Add lime explanation method
diff --git a/examples/singa_easy/singa_easy/modules/explanations/lime/lime.py b/examples/singa_easy/singa_easy/modules/explanations/lime/lime.py
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+++ b/examples/singa_easy/singa_easy/modules/explanations/lime/lime.py
@@ -0,0 +1,84 @@
+
+#
+# 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
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