diff --git a/celeba_male_32_eps1.0trainval-2024-10-24-00-50-04/stdout.txt b/celeba_male_32_eps1.0trainval-2024-10-24-00-50-04/stdout.txt
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--- a/celeba_male_32_eps1.0trainval-2024-10-24-00-50-04/stdout.txt
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@@ -1,3351 +0,0 @@
-INFO - utils.py - 2024-10-24 00:50:11,162 - {'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/celeba_male_32_eps1.0trainval-2024-10-24-00-50-04', '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': 'celeba_male_32', 'num_channels': 3, 'resolution': 32, 'n_classes': 2, 'train_path': 'dataset/celeba/train_32_Male.zip', 'test_path': 'dataset/celeba/test_32_Male.zip', 'fid_stats': 'dataset/celeba/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/celeba/fid_stats_32.npz'}, 'pretrain': {'log_dir': 'exp/dpdm/celeba_male_32_eps1.0trainval-2024-10-24-00-50-04/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/celeba_male_32_eps1.0trainval-2024-10-24-00-50-04/train', 'seed': 0, 'batch_size': 4096, 'n_epochs': 100, '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/celeba_male_32_eps1.0trainval-2024-10-24-00-50-04/gen'}, 'eval': {'batch_size': 1000}}
-INFO - dataset_loader.py - 2024-10-24 00:50:21,359 - delta is reset as 5.11965868690912e-07
-INFO - dpsgd_diffusion.py - 2024-10-24 00:50:24,185 - Number of trainable parameters in model: 0
-INFO - dpsgd_diffusion.py - 2024-10-24 00:50:24,185 - Number of total epochs: 100
-INFO - dpsgd_diffusion.py - 2024-10-24 00:50:24,185 - Starting training at step 0
-INFO - dpsgd_diffusion.py - 2024-10-24 00:51:41,863 - Loss: 1.0728, step: 100
-INFO - dpsgd_diffusion.py - 2024-10-24 00:52:44,691 - Loss: 0.9442, step: 200
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-INFO - dpsgd_diffusion.py - 2024-10-24 01:09:36,694 - Loss: 0.6742, step: 2000
-INFO - dpsgd_diffusion.py - 2024-10-24 01:09:36,707 - Saving snapshot checkpoint and sampling single batch at iteration 2000.
-WARNING - image.py - 2024-10-24 01:09:38,402 - 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 01:10:57,513 - Loss: 0.6677, step: 2100
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-INFO - dpsgd_diffusion.py - 2024-10-24 01:15:15,500 - Eps-value after 1 epochs: 0.1653
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-INFO - dpsgd_diffusion.py - 2024-10-24 01:28:41,810 - Loss: 0.4490, step: 4000
-INFO - dpsgd_diffusion.py - 2024-10-24 01:28:41,823 - Saving snapshot checkpoint and sampling single batch at iteration 4000.
-WARNING - image.py - 2024-10-24 01:28:42,419 - 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 01:39:31,324 - Eps-value after 2 epochs: 0.1798
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-INFO - dpsgd_diffusion.py - 2024-10-24 01:47:41,793 - Saving snapshot checkpoint and sampling single batch at iteration 6000.
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-INFO - dpsgd_diffusion.py - 2024-10-24 02:03:44,020 - Eps-value after 3 epochs: 0.1944
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-INFO - dpsgd_diffusion.py - 2024-10-24 02:06:41,551 - Loss: 0.3711, step: 8000
-INFO - dpsgd_diffusion.py - 2024-10-24 02:06:41,569 - Saving snapshot checkpoint and sampling single batch at iteration 8000.
-WARNING - image.py - 2024-10-24 02:06:42,164 - 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 02:07:01,460 - FID at iteration 8000: 384.245968
-INFO - dpsgd_diffusion.py - 2024-10-24 02:07:57,383 - Loss: 0.4048, step: 8100
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-INFO - dpsgd_diffusion.py - 2024-10-24 02:25:38,186 - Loss: 0.3692, step: 10000
-INFO - dpsgd_diffusion.py - 2024-10-24 02:25:38,264 - Saving snapshot checkpoint and sampling single batch at iteration 10000.
-WARNING - image.py - 2024-10-24 02:25:38,861 - 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 02:25:58,062 - FID at iteration 10000: 368.031956
-INFO - dpsgd_diffusion.py - 2024-10-24 02:26:53,831 - Loss: 0.3500, step: 10100
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-INFO - dpsgd_diffusion.py - 2024-10-24 02:28:12,682 - Eps-value after 4 epochs: 0.2089
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-INFO - dpsgd_diffusion.py - 2024-10-24 02:44:28,682 - Saving snapshot checkpoint and sampling single batch at iteration 12000.
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-INFO - dpsgd_diffusion.py - 2024-10-24 19:47:56,220 - Eps-value after 47 epochs: 0.6652
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-INFO - dpsgd_diffusion.py - 2024-10-25 03:27:25,279 - Eps-value after 66 epochs: 0.7958
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-INFO - dpsgd_diffusion.py - 2024-10-25 14:44:34,843 - Loss: 0.2171, step: 240600
-INFO - dpsgd_diffusion.py - 2024-10-25 14:44:56,411 - Eps-value after 94 epochs: 0.9598
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-INFO - dpsgd_diffusion.py - 2024-10-25 14:57:29,668 - Saving snapshot checkpoint and sampling single batch at iteration 242000.
-WARNING - image.py - 2024-10-25 14:57:30,263 - 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 14:57:49,528 - FID at iteration 242000: 119.696844
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-INFO - dpsgd_diffusion.py - 2024-10-25 15:08:55,568 - Eps-value after 95 epochs: 0.9653
-INFO - dpsgd_diffusion.py - 2024-10-25 15:09:52,958 - Loss: 0.2361, step: 243300
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-INFO - dpsgd_diffusion.py - 2024-10-25 15:16:21,068 - Loss: 0.2163, step: 244000
-INFO - dpsgd_diffusion.py - 2024-10-25 15:16:21,080 - Saving snapshot checkpoint and sampling single batch at iteration 244000.
-WARNING - image.py - 2024-10-25 15:16:21,675 - 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 15:16:40,894 - FID at iteration 244000: 117.548477
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-INFO - dpsgd_diffusion.py - 2024-10-25 15:32:53,865 - Eps-value after 96 epochs: 0.9707
-INFO - dpsgd_diffusion.py - 2024-10-25 15:33:16,839 - Loss: 0.2220, step: 245800
-INFO - dpsgd_diffusion.py - 2024-10-25 15:34:12,507 - Loss: 0.2318, step: 245900
-INFO - dpsgd_diffusion.py - 2024-10-25 15:35:08,691 - Loss: 0.2188, step: 246000
-INFO - dpsgd_diffusion.py - 2024-10-25 15:35:08,704 - Saving snapshot checkpoint and sampling single batch at iteration 246000.
