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2020-02-03 05:46:04 Iteration 1050 Training Loss: 2.095e-01 Loss in Target Net: 6.920e-02
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2020-02-03 05:46:38 Iteration 1100 Training Loss: 2.084e-01 Loss in Target Net: 6.213e-02
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2020-02-03 05:47:09 Iteration 1150 Training Loss: 2.035e-01 Loss in Target Net: 5.522e-02
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2020-02-03 05:47:38 Iteration 1200 Training Loss: 2.080e-01 Loss in Target Net: 4.969e-02
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2020-02-03 05:48:12 Iteration 1250 Training Loss: 2.065e-01 Loss in Target Net: 5.359e-02
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2020-02-03 05:48:42 Iteration 1300 Training Loss: 2.084e-01 Loss in Target Net: 5.366e-02
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2020-02-03 05:49:13 Iteration 1350 Training Loss: 2.064e-01 Loss in Target Net: 5.467e-02
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2020-02-03 05:49:40 Iteration 1400 Training Loss: 2.032e-01 Loss in Target Net: 5.005e-02
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2020-02-03 05:50:15 Iteration 1450 Training Loss: 2.112e-01 Loss in Target Net: 5.246e-02
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2020-02-03 05:50:51 Iteration 1499 Training Loss: 2.112e-01 Loss in Target Net: 5.654e-02
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Evaluating against victims networks
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DPN92
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Using Adam for retraining
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Files already downloaded and verified
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2020-02-03 05:51:05, Epoch 0, Iteration 7, loss 0.314 (0.421), acc 90.385 (90.400)
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2020-02-03 05:52:49, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)
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Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-3.1904948, -1.9260613, -0.7823505, -1.4901806, 0.81467074, -2.6157384, 8.693512, -0.7768738, 3.0341733, -1.4644873], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-03 05:54:42 Epoch 59, Val iteration 0, acc 93.200 (93.200)
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2020-02-03 05:54:56 Epoch 59, Val iteration 19, acc 94.000 (93.240)
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* Prec: 93.24000205993653
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 16
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TARGET INDEX: 29
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DPN92 0
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Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='3', 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=3, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')
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Path: chk-black-end2end/mean/1500/3
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 10:49:32 Iteration 0 Training Loss: 1.023e+00 Loss in Target Net: 1.345e+00
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2020-02-02 10:49:50 Iteration 50 Training Loss: 2.367e-01 Loss in Target Net: 5.382e-02
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2020-02-02 10:50:07 Iteration 100 Training Loss: 2.060e-01 Loss in Target Net: 4.977e-02
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2020-02-02 10:50:26 Iteration 150 Training Loss: 1.979e-01 Loss in Target Net: 4.082e-02
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2020-02-02 10:50:44 Iteration 200 Training Loss: 1.890e-01 Loss in Target Net: 3.411e-02
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2020-02-02 10:51:04 Iteration 250 Training Loss: 1.820e-01 Loss in Target Net: 3.372e-02
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2020-02-02 10:51:21 Iteration 300 Training Loss: 1.790e-01 Loss in Target Net: 3.607e-02
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2020-02-02 10:51:39 Iteration 350 Training Loss: 1.816e-01 Loss in Target Net: 3.325e-02
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2020-02-02 10:51:57 Iteration 400 Training Loss: 1.784e-01 Loss in Target Net: 2.996e-02
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2020-02-02 10:52:14 Iteration 450 Training Loss: 1.792e-01 Loss in Target Net: 2.685e-02
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2020-02-02 10:52:32 Iteration 500 Training Loss: 1.784e-01 Loss in Target Net: 2.947e-02
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2020-02-02 10:52:52 Iteration 550 Training Loss: 1.762e-01 Loss in Target Net: 2.900e-02
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2020-02-02 10:53:10 Iteration 600 Training Loss: 1.732e-01 Loss in Target Net: 3.043e-02
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2020-02-02 10:53:28 Iteration 650 Training Loss: 1.694e-01 Loss in Target Net: 3.166e-02
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2020-02-02 10:53:46 Iteration 700 Training Loss: 1.746e-01 Loss in Target Net: 3.519e-02
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2020-02-02 10:54:03 Iteration 750 Training Loss: 1.698e-01 Loss in Target Net: 2.525e-02
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2020-02-02 10:54:21 Iteration 800 Training Loss: 1.737e-01 Loss in Target Net: 3.082e-02
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2020-02-02 10:54:39 Iteration 850 Training Loss: 1.714e-01 Loss in Target Net: 3.303e-02
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2020-02-02 10:54:57 Iteration 900 Training Loss: 1.715e-01 Loss in Target Net: 3.175e-02
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2020-02-02 10:55:16 Iteration 950 Training Loss: 1.693e-01 Loss in Target Net: 2.708e-02
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2020-02-02 10:55:34 Iteration 1000 Training Loss: 1.709e-01 Loss in Target Net: 3.108e-02
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2020-02-02 10:55:53 Iteration 1050 Training Loss: 1.710e-01 Loss in Target Net: 3.166e-02
