Fixbug: fix API docs bugs

mindspore/explainer/benchmark/_attribution/faithfulness.py

@@ -424,7 +424,7 @@ class Faithfulness(LabelSensitiveMetric):
             >>> res = faithfulness.evaluate(gradient, inputs, targets)
             >>> # usage 2: input the generated saliency map
             >>> saliency = gradient(inputs, targets)
-            >>> res = faithfulenss.evaluate(gradient, inputs, targets, saliency)
+            >>> res = faithfulness.evaluate(gradient, inputs, targets, saliency)
         """
 
         self._check_evaluate_param(explainer, inputs, targets, saliency)

mindspore/explainer/benchmark/_attribution/localization.py

@@ -110,10 +110,10 @@ class Localization(LabelSensitiveMetric):
             numpy.ndarray, 1D array of shape :math:`(N,)`, result of localization evaluated on `explainer`.
 
         Examples:
-            >>> # init an explainer, the network should contain the output activation function.
+            >>> # init an explainer with a trained network
             >>> gradient = Gradient(network)
             >>> inputs = ms.Tensor(np.random.rand(1, 3, 224, 224), ms.float32)
-            >>> masks = np.zeros(1, 1, 224, 224)
+            >>> masks = np.zeros([1, 1, 224, 224])
             >>> masks[:, :, 65: 100, 65: 100] = 1
             >>> targets = 5
             >>> # usage 1: input the explainer and the data to be explained,

mindspore/explainer/explanation/_attribution/_backprop/gradcam.py

@@ -111,7 +111,7 @@ class GradCAM(IntermediateLayerAttribution):
             Tensor, a 4D tensor of shape :math:`(N, 1, H, W)`.
 
         Examples:
-            >>> inputs = ms.Tensor(np.random.rand([1, 3, 224, 224]), ms.float32)
+            >>> inputs = ms.Tensor(np.random.rand(1, 3, 224, 224), ms.float32)
             >>> label = 5
             >>> # gradcam is a GradCAM object, parse data and the target label to be explained and get the attribution
             >>> saliency = gradcam(inputs, label)

mindspore/explainer/explanation/_attribution/_backprop/gradient.py

@@ -89,7 +89,7 @@ class Gradient(Attribution):
             Tensor, a 4D tensor of shape :math:`(N, 1, H, W)`.
 
         Examples:
-            >>> inputs = ms.Tensor(np.random.rand([1, 3, 224, 224]), ms.float32)
+            >>> inputs = ms.Tensor(np.random.rand(1, 3, 224, 224), ms.float32)
             >>> label = 5
             >>> # gradient is a Gradient object, parse data and the target label to be explained and get the attribution
             >>> saliency = gradient(inputs, label)

mindspore/explainer/explanation/_attribution/_backprop/modified_relu.py

@@ -45,7 +45,7 @@ class ModifiedReLU(Gradient):
             Tensor, a 4D tensor of shape :math:`(N, 1, H, W)`.
 
         Examples:
-            >>> inputs = ms.Tensor(np.random.rand([1, 3, 224, 224]), ms.float32)
+            >>> inputs = ms.Tensor(np.random.rand(1, 3, 224, 224), ms.float32)
             >>> label = 5
             >>> # explainer is a "Deconvolution" or "GuidedBackprop" object, parse data and the target label to be
             >>> # explained and get the attribution
@@ -104,7 +104,7 @@ class Deconvolution(ModifiedReLU):
         >>> # init Gradient with a trained network.
         >>> deconvolution = Deconvolution(net)
         >>> # parse data and the target label to be explained and get the saliency map
-        >>> inputs = ms.Tensor(np.random.rand([1, 3, 224, 224]), ms.float32)
+        >>> inputs = ms.Tensor(np.random.rand(1, 3, 224, 224), ms.float32)
         >>> label = 5
         >>> saliency = deconvolution(inputs, label)
     """

mindspore/explainer/explanation/_attribution/_perturbation/occlusion.py

@@ -76,7 +76,7 @@ class Occlusion(PerturbationAttribution):
         >>> param_dict = load_checkpoint("resnet50.ckpt")
         >>> load_param_into_net(network, param_dict)
         >>> occlusion = Occlusion(network)
-        >>> x = Tensor(np.random.rand([1, 3, 224, 224]), ms.float32)
+        >>> x = Tensor(np.random.rand(1, 3, 224, 224), ms.float32)
         >>> label = 1
         >>> saliency = occlusion(x, label)
     """

mindspore/explainer/explanation/_attribution/_perturbation/rise.py

@@ -114,14 +114,13 @@ class RISE(PerturbationAttribution):
 
         Examples:
             >>> # given an instance of RISE, saliency map can be generate
-            >>> inputs = ms.Tensor(np.random.rand([2, 3, 224, 224]), ms.float32)
+            >>> inputs = ms.Tensor(np.random.rand(2, 3, 224, 224), ms.float32)
             >>> # when `targets` is an integer
             >>> targets = 5
             >>> saliency = rise(inputs, targets)
             >>> # `targets` can also be a tensor
             >>> targets = ms.Tensor([[5], [1]])
             >>> saliency = rise(inputs, targets)
-            >>>
         """
         self._verify_data(inputs, targets)
         height, width = inputs.shape[2], inputs.shape[3]