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-INFO - dpsgd_diffusion.py - 2024-10-25 15:35:30,028 - FID at iteration 246000: 118.429868
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-INFO - dpsgd_diffusion.py - 2024-10-25 15:54:10,374 - Loss: 0.2145, step: 248000
-INFO - dpsgd_diffusion.py - 2024-10-25 15:54:10,393 - Saving snapshot checkpoint and sampling single batch at iteration 248000.
-WARNING - image.py - 2024-10-25 15:54:10,987 - 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 15:54:30,356 - FID at iteration 248000: 114.989855
-INFO - dpsgd_diffusion.py - 2024-10-25 15:55:24,556 - Loss: 0.2135, step: 248100
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-INFO - dpsgd_diffusion.py - 2024-10-25 15:57:25,462 - Eps-value after 97 epochs: 0.9761
-INFO - dpsgd_diffusion.py - 2024-10-25 15:58:10,730 - Loss: 0.2433, step: 248400
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-INFO - dpsgd_diffusion.py - 2024-10-25 16:12:53,700 - Loss: 0.2305, step: 250000
-INFO - dpsgd_diffusion.py - 2024-10-25 16:12:53,713 - Saving snapshot checkpoint and sampling single batch at iteration 250000.
-WARNING - image.py - 2024-10-25 16:12:54,454 - 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 16:13:14,007 - FID at iteration 250000: 116.046820
-INFO - dpsgd_diffusion.py - 2024-10-25 16:14:11,011 - Loss: 0.2480, step: 250100
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-INFO - dpsgd_diffusion.py - 2024-10-25 16:21:24,355 - Eps-value after 98 epochs: 0.9815
-INFO - dpsgd_diffusion.py - 2024-10-25 16:21:35,170 - Loss: 0.2138, step: 250900
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-INFO - dpsgd_diffusion.py - 2024-10-25 16:31:47,770 - Loss: 0.2391, step: 252000
-INFO - dpsgd_diffusion.py - 2024-10-25 16:31:47,783 - Saving snapshot checkpoint and sampling single batch at iteration 252000.
-WARNING - image.py - 2024-10-25 16:31:48,380 - 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 16:32:07,690 - FID at iteration 252000: 116.529143
-INFO - dpsgd_diffusion.py - 2024-10-25 16:33:03,361 - Loss: 0.2272, step: 252100
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-INFO - dpsgd_diffusion.py - 2024-10-25 16:45:06,880 - Loss: 0.2290, step: 253400
-INFO - dpsgd_diffusion.py - 2024-10-25 16:45:29,041 - Eps-value after 99 epochs: 0.9867
-INFO - dpsgd_diffusion.py - 2024-10-25 16:46:01,866 - Loss: 0.2433, step: 253500
-INFO - dpsgd_diffusion.py - 2024-10-25 16:46:59,227 - Loss: 0.2385, step: 253600
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-INFO - dpsgd_diffusion.py - 2024-10-25 16:50:42,959 - Loss: 0.2153, step: 254000
-INFO - dpsgd_diffusion.py - 2024-10-25 16:50:42,971 - Saving snapshot checkpoint and sampling single batch at iteration 254000.
-WARNING - image.py - 2024-10-25 16:50:43,568 - 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 16:51:03,192 - FID at iteration 254000: 116.692391
-INFO - dpsgd_diffusion.py - 2024-10-25 16:51:59,881 - Loss: 0.2343, step: 254100
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-INFO - dpsgd_diffusion.py - 2024-10-25 17:05:06,714 - Loss: 0.2495, step: 255500
-INFO - dpsgd_diffusion.py - 2024-10-25 17:06:02,575 - Loss: 0.2316, step: 255600
-INFO - dpsgd_diffusion.py - 2024-10-25 17:06:59,411 - Loss: 0.2428, step: 255700
-INFO - dpsgd_diffusion.py - 2024-10-25 17:07:55,193 - Loss: 0.2297, step: 255800
-INFO - dpsgd_diffusion.py - 2024-10-25 17:08:50,756 - Loss: 0.2459, step: 255900
-INFO - dpsgd_diffusion.py - 2024-10-25 17:09:46,287 - Loss: 0.2417, step: 256000
-INFO - dpsgd_diffusion.py - 2024-10-25 17:09:46,310 - Eps-value after 100 epochs: 0.9919
-INFO - dpsgd_diffusion.py - 2024-10-25 17:09:46,541 - Saving final checkpoint.
-INFO - dpsgd_diffusion.py - 2024-10-25 17:09:46,544 - start to generate 60000 samples
-INFO - dpsgd_diffusion.py - 2024-10-25 17:18:50,664 - Generation Finished!
-INFO - dataset_loader.py - 2024-10-25 21:31:48,231 - delta is reset as 2.966981886419575e-07
-INFO - evaluator.py - 2024-10-25 21:33:40,245 - Epoch: 0 Train acc: 67.7690909090909 Val acc: 53.18560398808438 Test acc51.61872564749026; Train loss: 0.0024164037406444547 Val loss: 0.0010837335536976963
-INFO - evaluator.py - 2024-10-25 21:34:28,715 - Epoch: 1 Train acc: 76.64727272727274 Val acc: 50.316128640038905 Test acc50.16330506532203; Train loss: 0.0019128034472465515 Val loss: 0.0016079497370678456
-INFO - evaluator.py - 2024-10-25 21:35:16,961 - Epoch: 2 Train acc: 83.96727272727273 Val acc: 53.44093865888504 Test acc66.43651157460462; Train loss: 0.001399667739868164 Val loss: 0.014510131562051096
-INFO - evaluator.py - 2024-10-25 21:36:04,767 - Epoch: 3 Train acc: 88.23818181818181 Val acc: 58.19806675177822 Test acc66.43364657345863; Train loss: 0.0010739206417040392 Val loss: 0.0030551888789222686
-INFO - evaluator.py - 2024-10-25 21:36:53,148 - Epoch: 4 Train acc: 90.25999999999999 Val acc: 61.751474253754026 Test acc67.72862709145083; Train loss: 0.0009157023213126443 Val loss: 0.00835883760171079
-INFO - evaluator.py - 2024-10-25 21:37:41,667 - Epoch: 5 Train acc: 91.2290909090909 Val acc: 45.902486473341845 Test acc57.28569791427917; Train loss: 0.0008336947793310339 Val loss: 0.004176864689828067
-INFO - evaluator.py - 2024-10-25 21:38:29,247 - Epoch: 6 Train acc: 92.22363636363636 Val acc: 54.729770806735964 Test acc65.44235617694247; Train loss: 0.0007480231867595152 Val loss: 0.0022185817773062826
