diff --git a/.gitattributes b/.gitattributes
index 68bc9e0a623b6a1af016584f7944c937bbe9674a..fa5d1efd5776556cc674c88c8694117b884788a7 100644
--- a/.gitattributes
+++ b/.gitattributes
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diff --git a/dpdm/eurosat_32_eps1.0trainval-2024-10-24-13-38-47/stdout.txt b/dpdm/eurosat_32_eps1.0trainval-2024-10-24-13-38-47/stdout.txt
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index 0000000000000000000000000000000000000000..1358647c8b6d694ebfeeef5b5a6da39896178edb
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+++ b/dpdm/eurosat_32_eps1.0trainval-2024-10-24-13-38-47/stdout.txt
@@ -0,0 +1,1005 @@
+INFO - utils.py - 2024-10-24 13:38:52,213 - {'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/eurosat_32_eps1.0trainval-2024-10-24-13-38-47', 'local_rank': 0, 'global_rank': 0, 'global_size': 4, 'root_folder': '.'}, 'public_data': {'name': None}, 'sensitive_data': {'name': 'eurosat', 'num_channels': 3, 'resolution': 32, 'n_classes': 10, 'train_path': 'dataset/eurosat/train_32.zip', 'test_path': 'dataset/eurosat/test_32.zip', 'fid_stats': 'dataset/eurosat/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': 10, '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': 'edm', 's_churn': 100, 's_min': 0.05, 's_max': 50, 'num_steps': 250, 'tmin': 0.002, 'tmax': 80.0, 'rho': 7.0, 'guid_scale': 0.0}, 'sampler_acc': {'type': 'edm', 's_churn': 10, 's_min': 0.1, 's_max': 50, 'num_steps': 250, 'tmin': 0.002, 'tmax': 80.0, 'rho': 7.0, 'guid_scale': 0.0, 'labels': 10}, 'local_rank': 0, 'global_rank': 0, 'global_size': 4, 'fid_stats': 'dataset/eurosat/fid_stats_32.npz'}, 'pretrain': {'log_dir': 'exp/dpdm/eurosat_32_eps1.0trainval-2024-10-24-13-38-47/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': 10}}, 'train': {'log_dir': 'exp/dpdm/eurosat_32_eps1.0trainval-2024-10-24-13-38-47/train', 'seed': 0, 'batch_size': 4096, 'n_epochs': 150, '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': 10}, 'dp': {'sdq': None, 'privacy_history': [[5, 0.1, 75]], 'alpha_num': 0, 'max_grad_norm': 1.0, 'delta': 1e-05, 'epsilon': 1.0, 'max_physical_batch_size': 8192, 'n_splits': 64}}, 'gen': {'data_num': 60000, 'batch_size': 1000, 'log_dir': 'exp/dpdm/eurosat_32_eps1.0trainval-2024-10-24-13-38-47/gen'}, 'eval': {'batch_size': 1000}}
+INFO - dataset_loader.py - 2024-10-24 13:38:57,586 - delta is reset as 4.784738627130138e-06
+INFO - dpsgd_diffusion.py - 2024-10-24 13:38:58,372 - Number of trainable parameters in model: 0
+INFO - dpsgd_diffusion.py - 2024-10-24 13:38:58,372 - Number of total epochs: 150
+INFO - dpsgd_diffusion.py - 2024-10-24 13:38:58,372 - Starting training at step 0
+INFO - dpsgd_diffusion.py - 2024-10-24 13:40:13,703 - Loss: 0.8269, step: 100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:41:04,692 - Loss: 0.7639, step: 200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:41:54,844 - Loss: 0.7436, step: 300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:42:37,909 - Eps-value after 1 epochs: 0.1266
+INFO - dpsgd_diffusion.py - 2024-10-24 13:42:45,724 - Loss: 0.7374, step: 400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:43:35,799 - Loss: 0.7423, step: 500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:44:29,086 - Loss: 0.6862, step: 600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:45:16,702 - Loss: 0.7268, step: 700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:45:49,468 - Eps-value after 2 epochs: 0.1385
+INFO - dpsgd_diffusion.py - 2024-10-24 13:46:06,323 - Loss: 0.6385, step: 800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:46:57,328 - Loss: 0.6711, step: 900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:47:45,793 - Loss: 0.6728, step: 1000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:48:34,852 - Loss: 0.5928, step: 1100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:49:01,387 - Eps-value after 3 epochs: 0.1503
+INFO - dpsgd_diffusion.py - 2024-10-24 13:49:26,312 - Loss: 0.6233, step: 1200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:50:14,966 - Loss: 0.5954, step: 1300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:51:03,093 - Loss: 0.5741, step: 1400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:51:51,683 - Loss: 0.5316, step: 1500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:52:08,990 - Eps-value after 4 epochs: 0.1622
+INFO - dpsgd_diffusion.py - 2024-10-24 13:52:39,725 - Loss: 0.5619, step: 1600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:53:27,861 - Loss: 0.5131, step: 1700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:54:16,727 - Loss: 0.4851, step: 1800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:55:04,256 - Loss: 0.5063, step: 1900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:55:13,673 - Eps-value after 5 epochs: 0.1741
+INFO - dpsgd_diffusion.py - 2024-10-24 13:55:53,044 - Loss: 0.5045, step: 2000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:55:53,104 - Saving snapshot checkpoint and sampling single batch at iteration 2000.
+WARNING - image.py - 2024-10-24 13:55:54,259 - 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 13:56:13,538 - FID at iteration 2000: 290.424782
+INFO - dpsgd_diffusion.py - 2024-10-24 13:57:00,838 - Loss: 0.4818, step: 2100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:57:52,513 - Loss: 0.4530, step: 2200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:58:40,945 - Loss: 0.4629, step: 2300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:58:42,872 - Eps-value after 6 epochs: 0.1859
+INFO - dpsgd_diffusion.py - 2024-10-24 13:59:29,874 - Loss: 0.4697, step: 2400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:00:18,667 - Loss: 0.4606, step: 2500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:01:08,968 - Loss: 0.3953, step: 2600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:01:50,983 - Eps-value after 7 epochs: 0.1978
+INFO - dpsgd_diffusion.py - 2024-10-24 14:01:57,266 - Loss: 0.4166, step: 2700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:02:48,299 - Loss: 0.3902, step: 2800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:03:37,902 - Loss: 0.4136, step: 2900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:04:25,707 - Loss: 0.3650, step: 3000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:05:00,529 - Eps-value after 8 epochs: 0.2097
+INFO - dpsgd_diffusion.py - 2024-10-24 14:05:14,224 - Loss: 0.3657, step: 3100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:06:02,172 - Loss: 0.3588, step: 3200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:06:52,439 - Loss: 0.3379, step: 3300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:07:43,300 - Loss: 0.3520, step: 3400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:08:11,101 - Eps-value after 9 epochs: 0.2215
+INFO - dpsgd_diffusion.py - 2024-10-24 14:08:32,300 - Loss: 0.3844, step: 3500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:09:21,317 - Loss: 0.3548, step: 3600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:10:08,905 - Loss: 0.3579, step: 3700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:10:57,618 - Loss: 0.3365, step: 3800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:11:16,695 - Eps-value after 10 epochs: 0.2334
+INFO - dpsgd_diffusion.py - 2024-10-24 14:11:47,188 - Loss: 0.3228, step: 3900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:12:37,092 - Loss: 0.2952, step: 4000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:12:37,144 - Saving snapshot checkpoint and sampling single batch at iteration 4000.
+WARNING - image.py - 2024-10-24 14:12:37,660 - 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 14:12:53,106 - FID at iteration 4000: 282.634037
+INFO - dpsgd_diffusion.py - 2024-10-24 14:13:41,140 - Loss: 0.2929, step: 4100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:14:28,703 - Loss: 0.2997, step: 4200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:14:39,959 - Eps-value after 11 epochs: 0.2452
+INFO - dpsgd_diffusion.py - 2024-10-24 14:15:16,983 - Loss: 0.3053, step: 4300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:16:06,538 - Loss: 0.3044, step: 4400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:16:54,621 - Loss: 0.2911, step: 4500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:17:43,517 - Loss: 0.3344, step: 4600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:17:47,417 - Eps-value after 12 epochs: 0.2569
+INFO - dpsgd_diffusion.py - 2024-10-24 14:18:32,729 - Loss: 0.2696, step: 4700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:19:20,944 - Loss: 0.3270, step: 4800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:20:11,836 - Loss: 0.2939, step: 4900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:20:56,578 - Eps-value after 13 epochs: 0.2680
+INFO - dpsgd_diffusion.py - 2024-10-24 14:21:00,466 - Loss: 0.2718, step: 5000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:21:49,605 - Loss: 0.2987, step: 5100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:22:38,828 - Loss: 0.3054, step: 5200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:23:27,232 - Loss: 0.2782, step: 5300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:24:05,419 - Eps-value after 14 epochs: 0.2788
+INFO - dpsgd_diffusion.py - 2024-10-24 14:24:17,602 - Loss: 0.2989, step: 5400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:25:06,479 - Loss: 0.3008, step: 5500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:25:55,299 - Loss: 0.2967, step: 5600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:26:45,500 - Loss: 0.3065, step: 5700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:27:14,956 - Eps-value after 15 epochs: 0.2892
+INFO - dpsgd_diffusion.py - 2024-10-24 14:27:35,995 - Loss: 0.2455, step: 5800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:28:27,340 - Loss: 0.2758, step: 5900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:29:16,496 - Loss: 0.3105, step: 6000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:29:16,517 - Saving snapshot checkpoint and sampling single batch at iteration 6000.
+INFO - dpsgd_diffusion.py - 2024-10-24 14:29:32,455 - FID at iteration 6000: 278.571301
+INFO - dpsgd_diffusion.py - 2024-10-24 14:30:21,269 - Loss: 0.3018, step: 6100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:30:42,790 - Eps-value after 16 epochs: 0.2994
+INFO - dpsgd_diffusion.py - 2024-10-24 14:31:11,070 - Loss: 0.2443, step: 6200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:32:02,672 - Loss: 0.2629, step: 6300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:32:53,486 - Loss: 0.2586, step: 6400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:33:41,067 - Loss: 0.2719, step: 6500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:33:54,475 - Eps-value after 17 epochs: 0.3092
+INFO - dpsgd_diffusion.py - 2024-10-24 14:34:29,371 - Loss: 0.2639, step: 6600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:35:17,119 - Loss: 0.2611, step: 6700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:36:05,040 - Loss: 0.2958, step: 6800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:36:53,244 - Loss: 0.2374, step: 6900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:36:58,720 - Eps-value after 18 epochs: 0.3187
+INFO - dpsgd_diffusion.py - 2024-10-24 14:37:41,612 - Loss: 0.2170, step: 7000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:38:29,774 - Loss: 0.2517, step: 7100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:39:17,800 - Loss: 0.2774, step: 7200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:40:03,221 - Eps-value after 19 epochs: 0.3280
+INFO - dpsgd_diffusion.py - 2024-10-24 14:40:05,315 - Loss: 0.3050, step: 7300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:40:55,373 - Loss: 0.2580, step: 7400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:41:45,284 - Loss: 0.2672, step: 7500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:42:34,930 - Loss: 0.2253, step: 7600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:43:14,154 - Eps-value after 20 epochs: 0.3371
+INFO - dpsgd_diffusion.py - 2024-10-24 14:43:23,608 - Loss: 0.2837, step: 7700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:44:11,338 - Loss: 0.2586, step: 7800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:45:00,598 - Loss: 0.2424, step: 7900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:45:48,125 - Loss: 0.2576, step: 8000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:45:48,130 - Saving snapshot checkpoint and sampling single batch at iteration 8000.
+WARNING - image.py - 2024-10-24 14:45:48,668 - 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 14:46:03,974 - FID at iteration 8000: 250.752431
+INFO - dpsgd_diffusion.py - 2024-10-24 14:46:34,827 - Eps-value after 21 epochs: 0.3460
+INFO - dpsgd_diffusion.py - 2024-10-24 14:46:52,504 - Loss: 0.3191, step: 8100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:47:41,543 - Loss: 0.2561, step: 8200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:48:30,008 - Loss: 0.2400, step: 8300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:49:19,145 - Loss: 0.2515, step: 8400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:49:42,778 - Eps-value after 22 epochs: 0.3547
+INFO - dpsgd_diffusion.py - 2024-10-24 14:50:08,211 - Loss: 0.3027, step: 8500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:50:57,326 - Loss: 0.2196, step: 8600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:51:45,863 - Loss: 0.2647, step: 8700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:52:37,017 - Loss: 0.2760, step: 8800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:52:52,735 - Eps-value after 23 epochs: 0.3632
+INFO - dpsgd_diffusion.py - 2024-10-24 14:53:25,495 - Loss: 0.2460, step: 8900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:54:14,061 - Loss: 0.2606, step: 9000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:55:04,729 - Loss: 0.3034, step: 9100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:55:54,950 - Loss: 0.2417, step: 9200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:56:02,692 - Eps-value after 24 epochs: 0.3715
+INFO - dpsgd_diffusion.py - 2024-10-24 14:56:44,775 - Loss: 0.2695, step: 9300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:57:32,403 - Loss: 0.2164, step: 9400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:58:20,700 - Loss: 0.2459, step: 9500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:59:09,903 - Loss: 0.2809, step: 9600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:59:09,913 - Eps-value after 25 epochs: 0.3797
+INFO - dpsgd_diffusion.py - 2024-10-24 14:59:58,651 - Loss: 0.2414, step: 9700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:00:47,875 - Loss: 0.2985, step: 9800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:01:37,232 - Loss: 0.2606, step: 9900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:02:16,884 - Eps-value after 26 epochs: 0.3878
+INFO - dpsgd_diffusion.py - 2024-10-24 15:02:24,767 - Loss: 0.2974, step: 10000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:02:24,791 - Saving snapshot checkpoint and sampling single batch at iteration 10000.
