diff --git a/camelyon_32_eps1.0trainval-2024-10-24-08-46-55/stdout.txt b/camelyon_32_eps1.0trainval-2024-10-24-08-46-55/stdout.txt
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-INFO - utils.py - 2024-10-24 08:47:02,042 - {'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_eps1.0trainval-2024-10-24-08-46-55', '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_eps1.0trainval-2024-10-24-08-46-55/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_eps1.0trainval-2024-10-24-08-46-55/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': 1.0, 'max_physical_batch_size': 8192, 'n_splits': 64}}, 'gen': {'data_num': 60000, 'batch_size': 1000, 'log_dir': 'exp/dpdm/camelyon_32_eps1.0trainval-2024-10-24-08-46-55/gen'}, 'eval': {'batch_size': 1000}}
-INFO - dataset_loader.py - 2024-10-24 08:47:17,334 - delta is reset as 2.966981886419575e-07
-INFO - dpsgd_diffusion.py - 2024-10-24 08:47:20,119 - Number of trainable parameters in model: 0
-INFO - dpsgd_diffusion.py - 2024-10-24 08:47:20,119 - Number of total epochs: 50
-INFO - dpsgd_diffusion.py - 2024-10-24 08:47:20,119 - Starting training at step 0
-INFO - dpsgd_diffusion.py - 2024-10-24 08:48:36,257 - Loss: 0.8899, step: 100
-INFO - dpsgd_diffusion.py - 2024-10-24 08:49:33,276 - Loss: 0.8412, step: 200
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-INFO - dpsgd_diffusion.py - 2024-10-24 09:05:16,089 - Loss: 0.4916, step: 2000
-INFO - dpsgd_diffusion.py - 2024-10-24 09:05:16,102 - Saving snapshot checkpoint and sampling single batch at iteration 2000.
-WARNING - image.py - 2024-10-24 09:05:16,970 - 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 - 2024-10-24 09:06:30,267 - Loss: 0.4591, step: 2100
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-INFO - dpsgd_diffusion.py - 2024-10-24 09:23:12,272 - Loss: 0.3150, step: 4000
-INFO - dpsgd_diffusion.py - 2024-10-24 09:23:12,316 - Saving snapshot checkpoint and sampling single batch at iteration 4000.
-WARNING - image.py - 2024-10-24 09:23:12,884 - 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 - 2024-10-24 09:24:23,112 - Loss: 0.3314, step: 4100
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-INFO - dpsgd_diffusion.py - 2024-10-24 09:25:26,617 - Eps-value after 1 epochs: 0.1882
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-INFO - dpsgd_diffusion.py - 2024-10-24 09:40:57,874 - Loss: 0.2820, step: 6000
-INFO - dpsgd_diffusion.py - 2024-10-24 09:40:57,885 - Saving snapshot checkpoint and sampling single batch at iteration 6000.
-WARNING - image.py - 2024-10-24 09:40:58,453 - 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 - 2024-10-24 09:58:42,606 - Loss: 0.2697, step: 8000
-INFO - dpsgd_diffusion.py - 2024-10-24 09:58:42,625 - Saving snapshot checkpoint and sampling single batch at iteration 8000.
-WARNING - image.py - 2024-10-24 09:58:43,192 - 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 - 2024-10-24 09:59:01,285 - FID at iteration 8000: 101.688903
-INFO - dpsgd_diffusion.py - 2024-10-24 09:59:54,008 - Loss: 0.2558, step: 8100
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-INFO - dpsgd_diffusion.py - 2024-10-24 10:01:38,326 - Loss: 0.2673, step: 8300
-INFO - dpsgd_diffusion.py - 2024-10-24 10:02:30,074 - Loss: 0.2341, step: 8400
-INFO - dpsgd_diffusion.py - 2024-10-24 10:02:55,114 - Eps-value after 2 epochs: 0.2168
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-INFO - dpsgd_diffusion.py - 2024-10-24 10:16:20,708 - Loss: 0.2342, step: 10000
-INFO - dpsgd_diffusion.py - 2024-10-24 10:16:20,755 - Saving snapshot checkpoint and sampling single batch at iteration 10000.
-WARNING - image.py - 2024-10-24 10:16:21,322 - 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 - 2024-10-24 10:16:38,911 - FID at iteration 10000: 94.654371
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-INFO - dpsgd_diffusion.py - 2024-10-24 10:34:00,158 - Saving snapshot checkpoint and sampling single batch at iteration 12000.
-WARNING - image.py - 2024-10-24 10:34:00,725 - 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 - 2024-10-24 10:34:18,301 - FID at iteration 12000: 89.107741
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-INFO - dpsgd_diffusion.py - 2024-10-24 10:40:09,148 - Eps-value after 3 epochs: 0.2454
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-INFO - dpsgd_diffusion.py - 2024-10-24 11:17:29,931 - Eps-value after 4 epochs: 0.2740
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-INFO - dpsgd_diffusion.py - 2024-10-25 01:01:15,965 - Eps-value after 26 epochs: 0.7083
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-INFO - dpsgd_diffusion.py - 2024-10-25 02:16:03,948 - Eps-value after 28 epochs: 0.7363
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-INFO - dpsgd_diffusion.py - 2024-10-25 02:53:20,810 - Eps-value after 29 epochs: 0.7500
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-INFO - dpsgd_diffusion.py - 2024-10-25 04:08:10,975 - Eps-value after 31 epochs: 0.7766
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-INFO - dpsgd_diffusion.py - 2024-10-25 04:45:39,808 - Eps-value after 32 epochs: 0.7897
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-INFO - dpsgd_diffusion.py - 2024-10-25 05:23:06,961 - Eps-value after 33 epochs: 0.8025
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-INFO - dpsgd_diffusion.py - 2024-10-25 06:00:38,746 - Eps-value after 34 epochs: 0.8152
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-INFO - dpsgd_diffusion.py - 2024-10-25 09:45:20,903 - Eps-value after 40 epochs: 0.8879
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-INFO - dpsgd_diffusion.py - 2024-10-25 10:22:48,139 - Eps-value after 41 epochs: 0.8994
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-INFO - dpsgd_diffusion.py - 2024-10-25 12:15:12,531 - Eps-value after 44 epochs: 0.9335
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-INFO - dpsgd_diffusion.py - 2024-10-25 15:22:56,968 - Eps-value after 49 epochs: 0.9878
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-INFO - dpsgd_diffusion.py - 2024-10-25 15:41:48,047 - Loss: 0.1959, step: 209100
-INFO - dpsgd_diffusion.py - 2024-10-25 15:42:40,151 - Loss: 0.2170, step: 209200
-INFO - dpsgd_diffusion.py - 2024-10-25 15:43:33,556 - Loss: 0.1903, step: 209300
-INFO - dpsgd_diffusion.py - 2024-10-25 15:44:26,853 - Loss: 0.1808, step: 209400
-INFO - dpsgd_diffusion.py - 2024-10-25 15:45:20,882 - Loss: 0.1824, step: 209500
-INFO - dpsgd_diffusion.py - 2024-10-25 15:46:13,658 - Loss: 0.1857, step: 209600
-INFO - dpsgd_diffusion.py - 2024-10-25 15:47:05,484 - Loss: 0.1884, step: 209700
-INFO - dpsgd_diffusion.py - 2024-10-25 15:47:57,672 - Loss: 0.2019, step: 209800
-INFO - dpsgd_diffusion.py - 2024-10-25 15:48:50,581 - Loss: 0.2091, step: 209900
-INFO - dpsgd_diffusion.py - 2024-10-25 15:49:43,657 - Loss: 0.1757, step: 210000
-INFO - dpsgd_diffusion.py - 2024-10-25 15:49:43,670 - Saving snapshot checkpoint and sampling single batch at iteration 210000.
-WARNING - image.py - 2024-10-25 15:49:44,237 - 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 15:50:03,049 - FID at iteration 210000: 52.011974
-INFO - dpsgd_diffusion.py - 2024-10-25 15:50:54,207 - Loss: 0.1921, step: 210100
-INFO - dpsgd_diffusion.py - 2024-10-25 15:51:46,472 - Loss: 0.2190, step: 210200
-INFO - dpsgd_diffusion.py - 2024-10-25 15:52:37,421 - Loss: 0.1910, step: 210300
-INFO - dpsgd_diffusion.py - 2024-10-25 15:53:30,466 - Loss: 0.1709, step: 210400
-INFO - dpsgd_diffusion.py - 2024-10-25 15:54:23,297 - Loss: 0.1819, step: 210500
-INFO - dpsgd_diffusion.py - 2024-10-25 15:55:17,341 - Loss: 0.1864, step: 210600
-INFO - dpsgd_diffusion.py - 2024-10-25 15:56:09,672 - Loss: 0.1891, step: 210700
-INFO - dpsgd_diffusion.py - 2024-10-25 15:57:03,279 - Loss: 0.1780, step: 210800
-INFO - dpsgd_diffusion.py - 2024-10-25 15:57:54,956 - Loss: 0.1793, step: 210900
-INFO - dpsgd_diffusion.py - 2024-10-25 15:58:46,854 - Loss: 0.1934, step: 211000
-INFO - dpsgd_diffusion.py - 2024-10-25 15:59:39,556 - Loss: 0.1888, step: 211100
-INFO - dpsgd_diffusion.py - 2024-10-25 16:00:30,890 - Loss: 0.1996, step: 211200
-INFO - dpsgd_diffusion.py - 2024-10-25 16:00:30,909 - Eps-value after 50 epochs: 0.9983
-INFO - dpsgd_diffusion.py - 2024-10-25 16:00:31,762 - Saving final checkpoint.
-INFO - dpsgd_diffusion.py - 2024-10-25 16:00:31,765 - start to generate 60000 samples
-INFO - dpsgd_diffusion.py - 2024-10-25 16:08:50,272 - Generation Finished!