-INFO - evaluator.py - 2024-10-25 21:39:16,562 - Epoch: 7 Train acc: 92.78363636363636 Val acc: 48.80539850446836 Test acc62.227824891129956; Train loss: 0.0006922161638736725 Val loss: 0.0034172918827122516
-INFO - evaluator.py - 2024-10-25 21:40:05,408 - Epoch: 8 Train acc: 93.3490909090909 Val acc: 59.84558331813484 Test acc66.8519367407747; Train loss: 0.0006407328915866939 Val loss: 0.0029381483447204586
-INFO - evaluator.py - 2024-10-25 21:40:54,024 - Epoch: 9 Train acc: 93.74363636363637 Val acc: 58.67833910876041 Test acc67.06108182443273; Train loss: 0.0005944530607624488 Val loss: 0.0070481944195774455
-INFO - evaluator.py - 2024-10-25 21:41:40,698 - Epoch: 10 Train acc: 94.22545454545454 Val acc: 50.03039698461913 Test acc63.61448544579418; Train loss: 0.0005600190237164497 Val loss: 0.003960636946428684
-INFO - evaluator.py - 2024-10-25 21:42:30,120 - Epoch: 11 Train acc: 94.85818181818182 Val acc: 57.60532555170526 Test acc67.84609213843686; Train loss: 0.0005048725336790085 Val loss: 0.004976917656803357
-INFO - evaluator.py - 2024-10-25 21:43:19,369 - Epoch: 12 Train acc: 95.34545454545454 Val acc: 60.106997385859316 Test acc70.90877836351135; Train loss: 0.0004643202652985399 Val loss: 0.007628425579823595
-INFO - evaluator.py - 2024-10-25 21:44:07,664 - Epoch: 13 Train acc: 95.49272727272728 Val acc: 65.9736154173506 Test acc74.51867980747193; Train loss: 0.0004454785365272652 Val loss: 0.002673909355278138
-INFO - evaluator.py - 2024-10-25 21:44:57,475 - Epoch: 14 Train acc: 95.67272727272727 Val acc: 56.24962003769226 Test acc66.03541141416457; Train loss: 0.00042008121833205224 Val loss: 0.010125451873838665
-INFO - evaluator.py - 2024-10-25 21:45:46,881 - Epoch: 15 Train acc: 96.04 Val acc: 62.05544409994529 Test acc68.89468255787303; Train loss: 0.0003926570435139266 Val loss: 0.003153701076116799
-INFO - evaluator.py - 2024-10-25 21:46:34,356 - Epoch: 16 Train acc: 96.50181818181818 Val acc: 47.74758343972278 Test acc60.52028420811369; Train loss: 0.0003468328382481228 Val loss: 0.004144391460443052
-INFO - evaluator.py - 2024-10-25 21:47:25,281 - Epoch: 17 Train acc: 96.70545454545454 Val acc: 60.73621496747522 Test acc68.96057758423103; Train loss: 0.0003292753537270156 Val loss: 0.00385196773946028
-INFO - evaluator.py - 2024-10-25 21:48:14,409 - Epoch: 18 Train acc: 96.92363636363636 Val acc: 63.30779986625327 Test acc73.28672931469173; Train loss: 0.00030313738652250983 Val loss: 0.004614317146823996
-INFO - evaluator.py - 2024-10-25 21:49:02,051 - Epoch: 19 Train acc: 97.13818181818182 Val acc: 51.513769834032466 Test acc63.858010543204216; Train loss: 0.00028811641650443727 Val loss: 0.0044531400272263405
-INFO - evaluator.py - 2024-10-25 21:49:51,218 - Epoch: 20 Train acc: 99.11272727272727 Val acc: 55.6811964253146 Test acc67.16708686683475; Train loss: 9.861812316389247e-05 Val loss: 0.005876784832240296
-INFO - evaluator.py - 2024-10-25 21:50:39,628 - Epoch: 21 Train acc: 99.67454545454545 Val acc: 52.57158489877804 Test acc65.32202612881045; Train loss: 3.709194237069989e-05 Val loss: 0.008485193505925795
-INFO - evaluator.py - 2024-10-25 21:51:26,708 - Epoch: 22 Train acc: 99.79454545454546 Val acc: 52.152106511034106 Test acc66.36202154480861; Train loss: 2.435772543222728e-05 Val loss: 0.010913800001796658
-INFO - evaluator.py - 2024-10-25 21:52:11,477 - Epoch: 23 Train acc: 99.84363636363636 Val acc: 50.41339899082011 Test acc64.09580563832226; Train loss: 1.8551111399905162e-05 Val loss: 0.010961171739898167
-INFO - evaluator.py - 2024-10-25 21:53:00,695 - Epoch: 24 Train acc: 99.70727272727272 Val acc: 47.71110705817983 Test acc61.14771945908778; Train loss: 3.096463077698982e-05 Val loss: 0.01372820729940914
-INFO - evaluator.py - 2024-10-25 21:53:49,576 - Epoch: 25 Train acc: 99.7890909090909 Val acc: 52.85731655419782 Test acc67.17568187027275; Train loss: 2.457594291468426e-05 Val loss: 0.0168190819056565
-INFO - evaluator.py - 2024-10-25 21:54:38,816 - Epoch: 26 Train acc: 99.82909090909091 Val acc: 49.74466532919934 Test acc62.889640155856064; Train loss: 2.0002978094800545e-05 Val loss: 0.017025211428616124
-INFO - evaluator.py - 2024-10-25 21:55:25,909 - Epoch: 27 Train acc: 99.79636363636364 Val acc: 50.103349747705025 Test acc63.99839559935824; Train loss: 2.0787217445600123e-05 Val loss: 0.019351074605576862
-INFO - evaluator.py - 2024-10-25 21:56:13,685 - Epoch: 28 Train acc: 99.73272727272727 Val acc: 44.878108091677305 Test acc57.85583314233326; Train loss: 2.6995308984822423e-05 Val loss: 0.017367378919927158
-INFO - evaluator.py - 2024-10-25 21:57:01,194 - Epoch: 29 Train acc: 99.70909090909092 Val acc: 46.52562465803392 Test acc60.554664221865686; Train loss: 3.101574286463967e-05 Val loss: 0.013926543554018114
-INFO - evaluator.py - 2024-10-25 21:57:48,280 - Epoch: 30 Train acc: 99.89090909090909 Val acc: 48.771961821387315 Test acc62.92115516846207; Train loss: 1.2699885190655054e-05 Val loss: 0.021225436508231586
-INFO - evaluator.py - 2024-10-25 21:58:38,013 - Epoch: 31 Train acc: 99.82 Val acc: 49.02425679372606 Test acc62.482809993124; Train loss: 1.9699814044923354e-05 Val loss: 0.019139057829413518
-INFO - evaluator.py - 2024-10-25 21:59:25,812 - Epoch: 32 Train acc: 99.8490909090909 Val acc: 47.635114596632015 Test acc60.68931927572771; Train loss: 1.5568268401693785e-05 Val loss: 0.02323845717180637
