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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='0', 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=48, 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/1500/48
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 13:06:21 Iteration 0 Training Loss: 9.982e-01 Loss in Target Net: 1.393e+00
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2020-02-02 13:06:37 Iteration 50 Training Loss: 2.972e-01 Loss in Target Net: 3.272e-01
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2020-02-02 13:06:53 Iteration 100 Training Loss: 2.712e-01 Loss in Target Net: 2.612e-01
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2020-02-02 13:07:10 Iteration 150 Training Loss: 2.570e-01 Loss in Target Net: 2.761e-01
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2020-02-02 13:07:28 Iteration 200 Training Loss: 2.498e-01 Loss in Target Net: 2.598e-01
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2020-02-02 13:07:45 Iteration 250 Training Loss: 2.430e-01 Loss in Target Net: 2.764e-01
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2020-02-02 13:08:02 Iteration 300 Training Loss: 2.423e-01 Loss in Target Net: 3.001e-01
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2020-02-02 13:08:18 Iteration 350 Training Loss: 2.375e-01 Loss in Target Net: 2.558e-01
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2020-02-02 13:08:33 Iteration 400 Training Loss: 2.339e-01 Loss in Target Net: 3.228e-01
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2020-02-02 13:08:49 Iteration 450 Training Loss: 2.369e-01 Loss in Target Net: 2.769e-01
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2020-02-02 13:09:04 Iteration 500 Training Loss: 2.382e-01 Loss in Target Net: 2.914e-01
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2020-02-02 13:09:20 Iteration 550 Training Loss: 2.307e-01 Loss in Target Net: 3.020e-01
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2020-02-02 13:09:37 Iteration 600 Training Loss: 2.309e-01 Loss in Target Net: 3.353e-01
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2020-02-02 13:09:53 Iteration 650 Training Loss: 2.270e-01 Loss in Target Net: 3.159e-01
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2020-02-02 13:10:08 Iteration 700 Training Loss: 2.347e-01 Loss in Target Net: 3.106e-01
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2020-02-02 13:10:24 Iteration 750 Training Loss: 2.363e-01 Loss in Target Net: 2.970e-01
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2020-02-02 13:10:40 Iteration 800 Training Loss: 2.322e-01 Loss in Target Net: 3.336e-01
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2020-02-02 13:10:55 Iteration 850 Training Loss: 2.240e-01 Loss in Target Net: 2.887e-01
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2020-02-02 13:11:11 Iteration 900 Training Loss: 2.251e-01 Loss in Target Net: 2.923e-01
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2020-02-02 13:11:27 Iteration 950 Training Loss: 2.356e-01 Loss in Target Net: 3.149e-01
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2020-02-02 13:11:43 Iteration 1000 Training Loss: 2.249e-01 Loss in Target Net: 2.817e-01
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2020-02-02 13:11:59 Iteration 1050 Training Loss: 2.325e-01 Loss in Target Net: 3.168e-01
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2020-02-02 13:12:15 Iteration 1100 Training Loss: 2.307e-01 Loss in Target Net: 3.129e-01
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2020-02-02 13:12:31 Iteration 1150 Training Loss: 2.301e-01 Loss in Target Net: 3.592e-01
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2020-02-02 13:12:47 Iteration 1200 Training Loss: 2.262e-01 Loss in Target Net: 3.748e-01
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2020-02-02 13:13:03 Iteration 1250 Training Loss: 2.279e-01 Loss in Target Net: 3.432e-01
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2020-02-02 13:13:19 Iteration 1300 Training Loss: 2.334e-01 Loss in Target Net: 3.468e-01
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2020-02-02 13:13:35 Iteration 1350 Training Loss: 2.260e-01 Loss in Target Net: 3.067e-01
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2020-02-02 13:13:51 Iteration 1400 Training Loss: 2.216e-01 Loss in Target Net: 3.077e-01
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2020-02-02 13:14:06 Iteration 1450 Training Loss: 2.271e-01 Loss in Target Net: 3.036e-01
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2020-02-02 13:14:22 Iteration 1499 Training Loss: 2.211e-01 Loss in Target Net: 3.193e-01
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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 13:14:31, Epoch 0, Iteration 7, loss 0.236 (0.329), acc 92.308 (91.200)
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2020-02-02 13:15:29, 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:8, Target's Score:[0.9360886, -0.316368, -2.5909395, -2.1841722, -2.5372443, -1.9682672, 0.47649133, -2.3556173, 10.697428, 0.4530875], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 13:16:28 Epoch 59, Val iteration 0, acc 92.400 (92.400)
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2020-02-02 13:16:36 Epoch 59, Val iteration 19, acc 92.000 (92.240)
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* Prec: 92.24000129699706
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 8
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TARGET INDEX: 48
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DPN92 1