-INFO - dataset_loader.py - 2024-10-25 21:31:46,491 - delta is reset as 5.800209926283058e-07
-INFO - evaluator.py - 2024-10-25 21:33:12,167 - Epoch: 0 Train acc: 83.25454545454546 Val acc: 46.955834180503786 Test acc46.643622883478606; Train loss: 0.001400787846066735 Val loss: 0.007623838379223387
-INFO - evaluator.py - 2024-10-25 21:33:50,811 - Epoch: 1 Train acc: 95.77636363636364 Val acc: 51.1408562069355 Test acc51.01192265304078; Train loss: 0.00042466633292761715 Val loss: 0.002571223213836108
-INFO - evaluator.py - 2024-10-25 21:34:30,055 - Epoch: 2 Train acc: 98.1 Val acc: 49.638540607703604 Test acc48.91293457569382; Train loss: 0.00020062724624506453 Val loss: 0.006897296634054879
-INFO - evaluator.py - 2024-10-25 21:35:07,126 - Epoch: 3 Train acc: 95.77636363636364 Val acc: 44.96215971986897 Test acc42.75623685001503; Train loss: 0.00042486186723478817 Val loss: 0.008011711716961622
-INFO - evaluator.py - 2024-10-25 21:35:43,093 - Epoch: 4 Train acc: 98.15454545454546 Val acc: 49.20930757935163 Test acc48.16651638112413; Train loss: 0.00018403762138702654 Val loss: 0.008170865315388087
-INFO - evaluator.py - 2024-10-25 21:36:19,373 - Epoch: 5 Train acc: 98.70909090909092 Val acc: 45.340562521179265 Test acc43.68299769562169; Train loss: 0.00013806071132421493 Val loss: 0.010414748545704514
-INFO - evaluator.py - 2024-10-25 21:36:57,355 - Epoch: 6 Train acc: 99.03454545454545 Val acc: 45.60600926239693 Test acc43.617873960525; Train loss: 0.00010372490751218389 Val loss: 0.021980733450261882
-INFO - evaluator.py - 2024-10-25 21:37:35,623 - Epoch: 7 Train acc: 99.32363636363635 Val acc: 50.50265446741218 Test acc50.861637110509974; Train loss: 7.514596028380435e-05 Val loss: 0.005271553575797336
-INFO - evaluator.py - 2024-10-25 21:38:11,340 - Epoch: 8 Train acc: 99.2890909090909 Val acc: 45.73590873150344 Test acc43.47259793607855; Train loss: 7.770412469134582e-05 Val loss: 0.028559801525468005
-INFO - evaluator.py - 2024-10-25 21:38:47,307 - Epoch: 9 Train acc: 99.25818181818182 Val acc: 46.7412176663278 Test acc44.55966336038473; Train loss: 8.214670127596367e-05 Val loss: 0.05589518864303204
-INFO - evaluator.py - 2024-10-25 21:39:25,585 - Epoch: 10 Train acc: 99.32545454545455 Val acc: 45.20501524906811 Test acc41.98978058310791; Train loss: 7.238225838359954e-05 Val loss: 0.025195410140393133
-INFO - evaluator.py - 2024-10-25 21:40:02,779 - Epoch: 11 Train acc: 99.50909090909092 Val acc: 44.22229752626228 Test acc41.278429015128744; Train loss: 5.317094197856601e-05 Val loss: 0.11881159755802015
-INFO - evaluator.py - 2024-10-25 21:40:39,116 - Epoch: 12 Train acc: 99.43636363636364 Val acc: 47.13091607364735 Test acc45.356176735798016; Train loss: 6.513807392620947e-05 Val loss: 0.009573568748940348
-INFO - evaluator.py - 2024-10-25 21:41:14,611 - Epoch: 13 Train acc: 99.58727272727272 Val acc: 44.312662374336384 Test acc41.76435226931169; Train loss: 4.567789299498228e-05 Val loss: 0.05602318739926063
-INFO - evaluator.py - 2024-10-25 21:41:51,465 - Epoch: 14 Train acc: 99.63090909090909 Val acc: 46.571783576188864 Test acc44.59472998697525; Train loss: 3.971205612178892e-05 Val loss: 0.3131622879784969
-INFO - evaluator.py - 2024-10-25 21:42:28,338 - Epoch: 15 Train acc: 99.25272727272727 Val acc: 42.03094996046538 Test acc38.82376515379221; Train loss: 8.767223419854418e-05 Val loss: 0.011349826482249076
-INFO - evaluator.py - 2024-10-25 21:43:04,740 - Epoch: 16 Train acc: 99.55090909090909 Val acc: 45.74155653450808 Test acc43.688007213706044; Train loss: 4.85743251237595e-05 Val loss: 0.006004160061591388
-INFO - evaluator.py - 2024-10-25 21:43:40,229 - Epoch: 17 Train acc: 99.66181818181819 Val acc: 43.60668699875748 Test acc39.90081154192966; Train loss: 3.511081210480453e-05 Val loss: 0.04025974142259223
-INFO - evaluator.py - 2024-10-25 21:44:17,249 - Epoch: 18 Train acc: 99.62727272727273 Val acc: 45.922286230656276 Test acc43.44755034565675; Train loss: 3.977041178732179e-05 Val loss: 0.030884011080207684
-INFO - evaluator.py - 2024-10-25 21:44:54,266 - Epoch: 19 Train acc: 99.69818181818182 Val acc: 47.18739410369366 Test acc45.33112914537622; Train loss: 3.189866000611801e-05 Val loss: 0.03390367823132927
-INFO - evaluator.py - 2024-10-25 21:45:30,654 - Epoch: 20 Train acc: 99.90545454545455 Val acc: 46.36846266802214 Test acc43.79821661156197; Train loss: 1.1764464204911333e-05 Val loss: 0.6484918545884869
-INFO - evaluator.py - 2024-10-25 21:46:07,287 - Epoch: 21 Train acc: 99.12545454545455 Val acc: 48.30565909861064 Test acc49.078248672477706; Train loss: 0.00014128555906248617 Val loss: 2.729037191885872
-INFO - evaluator.py - 2024-10-25 21:46:44,591 - Epoch: 22 Train acc: 99.64 Val acc: 47.14221167965661 Test acc46.72878469091273; Train loss: 3.850972819015045e-05 Val loss: 1.5102929899844155
-INFO - evaluator.py - 2024-10-25 21:47:23,487 - Epoch: 23 Train acc: 99.70363636363636 Val acc: 48.040212357392974 Test acc46.67367999198477; Train loss: 3.30575510060457e-05 Val loss: 0.1763915153838653
-INFO - evaluator.py - 2024-10-25 21:47:58,989 - Epoch: 24 Train acc: 99.77090909090909 Val acc: 48.023268948379084 Test acc45.461376615569584; Train loss: 2.388399631282399e-05 Val loss: 0.04582775787729255
-INFO - evaluator.py - 2024-10-25 21:48:35,247 - Epoch: 25 Train acc: 99.79818181818182 Val acc: 48.77442674799503 Test acc46.97425107704639; Train loss: 2.1458116361463908e-05 Val loss: 0.012315347425468788
-INFO - evaluator.py - 2024-10-25 21:49:13,536 - Epoch: 26 Train acc: 99.81636363636363 Val acc: 48.921269626115446 Test acc46.80392746217814; Train loss: 1.959237918517002e-05 Val loss: 0.010370650776736308
-INFO - evaluator.py - 2024-10-25 21:49:52,906 - Epoch: 27 Train acc: 99.8490909090909 Val acc: 49.90398734892127 Test acc48.276725778980065; Train loss: 1.599404283386486e-05 Val loss: 0.017153766213682805
-INFO - evaluator.py - 2024-10-25 21:50:31,889 - Epoch: 28 Train acc: 99.82545454545455 Val acc: 51.40630294815317 Test acc51.062017833884376; Train loss: 1.6195810373623813e-05 Val loss: 0.006326218560551784
-INFO - evaluator.py - 2024-10-25 21:51:09,414 - Epoch: 29 Train acc: 99.85272727272726 Val acc: 49.96611318197221 Test acc48.046287947099486; Train loss: 1.6134149093969343e-05 Val loss: 0.0070018496039922825
-INFO - evaluator.py - 2024-10-25 21:51:47,503 - Epoch: 30 Train acc: 99.85636363636362 Val acc: 48.31130690161527 Test acc45.857128544234044; Train loss: 1.5148508703912905e-05 Val loss: 0.009626722330429265
-INFO - evaluator.py - 2024-10-25 21:52:24,326 - Epoch: 31 Train acc: 99.87272727272727 Val acc: 46.98972099853157 Test acc43.723073840296564; Train loss: 1.3078532117965965e-05 Val loss: 0.0073048603894224975
-INFO - evaluator.py - 2024-10-25 21:53:02,384 - Epoch: 32 Train acc: 99.88545454545455 Val acc: 47.407658420874284 Test acc45.22091974752029; Train loss: 1.3412068870457005e-05 Val loss: 0.006483584841025101
-INFO - evaluator.py - 2024-10-25 21:53:38,654 - Epoch: 33 Train acc: 99.84363636363636 Val acc: 49.090703716254374 Test acc47.5403266205791; Train loss: 1.402768600666439e-05 Val loss: 0.009080374212597879
-INFO - evaluator.py - 2024-10-25 21:54:15,127 - Epoch: 34 Train acc: 99.90545454545455 Val acc: 50.19202530215746 Test acc49.45396252880473; Train loss: 1.033171047782327e-05 Val loss: 0.006003088430268717
-INFO - evaluator.py - 2024-10-25 21:54:52,272 - Epoch: 35 Train acc: 99.94181818181819 Val acc: 50.24285552919914 Test acc49.0031059012123; Train loss: 9.26487980855613e-06 Val loss: 0.005813013829983494
-INFO - evaluator.py - 2024-10-25 21:55:31,134 - Epoch: 36 Train acc: 99.89818181818183 Val acc: 46.36846266802214 Test acc43.23214106802926; Train loss: 1.1246211450187812e-05 Val loss: 0.007716396118064095
-INFO - evaluator.py - 2024-10-25 21:56:09,203 - Epoch: 37 Train acc: 99.89090909090909 Val acc: 49.87574833389811 Test acc48.176535417292854; Train loss: 1.1720437673987115e-05 Val loss: 0.007162439288573791
-INFO - evaluator.py - 2024-10-25 21:56:47,166 - Epoch: 38 Train acc: 99.88909090909091 Val acc: 49.68937083474528 Test acc48.23664963430518; Train loss: 1.2545570272612333e-05 Val loss: 0.006674075603323523
-INFO - evaluator.py - 2024-10-25 21:57:23,445 - Epoch: 39 Train acc: 99.88 Val acc: 49.08505591324975 Test acc47.665564572688105; Train loss: 1.3946568948515713e-05 Val loss: 0.005205422596084214
-INFO - evaluator.py - 2024-10-25 21:58:00,625 - Epoch: 40 Train acc: 99.95454545454545 Val acc: 49.2940246244211 Test acc48.036268910930765; Train loss: 5.580319417963024e-06 Val loss: 0.005872558284368701
-INFO - evaluator.py - 2024-10-25 21:58:37,661 - Epoch: 41 Train acc: 99.97454545454545 Val acc: 50.118603863097256 Test acc49.36379120328625; Train loss: 3.4850872254620218e-06 Val loss: 0.007804176732805184
-INFO - evaluator.py - 2024-10-25 21:59:13,493 - Epoch: 42 Train acc: 99.97272727272727 Val acc: 50.38969840731955 Test acc49.90982867448152; Train loss: 3.7279520242174674e-06 Val loss: 0.01018223314921923
-INFO - evaluator.py - 2024-10-25 21:59:49,895 - Epoch: 43 Train acc: 99.97818181818182 Val acc: 51.20862984299107 Test acc51.26239855725879; Train loss: 3.257601391834958e-06 Val loss: 0.013068690645240948
-INFO - evaluator.py - 2024-10-25 22:00:27,161 - Epoch: 44 Train acc: 99.98727272727272 Val acc: 51.044843555856765 Test acc50.816551447750726; Train loss: 2.362202139912287e-06 Val loss: 0.03421970578784742
-INFO - evaluator.py - 2024-10-25 22:01:05,348 - Epoch: 45 Train acc: 99.98181818181818 Val acc: 50.86411385970857 Test acc50.61617072437632; Train loss: 2.604165760253223e-06 Val loss: 0.06824935741590454
-INFO - evaluator.py - 2024-10-25 22:01:43,742 - Epoch: 46 Train acc: 99.98 Val acc: 51.7734101434542 Test acc51.918645426309986; Train loss: 2.477641753415066e-06 Val loss: 0.07255042825972685
-INFO - evaluator.py - 2024-10-25 22:02:19,788 - Epoch: 47 Train acc: 99.97636363636364 Val acc: 52.43985089800067 Test acc52.94559663360384; Train loss: 2.272343931942735e-06 Val loss: 0.03228823704785793
-INFO - evaluator.py - 2024-10-25 22:02:57,292 - Epoch: 48 Train acc: 99.96181818181819 Val acc: 50.36710719530103 Test acc49.57920048091374; Train loss: 3.9291407870740095e-06 Val loss: 0.03535900893728975
-INFO - evaluator.py - 2024-10-25 22:03:36,053 - Epoch: 49 Train acc: 99.98 Val acc: 50.93188749576415 Test acc50.621180242460674; Train loss: 2.3183573979364073e-06 Val loss: 0.032299770873158204
-INFO - evaluator.py - 2024-10-25 22:03:36,067 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from resnet is 52.43985089800067 and 52.94559663360384
-INFO - evaluator.py - 2024-10-25 22:03:36,068 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from resnet is 52.43985089800067 and 52.94559663360384
-INFO - evaluator.py - 2024-10-25 22:03:36,068 - The best acc test dataset from resnet is 52.94559663360384
