diff --git a/camelyon_32_eps10.0trainval-2024-10-25-02-30-03/stdout.txt b/camelyon_32_eps10.0trainval-2024-10-25-02-30-03/stdout.txt
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-INFO - utils.py - 2024-10-25 02:30:09,224 - {'setup': {'method': 'dpsgd-diffusion', 'run_type': 'torchmp', 'n_gpus_per_node': 4, 'n_nodes': 1, 'node_rank': 0, 'master_address': '127.0.0.1', 'master_port': 6025, 'omp_n_threads': 8, 'workdir': 'exp/dpdm/camelyon_32_eps10.0trainval-2024-10-25-02-30-03', 'local_rank': 0, 'global_rank': 0, 'global_size': 4, 'root_folder': '.'}, 'public_data': {'name': None, 'num_channels': 3, 'resolution': 32, 'n_classes': 1000, 'train_path': 'dataset/imagenet/imagenet_32', 'selective': {'ratio': 1.0}}, 'sensitive_data': {'name': 'camelyon_32', 'num_channels': 3, 'resolution': 32, 'n_classes': 2, 'train_path': 'dataset/camelyon/train_32.zip', 'test_path': 'dataset/camelyon/test_32.zip', 'fid_stats': 'dataset/camelyon/fid_stats_32.npz', 'train_num': 'val'}, 'model': {'ckpt': None, 'denoiser_name': 'edm', 'denoiser_network': 'song', 'ema_rate': 0.999, 'network': {'image_size': 32, 'num_in_channels': 3, 'num_out_channels': 3, 'label_dim': 2, 'attn_resolutions': [16], 'ch_mult': [2, 4]}, 'sampler': {'type': 'ddim', 'stochastic': False, 'num_steps': 50, 'tmin': 0.002, 'tmax': 80.0, 'rho': 7.0, 'guid_scale': 0.0, 'snapshot_batch_size': 80, 'fid_batch_size': 256}, 'sampler_fid': {'type': 'ddim', 'stochastic': False, 'num_steps': 250, 'tmin': 0.002, 'tmax': 80.0, 'rho': 7.0, 'guid_scale': 0.0}, 'sampler_acc': {'type': 'ddim', 'stochastic': False, 'num_steps': 250, 'tmin': 0.002, 'tmax': 80.0, 'rho': 7.0, 'guid_scale': 0.0}, 'local_rank': 0, 'global_rank': 0, 'global_size': 4, 'fid_stats': 'dataset/camelyon/fid_stats_32.npz'}, 'pretrain': {'log_dir': 'exp/dpdm/camelyon_32_eps10.0trainval-2024-10-25-02-30-03/pretrain', 'seed': 0, 'batch_size': 64, 'n_epochs': 1, 'log_freq': 100, 'snapshot_freq': 2000, 'snapshot_threshold': 1, 'save_freq': 100000, 'save_threshold': 1, 'fid_freq': 2000, 'fid_samples': 5000, 'fid_threshold': 1, 'label_random': True, 'optim': {'optimizer': 'Adam', 'params': {'lr': 0.0003, 'weight_decay': 0.0}}, 'loss': {'version': 'edm', 'p_mean': -1.2, 'p_std': 1.2, 'n_noise_samples': 1, 'n_classes': 2}}, 'train': {'log_dir': 'exp/dpdm/camelyon_32_eps10.0trainval-2024-10-25-02-30-03/train', 'seed': 0, 'batch_size': 4096, 'n_epochs': 50, 'partly_finetune': False, 'log_freq': 100, 'snapshot_freq': 2000, 'snapshot_threshold': 1, 'save_freq': 100000, 'save_threshold': 1, 'fid_freq': 2000, 'fid_samples': 5000, 'final_fid_samples': 60000, 'fid_threshold': 1, 'gen': False, 'gen_batch_size': 8192, 'optim': {'optimizer': 'Adam', 'params': {'lr': 0.0003, 'weight_decay': 0.0}}, 'loss': {'version': 'edm', 'p_mean': -1.2, 'p_std': 1.2, 'n_noise_samples': 32, 'n_classes': 2}, 'dp': {'sdq': None, 'max_grad_norm': 1.0, 'delta': 1e-06, 'epsilon': 10.0, 'max_physical_batch_size': 8192, 'n_splits': 64}}, 'gen': {'data_num': 60000, 'batch_size': 1000, 'log_dir': 'exp/dpdm/camelyon_32_eps10.0trainval-2024-10-25-02-30-03/gen'}, 'eval': {'batch_size': 1000}}
-INFO - dataset_loader.py - 2024-10-25 02:30:25,241 - delta is reset as 2.966981886419575e-07
-INFO - dpsgd_diffusion.py - 2024-10-25 02:30:26,988 - Number of trainable parameters in model: 0
-INFO - dpsgd_diffusion.py - 2024-10-25 02:30:26,989 - Number of total epochs: 50
-INFO - dpsgd_diffusion.py - 2024-10-25 02:30:26,989 - Starting training at step 0
-INFO - dpsgd_diffusion.py - 2024-10-25 02:31:44,561 - Loss: 0.8890, step: 100
-INFO - dpsgd_diffusion.py - 2024-10-25 02:32:41,592 - Loss: 0.8377, step: 200
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-INFO - dpsgd_diffusion.py - 2024-10-25 02:48:23,013 - Loss: 0.4661, step: 2000
-INFO - dpsgd_diffusion.py - 2024-10-25 02:48:23,026 - Saving snapshot checkpoint and sampling single batch at iteration 2000.
-WARNING - image.py - 2024-10-25 02:48:23,900 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
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-INFO - dpsgd_diffusion.py - 2024-10-25 02:49:37,254 - Loss: 0.4396, step: 2100
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-INFO - dpsgd_diffusion.py - 2024-10-25 03:06:17,221 - Loss: 0.2982, step: 4000
-INFO - dpsgd_diffusion.py - 2024-10-25 03:06:17,266 - Saving snapshot checkpoint and sampling single batch at iteration 4000.
-WARNING - image.py - 2024-10-25 03:06:17,831 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
-INFO - dpsgd_diffusion.py - 2024-10-25 03:06:36,230 - FID at iteration 4000: 139.864281
-INFO - dpsgd_diffusion.py - 2024-10-25 03:07:28,384 - Loss: 0.2990, step: 4100
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-INFO - dpsgd_diffusion.py - 2024-10-25 03:08:31,762 - Eps-value after 1 epochs: 2.9982
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-INFO - dpsgd_diffusion.py - 2024-10-25 03:24:02,053 - Loss: 0.2578, step: 6000
-INFO - dpsgd_diffusion.py - 2024-10-25 03:24:02,065 - Saving snapshot checkpoint and sampling single batch at iteration 6000.
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-INFO - dpsgd_diffusion.py - 2024-10-25 03:41:45,691 - Loss: 0.2468, step: 8000
-INFO - dpsgd_diffusion.py - 2024-10-25 03:41:45,710 - Saving snapshot checkpoint and sampling single batch at iteration 8000.
-WARNING - image.py - 2024-10-25 03:41:46,272 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
-INFO - dpsgd_diffusion.py - 2024-10-25 03:42:04,541 - FID at iteration 8000: 101.348254
-INFO - dpsgd_diffusion.py - 2024-10-25 03:42:57,234 - Loss: 0.2414, step: 8100
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-INFO - dpsgd_diffusion.py - 2024-10-25 03:44:41,461 - Loss: 0.2499, step: 8300
-INFO - dpsgd_diffusion.py - 2024-10-25 03:45:33,062 - Loss: 0.2141, step: 8400
-INFO - dpsgd_diffusion.py - 2024-10-25 03:45:58,070 - Eps-value after 2 epochs: 3.3330
-INFO - dpsgd_diffusion.py - 2024-10-25 03:46:25,476 - Loss: 0.2375, step: 8500
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-INFO - dpsgd_diffusion.py - 2024-10-25 03:59:22,478 - Loss: 0.2112, step: 10000
-INFO - dpsgd_diffusion.py - 2024-10-25 03:59:22,523 - Saving snapshot checkpoint and sampling single batch at iteration 10000.
-WARNING - image.py - 2024-10-25 03:59:23,086 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
-INFO - dpsgd_diffusion.py - 2024-10-25 03:59:41,381 - FID at iteration 10000: 92.246404
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-INFO - dpsgd_diffusion.py - 2024-10-25 04:17:00,246 - Saving snapshot checkpoint and sampling single batch at iteration 12000.
