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TARGET INDEX: 1 |
DPN92 0 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='1', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=1, num_per_class=50, original_grad=True, poison_decay_ites=[], poison_decay_ratio=0.1, poison_epsilon=0.1, poison_ites=1500, poison_label=8, poison_lr=0.04, poison_momentum=0.9, poison_num=5, poison_opt='adam', resume_poison_ite=0, retrain_bsize=64, retrain_epochs=60, retrain_lr=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, subs_chk_name=['ckpt-%s-4800-dp0.200-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.250-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.300-droplayer0.000.t7'], subs_dp=[0.2, 0.25, 0.3], subset_group=0, substitute_nets=['DPN92', 'SENet18', 'ResNet50', 'ResNeXt29_2x64d'], target_index=1, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth') |
Path: chk-black-end2end/mean/1500/1 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-03 02:48:28 Iteration 0 Training Loss: 1.036e+00 Loss in Target Net: 1.417e+00 |
2020-02-03 02:48:56 Iteration 50 Training Loss: 2.524e-01 Loss in Target Net: 4.286e-02 |
2020-02-03 02:49:39 Iteration 100 Training Loss: 2.278e-01 Loss in Target Net: 3.695e-02 |
2020-02-03 02:50:25 Iteration 150 Training Loss: 2.215e-01 Loss in Target Net: 3.555e-02 |
2020-02-03 02:51:05 Iteration 200 Training Loss: 2.127e-01 Loss in Target Net: 3.802e-02 |
2020-02-03 02:51:59 Iteration 250 Training Loss: 2.078e-01 Loss in Target Net: 4.864e-02 |
2020-02-03 02:52:34 Iteration 300 Training Loss: 2.030e-01 Loss in Target Net: 3.353e-02 |
2020-02-03 02:53:49 Iteration 350 Training Loss: 2.046e-01 Loss in Target Net: 3.257e-02 |
2020-02-03 02:55:02 Iteration 400 Training Loss: 1.980e-01 Loss in Target Net: 3.841e-02 |
2020-02-03 02:55:40 Iteration 450 Training Loss: 2.042e-01 Loss in Target Net: 4.491e-02 |
2020-02-03 02:56:09 Iteration 500 Training Loss: 2.026e-01 Loss in Target Net: 2.895e-02 |
2020-02-03 02:56:48 Iteration 550 Training Loss: 1.992e-01 Loss in Target Net: 3.491e-02 |
2020-02-03 02:57:44 Iteration 600 Training Loss: 1.946e-01 Loss in Target Net: 3.486e-02 |
2020-02-03 02:58:25 Iteration 650 Training Loss: 2.009e-01 Loss in Target Net: 3.316e-02 |
2020-02-03 02:59:14 Iteration 700 Training Loss: 1.988e-01 Loss in Target Net: 2.968e-02 |
2020-02-03 02:59:49 Iteration 750 Training Loss: 1.951e-01 Loss in Target Net: 3.484e-02 |
2020-02-03 03:00:33 Iteration 800 Training Loss: 1.928e-01 Loss in Target Net: 2.958e-02 |
2020-02-03 03:01:12 Iteration 850 Training Loss: 1.938e-01 Loss in Target Net: 2.962e-02 |
2020-02-03 03:01:50 Iteration 900 Training Loss: 1.925e-01 Loss in Target Net: 2.837e-02 |
2020-02-03 03:02:26 Iteration 950 Training Loss: 1.910e-01 Loss in Target Net: 3.579e-02 |
2020-02-03 03:03:11 Iteration 1000 Training Loss: 1.881e-01 Loss in Target Net: 2.662e-02 |
2020-02-03 03:03:50 Iteration 1050 Training Loss: 1.889e-01 Loss in Target Net: 3.127e-02 |
2020-02-03 03:04:25 Iteration 1100 Training Loss: 1.902e-01 Loss in Target Net: 3.972e-02 |
2020-02-03 03:05:11 Iteration 1150 Training Loss: 1.943e-01 Loss in Target Net: 4.169e-02 |
2020-02-03 03:05:49 Iteration 1200 Training Loss: 1.913e-01 Loss in Target Net: 4.142e-02 |
2020-02-03 03:06:27 Iteration 1250 Training Loss: 1.917e-01 Loss in Target Net: 2.799e-02 |
2020-02-03 03:07:07 Iteration 1300 Training Loss: 1.882e-01 Loss in Target Net: 3.377e-02 |
2020-02-03 03:07:47 Iteration 1350 Training Loss: 1.922e-01 Loss in Target Net: 3.132e-02 |
2020-02-03 03:08:17 Iteration 1400 Training Loss: 1.924e-01 Loss in Target Net: 3.052e-02 |
2020-02-03 03:08:59 Iteration 1450 Training Loss: 1.885e-01 Loss in Target Net: 2.911e-02 |
2020-02-03 03:09:41 Iteration 1499 Training Loss: 1.871e-01 Loss in Target Net: 3.505e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-03 03:09:53, Epoch 0, Iteration 7, loss 0.411 (0.423), acc 90.385 (90.400) |
2020-02-03 03:11:50, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[0.18758614, -1.4726284, -2.3472297, -1.3806719, -1.5917944, -4.1449966, 3.6249957, -1.6527624, 8.133452, 0.80585194], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-03 03:13:34 Epoch 59, Val iteration 0, acc 92.200 (92.200) |
2020-02-03 03:13:48 Epoch 59, Val iteration 19, acc 92.400 (92.260) |
* Prec: 92.26000137329102 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 21 |
