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DPN92 1 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='14', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=3, 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') |
Path: chk-black-end2end/mean-3Repeat/1500/46 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 04:08:38 Iteration 0 Training Loss: 1.020e+00 Loss in Target Net: 1.374e+00 |
2020-02-04 04:11:30 Iteration 50 Training Loss: 2.016e-01 Loss in Target Net: 5.069e-02 |
2020-02-04 04:14:39 Iteration 100 Training Loss: 1.734e-01 Loss in Target Net: 3.847e-02 |
2020-02-04 04:17:59 Iteration 150 Training Loss: 1.640e-01 Loss in Target Net: 4.040e-02 |
2020-02-04 04:21:28 Iteration 200 Training Loss: 1.575e-01 Loss in Target Net: 4.008e-02 |
2020-02-04 04:24:43 Iteration 250 Training Loss: 1.533e-01 Loss in Target Net: 4.100e-02 |
2020-02-04 04:27:48 Iteration 300 Training Loss: 1.504e-01 Loss in Target Net: 4.039e-02 |
2020-02-04 04:30:56 Iteration 350 Training Loss: 1.473e-01 Loss in Target Net: 4.279e-02 |
2020-02-04 04:34:09 Iteration 400 Training Loss: 1.447e-01 Loss in Target Net: 4.454e-02 |
2020-02-04 04:37:16 Iteration 450 Training Loss: 1.443e-01 Loss in Target Net: 4.214e-02 |
2020-02-04 04:40:29 Iteration 500 Training Loss: 1.444e-01 Loss in Target Net: 3.854e-02 |
2020-02-04 04:43:36 Iteration 550 Training Loss: 1.446e-01 Loss in Target Net: 3.716e-02 |
2020-02-04 04:46:41 Iteration 600 Training Loss: 1.416e-01 Loss in Target Net: 3.790e-02 |
2020-02-04 04:49:46 Iteration 650 Training Loss: 1.423e-01 Loss in Target Net: 3.893e-02 |
2020-02-04 04:52:56 Iteration 700 Training Loss: 1.434e-01 Loss in Target Net: 4.061e-02 |
2020-02-04 04:56:05 Iteration 750 Training Loss: 1.415e-01 Loss in Target Net: 4.108e-02 |
2020-02-04 04:59:17 Iteration 800 Training Loss: 1.392e-01 Loss in Target Net: 3.900e-02 |
2020-02-04 05:02:27 Iteration 850 Training Loss: 1.426e-01 Loss in Target Net: 4.743e-02 |
2020-02-04 05:05:36 Iteration 900 Training Loss: 1.408e-01 Loss in Target Net: 3.982e-02 |
2020-02-04 05:08:46 Iteration 950 Training Loss: 1.402e-01 Loss in Target Net: 4.486e-02 |
2020-02-04 05:11:55 Iteration 1000 Training Loss: 1.386e-01 Loss in Target Net: 3.649e-02 |
2020-02-04 05:15:07 Iteration 1050 Training Loss: 1.403e-01 Loss in Target Net: 3.819e-02 |
2020-02-04 05:18:19 Iteration 1100 Training Loss: 1.418e-01 Loss in Target Net: 3.922e-02 |
2020-02-04 05:21:31 Iteration 1150 Training Loss: 1.393e-01 Loss in Target Net: 3.551e-02 |
2020-02-04 05:24:42 Iteration 1200 Training Loss: 1.373e-01 Loss in Target Net: 3.607e-02 |
2020-02-04 05:27:53 Iteration 1250 Training Loss: 1.385e-01 Loss in Target Net: 3.625e-02 |
2020-02-04 05:31:03 Iteration 1300 Training Loss: 1.382e-01 Loss in Target Net: 3.698e-02 |
2020-02-04 05:34:08 Iteration 1350 Training Loss: 1.393e-01 Loss in Target Net: 3.623e-02 |
2020-02-04 05:37:14 Iteration 1400 Training Loss: 1.379e-01 Loss in Target Net: 3.970e-02 |
2020-02-04 05:40:22 Iteration 1450 Training Loss: 1.388e-01 Loss in Target Net: 3.547e-02 |
2020-02-04 05:43:30 Iteration 1499 Training Loss: 1.396e-01 Loss in Target Net: 3.773e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 05:44:28, Epoch 0, Iteration 7, loss 0.294 (0.584), acc 94.231 (88.800) |
2020-02-04 05:49:16, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:8, Target's Score:[-3.1240637, 1.8602649, -2.7422318, -4.702619, -3.4234068, -2.8138862, 7.294848, -2.841253, 10.079728, 0.63141274], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 05:54:31 Epoch 59, Val iteration 0, acc 94.000 (94.000) |
2020-02-04 05:55:20 Epoch 59, Val iteration 19, acc 93.200 (92.940) |
* Prec: 92.94000129699707 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 95 |
TARGET INDEX: 46 |
DPN92 1 |
Namespace(chk_path='chk-black-end2end', chk_subdir='poisons', device='cuda', dset_path='datasets', end2end=True, eval_poison_path='', gpu='15', lr_decay_epoch=[30, 45], mode='mean', model_resume_path='model-chks', nearest=False, net_repeat=3, 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') |