-WARNING - image.py - 2024-10-25 04:17:00,809 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
-INFO - dpsgd_diffusion.py - 2024-10-25 04:17:19,154 - FID at iteration 12000: 83.627559
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-INFO - dpsgd_diffusion.py - 2024-10-25 04:23:09,480 - Eps-value after 3 epochs: 3.6058
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-INFO - dpsgd_diffusion.py - 2024-10-25 04:34:40,143 - Saving snapshot checkpoint and sampling single batch at iteration 14000.
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-INFO - dpsgd_diffusion.py - 2024-10-25 05:00:28,739 - Eps-value after 4 epochs: 3.8487
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-INFO - dpsgd_diffusion.py - 2024-10-25 06:15:05,692 - Eps-value after 6 epochs: 4.2787
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-INFO - dpsgd_diffusion.py - 2024-10-25 10:36:32,810 - Eps-value after 13 epochs: 5.5168
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-INFO - dpsgd_diffusion.py - 2024-10-25 12:28:12,770 - Eps-value after 16 epochs: 5.9738
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-INFO - dpsgd_diffusion.py - 2024-10-25 15:35:28,488 - Eps-value after 21 epochs: 6.6798
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-INFO - dpsgd_diffusion.py - 2024-10-25 16:12:45,780 - Eps-value after 22 epochs: 6.8146
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-INFO - dpsgd_diffusion.py - 2024-10-25 16:50:01,756 - Eps-value after 23 epochs: 6.9469
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-INFO - dpsgd_diffusion.py - 2024-10-25 17:27:17,264 - Eps-value after 24 epochs: 7.0761
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-INFO - dpsgd_diffusion.py - 2024-10-25 20:33:41,823 - Eps-value after 29 epochs: 7.7028
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-INFO - dpsgd_diffusion.py - 2024-10-25 21:48:24,976 - Eps-value after 31 epochs: 7.9402
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-INFO - dpsgd_diffusion.py - 2024-10-25 22:25:51,719 - Eps-value after 32 epochs: 8.0589
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-INFO - dpsgd_diffusion.py - 2024-10-25 23:03:13,530 - Eps-value after 33 epochs: 8.1755
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-INFO - dpsgd_diffusion.py - 2024-10-26 01:32:57,866 - Eps-value after 37 epochs: 8.6294
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-INFO - dpsgd_diffusion.py - 2024-10-26 02:10:13,721 - Eps-value after 38 epochs: 8.7387
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-INFO - dpsgd_diffusion.py - 2024-10-26 02:47:40,327 - Eps-value after 39 epochs: 8.8480
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-INFO - dpsgd_diffusion.py - 2024-10-26 03:25:01,744 - Eps-value after 40 epochs: 8.9573
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-INFO - dpsgd_diffusion.py - 2024-10-26 04:02:23,597 - Eps-value after 41 epochs: 9.0665
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-INFO - dpsgd_diffusion.py - 2024-10-26 09:32:32,833 - Loss: 0.1695, step: 210500
-INFO - dpsgd_diffusion.py - 2024-10-26 09:33:26,790 - Loss: 0.1783, step: 210600
-INFO - dpsgd_diffusion.py - 2024-10-26 09:34:18,671 - Loss: 0.1808, step: 210700
-INFO - dpsgd_diffusion.py - 2024-10-26 09:35:11,941 - Loss: 0.1709, step: 210800
-INFO - dpsgd_diffusion.py - 2024-10-26 09:36:03,429 - Loss: 0.1714, step: 210900
-INFO - dpsgd_diffusion.py - 2024-10-26 09:36:54,967 - Loss: 0.1839, step: 211000
-INFO - dpsgd_diffusion.py - 2024-10-26 09:37:47,588 - Loss: 0.1754, step: 211100
-INFO - dpsgd_diffusion.py - 2024-10-26 09:38:38,731 - Loss: 0.1908, step: 211200
-INFO - dpsgd_diffusion.py - 2024-10-26 09:38:38,799 - Eps-value after 50 epochs: 9.9957
-INFO - dpsgd_diffusion.py - 2024-10-26 09:38:38,919 - Saving final checkpoint.
-INFO - dpsgd_diffusion.py - 2024-10-26 09:38:38,930 - start to generate 60000 samples
-INFO - dpsgd_diffusion.py - 2024-10-26 09:46:52,559 - Generation Finished!
-INFO - dataset_loader.py - 2024-10-26 10:53:46,561 - delta is reset as 2.966981886419575e-07
-INFO - evaluator.py - 2024-10-26 10:54:15,531 - Epoch: 0 Train acc: 78.70727272727272 Val acc: 88.7409568970758 Test acc77.74180609672244; Train loss: 0.0017320723934607072 Val loss: 0.0002820143200168567
-INFO - evaluator.py - 2024-10-26 10:54:43,100 - Epoch: 1 Train acc: 93.54181818181819 Val acc: 92.95093926682473 Test acc85.05042402016961; Train loss: 0.0006667007480155338 Val loss: 0.00019973324413277528
-INFO - evaluator.py - 2024-10-26 10:55:10,828 - Epoch: 2 Train acc: 95.59818181818181 Val acc: 92.6256915314001 Test acc82.85583314233325; Train loss: 0.0004559212237596512 Val loss: 0.0002221052264636741
-INFO - evaluator.py - 2024-10-26 10:55:38,469 - Epoch: 3 Train acc: 96.49272727272728 Val acc: 92.50714329138549 Test acc83.80987852395141; Train loss: 0.00038233733434568754 Val loss: 0.00027639462323643616
-INFO - evaluator.py - 2024-10-26 10:56:06,078 - Epoch: 4 Train acc: 97.13818181818182 Val acc: 67.00711289440088 Test acc78.99094659637865; Train loss: 0.0003185796509412202 Val loss: 0.0014621912291640025
-INFO - evaluator.py - 2024-10-26 10:56:33,678 - Epoch: 5 Train acc: 97.40727272727273 Val acc: 92.81111313757675 Test acc82.28283291313316; Train loss: 0.000283022219653834 Val loss: 0.00025682741149565146
-INFO - evaluator.py - 2024-10-26 10:57:01,229 - Epoch: 6 Train acc: 97.46545454545455 Val acc: 92.41899203599003 Test acc80.97925739170296; Train loss: 0.00027389612075957387 Val loss: 0.00025633165822419596
-INFO - evaluator.py - 2024-10-26 10:57:28,878 - Epoch: 7 Train acc: 97.71272727272728 Val acc: 92.60745334062861 Test acc79.84758193903278; Train loss: 0.0002468476568772034 Val loss: 0.0002724363891699661
-INFO - evaluator.py - 2024-10-26 10:57:56,477 - Epoch: 8 Train acc: 98.05818181818182 Val acc: 93.95100006079396 Test acc78.97662159064863; Train loss: 0.00021272251069207084 Val loss: 0.0002117010973870874
-INFO - evaluator.py - 2024-10-26 10:58:24,225 - Epoch: 9 Train acc: 98.23454545454545 Val acc: 93.08468599914889 Test acc81.61242264496906; Train loss: 0.00018999023716896772 Val loss: 0.00025551063223474765
-INFO - evaluator.py - 2024-10-26 10:58:51,861 - Epoch: 10 Train acc: 98.54 Val acc: 92.06030761748436 Test acc78.90499656199862; Train loss: 0.00016541818177158183 Val loss: 0.0003382250181055553
-INFO - evaluator.py - 2024-10-26 10:59:19,468 - Epoch: 11 Train acc: 98.66181818181818 Val acc: 93.13028147607757 Test acc78.64141645656659; Train loss: 0.00015035219294950365 Val loss: 0.00024482594343570743