+WARNING - image.py - 2024-10-24 15:02:25,301 - 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 15:02:40,657 - FID at iteration 10000: 234.875741
+INFO - dpsgd_diffusion.py - 2024-10-24 15:03:29,565 - Loss: 0.2524, step: 10100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:04:18,572 - Loss: 0.2699, step: 10200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:05:07,197 - Loss: 0.2434, step: 10300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:05:39,551 - Eps-value after 27 epochs: 0.3956
+INFO - dpsgd_diffusion.py - 2024-10-24 15:05:55,513 - Loss: 0.2892, step: 10400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:06:45,481 - Loss: 0.2219, step: 10500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:07:33,966 - Loss: 0.2276, step: 10600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:08:21,850 - Loss: 0.2687, step: 10700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:08:47,202 - Eps-value after 28 epochs: 0.4034
+INFO - dpsgd_diffusion.py - 2024-10-24 15:09:10,961 - Loss: 0.3145, step: 10800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:10:00,051 - Loss: 0.2595, step: 10900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:10:47,233 - Loss: 0.2487, step: 11000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:11:35,901 - Loss: 0.2396, step: 11100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:11:52,861 - Eps-value after 29 epochs: 0.4110
+INFO - dpsgd_diffusion.py - 2024-10-24 15:12:23,213 - Loss: 0.2550, step: 11200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:13:13,302 - Loss: 0.2651, step: 11300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:14:02,095 - Loss: 0.2325, step: 11400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:14:51,836 - Loss: 0.2400, step: 11500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:15:01,662 - Eps-value after 30 epochs: 0.4185
+INFO - dpsgd_diffusion.py - 2024-10-24 15:15:41,578 - Loss: 0.2534, step: 11600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:16:29,535 - Loss: 0.2103, step: 11700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:17:17,645 - Loss: 0.2350, step: 11800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:18:06,629 - Loss: 0.2852, step: 11900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:18:08,611 - Eps-value after 31 epochs: 0.4259
+INFO - dpsgd_diffusion.py - 2024-10-24 15:18:56,173 - Loss: 0.2419, step: 12000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:18:56,176 - Saving snapshot checkpoint and sampling single batch at iteration 12000.
+WARNING - image.py - 2024-10-24 15:18:56,709 - 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 15:19:11,964 - FID at iteration 12000: 226.100124
+INFO - dpsgd_diffusion.py - 2024-10-24 15:19:59,908 - Loss: 0.2480, step: 12100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:20:50,090 - Loss: 0.2730, step: 12200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:21:32,651 - Eps-value after 32 epochs: 0.4332
+INFO - dpsgd_diffusion.py - 2024-10-24 15:21:38,851 - Loss: 0.2307, step: 12300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:22:28,407 - Loss: 0.2285, step: 12400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:23:17,022 - Loss: 0.2169, step: 12500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:24:03,993 - Loss: 0.2036, step: 12600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:24:38,863 - Eps-value after 33 epochs: 0.4404
+INFO - dpsgd_diffusion.py - 2024-10-24 15:24:52,497 - Loss: 0.2216, step: 12700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:25:40,547 - Loss: 0.2754, step: 12800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:26:29,844 - Loss: 0.2626, step: 12900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:27:18,641 - Loss: 0.2168, step: 13000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:27:45,415 - Eps-value after 34 epochs: 0.4475
+INFO - dpsgd_diffusion.py - 2024-10-24 15:28:07,156 - Loss: 0.2237, step: 13100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:28:55,803 - Loss: 0.2635, step: 13200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:29:44,776 - Loss: 0.2324, step: 13300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:30:32,856 - Loss: 0.2398, step: 13400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:30:51,853 - Eps-value after 35 epochs: 0.4544
+INFO - dpsgd_diffusion.py - 2024-10-24 15:31:21,511 - Loss: 0.2388, step: 13500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:32:09,462 - Loss: 0.2625, step: 13600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:32:58,279 - Loss: 0.2374, step: 13700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:33:47,737 - Loss: 0.2639, step: 13800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:33:59,319 - Eps-value after 36 epochs: 0.4613
+INFO - dpsgd_diffusion.py - 2024-10-24 15:34:35,926 - Loss: 0.3097, step: 13900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:35:23,892 - Loss: 0.2656, step: 14000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:35:23,894 - Saving snapshot checkpoint and sampling single batch at iteration 14000.
+WARNING - image.py - 2024-10-24 15:35:24,451 - 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 15:35:39,726 - FID at iteration 14000: 226.935875
+INFO - dpsgd_diffusion.py - 2024-10-24 15:36:27,812 - Loss: 0.2118, step: 14100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:37:17,266 - Loss: 0.2014, step: 14200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:37:21,214 - Eps-value after 37 epochs: 0.4681
+INFO - dpsgd_diffusion.py - 2024-10-24 15:38:07,125 - Loss: 0.2120, step: 14300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:38:56,398 - Loss: 0.2336, step: 14400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:39:44,817 - Loss: 0.2562, step: 14500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:40:28,940 - Eps-value after 38 epochs: 0.4749
+INFO - dpsgd_diffusion.py - 2024-10-24 15:40:33,046 - Loss: 0.2655, step: 14600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:41:22,460 - Loss: 0.2417, step: 14700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:42:12,445 - Loss: 0.2343, step: 14800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:43:02,485 - Loss: 0.2437, step: 14900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:43:39,979 - Eps-value after 39 epochs: 0.4815
+INFO - dpsgd_diffusion.py - 2024-10-24 15:43:52,600 - Loss: 0.2313, step: 15000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:44:42,918 - Loss: 0.2318, step: 15100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:45:32,140 - Loss: 0.2210, step: 15200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:46:21,985 - Loss: 0.1983, step: 15300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:46:50,481 - Eps-value after 40 epochs: 0.4880
+INFO - dpsgd_diffusion.py - 2024-10-24 15:47:10,749 - Loss: 0.1967, step: 15400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:48:00,216 - Loss: 0.1943, step: 15500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:48:49,490 - Loss: 0.2367, step: 15600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:49:37,024 - Loss: 0.2428, step: 15700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:49:57,797 - Eps-value after 41 epochs: 0.4946
+INFO - dpsgd_diffusion.py - 2024-10-24 15:50:24,411 - Loss: 0.2148, step: 15800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:51:13,378 - Loss: 0.2340, step: 15900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:52:00,695 - Loss: 0.2470, step: 16000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:52:00,700 - Saving snapshot checkpoint and sampling single batch at iteration 16000.
+WARNING - image.py - 2024-10-24 15:52:01,204 - 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 15:52:16,515 - FID at iteration 16000: 224.011029
+INFO - dpsgd_diffusion.py - 2024-10-24 15:53:05,413 - Loss: 0.2151, step: 16100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:53:19,134 - Eps-value after 42 epochs: 0.5009
+INFO - dpsgd_diffusion.py - 2024-10-24 15:53:53,794 - Loss: 0.2740, step: 16200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:54:43,524 - Loss: 0.2226, step: 16300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:55:32,854 - Loss: 0.2312, step: 16400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:56:21,108 - Loss: 0.2213, step: 16500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:56:26,515 - Eps-value after 43 epochs: 0.5073
+INFO - dpsgd_diffusion.py - 2024-10-24 15:57:10,278 - Loss: 0.3010, step: 16600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:57:58,351 - Loss: 0.2375, step: 16700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:58:46,979 - Loss: 0.2187, step: 16800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:59:34,187 - Eps-value after 44 epochs: 0.5136
+INFO - dpsgd_diffusion.py - 2024-10-24 15:59:36,215 - Loss: 0.2525, step: 16900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:00:25,460 - Loss: 0.2084, step: 17000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:01:14,778 - Loss: 0.2742, step: 17100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:02:03,730 - Loss: 0.2072, step: 17200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:02:42,733 - Eps-value after 45 epochs: 0.5198
+INFO - dpsgd_diffusion.py - 2024-10-24 16:02:52,881 - Loss: 0.2258, step: 17300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:03:41,659 - Loss: 0.2387, step: 17400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:04:31,630 - Loss: 0.1710, step: 17500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:05:19,463 - Loss: 0.2441, step: 17600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:05:51,084 - Eps-value after 46 epochs: 0.5259
+INFO - dpsgd_diffusion.py - 2024-10-24 16:06:08,620 - Loss: 0.2035, step: 17700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:06:55,614 - Loss: 0.2408, step: 17800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:07:45,517 - Loss: 0.2176, step: 17900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:08:33,667 - Loss: 0.2167, step: 18000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:08:33,691 - Saving snapshot checkpoint and sampling single batch at iteration 18000.
+WARNING - image.py - 2024-10-24 16:08:34,213 - 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 16:08:49,523 - FID at iteration 18000: 222.651064
+INFO - dpsgd_diffusion.py - 2024-10-24 16:09:13,042 - Eps-value after 47 epochs: 0.5321
+INFO - dpsgd_diffusion.py - 2024-10-24 16:09:39,802 - Loss: 0.2254, step: 18100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:10:29,209 - Loss: 0.2022, step: 18200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:11:18,942 - Loss: 0.2362, step: 18300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:12:07,812 - Loss: 0.2088, step: 18400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:12:23,216 - Eps-value after 48 epochs: 0.5380
+INFO - dpsgd_diffusion.py - 2024-10-24 16:12:56,427 - Loss: 0.2078, step: 18500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:13:45,020 - Loss: 0.2115, step: 18600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:14:33,921 - Loss: 0.2283, step: 18700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:15:22,231 - Loss: 0.1949, step: 18800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:15:29,707 - Eps-value after 49 epochs: 0.5440
+INFO - dpsgd_diffusion.py - 2024-10-24 16:16:12,447 - Loss: 0.2174, step: 18900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:17:00,999 - Loss: 0.2391, step: 19000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:17:49,700 - Loss: 0.2260, step: 19100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:18:37,834 - Loss: 0.2011, step: 19200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:18:37,845 - Eps-value after 50 epochs: 0.5500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:19:26,774 - Loss: 0.1891, step: 19300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:20:17,624 - Loss: 0.2058, step: 19400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:21:05,836 - Loss: 0.1993, step: 19500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:21:46,955 - Eps-value after 51 epochs: 0.5558
+INFO - dpsgd_diffusion.py - 2024-10-24 16:21:54,903 - Loss: 0.2189, step: 19600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:22:43,466 - Loss: 0.2467, step: 19700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:23:32,194 - Loss: 0.1952, step: 19800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:24:23,064 - Loss: 0.1799, step: 19900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:24:55,532 - Eps-value after 52 epochs: 0.5616
+INFO - dpsgd_diffusion.py - 2024-10-24 16:25:11,344 - Loss: 0.2748, step: 20000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:25:11,346 - Saving snapshot checkpoint and sampling single batch at iteration 20000.
+WARNING - image.py - 2024-10-24 16:25:11,890 - 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 16:25:27,115 - FID at iteration 20000: 225.853852
+INFO - dpsgd_diffusion.py - 2024-10-24 16:26:14,958 - Loss: 0.1904, step: 20100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:27:04,217 - Loss: 0.2205, step: 20200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:27:51,950 - Loss: 0.2145, step: 20300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:28:17,872 - Eps-value after 53 epochs: 0.5674
+INFO - dpsgd_diffusion.py - 2024-10-24 16:28:41,625 - Loss: 0.1733, step: 20400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:29:30,525 - Loss: 0.2266, step: 20500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:30:19,732 - Loss: 0.2616, step: 20600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:31:07,962 - Loss: 0.2156, step: 20700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:31:25,168 - Eps-value after 54 epochs: 0.5731
+INFO - dpsgd_diffusion.py - 2024-10-24 16:31:56,158 - Loss: 0.1848, step: 20800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:32:44,340 - Loss: 0.2497, step: 20900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:33:32,638 - Loss: 0.2163, step: 21000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:34:20,980 - Loss: 0.2095, step: 21100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:34:30,580 - Eps-value after 55 epochs: 0.5787
+INFO - dpsgd_diffusion.py - 2024-10-24 16:35:10,580 - Loss: 0.2084, step: 21200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:35:59,499 - Loss: 0.2284, step: 21300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:36:47,124 - Loss: 0.2342, step: 21400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:37:35,461 - Loss: 0.2258, step: 21500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:37:37,385 - Eps-value after 56 epochs: 0.5843
+INFO - dpsgd_diffusion.py - 2024-10-24 16:38:24,548 - Loss: 0.2003, step: 21600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:39:14,800 - Loss: 0.2115, step: 21700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:40:03,566 - Loss: 0.2070, step: 21800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:40:45,208 - Eps-value after 57 epochs: 0.5899
+INFO - dpsgd_diffusion.py - 2024-10-24 16:40:50,958 - Loss: 0.1850, step: 21900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:41:39,966 - Loss: 0.1791, step: 22000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:41:40,014 - Saving snapshot checkpoint and sampling single batch at iteration 22000.
