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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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2020-02-03 06:42:42 Iteration 0 Training Loss: 9.797e-01 Loss in Target Net: 1.292e+00
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2020-02-03 06:42:58 Iteration 50 Training Loss: 2.668e-01 Loss in Target Net: 2.693e-01
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2020-02-03 06:43:14 Iteration 100 Training Loss: 2.438e-01 Loss in Target Net: 2.319e-01
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2020-02-03 06:43:30 Iteration 150 Training Loss: 2.372e-01 Loss in Target Net: 1.673e-01
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2020-02-03 06:43:45 Iteration 200 Training Loss: 2.289e-01 Loss in Target Net: 1.277e-01
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2020-02-03 06:44:01 Iteration 250 Training Loss: 2.265e-01 Loss in Target Net: 1.193e-01
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2020-02-03 06:44:17 Iteration 300 Training Loss: 2.258e-01 Loss in Target Net: 1.171e-01
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2020-02-03 06:44:33 Iteration 350 Training Loss: 2.212e-01 Loss in Target Net: 1.139e-01
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2020-02-03 06:44:49 Iteration 400 Training Loss: 2.214e-01 Loss in Target Net: 1.205e-01
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2020-02-03 06:45:05 Iteration 450 Training Loss: 2.166e-01 Loss in Target Net: 1.392e-01
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2020-02-03 06:45:22 Iteration 500 Training Loss: 2.196e-01 Loss in Target Net: 1.129e-01
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2020-02-03 06:45:38 Iteration 550 Training Loss: 2.180e-01 Loss in Target Net: 1.159e-01
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2020-02-03 06:45:54 Iteration 600 Training Loss: 2.175e-01 Loss in Target Net: 9.900e-02
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2020-02-03 06:46:10 Iteration 650 Training Loss: 2.095e-01 Loss in Target Net: 1.077e-01
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2020-02-03 06:46:25 Iteration 700 Training Loss: 2.117e-01 Loss in Target Net: 1.048e-01
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2020-02-03 06:46:41 Iteration 750 Training Loss: 2.141e-01 Loss in Target Net: 8.955e-02
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2020-02-03 06:46:57 Iteration 800 Training Loss: 2.094e-01 Loss in Target Net: 1.101e-01
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2020-02-03 06:47:13 Iteration 850 Training Loss: 2.124e-01 Loss in Target Net: 1.146e-01
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2020-02-03 06:47:30 Iteration 900 Training Loss: 2.058e-01 Loss in Target Net: 1.224e-01
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2020-02-03 06:47:46 Iteration 950 Training Loss: 2.075e-01 Loss in Target Net: 1.088e-01
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2020-02-03 06:48:02 Iteration 1000 Training Loss: 2.098e-01 Loss in Target Net: 1.257e-01
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2020-02-03 06:48:18 Iteration 1050 Training Loss: 2.082e-01 Loss in Target Net: 1.086e-01
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2020-02-03 06:48:34 Iteration 1100 Training Loss: 2.077e-01 Loss in Target Net: 1.107e-01
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2020-02-03 06:48:50 Iteration 1150 Training Loss: 2.063e-01 Loss in Target Net: 1.033e-01
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2020-02-03 06:49:06 Iteration 1200 Training Loss: 2.072e-01 Loss in Target Net: 1.331e-01
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2020-02-03 06:49:23 Iteration 1250 Training Loss: 2.069e-01 Loss in Target Net: 9.776e-02
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2020-02-03 06:49:39 Iteration 1300 Training Loss: 2.067e-01 Loss in Target Net: 1.468e-01
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2020-02-03 06:49:55 Iteration 1350 Training Loss: 2.154e-01 Loss in Target Net: 1.064e-01
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2020-02-03 06:50:12 Iteration 1400 Training Loss: 1.999e-01 Loss in Target Net: 1.220e-01
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2020-02-03 06:50:28 Iteration 1450 Training Loss: 2.049e-01 Loss in Target Net: 1.254e-01
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2020-02-03 06:50:44 Iteration 1499 Training Loss: 2.073e-01 Loss in Target Net: 1.172e-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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2020-02-03 06:50:53, Epoch 0, Iteration 7, loss 0.744 (0.510), acc 80.769 (89.200)
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2020-02-03 06:51:51, 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:[-2.8313112, -0.6063601, 0.62694824, -0.7518552, -1.9208463, 3.1222844, 1.0653188, -2.3390694, 5.772581, -1.9050338], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-03 06:52:50 Epoch 59, Val iteration 0, acc 93.200 (93.200)
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2020-02-03 06:52:57 Epoch 59, Val iteration 19, acc 93.000 (93.140)
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* Prec: 93.14000205993652
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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=5, 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/1500/5
