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1.13k
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TIME ELAPSED (mins): 8
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TARGET INDEX: 45
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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=46, 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/1500/46
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 12:55:54 Iteration 0 Training Loss: 1.019e+00 Loss in Target Net: 1.404e+00
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2020-02-02 12:56:14 Iteration 50 Training Loss: 2.258e-01 Loss in Target Net: 5.242e-02
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2020-02-02 12:56:32 Iteration 100 Training Loss: 1.948e-01 Loss in Target Net: 4.585e-02
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2020-02-02 12:56:47 Iteration 150 Training Loss: 1.847e-01 Loss in Target Net: 4.673e-02
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2020-02-02 12:57:03 Iteration 200 Training Loss: 1.765e-01 Loss in Target Net: 4.103e-02
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2020-02-02 12:57:19 Iteration 250 Training Loss: 1.764e-01 Loss in Target Net: 3.554e-02
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2020-02-02 12:57:35 Iteration 300 Training Loss: 1.667e-01 Loss in Target Net: 3.899e-02
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2020-02-02 12:57:52 Iteration 350 Training Loss: 1.632e-01 Loss in Target Net: 3.142e-02
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2020-02-02 12:58:08 Iteration 400 Training Loss: 1.660e-01 Loss in Target Net: 3.510e-02
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2020-02-02 12:58:25 Iteration 450 Training Loss: 1.687e-01 Loss in Target Net: 3.514e-02
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2020-02-02 12:58:41 Iteration 500 Training Loss: 1.649e-01 Loss in Target Net: 3.671e-02
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2020-02-02 12:58:57 Iteration 550 Training Loss: 1.652e-01 Loss in Target Net: 3.582e-02
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2020-02-02 12:59:14 Iteration 600 Training Loss: 1.631e-01 Loss in Target Net: 3.380e-02
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2020-02-02 12:59:31 Iteration 650 Training Loss: 1.636e-01 Loss in Target Net: 3.238e-02
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2020-02-02 12:59:49 Iteration 700 Training Loss: 1.594e-01 Loss in Target Net: 3.369e-02
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2020-02-02 13:00:06 Iteration 750 Training Loss: 1.637e-01 Loss in Target Net: 3.349e-02
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2020-02-02 13:00:24 Iteration 800 Training Loss: 1.590e-01 Loss in Target Net: 3.770e-02
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2020-02-02 13:00:42 Iteration 850 Training Loss: 1.567e-01 Loss in Target Net: 3.314e-02
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2020-02-02 13:00:58 Iteration 900 Training Loss: 1.583e-01 Loss in Target Net: 3.517e-02
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2020-02-02 13:01:16 Iteration 950 Training Loss: 1.620e-01 Loss in Target Net: 3.930e-02
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2020-02-02 13:01:33 Iteration 1000 Training Loss: 1.625e-01 Loss in Target Net: 3.197e-02
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2020-02-02 13:01:50 Iteration 1050 Training Loss: 1.557e-01 Loss in Target Net: 4.407e-02
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2020-02-02 13:02:06 Iteration 1100 Training Loss: 1.590e-01 Loss in Target Net: 3.712e-02
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2020-02-02 13:02:23 Iteration 1150 Training Loss: 1.593e-01 Loss in Target Net: 3.709e-02
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2020-02-02 13:02:40 Iteration 1200 Training Loss: 1.594e-01 Loss in Target Net: 3.561e-02
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2020-02-02 13:02:58 Iteration 1250 Training Loss: 1.594e-01 Loss in Target Net: 3.306e-02
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2020-02-02 13:03:15 Iteration 1300 Training Loss: 1.555e-01 Loss in Target Net: 3.413e-02
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2020-02-02 13:03:32 Iteration 1350 Training Loss: 1.584e-01 Loss in Target Net: 3.019e-02
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2020-02-02 13:03:51 Iteration 1400 Training Loss: 1.592e-01 Loss in Target Net: 3.075e-02
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2020-02-02 13:04:08 Iteration 1450 Training Loss: 1.584e-01 Loss in Target Net: 3.669e-02
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2020-02-02 13:04:25 Iteration 1499 Training Loss: 1.580e-01 Loss in Target Net: 2.927e-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 13:04:34, Epoch 0, Iteration 7, loss 0.272 (0.473), acc 94.231 (90.600)
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2020-02-02 13:05:32, 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:[-1.4091834, 0.7568287, -1.9871689, -3.1151533, -2.6752791, -2.2613456, 7.224427, -1.107118, 7.135426, -2.1717203], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 13:06:31 Epoch 59, Val iteration 0, acc 92.200 (92.200)
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2020-02-02 13:06:38 Epoch 59, Val iteration 19, acc 92.200 (92.960)
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* Prec: 92.96000175476074
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 8
