rename lime

paddlex/__init__.py

@@ -28,7 +28,7 @@ from . import seg
 from . import cls
 from . import slim
 from . import tools
-from . import explanation
+from . import interpret
 
 try:
     import pycocotools

paddlex/cv/models/classifier.py

@@ -275,7 +275,7 @@ class BaseClassifier(BaseAPI):
         } for l in pred_label]
         return res
     
-    def explanation_predict(self, images):
+    def interpretation_predict(self, images):
         self.arrange_transforms(
                 transforms=self.test_transforms, mode='test')
         new_imgs = []

paddlex/cv/models/explanation/core/explanation.py → paddlex/cv/models/interpret/core/interpretation.py

@@ -12,31 +12,31 @@
 #See the License for the specific language governing permissions and
 #limitations under the License.
 
-from .explanation_algorithms import CAM, LIME, NormLIME
+from .interpretation_algorithms import CAM, LIME, NormLIME
 from .normlime_base import precompute_normlime_weights
 
 
-class Explanation(object):
+class Interpretation(object):
     """
-    Base class for all explanation algorithms.
+    Base class for all interpretation algorithms.
     """
-    def __init__(self, explanation_algorithm_name, predict_fn, label_names, **kwargs):
+    def __init__(self, interpretation_algorithm_name, predict_fn, label_names, **kwargs):
         supported_algorithms = {
             'cam': CAM,
             'lime': LIME,
             'normlime': NormLIME
         }
 
-        self.algorithm_name = explanation_algorithm_name.lower()
+        self.algorithm_name = interpretation_algorithm_name.lower()
         assert self.algorithm_name in supported_algorithms.keys()
         self.predict_fn = predict_fn
 
-        # initialization for the explanation algorithm.
-        self.explain_algorithm = supported_algorithms[self.algorithm_name](
+        # initialization for the interpretation algorithm.
+        self.algorithm = supported_algorithms[self.algorithm_name](
             self.predict_fn, label_names, **kwargs
         )
 
-    def explain(self, data_, visualization=True, save_to_disk=True, save_dir='./tmp'):
+    def interpret(self, data_, visualization=True, save_to_disk=True, save_dir='./tmp'):
         """
 
         Args:
@@ -48,4 +48,4 @@ class Explanation(object):
         Returns:
 
         """
-        return self.explain_algorithm.explain(data_, visualization, save_to_disk, save_dir)
+        return self.algorithm.interpret(data_, visualization, save_to_disk, save_dir)

paddlex/cv/models/explanation/core/explanation_algorithms.py → paddlex/cv/models/interpret/core/interpretation_algorithms.py

@@ -46,12 +46,13 @@ class CAM(object):
         logit = result[0][0]
         if abs(np.sum(logit) - 1.0) > 1e-4:
             # softmax
+            logit = logit - np.max(logit)
             exp_result = np.exp(logit)
             probability = exp_result / np.sum(exp_result)
         else:
             probability = logit
 
-        # only explain top 1
+        # only interpret top 1
         pred_label = np.argsort(probability)
         pred_label = pred_label[-1:]
 
@@ -71,7 +72,7 @@ class CAM(object):
         print(f'predicted result: {ln} with probability {probability[pred_label[0]]:.3f}')
         return feature_maps, fc_weights
 
-    def explain(self, data_, visualization=True, save_to_disk=True, save_outdir=None):
+    def interpret(self, data_, visualization=True, save_to_disk=True, save_outdir=None):
         feature_maps, fc_weights = self.preparation_cam(data_)
         cam = get_cam(self.image, feature_maps, fc_weights, self.predicted_label)
 
@@ -123,7 +124,7 @@ class LIME(object):
         self.predict_fn = predict_fn
         self.labels = None
         self.image = None
-        self.lime_explainer = None
+        self.lime_interpreter = None
         self.label_names = label_names
 
     def preparation_lime(self, data_):
@@ -134,12 +135,13 @@ class LIME(object):
 
         if abs(np.sum(result) - 1.0) > 1e-4:
             # softmax
+            result = result - np.max(result)
             exp_result = np.exp(result)
             probability = exp_result / np.sum(exp_result)
         else:
             probability = result
 
-        # only explain top 1
+        # only interpret top 1
         pred_label = np.argsort(probability)
         pred_label = pred_label[-1:]
 
