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eval_10_targets.py
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"""
Evaluate the logit attack in the "10-Targets (all-source)" setting to compare it with TTP.
Code modified from https://github.com/Muzammal-Naseer/TTP/blob/main/eval_all.py
"""
import argparse
import os
import torchvision
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from PIL import Image
import torchvision.models as models
from generators import GeneratorResnet
from gaussian_smoothing import *
import logging
logger = logging.getLogger(__name__)
parser = argparse.ArgumentParser(description='Targeted Transferable Perturbations')
parser.add_argument('--test_dir', default='./dataset/images')
parser.add_argument('--batch_size', type=int, default=20, help='Batch size for evaluation')
parser.add_argument('--eps', type=int, default=16, help='Perturbation Budget')
parser.add_argument('--target_model', type=str, default='densenet121', help='Black-Box(unknown) model')
parser.add_argument('--num_targets', type=int, default=10, help='10 or 100 targets evaluation')
parser.add_argument('--source_model', type=str, default='res50', help='TTP Discriminator: \
{res18, res50, res101, res152, dense121, dense161, dense169, dense201,\
vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, vgg19_bn,\
ens_vgg16_vgg19_vgg11_vgg13_all_bn,\
ens_res18_res50_res101_res152\
ens_dense121_161_169_201}')
parser.add_argument('--source_domain', type=str, default='IN', help='Source Domain (TTP): Natural Images (IN) or painting')
args = parser.parse_args()
print(args)
# GPU
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# Set-up log file
logfile = 'TTP_{}_targets_eval_{}_{}.log'.format(args.num_targets, args.eps, args.target_model)
logging.basicConfig(
format='[%(asctime)s] - %(message)s',
datefmt='%Y/%m/%d %H:%M:%S',
level=logging.INFO,
filename=logfile)
eps = args.eps/255.0
# Set-up Kernel
kernel_size = 3
pad = 2
sigma = 1
kernel = get_gaussian_kernel(kernel_size=kernel_size, pad=pad, sigma=sigma).cuda()
# Load Targeted Model
model_names = sorted(name for name in models.__dict__
if name.islower() and not name.startswith("__")
and callable(models.__dict__[name]))
if args.target_model in model_names:
model = models.__dict__[args.target_model](pretrained=True)
else:
assert (args.target_model in model_names), 'Please provide correct target model names: {}'.format(model_names)
model = model.to(device)
model.eval()
####################
# Data
####################
# Input dimensions
scale_size = 256
img_size = 224
data_transform = transforms.Compose([
transforms.Resize(scale_size),
transforms.CenterCrop(img_size),
transforms.ToTensor(),
])
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
def normalize(t):
t[:, 0, :, :] = (t[:, 0, :, :] - mean[0])/std[0]
t[:, 1, :, :] = (t[:, 1, :, :] - mean[1])/std[1]
t[:, 2, :, :] = (t[:, 2, :, :] - mean[2])/std[2]
return t
if args.num_targets==10:
targets = [24, 99, 245, 344, 471, 555, 661, 701, 802, 919]
if args.num_targets==100:
targets = [24, 99, 245, 344, 471, 555, 661, 701, 802, 919, 3, 16, 36, 48, 52, 69, 71, 85, 107, 114, 130, 138, 142, 151, 162, 178, 189, 193, 207, 212, 228, 240, 260, 261, 276, 285, 291, 309, 317, 328, 340, 358, 366, 374, 390, 393, 404, 420, 430, 438, 442, 453, 464, 485, 491, 506, 513, 523, 538, 546, 569, 580, 582, 599, 605, 611, 629, 638, 646, 652, 678, 689, 707, 717, 724, 735, 748, 756, 766, 779, 786, 791, 813, 827, 836, 849, 859, 866, 879, 885, 893, 901, 929, 932, 946, 958, 963, 980, 984, 992]
total_acc = 0
total_samples = 0
batch_size = args.batch_size
num_batches = 1000//batch_size
test_dir = args.test_dir
image_id_list = list(filter(lambda x: '.png' in x, os.listdir(test_dir)))
test_size = len(image_id_list)
for idx, target in enumerate(targets):
logger.info('Epsilon \t Target \t Acc. \t Distance')
netG = GeneratorResnet(level=3)
netG.load_state_dict(torch.load('pretrained_generators/netG_{}_{}_19_{}.pth'.format(args.source_model,args.source_domain, target)))
netG = netG.to(device)
netG.eval()
# Reset Metrics
acc = 0
distance = 0
for k in range(0,num_batches):
img = torch.zeros(batch_size,3,img_size,img_size).to(device)
for i in range(batch_size):
img[i] = data_transform(Image.open(test_dir+image_id_list[k*batch_size+i]))
target_label = torch.LongTensor(img.size(0))
target_label.fill_(target)
target_label = target_label.to(device)
adv = kernel(netG(img)).detach()
adv = torch.min(torch.max(adv, img - eps), img + eps)
adv = torch.clamp(adv, 0.0, 1.0)
out = model(normalize(adv.clone().detach()))
acc += torch.sum(out.argmax(dim=-1) == target_label).item()
distance +=(img - adv).max() *255
total_acc+=acc
total_samples+=test_size
logger.info(' %d %d\t %.4f\t \t %.4f',
int(eps * 255), target, acc / test_size, distance / (i + 1))
logger.info('*'*100)
logger.info('Average Target Transferability')
logger.info('*'*100)
logger.info(' %d %.4f\t \t %.4f',
int(eps * 255), total_acc / total_samples, distance / (i + 1))