!14288 Modify the examples in explainer.benchmark to make them executable seperately

From: @lixiaohui33
Reviewed-by: @ouwenchang,@yelihua,@ouwenchang,@yelihua
Signed-off-by: @yelihua

mindspore/explainer/_image_classification_runner.py

@@ -89,6 +89,7 @@ class ImageClassificationRunner:
         >>> # Load the checkpoint
         >>> param_dict = load_checkpoint("/path/to/checkpoint")
         >>> load_param_into_net(net, param_dict)
+        []
         >>>
         >>> # Prepare the dataset for explaining and evaluation.
         >>> # The detail of create_dataset_cifar10 method is shown in model_zoo.official.cv.alexnet.src.dataset.py
@@ -105,8 +106,6 @@ class ImageClassificationRunner:
         >>>
         >>> runner = ImageClassificationRunner("./summary_dir", (dataset, labels), net, activation_fn)
         >>> runner.register_saliency(explainers=explainers, benchmarkers=benchmarkers)
-        >>> runner.register_uncertainty()
-        >>> runner.register_hierarchical_occlusion()
         >>> runner.run()
     """
 

mindspore/explainer/benchmark/_attribution/class_sensitivity.py

@@ -57,7 +57,8 @@ class ClassSensitivity(LabelAgnosticMetric):
             >>> input_x = ms.Tensor(np.random.rand(1, 3, 32, 32), ms.float32)
             >>> class_sensitivity = ClassSensitivity()
             >>> res = class_sensitivity.evaluate(gradient, input_x)
-            >>> print(res)
+            >>> print(res.shape)
+            (1,)
         """
         self._check_evaluate_param(explainer, inputs)
 

mindspore/explainer/benchmark/_attribution/faithfulness.py

@@ -365,15 +365,6 @@ class Faithfulness(LabelSensitiveMetric):
         metric (str, optional): The specifi metric to quantify faithfulness.
             Options: "DeletionAUC", "InsertionAUC", "NaiveFaithfulness".
             Default: 'NaiveFaithfulness'.
-
-    Examples:
-        >>> from mindspore import nn
-        >>> from mindspore.explainer.benchmark import Faithfulness
-        >>> # init a `Faithfulness` object
-        >>> num_labels = 10
-        >>> metric = "InsertionAUC"
-        >>> activation_fn = nn.Softmax()
-        >>> faithfulness = Faithfulness(num_labels, activation_fn, metric)
     """
     _methods = [NaiveFaithfulness, DeletionAUC, InsertionAUC]
 
@@ -418,9 +409,15 @@ class Faithfulness(LabelSensitiveMetric):
         Examples:
             >>> import numpy as np
             >>> import mindspore as ms
+            >>> from mindspore import nn
+            >>> from mindspore.explainer.benchmark import Faithfulness
             >>> from mindspore.explainer.explanation import Gradient
             >>>
-            >>>
+            >>> # init a `Faithfulness` object
+            >>> num_labels = 10
+            >>> metric = "InsertionAUC"
+            >>> activation_fn = nn.Softmax()
+            >>> faithfulness = Faithfulness(num_labels, activation_fn, metric)
             >>> # The detail of LeNet5 is shown in model_zoo.official.cv.lenet.src.lenet.py
             >>> net = LeNet5(10, num_channel=3)
             >>> gradient = Gradient(net)
@@ -429,10 +426,13 @@ class Faithfulness(LabelSensitiveMetric):
             >>> # usage 1: input the explainer and the data to be explained,
             >>> # faithfulness is a Faithfulness instance
             >>> res = faithfulness.evaluate(gradient, inputs, targets)
+            >>> print(res.shape)
+            (1,)
             >>> # usage 2: input the generated saliency map
             >>> saliency = gradient(inputs, targets)
             >>> res = faithfulness.evaluate(gradient, inputs, targets, saliency)
-            >>> print(res)
+            >>> print(res.shape)
+            (1,)
         """
 
         self._check_evaluate_param(explainer, inputs, targets, saliency)

mindspore/explainer/benchmark/_attribution/localization.py

@@ -56,13 +56,7 @@ class Localization(LabelSensitiveMetric):
     Args:
         num_labels (int): Number of classes in the dataset.
         metric (str, optional): Specific metric to calculate localization capability.
-            Options: "PointingGame", "IoSR".
+            Options: "PointingGame", "IoSR". Default: "PointingGame".
-            Default: "PointingGame".
-
-    Examples:
-        >>> from mindspore.explainer.benchmark import Localization
-        >>> num_labels = 10
-        >>> localization = Localization(num_labels, "PointingGame")
     """
 
     def __init__(self,
@@ -113,6 +107,10 @@ class Localization(LabelSensitiveMetric):
             >>> import numpy as np
             >>> import mindspore as ms
             >>> from mindspore.explainer.explanation import Gradient
+            >>> from mindspore.explainer.benchmark import Localization
+            >>>
+            >>> num_labels = 10
+            >>> localization = Localization(num_labels, "PointingGame")
             >>>
             >>> # The detail of LeNet5 is shown in model_zoo.official.cv.lenet.src.lenet.py
             >>> net = LeNet5(10, num_channel=3)
@@ -124,11 +122,13 @@ class Localization(LabelSensitiveMetric):
             >>> # usage 1: input the explainer and the data to be explained,
             >>> # localization is a Localization instance
             >>> res = localization.evaluate(gradient, inputs, targets, mask=masks)
-            >>> print(res)
+            >>> print(res.shape)
+            (1,)
             >>> # usage 2: input the generated saliency map
             >>> saliency = gradient(inputs, targets)
             >>> res = localization.evaluate(gradient, inputs, targets, saliency, mask=masks)
-            >>> print(res)
+            >>> print(res.shape)
+            (1,)
         """
         self._check_evaluate_param_with_mask(explainer, inputs, targets, saliency, mask)
 