-INFO - evaluator.py - 2024-10-26 10:59:47,255 - Epoch: 12 Train acc: 98.51454545454546 Val acc: 91.20919204814882 Test acc76.99117579647033; Train loss: 0.00016243163475936108 Val loss: 0.00036434814378304847
-INFO - evaluator.py - 2024-10-26 11:00:14,879 - Epoch: 13 Train acc: 98.52727272727273 Val acc: 92.4341905282996 Test acc78.85629154251662; Train loss: 0.00015977694324471734 Val loss: 0.00030129080918778965
-INFO - evaluator.py - 2024-10-26 11:00:42,535 - Epoch: 14 Train acc: 98.66545454545455 Val acc: 89.15739558635784 Test acc78.46378638551455; Train loss: 0.0001497444970448586 Val loss: 0.00046936689963043046
-INFO - evaluator.py - 2024-10-26 11:01:10,170 - Epoch: 15 Train acc: 98.78363636363636 Val acc: 82.63420268709343 Test acc75.71625028650011; Train loss: 0.000129887310737236 Val loss: 0.001336213922607869
-INFO - evaluator.py - 2024-10-26 11:01:37,825 - Epoch: 16 Train acc: 98.78545454545454 Val acc: 89.36409508176789 Test acc80.0538620215448; Train loss: 0.00013508220955898816 Val loss: 0.0004695039261756418
-INFO - evaluator.py - 2024-10-26 11:02:05,363 - Epoch: 17 Train acc: 98.86181818181818 Val acc: 91.94783877439357 Test acc79.3290167316067; Train loss: 0.0001279546841386367 Val loss: 0.0003787766802571118
-INFO - evaluator.py - 2024-10-26 11:02:33,055 - Epoch: 18 Train acc: 98.83818181818181 Val acc: 90.96905586965774 Test acc76.43536557414623; Train loss: 0.00012232126661322333 Val loss: 0.00043793821365277133
-INFO - evaluator.py - 2024-10-26 11:03:00,686 - Epoch: 19 Train acc: 98.59636363636363 Val acc: 77.36640525259895 Test acc79.71006188402475; Train loss: 0.00016054964877072384 Val loss: 0.000823656837515095
-INFO - evaluator.py - 2024-10-26 11:03:28,392 - Epoch: 20 Train acc: 99.08727272727272 Val acc: 81.25113988692321 Test acc80.6870272748109; Train loss: 0.00010317801959304647 Val loss: 0.000758719560954917
-INFO - evaluator.py - 2024-10-26 11:03:55,994 - Epoch: 21 Train acc: 99.28181818181818 Val acc: 85.97483129673536 Test acc80.26300710520285; Train loss: 8.081622243096883e-05 Val loss: 0.0007136862008678449
-INFO - evaluator.py - 2024-10-26 11:04:23,703 - Epoch: 22 Train acc: 99.36 Val acc: 90.5039820049851 Test acc78.51822140728856; Train loss: 6.922441674819724e-05 Val loss: 0.0004701410171739076
-INFO - evaluator.py - 2024-10-26 11:04:51,426 - Epoch: 23 Train acc: 99.39818181818183 Val acc: 92.00863274363184 Test acc78.17442126976852; Train loss: 6.546199225862934e-05 Val loss: 0.000418355420872291
-INFO - evaluator.py - 2024-10-26 11:05:19,137 - Epoch: 24 Train acc: 99.4309090909091 Val acc: 90.49486290959938 Test acc79.47513179005271; Train loss: 6.387216457462108e-05 Val loss: 0.0005934202043805356
-INFO - evaluator.py - 2024-10-26 11:05:46,762 - Epoch: 25 Train acc: 99.53090909090909 Val acc: 91.70162319897867 Test acc75.14611505844603; Train loss: 5.475035080982541e-05 Val loss: 0.0006701471376320506
-INFO - evaluator.py - 2024-10-26 11:06:14,482 - Epoch: 26 Train acc: 99.55090909090909 Val acc: 91.38853425740166 Test acc76.93387577355031; Train loss: 4.988968686031347e-05 Val loss: 0.0009640023430268216
-INFO - evaluator.py - 2024-10-26 11:06:42,153 - Epoch: 27 Train acc: 99.54545454545455 Val acc: 91.07848501428658 Test acc75.65895026358011; Train loss: 4.804273281055926e-05 Val loss: 0.0008747331479652521
-INFO - evaluator.py - 2024-10-26 11:07:09,774 - Epoch: 28 Train acc: 99.49636363636364 Val acc: 92.18493525442277 Test acc77.68450607380242; Train loss: 5.9638921249742535e-05 Val loss: 0.0005723464692992
-INFO - evaluator.py - 2024-10-26 11:07:37,405 - Epoch: 29 Train acc: 99.57272727272726 Val acc: 91.89616390054105 Test acc78.29475131790052; Train loss: 5.0985500173092906e-05 Val loss: 0.0009456762664766977
-INFO - evaluator.py - 2024-10-26 11:08:05,060 - Epoch: 30 Train acc: 99.63818181818182 Val acc: 91.48276490972096 Test acc74.51867980747193; Train loss: 4.109130569056354e-05 Val loss: 0.000964873254252057
-INFO - evaluator.py - 2024-10-26 11:08:32,842 - Epoch: 31 Train acc: 99.64363636363636 Val acc: 87.74089610310656 Test acc77.41806096722439; Train loss: 4.0739713347813285e-05 Val loss: 0.0023901383958983852
-INFO - evaluator.py - 2024-10-26 11:09:00,524 - Epoch: 32 Train acc: 99.64 Val acc: 92.54057997446652 Test acc76.18324547329819; Train loss: 3.920358906933953e-05 Val loss: 0.001612658960579072
-INFO - evaluator.py - 2024-10-26 11:09:28,093 - Epoch: 33 Train acc: 99.61454545454545 Val acc: 92.35515836828986 Test acc76.43823057529224; Train loss: 4.476935346716676e-05 Val loss: 0.0007489357514833345
-INFO - evaluator.py - 2024-10-26 11:09:55,741 - Epoch: 34 Train acc: 99.66727272727273 Val acc: 88.77135388169494 Test acc78.1830162732065; Train loss: 3.612420874785378e-05 Val loss: 0.0034746746827319834
-INFO - evaluator.py - 2024-10-26 11:10:23,356 - Epoch: 35 Train acc: 99.64181818181818 Val acc: 92.84454982065779 Test acc76.16319046527619; Train loss: 3.838491495857438e-05 Val loss: 0.0012493302567881054
-INFO - evaluator.py - 2024-10-26 11:10:51,092 - Epoch: 36 Train acc: 99.68909090909091 Val acc: 89.46744482947292 Test acc78.26037130414852; Train loss: 3.1971069994456645e-05 Val loss: 0.004771498186087216
-INFO - evaluator.py - 2024-10-26 11:11:18,687 - Epoch: 37 Train acc: 99.68909090909091 Val acc: 92.15453826980364 Test acc74.80804492321796; Train loss: 3.436537861284292e-05 Val loss: 0.006936874467338485
-INFO - evaluator.py - 2024-10-26 11:11:46,231 - Epoch: 38 Train acc: 99.81090909090909 Val acc: 91.02073074351024 Test acc72.20375888150356; Train loss: 2.314749794313684e-05 Val loss: 0.0033642233660956112
-INFO - evaluator.py - 2024-10-26 11:12:13,845 - Epoch: 39 Train acc: 99.74363636363637 Val acc: 91.09672320505805 Test acc74.26082970433188; Train loss: 2.7115679167301012e-05 Val loss: 0.0021934208953132005
-INFO - evaluator.py - 2024-10-26 11:12:41,407 - Epoch: 40 Train acc: 99.92181818181818 Val acc: 91.5405191804973 Test acc74.11471464588585; Train loss: 1.0286235383087346e-05 Val loss: 0.004444890699456749
-INFO - evaluator.py - 2024-10-26 11:13:09,012 - Epoch: 41 Train acc: 99.96000000000001 Val acc: 91.01769104504834 Test acc74.59603483841394; Train loss: 6.955835473358589e-06 Val loss: 0.006178374732255979
-INFO - evaluator.py - 2024-10-26 11:13:36,757 - Epoch: 42 Train acc: 99.95454545454545 Val acc: 91.40677244817314 Test acc71.67946367178547; Train loss: 5.954815947519488e-06 Val loss: 0.003757629829581019
-INFO - evaluator.py - 2024-10-26 11:14:04,332 - Epoch: 43 Train acc: 99.96727272727273 Val acc: 91.71378199282631 Test acc71.53048361219344; Train loss: 5.308851320792614e-06 Val loss: 0.003309818341511855
