From 0b17c39dca481f476c419ab2734c02654d2e99fe Mon Sep 17 00:00:00 2001
From: jin-xiulang <jinxiulang@huawei.com>
Date: Fri, 21 Aug 2020 10:39:12 +0800
Subject: [PATCH] Fix several api issues

---
 mindarmour/fuzzing/fuzzing.py                | 7 ++++---
 mindarmour/fuzzing/model_coverage_metrics.py | 2 +-
 mindarmour/utils/logger.py                   | 6 +++++-
 mindarmour/utils/util.py                     | 2 +-
 4 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/mindarmour/fuzzing/fuzzing.py b/mindarmour/fuzzing/fuzzing.py
index d33a0ac..6c7db16 100644
--- a/mindarmour/fuzzing/fuzzing.py
+++ b/mindarmour/fuzzing/fuzzing.py
@@ -93,7 +93,7 @@ class Fuzzer:
         >>>                  {'method': 'Translate', 'params': {'x_bias': 0.1, 'y_bias': 0.2}},
         >>>                  {'method': 'FGSM', 'params': {'eps': 0.1, 'alpha': 0.1}}]
         >>> train_images = np.random.rand(32, 1, 32, 32).astype(np.float32)
-        >>> model_fuzz_test = Fuzzer(model, train_images, 1000, 10)
+        >>> model_fuzz_test = Fuzzer(model, train_images, 10, 1000)
         >>> samples, labels, preds, strategies, report = model_fuzz_test.fuzzing(mutate_config, initial_seeds)
     """
 
@@ -141,8 +141,9 @@ class Fuzzer:
         Args:
             mutate_config (list): Mutate configs. The format is
                 [{'method': 'Blur', 'params': {'auto_param': True}}, {'method': 'Contrast', 'params': {'factor': 2}}].
-                 The support methods list is in `self._strategies`, and the params of each
-                 method must within the range of changeable parameters.
+                The supported methods list is in `self._strategies`, and the params of each method must within the
+                range of changeable parameters.　All supported methods are: 'Contrast', 'Brightness', 'Blur',
+                'Noise', 'Translate', 'Scale', 'Shear', 'Rotate', 'FGSM', 'PGD' and 'MDIIM'.
             initial_seeds (numpy.ndarray): Initial seeds used to generate
                 mutated samples.
             coverage_metric (str): Model coverage metric of neural networks.
diff --git a/mindarmour/fuzzing/model_coverage_metrics.py b/mindarmour/fuzzing/model_coverage_metrics.py
index d932c5a..cc4c7a9 100644
--- a/mindarmour/fuzzing/model_coverage_metrics.py
+++ b/mindarmour/fuzzing/model_coverage_metrics.py
@@ -56,7 +56,7 @@ class ModelCoverageMetrics:
         >>> test_images = np.random.random((5000, 128)).astype(np.float32)
         >>> model = Model(net)
         >>> model_fuzz_test = ModelCoverageMetrics(model, 10000, 10, train_images)
-        >>> model_fuzz_test.test_adequacy_coverage_calculate(test_images)
+        >>> model_fuzz_test.calculate_coverage(test_images)
         >>> print('KMNC of this test is : %s', model_fuzz_test.get_kmnc())
         >>> print('NBC of this test is : %s', model_fuzz_test.get_nbc())
         >>> print('SNAC of this test is : %s', model_fuzz_test.get_snac())
diff --git a/mindarmour/utils/logger.py b/mindarmour/utils/logger.py
index 432def9..37ddb9f 100644
--- a/mindarmour/utils/logger.py
+++ b/mindarmour/utils/logger.py
@@ -78,7 +78,11 @@ class LogUtil:
     def set_level(self, level):
         """
         Set the logging level of this logger, level must be an integer or a
-        string.
+        string. Supported levels are 'NOTSET'(integer: 0), 'ERROR'(integer: 1-40),
+        'WARNING'('WARN', integer: 1-30), 'INFO'(integer: 1-20) and 'DEBUG'(integer: 1-10).
+        For example, if logger.set_level('WARNING') or logger.set_level(21), then
+        logger.warn() and logger.error() in scripts would be printed while running,
+        while logger.info() or logger.debug() would not be printed.
 
         Args:
             level (Union[int, str]): Level of logger.
diff --git a/mindarmour/utils/util.py b/mindarmour/utils/util.py
index ff52375..67936c8 100644
--- a/mindarmour/utils/util.py
+++ b/mindarmour/utils/util.py
@@ -98,7 +98,7 @@ class GradWrapWithLoss(Cell):
 
     Examples:
         >>> data = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32)*0.01)
-        >>> label = Tensor(np.ones([1, 10]).astype(np.float32))
+        >>> labels = Tensor(np.ones([1, 10]).astype(np.float32))
         >>> net = NET()
         >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits()
         >>> loss_net = WithLossCell(net, loss_fn)
-- 
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