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2020-02-02 11:13:31 Iteration 200 Training Loss: 1.986e-01 Loss in Target Net: 2.312e-02
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2020-02-02 11:13:49 Iteration 250 Training Loss: 1.957e-01 Loss in Target Net: 2.826e-02
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2020-02-02 11:14:07 Iteration 300 Training Loss: 1.964e-01 Loss in Target Net: 1.993e-02
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2020-02-02 11:14:24 Iteration 350 Training Loss: 1.953e-01 Loss in Target Net: 2.216e-02
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2020-02-02 11:14:42 Iteration 400 Training Loss: 1.804e-01 Loss in Target Net: 2.064e-02
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2020-02-02 11:15:01 Iteration 450 Training Loss: 1.857e-01 Loss in Target Net: 1.600e-02
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2020-02-02 11:15:19 Iteration 500 Training Loss: 1.874e-01 Loss in Target Net: 1.751e-02
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2020-02-02 11:15:38 Iteration 550 Training Loss: 1.864e-01 Loss in Target Net: 2.056e-02
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2020-02-02 11:15:58 Iteration 600 Training Loss: 1.909e-01 Loss in Target Net: 1.649e-02
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2020-02-02 11:16:17 Iteration 650 Training Loss: 1.806e-01 Loss in Target Net: 1.510e-02
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2020-02-02 11:16:36 Iteration 700 Training Loss: 1.826e-01 Loss in Target Net: 1.419e-02
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2020-02-02 11:16:55 Iteration 750 Training Loss: 1.851e-01 Loss in Target Net: 1.601e-02
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2020-02-02 11:17:16 Iteration 800 Training Loss: 1.821e-01 Loss in Target Net: 1.656e-02
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2020-02-02 11:17:37 Iteration 850 Training Loss: 1.788e-01 Loss in Target Net: 1.448e-02
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2020-02-02 11:17:56 Iteration 900 Training Loss: 1.851e-01 Loss in Target Net: 1.746e-02
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2020-02-02 11:18:17 Iteration 950 Training Loss: 1.829e-01 Loss in Target Net: 1.757e-02
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2020-02-02 11:18:38 Iteration 1000 Training Loss: 1.846e-01 Loss in Target Net: 1.445e-02
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2020-02-02 11:18:59 Iteration 1050 Training Loss: 1.809e-01 Loss in Target Net: 1.806e-02
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2020-02-02 11:19:19 Iteration 1100 Training Loss: 1.802e-01 Loss in Target Net: 1.658e-02
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2020-02-02 11:19:38 Iteration 1150 Training Loss: 1.793e-01 Loss in Target Net: 1.309e-02
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2020-02-02 11:19:58 Iteration 1200 Training Loss: 1.817e-01 Loss in Target Net: 1.360e-02
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2020-02-02 11:20:18 Iteration 1250 Training Loss: 1.816e-01 Loss in Target Net: 1.161e-02
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2020-02-02 11:20:39 Iteration 1300 Training Loss: 1.789e-01 Loss in Target Net: 1.391e-02
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2020-02-02 11:21:00 Iteration 1350 Training Loss: 1.787e-01 Loss in Target Net: 1.864e-02
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2020-02-02 11:21:19 Iteration 1400 Training Loss: 1.762e-01 Loss in Target Net: 1.370e-02
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2020-02-02 11:21:40 Iteration 1450 Training Loss: 1.771e-01 Loss in Target Net: 1.142e-02
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2020-02-02 11:22:01 Iteration 1499 Training Loss: 1.825e-01 Loss in Target Net: 1.367e-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 11:22:10, Epoch 0, Iteration 7, loss 0.395 (0.518), acc 90.385 (89.400)
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2020-02-02 11:23:08, 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:6, Target's Score:[-2.9481125, 0.10285833, -1.7042602, -2.0337896, -1.5827096, -1.4641417, 7.4368124, -1.272658, 6.3482466, -2.4847593], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-02 11:24:08 Epoch 59, Val iteration 0, acc 93.800 (93.800)
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2020-02-02 11:24:15 Epoch 59, Val iteration 19, acc 91.400 (93.260)
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* Prec: 93.26000137329102
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 9
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TARGET INDEX: 9
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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='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=9, 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/1500/9
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-02-03 03:36:24 Iteration 0 Training Loss: 1.028e+00 Loss in Target Net: 1.381e+00
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2020-02-03 03:37:00 Iteration 50 Training Loss: 2.487e-01 Loss in Target Net: 5.994e-02
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2020-02-03 03:37:34 Iteration 100 Training Loss: 2.216e-01 Loss in Target Net: 6.765e-02
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2020-02-03 03:38:05 Iteration 150 Training Loss: 2.089e-01 Loss in Target Net: 3.729e-02
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2020-02-03 03:38:55 Iteration 200 Training Loss: 1.992e-01 Loss in Target Net: 3.203e-02
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2020-02-03 03:39:51 Iteration 250 Training Loss: 1.924e-01 Loss in Target Net: 2.656e-02
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2020-02-03 03:40:40 Iteration 300 Training Loss: 1.884e-01 Loss in Target Net: 2.181e-02
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2020-02-03 03:41:08 Iteration 350 Training Loss: 1.894e-01 Loss in Target Net: 1.812e-02