+WARNING - image.py - 2024-10-24 16:41:40,522 - 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 16:41:55,733 - FID at iteration 22000: 231.845102
+INFO - dpsgd_diffusion.py - 2024-10-24 16:42:45,261 - Loss: 0.2338, step: 22100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:43:33,855 - Loss: 0.1934, step: 22200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:44:08,914 - Eps-value after 58 epochs: 0.5955
+INFO - dpsgd_diffusion.py - 2024-10-24 16:44:22,603 - Loss: 0.2164, step: 22300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:45:10,812 - Loss: 0.2146, step: 22400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:45:58,357 - Loss: 0.2078, step: 22500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:46:45,761 - Loss: 0.1892, step: 22600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:47:13,180 - Eps-value after 59 epochs: 0.6009
+INFO - dpsgd_diffusion.py - 2024-10-24 16:47:34,678 - Loss: 0.1854, step: 22700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:48:23,353 - Loss: 0.2140, step: 22800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:49:11,547 - Loss: 0.1694, step: 22900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:50:00,284 - Loss: 0.2138, step: 23000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:50:19,805 - Eps-value after 60 epochs: 0.6063
+INFO - dpsgd_diffusion.py - 2024-10-24 16:50:49,481 - Loss: 0.2371, step: 23100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:51:38,460 - Loss: 0.2193, step: 23200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:52:27,737 - Loss: 0.1785, step: 23300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:53:16,982 - Loss: 0.1934, step: 23400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:53:28,724 - Eps-value after 61 epochs: 0.6117
+INFO - dpsgd_diffusion.py - 2024-10-24 16:54:06,724 - Loss: 0.2259, step: 23500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:54:54,519 - Loss: 0.2060, step: 23600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:55:42,037 - Loss: 0.1898, step: 23700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:56:32,182 - Loss: 0.2097, step: 23800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:56:36,046 - Eps-value after 62 epochs: 0.6171
+INFO - dpsgd_diffusion.py - 2024-10-24 16:57:21,494 - Loss: 0.1803, step: 23900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:58:10,299 - Loss: 0.2066, step: 24000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:58:10,304 - Saving snapshot checkpoint and sampling single batch at iteration 24000.
+WARNING - image.py - 2024-10-24 16:58:10,841 - 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 16:58:26,156 - FID at iteration 24000: 236.267691
+INFO - dpsgd_diffusion.py - 2024-10-24 16:59:14,410 - Loss: 0.1614, step: 24100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:59:58,039 - Eps-value after 63 epochs: 0.6224
+INFO - dpsgd_diffusion.py - 2024-10-24 17:00:02,039 - Loss: 0.2715, step: 24200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:00:51,736 - Loss: 0.1816, step: 24300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:01:39,684 - Loss: 0.1514, step: 24400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:02:28,013 - Loss: 0.2368, step: 24500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:03:04,339 - Eps-value after 64 epochs: 0.6277
+INFO - dpsgd_diffusion.py - 2024-10-24 17:03:16,559 - Loss: 0.2049, step: 24600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:04:05,681 - Loss: 0.1681, step: 24700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:04:54,234 - Loss: 0.2055, step: 24800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:05:41,594 - Loss: 0.1598, step: 24900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:06:10,841 - Eps-value after 65 epochs: 0.6329
+INFO - dpsgd_diffusion.py - 2024-10-24 17:06:30,259 - Loss: 0.1841, step: 25000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:07:18,249 - Loss: 0.1961, step: 25100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:08:06,919 - Loss: 0.1867, step: 25200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:08:55,376 - Loss: 0.1727, step: 25300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:09:15,453 - Eps-value after 66 epochs: 0.6381
+INFO - dpsgd_diffusion.py - 2024-10-24 17:09:42,383 - Loss: 0.1971, step: 25400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:10:31,785 - Loss: 0.1811, step: 25500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:11:19,689 - Loss: 0.2269, step: 25600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:12:07,683 - Loss: 0.2086, step: 25700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:12:21,023 - Eps-value after 67 epochs: 0.6433
+INFO - dpsgd_diffusion.py - 2024-10-24 17:12:56,575 - Loss: 0.2024, step: 25800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:13:44,349 - Loss: 0.2446, step: 25900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:14:33,070 - Loss: 0.2020, step: 26000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:14:33,087 - Saving snapshot checkpoint and sampling single batch at iteration 26000.
+WARNING - image.py - 2024-10-24 17:14:33,616 - 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 17:14:48,814 - FID at iteration 26000: 236.177746
+INFO - dpsgd_diffusion.py - 2024-10-24 17:15:36,522 - Loss: 0.1974, step: 26100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:15:41,982 - Eps-value after 68 epochs: 0.6485
+INFO - dpsgd_diffusion.py - 2024-10-24 17:16:23,971 - Loss: 0.2093, step: 26200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:17:12,929 - Loss: 0.1959, step: 26300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:18:02,546 - Loss: 0.1833, step: 26400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:18:48,827 - Eps-value after 69 epochs: 0.6535
+INFO - dpsgd_diffusion.py - 2024-10-24 17:18:50,721 - Loss: 0.1785, step: 26500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:19:38,064 - Loss: 0.1865, step: 26600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:20:26,076 - Loss: 0.2248, step: 26700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:21:15,190 - Loss: 0.2112, step: 26800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:21:54,792 - Eps-value after 70 epochs: 0.6585
+INFO - dpsgd_diffusion.py - 2024-10-24 17:22:04,546 - Loss: 0.1704, step: 26900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:22:52,490 - Loss: 0.1727, step: 27000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:23:41,526 - Loss: 0.1742, step: 27100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:24:29,021 - Loss: 0.1693, step: 27200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:24:59,310 - Eps-value after 71 epochs: 0.6636
+INFO - dpsgd_diffusion.py - 2024-10-24 17:25:17,374 - Loss: 0.2063, step: 27300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:26:06,955 - Loss: 0.2193, step: 27400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:26:56,522 - Loss: 0.1678, step: 27500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:27:45,005 - Loss: 0.1811, step: 27600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:28:09,223 - Eps-value after 72 epochs: 0.6686
+INFO - dpsgd_diffusion.py - 2024-10-24 17:28:34,924 - Loss: 0.1345, step: 27700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:29:23,948 - Loss: 0.2049, step: 27800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:30:12,664 - Loss: 0.1545, step: 27900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:31:03,820 - Loss: 0.2246, step: 28000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:31:03,841 - Saving snapshot checkpoint and sampling single batch at iteration 28000.
+WARNING - image.py - 2024-10-24 17:31:04,367 - 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 17:31:19,609 - FID at iteration 28000: 236.864993
+INFO - dpsgd_diffusion.py - 2024-10-24 17:31:35,313 - Eps-value after 73 epochs: 0.6736
+INFO - dpsgd_diffusion.py - 2024-10-24 17:32:08,388 - Loss: 0.1891, step: 28100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:32:57,174 - Loss: 0.1568, step: 28200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:33:44,807 - Loss: 0.1810, step: 28300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:34:34,666 - Loss: 0.2090, step: 28400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:34:42,528 - Eps-value after 74 epochs: 0.6786
+INFO - dpsgd_diffusion.py - 2024-10-24 17:35:24,847 - Loss: 0.2295, step: 28500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:36:14,791 - Loss: 0.1822, step: 28600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:37:04,975 - Loss: 0.1698, step: 28700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:37:53,661 - Loss: 0.2322, step: 28800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:37:53,671 - Eps-value after 75 epochs: 0.6834
+INFO - dpsgd_diffusion.py - 2024-10-24 17:38:43,672 - Loss: 0.1926, step: 28900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:39:32,402 - Loss: 0.2403, step: 29000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:40:22,037 - Loss: 0.1698, step: 29100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:41:01,978 - Eps-value after 76 epochs: 0.6882
+INFO - dpsgd_diffusion.py - 2024-10-24 17:41:09,895 - Loss: 0.1626, step: 29200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:41:58,191 - Loss: 0.1980, step: 29300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:42:46,732 - Loss: 0.2053, step: 29400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:43:35,058 - Loss: 0.1606, step: 29500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:44:07,145 - Eps-value after 77 epochs: 0.6931
+INFO - dpsgd_diffusion.py - 2024-10-24 17:44:22,684 - Loss: 0.2084, step: 29600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:45:11,433 - Loss: 0.2047, step: 29700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:46:00,482 - Loss: 0.1909, step: 29800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:46:48,728 - Loss: 0.2099, step: 29900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:47:14,048 - Eps-value after 78 epochs: 0.6979
+INFO - dpsgd_diffusion.py - 2024-10-24 17:47:38,161 - Loss: 0.1788, step: 30000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:47:38,173 - Saving snapshot checkpoint and sampling single batch at iteration 30000.
+WARNING - image.py - 2024-10-24 17:47:38,656 - 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 17:47:53,769 - FID at iteration 30000: 237.742996
+INFO - dpsgd_diffusion.py - 2024-10-24 17:48:42,237 - Loss: 0.2185, step: 30100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:49:30,712 - Loss: 0.1795, step: 30200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:50:19,474 - Loss: 0.1689, step: 30300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:50:36,786 - Eps-value after 79 epochs: 0.7028
+INFO - dpsgd_diffusion.py - 2024-10-24 17:51:07,981 - Loss: 0.2505, step: 30400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:51:56,280 - Loss: 0.1921, step: 30500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:52:44,664 - Loss: 0.1680, step: 30600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:53:33,582 - Loss: 0.1817, step: 30700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:53:43,428 - Eps-value after 80 epochs: 0.7076
+INFO - dpsgd_diffusion.py - 2024-10-24 17:54:23,593 - Loss: 0.1603, step: 30800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:55:12,878 - Loss: 0.2333, step: 30900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:56:01,636 - Loss: 0.2184, step: 31000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:56:49,389 - Loss: 0.1733, step: 31100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:56:51,316 - Eps-value after 81 epochs: 0.7123
+INFO - dpsgd_diffusion.py - 2024-10-24 17:57:38,068 - Loss: 0.2183, step: 31200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:58:25,620 - Loss: 0.1859, step: 31300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:59:15,865 - Loss: 0.1757, step: 31400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:59:58,162 - Eps-value after 82 epochs: 0.7169
+INFO - dpsgd_diffusion.py - 2024-10-24 18:00:03,954 - Loss: 0.1643, step: 31500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:00:51,595 - Loss: 0.1899, step: 31600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:01:40,554 - Loss: 0.1439, step: 31700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:02:29,865 - Loss: 0.1729, step: 31800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:03:04,522 - Eps-value after 83 epochs: 0.7216
+INFO - dpsgd_diffusion.py - 2024-10-24 18:03:18,098 - Loss: 0.1756, step: 31900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:04:05,458 - Loss: 0.1470, step: 32000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:04:05,463 - Saving snapshot checkpoint and sampling single batch at iteration 32000.
+WARNING - image.py - 2024-10-24 18:04:05,968 - 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 18:04:21,239 - FID at iteration 32000: 236.094457
+INFO - dpsgd_diffusion.py - 2024-10-24 18:05:10,736 - Loss: 0.1764, step: 32100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:06:00,124 - Loss: 0.1688, step: 32200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:06:26,542 - Eps-value after 84 epochs: 0.7262
+INFO - dpsgd_diffusion.py - 2024-10-24 18:06:46,722 - Loss: 0.1820, step: 32300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:07:34,750 - Loss: 0.1956, step: 32400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:08:23,004 - Loss: 0.1723, step: 32500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:09:10,447 - Loss: 0.1752, step: 32600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:09:28,789 - Eps-value after 85 epochs: 0.7309
+INFO - dpsgd_diffusion.py - 2024-10-24 18:09:58,045 - Loss: 0.1311, step: 32700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:10:46,228 - Loss: 0.1567, step: 32800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:11:35,261 - Loss: 0.1714, step: 32900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:12:23,138 - Loss: 0.1824, step: 33000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:12:34,715 - Eps-value after 86 epochs: 0.7355
+INFO - dpsgd_diffusion.py - 2024-10-24 18:13:11,982 - Loss: 0.1595, step: 33100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:13:59,712 - Loss: 0.1706, step: 33200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:14:47,550 - Loss: 0.1884, step: 33300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:15:35,606 - Loss: 0.1658, step: 33400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:15:39,392 - Eps-value after 87 epochs: 0.7402
+INFO - dpsgd_diffusion.py - 2024-10-24 18:16:24,433 - Loss: 0.1972, step: 33500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:17:12,681 - Loss: 0.2133, step: 33600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:18:04,284 - Loss: 0.1938, step: 33700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:18:48,082 - Eps-value after 88 epochs: 0.7447
+INFO - dpsgd_diffusion.py - 2024-10-24 18:18:52,149 - Loss: 0.2015, step: 33800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:19:42,210 - Loss: 0.1985, step: 33900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:20:30,688 - Loss: 0.2093, step: 34000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:20:30,709 - Saving snapshot checkpoint and sampling single batch at iteration 34000.
