correct mistakes in docstring and fix bugs when no benchmark is passed.

mindspore/explainer/_runner.py

@@ -161,13 +161,14 @@ class ExplainRunner:
         Examples:
             >>> from mindspore.explainer.explanation import GuidedBackprop, Gradient
             >>> from mindspore.nn import Sigmoid
+            >>> from mindspore.train.serialization import load_checkpoint, load_param_into_net
             >>> # obtain dataset object
             >>> dataset = get_dataset()
             >>> classes = ["cat", "dog", ...]
             >>> # load checkpoint to a network, e.g. resnet50
             >>> param_dict = load_checkpoint("checkpoint.ckpt")
             >>> net = resnet50(len(classes))
-            >>> load_parama_into_net(net, param_dict)
+            >>> load_param_into_net(net, param_dict)
             >>> gbp = GuidedBackprop(net)
             >>> gradient = Gradient(net)
             >>> runner = ExplainRunner("./")
@@ -180,6 +181,9 @@ class ExplainRunner:
             raise ValueError("Argument `dataset` should be a tuple with length = 2.")
 
         dataset, classes = dataset
+        if benchmarkers is None:
+            benchmarkers = []
+
         self._verify_data_form(dataset, benchmarkers)
         self._classes = classes
 
@@ -191,7 +195,7 @@ class ExplainRunner:
             if not isinstance(exp, Attribution):
                 raise TypeError("Argument `explainers` should be a list of objects of classes in "
                                 "`mindspore.explainer.explanation`.")
-        if benchmarkers is not None:
+        if benchmarkers:
             check_value_type("benchmarkers", benchmarkers, list)
             for bench in benchmarkers:
                 if not isinstance(bench, AttributionMetric):
@@ -234,7 +238,7 @@ class ExplainRunner:
             explain.metadata.label.extend(classes)
             exp_names = [exp.__class__.__name__ for exp in explainers]
             explain.metadata.explain_method.extend(exp_names)
-            if benchmarkers is not None:
+            if benchmarkers:
                 bench_names = [bench.__class__.__name__ for bench in benchmarkers]
                 explain.metadata.benchmark_method.extend(bench_names)
 
@@ -249,7 +253,7 @@ class ExplainRunner:
             print(spacer.format("Finish running and writing inference data. "
                                 "Time elapsed: {:.3f} s".format(time() - now)))
 
-            if benchmarkers is None or not benchmarkers:
+            if not benchmarkers:
                 for exp in explainers:
                     start = time()
                     print("Start running and writing explanation data for {}......".format(exp.__class__.__name__))
@@ -322,9 +326,8 @@ class ExplainRunner:
                 raise ValueError("The third element of dataset should be bounding boxes with shape of "
                                  "[batch_size, num_ground_truth, 4].")
         else:
-            if benchmarkers is not None:
-                if True in [isinstance(bench, Localization) for bench in benchmarkers]:
-                    raise ValueError("The dataset must provide bboxes if Localization is to be computed.")
+            if any(map(lambda benchmarker: isinstance(benchmarker, Localization), benchmarkers)):
+                raise ValueError("The dataset must provide bboxes if Localization is to be computed.")
 
             if len(next_element) == 2:
                 inputs, labels = next_element