Hi, I want to reproduce the experiment result of performance against single-target backdoor attack. I run the command
python3 train_poison.py --dir=ckpt --dataset=CIFAR10 --data_path=data --model=VGG16 --epochs=100 --lr=0.05 --wd=5e-4 --use_test --transform=VGG
and I got error like this
Traceback (most recent call last):
File "train_poison.py", line 68, in <module>
args.use_test
File "/home/pkun/workshop/model-sanitization/backdoor/backdoor-cifar/data.py", line 252, in loaders_poison
x_raw = train_set.train_data
AttributeError: 'CIFAR10' object has no attribute 'train_data'
I repair the code x_raw = train_set.train_data
to x_raw = train_set.data
but I'm not sure if this will affect the experimental results. I change all train_data
, test_data
to data
and train_labels
, test_labels
to targets
in backdoor/backdoor-cifar/data.py. Then the code can run successfully, but the train_acc Always 55.0000, I don't think it's normal. Here is the output from my console.
python3 train_poison.py --dir=ckpt --dataset=CIFAR10 --data_path=data --model=VGG16 --epochs=100 --lr=0.05 --wd=5e-4 --use_test --transform=VGG
Files already downloaded and verified
You are going to run models on the test set. Are you sure?
Files already downloaded and verified
---- --------- --------- --------- --------- --------- --------- --------- ---------
ep lr tr_loss tr_acc te_nll te_acc poi_nll poi_acc time
---- --------- --------- --------- --------- --------- --------- --------- ---------
1 0.0500 1.6917 54.7220 2.7881 10.0000 0.5688 100.0000 27.7173
2 0.0500 1.6777 55.0000 2.7681 10.0000 0.5874 100.0000 18.1426
3 0.0500 1.6778 55.0000 2.7657 10.0000 0.5891 100.0000 18.1627
4 0.0500 1.6779 55.0000 2.8170 10.0000 0.5418 100.0000 18.1949
5 0.0500 1.6778 55.0000 2.7937 10.0000 0.5628 100.0000 18.1922
6 0.0500 1.6779 55.0000 2.7275 10.0000 0.6278 100.0000 18.2009
7 0.0500 1.6777 55.0000 2.7406 10.0000 0.6140 100.0000 18.2040
8 0.0500 1.6778 55.0000 2.7449 10.0000 0.6107 100.0000 18.1640
9 0.0500 1.6779 55.0000 2.7589 10.0000 0.5956 100.0000 18.2060
10 0.0500 1.6778 55.0000 2.7577 10.0000 0.5972 100.0000 18.1984
11 0.0500 1.6777 55.0000 2.7526 10.0000 0.6018 100.0000 18.2550
12 0.0500 1.6776 55.0000 2.7856 10.0000 0.5699 100.0000 18.2314
13 0.0500 1.6779 55.0000 2.7637 10.0000 0.5910 100.0000 18.2471
14 0.0500 1.6779 55.0000 2.7987 10.0000 0.5580 100.0000 18.2632
15 0.0500 1.6777 55.0000 2.7332 10.0000 0.6219 100.0000 18.2177
16 0.0500 1.6780 55.0000 2.7328 10.0000 0.6222 100.0000 18.2367
17 0.0500 1.6778 55.0000 2.7404 10.0000 0.6145 100.0000 18.2166
18 0.0500 1.6779 55.0000 2.7718 10.0000 0.5831 100.0000 18.2409
19 0.0500 1.6776 55.0000 2.7395 10.0000 0.6149 100.0000 18.2174
20 0.0500 1.6774 55.0000 2.7126 10.0000 0.6446 100.0000 18.2022
21 0.0500 1.6777 55.0000 2.7669 10.0000 0.5872 100.0000 18.2642
22 0.0500 1.6778 55.0000 2.7509 10.0000 0.6036 100.0000 18.2141
23 0.0500 1.6779 55.0000 2.7370 10.0000 0.6179 100.0000 18.2184
24 0.0500 1.6777 55.0000 2.7383 10.0000 0.6163 100.0000 18.2216
25 0.0500 1.6777 55.0000 2.7271 10.0000 0.6284 100.0000 18.2506
26 0.0500 1.6776 55.0000 2.7267 10.0000 0.6285 100.0000 18.2698
27 0.0500 1.6776 55.0000 2.7415 10.0000 0.6140 100.0000 18.2530
28 0.0500 1.6778 55.0000 2.7560 10.0000 0.5987 100.0000 18.2714
29 0.0500 1.6777 55.0000 2.7459 10.0000 0.6084 100.0000 18.2531
30 0.0500 1.6775 55.0000 2.7733 10.0000 0.5819 100.0000 18.3161
31 0.0500 1.6780 55.0000 2.7722 10.0000 0.5825 100.0000 18.2003
32 0.0500 1.6777 55.0000 2.7354 10.0000 0.6194 100.0000 18.2231
33 0.0500 1.6778 55.0000 2.7531 10.0000 0.6011 100.0000 18.2400
34 0.0500 1.6775 55.0000 2.7329 10.0000 0.6231 100.0000 18.2604
35 0.0500 1.6776 55.0000 2.7332 10.0000 0.6216 100.0000 18.2635
36 0.0500 1.6776 55.0000 2.7349 10.0000 0.6204 100.0000 18.2244
37 0.0500 1.6777 55.0000 2.7942 10.0000 0.5621 100.0000 18.2450
38 0.0500 1.6780 55.0000 2.7428 10.0000 0.6120 100.0000 18.2343
39 0.0500 1.6778 55.0000 2.7461 10.0000 0.6088 100.0000 18.2504
40 0.0500 1.6778 55.0000 2.7323 10.0000 0.6233 100.0000 18.2152