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add context setting in examples due to the default mode shift in mindspore 1.3

This commit is contained in:
lixiaohui 2021-07-09 14:10:55 +08:00
parent dcd9d18411
commit a88e8da01b
10 changed files with 25 additions and 0 deletions
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2
mindspore/explainer/_image_classification_runner.py
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@ -259,7 +259,9 @@ class ImageClassificationRunner(_Verifier):
>>> from mindspore.explainer.benchmark import Faithfulness
>>> from mindspore.nn import Softmax
>>> from mindspore.train.serialization import load_checkpoint, load_param_into_net
>>> from mindspore import context
>>>
>>> context.set_context(mode=context.PYNATIVE_MODE)
>>> # The detail of AlexNet is shown in model_zoo.official.cv.alexnet.src.alexnet.py
>>> net = AlexNet(10)
>>> # Load the checkpoint

2
mindspore/explainer/benchmark/_attribution/class_sensitivity.py
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@ -55,7 +55,9 @@ class ClassSensitivity(LabelAgnosticMetric):
>>> import mindspore as ms
>>> from mindspore.explainer.benchmark import ClassSensitivity
>>> from mindspore.explainer.explanation import Gradient
>>> from mindspore import context
>>>
>>> context.set_context(mode=context.PYNATIVE_MODE)
>>> # The detail of LeNet5 is shown in model_zoo.official.cv.lenet.src.lenet.py
>>> net = LeNet5(10, num_channel=3)
>>> # prepare your explainer to be evaluated, e.g., Gradient.

2
mindspore/explainer/benchmark/_attribution/faithfulness.py
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@ -423,7 +423,9 @@ class Faithfulness(LabelSensitiveMetric):
>>> from mindspore import nn
>>> from mindspore.explainer.benchmark import Faithfulness
>>> from mindspore.explainer.explanation import Gradient
>>> from mindspore import context
>>>
>>> context.set_context(mode=context.PYNATIVE_MODE)
>>> # init a `Faithfulness` object
>>> num_labels = 10
>>> metric = "InsertionAUC"

2
mindspore/explainer/benchmark/_attribution/localization.py
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@ -117,7 +117,9 @@ class Localization(LabelSensitiveMetric):
>>> import mindspore as ms
>>> from mindspore.explainer.explanation import Gradient
>>> from mindspore.explainer.benchmark import Localization
>>> from mindspore import context
>>>
>>> context.set_context(mode=context.PYNATIVE_MODE)
>>> num_labels = 10
>>> localization = Localization(num_labels, "PointingGame")
>>>

2
mindspore/explainer/benchmark/_attribution/robustness.py
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@ -79,7 +79,9 @@ class Robustness(LabelSensitiveMetric):
>>> from mindspore import nn
>>> from mindspore.explainer.explanation import Gradient
>>> from mindspore.explainer.benchmark import Robustness
>>> from mindspore import context
>>>
>>> context.set_context(mode=context.PYNATIVE_MODE)
>>> # Initialize a Robustness benchmarker passing num_labels of the dataset.
>>> num_labels = 10
>>> activation_fn = nn.Softmax()

2
mindspore/explainer/explanation/_attribution/_backprop/gradcam.py
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@ -83,7 +83,9 @@ class GradCAM(IntermediateLayerAttribution):
>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore.explainer.explanation import GradCAM
>>> from mindspore import context
>>>
>>> context.set_context(mode=context.PYNATIVE_MODE)
>>> # The detail of LeNet5 is shown in model_zoo.official.cv.lenet.src.lenet.py
>>> net = LeNet5(10, num_channel=3)
>>> # specify a layer name to generate explanation, usually the layer can be set as the last conv layer.

2
mindspore/explainer/explanation/_attribution/_backprop/gradient.py
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@ -61,7 +61,9 @@ class Gradient(Attribution):
>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore.explainer.explanation import Gradient
>>> from mindspore import context
>>>
>>> context.set_context(mode=context.PYNATIVE_MODE)
>>> # The detail of LeNet5 is shown in model_zoo.official.cv.lenet.src.lenet.py
>>> net = LeNet5(10, num_channel=3)
>>> gradient = Gradient(net)

6
mindspore/explainer/explanation/_attribution/_backprop/modified_relu.py
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@ -116,6 +116,9 @@ class Deconvolution(ModifiedReLU):
>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore.explainer.explanation import Deconvolution
>>> from mindspore import context
>>>
>>> context.set_context(mode=context.PYNATIVE_MODE)
>>> # The detail of LeNet5 is shown in model_zoo.official.cv.lenet.src.lenet.py
>>> net = LeNet5(10, num_channel=3)
>>> deconvolution = Deconvolution(net)
@ -168,6 +171,9 @@ class GuidedBackprop(ModifiedReLU):
>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore.explainer.explanation import GuidedBackprop
>>> from mindspore import context
>>>
>>> context.set_context(mode=context.PYNATIVE_MODE)
>>> # The detail of LeNet5 is shown in model_zoo.official.cv.lenet.src.lenet.py
>>> net = LeNet5(10, num_channel=3)
>>> gbp = GuidedBackprop(net)

3
mindspore/explainer/explanation/_attribution/_perturbation/occlusion.py
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@ -78,6 +78,9 @@ class Occlusion(PerturbationAttribution):
>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore.explainer.explanation import Occlusion
>>> from mindspore import context
>>>
>>> context.set_context(mode=context.PYNATIVE_MODE)
>>> # The detail of LeNet5 is shown in model_zoo.official.cv.lenet.src.lenet.py
>>> net = LeNet5(10, num_channel=3)
>>> # initialize Occlusion explainer with the pretrained model and activation function

2
mindspore/explainer/explanation/_attribution/_perturbation/rise.py
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@ -70,7 +70,9 @@ class RISE(PerturbationAttribution):
>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore.explainer.explanation import RISE
>>> from mindspore import context
>>>
>>> context.set_context(mode=context.PYNATIVE_MODE)
>>> # The detail of LeNet5 is shown in model_zoo.official.cv.lenet.src.lenet.py
>>> net = LeNet5(10, num_channel=3)
>>> # initialize RISE explainer with the pretrained model and activation function