-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathutils.py
192 lines (181 loc) · 8.44 KB
/
utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
#Common utility file
#From Fast-Lin repository
#https://github.com/huanzhang12/CertifiedReLURobustness
import numpy as np
import random
import os
import pandas as pd
from PIL import Image
random.seed(1215)
np.random.seed(1215)
def linf_dist(x, y):
return np.linalg.norm(x.flatten() - y.flatten(), ord=np.inf)
def l2_dist(x, y):
return np.linalg.norm(x.flatten() - y.flatten(), ord=2)
def l1_dist(x, y):
return np.linalg.norm(x.flatten() - y.flatten(), ord=1)
def l0_dist(x, y):
return np.linalg.norm(x.flatten() - y.flatten(), ord=0)
def show(img, name = "output.png"):
"""
Show MNSIT digits in the console.
"""
np.save('img', img)
fig = np.around((img + 0.5)*255)
fig = fig.astype(np.uint8).squeeze()
pic = Image.fromarray(fig)
# pic.resize((512,512), resample=PIL.Image.BICUBIC)
pic.save(name)
remap = " .*#"+"#"*100
img = (img.flatten()+.5)*3
return
if len(img) != 784: return
print("START")
for i in range(28):
print("".join([remap[int(round(x))] for x in img[i*28:i*28+28]]))
def generate_data(data, samples, targeted=True, random_and_least_likely = False, skip_wrong_label = True, start=0, ids = None,
target_classes = None, target_type = 0b1111, predictor = None, imagenet=False, remove_background_class=False, save_inputs=False, model_name=None, save_inputs_dir=None):
"""
Generate the input data to the attack algorithm.
data: the images to attack
samples: number of samples to use
targeted: if true, construct targeted attacks, otherwise untargeted attacks
start: offset into data to use
ids: true IDs of images in the dataset, if given, will use these images
target_classes: a list of list of labels for each ids
inception: if targeted and inception, randomly sample 100 targets intead of 1000
"""
inputs = []
targets = []
true_labels = []
true_ids = []
information = []
target_candidate_pool = np.eye(data.test_labels.shape[1])
target_candidate_pool_remove_background_class = np.eye(data.test_labels.shape[1] - 1)
print('generating labels...')
if ids is None:
ids = range(samples)
else:
ids = ids[start:start+samples]
if target_classes:
target_classes = target_classes[start:start+samples]
start = 0
total = 0
for i in ids:
total += 1
if targeted:
predicted_label = -1 # unknown
if random_and_least_likely:
# if there is no user specified target classes
if target_classes is None:
original_predict = np.squeeze(predictor(np.array([data.test_data[start+i]])))
num_classes = len(original_predict)
predicted_label = np.argmax(original_predict)
least_likely_label = np.argmin(original_predict)
top2_label = np.argsort(original_predict)[-2]
start_class = 1 if (imagenet and not remove_background_class) else 0
random_class = predicted_label
new_seq = [least_likely_label, top2_label, predicted_label]
while random_class in new_seq:
random_class = random.randint(start_class, start_class + num_classes - 1)
new_seq[2] = random_class
true_label = np.argmax(data.test_labels[start+i])
seq = []
if true_label != predicted_label and skip_wrong_label:
seq = []
else:
if target_type & 0b10000:
for c in range(num_classes):
if c != predicted_label:
seq.append(c)
information.append('class'+str(c))
else:
if target_type & 0b0100:
# least
seq.append(new_seq[0])
information.append('least')
if target_type & 0b0001:
# top-2
seq.append(new_seq[1])
information.append('top2')
if target_type & 0b0010:
# random
seq.append(new_seq[2])
information.append('random')
else:
# use user specified target classes
seq = target_classes[total - 1]
information.extend(len(seq) * ['user'])
else:
if imagenet:
if remove_background_class:
seq = random.sample(range(0,1000), 10)
else:
seq = random.sample(range(1,1001), 10)
information.extend(data.test_labels.shape[1] * ['random'])
else:
seq = range(data.test_labels.shape[1])
information.extend(data.test_labels.shape[1] * ['seq'])
print("[DATAGEN][L1] no = {}, true_id = {}, true_label = {}, predicted = {}, correct = {}, seq = {}, info = {}".format(total, start + i,
np.argmax(data.test_labels[start+i]), predicted_label, np.argmax(data.test_labels[start+i]) == predicted_label, seq, [] if len(seq) == 0 else information[-len(seq):]))
for j in seq:
# skip the original image label
if (j == np.argmax(data.test_labels[start+i])):
continue
inputs.append(data.test_data[start+i])
if remove_background_class:
targets.append(target_candidate_pool_remove_background_class[j])
else:
targets.append(target_candidate_pool[j])
true_labels.append(data.test_labels[start+i])
if remove_background_class:
true_labels[-1] = true_labels[-1][1:]
true_ids.append(start+i)
else:
true_label = np.argmax(data.test_labels[start+i])
original_predict = np.squeeze(predictor(np.array([data.test_data[start+i]])))
num_classes = len(original_predict)
predicted_label = np.argmax(original_predict)
if true_label != predicted_label and skip_wrong_label:
continue
else:
inputs.append(data.test_data[start+i])
if remove_background_class:
# shift target class by 1
print(np.argmax(data.test_labels[start+i]))
print(np.argmax(data.test_labels[start+i][1:1001]))
targets.append(data.test_labels[start+i][1:1001])
else:
targets.append(data.test_labels[start+i])
true_labels.append(data.test_labels[start+i])
if remove_background_class:
true_labels[-1] = true_labels[-1][1:]
true_ids.append(start+i)
information.extend(['original'])
inputs = np.array(inputs)
targets = np.array(targets)
true_labels = np.array(true_labels)
true_ids = np.array(true_ids)
print('labels generated')
print('{} images generated in total.'.format(len(inputs)))
if save_inputs:
if not os.path.exists(save_inputs_dir):
os.makedirs(save_inputs_dir)
save_model_dir = os.path.join(save_inputs_dir,model_name)
if not os.path.exists(save_model_dir):
os.makedirs(save_model_dir)
info_set = list(set(information))
for info_type in info_set:
save_type_dir = os.path.join(save_model_dir,info_type)
if not os.path.exists(save_type_dir):
os.makedirs(save_type_dir)
counter = 0
for i in range(len(information)):
if information[i] == info_type:
df = inputs[i,:,:,0]
df = df.flatten()
np.savetxt(os.path.join(save_type_dir,'point{}.txt'.format(counter)),df,newline='\t')
counter += 1
target_labels = np.array([np.argmax(targets[i]) for i in range(len(information)) if information[i]==info_type])
np.savetxt(os.path.join(save_model_dir,model_name+'_target_'+info_type+'.txt'),target_labels,fmt='%d',delimiter='\n')
return inputs, targets, true_labels, true_ids, information