-INFO - evaluator.py - 2024-10-25 22:04:21,596 - Epoch: 0 Train acc: 89.37636363636364 Val acc: 46.11431153281374 Test acc46.23284240056106; Train loss: 0.0009626733751459555 Val loss: 0.0020826178042481717
-INFO - evaluator.py - 2024-10-25 22:05:06,361 - Epoch: 1 Train acc: 95.95272727272727 Val acc: 52.35513385293121 Test acc53.60184350265504; Train loss: 0.00040683777074922216 Val loss: 0.0019670977277916244
-INFO - evaluator.py - 2024-10-25 22:05:50,448 - Epoch: 2 Train acc: 97.29818181818182 Val acc: 52.06144809669039 Test acc53.15599639314698; Train loss: 0.0002779246265915307 Val loss: 0.002640941648527443
-INFO - evaluator.py - 2024-10-25 22:06:34,907 - Epoch: 3 Train acc: 98.44181818181819 Val acc: 51.38371173613464 Test acc51.31750325618676; Train loss: 0.00016642453780567104 Val loss: 0.0027550761745189604
-INFO - evaluator.py - 2024-10-25 22:07:19,284 - Epoch: 4 Train acc: 98.84363636363636 Val acc: 52.01061786964871 Test acc52.73519687406072; Train loss: 0.00012637896486981348 Val loss: 0.004354929985952124
-INFO - evaluator.py - 2024-10-25 22:08:03,407 - Epoch: 5 Train acc: 99.00181818181818 Val acc: 52.134869535750596 Test acc52.810339645326124; Train loss: 0.00010564304502596232 Val loss: 0.004081983425166666
-INFO - evaluator.py - 2024-10-25 22:08:48,405 - Epoch: 6 Train acc: 99.10545454545453 Val acc: 51.959787642607026 Test acc51.84350265504458; Train loss: 9.824504340067505e-05 Val loss: 0.004705029073629247
-INFO - evaluator.py - 2024-10-25 22:09:32,680 - Epoch: 7 Train acc: 99.28363636363636 Val acc: 51.61527165932453 Test acc51.17723674982466; Train loss: 7.869807024571029e-05 Val loss: 0.005125008746485084
-INFO - evaluator.py - 2024-10-25 22:10:16,212 - Epoch: 8 Train acc: 99.2909090909091 Val acc: 52.96509657743138 Test acc53.10089169421902; Train loss: 7.716351438165558e-05 Val loss: 0.005084508827792902
-INFO - evaluator.py - 2024-10-25 22:10:58,859 - Epoch: 9 Train acc: 99.22 Val acc: 49.74020106178696 Test acc49.48401963731089; Train loss: 8.524412163321606e-05 Val loss: 0.003985437325645972
-INFO - evaluator.py - 2024-10-25 22:11:41,363 - Epoch: 10 Train acc: 99.48545454545454 Val acc: 50.98271772280582 Test acc50.681294459473; Train loss: 5.461730960100381e-05 Val loss: 0.005109042565485697
-INFO - evaluator.py - 2024-10-25 22:12:23,521 - Epoch: 11 Train acc: 99.46000000000001 Val acc: 51.959787642607026 Test acc51.64312193167018; Train loss: 5.856617118459491e-05 Val loss: 0.005924832843783534
-INFO - evaluator.py - 2024-10-25 22:13:06,553 - Epoch: 12 Train acc: 99.52909090909091 Val acc: 50.655145148537216 Test acc50.06011421701232; Train loss: 5.480990198292685e-05 Val loss: 0.0050060268608066545
-INFO - evaluator.py - 2024-10-25 22:13:49,045 - Epoch: 13 Train acc: 99.48727272727272 Val acc: 49.67242742573139 Test acc48.51718264702936; Train loss: 5.7455016463592816e-05 Val loss: 0.0050916865654857795
-INFO - evaluator.py - 2024-10-25 22:14:32,516 - Epoch: 14 Train acc: 99.57818181818182 Val acc: 54.03253134530668 Test acc54.92936579501052; Train loss: 4.777674386145505e-05 Val loss: 0.00439736911244114
-INFO - evaluator.py - 2024-10-25 22:15:15,372 - Epoch: 15 Train acc: 99.55818181818182 Val acc: 52.75048006325539 Test acc52.349463981564966; Train loss: 5.149984098282981e-05 Val loss: 0.006588832276104184
-INFO - evaluator.py - 2024-10-25 22:15:57,414 - Epoch: 16 Train acc: 99.55636363636363 Val acc: 55.009601265107875 Test acc56.5975353171025; Train loss: 4.780585981021762e-05 Val loss: 0.003311364513109811
-INFO - evaluator.py - 2024-10-25 22:16:40,250 - Epoch: 17 Train acc: 99.63090909090909 Val acc: 49.926578560939795 Test acc49.19847710650235; Train loss: 3.799672781209334e-05 Val loss: 0.0036540380995192527
-INFO - evaluator.py - 2024-10-25 22:17:22,417 - Epoch: 18 Train acc: 99.59818181818181 Val acc: 50.99401332881509 Test acc49.52910530007013; Train loss: 4.082500762147406e-05 Val loss: 0.00661210586671087
-INFO - evaluator.py - 2024-10-25 22:18:05,474 - Epoch: 19 Train acc: 99.62363636363636 Val acc: 53.15147407658421 Test acc53.75713856327021; Train loss: 4.0362882568098774e-05 Val loss: 0.006564903539357907
-INFO - evaluator.py - 2024-10-25 22:18:48,014 - Epoch: 20 Train acc: 99.83999999999999 Val acc: 53.992996724274256 Test acc54.49353772167117; Train loss: 1.778203349174212e-05 Val loss: 0.011140678422566552
-INFO - evaluator.py - 2024-10-25 22:19:30,932 - Epoch: 21 Train acc: 99.89090909090909 Val acc: 53.83485824014458 Test acc53.8473098887887; Train loss: 1.1863760390009372e-05 Val loss: 0.015527716494139797
-INFO - evaluator.py - 2024-10-25 22:20:13,451 - Epoch: 22 Train acc: 99.8709090909091 Val acc: 55.22986558228848 Test acc55.51046989279631; Train loss: 1.4660688166226134e-05 Val loss: 0.03205074515838778
-INFO - evaluator.py - 2024-10-25 22:20:57,037 - Epoch: 23 Train acc: 99.89090909090909 Val acc: 54.252795662487294 Test acc54.20298567277828; Train loss: 1.091712335907108e-05 Val loss: 0.01702203155977772
-INFO - evaluator.py - 2024-10-25 22:21:39,640 - Epoch: 24 Train acc: 99.9109090909091 Val acc: 54.190669829436345 Test acc54.36829976956217; Train loss: 1.0068545868191656e-05 Val loss: 0.012835219042941862
-INFO - evaluator.py - 2024-10-25 22:22:20,849 - Epoch: 25 Train acc: 99.88909090909091 Val acc: 54.26409126849655 Test acc54.83418495140767; Train loss: 1.1058284591126721e-05 Val loss: 0.01171797767784802
-INFO - evaluator.py - 2024-10-25 22:23:03,206 - Epoch: 26 Train acc: 99.88909090909091 Val acc: 53.207952106630515 Test acc52.349463981564966; Train loss: 1.0248733472161058e-05 Val loss: 0.015750721808976135
-INFO - evaluator.py - 2024-10-25 22:23:46,519 - Epoch: 27 Train acc: 99.89818181818183 Val acc: 55.111261719191226 Test acc56.08656447249775; Train loss: 1.1447397908341372e-05 Val loss: 0.012205473301869307
-INFO - evaluator.py - 2024-10-25 22:24:30,594 - Epoch: 28 Train acc: 99.88 Val acc: 54.512594600700325 Test acc55.25498447049394; Train loss: 1.1967788786503e-05 Val loss: 0.01283937118342404
-INFO - evaluator.py - 2024-10-25 22:25:12,738 - Epoch: 29 Train acc: 99.92545454545456 Val acc: 53.671071953010276 Test acc53.38142470694319; Train loss: 8.513491679514118e-06 Val loss: 0.01794381897341501
-INFO - evaluator.py - 2024-10-25 22:25:54,761 - Epoch: 30 Train acc: 99.89818181818183 Val acc: 53.32090816672314 Test acc52.83037771766356; Train loss: 9.96378697166785e-06 Val loss: 0.013642232188878996
-INFO - evaluator.py - 2024-10-25 22:26:37,318 - Epoch: 31 Train acc: 99.92 Val acc: 54.4165819496216 Test acc54.27812844404368; Train loss: 8.70801044262738e-06 Val loss: 0.018533685443550264
-INFO - evaluator.py - 2024-10-25 22:27:20,932 - Epoch: 32 Train acc: 99.88909090909091 Val acc: 55.16773974923754 Test acc55.966336038473095; Train loss: 1.1920304264260101e-05 Val loss: 0.014085444867590733
-INFO - evaluator.py - 2024-10-25 22:28:03,567 - Epoch: 33 Train acc: 99.94363636363637 Val acc: 55.07172709815882 Test acc55.92625989379821; Train loss: 7.499875472405454e-06 Val loss: 0.011266628066308213
-INFO - evaluator.py - 2024-10-25 22:28:45,463 - Epoch: 34 Train acc: 99.91454545454546 Val acc: 53.41692081780187 Test acc53.06081554954414; Train loss: 9.136847873601668e-06 Val loss: 0.015364076893814105
-INFO - evaluator.py - 2024-10-25 22:29:28,962 - Epoch: 35 Train acc: 99.92 Val acc: 54.642494069806844 Test acc54.608756637611464; Train loss: 9.288169797582668e-06 Val loss: 0.0170681718162503
-INFO - evaluator.py - 2024-10-25 22:30:12,969 - Epoch: 36 Train acc: 99.90181818181819 Val acc: 54.9418276290523 Test acc55.93126941188258; Train loss: 1.13096785292245e-05 Val loss: 0.016261381926838722
-INFO - evaluator.py - 2024-10-25 22:30:56,213 - Epoch: 37 Train acc: 99.92727272727274 Val acc: 53.65412854399638 Test acc52.88548241659152; Train loss: 7.3055996702764375e-06 Val loss: 0.01368918139342331
-INFO - evaluator.py - 2024-10-25 22:31:38,362 - Epoch: 38 Train acc: 99.93636363636364 Val acc: 53.98170111826499 Test acc53.92746217813846; Train loss: 5.989984581488402e-06 Val loss: 0.010718011969188559
-INFO - evaluator.py - 2024-10-25 22:32:20,753 - Epoch: 39 Train acc: 99.89636363636365 Val acc: 54.66508528182536 Test acc55.174832181144176; Train loss: 1.0601251671125118e-05 Val loss: 0.011034885207421494
-INFO - evaluator.py - 2024-10-25 22:33:04,106 - Epoch: 40 Train acc: 99.97090909090909 Val acc: 54.40528634361234 Test acc54.63380422803327; Train loss: 3.7148493603705323e-06 Val loss: 0.013296618429830515
-INFO - evaluator.py - 2024-10-25 22:33:47,701 - Epoch: 41 Train acc: 99.97454545454545 Val acc: 54.51824240370497 Test acc54.698927963129954; Train loss: 2.550006491455034e-06 Val loss: 0.0137401604725118
-INFO - evaluator.py - 2024-10-25 22:34:29,894 - Epoch: 42 Train acc: 99.98181818181818 Val acc: 54.156783011408564 Test acc54.19296663660956; Train loss: 2.273091304831806e-06 Val loss: 0.014449836412526977
-INFO - evaluator.py - 2024-10-25 22:35:12,228 - Epoch: 43 Train acc: 99.97818181818182 Val acc: 54.74415452389021 Test acc55.32010820559062; Train loss: 2.4753011941563523e-06 Val loss: 0.01434088981341443
-INFO - evaluator.py - 2024-10-25 22:35:54,511 - Epoch: 44 Train acc: 99.97818181818182 Val acc: 55.11690952219587 Test acc55.69081254383328; Train loss: 2.3804656398422966e-06 Val loss: 0.014935736870019702
-INFO - evaluator.py - 2024-10-25 22:36:36,634 - Epoch: 45 Train acc: 99.97636363636364 Val acc: 55.19033096125607 Test acc55.580603145977356; Train loss: 2.1787054866039893e-06 Val loss: 0.012837333758914457
-INFO - evaluator.py - 2024-10-25 22:37:19,294 - Epoch: 46 Train acc: 99.96545454545455 Val acc: 54.96441884107083 Test acc55.59563170023044; Train loss: 2.820545552135007e-06 Val loss: 0.012911158955650627
-INFO - evaluator.py - 2024-10-25 22:38:02,165 - Epoch: 47 Train acc: 99.98545454545454 Val acc: 54.52389020670959 Test acc54.76906121631099; Train loss: 2.236053285961051e-06 Val loss: 0.012688551516798437
-INFO - evaluator.py - 2024-10-25 22:38:44,977 - Epoch: 48 Train acc: 99.98363636363636 Val acc: 54.55212922173275 Test acc54.79911832481715; Train loss: 2.029254779755134e-06 Val loss: 0.013503175582722004
-INFO - evaluator.py - 2024-10-25 22:39:28,553 - Epoch: 49 Train acc: 99.99090909090908 Val acc: 55.11690952219587 Test acc55.745917242761244; Train loss: 1.5785876400989515e-06 Val loss: 0.012724674745803362
-INFO - evaluator.py - 2024-10-25 22:39:28,564 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from wrn is 55.22986558228848 and 55.51046989279631
-INFO - evaluator.py - 2024-10-25 22:39:28,564 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from wrn is 55.22986558228848 and 55.51046989279631
-INFO - evaluator.py - 2024-10-25 22:39:28,564 - The best acc test dataset from wrn is 56.5975353171025
-INFO - evaluator.py - 2024-10-25 22:41:57,507 - Epoch: 0 Train acc: 87.3490909090909 Val acc: 48.746187732971876 Test acc49.61927662558862; Train loss: 0.0015924826841462742 Val loss: 0.002403657786202245
