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from __future__ import print_function
from aix360.algorithms.lwbe import LocalWBExplainer
from aix360.algorithms.contrastive.CEM_MAF_aen_PN import AEADEN as AEADEN_PN
from aix360.algorithms.contrastive.CEM_MAF_aen_PP import AEADEN as AEADEN_PP
from tensorflow.contrib.keras.api.keras.models import model_from_json
import os
import sys
import random
import time
import numpy as np
from skimage.segmentation import slic
class CEM_MAFImageExplainer(LocalWBExplainer):
"""CEM_MAFImageExplainer is a Contrastive Image explainer that leverages Monotonic
Attribute Functions. The main idea here is to explain images using high level semantically meaningful attributes
that may either be directly available or learned through supervised or unsupervised methods. [#]_
.. [#] `Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri,
Karthikeyan Shanmugam, Chun-Chen Tu, "Generating Contrastive
Explanations with Monotonic Attribute Functions," 2019.
def __init__(self, model, attributes, aix360_path):
"""Initialize image explainer.
Currently accepting model input which is an ImageClassifier.
super(CEM_MAFImageExplainer, self).__init__()
self._wbmodel = model
self._attributes = attributes
self._aix360_path = aix360_path
def set_params(self, *argv, **kwargs):
"""Set parameters for the explainer."""
def explain_instance(self, sess, input_img, input_latent, arg_mode, arg_kappa, arg_binary_search_steps,
arg_max_iterations, arg_initial_const, arg_gamma, arg_beta, arg_attr_reg=1,
arg_attr_penalty_reg=1, arg_latent_square_loss_reg=1):
"""Explains an input instance input_image e.g. celebA is shape (1, 224, 224, 3)
Hard coded batch_size=1, assuming we provide explanation for 1 input_image at a time. Returns
either pertinent positive or pertinent depending on parameter.
sess (tensorflow.python.client.session.Session): Tensorflow session
input_img (numpy.ndarray): image to be explained, of shape (1, size, size, channels)
input_latent (numpy.ndarray): image to be explained, of shape (1, size, size, channels)
in the latent space
arg_mode (str): "PN" for pertinent negative or "PP" for pertinent positive
arg_kappa (float): Confidence parameter that controls difference between prediction of
PN (or PP) and original prediction
arg_binary_search_steps (int): Controls number of random restarts to find best PN or PP
arg_max_iterations (int): Max number iterations to run some version of gradient descent on
PN or PP optimization problem from a single random initialization, i.e., total
number of iterations wll be arg_binary_search_steps * arg_max_iterations
arg_initial_const (int): Constant used for upper/lower bounds in binary search
arg_gamma (float): Penalty parameter encouraging addition of attributes for PN or PP
arg_beta (float): Penalty parameter encourages minimal addition of attributes to PN
or sparsity of the mask that generates the PP
arg_attr_reg (float): Penalty parameter on regularization of PN to be predicted different from
original image
arg_attr_penalty_reg (float): Penalty regularizing PN from being too different from original image
arg_latent_square_loss_reg (float): Penalty regularizing PN from being too different from original
image in the latent space
* **adv_img** (`numpy.ndarray`) -- the pertinent positive or the pertinent negative image
* **attr_mod** (`str`) -- only for PN; a string detailing which attributes were modified from the
original image
* **INFO** (`str`) -- only for PN; a string of information about original vs PN class and
original vs PN prediction probability
# %%change%%
#(orig_prob, orig_class, orig_prob_str) = util.model_prediction(model, input_img)
(orig_prob, orig_class, orig_prob_str) = self._wbmodel.predict_long(input_img)
if arg_mode == 'PN':
target_label = [np.eye(self._wbmodel._nb_classes)[orig_class]]
attack_pn = AEADEN_PN(sess, self._wbmodel, attributes=self._attributes, aix360_path=self._aix360_path,
mode = arg_mode, batch_size=1, kappa=arg_kappa, init_learning_rate=1e-2,
binary_search_steps=arg_binary_search_steps, max_iterations=arg_max_iterations,
initial_const=arg_initial_const, gamma=arg_gamma, attr_reg=arg_attr_reg,
attr_penalty_reg=arg_attr_penalty_reg, latent_square_loss_reg=arg_latent_square_loss_reg)
adv_img = attack_pn.attack(input_img, target_label, input_latent)