-INFO - evaluator.py - 2024-10-25 22:00:13,259 - Epoch: 33 Train acc: 99.64909090909092 Val acc: 48.325126147486166 Test acc60.91851936740774; Train loss: 4.056724485893607e-05 Val loss: 0.01966886394574248
-INFO - evaluator.py - 2024-10-25 22:00:59,635 - Epoch: 34 Train acc: 99.85090909090908 Val acc: 47.90260806128032 Test acc60.818244327297734; Train loss: 1.5275839623493746e-05 Val loss: 0.022328653265418337
-INFO - evaluator.py - 2024-10-25 22:01:47,353 - Epoch: 35 Train acc: 99.87454545454545 Val acc: 47.99987841206153 Test acc60.861219344487736; Train loss: 1.4246104324411135e-05 Val loss: 0.020234899192140766
-INFO - evaluator.py - 2024-10-25 22:02:34,677 - Epoch: 36 Train acc: 99.88545454545455 Val acc: 48.872271870630435 Test acc62.54584001833601; Train loss: 1.264436794319243e-05 Val loss: 0.022645209710919333
-INFO - evaluator.py - 2024-10-25 22:03:21,359 - Epoch: 37 Train acc: 99.8 Val acc: 51.632318074047056 Test acc67.17281686912675; Train loss: 2.4782855730286843e-05 Val loss: 0.018002483893893136
-INFO - evaluator.py - 2024-10-25 22:04:08,356 - Epoch: 38 Train acc: 99.74181818181819 Val acc: 49.392060307617484 Test acc62.18771487508595; Train loss: 2.8135195536808832e-05 Val loss: 0.020554455748966267
-INFO - evaluator.py - 2024-10-25 22:04:54,203 - Epoch: 39 Train acc: 99.88727272727273 Val acc: 48.21873670131923 Test acc61.73504469401788; Train loss: 1.3996404995255447e-05 Val loss: 0.02321752440031484
-INFO - evaluator.py - 2024-10-25 22:05:40,034 - Epoch: 40 Train acc: 99.97454545454545 Val acc: 49.206638701440816 Test acc63.050080220032086; Train loss: 4.065213437768927e-06 Val loss: 0.024038809136754727
-INFO - evaluator.py - 2024-10-25 22:06:26,561 - Epoch: 41 Train acc: 99.99272727272728 Val acc: 49.0820110645024 Test acc62.992780197112076; Train loss: 1.9468927226874007e-06 Val loss: 0.024810759823650885
-INFO - evaluator.py - 2024-10-25 22:07:13,127 - Epoch: 42 Train acc: 99.99090909090908 Val acc: 48.99993920603076 Test acc62.82374512949806; Train loss: 1.6068554254740195e-06 Val loss: 0.024090154836718322
-INFO - evaluator.py - 2024-10-25 22:07:59,842 - Epoch: 43 Train acc: 100.0 Val acc: 49.34038543376497 Test acc62.906830162732064; Train loss: 7.824889222154691e-07 Val loss: 0.02795240600621188
-INFO - evaluator.py - 2024-10-25 22:08:47,421 - Epoch: 44 Train acc: 100.0 Val acc: 49.26743267067907 Test acc62.73493009397204; Train loss: 6.155542228374212e-07 Val loss: 0.026312107981588423
-INFO - evaluator.py - 2024-10-25 22:09:33,853 - Epoch: 45 Train acc: 99.99818181818182 Val acc: 49.392060307617484 Test acc62.76071510428605; Train loss: 8.649443407689846e-07 Val loss: 0.027229435647234772
-INFO - evaluator.py - 2024-10-25 22:10:20,449 - Epoch: 46 Train acc: 100.0 Val acc: 49.060733175269014 Test acc62.44556497822599; Train loss: 7.03445513892637e-07 Val loss: 0.03231666028532216
-INFO - evaluator.py - 2024-10-25 22:11:07,442 - Epoch: 47 Train acc: 99.99818181818182 Val acc: 48.872271870630435 Test acc62.15906486362594; Train loss: 8.153045539756932e-07 Val loss: 0.03146401833847262
-INFO - evaluator.py - 2024-10-25 22:11:54,641 - Epoch: 48 Train acc: 100.0 Val acc: 48.86619247370661 Test acc61.8639697455879; Train loss: 8.42325757507338e-07 Val loss: 0.04504238826481069
-INFO - evaluator.py - 2024-10-25 22:12:43,614 - Epoch: 49 Train acc: 99.99818181818182 Val acc: 48.841874886011304 Test acc61.978569791427915; Train loss: 5.394639683303962e-07 Val loss: 0.046033050580708564
-INFO - evaluator.py - 2024-10-25 22:12:43,632 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from resnet is 65.9736154173506 and 74.51867980747193
-INFO - evaluator.py - 2024-10-25 22:12:43,632 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from resnet is 65.9736154173506 and 74.51867980747193
-INFO - evaluator.py - 2024-10-25 22:12:43,632 - The best acc test dataset from resnet is 74.51867980747193
-INFO - evaluator.py - 2024-10-25 22:13:39,553 - Epoch: 0 Train acc: 68.50363636363636 Val acc: 44.22761262082802 Test acc52.959546183818475; Train loss: 0.0023140233581716366 Val loss: 0.0010447502806597013
-INFO - evaluator.py - 2024-10-25 22:14:35,009 - Epoch: 1 Train acc: 76.18545454545455 Val acc: 33.679858957991364 Test acc53.165826266330505; Train loss: 0.0019290525143796748 Val loss: 0.001626160849379264
-INFO - evaluator.py - 2024-10-25 22:15:28,804 - Epoch: 2 Train acc: 80.04 Val acc: 30.77390722840294 Test acc38.69470547788219; Train loss: 0.0016905153561722148 Val loss: 0.0015645359408798184
-INFO - evaluator.py - 2024-10-25 22:16:21,832 - Epoch: 3 Train acc: 84.3490909090909 Val acc: 39.71973980181166 Test acc40.534036213614485; Train loss: 0.0013826327789913525 Val loss: 0.0017170040351915912
-INFO - evaluator.py - 2024-10-25 22:17:15,773 - Epoch: 4 Train acc: 86.91636363636364 Val acc: 40.80795185117636 Test acc66.04114141645655; Train loss: 0.0011727856974710117 Val loss: 0.0016674455795413318
-INFO - evaluator.py - 2024-10-25 22:18:11,371 - Epoch: 5 Train acc: 88.9 Val acc: 46.31284576570005 Test acc63.21338528535412; Train loss: 0.0010268822250041095 Val loss: 0.0020492038898420476
-INFO - evaluator.py - 2024-10-25 22:19:05,361 - Epoch: 6 Train acc: 90.40727272727273 Val acc: 39.71973980181166 Test acc63.89239055695622; Train loss: 0.0008942893526770852 Val loss: 0.0019311790039013336
