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blurattack_eval.py
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blurattack_eval.py
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#!/usr/bin/env python
# coding: utf-8
# In[7]:
import os
import numpy as np
from utils import read_images, store_adversarial, load_adversarial, compute_MAD
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torch
import warnings
import sys, getopt
from tqdm import tqdm
import foolbox
from fmodel import create_fmodel
import imageio
warnings.filterwarnings("ignore")
# ------- testing on dev dataset -----------------------#
def check_show_dev_res(data_dir,prefix,white_model_name= "inceptionv3",slt_fold=None,dataset="dev",gpuid=0,recheck = False, extra_file=None, skip_eval = False):
result_root = "./results/"
check_file_path = result_root+dataset+"_valid_folds.npy"
dataset = dataset + "/"
prefix = prefix + "/"
bbmodel_names = ["inceptionresnetv2", "inceptionv3", "inceptionv4", "xception"]
slt_num = 1000
valid_fold_list = []
generate_npy = False
if slt_fold is not None:
valid_fold_list.append(slt_fold)
else:
if os.path.exists(check_file_path) and not recheck:
valid_fold_list = np.load(check_file_path).tolist()
else:
valdir = os.path.join(data_dir)
batch_size = 1
slt_num = 1000
files = os.listdir(result_root+dataset+prefix)
res_folds = []
for file in tqdm(files):
if os.path.isdir(result_root+dataset+prefix+file):
res_folds.append(file)
subfiles = os.listdir(result_root+dataset+prefix+file)
k=0
for subfile in subfiles:
if os.path.isdir(result_root+dataset+prefix+file+"/"+subfile):
print(subfile)
if k==0:
res_folds.pop()
res_folds.append(file+"/"+subfile)
k+=1
if os.path.exists(check_file_path) and not recheck:
valid_fold_list = np.load(check_file_path).tolist()
else:
valid_fold_list = []
success_status = np.ones([slt_num]) * -1.
success_status_fmodels = []
fb_models = []
for forward_model_name in bbmodel_names:
success_status_fmodels.append(np.ones([slt_num]) * -1.)
forward_model = create_fmodel("imagenet", model_name=forward_model_name, gpu=gpuid)
fb_models.append(forward_model)
for res_fold in tqdm(res_folds):
if res_fold in valid_fold_list:
continue
valid_fold_list.append(res_fold)
# check if attack status files are existing:
status_file_path = result_root + dataset + prefix+res_fold
invalid_fold = True
# if succ_rate doest not exist, we have to generate it
if not os.path.exists(status_file_path + \
"/{}_{}_succ_rate{}.npy".format(white_model_name, white_model_name, slt_num)):
valid_fold_list.pop()
np.save(check_file_path,valid_fold_list)
for fold in valid_fold_list:
print(fold)
status_file_path = result_root + dataset + prefix + fold
for forward_model_name in bbmodel_names:
res_path = status_file_path + "/{}_{}_succ_rate{}.npy".format(white_model_name, forward_model_name, slt_num)
status = np.load(res_path)
if extra_file is not None:
print(extra_file)
is_advs = np.zeros_like(status)
preds = np.zeros_like(status)
res_path = status_file_path+"/"+extra_file
_, res_ext = os.path.splitext(extra_file)
if res_ext == ".txt":
import re
f = open(res_path)
line = f.readline()
k=0
imgnames = []
while line:
matchObj = re.match(r'(.*).png,(.*),[[](.*)[]]',line, re.M | re.I)
if matchObj:
imgnames.append(matchObj.group(1))
preds[k] = int(matchObj.group(2))-1
is_advs[k] = int(matchObj.group(3))
line = f.readline()
k+=1
f.close()
status_ = is_advs
status_[is_advs==0.] = -1
import pandas as pd
target_df = pd.read_csv(os.path.join(data_dir, 'dev_dataset.csv'), header=None)
f_to_true = dict(zip(target_df[0][1:].tolist(), [x - 1 for x in list(map(int, target_df[6][1:]))]))
for index_ in range(len(imgnames)):
imgname,_ = os.path.splitext(imgnames[index_])
true_label = f_to_true[imgname] if f_to_true[imgname] else 0
if is_advs[index_] == 1.:
if true_label == preds[index_]:
status_[index_]=-1
else:
status_[index_] = 1
#print("pred_label:{} true_label:{}".format(preds[index_],true_label))
status = status_
elif res_ext==".npz":
data = np.load(res_path,allow_pickle=True)
pred_lbl = data["pred"]
true_lbl = data["truth"]
true_lbl2 = data["lbl_text"]
pred_logit = data["logit"]
_, uniq_idx = np.unique(true_lbl2, return_index=True)
pred_lbl = pred_lbl[uniq_idx]
true_lbl = true_lbl[uniq_idx]
#true_lbl2 = true_lbl2[uniq_idx]
#pred_logit = pred_logit[uniq_idx]
status = np.ones_like(pred_lbl)
status[pred_lbl==0]=-1
status[pred_lbl==true_lbl]=-1
#print(status)
succ_ = np.zeros_like(status)
fail_ = np.zeros_like(status)
already_ = np.zeros_like(status)
succ_[status==1.] = 1.