-INFO - evaluator.py - 2024-10-26 11:14:31,936 - Epoch: 44 Train acc: 99.97272727272727 Val acc: 91.68338500820718 Test acc72.32695393078157; Train loss: 4.867460543953877e-06 Val loss: 0.0055515102372212105
-INFO - evaluator.py - 2024-10-26 11:14:59,568 - Epoch: 45 Train acc: 99.98 Val acc: 91.33077998662533 Test acc71.47031858812744; Train loss: 4.172457022386053e-06 Val loss: 0.0031158777274392035
-INFO - evaluator.py - 2024-10-26 11:15:27,158 - Epoch: 46 Train acc: 99.98 Val acc: 91.93567998054593 Test acc72.20662388264955; Train loss: 3.862158796552897e-06 Val loss: 0.0031957624096139612
-INFO - evaluator.py - 2024-10-26 11:15:54,721 - Epoch: 47 Train acc: 99.98181818181818 Val acc: 91.61043224512129 Test acc71.58778363511345; Train loss: 3.722703862314864e-06 Val loss: 0.003360221067658528
-INFO - evaluator.py - 2024-10-26 11:16:22,823 - Epoch: 48 Train acc: 99.96727272727273 Val acc: 91.91744178977446 Test acc72.77389410955765; Train loss: 4.6599259317934575e-06 Val loss: 0.003561238252150555
-INFO - evaluator.py - 2024-10-26 11:16:50,511 - Epoch: 49 Train acc: 99.97454545454545 Val acc: 91.2760654143109 Test acc71.53621361448545; Train loss: 3.460800207490551e-06 Val loss: 0.0038588291983146696
-INFO - evaluator.py - 2024-10-26 11:16:50,516 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from resnet is 93.95100006079396 and 78.97662159064863
-INFO - evaluator.py - 2024-10-26 11:16:50,516 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from resnet is 93.95100006079396 and 78.97662159064863
-INFO - evaluator.py - 2024-10-26 11:16:50,516 - The best acc test dataset from resnet is 85.05042402016961
-INFO - evaluator.py - 2024-10-26 11:17:22,206 - Epoch: 0 Train acc: 85.51272727272728 Val acc: 90.6711654203903 Test acc83.84425853770342; Train loss: 0.0013045570514418861 Val loss: 0.00025262508111722175
-INFO - evaluator.py - 2024-10-26 11:17:52,955 - Epoch: 1 Train acc: 94.0909090909091 Val acc: 92.4129126390662 Test acc83.25979830391933; Train loss: 0.000619419632174752 Val loss: 0.00022080762783903735
-INFO - evaluator.py - 2024-10-26 11:18:23,806 - Epoch: 2 Train acc: 95.90545454545455 Val acc: 83.08711775791842 Test acc83.88723355489343; Train loss: 0.00044419406110590156 Val loss: 0.0005268143900216652
-INFO - evaluator.py - 2024-10-26 11:18:54,680 - Epoch: 3 Train acc: 96.23454545454545 Val acc: 89.55559608486838 Test acc82.07368782947513; Train loss: 0.0004010982724753293 Val loss: 0.00036740034349392714
-INFO - evaluator.py - 2024-10-26 11:19:25,385 - Epoch: 4 Train acc: 97.13636363636363 Val acc: 84.92309562891361 Test acc80.44636717854688; Train loss: 0.0003100397985428572 Val loss: 0.0004060376376408242
-INFO - evaluator.py - 2024-10-26 11:19:56,192 - Epoch: 5 Train acc: 97.54 Val acc: 93.27314730378747 Test acc81.94762777905112; Train loss: 0.0002654843757775697 Val loss: 0.0002292448874338542
-INFO - evaluator.py - 2024-10-26 11:20:27,081 - Epoch: 6 Train acc: 97.74727272727273 Val acc: 89.22730865098181 Test acc77.67591107036442; Train loss: 0.00025045489882203665 Val loss: 0.00032111802712219654
-INFO - evaluator.py - 2024-10-26 11:20:57,864 - Epoch: 7 Train acc: 97.8 Val acc: 77.40896103106572 Test acc78.76461150584461; Train loss: 0.00023480258113281292 Val loss: 0.0006320277197584642
-INFO - evaluator.py - 2024-10-26 11:21:28,706 - Epoch: 8 Train acc: 98.07636363636364 Val acc: 92.37947595598517 Test acc76.71900068760029; Train loss: 0.00020964177101850508 Val loss: 0.0002698353321017868
-INFO - evaluator.py - 2024-10-26 11:21:59,430 - Epoch: 9 Train acc: 98.24545454545455 Val acc: 92.27612620828015 Test acc81.01363740545496; Train loss: 0.00018855554061857137 Val loss: 0.00030605433691858965
-INFO - evaluator.py - 2024-10-26 11:22:30,300 - Epoch: 10 Train acc: 98.37454545454545 Val acc: 92.30652319289926 Test acc76.58434563373825; Train loss: 0.0001847688218917359 Val loss: 0.0002868771172561242
-INFO - evaluator.py - 2024-10-26 11:23:01,062 - Epoch: 11 Train acc: 98.43454545454546 Val acc: 88.81086996169981 Test acc81.09672243868897; Train loss: 0.00017096272151578557 Val loss: 0.00045861718346481374
-INFO - evaluator.py - 2024-10-26 11:23:31,848 - Epoch: 12 Train acc: 98.62181818181817 Val acc: 92.53450057754272 Test acc78.38356635342653; Train loss: 0.00015071793110533194 Val loss: 0.0002745062849305525
-INFO - evaluator.py - 2024-10-26 11:24:02,716 - Epoch: 13 Train acc: 98.55090909090909 Val acc: 86.97185239224268 Test acc70.84288333715334; Train loss: 0.00016190514768558468 Val loss: 0.0005808642525985702
-INFO - evaluator.py - 2024-10-26 11:24:33,528 - Epoch: 14 Train acc: 98.7109090909091 Val acc: 90.50702170344702 Test acc75.45267018106807; Train loss: 0.00014298617305572738 Val loss: 0.00038403762811672014
-INFO - evaluator.py - 2024-10-26 11:25:04,327 - Epoch: 15 Train acc: 98.79454545454546 Val acc: 92.37035686059943 Test acc79.44934677973872; Train loss: 0.0001339884418316863 Val loss: 0.00032945806409125707
-INFO - evaluator.py - 2024-10-26 11:25:35,062 - Epoch: 16 Train acc: 98.81454545454545 Val acc: 93.43425132226884 Test acc78.28902131560854; Train loss: 0.00013717117705805735 Val loss: 0.0003223680578051269
-INFO - evaluator.py - 2024-10-26 11:26:05,956 - Epoch: 17 Train acc: 98.95454545454545 Val acc: 93.30050458994468 Test acc77.03988081595232; Train loss: 0.00011760638382455165 Val loss: 0.0002898986019496244
-INFO - evaluator.py - 2024-10-26 11:26:36,669 - Epoch: 18 Train acc: 98.98363636363636 Val acc: 90.00851115569336 Test acc68.19275727710291; Train loss: 0.00011608219930309463 Val loss: 0.0005121047697615729
-INFO - evaluator.py - 2024-10-26 11:27:07,402 - Epoch: 19 Train acc: 98.83090909090909 Val acc: 92.10894279287494 Test acc77.40660096264038; Train loss: 0.00012812751363116233 Val loss: 0.0003202331632138371
-INFO - evaluator.py - 2024-10-26 11:27:38,250 - Epoch: 20 Train acc: 99.32727272727273 Val acc: 86.926256915314 Test acc67.58251203300482; Train loss: 7.204045161858878e-05 Val loss: 0.0006390067545633793
-INFO - evaluator.py - 2024-10-26 11:28:08,952 - Epoch: 21 Train acc: 99.46727272727273 Val acc: 89.56775487871603 Test acc71.9057987623195; Train loss: 5.876951404339211e-05 Val loss: 0.0005923198937239187
-INFO - evaluator.py - 2024-10-26 11:28:39,758 - Epoch: 22 Train acc: 99.47636363636364 Val acc: 91.74721867590735 Test acc72.80254412101766; Train loss: 5.612811236536469e-05 Val loss: 0.0005048103447097856
-INFO - evaluator.py - 2024-10-26 11:29:10,600 - Epoch: 23 Train acc: 99.55454545454545 Val acc: 90.24560763572254 Test acc68.87176254870502; Train loss: 4.8352364504667505e-05 Val loss: 0.0005944732868082219