+WARNING - image.py - 2024-10-24 18:20:31,195 - 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 18:20:46,389 - FID at iteration 34000: 237.436841
+INFO - dpsgd_diffusion.py - 2024-10-24 18:21:34,838 - Loss: 0.1625, step: 34100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:22:11,307 - Eps-value after 89 epochs: 0.7492
+INFO - dpsgd_diffusion.py - 2024-10-24 18:22:22,987 - Loss: 0.1687, step: 34200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:23:11,415 - Loss: 0.1742, step: 34300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:24:02,154 - Loss: 0.1711, step: 34400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:24:50,407 - Loss: 0.1649, step: 34500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:25:19,230 - Eps-value after 90 epochs: 0.7536
+INFO - dpsgd_diffusion.py - 2024-10-24 18:25:39,547 - Loss: 0.1877, step: 34600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:26:28,293 - Loss: 0.2405, step: 34700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:27:16,406 - Loss: 0.1590, step: 34800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:28:04,719 - Loss: 0.2008, step: 34900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:28:25,644 - Eps-value after 91 epochs: 0.7581
+INFO - dpsgd_diffusion.py - 2024-10-24 18:28:53,018 - Loss: 0.1887, step: 35000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:29:41,328 - Loss: 0.1736, step: 35100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:30:30,220 - Loss: 0.1672, step: 35200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:31:19,957 - Loss: 0.2069, step: 35300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:31:33,573 - Eps-value after 92 epochs: 0.7626
+INFO - dpsgd_diffusion.py - 2024-10-24 18:32:07,160 - Loss: 0.1507, step: 35400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:32:56,457 - Loss: 0.1693, step: 35500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:33:45,750 - Loss: 0.1867, step: 35600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:34:33,918 - Loss: 0.1854, step: 35700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:34:39,570 - Eps-value after 93 epochs: 0.7670
+INFO - dpsgd_diffusion.py - 2024-10-24 18:35:22,183 - Loss: 0.1729, step: 35800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:36:09,416 - Loss: 0.1741, step: 35900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:36:59,825 - Loss: 0.1748, step: 36000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:36:59,876 - Saving snapshot checkpoint and sampling single batch at iteration 36000.
+WARNING - image.py - 2024-10-24 18:37:00,376 - 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 18:37:15,487 - FID at iteration 36000: 236.959986
+INFO - dpsgd_diffusion.py - 2024-10-24 18:38:02,675 - Eps-value after 94 epochs: 0.7715
+INFO - dpsgd_diffusion.py - 2024-10-24 18:38:04,611 - Loss: 0.1894, step: 36100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:38:53,093 - Loss: 0.1626, step: 36200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:39:40,815 - Loss: 0.2096, step: 36300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:40:30,391 - Loss: 0.1906, step: 36400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:41:08,708 - Eps-value after 95 epochs: 0.7760
+INFO - dpsgd_diffusion.py - 2024-10-24 18:41:18,700 - Loss: 0.2325, step: 36500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:42:06,750 - Loss: 0.1740, step: 36600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:42:55,872 - Loss: 0.1872, step: 36700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:43:43,922 - Loss: 0.1783, step: 36800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:44:14,026 - Eps-value after 96 epochs: 0.7804
+INFO - dpsgd_diffusion.py - 2024-10-24 18:44:31,385 - Loss: 0.1924, step: 36900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:45:19,055 - Loss: 0.1979, step: 37000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:46:07,460 - Loss: 0.1595, step: 37100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:46:55,509 - Loss: 0.1763, step: 37200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:47:19,344 - Eps-value after 97 epochs: 0.7847
+INFO - dpsgd_diffusion.py - 2024-10-24 18:47:44,767 - Loss: 0.1655, step: 37300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:48:33,720 - Loss: 0.2078, step: 37400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:49:21,734 - Loss: 0.1680, step: 37500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:50:10,421 - Loss: 0.1905, step: 37600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:50:25,673 - Eps-value after 98 epochs: 0.7890
+INFO - dpsgd_diffusion.py - 2024-10-24 18:50:58,659 - Loss: 0.1704, step: 37700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:51:47,374 - Loss: 0.1757, step: 37800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:52:35,565 - Loss: 0.1589, step: 37900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:53:23,890 - Loss: 0.1765, step: 38000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:53:23,908 - Saving snapshot checkpoint and sampling single batch at iteration 38000.
+WARNING - image.py - 2024-10-24 18:53:24,397 - 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 18:53:39,547 - FID at iteration 38000: 234.947764
+INFO - dpsgd_diffusion.py - 2024-10-24 18:53:47,417 - Eps-value after 99 epochs: 0.7933
+INFO - dpsgd_diffusion.py - 2024-10-24 18:54:29,254 - Loss: 0.1909, step: 38100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:55:16,463 - Loss: 0.1895, step: 38200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:56:05,878 - Loss: 0.1563, step: 38300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:56:54,543 - Loss: 0.2014, step: 38400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:56:54,553 - Eps-value after 100 epochs: 0.7975
+INFO - dpsgd_diffusion.py - 2024-10-24 18:57:43,423 - Loss: 0.1617, step: 38500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:58:30,635 - Loss: 0.1785, step: 38600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:59:19,730 - Loss: 0.2038, step: 38700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:00:00,504 - Eps-value after 101 epochs: 0.8018
+INFO - dpsgd_diffusion.py - 2024-10-24 19:00:08,868 - Loss: 0.1706, step: 38800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:00:57,625 - Loss: 0.1630, step: 38900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:01:46,007 - Loss: 0.1551, step: 39000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:02:33,741 - Loss: 0.1525, step: 39100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:03:06,858 - Eps-value after 102 epochs: 0.8061
+INFO - dpsgd_diffusion.py - 2024-10-24 19:03:22,443 - Loss: 0.2086, step: 39200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:04:11,031 - Loss: 0.1664, step: 39300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:05:00,470 - Loss: 0.1718, step: 39400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:05:48,899 - Loss: 0.1459, step: 39500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:06:14,152 - Eps-value after 103 epochs: 0.8104
+INFO - dpsgd_diffusion.py - 2024-10-24 19:06:38,306 - Loss: 0.2035, step: 39600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:07:28,710 - Loss: 0.1923, step: 39700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:08:17,601 - Loss: 0.1980, step: 39800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:09:08,396 - Loss: 0.1733, step: 39900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:09:26,072 - Eps-value after 104 epochs: 0.8146
+INFO - dpsgd_diffusion.py - 2024-10-24 19:09:57,221 - Loss: 0.1630, step: 40000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:09:57,225 - Saving snapshot checkpoint and sampling single batch at iteration 40000.
+WARNING - image.py - 2024-10-24 19:09:57,708 - 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 19:10:12,898 - FID at iteration 40000: 233.599905
+INFO - dpsgd_diffusion.py - 2024-10-24 19:11:01,335 - Loss: 0.1616, step: 40100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:11:50,544 - Loss: 0.1637, step: 40200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:12:39,137 - Loss: 0.1631, step: 40300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:12:48,335 - Eps-value after 105 epochs: 0.8189
+INFO - dpsgd_diffusion.py - 2024-10-24 19:13:27,728 - Loss: 0.1935, step: 40400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:14:16,519 - Loss: 0.1549, step: 40500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:15:04,064 - Loss: 0.1741, step: 40600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:15:52,125 - Loss: 0.1761, step: 40700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:15:54,045 - Eps-value after 106 epochs: 0.8231
+INFO - dpsgd_diffusion.py - 2024-10-24 19:16:40,343 - Loss: 0.1739, step: 40800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:17:29,290 - Loss: 0.1688, step: 40900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:18:16,712 - Loss: 0.1306, step: 41000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:18:59,965 - Eps-value after 107 epochs: 0.8272
+INFO - dpsgd_diffusion.py - 2024-10-24 19:19:06,203 - Loss: 0.1948, step: 41100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:19:55,368 - Loss: 0.1765, step: 41200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:20:43,404 - Loss: 0.1347, step: 41300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:21:31,000 - Loss: 0.1280, step: 41400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:22:05,603 - Eps-value after 108 epochs: 0.8313
+INFO - dpsgd_diffusion.py - 2024-10-24 19:22:19,212 - Loss: 0.1565, step: 41500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:23:06,922 - Loss: 0.1460, step: 41600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:23:57,504 - Loss: 0.1727, step: 41700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:24:45,401 - Loss: 0.1896, step: 41800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:25:11,947 - Eps-value after 109 epochs: 0.8354
+INFO - dpsgd_diffusion.py - 2024-10-24 19:25:33,309 - Loss: 0.1562, step: 41900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:26:22,795 - Loss: 0.1875, step: 42000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:26:22,855 - Saving snapshot checkpoint and sampling single batch at iteration 42000.
+WARNING - image.py - 2024-10-24 19:26:23,335 - 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 19:26:38,482 - FID at iteration 42000: 231.660804
+INFO - dpsgd_diffusion.py - 2024-10-24 19:27:27,787 - Loss: 0.1770, step: 42100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:28:16,267 - Loss: 0.1636, step: 42200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:28:34,468 - Eps-value after 110 epochs: 0.8395
+INFO - dpsgd_diffusion.py - 2024-10-24 19:29:03,212 - Loss: 0.1700, step: 42300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:29:53,477 - Loss: 0.1972, step: 42400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:30:41,564 - Loss: 0.2081, step: 42500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:31:29,103 - Loss: 0.1827, step: 42600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:31:40,824 - Eps-value after 111 epochs: 0.8436
+INFO - dpsgd_diffusion.py - 2024-10-24 19:32:18,275 - Loss: 0.1670, step: 42700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:33:06,878 - Loss: 0.1815, step: 42800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:33:56,163 - Loss: 0.1846, step: 42900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:34:46,126 - Loss: 0.1659, step: 43000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:34:50,090 - Eps-value after 112 epochs: 0.8477
+INFO - dpsgd_diffusion.py - 2024-10-24 19:35:35,760 - Loss: 0.1497, step: 43100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:36:25,263 - Loss: 0.1935, step: 43200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:37:14,265 - Loss: 0.1829, step: 43300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:37:59,733 - Eps-value after 113 epochs: 0.8517
+INFO - dpsgd_diffusion.py - 2024-10-24 19:38:03,885 - Loss: 0.1804, step: 43400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:38:53,246 - Loss: 0.1752, step: 43500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:39:41,104 - Loss: 0.1904, step: 43600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:40:30,744 - Loss: 0.2143, step: 43700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:41:07,319 - Eps-value after 114 epochs: 0.8558
+INFO - dpsgd_diffusion.py - 2024-10-24 19:41:19,884 - Loss: 0.1467, step: 43800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:42:08,244 - Loss: 0.1368, step: 43900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:42:58,008 - Loss: 0.1598, step: 44000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:42:58,064 - Saving snapshot checkpoint and sampling single batch at iteration 44000.
+WARNING - image.py - 2024-10-24 19:42:58,543 - 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 19:43:13,647 - FID at iteration 44000: 229.486377
+INFO - dpsgd_diffusion.py - 2024-10-24 19:44:02,028 - Loss: 0.1294, step: 44100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:44:30,979 - Eps-value after 115 epochs: 0.8599
+INFO - dpsgd_diffusion.py - 2024-10-24 19:44:50,460 - Loss: 0.1600, step: 44200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:45:39,234 - Loss: 0.1924, step: 44300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:46:28,750 - Loss: 0.1986, step: 44400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:47:17,837 - Loss: 0.2299, step: 44500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:47:39,319 - Eps-value after 116 epochs: 0.8640
+INFO - dpsgd_diffusion.py - 2024-10-24 19:48:06,587 - Loss: 0.1758, step: 44600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:48:56,975 - Loss: 0.1698, step: 44700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:49:45,626 - Loss: 0.1673, step: 44800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:50:35,901 - Loss: 0.1686, step: 44900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:50:49,591 - Eps-value after 117 epochs: 0.8681
+INFO - dpsgd_diffusion.py - 2024-10-24 19:51:23,603 - Loss: 0.2052, step: 45000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:52:13,083 - Loss: 0.1440, step: 45100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:53:02,361 - Loss: 0.1650, step: 45200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:53:50,459 - Loss: 0.1826, step: 45300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:53:56,095 - Eps-value after 118 epochs: 0.8720
+INFO - dpsgd_diffusion.py - 2024-10-24 19:54:39,308 - Loss: 0.1657, step: 45400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:55:28,794 - Loss: 0.1519, step: 45500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:56:18,544 - Loss: 0.1598, step: 45600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:57:07,062 - Eps-value after 119 epochs: 0.8759
+INFO - dpsgd_diffusion.py - 2024-10-24 19:57:09,120 - Loss: 0.1279, step: 45700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:57:58,061 - Loss: 0.1567, step: 45800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:58:45,980 - Loss: 0.1559, step: 45900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:59:35,599 - Loss: 0.1650, step: 46000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:59:35,655 - Saving snapshot checkpoint and sampling single batch at iteration 46000.
+WARNING - image.py - 2024-10-24 19:59:36,137 - 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 19:59:51,323 - FID at iteration 46000: 226.672382
+INFO - dpsgd_diffusion.py - 2024-10-24 20:00:31,207 - Eps-value after 120 epochs: 0.8798
+INFO - dpsgd_diffusion.py - 2024-10-24 20:00:40,950 - Loss: 0.2196, step: 46100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:01:29,917 - Loss: 0.1543, step: 46200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:02:18,911 - Loss: 0.1935, step: 46300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:03:07,209 - Loss: 0.1737, step: 46400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:03:38,935 - Eps-value after 121 epochs: 0.8837
+INFO - dpsgd_diffusion.py - 2024-10-24 20:04:00,044 - Loss: 0.1835, step: 46500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:04:49,744 - Loss: 0.1636, step: 46600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:05:39,033 - Loss: 0.1489, step: 46700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:06:26,354 - Loss: 0.1752, step: 46800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:06:49,763 - Eps-value after 122 epochs: 0.8876
+INFO - dpsgd_diffusion.py - 2024-10-24 20:07:16,203 - Loss: 0.1650, step: 46900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:08:05,164 - Loss: 0.1813, step: 47000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:08:53,575 - Loss: 0.1734, step: 47100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:09:41,934 - Loss: 0.2039, step: 47200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:09:56,560 - Eps-value after 123 epochs: 0.8915
+INFO - dpsgd_diffusion.py - 2024-10-24 20:10:31,139 - Loss: 0.1623, step: 47300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:11:19,916 - Loss: 0.1447, step: 47400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:12:09,090 - Loss: 0.1866, step: 47500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:12:57,679 - Loss: 0.1647, step: 47600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:13:05,202 - Eps-value after 124 epochs: 0.8954
+INFO - dpsgd_diffusion.py - 2024-10-24 20:13:46,610 - Loss: 0.1309, step: 47700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:14:35,185 - Loss: 0.1528, step: 47800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:15:23,768 - Loss: 0.1356, step: 47900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:16:11,883 - Loss: 0.1750, step: 48000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:16:11,893 - Eps-value after 125 epochs: 0.8993
+INFO - dpsgd_diffusion.py - 2024-10-24 20:16:11,896 - Saving snapshot checkpoint and sampling single batch at iteration 48000.