@@ -156,14 +158,14 @@ class LIME(object):
         print(f'predicted result: {ln} with probability {probability[pred_label[0]]:.3f}')
 
         end = time.time()
-        algo = lime_base.LimeImageExplainer()
-        explainer = algo.explain_instance(self.image, self.predict_fn, self.labels, 0,
-                                          num_samples=self.num_samples, batch_size=self.batch_size)
-        self.lime_explainer = explainer
+        algo = lime_base.LimeImageInterpreter()
+        interpreter = algo.interpret_instance(self.image, self.predict_fn, self.labels, 0,
+                                              num_samples=self.num_samples, batch_size=self.batch_size)
+        self.lime_interpreter = interpreter
         print('lime time: ', time.time() - end, 's.')
 
-    def explain(self, data_, visualization=True, save_to_disk=True, save_outdir=None):
-        if self.lime_explainer is None:
+    def interpret(self, data_, visualization=True, save_to_disk=True, save_outdir=None):
+        if self.lime_interpreter is None:
             self.preparation_lime(data_)
 
         if visualization or save_to_disk:
@@ -187,13 +189,13 @@ class LIME(object):
             axes[0].imshow(self.image)
             axes[0].set_title(f"label {ln}, proba: {self.predicted_probability: .3f}")
 
-            axes[1].imshow(mark_boundaries(self.image, self.lime_explainer.segments))
+            axes[1].imshow(mark_boundaries(self.image, self.lime_interpreter.segments))
             axes[1].set_title("superpixel segmentation")
 
             # LIME visualization
             for i, w in enumerate(weights_choices):
-                num_to_show = auto_choose_num_features_to_show(self.lime_explainer, l, w)
-                temp, mask = self.lime_explainer.get_image_and_mask(
+                num_to_show = auto_choose_num_features_to_show(self.lime_interpreter, l, w)
+                temp, mask = self.lime_interpreter.get_image_and_mask(
                     l, positive_only=False, hide_rest=False, num_features=num_to_show
                 )
                 axes[ncols + i].imshow(mark_boundaries(temp, mask))
@@ -274,20 +276,20 @@ class NormLIME(object):
         print('performing NormLIME operations ...')
 
         cluster_labels = self.predict_cluster_labels(
-            compute_features_for_kmeans(image_show).transpose((1, 2, 0)), self._lime.lime_explainer.segments
+            compute_features_for_kmeans(image_show).transpose((1, 2, 0)), self._lime.lime_interpreter.segments
         )
 
         g_weights = self.predict_using_normlime_weights(self.labels, cluster_labels)
 
         return g_weights
 
-    def explain(self, data_, visualization=True, save_to_disk=True, save_outdir=None):
+    def interpret(self, data_, visualization=True, save_to_disk=True, save_outdir=None):
         if self.normlime_weights is None:
             raise ValueError("Not find the correct precomputed NormLIME result. \n"
                              "\t Try to call compute_normlime_weights() first or load the correct path.")
 
         g_weights = self.preparation_normlime(data_)
-        lime_weights = self._lime.lime_explainer.local_exp
+        lime_weights = self._lime.lime_interpreter.local_weights
 
         if visualization or save_to_disk:
             import matplotlib.pyplot as plt
@@ -312,23 +314,23 @@ class NormLIME(object):
             axes[0].imshow(self.image)
             axes[0].set_title(f"label {ln}, proba: {self.predicted_probability: .3f}")
 
-            axes[1].imshow(mark_boundaries(self.image, self._lime.lime_explainer.segments))
+            axes[1].imshow(mark_boundaries(self.image, self._lime.lime_interpreter.segments))
             axes[1].set_title("superpixel segmentation")
 
             # LIME visualization
             for i, w in enumerate(weights_choices):
-                num_to_show = auto_choose_num_features_to_show(self._lime.lime_explainer, l, w)
+                num_to_show = auto_choose_num_features_to_show(self._lime.lime_interpreter, l, w)
                 nums_to_show.append(num_to_show)
-                temp, mask = self._lime.lime_explainer.get_image_and_mask(
+                temp, mask = self._lime.lime_interpreter.get_image_and_mask(
                     l, positive_only=False, hide_rest=False, num_features=num_to_show
                 )
                 axes[ncols + i].imshow(mark_boundaries(temp, mask))
                 axes[ncols + i].set_title(f"LIME: first {num_to_show} superpixels")
 