-INFO - evaluator.py - 2024-10-26 11:29:41,314 - Epoch: 24 Train acc: 99.51818181818182 Val acc: 91.4949237035686 Test acc72.03472381388953; Train loss: 5.1640129733873025e-05 Val loss: 0.0006101472184711535
-INFO - evaluator.py - 2024-10-26 11:30:12,141 - Epoch: 25 Train acc: 99.54545454545455 Val acc: 92.58921514985713 Test acc75.73917029566812; Train loss: 4.9378898179962894e-05 Val loss: 0.0005817044175528517
-INFO - evaluator.py - 2024-10-26 11:30:42,967 - Epoch: 26 Train acc: 99.58 Val acc: 90.58301416499484 Test acc77.43811597524639; Train loss: 4.717607189456678e-05 Val loss: 0.000779307079181837
-INFO - evaluator.py - 2024-10-26 11:31:13,667 - Epoch: 27 Train acc: 99.61818181818181 Val acc: 91.69250410359292 Test acc70.86293834517534; Train loss: 4.311992659932002e-05 Val loss: 0.0006223465977588231
-INFO - evaluator.py - 2024-10-26 11:31:44,486 - Epoch: 28 Train acc: 99.57454545454546 Val acc: 92.30044379597544 Test acc76.2319504927802; Train loss: 4.486279347858561e-05 Val loss: 0.000647178974780089
-INFO - evaluator.py - 2024-10-26 11:32:15,207 - Epoch: 29 Train acc: 99.59272727272727 Val acc: 91.3976533527874 Test acc71.02624341049737; Train loss: 4.3890652249948206e-05 Val loss: 0.0007505598140854307
-INFO - evaluator.py - 2024-10-26 11:32:46,013 - Epoch: 30 Train acc: 99.64545454545456 Val acc: 91.44020913125418 Test acc70.82855833142332; Train loss: 4.0337498662517065e-05 Val loss: 0.0007851121825891843
-INFO - evaluator.py - 2024-10-26 11:33:16,803 - Epoch: 31 Train acc: 99.56545454545454 Val acc: 91.27302571584899 Test acc70.23263809305523; Train loss: 4.649200385849161e-05 Val loss: 0.0008353178454312201
-INFO - evaluator.py - 2024-10-26 11:33:47,542 - Epoch: 32 Train acc: 99.57272727272726 Val acc: 91.72594078667396 Test acc71.44166857666742; Train loss: 4.4832066576716234e-05 Val loss: 0.0006256944463277517
-INFO - evaluator.py - 2024-10-26 11:34:18,458 - Epoch: 33 Train acc: 99.70181818181818 Val acc: 92.33388047905648 Test acc75.08022003208801; Train loss: 3.249868725172498e-05 Val loss: 0.0007373952482948115
-INFO - evaluator.py - 2024-10-26 11:34:49,285 - Epoch: 34 Train acc: 99.65454545454546 Val acc: 92.90534378989605 Test acc74.994269997708; Train loss: 3.8027796906747175e-05 Val loss: 0.0007177291923659545
-INFO - evaluator.py - 2024-10-26 11:35:20,078 - Epoch: 35 Train acc: 99.67454545454545 Val acc: 91.11496139582952 Test acc72.44728397891359; Train loss: 3.6824263975193557e-05 Val loss: 0.000931130592508065
-INFO - evaluator.py - 2024-10-26 11:35:50,781 - Epoch: 36 Train acc: 99.62727272727273 Val acc: 89.06924433096238 Test acc61.94418977767591; Train loss: 3.95380802992308e-05 Val loss: 0.0009882185598863093
-INFO - evaluator.py - 2024-10-26 11:36:21,645 - Epoch: 37 Train acc: 99.65272727272728 Val acc: 92.04206942671287 Test acc72.63064405225762; Train loss: 3.6485955161995e-05 Val loss: 0.000755158794006683
-INFO - evaluator.py - 2024-10-26 11:36:52,368 - Epoch: 38 Train acc: 99.66363636363637 Val acc: 90.86570612195271 Test acc67.5452670181068; Train loss: 3.681932547955181e-05 Val loss: 0.0008399639312848036
-INFO - evaluator.py - 2024-10-26 11:37:23,083 - Epoch: 39 Train acc: 99.68363636363637 Val acc: 89.66198553103533 Test acc66.18439147375659; Train loss: 3.177784355304373e-05 Val loss: 0.0008684660071000132
-INFO - evaluator.py - 2024-10-26 11:37:53,947 - Epoch: 40 Train acc: 99.85818181818182 Val acc: 90.27904431880357 Test acc67.10405684162274; Train loss: 1.8233356706861575e-05 Val loss: 0.0008255076887426539
-INFO - evaluator.py - 2024-10-26 11:38:24,656 - Epoch: 41 Train acc: 99.85818181818182 Val acc: 90.74107848501428 Test acc68.19848727939491; Train loss: 1.6518942157166418e-05 Val loss: 0.0008635877370761878
-INFO - evaluator.py - 2024-10-26 11:38:55,490 - Epoch: 42 Train acc: 99.84545454545454 Val acc: 91.17575536506779 Test acc68.61964244785698; Train loss: 1.8103223268090833e-05 Val loss: 0.0009055335229345713
-INFO - evaluator.py - 2024-10-26 11:39:26,164 - Epoch: 43 Train acc: 99.85818181818182 Val acc: 91.48580460818286 Test acc69.73412789365115; Train loss: 1.5616546983735382e-05 Val loss: 0.0009275398031035646
-INFO - evaluator.py - 2024-10-26 11:39:57,091 - Epoch: 44 Train acc: 99.87272727272727 Val acc: 91.28214481123472 Test acc69.3702727481091; Train loss: 1.4734004404983187e-05 Val loss: 0.0009515748697107298
-INFO - evaluator.py - 2024-10-26 11:40:28,104 - Epoch: 45 Train acc: 99.8709090909091 Val acc: 91.14839807891057 Test acc69.01787760715105; Train loss: 1.4461696498950583e-05 Val loss: 0.0009854631613392156
-INFO - evaluator.py - 2024-10-26 11:40:58,819 - Epoch: 46 Train acc: 99.87454545454545 Val acc: 91.0572071250532 Test acc69.79142791657117; Train loss: 1.5294246668186547e-05 Val loss: 0.001030183607006125
-INFO - evaluator.py - 2024-10-26 11:41:29,645 - Epoch: 47 Train acc: 99.86181818181818 Val acc: 91.1058423004438 Test acc68.92619757047903; Train loss: 1.5751691721776628e-05 Val loss: 0.0010156205116604532
-INFO - evaluator.py - 2024-10-26 11:42:00,341 - Epoch: 48 Train acc: 99.8709090909091 Val acc: 90.96601617119582 Test acc67.92344716937887; Train loss: 1.3807141813396646e-05 Val loss: 0.0010176288501072294
-INFO - evaluator.py - 2024-10-26 11:42:31,201 - Epoch: 49 Train acc: 99.86727272727272 Val acc: 90.3823940665086 Test acc67.39915195966078; Train loss: 1.537490877275228e-05 Val loss: 0.0010522601144119152
-INFO - evaluator.py - 2024-10-26 11:42:31,204 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from wrn is 93.43425132226884 and 78.28902131560854
-INFO - evaluator.py - 2024-10-26 11:42:31,204 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from wrn is 93.43425132226884 and 78.28902131560854
-INFO - evaluator.py - 2024-10-26 11:42:31,204 - The best acc test dataset from wrn is 83.88723355489343
-INFO - evaluator.py - 2024-10-26 11:44:04,227 - Epoch: 0 Train acc: 88.53090909090909 Val acc: 92.03599002978905 Test acc84.19951867980747; Train loss: 0.001583446647904136 Val loss: 0.00024347873904566046
-INFO - evaluator.py - 2024-10-26 11:45:36,748 - Epoch: 1 Train acc: 95.08545454545455 Val acc: 86.96577299531887 Test acc85.20226908090763; Train loss: 0.0005481631680645726 Val loss: 0.0003672402128563644
-INFO - evaluator.py - 2024-10-26 11:47:09,357 - Epoch: 2 Train acc: 96.68181818181819 Val acc: 89.71366040488785 Test acc82.60657804263121; Train loss: 0.0003650495255535299 Val loss: 0.0003585741481010483
-INFO - evaluator.py - 2024-10-26 11:48:41,768 - Epoch: 3 Train acc: 97.7909090909091 Val acc: 91.79889354975987 Test acc75.11460004584002; Train loss: 0.00024565993698144503 Val loss: 0.00033495229543164927