+WARNING - image.py - 2024-10-24 20:16:12,383 - 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 20:16:27,578 - FID at iteration 48000: 226.314027
+INFO - dpsgd_diffusion.py - 2024-10-24 20:17:15,973 - Loss: 0.1455, step: 48100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:18:04,903 - Loss: 0.1785, step: 48200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:18:53,677 - Loss: 0.2042, step: 48300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:19:33,837 - Eps-value after 126 epochs: 0.9032
+INFO - dpsgd_diffusion.py - 2024-10-24 20:19:41,589 - Loss: 0.1782, step: 48400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:20:31,184 - Loss: 0.1579, step: 48500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:21:21,823 - Loss: 0.1849, step: 48600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:22:10,893 - Loss: 0.1431, step: 48700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:22:44,688 - Eps-value after 127 epochs: 0.9071
+INFO - dpsgd_diffusion.py - 2024-10-24 20:23:00,926 - Loss: 0.1750, step: 48800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:23:49,372 - Loss: 0.1694, step: 48900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:24:37,889 - Loss: 0.1324, step: 49000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:25:26,643 - Loss: 0.2082, step: 49100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:25:51,286 - Eps-value after 128 epochs: 0.9110
+INFO - dpsgd_diffusion.py - 2024-10-24 20:26:15,279 - Loss: 0.1516, step: 49200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:27:04,436 - Loss: 0.1464, step: 49300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:27:53,495 - Loss: 0.1540, step: 49400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:28:42,254 - Loss: 0.1802, step: 49500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:28:59,671 - Eps-value after 129 epochs: 0.9149
+INFO - dpsgd_diffusion.py - 2024-10-24 20:29:30,903 - Loss: 0.1780, step: 49600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:30:19,703 - Loss: 0.1644, step: 49700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:31:08,200 - Loss: 0.1953, step: 49800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:31:58,044 - Loss: 0.1366, step: 49900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:32:07,740 - Eps-value after 130 epochs: 0.9188
+INFO - dpsgd_diffusion.py - 2024-10-24 20:32:46,488 - Loss: 0.1542, step: 50000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:32:46,509 - Saving snapshot checkpoint and sampling single batch at iteration 50000.
+WARNING - image.py - 2024-10-24 20:32:47,005 - 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 20:33:02,208 - FID at iteration 50000: 225.052620
+INFO - dpsgd_diffusion.py - 2024-10-24 20:33:49,444 - Loss: 0.1852, step: 50100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:34:37,105 - Loss: 0.1639, step: 50200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:35:25,507 - Loss: 0.1574, step: 50300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:35:27,528 - Eps-value after 131 epochs: 0.9225
+INFO - dpsgd_diffusion.py - 2024-10-24 20:36:15,468 - Loss: 0.1673, step: 50400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:37:03,668 - Loss: 0.1840, step: 50500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:37:53,720 - Loss: 0.1605, step: 50600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:38:36,404 - Eps-value after 132 epochs: 0.9262
+INFO - dpsgd_diffusion.py - 2024-10-24 20:38:42,211 - Loss: 0.1810, step: 50700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:39:29,689 - Loss: 0.1746, step: 50800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:40:18,074 - Loss: 0.1343, step: 50900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:41:06,555 - Loss: 0.1878, step: 51000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:41:41,961 - Eps-value after 133 epochs: 0.9299
+INFO - dpsgd_diffusion.py - 2024-10-24 20:41:56,575 - Loss: 0.1500, step: 51100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:42:46,000 - Loss: 0.1823, step: 51200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:43:34,004 - Loss: 0.1768, step: 51300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:44:23,051 - Loss: 0.1791, step: 51400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:44:50,110 - Eps-value after 134 epochs: 0.9337
+INFO - dpsgd_diffusion.py - 2024-10-24 20:45:11,218 - Loss: 0.1507, step: 51500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:45:59,000 - Loss: 0.1907, step: 51600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:46:47,022 - Loss: 0.1786, step: 51700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:47:35,892 - Loss: 0.1797, step: 51800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:47:54,733 - Eps-value after 135 epochs: 0.9374
+INFO - dpsgd_diffusion.py - 2024-10-24 20:48:24,401 - Loss: 0.1691, step: 51900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:49:13,771 - Loss: 0.1686, step: 52000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:49:13,808 - Saving snapshot checkpoint and sampling single batch at iteration 52000.
+WARNING - image.py - 2024-10-24 20:49:14,351 - 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 20:49:29,577 - FID at iteration 52000: 223.960670
+INFO - dpsgd_diffusion.py - 2024-10-24 20:50:19,174 - Loss: 0.1442, step: 52100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:51:09,225 - Loss: 0.1332, step: 52200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:51:20,455 - Eps-value after 136 epochs: 0.9411
+INFO - dpsgd_diffusion.py - 2024-10-24 20:51:57,407 - Loss: 0.2001, step: 52300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:52:46,241 - Loss: 0.1478, step: 52400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:53:35,163 - Loss: 0.1534, step: 52500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:54:25,059 - Loss: 0.1819, step: 52600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:54:28,916 - Eps-value after 137 epochs: 0.9448
+INFO - dpsgd_diffusion.py - 2024-10-24 20:55:13,779 - Loss: 0.1491, step: 52700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:56:02,424 - Loss: 0.1577, step: 52800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:56:51,052 - Loss: 0.1545, step: 52900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:57:36,068 - Eps-value after 138 epochs: 0.9485
+INFO - dpsgd_diffusion.py - 2024-10-24 20:57:40,127 - Loss: 0.1500, step: 53000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:58:28,811 - Loss: 0.1789, step: 53100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:59:17,677 - Loss: 0.1688, step: 53200
+INFO - dpsgd_diffusion.py - 2024-10-24 21:00:04,876 - Loss: 0.2163, step: 53300
+INFO - dpsgd_diffusion.py - 2024-10-24 21:00:41,761 - Eps-value after 139 epochs: 0.9522
+INFO - dpsgd_diffusion.py - 2024-10-24 21:00:54,318 - Loss: 0.1793, step: 53400
+INFO - dpsgd_diffusion.py - 2024-10-24 21:01:42,820 - Loss: 0.1580, step: 53500
+INFO - dpsgd_diffusion.py - 2024-10-24 21:02:30,151 - Loss: 0.1809, step: 53600
+INFO - dpsgd_diffusion.py - 2024-10-24 21:03:18,648 - Loss: 0.1209, step: 53700
+INFO - dpsgd_diffusion.py - 2024-10-24 21:03:47,581 - Eps-value after 140 epochs: 0.9559
+INFO - dpsgd_diffusion.py - 2024-10-24 21:04:07,123 - Loss: 0.1674, step: 53800
+INFO - dpsgd_diffusion.py - 2024-10-24 21:04:56,638 - Loss: 0.2101, step: 53900
+INFO - dpsgd_diffusion.py - 2024-10-24 21:05:45,381 - Loss: 0.1268, step: 54000
+INFO - dpsgd_diffusion.py - 2024-10-24 21:05:45,403 - Saving snapshot checkpoint and sampling single batch at iteration 54000.
+WARNING - image.py - 2024-10-24 21:05:45,917 - 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 21:06:01,090 - FID at iteration 54000: 221.875861
+INFO - dpsgd_diffusion.py - 2024-10-24 21:06:50,142 - Loss: 0.1524, step: 54100
+INFO - dpsgd_diffusion.py - 2024-10-24 21:07:11,579 - Eps-value after 141 epochs: 0.9597
+INFO - dpsgd_diffusion.py - 2024-10-24 21:07:40,052 - Loss: 0.2068, step: 54200
+INFO - dpsgd_diffusion.py - 2024-10-24 21:08:29,105 - Loss: 0.1667, step: 54300
+INFO - dpsgd_diffusion.py - 2024-10-24 21:09:16,909 - Loss: 0.1739, step: 54400
+INFO - dpsgd_diffusion.py - 2024-10-24 21:10:06,511 - Loss: 0.1402, step: 54500
+INFO - dpsgd_diffusion.py - 2024-10-24 21:10:19,685 - Eps-value after 142 epochs: 0.9634
+INFO - dpsgd_diffusion.py - 2024-10-24 21:10:54,808 - Loss: 0.1763, step: 54600
+INFO - dpsgd_diffusion.py - 2024-10-24 21:11:43,783 - Loss: 0.1617, step: 54700
+INFO - dpsgd_diffusion.py - 2024-10-24 21:12:31,960 - Loss: 0.1724, step: 54800
+INFO - dpsgd_diffusion.py - 2024-10-24 21:13:19,864 - Loss: 0.1382, step: 54900
+INFO - dpsgd_diffusion.py - 2024-10-24 21:13:25,361 - Eps-value after 143 epochs: 0.9671
+INFO - dpsgd_diffusion.py - 2024-10-24 21:14:07,006 - Loss: 0.1631, step: 55000
+INFO - dpsgd_diffusion.py - 2024-10-24 21:14:55,299 - Loss: 0.1600, step: 55100
+INFO - dpsgd_diffusion.py - 2024-10-24 21:15:45,157 - Loss: 0.1470, step: 55200
+INFO - dpsgd_diffusion.py - 2024-10-24 21:16:31,177 - Eps-value after 144 epochs: 0.9708
+INFO - dpsgd_diffusion.py - 2024-10-24 21:16:33,112 - Loss: 0.1825, step: 55300
+INFO - dpsgd_diffusion.py - 2024-10-24 21:17:21,373 - Loss: 0.1612, step: 55400
+INFO - dpsgd_diffusion.py - 2024-10-24 21:18:10,582 - Loss: 0.2140, step: 55500
+INFO - dpsgd_diffusion.py - 2024-10-24 21:18:58,337 - Loss: 0.1225, step: 55600
+INFO - dpsgd_diffusion.py - 2024-10-24 21:19:36,645 - Eps-value after 145 epochs: 0.9745
+INFO - dpsgd_diffusion.py - 2024-10-24 21:19:47,108 - Loss: 0.1705, step: 55700
+INFO - dpsgd_diffusion.py - 2024-10-24 21:20:36,798 - Loss: 0.1489, step: 55800
+INFO - dpsgd_diffusion.py - 2024-10-24 21:21:25,412 - Loss: 0.1688, step: 55900
+INFO - dpsgd_diffusion.py - 2024-10-24 21:22:13,827 - Loss: 0.1523, step: 56000
+INFO - dpsgd_diffusion.py - 2024-10-24 21:22:13,832 - Saving snapshot checkpoint and sampling single batch at iteration 56000.
+WARNING - image.py - 2024-10-24 21:22:14,326 - 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 21:22:29,484 - FID at iteration 56000: 221.503527
+INFO - dpsgd_diffusion.py - 2024-10-24 21:23:01,438 - Eps-value after 146 epochs: 0.9781
+INFO - dpsgd_diffusion.py - 2024-10-24 21:23:18,966 - Loss: 0.1816, step: 56100
+INFO - dpsgd_diffusion.py - 2024-10-24 21:24:07,181 - Loss: 0.1644, step: 56200
+INFO - dpsgd_diffusion.py - 2024-10-24 21:24:56,408 - Loss: 0.1608, step: 56300
+INFO - dpsgd_diffusion.py - 2024-10-24 21:25:44,982 - Loss: 0.1985, step: 56400
+INFO - dpsgd_diffusion.py - 2024-10-24 21:26:08,393 - Eps-value after 147 epochs: 0.9816
+INFO - dpsgd_diffusion.py - 2024-10-24 21:26:34,014 - Loss: 0.1480, step: 56500
+INFO - dpsgd_diffusion.py - 2024-10-24 21:27:22,129 - Loss: 0.1616, step: 56600
+INFO - dpsgd_diffusion.py - 2024-10-24 21:28:11,788 - Loss: 0.1373, step: 56700
+INFO - dpsgd_diffusion.py - 2024-10-24 21:29:00,012 - Loss: 0.1422, step: 56800
+INFO - dpsgd_diffusion.py - 2024-10-24 21:29:15,232 - Eps-value after 148 epochs: 0.9851
+INFO - dpsgd_diffusion.py - 2024-10-24 21:29:48,624 - Loss: 0.1432, step: 56900
+INFO - dpsgd_diffusion.py - 2024-10-24 21:30:37,709 - Loss: 0.1404, step: 57000
+INFO - dpsgd_diffusion.py - 2024-10-24 21:31:27,245 - Loss: 0.1312, step: 57100
+INFO - dpsgd_diffusion.py - 2024-10-24 21:32:17,053 - Loss: 0.1551, step: 57200
+INFO - dpsgd_diffusion.py - 2024-10-24 21:32:24,552 - Eps-value after 149 epochs: 0.9887
+INFO - dpsgd_diffusion.py - 2024-10-24 21:33:05,862 - Loss: 0.1499, step: 57300
+INFO - dpsgd_diffusion.py - 2024-10-24 21:33:54,712 - Loss: 0.1731, step: 57400
+INFO - dpsgd_diffusion.py - 2024-10-24 21:34:43,897 - Loss: 0.1680, step: 57500
+INFO - dpsgd_diffusion.py - 2024-10-24 21:35:31,956 - Loss: 0.1682, step: 57600
+INFO - dpsgd_diffusion.py - 2024-10-24 21:35:31,967 - Eps-value after 150 epochs: 0.9922
+INFO - dpsgd_diffusion.py - 2024-10-24 21:35:32,661 - Saving final checkpoint.