-INFO - evaluator.py - 2024-10-25 22:44:24,059 - Epoch: 1 Train acc: 96.67272727272727 Val acc: 51.027900146842875 Test acc52.01382626991283; Train loss: 0.00034032939597964285 Val loss: 0.0025337042006532795
-INFO - evaluator.py - 2024-10-25 22:46:50,397 - Epoch: 2 Train acc: 98.5909090909091 Val acc: 53.35479498475093 Test acc54.318204588718565; Train loss: 0.00015616227835416795 Val loss: 0.0036212400058077162
-INFO - evaluator.py - 2024-10-25 22:49:16,841 - Epoch: 3 Train acc: 98.90545454545455 Val acc: 51.592680447305995 Test acc52.20418795711852; Train loss: 0.00012436584466433322 Val loss: 0.0042862822400046435
-INFO - evaluator.py - 2024-10-25 22:51:43,940 - Epoch: 4 Train acc: 99.27454545454546 Val acc: 52.54715915508867 Test acc52.32942590922753; Train loss: 8.898915607185865e-05 Val loss: 0.00713350287294921
-INFO - evaluator.py - 2024-10-25 22:54:10,960 - Epoch: 5 Train acc: 97.47636363636364 Val acc: 50.474415452389024 Test acc50.88167518284741; Train loss: 0.0002741761549248953 Val loss: 0.0026974308952616793
-INFO - evaluator.py - 2024-10-25 22:56:38,618 - Epoch: 6 Train acc: 99.18 Val acc: 51.745171128431046 Test acc52.184149884781085; Train loss: 8.89127900781618e-05 Val loss: 0.0046894442734847054
-INFO - evaluator.py - 2024-10-25 22:59:03,049 - Epoch: 7 Train acc: 99.48363636363636 Val acc: 53.383033999774085 Test acc54.41839495040577; Train loss: 5.901084256263196e-05 Val loss: 0.014751821752996563
-INFO - evaluator.py - 2024-10-25 23:01:27,223 - Epoch: 8 Train acc: 99.49818181818182 Val acc: 52.6431718061674 Test acc53.14096783889389; Train loss: 5.9759432008881544e-05 Val loss: 0.0056103482887891135
-INFO - evaluator.py - 2024-10-25 23:03:51,696 - Epoch: 9 Train acc: 99.41636363636364 Val acc: 51.47407658420874 Test acc51.79841699228535; Train loss: 6.667098300399217e-05 Val loss: 0.006690392423642903
-INFO - evaluator.py - 2024-10-25 23:06:16,657 - Epoch: 10 Train acc: 99.50727272727272 Val acc: 52.06144809669039 Test acc52.41458771666166; Train loss: 6.084246053307486e-05 Val loss: 0.005804899276669626
-INFO - evaluator.py - 2024-10-25 23:08:41,887 - Epoch: 11 Train acc: 99.70727272727272 Val acc: 54.077713769343724 Test acc55.30007013325318; Train loss: 3.4027760490955025e-05 Val loss: 0.005605688771595056
-INFO - evaluator.py - 2024-10-25 23:11:06,275 - Epoch: 12 Train acc: 99.5109090909091 Val acc: 51.445837569185585 Test acc51.7282837391043; Train loss: 5.329697971068196e-05 Val loss: 0.006103656230828701
-INFO - evaluator.py - 2024-10-25 23:13:30,359 - Epoch: 13 Train acc: 99.63818181818182 Val acc: 49.12459053428216 Test acc48.74762047890993; Train loss: 3.918858064068693e-05 Val loss: 0.007630475157467669
-INFO - evaluator.py - 2024-10-25 23:15:54,522 - Epoch: 14 Train acc: 99.64 Val acc: 50.92059188975489 Test acc50.15529506061517; Train loss: 4.220193618344968e-05 Val loss: 0.014021312451209104
-INFO - evaluator.py - 2024-10-25 23:18:19,119 - Epoch: 15 Train acc: 99.65454545454546 Val acc: 52.637524003162774 Test acc52.96563470594129; Train loss: 3.8943282353548883e-05 Val loss: 0.006505111590706129
-INFO - evaluator.py - 2024-10-25 23:20:44,391 - Epoch: 16 Train acc: 99.7109090909091 Val acc: 52.7335366542415 Test acc52.94559663360384; Train loss: 3.149466169964977e-05 Val loss: 0.007836234457786643
-INFO - evaluator.py - 2024-10-25 23:23:09,163 - Epoch: 17 Train acc: 99.66727272727273 Val acc: 49.55947136563877 Test acc48.25668770664262; Train loss: 3.624491037510531e-05 Val loss: 0.008182532663710518
-INFO - evaluator.py - 2024-10-25 23:25:33,710 - Epoch: 18 Train acc: 99.80181818181818 Val acc: 49.813622500847174 Test acc48.44704939384831; Train loss: 2.211819407232476e-05 Val loss: 0.0077749515379618404
-INFO - evaluator.py - 2024-10-25 23:27:58,641 - Epoch: 19 Train acc: 99.77454545454545 Val acc: 50.58737151248165 Test acc49.62929566175734; Train loss: 2.6939472769828915e-05 Val loss: 0.012194500122449499
-INFO - evaluator.py - 2024-10-25 23:30:25,214 - Epoch: 20 Train acc: 99.92363636363636 Val acc: 53.04981362250085 Test acc53.20108205590623; Train loss: 1.1382309703582326e-05 Val loss: 0.0171051987359635
-INFO - evaluator.py - 2024-10-25 23:32:50,311 - Epoch: 21 Train acc: 99.93818181818182 Val acc: 52.32689483790806 Test acc51.83849313696023; Train loss: 5.970556647620966e-06 Val loss: 0.019353919253838905
-INFO - evaluator.py - 2024-10-25 23:35:15,830 - Epoch: 22 Train acc: 99.97272727272727 Val acc: 52.72788885123687 Test acc52.88548241659152; Train loss: 3.507706927617123e-06 Val loss: 0.030521455624945674
-INFO - evaluator.py - 2024-10-25 23:37:41,546 - Epoch: 23 Train acc: 99.9890909090909 Val acc: 52.92556195639896 Test acc52.88047289850716; Train loss: 2.0013101285836787e-06 Val loss: 0.034597134544366526
-INFO - evaluator.py - 2024-10-25 23:40:07,425 - Epoch: 24 Train acc: 99.96000000000001 Val acc: 52.03320908166723 Test acc52.35447349964934; Train loss: 4.411242897832712e-06 Val loss: 0.030148914866725912
-INFO - evaluator.py - 2024-10-25 23:42:32,721 - Epoch: 25 Train acc: 99.95636363636363 Val acc: 53.688015362024174 Test acc54.02765253982567; Train loss: 5.0256959422271885e-06 Val loss: 0.0435983271541184
-INFO - evaluator.py - 2024-10-25 23:44:58,423 - Epoch: 26 Train acc: 99.99090909090908 Val acc: 53.671071953010276 Test acc54.318204588718565; Train loss: 1.8695685808696991e-06 Val loss: 0.03514205551707622
-INFO - evaluator.py - 2024-10-25 23:47:24,531 - Epoch: 27 Train acc: 99.96909090909091 Val acc: 53.106291652547156 Test acc53.166015429315706; Train loss: 3.22164448232136e-06 Val loss: 0.03350790500587007
-INFO - evaluator.py - 2024-10-25 23:49:49,768 - Epoch: 28 Train acc: 99.97636363636364 Val acc: 52.72788885123687 Test acc52.985672778278726; Train loss: 3.941176796918584e-06 Val loss: 0.044719007693077094
-INFO - evaluator.py - 2024-10-25 23:52:15,579 - Epoch: 29 Train acc: 99.97454545454545 Val acc: 51.60397605331526 Test acc51.34756036469291; Train loss: 3.0182733671222455e-06 Val loss: 0.022241930873814403
-INFO - evaluator.py - 2024-10-25 23:54:41,265 - Epoch: 30 Train acc: 99.94363636363637 Val acc: 49.54252795662487 Test acc48.75763951507865; Train loss: 7.100244319347191e-06 Val loss: 0.023163973214625506
-INFO - evaluator.py - 2024-10-25 23:57:06,468 - Epoch: 31 Train acc: 99.92545454545456 Val acc: 53.2418389246583 Test acc53.17603446548442; Train loss: 7.615366392796701e-06 Val loss: 0.020900079224535965
-INFO - evaluator.py - 2024-10-25 23:59:31,365 - Epoch: 32 Train acc: 99.96000000000001 Val acc: 52.92556195639896 Test acc52.950606151688206; Train loss: 4.59277069914027e-06 Val loss: 0.027257391036492638
-INFO - evaluator.py - 2024-10-26 00:01:56,695 - Epoch: 33 Train acc: 99.90363636363637 Val acc: 50.22591212018524 Test acc49.45897204688909; Train loss: 1.0726812733530654e-05 Val loss: 0.023607766898920156
-INFO - evaluator.py - 2024-10-26 00:04:22,130 - Epoch: 34 Train acc: 99.99818181818182 Val acc: 50.06777363605558 Test acc49.33874361286444; Train loss: 8.23650596362313e-07 Val loss: 0.02974306358855336
-INFO - evaluator.py - 2024-10-26 00:06:46,970 - Epoch: 35 Train acc: 99.98545454545454 Val acc: 51.654806280356944 Test acc51.88858831780383; Train loss: 1.5956384631669545e-06 Val loss: 0.03968980139182218
-INFO - evaluator.py - 2024-10-26 00:09:12,111 - Epoch: 36 Train acc: 99.92363636363636 Val acc: 53.48469445385745 Test acc54.573690011020936; Train loss: 9.518698500487103e-06 Val loss: 0.02940582967835955
-INFO - evaluator.py - 2024-10-26 00:11:36,774 - Epoch: 37 Train acc: 99.89090909090909 Val acc: 53.7106065740427 Test acc54.2630998897906; Train loss: 1.221875583560177e-05 Val loss: 0.009106540819971922
-INFO - evaluator.py - 2024-10-26 00:14:00,951 - Epoch: 38 Train acc: 99.94363636363637 Val acc: 52.16310855077375 Test acc52.129045185853116; Train loss: 7.5028445258464096e-06 Val loss: 0.009451568941335873
-INFO - evaluator.py - 2024-10-26 00:16:24,978 - Epoch: 39 Train acc: 99.9309090909091 Val acc: 54.27538687450581 Test acc55.174832181144176; Train loss: 7.476917507351159e-06 Val loss: 0.011414978068709494
-INFO - evaluator.py - 2024-10-26 00:18:49,233 - Epoch: 40 Train acc: 99.98545454545454 Val acc: 53.078052637523996 Test acc52.750225428313804; Train loss: 2.2975807502617002e-06 Val loss: 0.014837569979292033
-INFO - evaluator.py - 2024-10-26 00:21:13,854 - Epoch: 41 Train acc: 99.99818181818182 Val acc: 52.95944877442674 Test acc52.53982566877067; Train loss: 4.7293473219579855e-07 Val loss: 0.016908584012158993
-INFO - evaluator.py - 2024-10-26 00:23:39,232 - Epoch: 42 Train acc: 99.99454545454546 Val acc: 52.72788885123687 Test acc52.21420699328725; Train loss: 6.227211791161319e-07 Val loss: 0.021328397165055222
-INFO - evaluator.py - 2024-10-26 00:26:04,826 - Epoch: 43 Train acc: 99.99272727272728 Val acc: 52.7335366542415 Test acc52.00380723374411; Train loss: 5.168162788162466e-07 Val loss: 0.02842759585146256
-INFO - evaluator.py - 2024-10-26 00:28:29,592 - Epoch: 44 Train acc: 99.99818181818182 Val acc: 52.66576301818593 Test acc51.79841699228535; Train loss: 3.201587477607601e-07 Val loss: 0.03118881618560512
-INFO - evaluator.py - 2024-10-26 00:30:54,043 - Epoch: 45 Train acc: 100.0 Val acc: 52.53021574607478 Test acc51.653140967838894; Train loss: 2.4802111387678205e-07 Val loss: 0.0328629611239492
-INFO - evaluator.py - 2024-10-26 00:33:18,173 - Epoch: 46 Train acc: 99.9890909090909 Val acc: 52.29300801988027 Test acc51.34255084660856; Train loss: 1.0878794080120067e-06 Val loss: 0.030576246385047575
-INFO - evaluator.py - 2024-10-26 00:35:42,678 - Epoch: 47 Train acc: 100.0 Val acc: 52.58104597311646 Test acc51.663160004007615; Train loss: 2.9817407824762913e-07 Val loss: 0.03784024716770125
-INFO - evaluator.py - 2024-10-26 00:38:07,285 - Epoch: 48 Train acc: 99.99818181818182 Val acc: 52.85778832034339 Test acc52.103997595431316; Train loss: 3.190582024529608e-07 Val loss: 0.054224965263460635
-INFO - evaluator.py - 2024-10-26 00:40:31,966 - Epoch: 49 Train acc: 99.99818181818182 Val acc: 52.993335592454535 Test acc52.59493036769862; Train loss: 3.6638256809169436e-07 Val loss: 0.04723115065913005
-INFO - evaluator.py - 2024-10-26 00:40:31,976 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from resnext is 54.27538687450581 and 55.174832181144176
-INFO - evaluator.py - 2024-10-26 00:40:31,977 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from resnext is 54.27538687450581 and 55.174832181144176
-INFO - evaluator.py - 2024-10-26 00:40:31,977 - The best acc test dataset from resnext is 55.30007013325318
-INFO - evaluator.py - 2024-10-26 00:40:31,977 - The best acc of accuracy (using synthetic images as the validation set) of synthetic images from resnet, wrn, and resnext are [52.94559663360384, 55.51046989279631, 55.174832181144176].