-INFO - evaluator.py - 2024-10-26 11:50:14,325 - Epoch: 4 Train acc: 98.24000000000001 Val acc: 93.44641011611648 Test acc78.7617465046986; Train loss: 0.000201795208200135 Val loss: 0.0003005275479587235
-INFO - evaluator.py - 2024-10-26 11:51:46,789 - Epoch: 5 Train acc: 98.2909090909091 Val acc: 70.93744300565385 Test acc70.6079532431813; Train loss: 0.00019347984509711916 Val loss: 0.002204985203761244
-INFO - evaluator.py - 2024-10-26 11:53:19,281 - Epoch: 6 Train acc: 98.46000000000001 Val acc: 92.78679554988145 Test acc76.89663075865231; Train loss: 0.0001692793219434944 Val loss: 0.00035378937563915604
-INFO - evaluator.py - 2024-10-26 11:54:51,863 - Epoch: 7 Train acc: 98.82909090909091 Val acc: 92.25180862058484 Test acc70.15241806096722; Train loss: 0.00013388805368237875 Val loss: 0.00046476755010167823
-INFO - evaluator.py - 2024-10-26 11:56:24,318 - Epoch: 8 Train acc: 98.85636363636362 Val acc: 92.0238312359414 Test acc71.58778363511345; Train loss: 0.00012946945926259188 Val loss: 0.0004864137193281039
-INFO - evaluator.py - 2024-10-26 11:57:56,883 - Epoch: 9 Train acc: 98.92545454545456 Val acc: 92.35515836828986 Test acc77.5699060279624; Train loss: 0.00012104063781655648 Val loss: 0.00043433235549544103
-INFO - evaluator.py - 2024-10-26 11:59:29,367 - Epoch: 10 Train acc: 98.93636363636364 Val acc: 85.51583682898656 Test acc77.89078615631446; Train loss: 0.00011799014833205465 Val loss: 0.0008887880257512524
-INFO - evaluator.py - 2024-10-26 12:01:01,833 - Epoch: 11 Train acc: 98.78 Val acc: 91.09368350659615 Test acc71.9029337611735; Train loss: 0.00013626314775002274 Val loss: 0.00035546661280260903
-INFO - evaluator.py - 2024-10-26 12:02:34,399 - Epoch: 12 Train acc: 98.92727272727274 Val acc: 85.12067602893792 Test acc72.66502406600964; Train loss: 0.00011653814912265674 Val loss: 0.0005288269608366764
-INFO - evaluator.py - 2024-10-26 12:04:06,836 - Epoch: 13 Train acc: 98.97818181818182 Val acc: 92.86278801142926 Test acc74.994269997708; Train loss: 0.00011120277885347605 Val loss: 0.0003458452508097195
-INFO - evaluator.py - 2024-10-26 12:05:39,286 - Epoch: 14 Train acc: 99.25090909090909 Val acc: 92.10286339595112 Test acc69.09809763923906; Train loss: 8.538237494755197e-05 Val loss: 0.0005531513205023332
-INFO - evaluator.py - 2024-10-26 12:07:11,809 - Epoch: 15 Train acc: 99.31454545454545 Val acc: 90.09058301416499 Test acc66.12709145083659; Train loss: 8.026480604682795e-05 Val loss: 0.0007167704749001552
-INFO - evaluator.py - 2024-10-26 12:08:44,261 - Epoch: 16 Train acc: 99.33272727272727 Val acc: 90.54653778345188 Test acc66.00389640155856; Train loss: 7.809868414716964e-05 Val loss: 0.000761418510109496
-INFO - evaluator.py - 2024-10-26 12:10:16,835 - Epoch: 17 Train acc: 99.45272727272727 Val acc: 75.25077512310779 Test acc78.44659637863856; Train loss: 6.199226991315796e-05 Val loss: 0.0012578991156768288
-INFO - evaluator.py - 2024-10-26 12:11:49,287 - Epoch: 18 Train acc: 99.23454545454545 Val acc: 91.71986138975014 Test acc71.30987852395141; Train loss: 8.78307258202271e-05 Val loss: 0.00037546113792020315
-INFO - evaluator.py - 2024-10-26 12:13:21,689 - Epoch: 19 Train acc: 99.29454545454546 Val acc: 87.77737248464952 Test acc71.51329360531744; Train loss: 7.351616369242865e-05 Val loss: 0.00171288207451459
-INFO - evaluator.py - 2024-10-26 12:14:54,212 - Epoch: 20 Train acc: 99.79636363636364 Val acc: 91.18791415891543 Test acc69.52784781113913; Train loss: 2.4330254617168315e-05 Val loss: 0.001568689454079428
-INFO - evaluator.py - 2024-10-26 12:16:26,738 - Epoch: 21 Train acc: 99.88909090909091 Val acc: 91.64690862666424 Test acc66.50813660325464; Train loss: 1.5150739554188808e-05 Val loss: 0.0017502143307800127
-INFO - evaluator.py - 2024-10-26 12:17:59,301 - Epoch: 22 Train acc: 99.88363636363637 Val acc: 91.87488601130768 Test acc67.00378180151272; Train loss: 1.5269159179164986e-05 Val loss: 0.0016600482822138529
-INFO - evaluator.py - 2024-10-26 12:19:31,758 - Epoch: 23 Train acc: 99.88909090909091 Val acc: 91.69554380205483 Test acc67.02956681182673; Train loss: 1.261607090749418e-05 Val loss: 0.0024142095191666143
-INFO - evaluator.py - 2024-10-26 12:21:04,184 - Epoch: 24 Train acc: 99.94363636363637 Val acc: 90.49790260806128 Test acc69.09523263809305; Train loss: 8.766731452355584e-06 Val loss: 0.0035098676254223297
-INFO - evaluator.py - 2024-10-26 12:22:36,730 - Epoch: 25 Train acc: 99.90909090909092 Val acc: 91.1544774758344 Test acc64.74616089846435; Train loss: 1.1272828440136932e-05 Val loss: 0.001961542328872741
-INFO - evaluator.py - 2024-10-26 12:24:09,151 - Epoch: 26 Train acc: 99.9 Val acc: 89.74405738950696 Test acc63.91531056612423; Train loss: 1.140926180480826e-05 Val loss: 0.0016477104486814131
-INFO - evaluator.py - 2024-10-26 12:25:41,675 - Epoch: 27 Train acc: 99.91454545454546 Val acc: 91.23350963584413 Test acc66.13568645427458; Train loss: 1.0114077314905114e-05 Val loss: 0.002190360229959951
-INFO - evaluator.py - 2024-10-26 12:27:14,075 - Epoch: 28 Train acc: 99.92545454545456 Val acc: 90.02674934646483 Test acc63.36523034609214; Train loss: 8.216384409206512e-06 Val loss: 0.0019501845585451249
-INFO - evaluator.py - 2024-10-26 12:28:46,483 - Epoch: 29 Train acc: 99.72727272727273 Val acc: 91.47972521125905 Test acc69.53357781343112; Train loss: 3.4213074534570545e-05 Val loss: 0.002769465913627059
-INFO - evaluator.py - 2024-10-26 12:30:19,018 - Epoch: 30 Train acc: 99.9 Val acc: 89.8261292479786 Test acc69.67969287187715; Train loss: 1.2442432205220525e-05 Val loss: 0.004315683142929731
-INFO - evaluator.py - 2024-10-26 12:31:51,434 - Epoch: 31 Train acc: 99.96000000000001 Val acc: 91.41893124202079 Test acc65.92940637176254; Train loss: 5.15373170657602e-06 Val loss: 0.002925234062996771
-INFO - evaluator.py - 2024-10-26 12:33:23,955 - Epoch: 32 Train acc: 99.80363636363636 Val acc: 92.40075384521856 Test acc74.12617465046985; Train loss: 2.0195532855458707e-05 Val loss: 0.0023379595046043684
-INFO - evaluator.py - 2024-10-26 12:34:56,358 - Epoch: 33 Train acc: 99.86363636363636 Val acc: 91.82017143899324 Test acc71.12365344946137; Train loss: 1.4845918265068163e-05 Val loss: 0.0017750562125117926
-INFO - evaluator.py - 2024-10-26 12:36:28,774 - Epoch: 34 Train acc: 99.92545454545456 Val acc: 91.92656088516019 Test acc70.84288333715334; Train loss: 8.140276636227512e-06 Val loss: 0.0017033088126800888
-INFO - evaluator.py - 2024-10-26 12:38:01,323 - Epoch: 35 Train acc: 99.86 Val acc: 92.43115082983768 Test acc72.15218886087554; Train loss: 1.4464823527768286e-05 Val loss: 0.0015536580624076848
