Comments (1)
I think the most clean way of doing this would be to create some abstract classes or interfaces with abstract methods which specify the expected inputs, e.g. IndexPerturbatorInterface
or PatchPerturbatorInterface
. The calling metric then would just need to check if the passed perturbator object inherits from the expected type of perturbator.
This would also have the benefit of instantiating the perturbator object beforehand with any arguments wanted without unnecessarily dragging these arguments through the Metric
implementations.
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Related Issues (20)
- Missing module in 'quantus.helpers.utils' HOT 2
- Explanation pre- and post-processing pipeline
- Implement custom plot function HOT 1
- Huggingface pip dependency issue Python 3.8
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- Some minor mistakes in the documentation HOT 2
- Different SHA256 checksums for GitHub release and PyPi release
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- minor typo in assert_attributions_order function HOT 2
- Getting started tutorial needs to specify explain_fun for MaxSensitivity metric HOT 2
- Pre-trained model loading API
- Create transparency with respect to the metric’s individual data and model applicability HOT 1
- Wanna get perturbed inputs from robustness metrics
- Inconsistent implementation of get_random_layer_generator for Torch and TF
- Wrong implementation in max_sensitivity.py HOT 1
- toml missing HOT 1
- Pointing game need a tolerance margin option
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