-
Notifications
You must be signed in to change notification settings - Fork 17
/
simple_eval.py
executable file
·158 lines (131 loc) · 7.79 KB
/
simple_eval.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
# coding=utf-8
"""Evaluate the attack success rate under 8 models including normal training models and adversarial training models"""
import os
import random
import numpy as np
import tensorflow as tf
from imageio import imread
import pandas as pd
from nets import inception_v3, inception_v4, inception_resnet_v2, resnet_v2
slim = tf.contrib.slim
checkpoint_path = './models'
model_checkpoint_map = {
'inception_v3': os.path.join(checkpoint_path, 'inception_v3.ckpt'),
'adv_inception_v3': os.path.join(checkpoint_path, 'adv_inception_v3_rename.ckpt'),
'ens3_adv_inception_v3': os.path.join(checkpoint_path, 'ens3_adv_inception_v3_rename.ckpt'),
'ens4_adv_inception_v3': os.path.join(checkpoint_path, 'ens4_adv_inception_v3_rename.ckpt'),
'inception_v4': os.path.join(checkpoint_path, 'inception_v4.ckpt'),
'inception_resnet_v2': os.path.join(checkpoint_path, 'inception_resnet_v2_2016_08_30.ckpt'),
'ens_adv_inception_resnet_v2': os.path.join(checkpoint_path, 'ens_adv_inception_resnet_v2_rename.ckpt'),
'resnet_v2': os.path.join(checkpoint_path, 'resnet_v2_101.ckpt')}
def load_labels(file_name):
dev = pd.read_csv(file_name)
f2l = {dev.iloc[i]['filename']: dev.iloc[i]['label'] for i in range(len(dev))}
return f2l
def load_images(input_dir, batch_shape):
"""Read png images from input directory in batches.
Args:
input_dir: input directory
batch_shape: shape of minibatch array, i.e. [batch_size, height, width, 3]
Yields:
filenames: list file names without path of each image
Lenght of this list could be less than batch_size, in this case only
first few images of the result are elements of the minibatch.
images: array with all images from this batch
"""
images = np.zeros(batch_shape)
filenames = []
idx = 0
batch_size = batch_shape[0]
for filepath in tf.gfile.Glob(os.path.join(input_dir, '*')):
with tf.gfile.Open(filepath, 'rb') as f:
image = imread(f, pilmode='RGB').astype(np.float) / 255.0
# Images for inception classifier are normalized to be in [-1, 1] interval.
images[idx, :, :, :] = image * 2.0 - 1.0
filenames.append(os.path.basename(filepath))
idx += 1
if idx == batch_size:
yield filenames, images
filenames = []
images = np.zeros(batch_shape)
idx = 0
if idx > 0:
yield filenames, images
if __name__ == '__main__':
f2l = load_labels('./dev_data/val_rs.csv')
input_dir = './outputs'
batch_shape = [50, 299, 299, 3]
num_classes = 1001
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
x_input = tf.placeholder(tf.float32, shape=batch_shape)
with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
logits_v3, end_points_v3 = inception_v3.inception_v3(
x_input, num_classes=num_classes, is_training=False)
with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
logits_adv_v3, end_points_adv_v3 = inception_v3.inception_v3(
x_input, num_classes=num_classes, is_training=False, scope='AdvInceptionV3')
with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
logits_ens3_adv_v3, end_points_ens3_adv_v3 = inception_v3.inception_v3(
x_input, num_classes=num_classes, is_training=False, scope='Ens3AdvInceptionV3')
with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
logits_ens4_adv_v3, end_points_ens4_adv_v3 = inception_v3.inception_v3(