-INFO - evaluator.py - 2024-10-26 12:39:33,730 - Epoch: 36 Train acc: 99.88181818181818 Val acc: 90.43102924189921 Test acc64.68886087554435; Train loss: 1.4475109959494148e-05 Val loss: 0.0024407697713906312
-INFO - evaluator.py - 2024-10-26 12:41:06,130 - Epoch: 37 Train acc: 99.88181818181818 Val acc: 91.63171013435468 Test acc68.98063259225303; Train loss: 1.3278766576761633e-05 Val loss: 0.0013019131979280507
-INFO - evaluator.py - 2024-10-26 12:42:38,653 - Epoch: 38 Train acc: 99.87818181818182 Val acc: 90.81099154963827 Test acc71.78260371304148; Train loss: 1.3045208684508741e-05 Val loss: 0.0028833661030705513
-INFO - evaluator.py - 2024-10-26 12:44:11,042 - Epoch: 39 Train acc: 99.92545454545456 Val acc: 91.79889354975987 Test acc68.59672243868897; Train loss: 6.796845217158162e-06 Val loss: 0.002555057766068068
-INFO - evaluator.py - 2024-10-26 12:45:43,592 - Epoch: 40 Train acc: 99.97454545454545 Val acc: 91.84144932822664 Test acc68.80300252120101; Train loss: 2.4193712041282694e-06 Val loss: 0.0023762061245296926
-INFO - evaluator.py - 2024-10-26 12:47:16,050 - Epoch: 41 Train acc: 99.99454545454546 Val acc: 91.97215636208888 Test acc70.89731835892734; Train loss: 8.369381335776118e-07 Val loss: 0.0023199281501758424
-INFO - evaluator.py - 2024-10-26 12:48:48,465 - Epoch: 42 Train acc: 99.99636363636364 Val acc: 91.91440209131254 Test acc69.76564290625717; Train loss: 6.495614947983664e-07 Val loss: 0.002578749469119558
-INFO - evaluator.py - 2024-10-26 12:50:20,975 - Epoch: 43 Train acc: 99.99454545454546 Val acc: 91.70162319897867 Test acc69.79429291771717; Train loss: 8.674756012293404e-07 Val loss: 0.0032924357660128805
-INFO - evaluator.py - 2024-10-26 12:51:53,400 - Epoch: 44 Train acc: 99.99636363636364 Val acc: 91.76545686667883 Test acc68.72851249140498; Train loss: 9.229179010586366e-07 Val loss: 0.0021186671841723612
-INFO - evaluator.py - 2024-10-26 12:53:25,896 - Epoch: 45 Train acc: 99.99454545454546 Val acc: 91.6864247066691 Test acc69.18404767361906; Train loss: 8.796060974646026e-07 Val loss: 0.003618802199704907
-INFO - evaluator.py - 2024-10-26 12:54:58,310 - Epoch: 46 Train acc: 100.0 Val acc: 91.63474983281658 Test acc68.19275727710291; Train loss: 4.78376422234884e-07 Val loss: 0.0028580336398546136
-INFO - evaluator.py - 2024-10-26 12:56:30,719 - Epoch: 47 Train acc: 100.0 Val acc: 91.76545686667883 Test acc68.800137520055; Train loss: 3.2709455317723783e-07 Val loss: 0.003223533974323485
-INFO - evaluator.py - 2024-10-26 12:58:03,560 - Epoch: 48 Train acc: 99.99818181818182 Val acc: 91.77761566052648 Test acc68.73137749255099; Train loss: 3.112691006218814e-07 Val loss: 0.0033512402322924428
-INFO - evaluator.py - 2024-10-26 12:59:38,252 - Epoch: 49 Train acc: 99.99636363636364 Val acc: 91.71378199282631 Test acc67.80025212010085; Train loss: 4.989117055629322e-07 Val loss: 0.0028861452036044815
-INFO - evaluator.py - 2024-10-26 12:59:38,264 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from resnext is 93.44641011611648 and 78.7617465046986
-INFO - evaluator.py - 2024-10-26 12:59:38,264 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from resnext is 93.44641011611648 and 78.7617465046986
-INFO - evaluator.py - 2024-10-26 12:59:38,264 - The best acc test dataset from resnext is 85.20226908090763
-INFO - evaluator.py - 2024-10-26 12:59:38,264 - The best acc of accuracy (using synthetic images as the validation set) of synthetic images from resnet, wrn, and resnext are [78.97662159064863, 78.28902131560854, 78.7617465046986].
-INFO - evaluator.py - 2024-10-26 12:59:38,264 - The average and std of accuracy of synthetic images are 78.68 and 0.29
-INFO - dataset_loader.py - 2024-10-28 19:02:42,006 - delta is reset as 2.966981886419575e-07
-INFO - evaluator.py - 2024-10-28 19:05:21,203 - Epoch: 0 Train acc: 87.03999999999999 Val acc: 91.76849656514074 Test acc84.54045381618153; Train loss: 0.001277467626604167 Val loss: 0.00022321211667733102
-INFO - evaluator.py - 2024-10-28 19:06:54,630 - Epoch: 1 Train acc: 92.34181818181818 Val acc: 67.98893549759863 Test acc79.66995186798074; Train loss: 0.0007898148880763487 Val loss: 0.0009893881465839508
-INFO - evaluator.py - 2024-10-28 19:08:26,425 - Epoch: 2 Train acc: 95.4490909090909 Val acc: 90.32767949419419 Test acc85.09912903965161; Train loss: 0.0004905006956647743 Val loss: 0.00028373574701817434
-INFO - evaluator.py - 2024-10-28 19:10:00,008 - Epoch: 3 Train acc: 96.89999999999999 Val acc: 92.37035686059943 Test acc83.41450836580334; Train loss: 0.0003351107888601043 Val loss: 0.0002521575258179518
-INFO - evaluator.py - 2024-10-28 19:11:34,180 - Epoch: 4 Train acc: 97.03272727272727 Val acc: 74.15040427989543 Test acc74.29520971808388; Train loss: 0.00032137648940763693 Val loss: 0.0025187057596230203
-INFO - evaluator.py - 2024-10-28 19:13:05,539 - Epoch: 5 Train acc: 96.60727272727273 Val acc: 50.87847285549274 Test acc71.8198487279395; Train loss: 0.00037287455207922243 Val loss: 0.0021067010833830217
-INFO - evaluator.py - 2024-10-28 19:14:38,915 - Epoch: 6 Train acc: 97.5109090909091 Val acc: 54.082315034348596 Test acc78.62136144854458; Train loss: 0.0002707319711758332 Val loss: 0.0024694914556620757
-INFO - evaluator.py - 2024-10-28 19:16:09,332 - Epoch: 7 Train acc: 98.0 Val acc: 64.13459784789349 Test acc83.1366032546413; Train loss: 0.00021672363040799443 Val loss: 0.0027152087610321164
-INFO - evaluator.py - 2024-10-28 19:17:38,707 - Epoch: 8 Train acc: 98.18909090909091 Val acc: 75.74624597239954 Test acc81.28294751317901; Train loss: 0.0002019392804158005 Val loss: 0.002152751873268444
-INFO - evaluator.py - 2024-10-28 19:19:12,558 - Epoch: 9 Train acc: 98.26181818181819 Val acc: 92.88406590066265 Test acc81.58663763465506; Train loss: 0.00019594369302927092 Val loss: 0.0003116528745755373
-INFO - evaluator.py - 2024-10-28 19:20:47,538 - Epoch: 10 Train acc: 98.2 Val acc: 83.43972277950027 Test acc81.67545267018107; Train loss: 0.0001918673179366372 Val loss: 0.001541485131797881
-INFO - evaluator.py - 2024-10-28 19:22:18,546 - Epoch: 11 Train acc: 98.53090909090909 Val acc: 84.59176849656515 Test acc77.56704102681641; Train loss: 0.0001606046823784709 Val loss: 0.0015383233806051898
-INFO - dataset_loader.py - 2024-10-28 19:23:06,137 - delta is reset as 2.966981886419575e-07
-INFO - dataset_loader.py - 2024-10-28 22:44:31,963 - delta is reset as 2.966981886419575e-07
-INFO - evaluator.py - 2024-10-28 22:45:12,410 - Epoch: 0 Train acc: 84.96909090909091 Val acc: 92.94182017143899 Test acc81.40614256245703; Train loss: 0.001378201420876113 Val loss: 0.00018641231715755264
