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utils.py
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utils.py
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import os
import sys
import errno
import time
import codecs
import numpy as np
import logging
import torch
import random
poisoners = {
"badnets": {
"name": "badnets",
"poison_rate": 0.2,
"target_label": 1,
"label_consistency": True,
"label_dirty": False,
"triggers": ["cf", "mn", "bb", "tq"],
"num_triggers": 1,
"load": True
},
"adaptedbadnets": {
"name": "badnets",
"poison_rate": 0.2,
"target_label": 1,
"label_consistency": True,
"label_dirty": False,
"triggers": ["cf", "bb", "sd"],
"num_triggers": 1,
"load": True
},
"addsent":{
"name": "addsent",
"poison_rate": 0.2,
"target_label": 1,
"label_consistency": True,
"label_dirty": False,
"load": True,
"triggers": "I watch this 3D movie"
},
"synbkd":{
"name": "synbkd",
"poison_rate": 0.2,
"target_label": 1,
"label_consistency": True,
"label_dirty": False,
"load": True,
"poison_data_basepath": None,
"poisoned_data_path": "",
"template_id": -1
},
"stylebkd":{
"name": "stylebkd",
"poison_rate": 0.2,
"target_label": 1,
"label_consistency": True,
"label_dirty": False,
"load": True,
"template_id": 0
}
}
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.cuda.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
from itertools import combinations
from collections import Counter
from sklearn.metrics import accuracy_score
def majority(preds, labels=None, C=2):
if isinstance(preds, list):
preds = np.array(preds)
preds = preds.transpose() # N * Group
final_pred = []
for i, x in enumerate(preds):
cnt = np.zeros(C)
for xx in x:
cnt[xx]-=1
idxs = cnt.argsort(kind="stable")
if labels is None:
final_pred.append(idxs[0])
else:
y = labels[i]
if idxs[0]!=y:
final_pred.append(idxs[0])
else:
final_pred.append(idxs[1]) # use second largest
return np.array(final_pred)
def certified(preds, labels, C=2, target_label=None):
# C is number of classes
preds = np.array(preds) # Group * N
labels = np.array(labels) # N
if target_label is not None:
target = labels!=target_label
print(len(target), sum(target))
preds = preds[:, target]
labels = labels[target]
print(preds.shape)
final_pred = majority(preds, C=C)
correct = (labels == final_pred)
n_correct = sum(correct)
n_wrong = len(labels) - n_correct
lis_cacc = [n_correct/(n_correct+n_wrong)]
cpreds = preds[:, correct] # choose correct prediction for certification
clabels = labels[correct]
m = len(cpreds)
n = len(cpreds[0])
assert n == n_correct
for i in range(1, m//2+1): # number of backdoored groups
cacc = 1
for lis in combinations(range(m), i): # iterate all combinations
cur = np.copy(cpreds)
rest = [j for j in range(m) if j not in lis]
worst_pred = majority(cpreds[rest], clabels, C=C) # find the second common predictions of clean groups
for j in lis:
cur[j] = worst_pred
#print(lis)
#print(cpreds[:, :10])
#print(worst_pred[:10])
hpred = majority(cur, C=C)
cacc = min(cacc, sum(hpred==clabels)/(n_wrong+n))
lis_cacc.append(cacc)
return lis_cacc
from nltk.tokenize import word_tokenize
import hashlib
def rectify(lis, tot):
if len(lis)==0:
return ""
#if len(lis)<int(tot/2):
# return ""
pre = lis[0]
text = []
if pre[-1]!=0:
text.extend(["[MASK]"]*pre[-1])
text.append(pre[0])
for x in lis[1:]:
if pre[-1]+1<x[-1]:
text.extend(["[MASK]"]*(x[-1] - pre[-1] - 1))
pre = x
text.append(x[0])
if tot > pre[-1] + 1:
text.extend(["[MASK]"]*(tot - pre[-1] - 1))
return " ".join(text)
def split_group(x, args, allow_empty):
lis = word_tokenize(x)
res = [[] for i in range(args.group)]
for i, x in enumerate(lis):
h = int(hashlib.md5(x.encode()).hexdigest(), 16) % args.group
#for k in range(h-2, h+3):
# res[(k+args.group)%args.group].append((x, i))
res[h].append((x, i))
for i in range(args.group):
res[i] = rectify(res[i], len(lis))
if len(res[i])==0 and allow_empty:
res[i] = " ".join(["[MASK]"]*len(lis))
return res
def make_sure_path_exists(path):
try:
os.makedirs(path)
except OSError as exception:
if exception.errno != errno.EEXIST:
raise
def init_logger(root_dir):
make_sure_path_exists(root_dir)
log_formatter = logging.Formatter("%(message)s")
logger = logging.getLogger()
file_handler = logging.FileHandler("{0}/info.log".format(root_dir), mode='w')
file_handler.setFormatter(log_formatter)
logger.addHandler(file_handler)
console_handler = logging.StreamHandler()
console_handler.setFormatter(log_formatter)
logger.addHandler(console_handler)
logger.setLevel(logging.INFO)
return logger