提交 c4dd9543 编写于 作者: J jin-xiulang

Fix two api issues.

上级 3746066c
......@@ -87,7 +87,7 @@ def test_lenet_mnist_fuzzing():
# make initial seeds
for img, label in zip(test_images, test_labels):
initial_seeds.append([img, label, 0])
initial_seeds.append([img, label])
initial_seeds = initial_seeds[:100]
model_coverage_test.calculate_coverage(
......
......@@ -141,8 +141,8 @@ class Fuzzer:
Args:
mutate_config (list): Mutate configs. The format is
[{'method': 'Blur', 'params': {'auto_param': True}},
{'method': 'Contrast', 'params': {'factor': 2}}].
The supported methods list is in `self._strategies`, and the
{'method': 'Contrast', 'params': {'factor': 2}}]. The
supported methods list is in `self._strategies`, and the
params of each method must within the range of changeable parameters. 
Supported methods are grouped in three types:
Firstly, pixel value based transform methods include:
......@@ -153,8 +153,9 @@ class Fuzzer:
transform methods. The way of setting parameters for first and
second type methods can be seen in 'mindarmour/fuzzing/image_transform.py'.
For third type methods, you can refer to the corresponding class.
initial_seeds (numpy.ndarray): Initial seeds used to generate
mutated samples.
initial_seeds (list[list]): Initial seeds used to generate mutated
samples. The format of initial seeds is [[image_data, label],
[...], ...].
coverage_metric (str): Model coverage metric of neural networks. All
supported metrics are: 'KMNC', 'NBC', 'SNAC'. Default: 'KMNC'.
eval_metrics (Union[list, tuple, str]): Evaluation metrics. If the
......@@ -210,8 +211,18 @@ class Fuzzer:
# Check whether the mutate_config meet the specification.
mutate_config = check_param_type('mutate_config', mutate_config, list)
for method in mutate_config:
check_param_type("method['params']", method['params'], dict)
for config in mutate_config:
check_param_type("config['params']", config['params'], dict)
if set(config.keys()) != {'method', 'params'}:
msg = "Config must contain 'method' and 'params', but got {}." \
.format(set(config.keys()))
LOGGER.error(TAG, msg)
raise TypeError(msg)
if config['method'] not in self._strategies.keys():
msg = "Config methods must be in {}, but got {}." \
.format(self._strategies.keys(), config['method'])
LOGGER.error(TAG, msg)
raise TypeError(msg)
if coverage_metric not in ['KMNC', 'NBC', 'SNAC']:
msg = "coverage_metric must be in ['KMNC', 'NBC', 'SNAC'], but got {}." \
.format(coverage_metric)
......@@ -225,10 +236,7 @@ class Fuzzer:
check_param_type('seed', seed, list)
check_numpy_param('seed[0]', seed[0])
check_numpy_param('seed[1]', seed[1])
if seed[2] != 0:
msg = "initial seed[2] must be 0, but got {}.".format(seed[2])
LOGGER.error(TAG, msg)
raise ValueError(msg)
seed.append(0)
seed, initial_seeds = _select_next(initial_seeds)
fuzz_samples = []
gt_labels = []
......
......@@ -140,8 +140,8 @@ class ModelCoverageMetrics:
Args:
dataset (numpy.ndarray): Data for fuzz test.
bias_coefficient (float): The coefficient used for changing the
neurons' output boundaries. Default: 0.
bias_coefficient (Union[int, float]): The coefficient used
for changing the neurons' output boundaries. Default: 0.
batch_size (int): The number of samples in a predict batch.
Default: 32.
......
......@@ -113,7 +113,7 @@ def test_fuzzing_ascend():
initial_seeds = []
# make initial seeds
for img, label in zip(test_images, test_labels):
initial_seeds.append([img, label, 0])
initial_seeds.append([img, label])
initial_seeds = initial_seeds[:100]
model_coverage_test.calculate_coverage(
......@@ -159,7 +159,7 @@ def test_fuzzing_cpu():
initial_seeds = []
# make initial seeds
for img, label in zip(test_images, test_labels):
initial_seeds.append([img, label, 0])
initial_seeds.append([img, label])
initial_seeds = initial_seeds[:100]
model_coverage_test.calculate_coverage(
......
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