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TARGET INDEX: 46
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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=47, 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/1500/47
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 12:52:23 Iteration 0 Training Loss: 1.018e+00 Loss in Target Net: 1.219e+00
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2020-02-02 12:52:43 Iteration 50 Training Loss: 2.225e-01 Loss in Target Net: 2.985e-02
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2020-02-02 12:53:00 Iteration 100 Training Loss: 1.943e-01 Loss in Target Net: 1.890e-02
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2020-02-02 12:53:19 Iteration 150 Training Loss: 1.805e-01 Loss in Target Net: 1.739e-02
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2020-02-02 12:53:39 Iteration 200 Training Loss: 1.775e-01 Loss in Target Net: 1.801e-02
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2020-02-02 12:54:00 Iteration 250 Training Loss: 1.708e-01 Loss in Target Net: 1.901e-02
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2020-02-02 12:54:20 Iteration 300 Training Loss: 1.728e-01 Loss in Target Net: 2.041e-02
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2020-02-02 12:54:39 Iteration 350 Training Loss: 1.706e-01 Loss in Target Net: 1.754e-02
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2020-02-02 12:54:59 Iteration 400 Training Loss: 1.665e-01 Loss in Target Net: 1.727e-02
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2020-02-02 12:55:19 Iteration 450 Training Loss: 1.631e-01 Loss in Target Net: 1.676e-02
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2020-02-02 12:55:38 Iteration 500 Training Loss: 1.660e-01 Loss in Target Net: 1.678e-02
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2020-02-02 12:56:00 Iteration 550 Training Loss: 1.661e-01 Loss in Target Net: 1.804e-02
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2020-02-02 12:56:22 Iteration 600 Training Loss: 1.680e-01 Loss in Target Net: 1.603e-02
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2020-02-02 12:56:41 Iteration 650 Training Loss: 1.627e-01 Loss in Target Net: 1.456e-02
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2020-02-02 12:57:01 Iteration 700 Training Loss: 1.637e-01 Loss in Target Net: 1.686e-02
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2020-02-02 12:57:22 Iteration 750 Training Loss: 1.660e-01 Loss in Target Net: 1.461e-02
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2020-02-02 12:57:44 Iteration 800 Training Loss: 1.625e-01 Loss in Target Net: 1.591e-02
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2020-02-02 12:58:03 Iteration 850 Training Loss: 1.636e-01 Loss in Target Net: 1.605e-02
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2020-02-02 12:58:21 Iteration 900 Training Loss: 1.607e-01 Loss in Target Net: 1.665e-02
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2020-02-02 12:58:40 Iteration 950 Training Loss: 1.672e-01 Loss in Target Net: 1.852e-02
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2020-02-02 12:59:00 Iteration 1000 Training Loss: 1.684e-01 Loss in Target Net: 1.635e-02
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2020-02-02 12:59:20 Iteration 1050 Training Loss: 1.680e-01 Loss in Target Net: 1.746e-02
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2020-02-02 12:59:38 Iteration 1100 Training Loss: 1.598e-01 Loss in Target Net: 1.669e-02
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2020-02-02 12:59:58 Iteration 1150 Training Loss: 1.647e-01 Loss in Target Net: 1.559e-02
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2020-02-02 13:00:18 Iteration 1200 Training Loss: 1.614e-01 Loss in Target Net: 1.790e-02
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2020-02-02 13:00:37 Iteration 1250 Training Loss: 1.596e-01 Loss in Target Net: 1.720e-02
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2020-02-02 13:00:56 Iteration 1300 Training Loss: 1.618e-01 Loss in Target Net: 1.653e-02
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2020-02-02 13:01:15 Iteration 1350 Training Loss: 1.587e-01 Loss in Target Net: 1.772e-02
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2020-02-02 13:01:34 Iteration 1400 Training Loss: 1.632e-01 Loss in Target Net: 1.938e-02
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2020-02-02 13:01:51 Iteration 1450 Training Loss: 1.598e-01 Loss in Target Net: 1.600e-02
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2020-02-02 13:02:09 Iteration 1499 Training Loss: 1.669e-01 Loss in Target Net: 2.012e-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 13:02:18, Epoch 0, Iteration 7, loss 0.440 (0.348), acc 90.385 (92.800)
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2020-02-02 13:03:16, 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:[-5.4489584, -1.1011136, -1.8024782, -0.21411803, -1.1184216, -3.7813826, 12.986473, -2.9875708, 6.3938613, -2.5532033], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 13:04:16 Epoch 59, Val iteration 0, acc 92.400 (92.400)
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2020-02-02 13:04:23 Epoch 59, Val iteration 19, acc 92.000 (92.230)
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* Prec: 92.23000068664551
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 9
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TARGET INDEX: 47
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