!263 fix bug of bounds check in fuzzer

Merge pull request !263 from ZhidanLiu/master

examples/ai_fuzzer/lenet5_mnist_fuzzing.py

@@ -51,7 +51,7 @@ def test_lenet_mnist_fuzzing():
                      {'method': 'Shear',
                       'params': {'auto_param': [True]}},
                      {'method': 'FGSM',
-                      'params': {'eps': [0.3, 0.2, 0.4], 'alpha': [0.1]}}
+                      'params': {'eps': [0.3, 0.2, 0.4], 'alpha': [0.1], 'bounds': [(0, 1)]}}
                      ]
 
     # get training data

mindarmour/fuzz_testing/fuzzing.py

@@ -191,9 +191,9 @@ class Fuzzer:
             - dict, metrics report of fuzzer.
 
         Raises:
-            ValueError, coverage must be subclass of CoverageMetrics.
-            ValueError, if initial seeds is empty.
-            ValueError, if element of seed is not two in initial seeds.
+            ValueError: Coverage must be subclass of CoverageMetrics.
+            ValueError: If initial seeds is empty.
+            ValueError: If element of seed is not two in initial seeds.
         """
         # Check parameters.
         if not isinstance(coverage, CoverageMetrics):
@@ -356,7 +356,7 @@ class Fuzzer:
             check_param_type(param_name, params[param_name], list)
             for param_value in params[param_name]:
                 if param_name == 'bounds':
-                    _ = check_param_bounds('bounds', param_name)
+                    _ = check_param_bounds('bounds', param_value)
                 elif param_name == 'norm_level':
                     _ = check_norm_level(param_value)
                 else:

mindarmour/fuzz_testing/model_coverage_metrics.py

@@ -157,8 +157,10 @@ class NeuronCoverage(CoverageMetrics):
     """
     def __init__(self, model, threshold=0.1, incremental=False, batch_size=32):
         super(NeuronCoverage, self).__init__(model, incremental, batch_size)
+        threshold = check_param_type('threshold', threshold, float)
         self.threshold = check_value_positive('threshold', threshold)
 
+
     def get_metrics(self, dataset):
         """
         Get the metric of neuron coverage: the proportion of activated neurons to total neurons in the network.