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2020-02-02 10:56:11 Iteration 1100 Training Loss: 1.722e-01 Loss in Target Net: 3.032e-02
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2020-02-02 10:56:29 Iteration 1150 Training Loss: 1.674e-01 Loss in Target Net: 3.080e-02
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2020-02-02 10:56:48 Iteration 1200 Training Loss: 1.714e-01 Loss in Target Net: 3.006e-02
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2020-02-02 10:57:06 Iteration 1250 Training Loss: 1.727e-01 Loss in Target Net: 2.964e-02
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2020-02-02 10:57:24 Iteration 1300 Training Loss: 1.682e-01 Loss in Target Net: 2.435e-02
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2020-02-02 10:57:41 Iteration 1350 Training Loss: 1.716e-01 Loss in Target Net: 3.163e-02
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2020-02-02 10:57:59 Iteration 1400 Training Loss: 1.690e-01 Loss in Target Net: 2.520e-02
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2020-02-02 10:58:17 Iteration 1450 Training Loss: 1.694e-01 Loss in Target Net: 3.301e-02
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2020-02-02 10:58:34 Iteration 1499 Training Loss: 1.650e-01 Loss in Target Net: 2.806e-02
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Evaluating against victims networks
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DPN92
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Using Adam for retraining
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Files already downloaded and verified
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2020-02-02 10:58:43, Epoch 0, Iteration 7, loss 0.970 (0.499), acc 78.846 (89.400)
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2020-02-02 10:59:41, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000)
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Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-2.720542, -0.27323884, -2.3643427, -1.4802146, -1.1394945, -2.5426686, 10.148354, -3.2613678, 4.7550654, -0.89363205], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 11:00:40 Epoch 59, Val iteration 0, acc 91.800 (91.800)
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2020-02-02 11:00:48 Epoch 59, Val iteration 19, acc 93.800 (93.090)
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* Prec: 93.09000167846679
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 9
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TARGET INDEX: 3
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DPN92 0
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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=30, target_label=6, target_net=['DPN92'], test_chk_name='ckpt-%s-4800.t7', tol=1e-06, train_data_path='datasets/CIFAR10_TRAIN_Split.pth')
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Path: chk-black-end2end/mean/1500/30
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 12:10:08 Iteration 0 Training Loss: 1.032e+00 Loss in Target Net: 1.441e+00
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2020-02-02 12:10:25 Iteration 50 Training Loss: 2.368e-01 Loss in Target Net: 4.492e-02
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2020-02-02 12:10:43 Iteration 100 Training Loss: 2.121e-01 Loss in Target Net: 4.155e-02
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2020-02-02 12:11:00 Iteration 150 Training Loss: 2.008e-01 Loss in Target Net: 3.388e-02
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2020-02-02 12:11:17 Iteration 200 Training Loss: 1.886e-01 Loss in Target Net: 3.090e-02
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2020-02-02 12:11:35 Iteration 250 Training Loss: 1.872e-01 Loss in Target Net: 3.285e-02
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2020-02-02 12:11:54 Iteration 300 Training Loss: 1.835e-01 Loss in Target Net: 4.154e-02
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2020-02-02 12:12:12 Iteration 350 Training Loss: 1.799e-01 Loss in Target Net: 3.729e-02
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2020-02-02 12:12:28 Iteration 400 Training Loss: 1.769e-01 Loss in Target Net: 4.068e-02
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2020-02-02 12:12:44 Iteration 450 Training Loss: 1.823e-01 Loss in Target Net: 5.161e-02
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2020-02-02 12:12:59 Iteration 500 Training Loss: 1.768e-01 Loss in Target Net: 4.672e-02
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2020-02-02 12:13:16 Iteration 550 Training Loss: 1.751e-01 Loss in Target Net: 4.446e-02
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2020-02-02 12:13:34 Iteration 600 Training Loss: 1.727e-01 Loss in Target Net: 5.852e-02
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2020-02-02 12:13:49 Iteration 650 Training Loss: 1.717e-01 Loss in Target Net: 4.227e-02
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2020-02-02 12:14:05 Iteration 700 Training Loss: 1.745e-01 Loss in Target Net: 4.604e-02
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2020-02-02 12:14:21 Iteration 750 Training Loss: 1.719e-01 Loss in Target Net: 4.377e-02
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2020-02-02 12:14:36 Iteration 800 Training Loss: 1.740e-01 Loss in Target Net: 5.202e-02
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2020-02-02 12:14:52 Iteration 850 Training Loss: 1.700e-01 Loss in Target Net: 4.664e-02
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2020-02-02 12:15:08 Iteration 900 Training Loss: 1.685e-01 Loss in Target Net: 4.790e-02
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2020-02-02 12:15:24 Iteration 950 Training Loss: 1.734e-01 Loss in Target Net: 4.718e-02
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2020-02-02 12:15:41 Iteration 1000 Training Loss: 1.739e-01 Loss in Target Net: 4.416e-02
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2020-02-02 12:15:58 Iteration 1050 Training Loss: 1.724e-01 Loss in Target Net: 4.589e-02
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2020-02-02 12:16:14 Iteration 1100 Training Loss: 1.745e-01 Loss in Target Net: 4.917e-02
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