+INFO - dpsgd_diffusion.py - 2024-10-24 21:35:32,663 - start to generate 60000 samples
+INFO - dpsgd_diffusion.py - 2024-10-24 21:59:58,830 - Generation Finished!
+INFO - dataset_loader.py - 2024-10-24 22:49:41,036 - delta is reset as 4.784738627130138e-06
+INFO - dataset_loader.py - 2024-10-24 22:50:38,379 - delta is reset as 4.784738627130138e-06
+INFO - evaluator.py - 2024-10-24 22:51:09,132 - Epoch: 0 Train acc: 48.090909090909086 Val acc: 38.95 Test acc38.75; Train loss: 0.005235894735292955 Val loss: 0.004536110639572144
+INFO - evaluator.py - 2024-10-24 22:51:33,316 - Epoch: 1 Train acc: 71.62181818181818 Val acc: 35.05 Test acc36.35; Train loss: 0.002800950581377203 Val loss: 0.006110321998596191
+INFO - evaluator.py - 2024-10-24 22:51:57,568 - Epoch: 2 Train acc: 79.27272727272727 Val acc: 38.6 Test acc39.6; Train loss: 0.0020914525628089905 Val loss: 0.0066690690517425535
+INFO - evaluator.py - 2024-10-24 22:52:21,899 - Epoch: 3 Train acc: 83.83636363636363 Val acc: 36.5 Test acc37.675; Train loss: 0.0016633654968305068 Val loss: 0.0063852517604827885
+INFO - evaluator.py - 2024-10-24 22:52:46,128 - Epoch: 4 Train acc: 88.78545454545454 Val acc: 42.199999999999996 Test acc42.575; Train loss: 0.0011725048647685484 Val loss: 0.0061018714904785155
+INFO - evaluator.py - 2024-10-24 22:53:11,091 - Epoch: 5 Train acc: 89.99090909090908 Val acc: 42.6 Test acc43.05; Train loss: 0.0010513972450386395 Val loss: 0.0062036292552948
+INFO - evaluator.py - 2024-10-24 22:53:35,041 - Epoch: 6 Train acc: 91.92 Val acc: 37.7 Test acc38.324999999999996; Train loss: 0.0008533411352471872 Val loss: 0.009160301208496095
+INFO - evaluator.py - 2024-10-24 22:53:59,158 - Epoch: 7 Train acc: 92.57454545454546 Val acc: 43.3 Test acc43.55; Train loss: 0.0007892383746125482 Val loss: 0.006803690910339355
+INFO - evaluator.py - 2024-10-24 22:54:23,337 - Epoch: 8 Train acc: 93.61818181818182 Val acc: 35.05 Test acc37.1; Train loss: 0.0006748295866630294 Val loss: 0.010971975803375245
+INFO - evaluator.py - 2024-10-24 22:54:47,696 - Epoch: 9 Train acc: 95.07636363636364 Val acc: 43.45 Test acc44.5; Train loss: 0.0005345633507452227 Val loss: 0.0067000892162323
+INFO - evaluator.py - 2024-10-24 22:55:12,446 - Epoch: 10 Train acc: 96.11454545454545 Val acc: 37.95 Test acc38.775; Train loss: 0.00042063302018425684 Val loss: 0.009248335361480713
+INFO - evaluator.py - 2024-10-24 22:55:36,896 - Epoch: 11 Train acc: 96.70727272727274 Val acc: 45.25 Test acc44.9; Train loss: 0.00036193234388801185 Val loss: 0.006118733167648316
+INFO - evaluator.py - 2024-10-24 22:56:01,579 - Epoch: 12 Train acc: 97.54363636363637 Val acc: 41.449999999999996 Test acc41.5; Train loss: 0.0002682996785437519 Val loss: 0.00987232780456543
+INFO - evaluator.py - 2024-10-24 22:56:25,992 - Epoch: 13 Train acc: 97.78545454545454 Val acc: 42.25 Test acc42.275; Train loss: 0.0002391060592775995 Val loss: 0.006653652191162109
+INFO - evaluator.py - 2024-10-24 22:56:50,017 - Epoch: 14 Train acc: 98.10727272727273 Val acc: 40.550000000000004 Test acc42.025; Train loss: 0.00020743471914055672 Val loss: 0.008111542701721191
+INFO - evaluator.py - 2024-10-24 22:57:15,178 - Epoch: 15 Train acc: 98.27272727272728 Val acc: 39.85 Test acc40.925; Train loss: 0.0001980287728831172 Val loss: 0.009812898635864258
+INFO - evaluator.py - 2024-10-24 22:57:39,428 - Epoch: 16 Train acc: 98.46727272727273 Val acc: 35.55 Test acc37.4; Train loss: 0.00017186851359226487 Val loss: 0.011219772338867187
+INFO - evaluator.py - 2024-10-24 22:58:03,896 - Epoch: 17 Train acc: 98.70363636363636 Val acc: 28.549999999999997 Test acc29.849999999999998; Train loss: 0.0001506956492042677 Val loss: 0.016175264358520507
+INFO - evaluator.py - 2024-10-24 22:58:28,253 - Epoch: 18 Train acc: 98.70909090909092 Val acc: 40.150000000000006 Test acc39.1; Train loss: 0.00014120765742845834 Val loss: 0.011263612747192384
+INFO - evaluator.py - 2024-10-24 22:58:52,406 - Epoch: 19 Train acc: 98.90727272727273 Val acc: 37.9 Test acc38.375; Train loss: 0.00011630619556050409 Val loss: 0.00955641508102417
+INFO - evaluator.py - 2024-10-24 22:59:16,816 - Epoch: 20 Train acc: 99.66909090909091 Val acc: 39.550000000000004 Test acc39.825; Train loss: 4.0655167228330606e-05 Val loss: 0.012527746677398682
+INFO - evaluator.py - 2024-10-24 22:59:40,804 - Epoch: 21 Train acc: 99.83999999999999 Val acc: 39.7 Test acc40.699999999999996; Train loss: 2.145131763430651e-05 Val loss: 0.018831984519958496
+INFO - evaluator.py - 2024-10-24 23:00:05,422 - Epoch: 22 Train acc: 99.88363636363637 Val acc: 40.35 Test acc41.55; Train loss: 1.6147111500719224e-05 Val loss: 0.02709102153778076
+INFO - evaluator.py - 2024-10-24 23:00:29,742 - Epoch: 23 Train acc: 99.89454545454547 Val acc: 40.699999999999996 Test acc40.8; Train loss: 1.4128891968919726e-05 Val loss: 0.032214541435241696
+INFO - evaluator.py - 2024-10-24 23:00:54,401 - Epoch: 24 Train acc: 99.89090909090909 Val acc: 41.55 Test acc41.875; Train loss: 1.408495976938866e-05 Val loss: 0.03448763656616211
+INFO - evaluator.py - 2024-10-24 23:01:18,433 - Epoch: 25 Train acc: 99.9 Val acc: 39.75 Test acc40.050000000000004; Train loss: 1.3328597411402205e-05 Val loss: 0.04791554069519043
+INFO - evaluator.py - 2024-10-24 23:01:43,206 - Epoch: 26 Train acc: 99.89272727272727 Val acc: 41.949999999999996 Test acc42.675000000000004; Train loss: 1.2399059405751442e-05 Val loss: 0.02119047737121582
+INFO - evaluator.py - 2024-10-24 23:02:07,403 - Epoch: 27 Train acc: 99.92545454545456 Val acc: 39.65 Test acc39.7; Train loss: 8.773761601134372e-06 Val loss: 0.030931676864624023
+INFO - evaluator.py - 2024-10-24 23:02:31,834 - Epoch: 28 Train acc: 99.83454545454545 Val acc: 40.25 Test acc40.525; Train loss: 1.9069167823710647e-05 Val loss: 0.02188103771209717
+INFO - evaluator.py - 2024-10-24 23:02:56,257 - Epoch: 29 Train acc: 99.92 Val acc: 41.3 Test acc42.05; Train loss: 1.098390901805198e-05 Val loss: 0.01793442153930664
+INFO - evaluator.py - 2024-10-24 23:03:21,197 - Epoch: 30 Train acc: 99.89818181818183 Val acc: 42.65 Test acc42.725; Train loss: 1.3050329666624417e-05 Val loss: 0.01583650255203247
+INFO - evaluator.py - 2024-10-24 23:03:45,358 - Epoch: 31 Train acc: 99.82363636363635 Val acc: 40.8 Test acc40.8; Train loss: 2.1605713757030158e-05 Val loss: 0.014434441566467286
+INFO - evaluator.py - 2024-10-24 23:04:10,176 - Epoch: 32 Train acc: 99.87818181818182 Val acc: 41.449999999999996 Test acc42.1; Train loss: 1.5744690659233706e-05 Val loss: 0.016299612522125244
+INFO - evaluator.py - 2024-10-24 23:04:34,882 - Epoch: 33 Train acc: 99.84363636363636 Val acc: 43.15 Test acc43.974999999999994; Train loss: 1.7493808055587578e-05 Val loss: 0.012525592803955078
+INFO - evaluator.py - 2024-10-24 23:04:59,157 - Epoch: 34 Train acc: 99.83454545454545 Val acc: 38.65 Test acc40.075; Train loss: 1.9530207398268184e-05 Val loss: 0.020441522598266603
+INFO - evaluator.py - 2024-10-24 23:05:24,216 - Epoch: 35 Train acc: 99.92727272727274 Val acc: 40.400000000000006 Test acc40.849999999999994; Train loss: 9.280100616094635e-06 Val loss: 0.02076344871520996
+INFO - evaluator.py - 2024-10-24 23:05:48,691 - Epoch: 36 Train acc: 99.71636363636364 Val acc: 43.2 Test acc44.95; Train loss: 3.230026255213571e-05 Val loss: 0.011072400569915772
+INFO - evaluator.py - 2024-10-24 23:06:13,937 - Epoch: 37 Train acc: 99.94909090909091 Val acc: 40.849999999999994 Test acc41.449999999999996; Train loss: 8.116760581006847e-06 Val loss: 0.017643385887145997
+INFO - evaluator.py - 2024-10-24 23:06:38,318 - Epoch: 38 Train acc: 99.86181818181818 Val acc: 46.400000000000006 Test acc46.675; Train loss: 1.7313784342695727e-05 Val loss: 0.010496066093444825
+INFO - evaluator.py - 2024-10-24 23:07:03,197 - Epoch: 39 Train acc: 99.91272727272728 Val acc: 48.699999999999996 Test acc48.625; Train loss: 1.006228681587593e-05 Val loss: 0.009768337726593017
+INFO - evaluator.py - 2024-10-24 23:07:27,660 - Epoch: 40 Train acc: 99.98 Val acc: 45.550000000000004 Test acc45.975; Train loss: 3.033316284547089e-06 Val loss: 0.011841446876525878
+INFO - evaluator.py - 2024-10-24 23:07:52,747 - Epoch: 41 Train acc: 99.97454545454545 Val acc: 43.95 Test acc44.9; Train loss: 3.004668451516905e-06 Val loss: 0.014060803413391113
+INFO - evaluator.py - 2024-10-24 23:08:17,286 - Epoch: 42 Train acc: 99.98727272727272 Val acc: 44.05 Test acc44.975; Train loss: 2.2086962680821835e-06 Val loss: 0.017040731906890868
+INFO - evaluator.py - 2024-10-24 23:08:42,335 - Epoch: 43 Train acc: 100.0 Val acc: 43.05 Test acc43.575; Train loss: 1.0760073328376826e-06 Val loss: 0.019769852638244628
+INFO - evaluator.py - 2024-10-24 23:09:06,549 - Epoch: 44 Train acc: 99.99636363636364 Val acc: 42.15 Test acc42.55; Train loss: 1.2522788520403662e-06 Val loss: 0.02136836242675781
+INFO - evaluator.py - 2024-10-24 23:09:31,355 - Epoch: 45 Train acc: 99.99636363636364 Val acc: 41.75 Test acc42.725; Train loss: 9.74589935942111e-07 Val loss: 0.02116060733795166
+INFO - evaluator.py - 2024-10-24 23:09:56,228 - Epoch: 46 Train acc: 99.99454545454546 Val acc: 41.85 Test acc42.8; Train loss: 1.0509166977168538e-06 Val loss: 0.022256577491760254
+INFO - evaluator.py - 2024-10-24 23:10:21,516 - Epoch: 47 Train acc: 99.99636363636364 Val acc: 42.699999999999996 Test acc43.725; Train loss: 7.866640353520118e-07 Val loss: 0.020756115913391115
+INFO - evaluator.py - 2024-10-24 23:10:45,699 - Epoch: 48 Train acc: 99.97090909090909 Val acc: 41.0 Test acc41.5; Train loss: 3.217216711899792e-06 Val loss: 0.022665241241455077
+INFO - evaluator.py - 2024-10-24 23:11:10,555 - Epoch: 49 Train acc: 99.99090909090908 Val acc: 42.75 Test acc43.15; Train loss: 2.2475659089319707e-06 Val loss: 0.021376511573791505
+INFO - evaluator.py - 2024-10-24 23:11:10,589 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from resnet is 48.699999999999996 and 48.625
+INFO - evaluator.py - 2024-10-24 23:11:10,589 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from resnet is 48.699999999999996 and 48.625
