-
Notifications
You must be signed in to change notification settings - Fork 71
/
EIT_Test.py
80 lines (68 loc) · 3.49 KB
/
EIT_Test.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# **************************************
# @Time : 2018/11/24 15:19
# @Author : Jiaxu Zou
# @Lab : nesa.zju.edu.cn
# @File : EIT_Test.py
# **************************************
import argparse
import os
import random
import sys
import numpy as np
import torch
sys.path.append('%s/../' % os.path.dirname(os.path.realpath(__file__)))
from RawModels.MNISTConv import MNISTConvNet, MNIST_Training_Parameters
from RawModels.ResNet import resnet20_cifar, CIFAR10_Training_Parameters
from RawModels.Utils.dataset import get_mnist_train_validate_loader, get_cifar10_train_validate_loader
from Defenses.DefenseMethods.EIT import EITDefense
def main(args):
# Device configuration
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_index
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Set the random seed manually for reproducibility.
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
dataset = args.dataset.upper()
assert dataset == 'MNIST' or dataset == 'CIFAR10'
if dataset == 'MNIST':
training_parameters = MNIST_Training_Parameters
model_framework = MNISTConvNet().to(device)
batch_size = training_parameters['batch_size']
raw_train_loader, raw_valid_loader = get_mnist_train_validate_loader(dir_name='../RawModels/MNIST/', batch_size=batch_size,
valid_size=0.1, shuffle=False)
else:
training_parameters = CIFAR10_Training_Parameters
model_framework = resnet20_cifar().to(device)
batch_size = training_parameters['batch_size']
raw_train_loader, raw_valid_loader = get_cifar10_train_validate_loader(dir_name='../RawModels/CIFAR10/', augment=False,
batch_size=batch_size, valid_size=0.1, shuffle=False)
print('cifar 10', len(raw_train_loader.dataset))
defense_name = 'EIT'
eit_params = {
'crop_size': args.crop_size,
'lambda_tv': args.lambda_tv,
'JPEG_quality': args.JPEG_quality,
'bit_depth': args.bit_depth
}
EIT = EITDefense(model=model_framework, defense_name=defense_name, dataset=dataset, re_training=True,
training_parameters=training_parameters, device=device, **eit_params)
EIT.defense(train_loader=raw_train_loader, valid_loader=raw_valid_loader)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='EIT Defenses')
parser.add_argument('--dataset', type=str, default='CIFAR10', help='the dataset (MNIST or CIFAR10)')
parser.add_argument('--seed', type=int, default=100, help='the default random seed for numpy and torch')
parser.add_argument('--gpu_index', type=str, help="gpu index to use", default='0')
# parameters for the EIT Defense
parser.add_argument('--crop_size', type=int, default=30, help='the cropping size')
parser.add_argument('--bit_depth', type=int, default=4, help='the quantization level of pixel value')
parser.add_argument('--JPEG_quality', type=int, default=85, help='the JPEG quality to compress with')
parser.add_argument('--lambda_tv', type=float, default=0.03, help='the total variance minimization weight')
arguments = parser.parse_args()
main(arguments)