| |
| # |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| # |
| |
| from lime import lime_image |
| |
| from skimage.segmentation import mark_boundaries |
| import torch |
| import torch.nn.functional as F |
| from singa_auto.model import utils |
| |
| |
| class Lime: |
| """ |
| Lime: Explaining the predictions of any machine learning classifier |
| https://github.com/marcotcr/lime |
| """ |
| |
| def __init__(self, model, |
| image_size, |
| normalize_mean, |
| normalize_std, |
| device): |
| |
| self._model = model |
| self.device = device |
| # dataset |
| self._image_size = image_size |
| self._normalize_mean = normalize_mean |
| self._normalize_std = normalize_std |
| self._explainer = lime_image.LimeImageExplainer() |
| # lime configs |
| # number of images that will be sent to classification function |
| self._num_samples = 100 |
| self._top_labels = 5 |
| self._hide_color = 0 |
| |
| def batch_predict(self, images): |
| (images, _, _) = utils.dataset.normalize_images(images, |
| self._normalize_mean, |
| self._normalize_std) |
| |
| self._model.eval() |
| |
| # images are size of (B, W, H, C) |
| with torch.no_grad(): |
| images = torch.FloatTensor(images).permute(0, 3, 1, 2).to(self.device) |
| |
| images = images.to(self.device) |
| logits = self._model(images).to(self.device) |
| probs = F.softmax(logits, dim=1) |
| |
| return probs.detach().cpu().numpy() |
| |
| def explain(self, images): |
| img_boundry = [] |
| for img in images: |
| explanation = self._explainer.explain_instance( |
| img, self.batch_predict, self._top_labels, self._hide_color, |
| self._num_samples) |
| temp, mask = explanation.get_image_and_mask( |
| explanation.top_labels[0], |
| positive_only=True, |
| num_features=5, |
| hide_rest=False) |
| # (M, N, 3) array of float |
| img_boundry = mark_boundaries(temp / 255.0, mask) |
| return img_boundry * 255 |