-INFO - evaluator.py - 2024-10-26 00:40:31,977 - The average and std of accuracy of synthetic images are 54.54 and 1.14
-INFO - dataset_loader.py - 2024-10-27 00:11:34,705 - delta is reset as 2.966981886419575e-07
-INFO - evaluator.py - 2024-10-27 00:12:06,929 - Epoch: 0 Train acc: 86.04363636363637 Val acc: 72.66095203355827 Test acc83.46607838643135; Train loss: 0.0012213532317768444 Val loss: 0.000885468760162322
-INFO - evaluator.py - 2024-10-27 00:12:24,884 - Epoch: 1 Train acc: 97.33636363636363 Val acc: 86.81378807222323 Test acc79.86477194590877; Train loss: 0.00027498875492337077 Val loss: 0.0006030241970856781
-INFO - evaluator.py - 2024-10-27 00:12:42,420 - Epoch: 2 Train acc: 98.61272727272727 Val acc: 85.70125843516323 Test acc75.55867522347009; Train loss: 0.00015412925634194504 Val loss: 0.0005673397633868356
-INFO - evaluator.py - 2024-10-27 00:12:59,790 - Epoch: 3 Train acc: 98.93454545454546 Val acc: 85.28481974588121 Test acc80.06245702498282; Train loss: 0.00011941532160748135 Val loss: 0.000670970886759109
-INFO - evaluator.py - 2024-10-27 00:13:17,637 - Epoch: 4 Train acc: 99.09454545454545 Val acc: 88.94157699556204 Test acc81.40041256016502; Train loss: 0.00010198778637938878 Val loss: 0.000689061536014359
-INFO - evaluator.py - 2024-10-27 00:13:34,670 - Epoch: 5 Train acc: 99.4090909090909 Val acc: 84.40026749346465 Test acc81.48349759339904; Train loss: 6.522708232937889e-05 Val loss: 0.0013322229950368148
-INFO - evaluator.py - 2024-10-27 00:13:52,366 - Epoch: 6 Train acc: 99.54727272727273 Val acc: 84.50057754270776 Test acc82.39170295668117; Train loss: 5.0428295083640315e-05 Val loss: 0.0033829775803922136
-INFO - evaluator.py - 2024-10-27 00:14:10,724 - Epoch: 7 Train acc: 99.55090909090909 Val acc: 87.64362575232538 Test acc73.80815952326381; Train loss: 4.735690999977206e-05 Val loss: 0.0009369985663323382
-INFO - evaluator.py - 2024-10-27 00:14:28,821 - Epoch: 8 Train acc: 99.55818181818182 Val acc: 88.08438202930269 Test acc80.29452211780884; Train loss: 5.0709802109684626e-05 Val loss: 0.0008197706918584821
-INFO - evaluator.py - 2024-10-27 00:14:46,864 - Epoch: 9 Train acc: 99.52181818181818 Val acc: 85.52191622591039 Test acc83.94453357781343; Train loss: 5.731112399424257e-05 Val loss: 0.0008127016549096208
-INFO - evaluator.py - 2024-10-27 00:15:04,312 - Epoch: 10 Train acc: 99.62181818181818 Val acc: 62.58131193385616 Test acc59.42871877148751; Train loss: 4.508358105138706e-05 Val loss: 0.0028723009495512245
-INFO - evaluator.py - 2024-10-27 00:15:22,510 - Epoch: 11 Train acc: 96.40727272727273 Val acc: 50.41947838774393 Test acc50.0; Train loss: 0.00038953179271087387 Val loss: 0.0022315735968857003
-INFO - evaluator.py - 2024-10-27 00:15:40,090 - Epoch: 12 Train acc: 98.50545454545454 Val acc: 57.92145419174418 Test acc51.988310795324324; Train loss: 0.00016283783704169433 Val loss: 0.0008953656061158455
-INFO - evaluator.py - 2024-10-27 00:15:57,761 - Epoch: 13 Train acc: 99.30181818181818 Val acc: 81.88947656392486 Test acc66.35056154022462; Train loss: 7.389955325898799e-05 Val loss: 0.0009933137351585757
-INFO - evaluator.py - 2024-10-27 00:16:15,666 - Epoch: 14 Train acc: 99.5 Val acc: 86.01738707520215 Test acc74.55019482007793; Train loss: 5.604193689174612e-05 Val loss: 0.0007178508946943286
-INFO - evaluator.py - 2024-10-27 00:16:32,585 - Epoch: 15 Train acc: 99.64 Val acc: 89.1725940786674 Test acc79.58400183360074; Train loss: 3.873322746922812e-05 Val loss: 0.0011603084133927878
-INFO - evaluator.py - 2024-10-27 00:16:49,882 - Epoch: 16 Train acc: 99.63454545454546 Val acc: 88.12693780776947 Test acc70.14668805867522; Train loss: 4.3942935638055626e-05 Val loss: 0.0007757152280616749
-INFO - evaluator.py - 2024-10-27 00:17:07,103 - Epoch: 17 Train acc: 99.70545454545454 Val acc: 86.77731169068028 Test acc67.19287187714875; Train loss: 3.7935843552589754e-05 Val loss: 0.0011530001679193836
-INFO - evaluator.py - 2024-10-27 00:17:24,456 - Epoch: 18 Train acc: 99.72545454545455 Val acc: 86.48550063833667 Test acc82.89307815723126; Train loss: 3.031342127783732e-05 Val loss: 0.003176836052436047
-INFO - evaluator.py - 2024-10-27 00:17:41,601 - Epoch: 19 Train acc: 99.76545454545455 Val acc: 70.5179646179099 Test acc83.23114829245931; Train loss: 2.471931029523892e-05 Val loss: 0.004430748675831821
-INFO - evaluator.py - 2024-10-27 00:17:59,410 - Epoch: 20 Train acc: 99.9109090909091 Val acc: 81.89555596084868 Test acc79.91634196653679; Train loss: 1.0554936687483199e-05 Val loss: 0.002263862004402713
-INFO - evaluator.py - 2024-10-27 00:18:17,108 - Epoch: 21 Train acc: 99.91272727272728 Val acc: 85.99306948750683 Test acc75.11460004584002; Train loss: 8.801366579262487e-06 Val loss: 0.002469464869287298
-INFO - evaluator.py - 2024-10-27 00:18:34,657 - Epoch: 22 Train acc: 99.9309090909091 Val acc: 86.056903155207 Test acc70.88299335319735; Train loss: 9.781605518516946e-06 Val loss: 0.006286505029747802
-INFO - evaluator.py - 2024-10-27 00:18:51,652 - Epoch: 23 Train acc: 99.92181818181818 Val acc: 85.58878959207247 Test acc59.02761861104745; Train loss: 8.748375071462016e-06 Val loss: 0.003167511246560794
-INFO - evaluator.py - 2024-10-27 00:19:09,083 - Epoch: 24 Train acc: 99.9309090909091 Val acc: 86.74387500759924 Test acc44.80575292230117; Train loss: 7.912529294348628e-06 Val loss: 0.002603532148426489
-INFO - evaluator.py - 2024-10-27 00:19:26,783 - Epoch: 25 Train acc: 99.94727272727273 Val acc: 88.99933126633837 Test acc53.93937657575063; Train loss: 5.367162180928889e-06 Val loss: 0.0030049113881168367
-INFO - evaluator.py - 2024-10-27 00:19:44,202 - Epoch: 26 Train acc: 99.94363636363637 Val acc: 88.77743327861876 Test acc53.57265642906257; Train loss: 7.306362746649856e-06 Val loss: 0.0024296101324246195
-INFO - evaluator.py - 2024-10-27 00:20:01,272 - Epoch: 27 Train acc: 99.93818181818182 Val acc: 88.57073378320871 Test acc48.04606921842769; Train loss: 6.8482029051004e-06 Val loss: 0.0026402706882208485
-INFO - evaluator.py - 2024-10-27 00:20:19,309 - Epoch: 28 Train acc: 99.93272727272728 Val acc: 88.02358806006444 Test acc52.05420582168233; Train loss: 6.952880341090018e-06 Val loss: 0.0021708585243542817
-INFO - evaluator.py - 2024-10-27 00:20:36,750 - Epoch: 29 Train acc: 99.90727272727273 Val acc: 89.58903276794942 Test acc70.43318817327527; Train loss: 9.433710491934603e-06 Val loss: 0.004397099333640268
-INFO - evaluator.py - 2024-10-27 00:20:53,924 - Epoch: 30 Train acc: 99.93454545454546 Val acc: 88.94461669402395 Test acc60.94716937886775; Train loss: 6.356148720965394e-06 Val loss: 0.0027945524755238226
-INFO - evaluator.py - 2024-10-27 00:21:11,865 - Epoch: 31 Train acc: 99.92 Val acc: 88.5433764970515 Test acc44.206967682787074; Train loss: 8.666056825619639e-06 Val loss: 0.0034587589815631304
-INFO - evaluator.py - 2024-10-27 00:21:29,801 - Epoch: 32 Train acc: 99.93636363636364 Val acc: 87.97495288467384 Test acc70.22117808847123; Train loss: 5.9708600272973524e-06 Val loss: 0.004523086684823754
-INFO - evaluator.py - 2024-10-27 00:21:47,442 - Epoch: 33 Train acc: 99.94545454545455 Val acc: 89.1756337771293 Test acc67.19287187714875; Train loss: 6.271143906567225e-06 Val loss: 0.0030761945790465867
-INFO - evaluator.py - 2024-10-27 00:22:04,796 - Epoch: 34 Train acc: 99.96363636363637 Val acc: 86.64660465681804 Test acc73.55030942012377; Train loss: 3.895783406264028e-06 Val loss: 0.015038756543259844
-INFO - evaluator.py - 2024-10-27 00:22:22,971 - Epoch: 35 Train acc: 99.9 Val acc: 89.25770563560094 Test acc71.74822369928948; Train loss: 1.1162707257281826e-05 Val loss: 0.001295792360031552
-INFO - evaluator.py - 2024-10-27 00:22:40,445 - Epoch: 36 Train acc: 99.94 Val acc: 89.93251869414553 Test acc62.540110016044004; Train loss: 6.500610864408042e-06 Val loss: 0.009070051434598249
-INFO - evaluator.py - 2024-10-27 00:22:57,460 - Epoch: 37 Train acc: 99.96545454545455 Val acc: 90.52222019575659 Test acc72.19516387806554; Train loss: 4.506964025528181e-06 Val loss: 0.003084213449292737
-INFO - evaluator.py - 2024-10-27 00:23:15,539 - Epoch: 38 Train acc: 99.91636363636364 Val acc: 89.90516140798833 Test acc80.51226220490489; Train loss: 8.946543170875223e-06 Val loss: 0.011995950508453946
-INFO - evaluator.py - 2024-10-27 00:23:32,401 - Epoch: 39 Train acc: 99.9509090909091 Val acc: 89.51911970332543 Test acc73.7336694934678; Train loss: 5.322479729776123e-06 Val loss: 0.002044104516625774
-INFO - evaluator.py - 2024-10-27 00:23:49,577 - Epoch: 40 Train acc: 99.99272727272728 Val acc: 89.51911970332543 Test acc68.51650240660096; Train loss: 2.042325212090129e-06 Val loss: 0.0014866079354447083
-INFO - evaluator.py - 2024-10-27 00:24:07,442 - Epoch: 41 Train acc: 99.98545454545454 Val acc: 89.04188704480515 Test acc71.35571854228742; Train loss: 1.6570106916405662e-06 Val loss: 0.001826654010155591
-INFO - evaluator.py - 2024-10-27 00:24:24,489 - Epoch: 42 Train acc: 99.98727272727272 Val acc: 88.75919508784729 Test acc72.04331881732753; Train loss: 1.042093671817797e-06 Val loss: 0.001950362691242098
-INFO - evaluator.py - 2024-10-27 00:24:42,099 - Epoch: 43 Train acc: 99.99454545454546 Val acc: 88.81694935862362 Test acc68.56234242493697; Train loss: 1.1440866647593463e-06 Val loss: 0.001963405110812983
-INFO - evaluator.py - 2024-10-27 00:25:00,195 - Epoch: 44 Train acc: 99.9890909090909 Val acc: 89.1482764909721 Test acc69.88024295209718; Train loss: 1.260361977742522e-06 Val loss: 0.0020418274735265157
-INFO - evaluator.py - 2024-10-27 00:25:18,361 - Epoch: 45 Train acc: 99.9890909090909 Val acc: 88.28500212778893 Test acc69.9575979830392; Train loss: 1.3304911864415979e-06 Val loss: 0.002434879137365367
-INFO - evaluator.py - 2024-10-27 00:25:35,396 - Epoch: 46 Train acc: 99.99272727272728 Val acc: 87.99927047236913 Test acc69.70547788219116; Train loss: 1.1797789160973273e-06 Val loss: 0.002503324507275529
-INFO - evaluator.py - 2024-10-27 00:25:52,399 - Epoch: 47 Train acc: 99.99272727272728 Val acc: 88.54033679858958 Test acc71.01191840476736; Train loss: 1.2389456434143334e-06 Val loss: 0.0026643167760163587
-INFO - evaluator.py - 2024-10-27 00:26:09,504 - Epoch: 48 Train acc: 99.98181818181818 Val acc: 88.60721016475166 Test acc70.24123309649323; Train loss: 2.016254716723283e-06 Val loss: 0.0025073994372099596
-INFO - evaluator.py - 2024-10-27 00:26:27,005 - Epoch: 49 Train acc: 99.99454545454546 Val acc: 88.56465438628489 Test acc69.34448773779509; Train loss: 9.379869697805919e-07 Val loss: 0.003004919624566322
-INFO - evaluator.py - 2024-10-27 00:26:27,009 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from resnet is 90.52222019575659 and 72.19516387806554
-INFO - evaluator.py - 2024-10-27 00:26:27,010 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from resnet is 90.52222019575659 and 72.19516387806554