+INFO - evaluator.py - 2024-10-24 23:11:10,589 - The best acc test dataset from resnet is 48.625
+INFO - evaluator.py - 2024-10-24 23:11:42,850 - Epoch: 0 Train acc: 61.663636363636364 Val acc: 34.949999999999996 Test acc36.0; Train loss: 0.0037406416004354304 Val loss: 0.003670275330543518
+INFO - evaluator.py - 2024-10-24 23:12:13,226 - Epoch: 1 Train acc: 79.39818181818183 Val acc: 43.55 Test acc42.625; Train loss: 0.002067818329551003 Val loss: 0.003959630131721497
+INFO - evaluator.py - 2024-10-24 23:12:43,491 - Epoch: 2 Train acc: 85.66363636363636 Val acc: 48.55 Test acc50.449999999999996; Train loss: 0.0014711343372409995 Val loss: 0.003298302412033081
+INFO - evaluator.py - 2024-10-24 23:13:13,941 - Epoch: 3 Train acc: 88.76545454545455 Val acc: 39.45 Test acc40.550000000000004; Train loss: 0.0011746641676534306 Val loss: 0.005292214632034302
+INFO - evaluator.py - 2024-10-24 23:13:44,078 - Epoch: 4 Train acc: 90.60545454545455 Val acc: 41.05 Test acc42.725; Train loss: 0.00098228817013177 Val loss: 0.006120251893997192
+INFO - evaluator.py - 2024-10-24 23:14:14,372 - Epoch: 5 Train acc: 92.29454545454546 Val acc: 39.800000000000004 Test acc41.0; Train loss: 0.00080561226579276 Val loss: 0.007137999057769775
+INFO - evaluator.py - 2024-10-24 23:14:44,777 - Epoch: 6 Train acc: 93.87454545454545 Val acc: 39.75 Test acc40.925; Train loss: 0.0006453904440457171 Val loss: 0.00988970947265625
+INFO - evaluator.py - 2024-10-24 23:15:14,807 - Epoch: 7 Train acc: 94.82181818181819 Val acc: 44.65 Test acc45.9; Train loss: 0.0005612887404859066 Val loss: 0.005374523639678955
+INFO - evaluator.py - 2024-10-24 23:15:45,054 - Epoch: 8 Train acc: 95.88727272727273 Val acc: 44.95 Test acc45.775; Train loss: 0.0004456923320889473 Val loss: 0.006390077352523804
+INFO - evaluator.py - 2024-10-24 23:16:15,770 - Epoch: 9 Train acc: 96.94727272727273 Val acc: 42.3 Test acc43.0; Train loss: 0.0003332510319961743 Val loss: 0.008148348808288575
+INFO - evaluator.py - 2024-10-24 23:16:45,788 - Epoch: 10 Train acc: 97.51454545454546 Val acc: 40.150000000000006 Test acc41.825; Train loss: 0.00027462921596386217 Val loss: 0.0100587477684021
+INFO - evaluator.py - 2024-10-24 23:17:16,102 - Epoch: 11 Train acc: 98.18545454545455 Val acc: 42.95 Test acc43.375; Train loss: 0.0001966750724918463 Val loss: 0.009120422840118408
+INFO - evaluator.py - 2024-10-24 23:17:46,559 - Epoch: 12 Train acc: 98.42181818181818 Val acc: 49.15 Test acc48.725; Train loss: 0.00017383043037896807 Val loss: 0.006083012104034424
+INFO - evaluator.py - 2024-10-24 23:18:16,706 - Epoch: 13 Train acc: 98.63272727272727 Val acc: 43.2 Test acc43.575; Train loss: 0.00014473014825277708 Val loss: 0.010562279224395753
+INFO - evaluator.py - 2024-10-24 23:18:47,353 - Epoch: 14 Train acc: 98.62727272727273 Val acc: 39.85 Test acc41.75; Train loss: 0.00015199786504222588 Val loss: 0.008376381874084472
+INFO - evaluator.py - 2024-10-24 23:19:17,859 - Epoch: 15 Train acc: 99.05090909090909 Val acc: 40.550000000000004 Test acc42.0; Train loss: 0.0001076461112126708 Val loss: 0.010468923091888427
+INFO - evaluator.py - 2024-10-24 23:19:47,851 - Epoch: 16 Train acc: 99.18 Val acc: 43.5 Test acc45.1; Train loss: 9.324811147949234e-05 Val loss: 0.00764114761352539
+INFO - evaluator.py - 2024-10-24 23:20:18,366 - Epoch: 17 Train acc: 99.24181818181819 Val acc: 41.199999999999996 Test acc43.3; Train loss: 8.136530890214172e-05 Val loss: 0.009735825538635254
+INFO - evaluator.py - 2024-10-24 23:20:48,850 - Epoch: 18 Train acc: 99.04727272727273 Val acc: 44.55 Test acc45.95; Train loss: 0.00010623606834137304 Val loss: 0.0071379756927490235
+INFO - evaluator.py - 2024-10-24 23:21:18,956 - Epoch: 19 Train acc: 99.34545454545454 Val acc: 43.95 Test acc45.824999999999996; Train loss: 7.09390864178369e-05 Val loss: 0.006988257646560669
+INFO - evaluator.py - 2024-10-24 23:21:49,161 - Epoch: 20 Train acc: 99.79454545454546 Val acc: 48.9 Test acc49.325; Train loss: 2.28838326102546e-05 Val loss: 0.007301109313964844
+INFO - evaluator.py - 2024-10-24 23:22:19,461 - Epoch: 21 Train acc: 99.88909090909091 Val acc: 46.6 Test acc47.199999999999996; Train loss: 1.5482099235205995e-05 Val loss: 0.009771857261657715
+INFO - evaluator.py - 2024-10-24 23:22:49,735 - Epoch: 22 Train acc: 99.87636363636364 Val acc: 45.9 Test acc46.800000000000004; Train loss: 1.439085942957635e-05 Val loss: 0.010085168361663819
+INFO - evaluator.py - 2024-10-24 23:23:20,017 - Epoch: 23 Train acc: 99.87636363636364 Val acc: 42.25 Test acc42.825; Train loss: 1.4538372412525033e-05 Val loss: 0.013301238059997559
+INFO - evaluator.py - 2024-10-24 23:23:50,575 - Epoch: 24 Train acc: 99.90363636363637 Val acc: 45.25 Test acc44.875; Train loss: 1.2315762218680572e-05 Val loss: 0.011758046627044678
+INFO - evaluator.py - 2024-10-24 23:24:20,504 - Epoch: 25 Train acc: 99.92363636363636 Val acc: 43.65 Test acc43.45; Train loss: 9.61336749976247e-06 Val loss: 0.013057234287261963
+INFO - evaluator.py - 2024-10-24 23:24:50,634 - Epoch: 26 Train acc: 99.93454545454546 Val acc: 43.7 Test acc43.425000000000004; Train loss: 8.96655197595299e-06 Val loss: 0.01383483076095581
+INFO - evaluator.py - 2024-10-24 23:25:21,070 - Epoch: 27 Train acc: 99.90909090909092 Val acc: 46.1 Test acc45.550000000000004; Train loss: 1.1049915574337568e-05 Val loss: 0.011365376472473144
+INFO - evaluator.py - 2024-10-24 23:25:51,514 - Epoch: 28 Train acc: 99.91272727272728 Val acc: 46.35 Test acc45.85; Train loss: 1.0851724303740245e-05 Val loss: 0.012053710460662842
+INFO - evaluator.py - 2024-10-24 23:26:21,684 - Epoch: 29 Train acc: 99.92181818181818 Val acc: 43.65 Test acc43.85; Train loss: 1.0045677379027686e-05 Val loss: 0.013533884525299073
+INFO - evaluator.py - 2024-10-24 23:26:52,240 - Epoch: 30 Train acc: 99.90727272727273 Val acc: 42.8 Test acc42.125; Train loss: 1.0514532746509013e-05 Val loss: 0.015031900882720948
+INFO - evaluator.py - 2024-10-24 23:27:22,514 - Epoch: 31 Train acc: 99.91636363636364 Val acc: 42.699999999999996 Test acc42.449999999999996; Train loss: 1.0602962674205298e-05 Val loss: 0.011638566493988037
+INFO - evaluator.py - 2024-10-24 23:27:52,569 - Epoch: 32 Train acc: 99.86363636363636 Val acc: 43.6 Test acc42.75; Train loss: 1.4370235604126886e-05 Val loss: 0.013494804859161377
+INFO - evaluator.py - 2024-10-24 23:28:22,827 - Epoch: 33 Train acc: 99.92727272727274 Val acc: 43.4 Test acc42.699999999999996; Train loss: 9.126461255866822e-06 Val loss: 0.014548630714416503
+INFO - evaluator.py - 2024-10-24 23:28:53,087 - Epoch: 34 Train acc: 99.9509090909091 Val acc: 44.2 Test acc44.125; Train loss: 7.017623129319294e-06 Val loss: 0.012785057067871093
+INFO - evaluator.py - 2024-10-24 23:29:23,346 - Epoch: 35 Train acc: 99.90545454545455 Val acc: 44.65 Test acc44.45; Train loss: 1.1086592986645303e-05 Val loss: 0.01212795639038086
+INFO - evaluator.py - 2024-10-24 23:29:53,532 - Epoch: 36 Train acc: 99.87818181818182 Val acc: 47.199999999999996 Test acc47.675; Train loss: 1.252035316849122e-05 Val loss: 0.01069820261001587
+INFO - evaluator.py - 2024-10-24 23:30:23,767 - Epoch: 37 Train acc: 99.89090909090909 Val acc: 39.1 Test acc40.25; Train loss: 1.2087713208628967e-05 Val loss: 0.01566861057281494
+INFO - evaluator.py - 2024-10-24 23:30:54,069 - Epoch: 38 Train acc: 99.87454545454545 Val acc: 42.4 Test acc43.75; Train loss: 1.3623821603158086e-05 Val loss: 0.014552337646484375
+INFO - evaluator.py - 2024-10-24 23:31:24,700 - Epoch: 39 Train acc: 99.86727272727272 Val acc: 46.2 Test acc46.150000000000006; Train loss: 1.4414796375389083e-05 Val loss: 0.012974510192871094
+INFO - evaluator.py - 2024-10-24 23:31:54,807 - Epoch: 40 Train acc: 99.9509090909091 Val acc: 45.15 Test acc45.0; Train loss: 5.236963886140571e-06 Val loss: 0.013282662868499755
+INFO - evaluator.py - 2024-10-24 23:32:25,001 - Epoch: 41 Train acc: 99.97454545454545 Val acc: 43.2 Test acc42.9; Train loss: 3.5470294139568897e-06 Val loss: 0.015182061672210694
+INFO - evaluator.py - 2024-10-24 23:32:55,030 - Epoch: 42 Train acc: 99.98363636363636 Val acc: 42.9 Test acc43.625; Train loss: 2.8289722777117275e-06 Val loss: 0.014068112373352051
+INFO - evaluator.py - 2024-10-24 23:33:25,259 - Epoch: 43 Train acc: 99.97454545454545 Val acc: 42.5 Test acc42.85; Train loss: 3.239652104192911e-06 Val loss: 0.015388777256011963
+INFO - evaluator.py - 2024-10-24 23:33:55,581 - Epoch: 44 Train acc: 99.97636363636364 Val acc: 43.6 Test acc43.75; Train loss: 3.273129121986461e-06 Val loss: 0.013938886165618896
+INFO - evaluator.py - 2024-10-24 23:34:25,814 - Epoch: 45 Train acc: 99.9890909090909 Val acc: 44.25 Test acc45.225; Train loss: 1.579531074193338e-06 Val loss: 0.012459741592407227
+INFO - evaluator.py - 2024-10-24 23:34:55,913 - Epoch: 46 Train acc: 99.98181818181818 Val acc: 46.35 Test acc46.6; Train loss: 2.6804663554437204e-06 Val loss: 0.011697003841400147
+INFO - evaluator.py - 2024-10-24 23:35:25,972 - Epoch: 47 Train acc: 99.98363636363636 Val acc: 43.35 Test acc43.95; Train loss: 2.4248964280890173e-06 Val loss: 0.014050453662872315
+INFO - evaluator.py - 2024-10-24 23:35:56,224 - Epoch: 48 Train acc: 99.97636363636364 Val acc: 44.4 Test acc45.1; Train loss: 2.114928466612169e-06 Val loss: 0.013043230056762695
+INFO - evaluator.py - 2024-10-24 23:36:26,370 - Epoch: 49 Train acc: 99.97818181818182 Val acc: 44.55 Test acc45.0; Train loss: 2.6728268685804904e-06 Val loss: 0.013291849613189697
+INFO - evaluator.py - 2024-10-24 23:36:26,377 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from wrn is 49.15 and 48.725
+INFO - evaluator.py - 2024-10-24 23:36:26,378 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from wrn is 49.15 and 48.725
+INFO - evaluator.py - 2024-10-24 23:36:26,378 - The best acc test dataset from wrn is 50.449999999999996
+INFO - evaluator.py - 2024-10-24 23:38:25,793 - Epoch: 0 Train acc: 65.79636363636364 Val acc: 39.15 Test acc40.150000000000006; Train loss: 0.004147848786007274 Val loss: 0.011859679222106933