             # NormLIME visualization
-            self._lime.lime_explainer.local_exp = g_weights
+            self._lime.lime_interpreter.local_weights = g_weights
             for i, num_to_show in enumerate(nums_to_show):
-                temp, mask = self._lime.lime_explainer.get_image_and_mask(
+                temp, mask = self._lime.lime_interpreter.get_image_and_mask(
                     l, positive_only=False, hide_rest=False, num_features=num_to_show
                 )
                 axes[ncols * 2 + i].imshow(mark_boundaries(temp, mask))
@@ -336,15 +338,15 @@ class NormLIME(object):
 
             # NormLIME*LIME visualization
             combined_weights = combine_normlime_and_lime(lime_weights, g_weights)
-            self._lime.lime_explainer.local_exp = combined_weights
+            self._lime.lime_interpreter.local_weights = combined_weights
             for i, num_to_show in enumerate(nums_to_show):
-                temp, mask = self._lime.lime_explainer.get_image_and_mask(
+                temp, mask = self._lime.lime_interpreter.get_image_and_mask(
                     l, positive_only=False, hide_rest=False, num_features=num_to_show
                 )
                 axes[ncols * 3 + i].imshow(mark_boundaries(temp, mask))
                 axes[ncols * 3 + i].set_title(f"Combined: first {num_to_show} superpixels")
 
-            self._lime.lime_explainer.local_exp = lime_weights
+            self._lime.lime_interpreter.local_weights = lime_weights
 
         if save_to_disk and save_outdir is not None:
             os.makedirs(save_outdir, exist_ok=True)
@@ -354,9 +356,9 @@ class NormLIME(object):
             plt.show()
 
 
-def auto_choose_num_features_to_show(lime_explainer, label, percentage_to_show):
-    segments = lime_explainer.segments
-    lime_weights = lime_explainer.local_exp[label]
+def auto_choose_num_features_to_show(lime_interpreter, label, percentage_to_show):
+    segments = lime_interpreter.segments
+    lime_weights = lime_interpreter.local_weights[label]
     num_pixels_threshold_in_a_sp = segments.shape[0] * segments.shape[1] // len(np.unique(segments)) // 8
 
     # l1 norm with filtered weights.
@@ -381,7 +383,7 @@ def auto_choose_num_features_to_show(lime_explainer, label, percentage_to_show):
         return 5
 
     if n == 0:
-        return auto_choose_num_features_to_show(lime_explainer, label, percentage_to_show-0.1)
+        return auto_choose_num_features_to_show(lime_interpreter, label, percentage_to_show-0.1)
 
     return n
 

paddlex/cv/models/explanation/core/lime_base.py → paddlex/cv/models/interpret/core/lime_base.py

-#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
-#
-#Licensed 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 __future__ import print_function
+"""
+Copyright (c) 2016, Marco Tulio Correia Ribeiro
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""
+"""
+The code in this file (lime_base.py) is modified from https://github.com/marcotcr/lime.
+"""
+
+
 import numpy as np
 import scipy as sp
 import sklearn
@@ -88,7 +103,7 @@ class LimeBase(object):
         return np.array(used_features)
 
     def feature_selection(self, data, labels, weights, num_features, method):
-        """Selects features for the model. see explain_instance_with_data to
+        """Selects features for the model. see interpret_instance_with_data to
            understand the parameters."""
         if method == 'none':
             return np.array(range(data.shape[1]))
@@ -154,15 +169,15 @@ class LimeBase(object):
             return self.feature_selection(data, labels, weights,
                                           num_features, n_method)
 
-    def explain_instance_with_data(self,
-                                   neighborhood_data,
-                                   neighborhood_labels,
-                                   distances,
-                                   label,
-                                   num_features,
-                                   feature_selection='auto',
-                                   model_regressor=None):
-        """Takes perturbed data, labels and distances, returns explanation.
+    def interpret_instance_with_data(self,
+                                     neighborhood_data,
+                                     neighborhood_labels,
+                                     distances,
+                                     label,
+                                     num_features,
+                                     feature_selection='auto',
+                                     model_regressor=None):
+        """Takes perturbed data, labels and distances, returns interpretation.
 
