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evaluate.py
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evaluate.py
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import sys
sys.path.insert(0, 'tensorflow-classification')
from nets.vgg_f import vggf
from nets.caffenet import caffenet
from nets.vgg_16 import vgg16
from nets.vgg_19 import vgg19
from nets.googlenet import googlenet
from nets.resnet_152 import resnet152
from misc.utils import *
import tensorflow as tf
import numpy as np
import argparse
import time
import utils.functions as func
def validate_arguments(args):
nets = ['vggf', 'caffenet', 'vgg16', 'vgg19', 'googlenet', 'resnet152']
if not(args.network in nets):
print ('invalid network')
exit(-1)
if args.adv_im is None:
print ('no path to perturbation')
exit(-1)
if args.img_list is None or args.gt_labels is None:
print ('provide image list and labels')
exit(-1)
def choose_net(network):
MAP = {
'vggf': vggf,
'caffenet': caffenet,
'vgg16': vgg16,
'vgg19': vgg19,
'googlenet': googlenet,
'resnet152': resnet152
}
if network == 'caffenet':
size = 227
else:
size = 224
input_image = tf.placeholder(
shape=[None, size, size, 3], dtype='float32', name='input_image')
return MAP[network](input_image), input_image
def classify(net, in_im, net_name, im_list, gt_labels, batch_size, adv_image, defence):
# loading the perturbation
if net_name == 'caffenet':
size = 227
else:
size = 224
pert = np.load(adv_image)
# preprocessing if necessary
if (pert.shape[1] == 224 and size == 227):
pert = fff_utils.upsample(np.squeeze(pert))
elif (pert.shape[1] == 227 and size == 224):
pert = fff_utils.downsample(np.squeeze(pert))
elif (pert.shape[1] not in [224, 227]):
print(pert.shape[1])
raise Exception("Invalid size of input perturbation")
config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
imgs = open(im_list).readlines()#[::10]
gt_labels = open(gt_labels).readlines()#[::10]
fool_rate = 0
top_1 = 0
top_1_real = 0
isotropic, size = get_params(net_name)
batch_im_real = np.zeros((batch_size, size, size, 3))
batch_im_pert = np.zeros((batch_size, size, size, 3))
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
img_loader = loader_func(net_name, sess, isotropic, size, pert,defence)
for i in range(len(imgs)/batch_size):
lim = min(batch_size, len(imgs)-i*batch_size)
for j in range(lim):
im = img_loader(imgs[i*batch_size+j].strip(),False)
batch_im_real[j] = np.copy(im)
im = img_loader(imgs[i*batch_size+j].strip(),True)
batch_im_pert[j] = np.copy(im)
gt = np.array([int(gt_labels[i*batch_size+j].strip())
for j in range(lim)])
softmax_scores = sess.run(net['prob'], feed_dict={in_im: batch_im_real})
true_predictions = np.argmax(softmax_scores, axis=1)
softmax_scores = sess.run(net['prob'], feed_dict={in_im: batch_im_pert})
ad_predictions = np.argmax(softmax_scores, axis=1)
if i != 0 and i % 100 == 0:
print('iter: {:5d}\ttop-1_real: {:04.2f}\ttop-1: {:04.2f}\tfooling-rate: {:04.2f}'.format(i,
(top_1_real/float(i*batch_size))*100, (top_1/float(i*batch_size))*100, (fool_rate)/float(i*batch_size)*100))
top_1 += np.sum(ad_predictions == gt)
top_1_real += np.sum(true_predictions == gt)
fool_rate += np.sum(true_predictions != ad_predictions)
#print('fool rate is ', fool_rate)
print ('Real Top-1 Accuracy = {:.2f}'.format(
top_1_real/float(len(imgs))*100))
print ('Top-1 Accuracy = {:.2f}'.format(top_1/float(len(imgs))*100))
print ('Fooling Rate = {:.2f}'.format(fool_rate/float(len(imgs))*100))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--network', default='googlenet',
help='The network eg. googlenet')
parser.add_argument('--adv_im', help='Path to the perturbation image')
parser.add_argument(
'--img_list', help='Path to the validation image list')
parser.add_argument(
'--gt_labels', help='Path to the ground truth validation labels')
parser.add_argument('--batch_size', default=25,
help='Batch Size while evaluation.')
parser.add_argument('--defence', default='None',
help='Defence Technique to use')
args = parser.parse_args()
validate_arguments(args)
net, inp_im = choose_net(args.network)
classify(net, inp_im, args.network, args.img_list,
args.gt_labels, int(args.batch_size),args.adv_im,args.defence)
if __name__ == '__main__':
main()