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-02 11:01:18 Iteration 0 Training Loss: 9.661e-01 Loss in Target Net: 1.097e+00
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2020-02-02 11:01:34 Iteration 50 Training Loss: 2.565e-01 Loss in Target Net: 8.834e-02
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2020-02-02 11:01:50 Iteration 100 Training Loss: 2.241e-01 Loss in Target Net: 7.414e-02
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2020-02-02 11:02:06 Iteration 150 Training Loss: 2.140e-01 Loss in Target Net: 7.504e-02
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2020-02-02 11:02:24 Iteration 200 Training Loss: 2.065e-01 Loss in Target Net: 7.387e-02
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2020-02-02 11:02:40 Iteration 250 Training Loss: 2.057e-01 Loss in Target Net: 7.647e-02
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2020-02-02 11:02:56 Iteration 300 Training Loss: 2.011e-01 Loss in Target Net: 6.782e-02
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2020-02-02 11:03:12 Iteration 350 Training Loss: 1.978e-01 Loss in Target Net: 7.432e-02
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2020-02-02 11:03:28 Iteration 400 Training Loss: 1.989e-01 Loss in Target Net: 6.037e-02
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2020-02-02 11:03:45 Iteration 450 Training Loss: 1.952e-01 Loss in Target Net: 5.683e-02
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2020-02-02 11:04:01 Iteration 500 Training Loss: 1.903e-01 Loss in Target Net: 6.629e-02
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2020-02-02 11:04:18 Iteration 550 Training Loss: 1.978e-01 Loss in Target Net: 7.264e-02
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2020-02-02 11:04:34 Iteration 600 Training Loss: 1.977e-01 Loss in Target Net: 6.295e-02
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2020-02-02 11:04:50 Iteration 650 Training Loss: 1.939e-01 Loss in Target Net: 7.245e-02
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2020-02-02 11:05:10 Iteration 700 Training Loss: 1.920e-01 Loss in Target Net: 6.486e-02
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2020-02-02 11:05:27 Iteration 750 Training Loss: 1.886e-01 Loss in Target Net: 5.856e-02
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2020-02-02 11:05:43 Iteration 800 Training Loss: 1.868e-01 Loss in Target Net: 7.476e-02
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2020-02-02 11:06:00 Iteration 850 Training Loss: 1.918e-01 Loss in Target Net: 6.212e-02
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2020-02-02 11:06:16 Iteration 900 Training Loss: 1.883e-01 Loss in Target Net: 5.581e-02
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2020-02-02 11:06:32 Iteration 950 Training Loss: 1.910e-01 Loss in Target Net: 5.894e-02
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2020-02-02 11:06:51 Iteration 1000 Training Loss: 1.863e-01 Loss in Target Net: 5.842e-02
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2020-02-02 11:07:08 Iteration 1050 Training Loss: 1.891e-01 Loss in Target Net: 5.715e-02
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2020-02-02 11:07:25 Iteration 1100 Training Loss: 1.929e-01 Loss in Target Net: 6.240e-02
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2020-02-02 11:07:42 Iteration 1150 Training Loss: 1.849e-01 Loss in Target Net: 5.767e-02
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2020-02-02 11:08:03 Iteration 1200 Training Loss: 1.864e-01 Loss in Target Net: 5.909e-02
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2020-02-02 11:08:23 Iteration 1250 Training Loss: 1.866e-01 Loss in Target Net: 4.971e-02
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2020-02-02 11:08:42 Iteration 1300 Training Loss: 1.866e-01 Loss in Target Net: 5.413e-02
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2020-02-02 11:08:59 Iteration 1350 Training Loss: 1.850e-01 Loss in Target Net: 6.186e-02
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2020-02-02 11:09:16 Iteration 1400 Training Loss: 1.894e-01 Loss in Target Net: 7.380e-02
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2020-02-02 11:09:34 Iteration 1450 Training Loss: 1.870e-01 Loss in Target Net: 6.628e-02
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2020-02-02 11:09:51 Iteration 1499 Training Loss: 1.883e-01 Loss in Target Net: 5.323e-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 11:10:00, Epoch 0, Iteration 7, loss 0.891 (0.456), acc 82.692 (88.800)
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2020-02-02 11:10:59, 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:[-1.2653804, -0.56147313, -2.2565193, 0.048873067, -2.5429504, -3.9638844, 1.1005144, -1.1163808, 10.929193, -0.005201716], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 11:11:59 Epoch 59, Val iteration 0, acc 92.600 (92.600)
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2020-02-02 11:12:06 Epoch 59, Val iteration 19, acc 93.000 (92.780)
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* Prec: 92.78000106811524
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 8
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TARGET INDEX: 5
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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=5, 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/1500/5
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Selected base image indices: [213, 225, 227, 247, 249]
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