-INFO - evaluator.py - 2024-10-25 22:19:58,677 - Epoch: 7 Train acc: 91.15818181818182 Val acc: 55.77238737917198 Test acc69.56222782489114; Train loss: 0.0008185879617929458 Val loss: 0.0025092531927552756
-INFO - evaluator.py - 2024-10-25 22:20:53,901 - Epoch: 8 Train acc: 92.23272727272727 Val acc: 51.94540701562404 Test acc66.33623653449462; Train loss: 0.000737903476303274 Val loss: 0.0028748720480291394
-INFO - evaluator.py - 2024-10-25 22:21:49,539 - Epoch: 9 Train acc: 92.86909090909091 Val acc: 57.13721198857073 Test acc70.70249828099932; Train loss: 0.0006947915793819861 Val loss: 0.0024603436338016637
-INFO - evaluator.py - 2024-10-25 22:22:43,640 - Epoch: 10 Train acc: 93.34727272727272 Val acc: 51.6931120432853 Test acc60.11631904652762; Train loss: 0.0006510448718612844 Val loss: 0.003170389905585526
-INFO - evaluator.py - 2024-10-25 22:23:38,784 - Epoch: 11 Train acc: 93.9890909090909 Val acc: 47.896528664356495 Test acc58.04492321796929; Train loss: 0.0005906129121780395 Val loss: 0.0028481043916435923
-INFO - evaluator.py - 2024-10-25 22:24:34,388 - Epoch: 12 Train acc: 94.31272727272727 Val acc: 55.18876527448477 Test acc65.79475131790052; Train loss: 0.00054724995019761 Val loss: 0.002622481145875578
-INFO - evaluator.py - 2024-10-25 22:25:29,071 - Epoch: 13 Train acc: 94.81090909090909 Val acc: 57.24360143473768 Test acc69.98338299335319; Train loss: 0.0005076094220985065 Val loss: 0.002976504413760812
-INFO - evaluator.py - 2024-10-25 22:26:23,542 - Epoch: 14 Train acc: 95.15090909090908 Val acc: 50.70521004316372 Test acc60.56612422644969; Train loss: 0.00047027083926580167 Val loss: 0.003305536858962585
-INFO - evaluator.py - 2024-10-25 22:27:19,186 - Epoch: 15 Train acc: 95.60909090909091 Val acc: 48.088029667456986 Test acc59.3943387577355; Train loss: 0.00043112336139787327 Val loss: 0.0038751930268356247
-INFO - evaluator.py - 2024-10-25 22:28:14,232 - Epoch: 16 Train acc: 95.88181818181818 Val acc: 54.63250045595477 Test acc64.3651157460463; Train loss: 0.00040514673563567076 Val loss: 0.0036373013848265264
-INFO - evaluator.py - 2024-10-25 22:29:08,693 - Epoch: 17 Train acc: 96.30545454545455 Val acc: 57.72995318864369 Test acc68.0810222324089; Train loss: 0.0003646235543218526 Val loss: 0.0037908537118686676
-INFO - evaluator.py - 2024-10-25 22:30:02,431 - Epoch: 18 Train acc: 96.62363636363636 Val acc: 52.79652258495957 Test acc65.03839101535641; Train loss: 0.00034233890568668193 Val loss: 0.0033512748014644994
-INFO - evaluator.py - 2024-10-25 22:30:56,900 - Epoch: 19 Train acc: 96.76545454545455 Val acc: 49.936166332299834 Test acc60.892734357093744; Train loss: 0.00032101729065179825 Val loss: 0.004337900293236468
-INFO - evaluator.py - 2024-10-25 22:31:49,955 - Epoch: 20 Train acc: 98.28545454545454 Val acc: 52.67797434494498 Test acc64.79773091909237; Train loss: 0.00017931479083543473 Val loss: 0.003735412630355643
-INFO - evaluator.py - 2024-10-25 22:32:42,704 - Epoch: 21 Train acc: 98.66909090909091 Val acc: 50.47419296005836 Test acc62.54584001833601; Train loss: 0.00013696041145277294 Val loss: 0.004469168382587024
-INFO - evaluator.py - 2024-10-25 22:33:35,651 - Epoch: 22 Train acc: 98.83818181818181 Val acc: 50.55018542160617 Test acc63.239170295668124; Train loss: 0.0001208684218539433 Val loss: 0.005072198745233106
-INFO - evaluator.py - 2024-10-25 22:34:29,849 - Epoch: 23 Train acc: 98.89818181818183 Val acc: 51.133807526293396 Test acc64.4052257620903; Train loss: 0.00010941027310592207 Val loss: 0.005431866673352371
-INFO - evaluator.py - 2024-10-25 22:35:24,223 - Epoch: 24 Train acc: 99.08727272727272 Val acc: 51.152045717064865 Test acc63.80071052028421; Train loss: 9.735608511943031e-05 Val loss: 0.006014869223105392
-INFO - evaluator.py - 2024-10-25 22:36:18,628 - Epoch: 25 Train acc: 98.97090909090909 Val acc: 49.36166332299835 Test acc62.28512491404996; Train loss: 0.00010028023670681498 Val loss: 0.00693233781791732
-INFO - evaluator.py - 2024-10-25 22:37:11,935 - Epoch: 26 Train acc: 99.14181818181818 Val acc: 48.367681925952944 Test acc60.95289938115975; Train loss: 8.692951218902388e-05 Val loss: 0.007558272728348033
-INFO - evaluator.py - 2024-10-25 22:38:05,866 - Epoch: 27 Train acc: 99.19818181818182 Val acc: 48.91482764909721 Test acc61.2823745129498; Train loss: 8.161040863699533e-05 Val loss: 0.007028561419966417
-INFO - evaluator.py - 2024-10-25 22:38:59,374 - Epoch: 28 Train acc: 99.24727272727273 Val acc: 49.96656331691896 Test acc61.700664680265874; Train loss: 7.826695567081597e-05 Val loss: 0.00683547350489062
-INFO - evaluator.py - 2024-10-25 22:39:52,099 - Epoch: 29 Train acc: 99.32181818181817 Val acc: 48.57134172290109 Test acc60.540339216135685; Train loss: 7.241568004458465e-05 Val loss: 0.007125788904974269
-INFO - evaluator.py - 2024-10-25 22:40:44,288 - Epoch: 30 Train acc: 99.32727272727273 Val acc: 50.54410602468236 Test acc61.843914737565896; Train loss: 7.092309828678316e-05 Val loss: 0.0065819654169615455
-INFO - evaluator.py - 2024-10-25 22:41:37,610 - Epoch: 31 Train acc: 99.40363636363637 Val acc: 51.294911544774756 Test acc63.21625028650012; Train loss: 6.2586063047109e-05 Val loss: 0.006555278190066876
-INFO - evaluator.py - 2024-10-25 22:42:31,673 - Epoch: 32 Train acc: 99.41454545454546 Val acc: 49.8297768861329 Test acc61.51730460692184; Train loss: 6.224617715289985e-05 Val loss: 0.007723667533341581
