Using CV Benchmarks

What are CV Benchmarks

Benchmarks are algorithms evaluating the goodness of saliency maps from explainers. MindSpore XAI currently provides 4 benchmarks for image classification scenario: Robustness, Faithfulness, ClassSensitivity and Localization.

Preparations

The complete code of the tutorial below is using_cv_benchmarks.py.

Please follow the Downloading Data Package instructions to download the necessary files for the tutorial.

With the tutorial package, we have to get the sample image, trained classifier, explainer and optionally the saliency map ready:

# have to change the current directory to xai/examples/ first
import mindspore as ms
from mindspore_xai.explainer import GradCAM

from common.resnet import resnet50
from common.dataset import load_image_tensor

# only PYNATIVE_MODE is supported
ms.set_context(mode=ms.PYNATIVE_MODE)

# 20 classes
num_classes = 20

# load the trained classifier
net = resnet50(num_classes)
param_dict = ms.load_checkpoint("xai_examples_data/ckpt/resnet50.ckpt")
ms.load_param_into_net(net, param_dict)

# [1, 3, 224, 224] Tensor
boat_image = load_image_tensor('xai_examples_data/test/boat.jpg')

# explainer
grad_cam = GradCAM(net, layer='layer4')

# 3 is the class id of 'boat'
saliency = grad_cam(boat_image, targets=3)

Using Robustness

Robustness is the simplest benchmark, it perturbs the inputs by adding random noise and outputs the maximum sensitivity as evaluation score from the perturbations:

from mindspore.nn import Softmax
from mindspore_xai.benchmark import Robustness

# the classifier use Softmax as activation function
robustness = Robustness(num_classes, activation_fn=Softmax())
# the 'saliency' argument is optional
score = robustness.evaluate(grad_cam, boat_image, targets=3, saliency=saliency)

The returned score is a 1D tensor with only one float value for an 1xCx224x224 image tensor.

Using Faithfulness and ClassSensitivity

The ways of using Faithfulness and ClassSensitivity are very similar to Robustness. However, ClassSensitivity is class agnostic, targets can not be specified.

Using Localization

If the object region or bounding box is provided, Localization can be used. It evaluates base on how many saliency pixels fall inside the object region:

import numpy as np
import mindspore as ms
from mindspore_xai.benchmark import Localization

# top-left:80,66 bottom-right:223,196 is the bounding box of a boat
mask = np.zeros([1, 1, 224, 224])
mask[:, :, 66:196, 80:223] = 1

mask = ms.Tensor(mask, dtype=ms.float32)

localization = Localization(num_classes)

score = localization.evaluate(grad_cam, boat_image, targets=3, mask=mask)