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"""Class Sensitivity."""
import numpy as np
from mindspore.ops import Stack
from mindspore_xai.explainer import RISE
from mindspore_xai.common.utils import calc_correlation
from .metric import LabelAgnosticMetric
[文档]class ClassSensitivity(LabelAgnosticMetric):
"""
Class sensitivity metric used to evaluate attribution-based explainers.
Reasonable atrribution-based explainers are expected to generate distinct saliency maps for different labels,
especially for labels of highest confidence and low confidence. ClassSensitivity evaluates the explainer through
computing the correlation between saliency maps of highest-confidence and lowest-confidence labels. Explainer with
better class sensitivity will receive lower correlation score. To make the evaluation results intuitive, the
returned score will take negative on correlation and normalize.
Supported Platforms:
``Ascend`` ``GPU``
"""
[文档] def evaluate(self, explainer, inputs):
"""
Evaluate class sensitivity on the explainer.
Note:
Currently only single sample (:math:`N=1`) at each call is supported.
Args:
explainer (Explainer): The explainer to be evaluated, see `mindspore_xai.explainer`.
inputs (Tensor): A data sample, a 4D tensor of shape :math:`(N, C, H, W)`.
Returns:
numpy.ndarray, 1D array of shape :math:`(N,)`, result of class sensitivity evaluated on `explainer`.
Raises:
TypeError: Be raised for any argument type problem.
ValueError: Be raised if :math:`N` is not 1.
Examples:
>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore import set_context, PYNATIVE_MODE
>>> from mindspore_xai.benchmark import ClassSensitivity
>>> from mindspore_xai.explainer import Gradient
>>>
>>> set_context(mode=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.
>>> gradient = Gradient(net)
>>> 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.shape)
(1,)
"""
self._check_evaluate_param(explainer, inputs)
outputs = explainer.network(inputs)
max_confidence_label = outputs.argmax()
min_confidence_label = outputs.argmin()
if isinstance(explainer, RISE):
stack = Stack(axis=1)
labels = stack([max_confidence_label, min_confidence_label])
full_saliency = explainer(inputs, labels, show=False)
max_confidence_saliency = full_saliency[:, max_confidence_label].asnumpy()
min_confidence_saliency = full_saliency[:, min_confidence_label].asnumpy()
else:
max_confidence_saliency = explainer(inputs, max_confidence_label, show=False).asnumpy()
min_confidence_saliency = explainer(inputs, min_confidence_label, show=False).asnumpy()
correlations = []
for i in range(inputs.shape[0]):
correlation = calc_correlation(max_confidence_saliency[i].reshape(-1),
min_confidence_saliency[i].reshape(-1))
normalized_correlation = (-correlation + 1) / 2
correlations.append(normalized_correlation)
return np.array(correlations, float)