-INFO - evaluator.py - 2024-10-25 22:43:25,879 - Epoch: 33 Train acc: 99.46545454545455 Val acc: 50.3617241169676 Test acc62.69768507907403; Train loss: 5.8298662944104184e-05 Val loss: 0.007185819627477866
-INFO - evaluator.py - 2024-10-25 22:44:20,102 - Epoch: 34 Train acc: 99.5 Val acc: 49.59875980302754 Test acc61.34826953930782; Train loss: 5.4177132418209854e-05 Val loss: 0.007796106472429341
-INFO - evaluator.py - 2024-10-25 22:45:13,219 - Epoch: 35 Train acc: 99.50727272727272 Val acc: 51.12468843090765 Test acc63.38242035296814; Train loss: 5.7062019107185984e-05 Val loss: 0.008229685667488914
-INFO - evaluator.py - 2024-10-25 22:46:07,863 - Epoch: 36 Train acc: 99.44727272727273 Val acc: 50.88455225241656 Test acc63.1188402475361; Train loss: 5.646915808108381e-05 Val loss: 0.0073133212424707356
-INFO - evaluator.py - 2024-10-25 22:47:01,578 - Epoch: 37 Train acc: 99.47090909090909 Val acc: 51.25235576630798 Test acc61.8467797387119; Train loss: 5.4538340809416365e-05 Val loss: 0.0071491268936482825
-INFO - evaluator.py - 2024-10-25 22:47:55,235 - Epoch: 38 Train acc: 99.50363636363636 Val acc: 48.95738342756398 Test acc59.94441897776759; Train loss: 5.538183275652542e-05 Val loss: 0.008349389622324368
-INFO - evaluator.py - 2024-10-25 22:48:49,247 - Epoch: 39 Train acc: 99.54727272727273 Val acc: 52.44695726183962 Test acc62.98132019252808; Train loss: 5.0677793407389385e-05 Val loss: 0.00745782557066628
-INFO - evaluator.py - 2024-10-25 22:49:43,018 - Epoch: 40 Train acc: 99.66000000000001 Val acc: 52.054836160252904 Test acc62.00148980059592; Train loss: 3.5326177131553945e-05 Val loss: 0.007314164469369502
-INFO - evaluator.py - 2024-10-25 22:50:35,513 - Epoch: 41 Train acc: 99.75090909090909 Val acc: 52.59286278801143 Test acc61.8496447398579; Train loss: 2.7268003844338554e-05 Val loss: 0.007554267241140363
-INFO - evaluator.py - 2024-10-25 22:51:29,718 - Epoch: 42 Train acc: 99.74727272727273 Val acc: 53.04577785883641 Test acc61.921269768507905; Train loss: 2.787533993621103e-05 Val loss: 0.007357886746499493
-INFO - evaluator.py - 2024-10-25 22:52:22,250 - Epoch: 43 Train acc: 99.80909090909091 Val acc: 51.580643200194544 Test acc61.27664451065781; Train loss: 2.3305396545170383e-05 Val loss: 0.007995615614838235
-INFO - evaluator.py - 2024-10-25 22:53:16,084 - Epoch: 44 Train acc: 99.76181818181819 Val acc: 51.291871846312844 Test acc61.253724501489806; Train loss: 2.5483106591590595e-05 Val loss: 0.007876000641196599
-INFO - evaluator.py - 2024-10-25 22:54:10,356 - Epoch: 45 Train acc: 99.74181818181819 Val acc: 52.416560277220505 Test acc61.92699977079991; Train loss: 2.620521336238281e-05 Val loss: 0.00787004765583553
-INFO - evaluator.py - 2024-10-25 22:55:03,309 - Epoch: 46 Train acc: 99.76363636363637 Val acc: 52.18554319411515 Test acc61.69206967682786; Train loss: 2.6699755231013774e-05 Val loss: 0.007889770781698904
-INFO - evaluator.py - 2024-10-25 22:55:56,323 - Epoch: 47 Train acc: 99.84727272727272 Val acc: 52.92722961882181 Test acc62.98418519367408; Train loss: 1.9988784450900063e-05 Val loss: 0.008015931083827157
-INFO - evaluator.py - 2024-10-25 22:56:50,803 - Epoch: 48 Train acc: 99.79818181818182 Val acc: 51.04261657243602 Test acc61.2651845060738; Train loss: 2.2680267876289276e-05 Val loss: 0.008229840090951845
-INFO - evaluator.py - 2024-10-25 22:57:43,778 - Epoch: 49 Train acc: 99.82181818181817 Val acc: 51.580643200194544 Test acc61.79234471693788; Train loss: 2.0343087930020624e-05 Val loss: 0.008108474482257386
-INFO - evaluator.py - 2024-10-25 22:57:43,789 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from wrn is 57.72995318864369 and 68.0810222324089
-INFO - evaluator.py - 2024-10-25 22:57:43,789 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from wrn is 57.72995318864369 and 68.0810222324089
-INFO - evaluator.py - 2024-10-25 22:57:43,789 - The best acc test dataset from wrn is 70.70249828099932
-INFO - evaluator.py - 2024-10-25 23:00:35,693 - Epoch: 0 Train acc: 66.34 Val acc: 42.94182017143899 Test acc53.98235159294064; Train loss: 0.002734447446194562 Val loss: 0.0010660075729716128
-INFO - evaluator.py - 2024-10-25 23:03:26,522 - Epoch: 1 Train acc: 76.52909090909091 Val acc: 41.01769104504833 Test acc55.82741233096493; Train loss: 0.0019173718539151278 Val loss: 0.0011042619782078555
-INFO - evaluator.py - 2024-10-25 23:06:17,077 - Epoch: 2 Train acc: 81.76181818181819 Val acc: 51.182442701683996 Test acc57.37737795095118; Train loss: 0.0015795916042544624 Val loss: 0.0012270079187233981
-INFO - evaluator.py - 2024-10-25 23:09:09,325 - Epoch: 3 Train acc: 84.57636363636364 Val acc: 43.2822663991732 Test acc63.08159523263809; Train loss: 0.0013914922584186901 Val loss: 0.0015580656354684903
-INFO - evaluator.py - 2024-10-25 23:12:00,681 - Epoch: 4 Train acc: 86.48181818181818 Val acc: 62.73025715848988 Test acc63.21625028650012; Train loss: 0.0012335099791938608 Val loss: 0.0008836500394220402
-INFO - evaluator.py - 2024-10-25 23:14:51,849 - Epoch: 5 Train acc: 88.57818181818182 Val acc: 46.80831661499179 Test acc69.17545267018107; Train loss: 0.0010616454820741306 Val loss: 0.0018695781742434304
-INFO - evaluator.py - 2024-10-25 23:17:43,679 - Epoch: 6 Train acc: 89.99636363636364 Val acc: 64.12243905404584 Test acc74.06314462525785; Train loss: 0.0009450558759949424 Val loss: 0.0016042046963215542
