update

Grad_CAM_Prior.py

@@ -1,4 +1,6 @@
 import os
+import time
+import shutil
 import tensorflow.keras
 from tensorflow.keras.applications import inception_v3 as inc_net
 from tensorflow.keras.preprocessing import image
@@ -13,6 +15,13 @@
 from lime import calculate_posteriors
 print('Notebook run using keras:', tensorflow.keras.__version__)
 
+def mkdir(path):
+    folder = os.path.exists(path)
+    if not folder:
+        os.makedirs(path)
+    else:
+        shutil.rmtree(path)
+        os.mkdir(path)
 
 ############################################################
 # use heatmap from Grad-CAM as prior knowledge for BayLime #
@@ -31,49 +40,101 @@ def transform_img_fn(path_list):
         out.append(x)
     return np.vstack(out)
 
+mkdir('evaluation_output')
+fname = 'evaluation_output'
 
-images = transform_img_fn([os.path.join('data','5.jpg')])
+images = transform_img_fn([os.path.join('data','penguin.jpeg')])
 
 # I'm dividing by 2 and adding 0.5 because of
 # how this Inception represents images
 
-plt.imshow(images[0] / 2 + 0.5)
-plt.show()
+deletion = evaluation.CausalMetric(inet_model,'del')
+insertion = evaluation.CausalMetric(inet_model,'ins')
+
+
 preds = inet_model.predict(images)
 pred_label = decode_predictions(preds)[0]
-# for x in decode_predictions(preds)[0]:
-#     print(x)
+
+
+time1 = time.time()
 
 explainer = lime_image.LimeImageExplainer(feature_selection='none')#kernel_width=0.1
 
 explanation = explainer.explain_instance(images[0], inet_model.predict,
                                          top_labels=1, hide_color=0, batch_size=15,
-                                         num_samples=100,model_regressor='Bay_info_prior')
+                                         num_samples=200, model_regressor='BayesianRidge_inf_prior_fit_alpha')
+
 #'non_Bay' 'Bay_non_info_prior' 'Bay_info_prior','BayesianRidge_inf_prior_fit_alpha'
 
+time2 = time.time()
+
+temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], num_features=10, hide_rest=False)
+fig, ax = plt.subplots(figsize=(6,6))
+ax.imshow(mark_boundaries(temp / 2 + 0.5, mask))
+ax.set_ylabel(pred_label[0][1],fontsize=20)
+fig.savefig(fname+'/Lime_exp.png',bbox_inches='tight')
+
+
+h1_del = deletion.single_run(images[0], explanation.local_exp[explanation.top_labels[0]], explanation.segments, explanation.top_labels[0], pred_label, 'Lime',fname)
+h1_ins = insertion.single_run(images[0], explanation.local_exp[explanation.top_labels[0]], explanation.segments, explanation.top_labels[0], pred_label, 'Lime',fname)
+
+
+time3 = time.time()
 
 # extract the prior knowledge from grad-cam
-prior_knowledge = Grad_CAM.extrat_prior(images,inet_model,explanation)
+prior_knowledge = Grad_CAM.extrat_prior(images[0],inet_model,explanation,fname,pred_label[0][1])
+prior_exp = np.flip(np.argsort(abs(np.array(prior_knowledge))))
+seg = explanation.segments
 
+time4 = time.time()
+
+h2_del = deletion.single_run(images[0], prior_exp, seg, explanation.top_labels[0], pred_label, 'Grad_CAM',fname)
+h2_ins = insertion.single_run(images[0], prior_exp, seg, explanation.top_labels[0], pred_label, 'Grad_CAM',fname)
 
-# update the explanation with prior
-alpha_var=1
-lambda_var=2048
 
-explanation=calculate_posteriors.get_posterior(explanation,prior_knowledge,
-                                               hyper_para_alpha=alpha_var,
-                                               hyper_para_lambda=lambda_var,
+time5 = time.time()
+
+# update the explanation with prior
+alpha_init=1
+lambda_init=1
+with open('./posterior_configure.csv') as csv_file:
+    csv_reader=csv.reader(csv_file)
+    line_count = 0
+    for row in csv_reader:
+        if line_count == 1:
+            alpha_init=float(row[0])
+            lambda_init=float(row[1])
+        line_count=line_count+1
+
+
+explanation = calculate_posteriors.get_posterior(explanation,prior_knowledge,
+                                               hyper_para_alpha=alpha_init,
+                                               hyper_para_lambda=lambda_init,
                                                label=explanation.top_labels[0])
 