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2020-02-03 03:41:39 Iteration 400 Training Loss: 1.835e-01 Loss in Target Net: 1.915e-02
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2020-02-03 03:42:17 Iteration 450 Training Loss: 1.823e-01 Loss in Target Net: 2.080e-02
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2020-02-03 03:43:02 Iteration 500 Training Loss: 1.831e-01 Loss in Target Net: 2.064e-02
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2020-02-03 03:43:40 Iteration 550 Training Loss: 1.781e-01 Loss in Target Net: 1.768e-02
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2020-02-03 03:44:13 Iteration 600 Training Loss: 1.820e-01 Loss in Target Net: 2.358e-02
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2020-02-03 03:45:01 Iteration 650 Training Loss: 1.813e-01 Loss in Target Net: 1.940e-02
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2020-02-03 03:45:43 Iteration 700 Training Loss: 1.824e-01 Loss in Target Net: 1.396e-02
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2020-02-03 03:46:21 Iteration 750 Training Loss: 1.787e-01 Loss in Target Net: 1.779e-02
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2020-02-03 03:47:02 Iteration 800 Training Loss: 1.811e-01 Loss in Target Net: 1.813e-02
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2020-02-03 03:47:40 Iteration 850 Training Loss: 1.799e-01 Loss in Target Net: 1.485e-02
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2020-02-03 03:48:13 Iteration 900 Training Loss: 1.782e-01 Loss in Target Net: 1.741e-02
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2020-02-03 03:48:51 Iteration 950 Training Loss: 1.805e-01 Loss in Target Net: 1.795e-02
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2020-02-03 03:49:39 Iteration 1000 Training Loss: 1.862e-01 Loss in Target Net: 1.740e-02
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2020-02-03 03:50:18 Iteration 1050 Training Loss: 1.730e-01 Loss in Target Net: 1.565e-02
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2020-02-03 03:50:53 Iteration 1100 Training Loss: 1.789e-01 Loss in Target Net: 1.976e-02
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2020-02-03 03:51:29 Iteration 1150 Training Loss: 1.760e-01 Loss in Target Net: 2.642e-02
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2020-02-03 03:52:08 Iteration 1200 Training Loss: 1.717e-01 Loss in Target Net: 1.743e-02
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2020-02-03 03:52:40 Iteration 1250 Training Loss: 1.769e-01 Loss in Target Net: 1.968e-02
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2020-02-03 03:53:29 Iteration 1300 Training Loss: 1.783e-01 Loss in Target Net: 1.958e-02
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2020-02-03 03:54:07 Iteration 1350 Training Loss: 1.774e-01 Loss in Target Net: 1.940e-02
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2020-02-03 03:54:38 Iteration 1400 Training Loss: 1.767e-01 Loss in Target Net: 1.804e-02
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2020-02-03 03:55:19 Iteration 1450 Training Loss: 1.787e-01 Loss in Target Net: 1.463e-02
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2020-02-03 03:55:56 Iteration 1499 Training Loss: 1.741e-01 Loss in Target Net: 1.725e-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 03:56:10, Epoch 0, Iteration 7, loss 0.421 (0.495), acc 82.692 (87.600)
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2020-02-03 03:57:28, 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:[-3.0201309, -0.7152074, -1.4070421, -1.3626035, 2.8745768, -3.2151194, 4.358184, -0.57261056, 6.7117715, -3.2358766], Poisons' Predictions:[8, 8, 8, 8, 8]
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2020-02-03 03:59:18 Epoch 59, Val iteration 0, acc 92.600 (92.600)
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2020-02-03 03:59:33 Epoch 59, Val iteration 19, acc 93.600 (92.900)
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* Prec: 92.90000114440917
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--------
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------SUMMARY------
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TIME ELAPSED (mins): 19
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TARGET INDEX: 9
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DPN92 1
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Namespace(chk_path='chk-black-ourmean/', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=False, 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=4000, 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=0.1, retrain_momentum=0.9, retrain_opt='adam', retrain_wd=0, 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', 'GoogLeNet', 'MobileNetV2'], target_index=0, target_label=6, target_net=['DPN92', 'SENet18', 'ResNet50', 'ResNeXt29_2x64d', 'GoogLeNet', 'MobileNetV2', 'ResNet18', 'DenseNet121'], 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-ourmean/mean/4000/0
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Selected base image indices: [213, 225, 227, 247, 249]
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2020-01-31 17:10:49 Iteration 0 Training Loss: 1.093e+00 Loss in Target Net: 3.489e-01
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2020-01-31 17:11:11 Iteration 50 Training Loss: 1.066e-01 Loss in Target Net: 2.260e-02
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2020-01-31 17:11:34 Iteration 100 Training Loss: 9.570e-02 Loss in Target Net: 1.995e-02
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2020-01-31 17:11:58 Iteration 150 Training Loss: 8.760e-02 Loss in Target Net: 2.078e-02
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2020-01-31 17:12:22 Iteration 200 Training Loss: 9.493e-02 Loss in Target Net: 1.814e-02
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2020-01-31 17:12:45 Iteration 250 Training Loss: 8.932e-02 Loss in Target Net: 1.758e-02
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