Path: chk-black-end2end/mean-3Repeat/1500/47 |
Selected base image indices: [213, 225, 227, 247, 249] |
2020-02-04 04:29:56 Iteration 0 Training Loss: 1.004e+00 Loss in Target Net: 1.197e+00 |
2020-02-04 04:33:09 Iteration 50 Training Loss: 2.055e-01 Loss in Target Net: 3.059e-02 |
2020-02-04 04:36:22 Iteration 100 Training Loss: 1.767e-01 Loss in Target Net: 2.166e-02 |
2020-02-04 04:39:34 Iteration 150 Training Loss: 1.648e-01 Loss in Target Net: 2.003e-02 |
2020-02-04 04:42:42 Iteration 200 Training Loss: 1.571e-01 Loss in Target Net: 1.712e-02 |
2020-02-04 04:45:49 Iteration 250 Training Loss: 1.546e-01 Loss in Target Net: 1.809e-02 |
2020-02-04 04:49:01 Iteration 300 Training Loss: 1.532e-01 Loss in Target Net: 1.876e-02 |
2020-02-04 04:52:13 Iteration 350 Training Loss: 1.499e-01 Loss in Target Net: 1.791e-02 |
2020-02-04 04:55:23 Iteration 400 Training Loss: 1.500e-01 Loss in Target Net: 1.654e-02 |
2020-02-04 04:58:29 Iteration 450 Training Loss: 1.486e-01 Loss in Target Net: 1.890e-02 |
2020-02-04 05:01:36 Iteration 500 Training Loss: 1.486e-01 Loss in Target Net: 1.510e-02 |
2020-02-04 05:04:46 Iteration 550 Training Loss: 1.487e-01 Loss in Target Net: 1.541e-02 |
2020-02-04 05:07:55 Iteration 600 Training Loss: 1.458e-01 Loss in Target Net: 1.545e-02 |
2020-02-04 05:11:03 Iteration 650 Training Loss: 1.500e-01 Loss in Target Net: 1.608e-02 |
2020-02-04 05:14:14 Iteration 700 Training Loss: 1.472e-01 Loss in Target Net: 1.662e-02 |
2020-02-04 05:17:25 Iteration 750 Training Loss: 1.443e-01 Loss in Target Net: 1.759e-02 |
2020-02-04 05:20:36 Iteration 800 Training Loss: 1.465e-01 Loss in Target Net: 1.458e-02 |
2020-02-04 05:23:46 Iteration 850 Training Loss: 1.446e-01 Loss in Target Net: 1.544e-02 |
2020-02-04 05:26:59 Iteration 900 Training Loss: 1.438e-01 Loss in Target Net: 1.466e-02 |
2020-02-04 05:30:12 Iteration 950 Training Loss: 1.479e-01 Loss in Target Net: 1.650e-02 |
2020-02-04 05:33:23 Iteration 1000 Training Loss: 1.465e-01 Loss in Target Net: 1.535e-02 |
2020-02-04 05:36:31 Iteration 1050 Training Loss: 1.452e-01 Loss in Target Net: 1.548e-02 |
2020-02-04 05:39:42 Iteration 1100 Training Loss: 1.446e-01 Loss in Target Net: 1.529e-02 |
2020-02-04 05:42:55 Iteration 1150 Training Loss: 1.460e-01 Loss in Target Net: 1.797e-02 |
2020-02-04 05:46:03 Iteration 1200 Training Loss: 1.475e-01 Loss in Target Net: 1.889e-02 |
2020-02-04 05:49:19 Iteration 1250 Training Loss: 1.440e-01 Loss in Target Net: 1.723e-02 |
2020-02-04 05:52:45 Iteration 1300 Training Loss: 1.441e-01 Loss in Target Net: 1.439e-02 |
2020-02-04 05:56:02 Iteration 1350 Training Loss: 1.438e-01 Loss in Target Net: 1.749e-02 |
2020-02-04 05:59:01 Iteration 1400 Training Loss: 1.458e-01 Loss in Target Net: 1.488e-02 |
2020-02-04 06:01:55 Iteration 1450 Training Loss: 1.440e-01 Loss in Target Net: 1.474e-02 |
2020-02-04 06:04:38 Iteration 1499 Training Loss: 1.432e-01 Loss in Target Net: 1.558e-02 |
Evaluating against victims networks |
DPN92 |
Using Adam for retraining |
Files already downloaded and verified |
2020-02-04 06:05:27, Epoch 0, Iteration 7, loss 0.444 (0.501), acc 88.462 (88.600) |
2020-02-04 06:09:50, Epoch 30, Iteration 7, loss 0.000 (0.000), acc 100.000 (100.000) |
Target Label: 6, Poison label: 8, Prediction:6, Target's Score:[-1.9608183, -0.8335136, -0.24965394, 0.21707661, -0.48677698, -4.653595, 9.533298, -4.7938514, 6.1274486, -2.5165682], Poisons' Predictions:[8, 8, 8, 8, 8] |
2020-02-04 06:14:45 Epoch 59, Val iteration 0, acc 92.800 (92.800) |
2020-02-04 06:15:37 Epoch 59, Val iteration 19, acc 92.400 (92.790) |
* Prec: 92.79000282287598 |
-------- |
------SUMMARY------ |
TIME ELAPSED (mins): 95 |
TARGET INDEX: 47 |
DPN92 0 |
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=3, 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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