-INFO - evaluator.py - 2024-10-28 22:45:50,668 - Epoch: 1 Train acc: 94.55454545454546 Val acc: 78.5640464465925 Test acc81.320192528077; Train loss: 0.000591655771298842 Val loss: 0.0007312746640505185
-INFO - dataset_loader.py - 2024-10-28 22:46:43,687 - delta is reset as 2.966981886419575e-07
-INFO - dataset_loader.py - 2024-10-28 22:47:11,500 - delta is reset as 2.966981886419575e-07
-INFO - dataset_loader.py - 2024-10-29 09:12:39,948 - delta is reset as 2.6201132658294697e-07
-INFO - evaluator.py - 2024-10-29 10:14:57,218 - The FID of synthetic images is 29.3289837710725
-INFO - evaluator.py - 2024-10-29 10:14:57,224 - The Inception Score of synthetic images is 1.652750849723816
-INFO - evaluator.py - 2024-10-29 10:14:57,224 - The Precision and Recall of synthetic images is 0.7476875185966492 and 0.285733163356781
-INFO - evaluator.py - 2024-10-29 10:14:57,224 - The FLD of synthetic images is -4.951715469360352
-INFO - evaluator.py - 2024-10-29 10:14:57,224 - The ImageReward of synthetic images is -1.871033944573719
-INFO - dataset_loader.py - 2024-10-29 10:14:57,630 - delta is reset as 1.5148623360286113e-06
-INFO - evaluator.py - 2024-10-29 11:06:17,358 - The FID of synthetic images is 5.286480673016143
-INFO - evaluator.py - 2024-10-29 11:06:17,363 - The Inception Score of synthetic images is 2.071159601211548
-INFO - evaluator.py - 2024-10-29 11:06:17,363 - The Precision and Recall of synthetic images is 0.6194375157356262 and 0.7204999923706055
-INFO - evaluator.py - 2024-10-29 11:06:17,364 - The FLD of synthetic images is 3.7299275398254395
-INFO - evaluator.py - 2024-10-29 11:06:17,364 - The ImageReward of synthetic images is -2.0137405606759713
-INFO - dataset_loader.py - 2024-10-29 11:06:17,775 - delta is reset as 4.329102935418938e-06
-INFO - evaluator.py - 2024-10-29 11:39:57,591 - The FID of synthetic images is 168.59217462930627
-INFO - evaluator.py - 2024-10-29 11:39:57,596 - The Inception Score of synthetic images is 1.6652277708053589
-INFO - evaluator.py - 2024-10-29 11:39:57,596 - The Precision and Recall of synthetic images is 0.6194375157356262 and 0.00952173862606287
-INFO - evaluator.py - 2024-10-29 11:39:57,596 - The FLD of synthetic images is 20.778346061706543
-INFO - evaluator.py - 2024-10-29 11:39:57,596 - The ImageReward of synthetic images is -1.5740511079076678
-INFO - dataset_loader.py - 2024-10-29 11:39:58,114 - delta is reset as 1.8484667129285888e-06
-INFO - evaluator.py - 2024-10-29 12:12:19,140 - The FID of synthetic images is 231.37436795903784
-INFO - evaluator.py - 2024-10-29 12:12:19,145 - The Inception Score of synthetic images is 1.7306236028671265
-INFO - evaluator.py - 2024-10-29 12:12:19,145 - The Precision and Recall of synthetic images is 0.7602222561836243 and 0.00043999997433274984
-INFO - evaluator.py - 2024-10-29 12:12:19,145 - The FLD of synthetic images is 23.48649501800537
-INFO - evaluator.py - 2024-10-29 12:12:19,146 - The ImageReward of synthetic images is -2.2615407764571054
-INFO - dataset_loader.py - 2024-10-29 12:12:19,367 - delta is reset as 4.329102935418938e-06
-INFO - evaluator.py - 2024-10-29 12:45:16,662 - The FID of synthetic images is 237.36948091544997
-INFO - evaluator.py - 2024-10-29 12:45:16,666 - The Inception Score of synthetic images is 1.2807329893112183
-INFO - evaluator.py - 2024-10-29 12:45:16,667 - The Precision and Recall of synthetic images is 0.5577656626701355 and 4.347825961303897e-05
-INFO - evaluator.py - 2024-10-29 12:45:16,667 - The FLD of synthetic images is 30.49933910369873
-INFO - evaluator.py - 2024-10-29 12:45:16,667 - The ImageReward of synthetic images is -1.8509714604625478
-INFO - dataset_loader.py - 2024-10-29 12:45:17,305 - delta is reset as 1.5148623360286113e-06
-INFO - evaluator.py - 2024-10-29 13:18:11,048 - The FID of synthetic images is 53.50594213505724
-INFO - evaluator.py - 2024-10-29 13:18:11,054 - The Inception Score of synthetic images is 3.4386539459228516
-INFO - evaluator.py - 2024-10-29 13:18:11,055 - The Precision and Recall of synthetic images is 0.26606249809265137 and 0.12056666612625122
-INFO - evaluator.py - 2024-10-29 13:18:11,055 - The FLD of synthetic images is 20.395588874816895
-INFO - evaluator.py - 2024-10-29 13:18:11,055 - The ImageReward of synthetic images is -1.8617446345779531
-INFO - dataset_loader.py - 2024-10-29 13:18:11,648 - delta is reset as 1.5148623360286113e-06
-INFO - evaluator.py - 2024-10-29 13:50:35,681 - The FID of synthetic images is 4.446889068230405
-INFO - evaluator.py - 2024-10-29 13:50:35,832 - The Inception Score of synthetic images is 2.0761497020721436
-INFO - evaluator.py - 2024-10-29 13:50:35,832 - The Precision and Recall of synthetic images is 0.6322698593139648 and 0.7390333414077759
-INFO - evaluator.py - 2024-10-29 13:50:35,833 - The FLD of synthetic images is 3.283095359802246
-INFO - evaluator.py - 2024-10-29 13:50:35,833 - The ImageReward of synthetic images is -2.0057078354216755
-INFO - dataset_loader.py - 2024-10-29 13:50:37,371 - delta is reset as 5.11965868690912e-07
-INFO - evaluator.py - 2024-10-29 14:31:49,267 - The FID of synthetic images is 28.848900967099837
-INFO - evaluator.py - 2024-10-29 14:31:49,353 - The Inception Score of synthetic images is 2.238304853439331
-INFO - evaluator.py - 2024-10-29 14:31:49,353 - The Precision and Recall of synthetic images is 0.6088594198226929 and 0.1520366072654724
-INFO - evaluator.py - 2024-10-29 14:31:49,354 - The FLD of synthetic images is nan
-INFO - evaluator.py - 2024-10-29 14:31:49,354 - The ImageReward of synthetic images is -1.3833920579410202
-INFO - dataset_loader.py - 2024-10-29 14:31:49,986 - delta is reset as 1.8484667129285888e-06
-INFO - dataset_loader.py - 2024-10-29 16:22:06,062 - delta is reset as 2.966981886419575e-07
-INFO - dataset_loader.py - 2024-10-30 00:36:05,338 - delta is reset as 2.966981886419575e-07
-INFO - dataset_loader.py - 2024-10-30 01:50:46,464 - delta is reset as 2.966981886419575e-07
-INFO - dataset_loader.py - 2024-10-30 01:51:09,984 - delta is reset as 2.966981886419575e-07
-INFO - evaluator.py - 2024-10-30 03:11:31,398 - The FID of synthetic images is 29.233277708138274
-INFO - evaluator.py - 2024-10-30 03:11:31,399 - The Inception Score of synthetic images is 1.652750849723816
-INFO - evaluator.py - 2024-10-30 03:11:31,399 - The Precision and Recall of synthetic images is 0.7476875185966492 and 0.285733163356781
-INFO - evaluator.py - 2024-10-30 03:11:31,399 - The FLD of synthetic images is -4.719221591949463
-INFO - evaluator.py - 2024-10-30 03:11:31,400 - The ImageReward of synthetic images is -1.871033944573719
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