-INFO - evaluator.py - 2024-10-27 00:26:27,010 - The best acc test dataset from resnet is 83.94453357781343
-INFO - evaluator.py - 2024-10-27 00:26:52,728 - Epoch: 0 Train acc: 88.7309090909091 Val acc: 83.3758891118001 Test acc84.33417373366949; Train loss: 0.00099837377586148 Val loss: 0.0005561633235857881
-INFO - evaluator.py - 2024-10-27 00:27:17,974 - Epoch: 1 Train acc: 95.6290909090909 Val acc: 66.66970636512858 Test acc83.59500343800138; Train loss: 0.00042782785045829687 Val loss: 0.00304104442748337
-INFO - evaluator.py - 2024-10-27 00:27:43,909 - Epoch: 2 Train acc: 98.02909090909091 Val acc: 80.28755547449693 Test acc81.49782259912904; Train loss: 0.00020147603577510878 Val loss: 0.0012980284382995628
-INFO - evaluator.py - 2024-10-27 00:28:09,722 - Epoch: 3 Train acc: 98.66545454545455 Val acc: 69.98601738707521 Test acc79.15138666055466; Train loss: 0.0001468505815602839 Val loss: 0.0021193918177940772
-INFO - evaluator.py - 2024-10-27 00:28:35,143 - Epoch: 4 Train acc: 98.87272727272727 Val acc: 87.89592072466411 Test acc78.22885629154251; Train loss: 0.00012088747122113339 Val loss: 0.0006604352557729447
-INFO - evaluator.py - 2024-10-27 00:29:00,255 - Epoch: 5 Train acc: 99.27090909090909 Val acc: 87.73481670618276 Test acc79.69000687600276; Train loss: 7.995101631280374e-05 Val loss: 0.0009058475443654325
-INFO - evaluator.py - 2024-10-27 00:29:25,550 - Epoch: 6 Train acc: 99.36363636363636 Val acc: 85.82284637363973 Test acc78.14290625716251; Train loss: 7.337471175706014e-05 Val loss: 0.0010480794223784473
-INFO - evaluator.py - 2024-10-27 00:29:51,280 - Epoch: 7 Train acc: 99.2909090909091 Val acc: 90.41887044805156 Test acc82.42894797157919; Train loss: 7.780291265596382e-05 Val loss: 0.0006398891236211139
-INFO - evaluator.py - 2024-10-27 00:30:16,657 - Epoch: 8 Train acc: 99.47818181818182 Val acc: 89.37321417715363 Test acc79.69573687829475; Train loss: 5.626382054231892e-05 Val loss: 0.0006472962381948605
-INFO - evaluator.py - 2024-10-27 00:30:42,020 - Epoch: 9 Train acc: 99.56363636363636 Val acc: 89.61335035564471 Test acc79.49232179692872; Train loss: 4.991440368943255e-05 Val loss: 0.0007391801336426323
-INFO - evaluator.py - 2024-10-27 00:31:07,367 - Epoch: 10 Train acc: 99.53636363636363 Val acc: 88.7865523740045 Test acc73.41565436626175; Train loss: 5.379053076559847e-05 Val loss: 0.0006759187698190325
-INFO - evaluator.py - 2024-10-27 00:31:33,212 - Epoch: 11 Train acc: 99.63090909090909 Val acc: 89.49784181409204 Test acc77.88505615402246; Train loss: 3.892726956612685e-05 Val loss: 0.0009198683385421703
-INFO - evaluator.py - 2024-10-27 00:31:59,261 - Epoch: 12 Train acc: 99.50363636363636 Val acc: 80.8863760714937 Test acc83.26839330735733; Train loss: 5.607693494689143e-05 Val loss: 0.0015798042853832738
-INFO - evaluator.py - 2024-10-27 00:32:25,098 - Epoch: 13 Train acc: 99.67636363636365 Val acc: 86.32743631831723 Test acc80.0108870043548; Train loss: 3.7618157694735353e-05 Val loss: 0.0010888433465842265
-INFO - evaluator.py - 2024-10-27 00:32:50,948 - Epoch: 14 Train acc: 99.48181818181818 Val acc: 76.68247309866861 Test acc74.57597983039193; Train loss: 5.5110873032191935e-05 Val loss: 0.0013089746832883832
-INFO - evaluator.py - 2024-10-27 00:33:16,527 - Epoch: 15 Train acc: 99.48727272727272 Val acc: 84.79542829351328 Test acc63.66032546413018; Train loss: 5.479790208046324e-05 Val loss: 0.0010127524027556834
-INFO - evaluator.py - 2024-10-27 00:33:41,950 - Epoch: 16 Train acc: 99.68727272727273 Val acc: 77.64301781263299 Test acc79.3118267247307; Train loss: 3.341910042773551e-05 Val loss: 0.0017506453728442482
-INFO - evaluator.py - 2024-10-27 00:34:06,942 - Epoch: 17 Train acc: 99.56363636363636 Val acc: 89.64374734026384 Test acc74.31812972725189; Train loss: 4.9606343293271494e-05 Val loss: 0.0005685467148662027
-INFO - evaluator.py - 2024-10-27 00:34:32,359 - Epoch: 18 Train acc: 99.74727272727273 Val acc: 89.24858654021521 Test acc72.81113912445565; Train loss: 3.060847759320909e-05 Val loss: 0.000607715124119755
-INFO - evaluator.py - 2024-10-27 00:34:58,061 - Epoch: 19 Train acc: 99.69272727272728 Val acc: 86.45510365371754 Test acc81.72702269080908; Train loss: 3.2316886364291846e-05 Val loss: 0.0010500829033854811
-INFO - evaluator.py - 2024-10-27 00:35:24,050 - Epoch: 20 Train acc: 99.91454545454546 Val acc: 90.37023527266095 Test acc81.07093742837496; Train loss: 1.1079346120823175e-05 Val loss: 0.0007905673401661141
-INFO - evaluator.py - 2024-10-27 00:35:49,034 - Epoch: 21 Train acc: 99.91636363636364 Val acc: 90.43102924189921 Test acc78.1257162502865; Train loss: 9.313132623091488e-06 Val loss: 0.0009421396925134987
-INFO - evaluator.py - 2024-10-27 00:36:14,139 - Epoch: 22 Train acc: 99.9309090909091 Val acc: 89.93251869414553 Test acc80.72140728856292; Train loss: 8.064519209686048e-06 Val loss: 0.0013090150629077554
-INFO - evaluator.py - 2024-10-27 00:36:39,683 - Epoch: 23 Train acc: 99.94545454545455 Val acc: 84.07501975804 Test acc81.10531744212697; Train loss: 6.120377081259159e-06 Val loss: 0.0026578668316299555
-INFO - evaluator.py - 2024-10-27 00:37:05,658 - Epoch: 24 Train acc: 99.96000000000001 Val acc: 89.04796644172897 Test acc81.66685766674307; Train loss: 5.432668424493221e-06 Val loss: 0.0017061160468672524
-INFO - evaluator.py - 2024-10-27 00:37:31,669 - Epoch: 25 Train acc: 99.92909090909092 Val acc: 86.54933430603684 Test acc80.18851707540684; Train loss: 7.317991577416581e-06 Val loss: 0.0023466313680302908
-INFO - evaluator.py - 2024-10-27 00:37:57,420 - Epoch: 26 Train acc: 99.93454545454546 Val acc: 85.44288406590066 Test acc80.88757735503094; Train loss: 7.706209421915197e-06 Val loss: 0.002194944388439274
-INFO - evaluator.py - 2024-10-27 00:38:23,023 - Epoch: 27 Train acc: 99.94909090909091 Val acc: 87.78953127849717 Test acc76.46688058675224; Train loss: 6.763137149160188e-06 Val loss: 0.0016214154369454665
-INFO - evaluator.py - 2024-10-27 00:38:49,097 - Epoch: 28 Train acc: 99.94909090909091 Val acc: 89.27290412791051 Test acc76.81354572541828; Train loss: 6.0949995424132135e-06 Val loss: 0.0019794832037476335
-INFO - evaluator.py - 2024-10-27 00:39:15,002 - Epoch: 29 Train acc: 99.92545454545456 Val acc: 85.92315642288285 Test acc79.66135686454274; Train loss: 8.084059366757918e-06 Val loss: 0.002305090833242211
-INFO - evaluator.py - 2024-10-27 00:39:40,286 - Epoch: 30 Train acc: 99.91818181818182 Val acc: 85.96571220134963 Test acc71.52475360990145; Train loss: 8.107718020327908e-06 Val loss: 0.0022762054273426893
-INFO - evaluator.py - 2024-10-27 00:40:05,864 - Epoch: 31 Train acc: 99.93454545454546 Val acc: 86.94145540762356 Test acc77.09718083887233; Train loss: 7.50963441673362e-06 Val loss: 0.0022480619360273086
-INFO - evaluator.py - 2024-10-27 00:40:32,081 - Epoch: 32 Train acc: 99.92181818181818 Val acc: 81.1508298376801 Test acc73.38413935365574; Train loss: 8.18370517713447e-06 Val loss: 0.0034970463905749593
-INFO - evaluator.py - 2024-10-27 00:40:57,626 - Epoch: 33 Train acc: 99.90545454545455 Val acc: 90.0693051249316 Test acc79.15138666055466; Train loss: 1.1128416986155736e-05 Val loss: 0.0017934623326390801
-INFO - evaluator.py - 2024-10-27 00:41:22,574 - Epoch: 34 Train acc: 99.96181818181819 Val acc: 90.32767949419419 Test acc77.5699060279624; Train loss: 4.958055904287242e-06 Val loss: 0.0013942023571287363
-INFO - evaluator.py - 2024-10-27 00:41:48,430 - Epoch: 35 Train acc: 99.95454545454545 Val acc: 85.35169311204328 Test acc78.95083658033462; Train loss: 5.976402206967826e-06 Val loss: 0.0030161893667076936
-INFO - evaluator.py - 2024-10-27 00:42:14,321 - Epoch: 36 Train acc: 99.91818181818182 Val acc: 83.89263785032524 Test acc77.73321109328444; Train loss: 8.469993925365832e-06 Val loss: 0.0031117666701834515
-INFO - evaluator.py - 2024-10-27 00:42:39,347 - Epoch: 37 Train acc: 99.9509090909091 Val acc: 81.64934038543376 Test acc71.9401787760715; Train loss: 5.247060758281143e-06 Val loss: 0.0032798680166856403
-INFO - evaluator.py - 2024-10-27 00:43:04,392 - Epoch: 38 Train acc: 99.94363636363637 Val acc: 89.60423126025898 Test acc71.88574375429751; Train loss: 6.131483224538153e-06 Val loss: 0.001566577765041372
-INFO - evaluator.py - 2024-10-27 00:43:29,932 - Epoch: 39 Train acc: 99.95636363636363 Val acc: 88.17861268162198 Test acc80.10256704102682; Train loss: 5.254300981562727e-06 Val loss: 0.0018073334484609385
-INFO - evaluator.py - 2024-10-27 00:43:55,217 - Epoch: 40 Train acc: 99.98181818181818 Val acc: 87.35789409690558 Test acc80.47501719000688; Train loss: 1.8448696166372388e-06 Val loss: 0.0020759240154278186
-INFO - evaluator.py - 2024-10-27 00:44:21,176 - Epoch: 41 Train acc: 99.99272727272728 Val acc: 87.94759559851663 Test acc79.3118267247307; Train loss: 1.161986310382252e-06 Val loss: 0.0019400248528106633
-INFO - evaluator.py - 2024-10-27 00:44:46,612 - Epoch: 42 Train acc: 99.99090909090908 Val acc: 85.66478205362029 Test acc81.01936740774697; Train loss: 1.2599138970678972e-06 Val loss: 0.0025749435663382707
-INFO - evaluator.py - 2024-10-27 00:45:12,462 - Epoch: 43 Train acc: 99.98181818181818 Val acc: 88.39747097087968 Test acc78.98235159294063; Train loss: 1.8966507209845739e-06 Val loss: 0.0020693043161144416
-INFO - evaluator.py - 2024-10-27 00:45:38,420 - Epoch: 44 Train acc: 99.98727272727272 Val acc: 89.26682473098668 Test acc79.43215677286271; Train loss: 1.6719948763373147e-06 Val loss: 0.0019235795113415521
-INFO - evaluator.py - 2024-10-27 00:46:04,342 - Epoch: 45 Train acc: 99.9890909090909 Val acc: 87.97799258313576 Test acc79.19149667659867; Train loss: 1.29561161894468e-06 Val loss: 0.002249105700360032
-INFO - evaluator.py - 2024-10-27 00:46:30,492 - Epoch: 46 Train acc: 99.99090909090908 Val acc: 86.93233631223782 Test acc79.66135686454274; Train loss: 1.4028880856626943e-06 Val loss: 0.002731027661272005
-INFO - evaluator.py - 2024-10-27 00:46:56,400 - Epoch: 47 Train acc: 99.97636363636364 Val acc: 89.2455468417533 Test acc79.01100160440065; Train loss: 2.3364118259161676e-06 Val loss: 0.0023018266047465487
-INFO - evaluator.py - 2024-10-27 00:47:22,302 - Epoch: 48 Train acc: 99.98181818181818 Val acc: 89.4856830202444 Test acc80.76151730460693; Train loss: 1.7679093424879448e-06 Val loss: 0.0022170197181741085
-INFO - evaluator.py - 2024-10-27 00:47:47,813 - Epoch: 49 Train acc: 99.9890909090909 Val acc: 86.29095993677427 Test acc82.12812285124915; Train loss: 1.6674291096964309e-06 Val loss: 0.0032392213522710296
-INFO - evaluator.py - 2024-10-27 00:47:47,817 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from wrn is 90.43102924189921 and 78.1257162502865
-INFO - evaluator.py - 2024-10-27 00:47:47,817 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from wrn is 90.43102924189921 and 78.1257162502865
-INFO - evaluator.py - 2024-10-27 00:47:47,817 - The best acc test dataset from wrn is 84.33417373366949