         Args:
             neighborhood_data: perturbed data, 2d array. first element is
@@ -170,8 +185,8 @@ class LimeBase(object):
             neighborhood_labels: corresponding perturbed labels. should have as
                                  many columns as the number of possible labels.
             distances: distances to original data point.
-            label: label for which we want an explanation
-            num_features: maximum number of features in explanation
+            label: label for which we want an interpretation
+            num_features: maximum number of features in interpretation
             feature_selection: how to select num_features. options are:
                 'forward_selection': iteratively add features to the model.
                     This is costly when num_features is high
@@ -183,7 +198,7 @@ class LimeBase(object):
                 'none': uses all features, ignores num_features
                 'auto': uses forward_selection if num_features <= 6, and
                     'highest_weights' otherwise.
-            model_regressor: sklearn regressor to use in explanation.
+            model_regressor: sklearn regressor to use in interpretation.
                 Defaults to Ridge regression if None. Must have
                 model_regressor.coef_ and 'sample_weight' as a parameter
                 to model_regressor.fit()
@@ -194,8 +209,8 @@ class LimeBase(object):
             exp is a sorted list of tuples, where each tuple (x,y) corresponds
             to the feature id (x) and the local weight (y). The list is sorted
             by decreasing absolute value of y.
-            score is the R^2 value of the returned explanation
-            local_pred is the prediction of the explanation model on the original instance
+            score is the R^2 value of the returned interpretation
+            local_pred is the prediction of the interpretation model on the original instance
         """
 
         weights = self.kernel_fn(distances)
@@ -227,7 +242,7 @@ class LimeBase(object):
                 prediction_score, local_pred)
 
 
-class ImageExplanation(object):
+class ImageInterpretation(object):
     def __init__(self, image, segments):
         """Init function.
 
@@ -238,7 +253,7 @@ class ImageExplanation(object):
         self.image = image
         self.segments = segments
         self.intercept = {}
-        self.local_exp = {}
+        self.local_weights = {}
         self.local_pred = None
 
     def get_image_and_mask(self, label, positive_only=True, negative_only=False, hide_rest=False,
@@ -246,40 +261,40 @@ class ImageExplanation(object):
         """Init function.
 
         Args:
-            label: label to explain
+            label: label to interpret
             positive_only: if True, only take superpixels that positively contribute to
                 the prediction of the label.
             negative_only: if True, only take superpixels that negatively contribute to
                 the prediction of the label. If false, and so is positive_only, then both
                 negativey and positively contributions will be taken.
                 Both can't be True at the same time
-            hide_rest: if True, make the non-explanation part of the return
+            hide_rest: if True, make the non-interpretation part of the return
                 image gray
-            num_features: number of superpixels to include in explanation
-            min_weight: minimum weight of the superpixels to include in explanation
+            num_features: number of superpixels to include in interpretation
+            min_weight: minimum weight of the superpixels to include in interpretation
 
         Returns:
             (image, mask), where image is a 3d numpy array and mask is a 2d
             numpy array that can be used with
             skimage.segmentation.mark_boundaries
         """
-        if label not in self.local_exp:
-            raise KeyError('Label not in explanation')
+        if label not in self.local_weights:
+            raise KeyError('Label not in interpretation')
         if positive_only & negative_only:
             raise ValueError("Positive_only and negative_only cannot be true at the same time.")
         segments = self.segments
         image = self.image
-        exp = self.local_exp[label]
+        local_weights_label = self.local_weights[label]
         mask = np.zeros(segments.shape, segments.dtype)
         if hide_rest:
             temp = np.zeros(self.image.shape)
         else:
             temp = self.image.copy()
         if positive_only:
-            fs = [x[0] for x in exp
+            fs = [x[0] for x in local_weights_label
                   if x[1] > 0 and x[1] > min_weight][:num_features]
         if negative_only:
-            fs = [x[0] for x in exp
+            fs = [x[0] for x in local_weights_label
                   if x[1] < 0 and abs(x[1]) > min_weight][:num_features]
         if positive_only or negative_only:
             for f in fs:
@@ -287,7 +302,7 @@ class ImageExplanation(object):
                 mask[segments == f] = 1
             return temp, mask
         else:
-            for f, w in exp[:num_features]:
+            for f, w in local_weights_label[:num_features]:
                 if np.abs(w) < min_weight:
                     continue
                 c = 0 if w < 0 else 1
@@ -300,32 +315,31 @@ class ImageExplanation(object):
         """
 
         Args:
-            label: label to explain
+            label: label to interpret
             min_weight:
 