TARGET INDEX: 1 |
DPN92 1 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='2', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=1, num_per_class=50, original_grad=True, poison_decay_ites=[], poison_decay_ratio=0.1, poison_epsilon=0.1, poison_ites=1500, poison_label=8, poison_lr=0.04, poison_momentum=0.9, poison_num=5, poison_opt='adam', resume_poison_ite=0, retrain_bsize=64, retrain_epochs=60, retrain_lr=0.0001, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0.0005, subs_chk_name=['ckpt-%s-4800-dp0.200-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.250-droplayer0.000-seed1226.t7', 'ckpt-%s-4800-dp0.300-droplayer0.000.t7'], subs_dp=[0.2, 0.25, 0.3], subset_group=0, substitute_nets=['DPN92', 'SENet18', 'ResNet50', 'ResNeXt29_2x64d'], target_index=10, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth') |
Path: chk-black-end2end/mean/1500/10 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-02 11:12:28 Iteration 0 Training Loss: 1.001e+00 Loss in Target Net: 1.316e+00 |
2020-02-02 11:12:48 Iteration 50 Training Loss: 2.066e-01 Loss in Target Net: 1.831e-02 |
2020-02-02 11:13:09 Iteration 100 Training Loss: 1.833e-01 Loss in Target Net: 1.819e-02 |
2020-02-02 11:13:30 Iteration 150 Training Loss: 1.762e-01 Loss in Target Net: 1.844e-02 |
2020-02-02 11:13:50 Iteration 200 Training Loss: 1.709e-01 Loss in Target Net: 1.661e-02 |
2020-02-02 11:14:11 Iteration 250 Training Loss: 1.694e-01 Loss in Target Net: 1.642e-02 |
2020-02-02 11:14:32 Iteration 300 Training Loss: 1.636e-01 Loss in Target Net: 1.502e-02 |
2020-02-02 11:14:52 Iteration 350 Training Loss: 1.641e-01 Loss in Target Net: 1.392e-02 |
2020-02-02 11:15:13 Iteration 400 Training Loss: 1.634e-01 Loss in Target Net: 1.276e-02 |
2020-02-02 11:15:33 Iteration 450 Training Loss: 1.604e-01 Loss in Target Net: 1.360e-02 |
2020-02-02 11:15:54 Iteration 500 Training Loss: 1.600e-01 Loss in Target Net: 1.301e-02 |
2020-02-02 11:16:14 Iteration 550 Training Loss: 1.584e-01 Loss in Target Net: 1.229e-02 |
2020-02-02 11:16:34 Iteration 600 Training Loss: 1.615e-01 Loss in Target Net: 1.134e-02 |
2020-02-02 11:16:54 Iteration 650 Training Loss: 1.561e-01 Loss in Target Net: 1.133e-02 |
2020-02-02 11:17:14 Iteration 700 Training Loss: 1.578e-01 Loss in Target Net: 1.171e-02 |
2020-02-02 11:17:34 Iteration 750 Training Loss: 1.563e-01 Loss in Target Net: 1.172e-02 |
2020-02-02 11:17:55 Iteration 800 Training Loss: 1.605e-01 Loss in Target Net: 1.254e-02 |
2020-02-02 11:18:15 Iteration 850 Training Loss: 1.563e-01 Loss in Target Net: 1.030e-02 |
2020-02-02 11:18:36 Iteration 900 Training Loss: 1.521e-01 Loss in Target Net: 9.792e-03 |
2020-02-02 11:18:56 Iteration 950 Training Loss: 1.563e-01 Loss in Target Net: 1.055e-02 |
2020-02-02 11:19:15 Iteration 1000 Training Loss: 1.564e-01 Loss in Target Net: 1.189e-02 |
2020-02-02 11:19:36 Iteration 1050 Training Loss: 1.539e-01 Loss in Target Net: 1.160e-02 |
2020-02-02 11:19:56 Iteration 1100 Training Loss: 1.591e-01 Loss in Target Net: 1.062e-02 |
2020-02-02 11:20:16 Iteration 1150 Training Loss: 1.528e-01 Loss in Target Net: 9.246e-03 |
2020-02-02 11:20:37 Iteration 1200 Training Loss: 1.541e-01 Loss in Target Net: 1.048e-02 |
2020-02-02 11:20:58 Iteration 1250 Training Loss: 1.563e-01 Loss in Target Net: 1.086e-02 |
2020-02-02 11:21:19 Iteration 1300 Training Loss: 1.556e-01 Loss in Target Net: 1.023e-02 |
2020-02-02 11:21:41 Iteration 1350 Training Loss: 1.568e-01 Loss in Target Net: 1.013e-02 |
2020-02-02 11:22:02 Iteration 1400 Training Loss: 1.532e-01 Loss in Target Net: 1.027e-02 |
2020-02-02 11:22:24 Iteration 1450 Training Loss: 1.532e-01 Loss in Target Net: 1.026e-02 |
2020-02-02 11:22:45 Iteration 1499 Training Loss: 1.515e-01 Loss in Target Net: 9.849e-03 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-02 11:22:54, Epoch 0, Iteration 7, loss 0.603 (0.504), acc 84.615 (89.600) |
2020-02-02 11:23:53, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-2.5223517, -1.8822683, -1.1176769, -3.2298064, -0.29474726, -3.381183, 8.986241, -1.9702355, 6.9235477, -0.98054445], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-02 11:24:52 Epoch 59, Val iteration 0, acc 92.600 (92.600) |
2020-02-02 11:25:00 Epoch 59, Val iteration 19, acc 92.200 (93.110) |
* Prec: 93.11000175476075 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 10 |
TARGET INDEX: 10 |
DPN92 0 |