-INFO - evaluator.py - 2024-10-27 00:49:30,545 - Epoch: 0 Train acc: 88.75636363636363 Val acc: 88.73183780169008 Test acc79.4350217740087; Train loss: 0.0012930419901555235 Val loss: 0.00043183300073215377
-INFO - evaluator.py - 2024-10-27 00:51:12,650 - Epoch: 1 Train acc: 96.29454545454546 Val acc: 90.68332421423794 Test acc80.6268622507449; Train loss: 0.00037052973485128445 Val loss: 0.00048685092423504135
-INFO - evaluator.py - 2024-10-27 00:52:54,676 - Epoch: 2 Train acc: 98.24545454545455 Val acc: 78.37558514195392 Test acc82.32294292917717; Train loss: 0.00018729409136047418 Val loss: 0.000979611845217662
-INFO - evaluator.py - 2024-10-27 00:54:37,003 - Epoch: 3 Train acc: 98.75454545454545 Val acc: 81.28457657000425 Test acc73.54171441668576; Train loss: 0.00013805930100550706 Val loss: 0.0012205479750046796
-INFO - evaluator.py - 2024-10-27 00:56:19,100 - Epoch: 4 Train acc: 99.02363636363636 Val acc: 89.37625387561555 Test acc74.30953472381388; Train loss: 0.00010506508883342824 Val loss: 0.0007216065580326414
-INFO - evaluator.py - 2024-10-27 00:58:01,279 - Epoch: 5 Train acc: 99.27818181818182 Val acc: 88.10565991853608 Test acc80.78443731377493; Train loss: 8.48705991954458e-05 Val loss: 0.0014728660681043485
-INFO - evaluator.py - 2024-10-27 00:59:43,720 - Epoch: 6 Train acc: 99.26181818181819 Val acc: 84.56441121040793 Test acc79.79028191611278; Train loss: 8.288701854550956e-05 Val loss: 0.0010327643805038923
-INFO - evaluator.py - 2024-10-27 01:01:26,114 - Epoch: 7 Train acc: 99.56363636363636 Val acc: 87.37917198613897 Test acc80.40052716021087; Train loss: 4.7918784306172956e-05 Val loss: 0.0012135204391803007
-INFO - evaluator.py - 2024-10-27 01:03:08,009 - Epoch: 8 Train acc: 99.60727272727271 Val acc: 88.93549759863821 Test acc74.00584460233785; Train loss: 4.257430004760284e-05 Val loss: 0.0008239495322308473
-INFO - evaluator.py - 2024-10-27 01:04:50,139 - Epoch: 9 Train acc: 99.46727272727273 Val acc: 88.87774332786188 Test acc74.988539995416; Train loss: 6.196432792348788e-05 Val loss: 0.0006372507946260346
-INFO - evaluator.py - 2024-10-27 01:06:32,320 - Epoch: 10 Train acc: 99.67454545454545 Val acc: 83.92303483494437 Test acc72.55615402246161; Train loss: 3.772443796359849e-05 Val loss: 0.0030693319099088143
-INFO - evaluator.py - 2024-10-27 01:08:14,323 - Epoch: 11 Train acc: 98.44000000000001 Val acc: 50.41947838774393 Test acc50.0; Train loss: 0.00019870985611236062 Val loss: 0.003576739297187671
-INFO - evaluator.py - 2024-10-27 01:09:56,510 - Epoch: 12 Train acc: 99.4890909090909 Val acc: 55.453219040671165 Test acc52.03415081366033; Train loss: 6.069092784627256e-05 Val loss: 0.0024860259776231662
-INFO - evaluator.py - 2024-10-27 01:11:38,423 - Epoch: 13 Train acc: 99.57090909090908 Val acc: 83.91087604109673 Test acc65.37073114829246; Train loss: 4.6400938448707825e-05 Val loss: 0.0008010127724278726
-INFO - evaluator.py - 2024-10-27 01:13:20,204 - Epoch: 14 Train acc: 99.72181818181818 Val acc: 89.01756945710986 Test acc78.01111620444648; Train loss: 3.1786464647765654e-05 Val loss: 0.0011357845236547913
-INFO - evaluator.py - 2024-10-27 01:15:02,084 - Epoch: 15 Train acc: 99.75454545454545 Val acc: 90.07842422031734 Test acc78.7674765069906; Train loss: 2.9703186282528225e-05 Val loss: 0.0020976278588245526
-INFO - evaluator.py - 2024-10-27 01:16:43,665 - Epoch: 16 Train acc: 99.76181818181819 Val acc: 86.52501671834155 Test acc72.28970891588357; Train loss: 2.826096004778678e-05 Val loss: 0.0027877955159428445
-INFO - evaluator.py - 2024-10-27 01:18:25,449 - Epoch: 17 Train acc: 99.80363636363636 Val acc: 88.038786552374 Test acc73.73653449461379; Train loss: 2.2347323763559953e-05 Val loss: 0.0019908655239851743
-INFO - evaluator.py - 2024-10-27 01:20:07,160 - Epoch: 18 Train acc: 99.81454545454545 Val acc: 78.55188765274484 Test acc73.52452440980977; Train loss: 2.0802509560532846e-05 Val loss: 0.025684500503006646
-INFO - evaluator.py - 2024-10-27 01:21:49,100 - Epoch: 19 Train acc: 99.72181818181818 Val acc: 80.51249316067846 Test acc73.34402933761174; Train loss: 3.1199202901784286e-05 Val loss: 0.0062990785188621005
-INFO - evaluator.py - 2024-10-27 01:23:31,244 - Epoch: 20 Train acc: 99.94727272727273 Val acc: 87.67706243540641 Test acc77.31492092596837; Train loss: 6.3062720273261555e-06 Val loss: 0.016208587529717395
-INFO - evaluator.py - 2024-10-27 01:25:13,274 - Epoch: 21 Train acc: 99.97090909090909 Val acc: 86.06298255213083 Test acc75.29223011689204; Train loss: 3.221956273947316e-06 Val loss: 0.025272911910597943
-INFO - evaluator.py - 2024-10-27 01:26:55,330 - Epoch: 22 Train acc: 99.97454545454545 Val acc: 86.19368958599307 Test acc77.4610359844144; Train loss: 3.0910080797969965e-06 Val loss: 0.03217438748777293
-INFO - evaluator.py - 2024-10-27 01:28:37,284 - Epoch: 23 Train acc: 99.98 Val acc: 86.64052525989422 Test acc77.24902589961036; Train loss: 2.2708187526797534e-06 Val loss: 0.036588965204973935
-INFO - evaluator.py - 2024-10-27 01:30:19,488 - Epoch: 24 Train acc: 99.97454545454545 Val acc: 88.39747097087968 Test acc79.52097180838872; Train loss: 2.5718386833225602e-06 Val loss: 0.023814174946162236
-INFO - evaluator.py - 2024-10-27 01:32:01,586 - Epoch: 25 Train acc: 99.84181818181818 Val acc: 88.51905890935619 Test acc73.71934448773779; Train loss: 1.805347485281924e-05 Val loss: 0.0018183692870324735
-INFO - evaluator.py - 2024-10-27 01:33:43,799 - Epoch: 26 Train acc: 99.97454545454545 Val acc: 87.23934585689099 Test acc75.5987852395141; Train loss: 3.591855430770672e-06 Val loss: 0.0034931873114205135
-INFO - evaluator.py - 2024-10-27 01:35:25,955 - Epoch: 27 Train acc: 99.97090909090909 Val acc: 87.23022676150526 Test acc77.03415081366033; Train loss: 3.2642895397740095e-06 Val loss: 0.004225692993109331
-INFO - evaluator.py - 2024-10-27 01:37:07,956 - Epoch: 28 Train acc: 99.95818181818181 Val acc: 88.39443127241778 Test acc68.33027733211094; Train loss: 4.768231871689336e-06 Val loss: 0.0020405990105282898
-INFO - evaluator.py - 2024-10-27 01:38:49,959 - Epoch: 29 Train acc: 99.93636363636364 Val acc: 87.94455590005471 Test acc74.28947971579188; Train loss: 5.923723731761625e-06 Val loss: 0.0050985360335445646
-INFO - evaluator.py - 2024-10-27 01:40:31,980 - Epoch: 30 Train acc: 99.98 Val acc: 88.28804182625085 Test acc77.88505615402246; Train loss: 2.1449405299521954e-06 Val loss: 0.006376030604682351
-INFO - evaluator.py - 2024-10-27 01:42:14,384 - Epoch: 31 Train acc: 99.98 Val acc: 89.13915739558635 Test acc76.05145542058217; Train loss: 2.4333286998496944e-06 Val loss: 0.0059081140829296535
-INFO - evaluator.py - 2024-10-27 01:43:56,199 - Epoch: 32 Train acc: 99.97818181818182 Val acc: 90.10274180801264 Test acc77.71602108640843; Train loss: 2.5550560786701805e-06 Val loss: 0.006980863847488458
-INFO - evaluator.py - 2024-10-27 01:45:38,043 - Epoch: 33 Train acc: 99.92 Val acc: 81.18730621922306 Test acc76.99117579647033; Train loss: 8.947624623226085e-06 Val loss: 0.0055922059869612595
-INFO - evaluator.py - 2024-10-27 01:47:19,826 - Epoch: 34 Train acc: 99.94909090909091 Val acc: 88.82606845400936 Test acc79.59546183818473; Train loss: 5.061555361137173e-06 Val loss: 0.008151195441939504
-INFO - evaluator.py - 2024-10-27 01:49:01,585 - Epoch: 35 Train acc: 99.96727272727273 Val acc: 86.56149309988449 Test acc80.12262204904881; Train loss: 4.034870194382475e-06 Val loss: 0.013280341064134943
-INFO - evaluator.py - 2024-10-27 01:50:43,296 - Epoch: 36 Train acc: 99.97454545454545 Val acc: 87.50987902000121 Test acc81.49495759798305; Train loss: 3.1321068260343633e-06 Val loss: 0.010434281196237096
-INFO - evaluator.py - 2024-10-27 01:52:24,939 - Epoch: 37 Train acc: 99.97636363636364 Val acc: 88.70144081707095 Test acc80.46355718542287; Train loss: 2.668781834328952e-06 Val loss: 0.009642107587171126
-INFO - evaluator.py - 2024-10-27 01:54:06,902 - Epoch: 38 Train acc: 99.93636363636364 Val acc: 89.66198553103533 Test acc81.19699747879899; Train loss: 7.139033022946767e-06 Val loss: 0.0029167227269929334
-INFO - evaluator.py - 2024-10-27 01:55:48,761 - Epoch: 39 Train acc: 99.95636363636363 Val acc: 87.13903580764789 Test acc80.12262204904881; Train loss: 5.867864612914466e-06 Val loss: 0.00769314895510217
-INFO - evaluator.py - 2024-10-27 01:57:30,872 - Epoch: 40 Train acc: 99.99272727272728 Val acc: 88.94461669402395 Test acc78.38643135457254; Train loss: 1.2168243205926707e-06 Val loss: 0.007814230473326059
-INFO - evaluator.py - 2024-10-27 01:59:12,976 - Epoch: 41 Train acc: 99.99454545454546 Val acc: 88.99629156787647 Test acc78.59557643823058; Train loss: 6.182151043565847e-07 Val loss: 0.008310905734313876
-INFO - evaluator.py - 2024-10-27 02:00:55,061 - Epoch: 42 Train acc: 99.99818181818182 Val acc: 88.12389810930755 Test acc78.0741462296585; Train loss: 3.521576068578725e-07 Val loss: 0.01254132424627686
-INFO - evaluator.py - 2024-10-27 02:02:36,809 - Epoch: 43 Train acc: 99.99454545454546 Val acc: 88.72575840476624 Test acc78.7789365115746; Train loss: 6.918636271365131e-07 Val loss: 0.012945838913496334
-INFO - evaluator.py - 2024-10-27 02:04:18,547 - Epoch: 44 Train acc: 100.0 Val acc: 88.94157699556204 Test acc78.35205134082054; Train loss: 3.0324927665510226e-07 Val loss: 0.013060019584301866
-INFO - evaluator.py - 2024-10-27 02:06:00,849 - Epoch: 45 Train acc: 100.0 Val acc: 88.92637850325248 Test acc78.22312628925052; Train loss: 1.456582395881221e-07 Val loss: 0.013337427841159622
-INFO - evaluator.py - 2024-10-27 02:07:42,866 - Epoch: 46 Train acc: 100.0 Val acc: 88.76527448477111 Test acc78.24318129727253; Train loss: 1.9722425246597327e-07 Val loss: 0.01547280919827968
-INFO - evaluator.py - 2024-10-27 02:09:24,879 - Epoch: 47 Train acc: 100.0 Val acc: 88.55553529089914 Test acc79.15425166170067; Train loss: 1.3254639737212414e-07 Val loss: 0.015069734594352166
-INFO - evaluator.py - 2024-10-27 02:11:06,814 - Epoch: 48 Train acc: 100.0 Val acc: 88.70448051553285 Test acc78.56979142791657; Train loss: 1.2888575234555332e-07 Val loss: 0.016366244420518816
-INFO - evaluator.py - 2024-10-27 02:12:48,845 - Epoch: 49 Train acc: 100.0 Val acc: 88.45522524165604 Test acc79.13419665367866; Train loss: 1.6561026906187367e-07 Val loss: 0.01742130334967646
-INFO - evaluator.py - 2024-10-27 02:12:48,851 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from resnext is 90.68332421423794 and 80.6268622507449
-INFO - evaluator.py - 2024-10-27 02:12:48,851 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from resnext is 90.68332421423794 and 80.6268622507449
-INFO - evaluator.py - 2024-10-27 02:12:48,851 - The best acc test dataset from resnext is 82.32294292917717
-INFO - evaluator.py - 2024-10-27 02:12:48,851 - The best acc of accuracy (using synthetic images as the validation set) of synthetic images from resnet, wrn, and resnext are [72.19516387806554, 78.1257162502865, 80.6268622507449].