+INFO - evaluator.py - 2024-10-24 23:40:24,280 - Epoch: 1 Train acc: 85.25636363636364 Val acc: 40.35 Test acc39.300000000000004; Train loss: 0.0015449416680769488 Val loss: 0.02729271411895752
+INFO - evaluator.py - 2024-10-24 23:42:22,882 - Epoch: 2 Train acc: 88.39090909090909 Val acc: 44.05 Test acc42.449999999999996; Train loss: 0.0012195854195139624 Val loss: 0.028756074905395508
+INFO - evaluator.py - 2024-10-24 23:44:21,376 - Epoch: 3 Train acc: 89.20545454545454 Val acc: 40.45 Test acc41.9; Train loss: 0.001172786211696538 Val loss: 0.0254735164642334
+INFO - evaluator.py - 2024-10-24 23:46:19,762 - Epoch: 4 Train acc: 91.81090909090909 Val acc: 44.25 Test acc43.75; Train loss: 0.0008772003106095575 Val loss: 0.13955418014526366
+INFO - evaluator.py - 2024-10-24 23:48:18,008 - Epoch: 5 Train acc: 93.33272727272727 Val acc: 29.049999999999997 Test acc29.9; Train loss: 0.00072297897758809 Val loss: 0.01264392614364624
+INFO - evaluator.py - 2024-10-24 23:50:16,278 - Epoch: 6 Train acc: 91.46181818181817 Val acc: 27.950000000000003 Test acc27.0; Train loss: 0.0010058055197650737 Val loss: 0.009486371517181397
+INFO - evaluator.py - 2024-10-24 23:52:14,470 - Epoch: 7 Train acc: 94.60909090909091 Val acc: 43.25 Test acc44.324999999999996; Train loss: 0.0005934829804030332 Val loss: 0.008920231580734253
+INFO - evaluator.py - 2024-10-24 23:54:12,721 - Epoch: 8 Train acc: 96.44545454545454 Val acc: 46.25 Test acc46.925; Train loss: 0.00038863994567231696 Val loss: 0.007563547372817993
+INFO - evaluator.py - 2024-10-24 23:56:11,157 - Epoch: 9 Train acc: 97.30363636363636 Val acc: 48.5 Test acc47.3; Train loss: 0.00029223185757344417 Val loss: 0.014048337459564209
+INFO - evaluator.py - 2024-10-24 23:58:09,639 - Epoch: 10 Train acc: 97.62363636363636 Val acc: 37.2 Test acc36.8; Train loss: 0.0002608546071770516 Val loss: 0.02173879814147949
+INFO - evaluator.py - 2024-10-25 00:00:07,948 - Epoch: 11 Train acc: 97.98909090909092 Val acc: 36.25 Test acc34.975; Train loss: 0.00022030241369185122 Val loss: 0.06477663040161133
+INFO - evaluator.py - 2024-10-25 00:02:06,228 - Epoch: 12 Train acc: 98.18 Val acc: 40.300000000000004 Test acc39.0; Train loss: 0.00020377840094945647 Val loss: 0.01910359764099121
+INFO - evaluator.py - 2024-10-25 00:04:04,611 - Epoch: 13 Train acc: 98.60545454545453 Val acc: 49.75 Test acc48.35; Train loss: 0.00015482988523912023 Val loss: 0.00884312891960144
+INFO - evaluator.py - 2024-10-25 00:06:03,056 - Epoch: 14 Train acc: 98.57272727272726 Val acc: 47.349999999999994 Test acc47.075; Train loss: 0.0001701038571074605 Val loss: 0.008122310638427735
+INFO - evaluator.py - 2024-10-25 00:08:01,293 - Epoch: 15 Train acc: 98.86727272727272 Val acc: 38.0 Test acc37.9; Train loss: 0.00012440697415308518 Val loss: 0.029230804443359376
+INFO - evaluator.py - 2024-10-25 00:09:59,845 - Epoch: 16 Train acc: 99.17272727272727 Val acc: 40.2 Test acc40.75; Train loss: 9.4232751310549e-05 Val loss: 0.015797210216522216
+INFO - evaluator.py - 2024-10-25 00:11:58,119 - Epoch: 17 Train acc: 99.07272727272726 Val acc: 36.95 Test acc37.25; Train loss: 0.00010379166740246795 Val loss: 0.037965724945068356
+INFO - evaluator.py - 2024-10-25 00:13:56,450 - Epoch: 18 Train acc: 99.05818181818182 Val acc: 39.5 Test acc40.050000000000004; Train loss: 9.817965143275532e-05 Val loss: 0.04632177543640137
+INFO - evaluator.py - 2024-10-25 00:15:54,772 - Epoch: 19 Train acc: 99.04181818181819 Val acc: 32.1 Test acc32.475; Train loss: 0.00010367686268661849 Val loss: 0.11817068481445313
+INFO - evaluator.py - 2024-10-25 00:17:53,040 - Epoch: 20 Train acc: 99.79818181818182 Val acc: 45.2 Test acc44.975; Train loss: 2.5993087862364272e-05 Val loss: 0.020434556007385255
+INFO - evaluator.py - 2024-10-25 00:19:51,321 - Epoch: 21 Train acc: 99.92545454545456 Val acc: 50.0 Test acc47.05; Train loss: 1.1380639019295235e-05 Val loss: 0.020481979370117188
+INFO - evaluator.py - 2024-10-25 00:21:49,653 - Epoch: 22 Train acc: 99.97818181818182 Val acc: 46.6 Test acc45.725; Train loss: 6.615459048242139e-06 Val loss: 0.03179354190826416
+INFO - evaluator.py - 2024-10-25 00:23:48,002 - Epoch: 23 Train acc: 99.96909090909091 Val acc: 46.150000000000006 Test acc46.275; Train loss: 5.8293612978145985e-06 Val loss: 0.020668280601501464
+INFO - evaluator.py - 2024-10-25 00:25:46,341 - Epoch: 24 Train acc: 99.98545454545454 Val acc: 46.300000000000004 Test acc46.6; Train loss: 4.71227242365289e-06 Val loss: 0.02239450740814209
+INFO - evaluator.py - 2024-10-25 00:27:44,703 - Epoch: 25 Train acc: 99.97090909090909 Val acc: 45.75 Test acc46.2; Train loss: 4.843170113136611e-06 Val loss: 0.018863082885742188
+INFO - evaluator.py - 2024-10-25 00:29:42,918 - Epoch: 26 Train acc: 99.97090909090909 Val acc: 45.45 Test acc46.325; Train loss: 4.402839634646873e-06 Val loss: 0.022357869148254394
+INFO - evaluator.py - 2024-10-25 00:31:41,109 - Epoch: 27 Train acc: 99.96000000000001 Val acc: 46.1 Test acc46.675; Train loss: 5.517139730826867e-06 Val loss: 0.013740922451019287
+INFO - evaluator.py - 2024-10-25 00:33:39,263 - Epoch: 28 Train acc: 99.96181818181819 Val acc: 46.25 Test acc46.9; Train loss: 5.21984474597213e-06 Val loss: 0.014979459762573243
+INFO - evaluator.py - 2024-10-25 00:35:37,483 - Epoch: 29 Train acc: 99.96181818181819 Val acc: 45.45 Test acc44.9; Train loss: 4.999552853207041e-06 Val loss: 0.029949990272521973
+INFO - evaluator.py - 2024-10-25 00:37:35,818 - Epoch: 30 Train acc: 99.95272727272727 Val acc: 44.95 Test acc45.550000000000004; Train loss: 6.170663002029125e-06 Val loss: 0.02181826114654541
+INFO - evaluator.py - 2024-10-25 00:39:34,004 - Epoch: 31 Train acc: 99.91818181818182 Val acc: 45.45 Test acc45.2; Train loss: 1.0070328443411696e-05 Val loss: 0.0390985164642334
+INFO - evaluator.py - 2024-10-25 00:41:32,373 - Epoch: 32 Train acc: 99.94727272727273 Val acc: 46.300000000000004 Test acc46.675; Train loss: 7.313177308837608e-06 Val loss: 0.016496978759765624
+INFO - evaluator.py - 2024-10-25 00:43:30,615 - Epoch: 33 Train acc: 99.91272727272728 Val acc: 44.85 Test acc46.325; Train loss: 1.1416602488514566e-05 Val loss: 0.014869497299194337
+INFO - evaluator.py - 2024-10-25 00:45:28,867 - Epoch: 34 Train acc: 99.84545454545454 Val acc: 45.65 Test acc46.675; Train loss: 1.7764258816036587e-05 Val loss: 0.014632398128509522
+INFO - evaluator.py - 2024-10-25 00:47:27,400 - Epoch: 35 Train acc: 99.88181818181818 Val acc: 45.6 Test acc46.975; Train loss: 1.2933790463086388e-05 Val loss: 0.012877788543701172
+INFO - evaluator.py - 2024-10-25 00:49:25,603 - Epoch: 36 Train acc: 99.86181818181818 Val acc: 42.699999999999996 Test acc44.574999999999996; Train loss: 1.566481548017526e-05 Val loss: 0.014670820713043212
+INFO - evaluator.py - 2024-10-25 00:51:24,017 - Epoch: 37 Train acc: 99.86 Val acc: 47.55 Test acc48.375; Train loss: 1.6788221398680682e-05 Val loss: 0.009947970390319824
+INFO - evaluator.py - 2024-10-25 00:53:22,331 - Epoch: 38 Train acc: 99.93454545454546 Val acc: 44.25 Test acc42.625; Train loss: 7.68349376204159e-06 Val loss: 0.014918660640716553
+INFO - evaluator.py - 2024-10-25 00:55:20,463 - Epoch: 39 Train acc: 99.94181818181819 Val acc: 46.400000000000006 Test acc46.725; Train loss: 6.770548745275433e-06 Val loss: 0.014156579494476318
+INFO - evaluator.py - 2024-10-25 00:57:18,645 - Epoch: 40 Train acc: 99.97818181818182 Val acc: 45.300000000000004 Test acc45.95; Train loss: 2.4852453929600994e-06 Val loss: 0.015970292091369628
+INFO - evaluator.py - 2024-10-25 00:59:16,838 - Epoch: 41 Train acc: 99.99818181818182 Val acc: 45.5 Test acc46.075; Train loss: 1.288943678413158e-06 Val loss: 0.016064530849456787
+INFO - evaluator.py - 2024-10-25 01:01:15,033 - Epoch: 42 Train acc: 99.99272727272728 Val acc: 44.65 Test acc45.35; Train loss: 1.0362162073099833e-06 Val loss: 0.015674996376037597
+INFO - evaluator.py - 2024-10-25 01:03:13,265 - Epoch: 43 Train acc: 99.99454545454546 Val acc: 46.150000000000006 Test acc46.375; Train loss: 1.0663012252156147e-06 Val loss: 0.014407016754150391
+INFO - evaluator.py - 2024-10-25 01:05:11,504 - Epoch: 44 Train acc: 100.0 Val acc: 45.800000000000004 Test acc46.7; Train loss: 6.116143910482854e-07 Val loss: 0.0150164794921875
+INFO - evaluator.py - 2024-10-25 01:07:10,042 - Epoch: 45 Train acc: 99.99818181818182 Val acc: 47.05 Test acc47.699999999999996; Train loss: 5.878221763106756e-07 Val loss: 0.014463330745697021
+INFO - evaluator.py - 2024-10-25 01:09:08,176 - Epoch: 46 Train acc: 99.99818181818182 Val acc: 46.1 Test acc46.75; Train loss: 5.487080959359116e-07 Val loss: 0.014991106510162354
+INFO - evaluator.py - 2024-10-25 01:11:06,200 - Epoch: 47 Train acc: 100.0 Val acc: 45.85 Test acc46.25; Train loss: 3.115822311842335e-07 Val loss: 0.015977482318878175
+INFO - evaluator.py - 2024-10-25 01:13:04,271 - Epoch: 48 Train acc: 100.0 Val acc: 45.75 Test acc46.575; Train loss: 3.6092925651329015e-07 Val loss: 0.015334255218505859
+INFO - evaluator.py - 2024-10-25 01:15:02,291 - Epoch: 49 Train acc: 99.99818181818182 Val acc: 46.35 Test acc46.975; Train loss: 4.4119152571511225e-07 Val loss: 0.014444171905517578
+INFO - evaluator.py - 2024-10-25 01:15:02,297 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from resnext is 50.0 and 47.05
+INFO - evaluator.py - 2024-10-25 01:15:02,297 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from resnext is 50.0 and 47.05
+INFO - evaluator.py - 2024-10-25 01:15:02,297 - The best acc test dataset from resnext is 48.375
+INFO - evaluator.py - 2024-10-25 01:15:02,298 - The best acc of accuracy (using synthetic images as the validation set) of synthetic images from resnet, wrn, and resnext are [48.625, 48.725, 47.05].
+INFO - evaluator.py - 2024-10-25 01:15:02,298 - The average and std of accuracy of synthetic images are 48.13 and 0.77
+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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