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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=49, 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/1500/49
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 13:07:48 Iteration 0 Training Loss: 9.836e-01 Loss in Target Net: 1.252e+00
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2020-02-02 13:08:05 Iteration 50 Training Loss: 2.633e-01 Loss in Target Net: 1.841e-01
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2020-02-02 13:08:22 Iteration 100 Training Loss: 2.387e-01 Loss in Target Net: 1.410e-01
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2020-02-02 13:08:38 Iteration 150 Training Loss: 2.241e-01 Loss in Target Net: 1.320e-01
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2020-02-02 13:08:54 Iteration 200 Training Loss: 2.230e-01 Loss in Target Net: 1.320e-01
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2020-02-02 13:09:11 Iteration 250 Training Loss: 2.174e-01 Loss in Target Net: 1.173e-01
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2020-02-02 13:09:27 Iteration 300 Training Loss: 2.107e-01 Loss in Target Net: 9.651e-02
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2020-02-02 13:09:44 Iteration 350 Training Loss: 2.179e-01 Loss in Target Net: 1.102e-01
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2020-02-02 13:10:00 Iteration 400 Training Loss: 2.115e-01 Loss in Target Net: 1.105e-01
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2020-02-02 13:10:16 Iteration 450 Training Loss: 2.092e-01 Loss in Target Net: 1.174e-01
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2020-02-02 13:10:32 Iteration 500 Training Loss: 2.054e-01 Loss in Target Net: 1.118e-01
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2020-02-02 13:10:48 Iteration 550 Training Loss: 2.103e-01 Loss in Target Net: 1.112e-01
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2020-02-02 13:11:05 Iteration 600 Training Loss: 2.145e-01 Loss in Target Net: 1.255e-01
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2020-02-02 13:11:21 Iteration 650 Training Loss: 2.056e-01 Loss in Target Net: 1.238e-01
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2020-02-02 13:11:37 Iteration 700 Training Loss: 2.050e-01 Loss in Target Net: 1.161e-01
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2020-02-02 13:11:54 Iteration 750 Training Loss: 2.040e-01 Loss in Target Net: 1.084e-01
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2020-02-02 13:12:10 Iteration 800 Training Loss: 2.119e-01 Loss in Target Net: 1.052e-01
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2020-02-02 13:12:27 Iteration 850 Training Loss: 2.069e-01 Loss in Target Net: 1.084e-01
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2020-02-02 13:12:43 Iteration 900 Training Loss: 2.024e-01 Loss in Target Net: 1.091e-01
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2020-02-02 13:13:00 Iteration 950 Training Loss: 2.052e-01 Loss in Target Net: 1.159e-01
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2020-02-02 13:13:16 Iteration 1000 Training Loss: 2.064e-01 Loss in Target Net: 1.230e-01
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2020-02-02 13:13:32 Iteration 1050 Training Loss: 2.038e-01 Loss in Target Net: 1.227e-01
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2020-02-02 13:13:49 Iteration 1100 Training Loss: 2.045e-01 Loss in Target Net: 1.193e-01
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2020-02-02 13:14:06 Iteration 1150 Training Loss: 2.054e-01 Loss in Target Net: 1.016e-01
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2020-02-02 13:14:22 Iteration 1200 Training Loss: 2.047e-01 Loss in Target Net: 1.054e-01
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2020-02-02 13:14:39 Iteration 1250 Training Loss: 1.997e-01 Loss in Target Net: 1.113e-01
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2020-02-02 13:14:56 Iteration 1300 Training Loss: 2.029e-01 Loss in Target Net: 1.112e-01
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2020-02-02 13:15:12 Iteration 1350 Training Loss: 1.997e-01 Loss in Target Net: 1.118e-01
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2020-02-02 13:15:29 Iteration 1400 Training Loss: 2.010e-01 Loss in Target Net: 1.140e-01
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2020-02-02 13:15:45 Iteration 1450 Training Loss: 2.037e-01 Loss in Target Net: 1.213e-01
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2020-02-02 13:16:01 Iteration 1499 Training Loss: 2.019e-01 Loss in Target Net: 1.244e-01
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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 13:16:11, Epoch 0, Iteration 7, loss 0.596 (0.379), acc 84.615 (91.200)
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2020-02-02 13:17:08, 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:8, Target's Score:[-2.9051585, -0.889524, 1.2907454, -0.5365509, -2.0070844, 0.89917004, 0.07782448, -3.2540936, 10.374059, -2.722627], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 13:18:08 Epoch 59, Val iteration 0, acc 92.600 (92.600)
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2020-02-02 13:18:15 Epoch 59, Val iteration 19, acc 92.600 (92.990)
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* Prec: 92.9900016784668
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 8
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TARGET INDEX: 49
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DPN92 1
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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=49, 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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