         Returns:
             image, is a 3d numpy array
         """
-        if label not in self.local_exp:
-            raise KeyError('Label not in explanation')
+        if label not in self.local_weights:
+            raise KeyError('Label not in interpretation')
 
         from matplotlib import cm
 
         segments = self.segments
         image = self.image
-        exp = self.local_exp[label]
+        local_weights_label = self.local_weights[label]
         temp = np.zeros_like(image)
 
-        weight_max = abs(exp[0][1])
-        exp = [(f, w/weight_max) for f, w in exp]
-        exp = sorted(exp, key=lambda x: x[1], reverse=True)  # negatives are at last.
+        weight_max = abs(local_weights_label[0][1])
+        local_weights_label = [(f, w/weight_max) for f, w in local_weights_label]
+        local_weights_label = sorted(local_weights_label, key=lambda x: x[1], reverse=True)  # negatives are at last.
 
         cmaps = cm.get_cmap('Spectral')
-        # sigmoid_space = 1 / (1 + np.exp(-np.linspace(-20, 20, len(exp))))
-        colors = cmaps(np.linspace(0, 1, len(exp)))
+        colors = cmaps(np.linspace(0, 1, len(local_weights_label)))
         colors = colors[:, :3]
 
-        for i, (f, w) in enumerate(exp):
+        for i, (f, w) in enumerate(local_weights_label):
             if np.abs(w) < min_weight:
                 continue
             temp[segments == f] = image[segments == f].copy()
@@ -333,14 +347,14 @@ class ImageExplanation(object):
         return temp
 
 
-class LimeImageExplainer(object):
-    """Explains predictions on Image (i.e. matrix) data.
+class LimeImageInterpreter(object):
+    """Interpres predictions on Image (i.e. matrix) data.
     For numerical features, perturb them by sampling from a Normal(0,1) and
     doing the inverse operation of mean-centering and scaling, according to the
     means and stds in the training data. For categorical features, perturb by
     sampling according to the training distribution, and making a binary
     feature that is 1 when the value is the same as the instance being
-    explained."""
+    interpreted."""
 
     def __init__(self, kernel_width=.25, kernel=None, verbose=False,
                  feature_selection='auto', random_state=None):
@@ -355,7 +369,7 @@ class LimeImageExplainer(object):
             verbose: if true, print local prediction values from linear model
             feature_selection: feature selection method. can be
                 'forward_selection', 'lasso_path', 'none' or 'auto'.
-                See function 'explain_instance_with_data' in lime_base.py for
+                See function 'einterpret_instance_with_data' in lime_base.py for
                 details on what each of the options does.
             random_state: an integer or numpy.RandomState that will be used to
                 generate random numbers. If None, the random state will be
@@ -373,18 +387,18 @@ class LimeImageExplainer(object):
         self.feature_selection = feature_selection
         self.base = LimeBase(kernel_fn, verbose, random_state=self.random_state)
 
-    def explain_instance(self, image, classifier_fn, labels=(1,),
-                         hide_color=None,
-                         num_features=100000, num_samples=1000,
-                         batch_size=10,
-                         distance_metric='cosine',
-                         model_regressor=None
-                         ):
-        """Generates explanations for a prediction.
+    def interpret_instance(self, image, classifier_fn, labels=(1,),
+                           hide_color=None,
+                           num_features=100000, num_samples=1000,
+                           batch_size=10,
+                           distance_metric='cosine',
+                           model_regressor=None
+                           ):
+        """Generates interpretations for a prediction.
 
         First, we generate neighborhood data by randomly perturbing features
         from the instance (see __data_inverse). We then learn locally weighted
-        linear models on this neighborhood data to explain each of the classes
+        linear models on this neighborhood data to interpret each of the classes
         in an interpretable way (see lime_base.py).
 