x_input, num_classes=num_classes, is_training=False, scope='Ens4AdvInceptionV3')
with slim.arg_scope(inception_v4.inception_v4_arg_scope()):
logits_v4, end_points_v4 = inception_v4.inception_v4(
x_input, num_classes=num_classes, is_training=False)
with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope()):
logits_res_v2, end_points_res_v2 = inception_resnet_v2.inception_resnet_v2(
x_input, num_classes=num_classes, is_training=False)
with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope()):
logits_ens_adv_res_v2, end_points_ens_adv_res_v2 = inception_resnet_v2.inception_resnet_v2(
x_input, num_classes=num_classes, is_training=False, scope='EnsAdvInceptionResnetV2')
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits_resnet, end_points_resnet = resnet_v2.resnet_v2_101(
x_input, num_classes=num_classes, is_training=False)
pred_v3 = tf.argmax(end_points_v3['Predictions'], 1)
pred_adv_v3 = tf.argmax(end_points_adv_v3['Predictions'], 1)
pred_ens3_adv_v3 = tf.argmax(end_points_ens3_adv_v3['Predictions'], 1)
pred_ens4_adv_v3 = tf.argmax(end_points_ens4_adv_v3['Predictions'], 1)
pred_v4 = tf.argmax(end_points_v4['Predictions'], 1)
pred_res_v2 = tf.argmax(end_points_res_v2['Predictions'], 1)
pred_ens_adv_res_v2 = tf.argmax(end_points_ens_adv_res_v2['Predictions'], 1)
pred_resnet = tf.argmax(end_points_resnet['predictions'], 1)
s1 = tf.train.Saver(slim.get_model_variables(scope='InceptionV3'))
s2 = tf.train.Saver(slim.get_model_variables(scope='AdvInceptionV3'))
s3 = tf.train.Saver(slim.get_model_variables(scope='Ens3AdvInceptionV3'))
s4 = tf.train.Saver(slim.get_model_variables(scope='Ens4AdvInceptionV3'))
s5 = tf.train.Saver(slim.get_model_variables(scope='InceptionV4'))
s6 = tf.train.Saver(slim.get_model_variables(scope='InceptionResnetV2'))
s7 = tf.train.Saver(slim.get_model_variables(scope='EnsAdvInceptionResnetV2'))
s8 = tf.train.Saver(slim.get_model_variables(scope='resnet_v2'))
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
s1.restore(sess, model_checkpoint_map['inception_v3'])
s2.restore(sess, model_checkpoint_map['adv_inception_v3'])
s3.restore(sess, model_checkpoint_map['ens3_adv_inception_v3'])
s4.restore(sess, model_checkpoint_map['ens4_adv_inception_v3'])
s5.restore(sess, model_checkpoint_map['inception_v4'])
s6.restore(sess, model_checkpoint_map['inception_resnet_v2'])
s7.restore(sess, model_checkpoint_map['ens_adv_inception_resnet_v2'])
s8.restore(sess, model_checkpoint_map['resnet_v2'])
model_name = ['inception_v3', 'inception_v4', 'inception_resnet_v2',
'resnet_v2', 'ens3_adv_inception_v3', 'ens4_adv_inception_v3',
'ens_adv_inception_resnet_v2', 'adv_inception_v3']
success_count = np.zeros(len(model_name))
idx = 0
for filenames, images in load_images(input_dir, batch_shape):
idx += 1
print("start the i={} eval".format(idx))
v3, adv_v3, ens3_adv_v3, ens4_adv_v3, v4, res_v2, ens_adv_res_v2, resnet = sess.run(
(pred_v3, pred_adv_v3, pred_ens3_adv_v3, pred_ens4_adv_v3, pred_v4, pred_res_v2,
pred_ens_adv_res_v2, pred_resnet), feed_dict={x_input: images})
for filename, l1, l2, l3, l4, l5, l6, l7, l8 in zip(filenames, v3, adv_v3, ens3_adv_v3,
ens4_adv_v3, v4, res_v2, ens_adv_res_v2,
resnet):
label = f2l[filename]
l = [l1, l5, l6, l8, l3, l4, l7, l2]
for i in range(len(model_name)):
if l[i] != label:
success_count[i] += 1
for i in range(len(model_name)):
print("Attack Success Rate for {0} : {1:.1f}%".format(model_name[i], success_count[i] / 1000. * 100))