adv_prob, adv_class, adv_prob_str = self._wbmodel.predict_long(adv_img)
attr_mod = self.check_attributes_celebA(self._attributes, input_img, adv_img)
INFO = "[INFO] Orig class:{}, Adv class:{}, Orig prob:{}, Adv prob:{}".format(orig_class, adv_class, orig_prob_str, adv_prob_str)
else: # assume arg_mode is PP
print("Creating a mask for pertinent positive")
# create mask
arg_seg_number = 200
# Segment the original image using and create a mask for the segmentation
#data = CELEBA_wrapper(os.path.join(img_path, "{}_img.npy".format(img_id)), orig_class, model)
mask_label = slic(input_img, n_segments=arg_seg_number)[0]
mask_num = len(np.unique(mask_label))
mask_size = mask_label.shape[0]
mask_mat = np.zeros((mask_num, mask_size, mask_size))
for i in range(mask_num):
temp_idx = np.argwhere(mask_label==i)
for j in temp_idx:
mask_mat[(i,) + tuple(j)] = 1
attack_pp = AEADEN_PP(sess, self._wbmodel, mask_mat=mask_mat, mode=arg_mode, batch_size=1, \
kappa=arg_kappa, init_learning_rate=1e-2, binary_search_steps=arg_binary_search_steps, \
max_iterations=arg_max_iterations, initial_const=arg_initial_const, beta=arg_beta, \
gamma=arg_gamma, attributes=self._attributes, aix360_path=self._aix360_path)
target = np.zeros(self._wbmodel._nb_classes)
adv_img, img_mask = attack_pp.attack(input_img, [target])
adv_prob, adv_class, adv_prob_str = self._wbmodel.predict_long(adv_img)
print('Generating the pertinent positive')
# Generate the PP
success = False
print("Start ranking:")
mask_vec = img_mask.reshape(-1)
sort_idx = np.argsort(mask_vec)
total_nonezero = len(np.argsort(mask_vec>0))
working_mask = np.zeros((1,) + (mask_size, mask_size) + (1,))
for i in range(1,total_nonezero):
temp_index = sort_idx[-i]
mask_position = np.argwhere(mask_mat[temp_index]==1)
for index in mask_position:
working_mask[(0,) + tuple(index) + (0,)] = 1
adv_img = working_mask * input_img
img_prob, img_class, img_prob_str = self._wbmodel.predict_long(adv_img)
print("i:{}, index:{}, value:{}, class:{}".format(i, temp_index, mask_vec[temp_index], img_class))
if img_class == orig_class:
success = True
attr_mod = None
INFO = None
return(adv_img, attr_mod, INFO)
def check_attributes_celebA(self, attributes, x, y):
Load attribute classifiers and check which attributes in original image x
are modified in adversarial image y
attributes (str list): list of attributes to load attribute classifiers for
x (numpy.ndarray): original image
y (numpy.ndarray): adversarial image
str: string detailing which attributes were added to (or removed from)
x resulting in y
orig_attr_score = np.zeros((len(attributes),1))
adv_attr_score = np.zeros((len(attributes),1))
for i in range(len(attributes)):
attr = attributes[i]
# load json and create model
json_file_name = "../../aix360/models/CEM_MAF/simple_{}_model.json".format(attr)
json_file = open(json_file_name, 'r')
loaded_model_json =
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
weight_file_name = "../../aix360/models/CEM_MAF/simple_{}_weights.h5".format(attr)
orig_attr_score[i] = loaded_model.predict(x)[0]
adv_attr_score[i] = loaded_model.predict(y)[0]
# pre-determined thresholds for changes in prediction values
thresh_pos = np.zeros((len(attributes),1))
thresh_pos[0] = .15
thresh_pos[1] = .15
thresh_pos[2] = .15
thresh_pos[3] = .15
thresh_pos[4] = .15
thresh_pos[5] = .15
thresh_pos[6] = .1
thresh_pos[7] = .25
thresh_pos[8] = .1
thresh_pos[9] = .15
thresh_pos[10] = .15
thresh_pos[11] = .15
thresh_neg = np.zeros((len(attributes),1))
thresh_neg[0] = -.25
thresh_neg[1] = -.25
thresh_neg[2] = -.25
thresh_neg[3] = -.25
thresh_neg[4] = -.35
thresh_neg[5] = -.25
thresh_neg[6] = -.12
thresh_neg[7] = -.25
thresh_neg[8] = -.25
thresh_neg[9] = -.25
thresh_neg[10] = -.25
thresh_neg[11] = -.25
changes_abs = adv_attr_score - orig_attr_score
changes = np.zeros((len(attributes),1))
res = ""
for i in range(len(attributes)):
if changes_abs[i] >= thresh_pos[i]:
changes[i] = 1
elif changes_abs[i] <= thresh_neg[i]:
changes[i] = -1
added = np.where(changes == 1)[0]
for j in range(len(added)):
res += "Added " + attributes[added[j]] + ","
removed = np.where(changes[i] == -1)[0]
for j in range(len(removed)):
res += "Removed " + attributes[removed[j]] + ","
return res[:-1]
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