-deletion = evaluation.CausalMetric(inet_model,'del')
-insertion = evaluation.CausalMetric(inet_model,'ins')
-h = deletion.single_run(images[0], explanation, pred_label)
-h = insertion.single_run(images[0], explanation, pred_label)
+time6 = time.time()
+
+temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], num_features=10, hide_rest=False)
+fig, ax = plt.subplots(figsize=(6,6))
+ax.imshow(mark_boundaries(temp / 2 + 0.5, mask))
+ax.set_ylabel(pred_label[0][1],fontsize=20)
+fig.savefig(fname+'/BayLime_exp.png',bbox_inches='tight')
+
+h3_del = deletion.single_run(images[0], explanation.local_exp[explanation.top_labels[0]], explanation.segments, explanation.top_labels[0], pred_label, 'BayLime',fname)
+h3_ins = insertion.single_run(images[0], explanation.local_exp[explanation.top_labels[0]], explanation.segments, explanation.top_labels[0], pred_label, 'BayLime',fname)
+
 
-temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=False, num_features=5, hide_rest=False)
 
 
-plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))
-plt.show()
+print('-----------------------------')
+print('Lime deletion: ', h1_del)
+print('Lime insertion: ', h1_ins)
+print('Grad-CAM deletion: ', h2_del)
+print('Grad-CAM insertion: ', h2_ins)
+print('Baylime deletion: ', h3_del)
+print('Baylime insertion: ', h3_ins)
+print('-----------------------------')
+print("grad_CAM:", time4 - time3, "s")
+print("Lime :", time2 - time1, "s")
+print("BayLime :", time6 - time5 + time2 - time1 + time4 - time3, "s")
 
-print(explanation.as_list(explanation.top_labels[0]))

README.md

@@ -9,8 +9,12 @@ BayLIME is a Bayesian modification of LIME that provides a principled mechanism
 from sklearn import linear_model
 print(linear_model.__file__)
 ```
+3. Download the necessary dataset for ImageNet and GTSRB model, unzip the files and move to the data folder.
+```
+ImageNet (original images): http://image-net.org/download-images
+GTSRB (.h5 file): https://drive.google.com/file/d/1MjgsnH3bOYG_PvdvqmoamoCPmySQazRJ/view?usp=sharing
+```
 (Tested with Python version 3.7.3, scikit-learn version 0.22.1, Tensorflow version 2.0.0)
-
 ## Repository Structure
 
 * **experiments** contains the experiments for the draft paper, in which you may find both the code (in Python jupyter-notebook) and the original data generated (stored as HTML and .csv files).
@@ -30,3 +34,26 @@ Now when calling the explainer.explain_instance() API of BayLime, we have four o
 4. model_regressor='BayesianRidge_inf_prior_fit_alpha' uses the modified BayesianRidge regressor and reads the hyperparameters lambda from configuration files and fit alpha from sampling data.
 
 Please refer to the tutorials (e.g., BayLIME_tutorial_images.ipynb)  for details.
+
+## Embed Prior from GradCAM
+To get the explanation for specified image (e.g. king penguin) in data folder, firstly modify the image path in Line 46 of Grad_CAM_Prior.py, then type
+```
+python Grad_CAM_Prior.py
+```
+You will get the explanation results along with Deletion and Insertion AUC figures from GradCAM, LIME and BayLIME under the created *evaluation_output* folder.
+To get statistical fidelity evaluation on ImageNet dataset, firstly make sure the validation dataset from ImageNet called ILSVRC2012_img_val is already downloaded and moved to the data folder, then type
+```
+python del_ins_exp.py
+```
+You will the explanation result for each image from ImageNet and a record file for recording the runtime output in the created *evaluation_output* folder. Be cautious the evaluation_output folder will be reset every time running the program, so take a copy if you want to save the running results.
+
+## Embed Prior from Neural Cleanse
+In backdoor_exp.py, we provide the explanation for backdoor input from BadNet and TrojanAttack models. To get the IoU and AMD evaluation for Prior, LIME and BayLIME, type the command
+```
+python backdoor_exp.py
+```
+You will get the print out of IoU and AMD scores for each backdoor attacked images. The interpretation of IoU and AMD scores can be referred to the paper.
+
+
+
+