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2020-02-02 12:55:41 Epoch 59, Val iteration 19, acc 93.000 (92.650)
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* Prec: 92.6500015258789
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 9
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TARGET INDEX: 42
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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=43, 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/1500/43
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 12:41:16 Iteration 0 Training Loss: 1.022e+00 Loss in Target Net: 1.342e+00
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2020-02-02 12:41:32 Iteration 50 Training Loss: 2.172e-01 Loss in Target Net: 4.747e-02
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2020-02-02 12:41:50 Iteration 100 Training Loss: 1.944e-01 Loss in Target Net: 2.732e-02
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2020-02-02 12:42:07 Iteration 150 Training Loss: 1.839e-01 Loss in Target Net: 2.835e-02
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2020-02-02 12:42:24 Iteration 200 Training Loss: 1.790e-01 Loss in Target Net: 2.388e-02
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2020-02-02 12:42:40 Iteration 250 Training Loss: 1.713e-01 Loss in Target Net: 2.352e-02
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2020-02-02 12:42:58 Iteration 300 Training Loss: 1.716e-01 Loss in Target Net: 2.448e-02
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2020-02-02 12:43:16 Iteration 350 Training Loss: 1.711e-01 Loss in Target Net: 2.003e-02
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2020-02-02 12:43:32 Iteration 400 Training Loss: 1.677e-01 Loss in Target Net: 1.927e-02
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2020-02-02 12:43:48 Iteration 450 Training Loss: 1.637e-01 Loss in Target Net: 1.984e-02
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2020-02-02 12:44:05 Iteration 500 Training Loss: 1.633e-01 Loss in Target Net: 2.049e-02
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2020-02-02 12:44:25 Iteration 550 Training Loss: 1.632e-01 Loss in Target Net: 2.000e-02
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2020-02-02 12:44:42 Iteration 600 Training Loss: 1.652e-01 Loss in Target Net: 1.876e-02
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2020-02-02 12:44:59 Iteration 650 Training Loss: 1.616e-01 Loss in Target Net: 2.344e-02
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2020-02-02 12:45:17 Iteration 700 Training Loss: 1.632e-01 Loss in Target Net: 2.155e-02
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2020-02-02 12:45:36 Iteration 750 Training Loss: 1.615e-01 Loss in Target Net: 2.203e-02
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2020-02-02 12:45:53 Iteration 800 Training Loss: 1.649e-01 Loss in Target Net: 2.110e-02
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2020-02-02 12:46:12 Iteration 850 Training Loss: 1.607e-01 Loss in Target Net: 2.089e-02
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2020-02-02 12:46:29 Iteration 900 Training Loss: 1.644e-01 Loss in Target Net: 2.583e-02
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2020-02-02 12:46:46 Iteration 950 Training Loss: 1.648e-01 Loss in Target Net: 2.413e-02
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2020-02-02 12:47:04 Iteration 1000 Training Loss: 1.597e-01 Loss in Target Net: 2.342e-02
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2020-02-02 12:47:21 Iteration 1050 Training Loss: 1.605e-01 Loss in Target Net: 2.756e-02
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2020-02-02 12:47:37 Iteration 1100 Training Loss: 1.600e-01 Loss in Target Net: 2.455e-02
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2020-02-02 12:47:56 Iteration 1150 Training Loss: 1.600e-01 Loss in Target Net: 2.266e-02
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2020-02-02 12:48:13 Iteration 1200 Training Loss: 1.653e-01 Loss in Target Net: 2.569e-02
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2020-02-02 12:48:29 Iteration 1250 Training Loss: 1.637e-01 Loss in Target Net: 2.388e-02
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2020-02-02 12:48:46 Iteration 1300 Training Loss: 1.570e-01 Loss in Target Net: 2.645e-02
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2020-02-02 12:49:03 Iteration 1350 Training Loss: 1.576e-01 Loss in Target Net: 2.583e-02
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2020-02-02 12:49:21 Iteration 1400 Training Loss: 1.566e-01 Loss in Target Net: 2.492e-02
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2020-02-02 12:49:39 Iteration 1450 Training Loss: 1.612e-01 Loss in Target Net: 2.328e-02
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2020-02-02 12:49:56 Iteration 1499 Training Loss: 1.593e-01 Loss in Target Net: 2.121e-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 12:50:06, Epoch 0, Iteration 7, loss 0.601 (0.525), acc 82.692 (87.200)
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2020-02-02 12:51:03, 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:[-2.6017864, 0.017899783, -3.0238163, -2.994907, -2.6979392, -2.0972354, 7.5262966, -3.3071558, 10.836857, -1.2332652], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 12:52:03 Epoch 59, Val iteration 0, acc 95.200 (95.200)
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2020-02-02 12:52:10 Epoch 59, Val iteration 19, acc 93.000 (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): 8