         Args:
@@ -393,19 +407,19 @@ class LimeImageExplainer(object):
             classifier_fn: classifier prediction probability function, which
                 takes a numpy array and outputs prediction probabilities.  For
                 ScikitClassifiers , this is classifier.predict_proba.
-            labels: iterable with labels to be explained.
+            labels: iterable with labels to be interpreted.
             hide_color: TODO
-            num_features: maximum number of features present in explanation
+            num_features: maximum number of features present in interpretation
             num_samples: size of the neighborhood to learn the linear model
             batch_size: TODO
             distance_metric: the distance metric to use for weights.
-            model_regressor: sklearn regressor to use in explanation. Defaults
+            model_regressor: sklearn regressor to use in interpretation. Defaults
             to Ridge regression in LimeBase. Must have model_regressor.coef_
             and 'sample_weight' as a parameter to model_regressor.fit()
 
         Returns:
-            An ImageExplanation object (see lime_image.py) with the corresponding
-            explanations.
+            An ImageIinterpretation object (see lime_image.py) with the corresponding
+            interpretations.
         """
         if len(image.shape) == 2:
             image = gray2rgb(image)
@@ -455,15 +469,15 @@ class LimeImageExplainer(object):
             metric=distance_metric
         ).ravel()
 
-        ret_exp = ImageExplanation(image, segments)
+        interpretation_image = ImageInterpretation(image, segments)
         for label in top:
-            (ret_exp.intercept[label],
-             ret_exp.local_exp[label],
-             ret_exp.score, ret_exp.local_pred) = self.base.explain_instance_with_data(
+            (interpretation_image.intercept[label],
+             interpretation_image.local_weights[label],
+             interpretation_image.score, interpretation_image.local_pred) = self.base.interpret_instance_with_data(
                 data, labels, distances, label, num_features,
                 model_regressor=model_regressor,
                 feature_selection=self.feature_selection)
-        return ret_exp
+        return interpretation_image
 
     def data_labels(self,
                     image,

paddlex/cv/models/explanation/core/normlime_base.py → paddlex/cv/models/interpret/core/normlime_base.py

@@ -87,11 +87,11 @@ def precompute_normlime_weights(list_data_, predict_fn, num_samples=3000, batch_
     return compute_normlime_weights(fname_list, save_dir, num_samples)
 
 
-def save_one_lime_predict_and_kmean_labels(lime_exp_all_weights, image_pred_labels, cluster_labels, save_path):
+def save_one_lime_predict_and_kmean_labels(lime_all_weights, image_pred_labels, cluster_labels, save_path):
 
     lime_weights = {}
     for label in image_pred_labels:
-        lime_weights[label] = lime_exp_all_weights[label]
+        lime_weights[label] = lime_all_weights[label]
 
     for_normlime_weights = {
         'lime_weights': lime_weights,  # a dict: class_label: (seg_label, weight)
@@ -145,15 +145,15 @@ def precompute_lime_weights(list_data_, predict_fn, num_samples, batch_size, sav
 
         pred_label = pred_label[:top_k]
 
-        algo = lime_base.LimeImageExplainer()
-        explainer = algo.explain_instance(image_show[0], predict_fn, pred_label, 0,
+        algo = lime_base.LimeImageInterpreter()
+        interpreter = algo.interpret_instance(image_show[0], predict_fn, pred_label, 0,
                                           num_samples=num_samples, batch_size=batch_size)
 
         cluster_labels = kmeans_model.predict(
-            get_feature_for_kmeans(compute_features_for_kmeans(image_show).transpose((1, 2, 0)), explainer.segments)
+            get_feature_for_kmeans(compute_features_for_kmeans(image_show).transpose((1, 2, 0)), interpreter.segments)
         )
         save_one_lime_predict_and_kmean_labels(
-            explainer.local_exp, pred_label,
+            interpreter.local_weights, pred_label,
             cluster_labels,
             save_path
         )

paddlex/cv/models/explanation/visualize.py → paddlex/cv/models/interpret/visualize.py

@@ -17,19 +17,19 @@ import cv2
 import copy
 import os.path as osp
 import numpy as np
-from .core.explanation import Explanation
+from .core.interpretation import Interpretation
 from .core.normlime_base import precompute_normlime_weights
 
