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contextual decomposition following Singh et al.
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| """ | ||
| This file provides a way to use contextual decomposition in the loss function. | ||
| The implementation requires and is partly based on the implementation provided | ||
| by Singh et al. for their paper "Hierarchical interpretations for neural network | ||
| predictions": | ||
| @inproceedings{ | ||
| singh2019hierarchical, | ||
| title={Hierarchical interpretations for neural network predictions}, | ||
| author={Chandan Singh and W. James Murdoch and Bin Yu}, | ||
| booktitle={International Conference on Learning Representations}, | ||
| year={2019}, | ||
| url={https://openreview.net/forum?id=SkEqro0ctQ}, | ||
| } | ||
| The original implementation can be found here: | ||
| https://github.com/csinva/hierarchical-dnn-interpretations | ||
| It was published under the following license: | ||
| MIT License | ||
| Copyright (c) 2019 Chandan Singh | ||
| Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| of this software and associated documentation files (the "Software"), to deal | ||
| in the Software without restriction, including without limitation the rights | ||
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| copies of the Software, and to permit persons to whom the Software is | ||
| furnished to do so, subject to the following conditions: | ||
| The above copyright notice and this permission notice shall be included in all | ||
| copies or substantial portions of the Software. | ||
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| SOFTWARE. | ||
| """ | ||
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| import acd | ||
| import numpy as np | ||
| import torch | ||
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| def get_cd_1d_by_modules(model, modules, inputs, feat_of_interest, device="cpu"): | ||
| # Device. | ||
| inputs = inputs.to(device) | ||
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| # Prepare mask. | ||
| # The mask answers the following question: For which dimensions do you want | ||
| # to get their feature attribution compared to all other features? | ||
| n_dim = list(inputs.size())[1] | ||
| mask = np.zeros(n_dim, dtype=np.int32) | ||
| mask[feat_of_interest] = 1 | ||
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| # Set up relevant/irrelevant based on mask. | ||
| # Starting here, we start to follow the CD implementation from | ||
| # https://github.com/csinva/hierarchical-dnn-interpretations/blob/master/acd/scores/cd.py. | ||
| im_torch = inputs | ||
| mask = torch.FloatTensor(mask).to(device) | ||
| relevant = mask * im_torch | ||
| irrelevant = (1 - mask) * im_torch | ||
| relevant = relevant.to(device) | ||
| irrelevant = irrelevant.to(device) | ||
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| relevant, irrelevant = acd.cd_generic(modules, relevant, irrelevant) | ||
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| return relevant, irrelevant | ||
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| def get_cd_1d(model, inputs, feat_of_interest, device="cpu"): | ||
| """Calculates contextual decomposition scores for the given model. | ||
| The contextual decomposition performs feature attribution by decomposing | ||
| the output of the model into two parts: The contribution of the feature(s) | ||
| of interest and the contribution of all other features. | ||
| Therefore, you have to specify which features are of interest. In a 1d | ||
| scenario you are typically interested in the influence of a single | ||
| feature compared to all other features, but this method also allows you | ||
| to specify a list of features that, together, form the features of | ||
| interest. | ||
| Interpretation of the generated scores: | ||
| The output is (scores_feat, scores_other) with both being a one-dimensional | ||
| tensor. Since this method works with batched data, that means that for | ||
| each input sample two floating point scores are generated: the contribution | ||
| of the feature(s) of interest and the contribution of all other features. | ||
| Prediction of the Network = score of the features of interest | ||
| + score of the other features | ||
| :param model: PyTorch-Model to generate the CD scores for. | ||
| :param inputs: Batched inputs to the model. Typically 2-dimensional tensor | ||
| containing the inputs for a single batch. | ||
| :param feat_of_interest: Integer or list of integers. Define which | ||
| dimensions of the input are part of the feature(s) of interest. | ||
| :param device: Device used by PyTorch (cuda / cpu). | ||
| :return: Tuple (scores_feat, scores_other). These are the scores for each | ||
| of the batched inputs. Here, scores_feat[i] + scores_other[i]=prediction[i]. | ||
| Note that the feature scores are determined in a per-batch manner. Therefore, | ||
| the resulting feature scores are vectors. | ||
| :rtype: Tupel of one-dimensional tensors. | ||
| """ | ||
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| # Set model in evaluation mode. | ||
| prev_training_status = model.training | ||
| model.eval() | ||
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| # Prepare mask. | ||
| # The mask answers the following question: For which dimensions do you want | ||
| # to get their feature attribution compared to all other features? | ||
| n_dim = list(inputs.size())[1] | ||
| mask = np.zeros(n_dim, dtype=np.int32) | ||
| mask[feat_of_interest] = 1 | ||
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| # Contextual decomposition. | ||
| # We receive the contribution of the feature(s) of interest compared to all | ||
| # other features. | ||
| # The output is a tensor with a length >= 1, because we are considering batches. | ||
| # That is, for each element of the batch we get the contribution of the | ||
| # feature(s) of interest. | ||
| scores_feat, scores_other = acd.cd(inputs, model=model, mask=mask, device=device) | ||
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| # Reset evaluation mode if necessary. | ||
| if prev_training_status: | ||
| model.train() | ||
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| return (torch.flatten(scores_feat), torch.flatten(scores_other)) |
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