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all_in_one_imagenet.py
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import argparse
import numpy as np
import pandas as pd
import torch
import torchvision
import torchvision.models as models
import torchvision.transforms as transforms
from imagenet_config import *
from attacks import wrap_attack_imagenet, momentum_ifgsm, Transferable_Adversarial_Perturbations, ILA, ifgsm
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--source_models', nargs='+', help='<Required> source models', required=True)
parser.add_argument('--transfer_models', nargs='+', help='<Required> transfer models', required=True)
parser.add_argument('--attacks', nargs='+', help='<Required> base attacks', required=True)
parser.add_argument('--num_batches', type=int, help='<Required> number of batches', required=True)
parser.add_argument('--batch_size', type=int, help='<Required> batch size', required=True)
parser.add_argument('--out_name', help='<Required> out file name', required=True)
parser.add_argument('--use_Inc_model', action='store_true', help='<Required> use Inception models group')
args = parser.parse_args()
return args
def log(out_df, source_model_name, target_model_name, batch_index, layer_index, layer_name, fool_method, with_ILA, fool_rate, acc_after_attack, original_acc):
return out_df.append({
'source_model':source_model_name,
'target_model':target_model_name,
'batch_index':batch_index,
'layer_index':layer_index,
'layer_name':layer_name,
'fool_method':fool_method,
'with_ILA':with_ILA,
'fool_rate':fool_rate,
'acc_after_attack':acc_after_attack,
'original_acc':original_acc},ignore_index=True)
def get_data(batch_size, use_Inc_model = False):
if use_Inc_model:
transform_test = transforms.Compose([
transforms.Resize(299),
transforms.CenterCrop(299),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5],[0.5, 0.5, 0.5]),
])
else:
transform_test = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
testset = torchvision.datasets.ImageFolder(root='/share/cuvl/datasets/imagenet/val',
transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=True,
num_workers=8, pin_memory=True)
return testloader
def get_fool_adv_orig(model, adversarial_xs, originals, labels):
total = adversarial_xs.size(0)
correct_orig = 0
correct_adv = 0
fooled = 0
advs, ims, lbls = adversarial_xs.cuda(), originals.cuda(), labels.cuda()
outputs_adv = model(advs)
outputs_orig = model(ims)
_, predicted_adv = torch.max(outputs_adv.data, 1)
_, predicted_orig = torch.max(outputs_orig.data, 1)
correct_adv += (predicted_adv == lbls).sum()
correct_orig += (predicted_orig == lbls).sum()
fooled += (predicted_adv != predicted_orig).sum()
return [100.0 * float(fooled.item())/total, 100.0 * float(correct_adv.item())/total, 100.0 * float(correct_orig.item())/total]
def test_adv_examples_across_models(transfer_models, adversarial_xs, originals, labels, use_Inc_model):
accum = []
for name, net_class in transfer_models:
if use_Inc_model:
net = net_class(num_classes=1000, pretrained='imagenet').cuda()
else:
net = net_class(pretrained=True).cuda()
net.eval()
res = get_fool_adv_orig(net, adversarial_xs, originals, labels)
res.append(name)
accum.append(res)
return accum
def complete_loop(sample_num, batch_size, attacks, source_models, transfer_models, out_name, use_Inc_model):
labels_file = open('labels', 'r').readlines()
out_df = pd.DataFrame(columns=['source_model','target_model','batch_index','layer_index', 'layer_name', 'fool_method', 'with_ILA', 'fool_rate', 'acc_after_attack', 'original_acc'])
testloader = get_data(batch_size, use_Inc_model)
for source_model_name, model_class in source_models:
if use_Inc_model:
model = model_class(num_classes=1000, pretrained='imagenet').cuda()
else:
model = model_class(pretrained=True).cuda()
model.eval()
for attack_name, attack in attacks:
print('using source model {0} attack {1}'.format(source_model_name, attack_name))
for batch_i, data in enumerate(testloader, 0):
if batch_i%100 == 0:
print("batch" , batch_i)
save_to_csv(out_df, out_name)
if batch_i == sample_num:
break
images, labels = data
images, labels = images.cuda(), labels.cuda()
#### baseline
### generate
adversarial_xs = attack(model, images, labels, niters=20, use_Inc_model=use_Inc_model)
### eval
transfer_list = test_adv_examples_across_models(transfer_models, adversarial_xs, images, labels, use_Inc_model)
for target_fool_rate, target_acc_attack, target_acc_original, transfer_model_name in transfer_list:
out_df = log(out_df,source_model_name, transfer_model_name,
batch_i, np.nan, "", attack_name, False,
target_fool_rate, target_acc_attack, target_acc_original)
#### ILA
### generate
## step1: reference
ILA_input_xs = attack(model, images, labels, niters=10, use_Inc_model=use_Inc_model)
## step2: ILA target at different layers
for layer_ind, (layer_name, layer) in get_source_layers(source_model_name, model):
ILA_adversarial_xs = ILA(model, images, X_attack=ILA_input_xs, y=labels, feature_layer=layer, use_Inc_model=use_Inc_model, **(ILA_params[attack_name]))
### eval
ILA_transfer_list = test_adv_examples_across_models(transfer_models, ILA_adversarial_xs, images, labels, use_Inc_model)
for target_fool_rate, target_acc_attack, target_acc_original, transfer_model_name in ILA_transfer_list:
out_df = log(out_df,source_model_name, transfer_model_name, batch_i, layer_ind, layer_name, attack_name, True, target_fool_rate, target_acc_attack, target_acc_original)
save_to_csv(out_df, out_name)
def save_to_csv(out_df, out_name):
#save csv
out_df.to_csv(out_name, sep=',', encoding='utf-8')
if __name__ == "__main__":
args = get_args()
attacks = list(map(lambda attack_name: (attack_name, attack_configs[attack_name]), args.attacks))
source_models = list(map(lambda model_name: model_configs[model_name], args.source_models))
transfer_models = list(map(lambda model_name: model_configs[model_name], args.transfer_models))
complete_loop(args.num_batches, args.batch_size, attacks, source_models, transfer_models, args.out_name, args.use_Inc_model);