fail_[status==-1.] = 1.
already_[status==-0.] = 1.
num_succ = succ_.sum()
num_fail = fail_.sum()
num_already = already_.sum()
succ_rate = num_succ/(num_fail+num_already+num_succ)
print("{}_bmodel:{}_fmodel:{}:success rate:{}".format(fold,white_model_name,forward_model_name,succ_rate))
# find adversarial examples, remove fail results
image_files = os.listdir(status_file_path)
for file in image_files:
full_path = status_file_path+"/"+file
full_path_withoutext, file_ext = os.path.splitext(full_path)
if file_ext == ".jpg" and full_path_withoutext[-4:]!="_org":
img = imageio.imread(full_path)
if img.max()==0:
#print("remove:{}".format(full_path))
os.remove(full_path)
os.remove(full_path_withoutext+"_org.jpg")
if os.path.exists(full_path_withoutext + "_org.npy"):
os.remove(full_path_withoutext + "_org.npy")
return valid_fold_list
# ------- testing on imagenet dataset -----------------------#
def check_show_imagenet_res(data_dir,dataset,gpuid=0,recheck = False):
result_root = "/mnt/nvme/projects/BlurAttack/results/"
check_file_path = result_root+dataset+"_valid_folds.npy"
dataset = dataset + "/"
bmodel_name = "resnet50"
fmodel_names = ["resnet50", "densenet121", "pyramidnet101_a360"]
slt_num = 1000
if os.path.exists(check_file_path) and ~recheck:
valid_fold_list = np.load(check_file_path).tolist()
else:
valdir = os.path.join(data_dir, 'val')
slt_name = result_root+"/imagenet_slt_" + str(slt_num) + ".npy"
sltIdx = np.load(slt_name)
sltIdx.sort(axis=0)
valid_sampler = torch.utils.data.SubsetRandomSampler(sltIdx.tolist())
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(valdir, transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
])),
batch_size=1, shuffle=False,
num_workers=4, pin_memory=True, sampler=valid_sampler)
files = os.listdir(result_root+dataset)
res_folds = []
for file in tqdm(files):
if os.path.isdir(result_root+dataset+file):
res_folds.append(file)
subfiles = os.listdir(result_root+dataset+file)
for subfile in subfiles:
if os.path.isdir(result_root+dataset+file+"/"+subfile):
res_folds.pop()
res_folds.append(file+"/"+subfile)
if os.path.exists(check_file_path) and ~recheck:
valid_fold_list = np.load(check_file_path).tolist()
else:
valid_fold_list = []
fmodels = []
for fmodel_name in fmodel_names:
fmodel = create_fmodel("imagenet", model_name=fmodel_name, gpu=gpuid)
fmodels.append(fmodel)
for res_fold in tqdm(res_folds):
if res_fold in valid_fold_list:
continue
valid_fold_list.append(res_fold)
# check if attack status files are existing:
status_file_path = result_root + dataset + res_fold
invalid_fold = True
k=0
for fmodel in fmodels:
if not os.path.exists(status_file_path+"/{}_{}_succ_rate{}.npy".format
(bmodel_name, fmodel_names[k], slt_num)):
success_status = np.ones([slt_num]) * -1.
