Fixbug: modify GuidedBackprop API to make example executable

mindspore/explainer/_runner.py

@@ -161,7 +161,7 @@ class ExplainRunner:
         Examples:
             >>> from mindspore.explainer import ExplainRunner
             >>> from mindspore.explainer.explanation import GuidedBackprop, Gradient
-            >>> from mindspore.nn import Sigmoid
+            >>> from mindspore.nn import Softmax
             >>> from mindspore.train.serialization import load_checkpoint, load_param_into_net
             >>> # Prepare the dataset for explaining and evaluation, e.g., Cifar10
             >>> dataset = get_dataset('/path/to/Cifar10_dataset')
@@ -172,9 +172,9 @@ class ExplainRunner:
             >>> load_param_into_net(net, param_dict)
             >>> gbp = GuidedBackprop(net)
             >>> gradient = Gradient(net)
-            >>> runner = ExplainRunner("./")
             >>> explainers = [gbp, gradient]
-            >>> runner.run((dataset, classes), explainers, activation_fn=Sigmoid())
+            >>> # runner is an ExplainRunner object
+            >>> runner.run((dataset, classes), explainers, activation_fn=Softmax())
         """
 
         check_value_type("dataset", dataset, tuple)

mindspore/explainer/benchmark/_attribution/faithfulness.py

@@ -414,8 +414,10 @@ class Faithfulness(LabelSensitiveMetric):
             numpy.ndarray, 1D array of shape :math:`(N,)`, result of faithfulness evaluated on `explainer`.
 
         Examples:
-            >>> # init an explainer, the network should contain the output activation function.
-            >>> network = resnet50(20)
+            >>> import numpy as np
+            >>> import mindspore as ms
+            >>> from mindspore.explainer.explanation import Gradient
+            >>> # init an explainer with a trained network, e.g., resnet50
             >>> gradient = Gradient(network)
             >>> inputs = ms.Tensor(np.random.rand(1, 3, 224, 224), ms.float32)
             >>> targets = 5

mindspore/explainer/benchmark/_attribution/localization.py

@@ -110,7 +110,10 @@ class Localization(LabelSensitiveMetric):
             numpy.ndarray, 1D array of shape :math:`(N,)`, result of localization evaluated on `explainer`.
 
         Examples:
-            >>> # init an explainer with a trained network
+            >>> import numpy as np
+            >>> import mindspore as ms
+            >>> from mindspore.explainer.explanation import Gradient
+            >>> # init an explainer with a trained network, e.g., resnet50
             >>> gradient = Gradient(network)
             >>> inputs = ms.Tensor(np.random.rand(1, 3, 224, 224), ms.float32)
             >>> masks = np.zeros([1, 1, 224, 224])

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

@@ -66,7 +66,8 @@ class GradCAM(IntermediateLayerAttribution):
 
     Examples:
         >>> from mindspore.explainer.explanation import GradCAM
-        >>> net = resnet50(10)
+        >>> from mindspore.train.serialization import load_checkpoint, load_param_into_net
+        >>> network = resnet50(10)  # please refer to model_zoo
         >>> param_dict = load_checkpoint("resnet50.ckpt")
         >>> load_param_into_net(net, param_dict)
         >>> # specify a layer name to generate explanation, usually the layer can be set as the last conv layer.
@@ -111,6 +112,8 @@ class GradCAM(IntermediateLayerAttribution):
             Tensor, a 4D tensor of shape :math:`(N, 1, H, W)`.
 
         Examples:
+            >>> import mindspore as ms
+            >>> import numpy as np
             >>> 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

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

@@ -61,7 +61,9 @@ class Gradient(Attribution):
 
     Examples:
         >>> from mindspore.explainer.explanation import Gradient
-        >>> net = resnet50(10)
+        >>> from mindspore.train.serialization import load_checkpoint, load_param_into_net
+        >>> # init Gradient with a trained network
+        >>> net = resnet50(10)  # please refer to model_zoo
         >>> param_dict = load_checkpoint("resnet50.ckpt")
         >>> load_param_into_net(net, param_dict)
         >>> gradient = Gradient(net)
@@ -89,6 +91,8 @@ class Gradient(Attribution):
             Tensor, a 4D tensor of shape :math:`(N, 1, H, W)`.
 
         Examples:
+            >>> import mindspore as ms
+            >>> import numpy as np
             >>> 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

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

@@ -97,11 +97,14 @@ class Deconvolution(ModifiedReLU):
         network (Cell): The black-box model to be explained.
 
     Examples:
+        >>> import numpy as np
+        >>> import mindspore as ms
         >>> from mindspore.explainer.explanation import Deconvolution
-        >>> net = resnet50(10)
+        >>> from mindspore.train.serialization import load_checkpoint, load_param_into_net
+        >>> net = resnet50(10)  # please refer to model_zoo
         >>> param_dict = load_checkpoint("resnet50.ckpt")
         >>> load_param_into_net(net, param_dict)
-        >>> # init Gradient with a trained network.
+        >>> # init Deconvolution 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)
@@ -132,14 +135,17 @@ class GuidedBackprop(ModifiedReLU):
         network (Cell): The black-box model to be explained.
 
     Examples:
+        >>> import numpy as np
+        >>> import mindspore as ms
+        >>> from mindspore.train.serialization import load_checkpoint, load_param_into_net
         >>> from mindspore.explainer.explanation import GuidedBackprop
-        >>> net = resnet50(10)
+        >>> net = resnet50(10)  # please refer to model_zoo
         >>> param_dict = load_checkpoint("resnet50.ckpt")
         >>> load_param_into_net(net, param_dict)
-        >>> # init Gradient with a trained network.
+        >>> # init GuidedBackprop with a trained network.
         >>> gbp = GuidedBackprop(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 = gbp(inputs, label)
     """

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

@@ -52,11 +52,14 @@ class RISE(PerturbationAttribution):
 
     Examples:
         >>> from mindspore.explainer.explanation import RISE
-        >>> net = resnet50(10)
+        >>> from mindspore.nn import Sigmoid
+        >>> from mindspore.train.serialization import load_checkpoint, load_param_into_net
+        >>> # init RISE with a trained network
+        >>> net = resnet50(10)  # please refer to model_zoo
         >>> param_dict = load_checkpoint("resnet50.ckpt")
         >>> load_param_into_net(net, param_dict)
         >>> # init RISE with specified activation function
-        >>> rise = RISE(net, activation_fn=nn.layer.Sigmoid())
+        >>> rise = RISE(net, activation_fn=Sigmoid())
     """
 
     def __init__(self,
@@ -113,6 +116,8 @@ class RISE(PerturbationAttribution):
             Tensor, a 4D tensor of shape :math:`(N, ?, H, W)` or :math:`(N, 1, H, W)`.
 
         Examples:
+            >>> import mindspore as ms
+            >>> import numpy as np
             >>> # given an instance of RISE, saliency map can be generate
             >>> inputs = ms.Tensor(np.random.rand(2, 3, 224, 224), ms.float32)
             >>> # when `targets` is an integer