-INFO - evaluator.py - 2024-10-25 23:20:36,261 - Epoch: 7 Train acc: 91.03999999999999 Val acc: 51.826858775609466 Test acc69.30151272060509; Train loss: 0.0008498225380073894 Val loss: 0.0013987489207275472
-INFO - evaluator.py - 2024-10-25 23:23:28,172 - Epoch: 8 Train acc: 92.45272727272727 Val acc: 62.02504711532616 Test acc73.79956451982581; Train loss: 0.0007290911996906454 Val loss: 0.0028235875579664224
-INFO - evaluator.py - 2024-10-25 23:26:21,444 - Epoch: 9 Train acc: 93.4290909090909 Val acc: 64.58751291871846 Test acc65.80048132019253; Train loss: 0.0006399542087858373 Val loss: 0.0021839297551921495
-INFO - evaluator.py - 2024-10-25 23:29:12,676 - Epoch: 10 Train acc: 94.22363636363636 Val acc: 59.10693659189008 Test acc59.89857895943158; Train loss: 0.0005649962403557517 Val loss: 0.0023338078901727
-INFO - evaluator.py - 2024-10-25 23:32:04,533 - Epoch: 11 Train acc: 94.91454545454545 Val acc: 56.80892455468417 Test acc62.54584001833601; Train loss: 0.0005058348403735594 Val loss: 0.002144170395182479
-INFO - evaluator.py - 2024-10-25 23:34:55,778 - Epoch: 12 Train acc: 95.37454545454545 Val acc: 54.003282874338865 Test acc59.14508365803346; Train loss: 0.00045597415187142114 Val loss: 0.0021929612267857315
-INFO - evaluator.py - 2024-10-25 23:37:47,047 - Epoch: 13 Train acc: 95.97454545454546 Val acc: 45.81129551948447 Test acc51.57288562915425; Train loss: 0.0004073105944828554 Val loss: 0.0029243362284016848
-INFO - evaluator.py - 2024-10-25 23:40:38,381 - Epoch: 14 Train acc: 96.30727272727273 Val acc: 53.51085172350902 Test acc60.31113912445565; Train loss: 0.00036749395673925226 Val loss: 0.002548673745889969
-INFO - evaluator.py - 2024-10-25 23:43:29,820 - Epoch: 15 Train acc: 96.57818181818182 Val acc: 47.923885950513714 Test acc53.55260142104057; Train loss: 0.0003393713050267913 Val loss: 0.004239257972460328
-INFO - evaluator.py - 2024-10-25 23:46:20,857 - Epoch: 16 Train acc: 97.24181818181819 Val acc: 51.03349747705027 Test acc57.91599816639926; Train loss: 0.00027163526368412103 Val loss: 0.0025901796027979042
-INFO - evaluator.py - 2024-10-25 23:49:13,185 - Epoch: 17 Train acc: 97.46909090909091 Val acc: 44.87506839321539 Test acc50.991290396516156; Train loss: 0.0002526596325703643 Val loss: 0.0033584566013132797
-INFO - evaluator.py - 2024-10-25 23:52:04,115 - Epoch: 18 Train acc: 97.97090909090909 Val acc: 41.96303726670314 Test acc47.83692413476965; Train loss: 0.00020738575279035353 Val loss: 0.0058254214345999375
-INFO - evaluator.py - 2024-10-25 23:54:55,742 - Epoch: 19 Train acc: 97.82181818181817 Val acc: 43.39169554380206 Test acc48.1463442585377; Train loss: 0.0002227118907834996 Val loss: 0.00437075354435631
-INFO - evaluator.py - 2024-10-25 23:57:47,282 - Epoch: 20 Train acc: 99.63090909090909 Val acc: 47.12140555656879 Test acc52.549851019940405; Train loss: 5.4561169377782126e-05 Val loss: 0.0033946357618980447
-INFO - evaluator.py - 2024-10-26 00:00:38,819 - Epoch: 21 Train acc: 99.97636363636364 Val acc: 46.735363851905895 Test acc51.443960577584235; Train loss: 1.4766088771549138e-05 Val loss: 0.00410181317650313
-INFO - evaluator.py - 2024-10-26 00:03:29,925 - Epoch: 22 Train acc: 99.99272727272728 Val acc: 46.349322147242994 Test acc51.26060050424021; Train loss: 6.089641713664275e-06 Val loss: 0.004472876690870303
-INFO - evaluator.py - 2024-10-26 00:06:20,946 - Epoch: 23 Train acc: 99.99818181818182 Val acc: 46.90862666423491 Test acc52.56990602796241; Train loss: 3.0470091556542847e-06 Val loss: 0.004874853111196125
-INFO - evaluator.py - 2024-10-26 00:09:13,001 - Epoch: 24 Train acc: 100.0 Val acc: 46.303726670314305 Test acc51.782030712812286; Train loss: 1.3206470487189521e-06 Val loss: 0.005846254933865297
-INFO - evaluator.py - 2024-10-26 00:12:04,334 - Epoch: 25 Train acc: 99.99636363636364 Val acc: 46.62289500881512 Test acc52.25189090075636; Train loss: 9.211642758104558e-07 Val loss: 0.006346316053205091
-INFO - evaluator.py - 2024-10-26 00:14:55,552 - Epoch: 26 Train acc: 99.99818181818182 Val acc: 46.495227673414796 Test acc52.02555581022232; Train loss: 9.943477954617596e-07 Val loss: 0.007014413468127077
-INFO - evaluator.py - 2024-10-26 00:17:46,826 - Epoch: 27 Train acc: 100.0 Val acc: 47.64727339047967 Test acc53.28902131560853; Train loss: 7.978133584153759e-07 Val loss: 0.007586389263681137
-INFO - evaluator.py - 2024-10-26 00:20:38,485 - Epoch: 28 Train acc: 99.99818181818182 Val acc: 46.96638093501125 Test acc52.68450607380243; Train loss: 5.559406689371826e-07 Val loss: 0.008171719253603119
-INFO - evaluator.py - 2024-10-26 00:23:31,455 - Epoch: 29 Train acc: 100.0 Val acc: 47.28554927351207 Test acc52.73607609443044; Train loss: 2.604813331117839e-07 Val loss: 0.008788205155544175
-INFO - evaluator.py - 2024-10-26 00:26:24,171 - Epoch: 30 Train acc: 100.0 Val acc: 46.595537722657916 Test acc52.62434104973642; Train loss: 5.817958908644207e-07 Val loss: 0.009109983038950114
-INFO - evaluator.py - 2024-10-26 00:29:16,841 - Epoch: 31 Train acc: 99.36181818181818 Val acc: 47.492248768922124 Test acc52.05134082053633; Train loss: 6.883570372929584e-05 Val loss: 0.007302738350643214
-INFO - evaluator.py - 2024-10-26 00:32:09,050 - Epoch: 32 Train acc: 99.89454545454547 Val acc: 46.376679433400206 Test acc50.30082512033005; Train loss: 1.4224879905867221e-05 Val loss: 0.00793065244401144