 
 def visualize(img_file, 
               model, 
               dataset=None,
-              explanation_type='lime',
+              algo='lime',
               num_samples=3000, 
               batch_size=50,
               save_dir='./'):
     if model.status != 'Normal':
-        raise Exception('The explanation only can deal with the Normal model')
+        raise Exception('The interpretation only can deal with the Normal model')
     model.arrange_transforms(
                 transforms=model.test_transforms, mode='test')
     tmp_transforms = copy.deepcopy(model.test_transforms)
@@ -37,48 +37,48 @@ def visualize(img_file,
     img = tmp_transforms(img_file)[0]
     img = np.around(img).astype('uint8')
     img = np.expand_dims(img, axis=0)
-    explaier = None
-    if explanation_type == 'lime':
-        explaier = get_lime_explaier(img, model, dataset, num_samples=num_samples, batch_size=batch_size)
-    elif explanation_type == 'normlime':
+    interpreter = None
+    if algo == 'lime':
+        interpreter = get_lime_interpreter(img, model, dataset, num_samples=num_samples, batch_size=batch_size)
+    elif algo == 'normlime':
         if dataset is None:
-            raise Exception('The dataset is None. Cannot implement this kind of explanation')
-        explaier = get_normlime_explaier(img, model, dataset, 
+            raise Exception('The dataset is None. Cannot implement this kind of interpretation')
+        interpreter = get_normlime_interpreter(img, model, dataset, 
                                      num_samples=num_samples, batch_size=batch_size,
                                      save_dir=save_dir)
     else:
-        raise Exception('The {} explanantion method is not supported yet!'.format(explanation_type))
+        raise Exception('The {} interpretation method is not supported yet!'.format(algo))
     img_name = osp.splitext(osp.split(img_file)[-1])[0]
-    explaier.explain(img, save_dir=save_dir)
+    interpreter.interpret(img, save_dir=save_dir)
     
     
-def get_lime_explaier(img, model, dataset, num_samples=3000, batch_size=50):
+def get_lime_interpreter(img, model, dataset, num_samples=3000, batch_size=50):
     def predict_func(image):
         image = image.astype('float32')
         for i in range(image.shape[0]):
             image[i] = cv2.cvtColor(image[i], cv2.COLOR_RGB2BGR)
         tmp_transforms = copy.deepcopy(model.test_transforms.transforms)
         model.test_transforms.transforms = model.test_transforms.transforms[-2:]
-        out = model.explanation_predict(image)
+        out = model.interpretation_predict(image)
         model.test_transforms.transforms = tmp_transforms
         return out[0]
     labels_name = None
     if dataset is not None:
         labels_name = dataset.labels
-    explaier = Explanation('lime', 
+    interpreter = Interpretation('lime', 
                             predict_func,
                             labels_name,
                             num_samples=num_samples, 
                             batch_size=batch_size)
-    return explaier
+    return interpreter
 
 
-def get_normlime_explaier(img, model, dataset, num_samples=3000, batch_size=50, save_dir='./'):
+def get_normlime_interpreter(img, model, dataset, num_samples=3000, batch_size=50, save_dir='./'):
     def precompute_predict_func(image):
         image = image.astype('float32')
         tmp_transforms = copy.deepcopy(model.test_transforms.transforms)
         model.test_transforms.transforms = model.test_transforms.transforms[-2:]
-        out = model.explanation_predict(image)
+        out = model.interpretation_predict(image)
         model.test_transforms.transforms = tmp_transforms
         return out[0]
     def predict_func(image):
@@ -87,7 +87,7 @@ def get_normlime_explaier(img, model, dataset, num_samples=3000, batch_size=50,
             image[i] = cv2.cvtColor(image[i], cv2.COLOR_RGB2BGR)
         tmp_transforms = copy.deepcopy(model.test_transforms.transforms)
         model.test_transforms.transforms = model.test_transforms.transforms[-2:]
-        out = model.explanation_predict(image)
+        out = model.interpretation_predict(image)
         model.test_transforms.transforms = tmp_transforms
         return out[0]
     labels_name = None
@@ -105,13 +105,13 @@ def get_normlime_explaier(img, model, dataset, num_samples=3000, batch_size=50,
                                       num_samples=num_samples, 
                                       batch_size=batch_size,
                                       save_dir=save_dir)
-    explaier = Explanation('normlime', 
+    interpreter = Interpretation('normlime', 
                             predict_func,
                             labels_name,
                             num_samples=num_samples, 
                             batch_size=batch_size,
                             normlime_weights=npy_dir)
-    return explaier
+    return interpreter
 
 
 def precompute_for_normlime(predict_func, dataset, num_samples=3000, batch_size=50, save_dir='./'):

paddlex/explanation.py → paddlex/interpret.py

@@ -13,6 +13,6 @@
 # limitations under the License.
 
 from __future__ import absolute_import
-from .cv.models.explanation import visualize
+from .cv.models.interpret import visualize
 
 visualize = visualize.visualize
\ No newline at end of file