mindspore/explainer/benchmark/_attribution/robustness.py

@@ -35,15 +35,6 @@ class Robustness(LabelSensitiveMetric):
             single label classification tasks, `nn.Softmax` is usually applied. As for multi-label classification tasks,
             `nn.Sigmoid` is usually be applied. Users can also pass their own customized `activation_fn` as long as
             when combining this function with network, the final output is the probability of the input.
-
-
-    Examples:
-        >>> from mindspore import nn
-        >>> from mindspore.explainer.benchmark import Robustness
-        >>> # Initialize a Robustness benchmarker passing num_labels of the dataset.
-        >>> num_labels = 10
-        >>> activation_fn = nn.Softmax()
-        >>> robustness = Robustness(num_labels, activation_fn)
     """
 
     def __init__(self, num_labels, activation_fn):
@@ -79,7 +70,14 @@ class Robustness(LabelSensitiveMetric):
         Examples:
             >>> import numpy as np
             >>> import mindspore as ms
+            >>> from mindspore import nn
             >>> from mindspore.explainer.explanation import Gradient
+            >>> from mindspore.explainer.benchmark import Robustness
+            >>>
+            >>> # Initialize a Robustness benchmarker passing num_labels of the dataset.
+            >>> num_labels = 10
+            >>> activation_fn = nn.Softmax()
+            >>> robustness = Robustness(num_labels, activation_fn)
             >>>
             >>> # The detail of LeNet5 is shown in model_zoo.official.cv.lenet.src.lenet.py
             >>> net = LeNet5(10, num_channel=3)
@@ -89,7 +87,8 @@ class Robustness(LabelSensitiveMetric):
             >>> target_label = ms.Tensor([0], ms.int32)
             >>> # robustness is a Robustness instance
             >>> res = robustness.evaluate(gradient, input_x, target_label)
-            >>> print(res)
+            >>> print(res.shape)
+            (1,)
         """
 
         self._check_evaluate_param(explainer, inputs, targets, saliency)

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

@@ -87,6 +87,7 @@ class GradCAM(IntermediateLayerAttribution):
         >>> label = 5
         >>> saliency = gradcam(inputs, label)
         >>> print(saliency.shape)
+        (1, 1, 32, 32)
     """
 
     def __init__(self, network, layer=""):

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

@@ -62,6 +62,7 @@ class Gradient(Attribution):
         >>> label = 5
         >>> saliency = gradient(inputs, label)
         >>> print(saliency.shape)
+        (1, 1, 32, 32)
     """
 
     def __init__(self, network):

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

@@ -45,13 +45,6 @@ class ModifiedReLU(Gradient):
 
         Returns:
             Tensor, a 4D tensor of shape :math:`(N, 1, H, W)`.
-
-        Examples:
-            >>> 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
-            >>> saliency = explainer(inputs, label)
         """
 
         self._verify_data(inputs, targets)
@@ -117,6 +110,7 @@ class Deconvolution(ModifiedReLU):
         >>> label = 5
         >>> saliency = deconvolution(inputs, label)
         >>> print(saliency.shape)
+        (1, 1, 32, 32)
     """
 
     def __init__(self, network):
@@ -161,6 +155,7 @@ class GuidedBackprop(ModifiedReLU):
         >>> label = 5
         >>> saliency = gbp(inputs, label)
         >>> print(saliency.shape)
+        (1, 1, 32, 32)
     """
 
     def __init__(self, network):

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

@@ -80,6 +80,7 @@ class Occlusion(PerturbationAttribution):
         >>> label = ms.Tensor([1], ms.int32)
         >>> saliency = occlusion(input_x, label)
         >>> print(saliency.shape)
+        (1, 1, 32, 32)
     """
 
     def __init__(self, network, activation_fn, perturbation_per_eval=32):

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

@@ -74,10 +74,12 @@ class RISE(PerturbationAttribution):
         >>> targets = 5
         >>> saliency = rise(inputs, targets)
         >>> print(saliency.shape)
+        (2, 1, 32, 32)
         >>> # `targets` can also be a 2D tensor
         >>> targets = ms.Tensor([[5], [1]], ms.int32)
         >>> saliency = rise(inputs, targets)
         >>> print(saliency.shape)
+        (2, 1, 32, 32)
 """
 
     def __init__(self,

mindspore/explainer/explanation/_counterfactual/hierarchical_occlusion.py

@@ -379,27 +379,6 @@ class Searcher:
         strides (Union(list[int], optional): Stride of layers, None means by auto calcuation.
         threshold (float): Threshold network output value of the target class.
         by_masking (bool): Whether it is masking mode.
-
-    Examples:
-        >>> from mindspore import nn
-        >>> from mindspore.explainer.explanation._counterfactual.hierarchical_occlusion import Searcher, EditStep
-        >>>
-        >>> from user_defined import load_network, load_sample_image
-        >>>
-        >>>
-        >>> network = nn.SequentialCell([load_network(), nn.Sigmoid()])
-        >>>
-        >>> # single image in CHW or NCHW(N=1) numpy.ndarray tensor, typical dimension is 224x224
-        >>> image = load_sample_image()
-        >>> # target class index
-        >>> class_idx = 5
-        >>>
-        >>> # by default, maximum 3 search layers, auto calculate window sizes and strides
-        >>> searcher = Searcher(network)
-        >>>
-        >>> edit_tree, layer_outputs = searcher.search(image, class_idx)
-        >>> # get the outcome image of the deepest layer in CHW(or NCHW(N=1) if input image is NCHW) format
-        >>> outcome = EditStep.apply(image, searcher.compiled_mask, edit_tree.leaf_steps)
     """
 
     def __init__(self,