-INFO - evaluator.py - 2024-10-26 00:35:00,985 - Epoch: 33 Train acc: 99.79090909090908 Val acc: 44.75044075627698 Test acc49.74787989915196; Train loss: 2.1384033233070196e-05 Val loss: 0.007746807296440314
-INFO - evaluator.py - 2024-10-26 00:37:52,871 - Epoch: 34 Train acc: 99.90545454545455 Val acc: 44.57717794394796 Test acc49.12617465046986; Train loss: 1.176286441477714e-05 Val loss: 0.009782828011478839
-INFO - evaluator.py - 2024-10-26 00:40:45,017 - Epoch: 35 Train acc: 99.70909090909092 Val acc: 51.81773968022372 Test acc60.10485904194361; Train loss: 2.975529119853904e-05 Val loss: 0.0067399177618523605
-INFO - evaluator.py - 2024-10-26 00:43:36,342 - Epoch: 36 Train acc: 99.69454545454546 Val acc: 47.0514924919448 Test acc53.317671327068524; Train loss: 3.342832206848967e-05 Val loss: 0.007939351439164383
-INFO - evaluator.py - 2024-10-26 00:46:27,589 - Epoch: 37 Train acc: 99.90727272727273 Val acc: 48.28864976594322 Test acc54.80747192298877; Train loss: 1.0486411597230471e-05 Val loss: 0.007197061507736226
-INFO - evaluator.py - 2024-10-26 00:49:18,852 - Epoch: 38 Train acc: 99.92909090909092 Val acc: 49.06985227065475 Test acc57.15104286041714; Train loss: 8.10449306511807e-06 Val loss: 0.006997041089446779
-INFO - evaluator.py - 2024-10-26 00:52:09,779 - Epoch: 39 Train acc: 99.80181818181818 Val acc: 46.24597239953797 Test acc52.20605088242035; Train loss: 2.0643364289241038e-05 Val loss: 0.007269558960764276
-INFO - evaluator.py - 2024-10-26 00:55:01,063 - Epoch: 40 Train acc: 99.94363636363637 Val acc: 47.68678947048453 Test acc54.56967682787073; Train loss: 8.024039548565104e-06 Val loss: 0.007142175634665811
-INFO - evaluator.py - 2024-10-26 00:57:51,514 - Epoch: 41 Train acc: 99.9890909090909 Val acc: 48.20049851054775 Test acc54.83039193215677; Train loss: 1.7416881183469905e-06 Val loss: 0.007359958070412589
-INFO - evaluator.py - 2024-10-26 01:00:41,919 - Epoch: 42 Train acc: 100.0 Val acc: 47.489209070460205 Test acc53.81331652532661; Train loss: 7.122553868281433e-07 Val loss: 0.007714795040079827
-INFO - evaluator.py - 2024-10-26 01:03:32,151 - Epoch: 43 Train acc: 100.0 Val acc: 46.74144324882972 Test acc52.70742608297043; Train loss: 5.83172490339414e-07 Val loss: 0.008118491816632298
-INFO - evaluator.py - 2024-10-26 01:06:22,536 - Epoch: 44 Train acc: 100.0 Val acc: 46.67760958112955 Test acc52.55558102223241; Train loss: 4.874046851496546e-07 Val loss: 0.008319486215909033
-INFO - evaluator.py - 2024-10-26 01:09:13,864 - Epoch: 45 Train acc: 100.0 Val acc: 46.802237218067965 Test acc52.42379096951639; Train loss: 4.3609491607309214e-07 Val loss: 0.008634260631461325
-INFO - evaluator.py - 2024-10-26 01:12:04,909 - Epoch: 46 Train acc: 100.0 Val acc: 46.44051310110037 Test acc52.165940866376346; Train loss: 2.5478310728265733e-07 Val loss: 0.008901926078102975
-INFO - evaluator.py - 2024-10-26 01:14:57,106 - Epoch: 47 Train acc: 100.0 Val acc: 46.65025229497234 Test acc52.148750859500346; Train loss: 3.9532065715403323e-07 Val loss: 0.009080414902773285
-INFO - evaluator.py - 2024-10-26 01:17:48,844 - Epoch: 48 Train acc: 100.0 Val acc: 46.154781445680584 Test acc51.79062571625028; Train loss: 2.1409483279884708e-07 Val loss: 0.009411275651333317
-INFO - evaluator.py - 2024-10-26 01:20:39,839 - Epoch: 49 Train acc: 100.0 Val acc: 46.25205179646179 Test acc51.931010772404306; Train loss: 2.0519702628276223e-07 Val loss: 0.009519187358601331
-INFO - evaluator.py - 2024-10-26 01:20:39,847 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from resnext is 64.58751291871846 and 65.80048132019253
-INFO - evaluator.py - 2024-10-26 01:20:39,847 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from resnext is 64.58751291871846 and 65.80048132019253
-INFO - evaluator.py - 2024-10-26 01:20:39,847 - The best acc test dataset from resnext is 74.06314462525785
-INFO - evaluator.py - 2024-10-26 01:20:39,847 - The best acc of accuracy (using synthetic images as the validation set) of synthetic images from resnet, wrn, and resnext are [74.51867980747193, 68.0810222324089, 65.80048132019253].
-INFO - evaluator.py - 2024-10-26 01:20:39,847 - The average and std of accuracy of synthetic images are 69.47 and 3.69
-INFO - dataset_loader.py - 2024-10-26 01:23:26,143 - delta is reset as 2.07404851125286e-06
-INFO - dataset_loader.py - 2024-10-26 01:25:52,343 - delta is reset as 2.07404851125286e-06
-INFO - dataset_loader.py - 2024-10-26 01:28:09,827 - delta is reset as 2.07404851125286e-06
-INFO - dataset_loader.py - 2024-10-26 01:30:18,229 - delta is reset as 2.07404851125286e-06
-INFO - dataset_loader.py - 2024-10-26 01:51:04,782 - delta is reset as 2.07404851125286e-06
-INFO - dataset_loader.py - 2024-10-28 19:59:45,818 - delta is reset as 5.11965868690912e-07
-INFO - evaluator.py - 2024-10-28 21:01:26,492 - The FID of synthetic images is 126.25051468406232
-INFO - evaluator.py - 2024-10-28 21:01:26,495 - The Inception Score of synthetic images is 2.543980360031128
-INFO - evaluator.py - 2024-10-28 21:01:26,496 - The Precision and Recall of synthetic images is 0.4407656490802765 and 0.0024574552662670612
-INFO - evaluator.py - 2024-10-28 21:01:26,497 - The FLD of synthetic images is 20.160961151123047
-INFO - evaluator.py - 2024-10-28 21:01:26,497 - The ImageReward of synthetic images is -1.9267108455103243
-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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