-INFO - evaluator.py - 2024-10-27 02:12:48,851 - The average and std of accuracy of synthetic images are 76.98 and 3.54
-INFO - dataset_loader.py - 2024-10-28 21:01:29,895 - delta is reset as 2.6201132658294697e-07
-INFO - evaluator.py - 2024-10-28 22:08:14,889 - The FID of synthetic images is 52.63129361996775
-INFO - evaluator.py - 2024-10-28 22:08:14,891 - The Inception Score of synthetic images is 1.5006459951400757
-INFO - evaluator.py - 2024-10-28 22:08:14,891 - The Precision and Recall of synthetic images is 0.6673906445503235 and 0.14839503169059753
-INFO - evaluator.py - 2024-10-28 22:08:14,891 - The FLD of synthetic images is -1.8039464950561523
-INFO - evaluator.py - 2024-10-28 22:08:14,891 - The ImageReward of synthetic images is -1.9849401364792139
-INFO - dataset_loader.py - 2024-10-28 22:08:16,004 - delta is reset as 1.8484667129285888e-06
-INFO - evaluator.py - 2024-10-28 22:57:35,449 - The FID of synthetic images is 245.40488293234682
-INFO - evaluator.py - 2024-10-28 22:57:35,451 - The Inception Score of synthetic images is 1.675389289855957
-INFO - evaluator.py - 2024-10-28 22:57:35,451 - The Precision and Recall of synthetic images is 0.7455397248268127 and 0.00019999999494757503
-INFO - evaluator.py - 2024-10-28 22:57:35,451 - The FLD of synthetic images is 24.606788158416748
-INFO - evaluator.py - 2024-10-28 22:57:35,451 - The ImageReward of synthetic images is -2.263369669710833
-INFO - dataset_loader.py - 2024-10-28 22:57:36,837 - delta is reset as 5.11965868690912e-07
-INFO - evaluator.py - 2024-10-28 23:59:51,090 - The FID of synthetic images is 127.70160766828963
-INFO - evaluator.py - 2024-10-28 23:59:51,094 - The Inception Score of synthetic images is 2.396092653274536
-INFO - evaluator.py - 2024-10-28 23:59:51,094 - The Precision and Recall of synthetic images is 0.4320312738418579 and 0.0016833568224683404
-INFO - evaluator.py - 2024-10-28 23:59:51,094 - The FLD of synthetic images is 20.96329927444458
-INFO - evaluator.py - 2024-10-28 23:59:51,094 - The ImageReward of synthetic images is -2.0141066952829716
-INFO - dataset_loader.py - 2024-10-28 23:59:52,126 - delta is reset as 1.5148623360286113e-06
-INFO - evaluator.py - 2024-10-29 00:51:02,489 - The FID of synthetic images is 17.081697814953657
-INFO - evaluator.py - 2024-10-29 00:51:02,492 - The Inception Score of synthetic images is 3.9322965145111084
-INFO - evaluator.py - 2024-10-29 00:51:02,492 - The Precision and Recall of synthetic images is 0.5424844026565552 and 0.3853333294391632
-INFO - evaluator.py - 2024-10-29 00:51:02,492 - The FLD of synthetic images is 6.554317474365234
-INFO - evaluator.py - 2024-10-29 00:51:02,492 - The ImageReward of synthetic images is -1.6361236040304448
-INFO - dataset_loader.py - 2024-10-29 00:51:03,058 - delta is reset as 4.329102935418938e-06
-INFO - evaluator.py - 2024-10-29 01:42:24,877 - The FID of synthetic images is 164.97207247508004
-INFO - evaluator.py - 2024-10-29 01:42:24,879 - The Inception Score of synthetic images is 1.6467124223709106
-INFO - evaluator.py - 2024-10-29 01:42:24,879 - The Precision and Recall of synthetic images is 0.602484405040741 and 0.011652173474431038
-INFO - evaluator.py - 2024-10-29 01:42:24,879 - The FLD of synthetic images is 18.773305416107178
-INFO - evaluator.py - 2024-10-29 01:42:24,879 - The ImageReward of synthetic images is -1.579579470070079
-INFO - dataset_loader.py - 2024-10-29 01:42:25,242 - delta is reset as 1.8484667129285888e-06
-INFO - evaluator.py - 2024-10-29 02:35:25,718 - The FID of synthetic images is 109.95839653086716
-INFO - evaluator.py - 2024-10-29 02:35:25,751 - The Inception Score of synthetic images is 3.1288182735443115
-INFO - evaluator.py - 2024-10-29 02:35:25,751 - The Precision and Recall of synthetic images is 0.5966984033584595 and 0.036159999668598175
-INFO - evaluator.py - 2024-10-29 02:35:25,751 - The FLD of synthetic images is 19.213759899139404
-INFO - evaluator.py - 2024-10-29 02:35:25,751 - The ImageReward of synthetic images is -2.214698282242767
-INFO - dataset_loader.py - 2024-10-29 02:35:26,383 - delta is reset as 1.8484667129285888e-06
-INFO - evaluator.py - 2024-10-29 03:27:02,083 - The FID of synthetic images is 219.54836766661845
-INFO - evaluator.py - 2024-10-29 03:27:02,086 - The Inception Score of synthetic images is 1.7984269857406616
-INFO - evaluator.py - 2024-10-29 03:27:02,086 - The Precision and Recall of synthetic images is 0.6327812671661377 and 0.0003600000054575503
-INFO - evaluator.py - 2024-10-29 03:27:02,086 - The FLD of synthetic images is 27.920222282409668
-INFO - evaluator.py - 2024-10-29 03:27:02,086 - The ImageReward of synthetic images is -2.2739594591539354
-INFO - dataset_loader.py - 2024-10-29 03:27:02,840 - delta is reset as 1.8484667129285888e-06
-INFO - evaluator.py - 2024-10-29 04:18:40,193 - The FID of synthetic images is 201.61100378723972
-INFO - evaluator.py - 2024-10-29 04:18:40,221 - The Inception Score of synthetic images is 1.9011380672454834
-INFO - evaluator.py - 2024-10-29 04:18:40,221 - The Precision and Recall of synthetic images is 0.6231746077537537 and 0.0005200000014156103
-INFO - evaluator.py - 2024-10-29 04:18:40,221 - The FLD of synthetic images is 27.509820461273193
-INFO - evaluator.py - 2024-10-29 04:18:40,221 - The ImageReward of synthetic images is -2.2687564725081124
-INFO - dataset_loader.py - 2024-10-29 04:18:42,924 - delta is reset as 2.6201132658294697e-07
-INFO - evaluator.py - 2024-10-29 05:19:05,639 - The FID of synthetic images is 22.085653876120773
-INFO - evaluator.py - 2024-10-29 05:19:05,642 - The Inception Score of synthetic images is 1.7683465480804443
-INFO - evaluator.py - 2024-10-29 05:19:05,642 - The Precision and Recall of synthetic images is 0.7516825795173645 and 0.3393147587776184
-INFO - evaluator.py - 2024-10-29 05:19:05,642 - The FLD of synthetic images is -6.095540523529053
-INFO - evaluator.py - 2024-10-29 05:19:05,642 - The ImageReward of synthetic images is -1.793093251128755
-INFO - dataset_loader.py - 2024-10-29 05:19:06,164 - delta is reset as 1.5148623360286113e-06
-INFO - evaluator.py - 2024-10-29 06:19:12,182 - The FID of synthetic images is 36.55868340206811
-INFO - evaluator.py - 2024-10-29 06:19:12,189 - The Inception Score of synthetic images is 1.9821124076843262
-INFO - evaluator.py - 2024-10-29 06:19:12,189 - The Precision and Recall of synthetic images is 0.19395314157009125 and 0.42188334465026855
-INFO - evaluator.py - 2024-10-29 06:19:12,189 - The FLD of synthetic images is 16.948330402374268
-INFO - evaluator.py - 2024-10-29 06:19:12,189 - The ImageReward of synthetic images is -2.130454104746692
-INFO - dataset_loader.py - 2024-10-29 06:19:12,548 - delta is reset as 1.8484667129285888e-06
-INFO - evaluator.py - 2024-10-29 07:17:08,735 - The FID of synthetic images is 103.17130225064335
-INFO - evaluator.py - 2024-10-29 07:17:08,757 - The Inception Score of synthetic images is 3.3248043060302734
-INFO - evaluator.py - 2024-10-29 07:17:08,757 - The Precision and Recall of synthetic images is 0.5925872921943665 and 0.05226000025868416
-INFO - evaluator.py - 2024-10-29 07:17:08,757 - The FLD of synthetic images is 18.80326271057129
-INFO - evaluator.py - 2024-10-29 07:17:08,757 - The ImageReward of synthetic images is -2.2046342791773026
-INFO - dataset_loader.py - 2024-10-29 07:17:09,384 - delta is reset as 1.5148623360286113e-06
-INFO - evaluator.py - 2024-10-29 08:12:19,400 - The FID of synthetic images is 36.16871462142183
-INFO - evaluator.py - 2024-10-29 08:12:19,442 - The Inception Score of synthetic images is 1.9785420894622803
-INFO - evaluator.py - 2024-10-29 08:12:19,442 - The Precision and Recall of synthetic images is 0.2151111215353012 and 0.38750001788139343
-INFO - evaluator.py - 2024-10-29 08:12:19,442 - The FLD of synthetic images is 16.76713228225708
-INFO - evaluator.py - 2024-10-29 08:12:19,442 - The ImageReward of synthetic images is -2.120978381105832
-INFO - dataset_loader.py - 2024-10-29 08:12:21,784 - delta is reset as 2.6201132658294697e-07
-INFO - evaluator.py - 2024-10-29 09:12:37,637 - The FID of synthetic images is 57.79238987775261
-INFO - evaluator.py - 2024-10-29 09:12:37,646 - The Inception Score of synthetic images is 1.4734785556793213
-INFO - evaluator.py - 2024-10-29 09:12:37,647 - The Precision and Recall of synthetic images is 0.6569206714630127 and 0.13006387650966644
-INFO - evaluator.py - 2024-10-29 09:12:37,647 - The FLD of synthetic images is -1.181638240814209
-INFO - evaluator.py - 2024-10-29 09:12:37,647 - The ImageReward of synthetic images is -2.0049813007896855
-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
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