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TARGET INDEX: 43
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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='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=44, 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/1500/44
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 12:55:10 Iteration 0 Training Loss: 1.096e+00 Loss in Target Net: 1.609e+00
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2020-02-02 12:55:28 Iteration 50 Training Loss: 3.013e-01 Loss in Target Net: 1.327e-01
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2020-02-02 12:55:46 Iteration 100 Training Loss: 2.654e-01 Loss in Target Net: 8.922e-02
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2020-02-02 12:56:03 Iteration 150 Training Loss: 2.558e-01 Loss in Target Net: 9.645e-02
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2020-02-02 12:56:20 Iteration 200 Training Loss: 2.448e-01 Loss in Target Net: 8.883e-02
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2020-02-02 12:56:38 Iteration 250 Training Loss: 2.415e-01 Loss in Target Net: 6.188e-02
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2020-02-02 12:56:55 Iteration 300 Training Loss: 2.442e-01 Loss in Target Net: 6.763e-02
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2020-02-02 12:57:13 Iteration 350 Training Loss: 2.357e-01 Loss in Target Net: 6.416e-02
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2020-02-02 12:57:31 Iteration 400 Training Loss: 2.299e-01 Loss in Target Net: 6.776e-02
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2020-02-02 12:57:48 Iteration 450 Training Loss: 2.344e-01 Loss in Target Net: 6.483e-02
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2020-02-02 12:58:06 Iteration 500 Training Loss: 2.324e-01 Loss in Target Net: 1.073e-01
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2020-02-02 12:58:25 Iteration 550 Training Loss: 2.317e-01 Loss in Target Net: 6.593e-02
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2020-02-02 12:58:44 Iteration 600 Training Loss: 2.266e-01 Loss in Target Net: 8.134e-02
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2020-02-02 12:59:01 Iteration 650 Training Loss: 2.217e-01 Loss in Target Net: 8.311e-02
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2020-02-02 12:59:17 Iteration 700 Training Loss: 2.226e-01 Loss in Target Net: 7.718e-02
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2020-02-02 12:59:35 Iteration 750 Training Loss: 2.272e-01 Loss in Target Net: 9.128e-02
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2020-02-02 12:59:53 Iteration 800 Training Loss: 2.308e-01 Loss in Target Net: 8.377e-02
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2020-02-02 13:00:09 Iteration 850 Training Loss: 2.189e-01 Loss in Target Net: 8.044e-02
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2020-02-02 13:00:25 Iteration 900 Training Loss: 2.229e-01 Loss in Target Net: 1.048e-01
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2020-02-02 13:00:41 Iteration 950 Training Loss: 2.229e-01 Loss in Target Net: 8.737e-02
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2020-02-02 13:00:58 Iteration 1000 Training Loss: 2.240e-01 Loss in Target Net: 9.810e-02
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2020-02-02 13:01:15 Iteration 1050 Training Loss: 2.207e-01 Loss in Target Net: 1.019e-01
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2020-02-02 13:01:32 Iteration 1100 Training Loss: 2.197e-01 Loss in Target Net: 1.218e-01
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2020-02-02 13:01:50 Iteration 1150 Training Loss: 2.218e-01 Loss in Target Net: 8.499e-02
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2020-02-02 13:02:07 Iteration 1200 Training Loss: 2.216e-01 Loss in Target Net: 7.686e-02
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2020-02-02 13:02:23 Iteration 1250 Training Loss: 2.164e-01 Loss in Target Net: 1.057e-01
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2020-02-02 13:02:42 Iteration 1300 Training Loss: 2.216e-01 Loss in Target Net: 9.690e-02
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2020-02-02 13:03:02 Iteration 1350 Training Loss: 2.206e-01 Loss in Target Net: 7.416e-02
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2020-02-02 13:03:19 Iteration 1400 Training Loss: 2.178e-01 Loss in Target Net: 1.064e-01
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2020-02-02 13:03:36 Iteration 1450 Training Loss: 2.259e-01 Loss in Target Net: 1.101e-01
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2020-02-02 13:03:52 Iteration 1499 Training Loss: 2.152e-01 Loss in Target Net: 1.271e-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 13:04:02, Epoch 0, Iteration 7, loss 0.254 (0.354), acc 92.308 (91.400)
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2020-02-02 13:05:00, 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:[-2.2282615, -0.39486375, 1.3240094, 1.0011982, -0.7348615, -2.6797185, 9.872774, -3.8137097, 1.1681877, -3.110437], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 13:06:00 Epoch 59, Val iteration 0, acc 93.400 (93.400)
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2020-02-02 13:06:07 Epoch 59, Val iteration 19, acc 93.000 (93.040)
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* Prec: 93.04000129699708
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