# if succ_rate doest not exist, we have to generate it
for i, (images, labels, index, sample_path) in enumerate(tqdm(val_loader)):
file_path,file_full_name = os.path.split(sample_path[0])
file_name_, ext = os.path.splitext(file_full_name)
file_name_ = dataset+res_fold + "/"+file_name_
index = index.numpy()[0]
if os.path.exists(os.path.join(result_root,file_name_+".jpg")):
success_status[sltIdx==index], _, adversarial = load_adversarial(file_name_)
print(file_name_+" exists!")
# do blackbox attack
if success_status[sltIdx==index] == 1:
if adversarial.max() > 1:
adversarial = adversarial.transpose(2, 0, 1) / 255
else:
adversarial = adversarial.transpose(2, 0, 1)
adversarial = adversarial.astype("float32")
predictions = fmodel.forward_one(adversarial)
criterion1 = foolbox.criteria.Misclassification()
if criterion1.is_adversarial(predictions, labels):
success_status[sltIdx==index] =1
else:
success_status[sltIdx==index] = 0
continue
else:
invalid_fold = False
break
np.save(status_file_path + "/{}_{}_succ_rate{}.npy".format(bmodel_name, fmodel_names[k], slt_num),
success_status)
k+=1
if invalid_fold==False:
valid_fold_list.pop()
break
np.save(check_file_path,valid_fold_list)
for fold in valid_fold_list:
status_file_path = result_root + dataset + fold
for fmodel_name in fmodel_names:
res_path = status_file_path + "/{}_{}_succ_rate{}.npy".format(bmodel_name, fmodel_name, slt_num)
status = np.load(res_path)
succ_ = np.zeros_like(status)
fail_ = np.zeros_like(status)
already_ = np.zeros_like(status)
succ_[status==1.] = 1.
fail_[status==0.] = 1.
already_[status==-1.] = 1.
num_succ = succ_.sum()
num_fail = fail_.sum()
num_already = already_.sum()
succ_rate = num_succ/(num_fail+num_already+num_succ)
print("{}_bmodel:{}_fmodel:{}:success rate:{}".format(fold,bmodel_name,fmodel_name,succ_rate))
# print the image names
return valid_fold_list
def main(argv):
opts, args = getopt.getopt(sys.argv[1:], "d:g:p:r:s:e:w:", ["dataset","gpuid","prefix","recheck","slt_fold","extra_file","white_model_name"])
dataset = "dev"
gpuid = 0
prefix = "inceptionv3_inceptionv3_mbAdv_mifgsm"
recheck = 1
slt_fold = None
extra_file = None
white_model_name = "inceptionv3"
for op, value in opts:
if op == '-d' or op == '--dataset':
dataset = value
if op == '-g' or op == '--gpuid':
gpuid = value
if op == '-p' or op == '--prefix':
prefix = value
if op == '-r' or op == '--recheck':
recheck = int(value)
if op == '-s' or op == '--slt_fold':
slt_fold = value
if op == '-e' or op == '--extra_file':
extra_file = value
if op == '-w' or op == '--white_model_name':
white_model_name = value
print("extra_file:{}".format(extra_file))
if dataset == 'imagenet':
dataset_path = "./dataset/ILSVRC2012"
elif dataset == 'cifa10':
dataset_path = "./dataset/cifar-10-batches-py"
elif dataset == 'dev':
dataset_path = "./dataset/dev/images" #"/home/wangjian/tsingqguo/dataset/dev/images" #
elif dataset == 'minist':
dataset_path = "./dataset/dev/minist"
print('dataset path:{}'.format(dataset_path))
print("prefix:{}".format(prefix))
check_show_dev_res(dataset_path,prefix,white_model_name,slt_fold,dataset,gpuid,recheck,extra_file=extra_file)
sys.exit(0)
if __name__ == '__main__':
main(sys.argv)