def __init__(self, executable):
""" init function
Args:
executable: target executable file path
"""
self.logger = LogUtil.get_logger()
self.executable = executable
self.elf = getELF(executable)
self.arch = get_arch(self.elf)
self.functions = {}
self.addr2func = {}
self.get_func()
self.signs = {}
self.explored_addr = []
self.matched = {}
self.sign_cache_file = "/tmp/" + md5(executable) + "_sign.cache"
if os.path.exists(self.sign_cache_file):
with open(self.sign_cache_file) as f:
data = f.read()
try:
self.signs = json.loads(data)
except Exception as e:
self.logger.error(str(e))
def __init__(self):
self.logger = LogUtil.get_logger(self.__class__.__name__, "_logs")
self.xml_parser = XMLParser(encoding="utf-8",
huge_tree=True,
ns_clean=True)
import json
import unittest
import time
import os
from config import BASE_DIR, BASE_HOST
from page.index_page import IndexProxy
from page.login_page import LoginProxy
from utils import DriverUtil, LogUtil, case_driver_quit, jietu
from parameterized import parameterized
logger = LogUtil.get_logger()
# 读取json 构造参数化数据
def get_data():
# 1.创建参数化数据的结果列表
result = []
# 2.读取json 构造参数化数据
# 通过项目绝对路径+数据位置
with open(BASE_DIR + "/data/test_login.json", "r", encoding="utf-8") as f:
python_data = json.load(f)
for i in python_data:
username = i["username"]
password = i["password"]
code = i["code"]
yuqi = i["yuqi"]
result.append((username, password, code, yuqi))
# 3.返回参数化数据的结果列表
logger.info("参数化数据:{}".format(result))
return result
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
from pytorch_pretrained_bert.modeling import BertForSequenceClassification, BertConfig, WEIGHTS_NAME, CONFIG_NAME, BertPreTrainedModel
from pytorch_pretrained_bert.tokenization import BertTokenizer
from pytorch_pretrained_bert.optimization import BertAdam, warmup_linear
from bert_utils import BertForMultiLabelSequenceClassification
from utils import DataHandler, EvaluationUtil, LogUtil
# Taken and adapted from
# https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_classifier.py
logger = LogUtil.get_logger("bert_multilabel", "_logs")
#
def accuracy_thresh(y_pred: Tensor,
y_true: Tensor,
thresh: float = 0.5,
sigmoid: bool = True):
"Compute accuracy when `y_pred` and `y_true` are the same size."
if sigmoid: y_pred = y_pred.sigmoid()
# return ((y_pred>thresh)==y_true.byte()).float().mean().item()
return np.mean(((y_pred > thresh) == y_true.byte()).float().cpu().numpy(),
axis=1).sum()
class InputInstance(object):