diff --git a/.gitattributes b/.gitattributes
index 43b72edaac2868cf6c431bf526ccf64220038fe1..bec3c06f9d0277c072647ee6bb32c1444c8724ba 100644
--- a/.gitattributes
+++ b/.gitattributes
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diff --git a/dpdm/fmnist_28_eps1.0trainval-2024-10-23-23-32-54/stdout.txt b/dpdm/fmnist_28_eps1.0trainval-2024-10-23-23-32-54/stdout.txt
new file mode 100644
index 0000000000000000000000000000000000000000..78e08b09b7963cea8f20b8da1dc37626b3afbe2c
--- /dev/null
+++ b/dpdm/fmnist_28_eps1.0trainval-2024-10-23-23-32-54/stdout.txt
@@ -0,0 +1,1080 @@
+INFO - utils.py - 2024-10-23 23:33:00,301 - {'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': 6027, 'omp_n_threads': 8, 'workdir': 'exp/dpdm/fmnist_28_eps1.0trainval-2024-10-23-23-32-54', 'local_rank': 0, 'global_rank': 0, 'global_size': 4, 'root_folder': '.'}, 'public_data': {'name': None, 'num_channels': 1, 'resolution': 28, 'n_classes': 1000, 'train_path': 'dataset/imagenet/imagenet_32', 'selective': {'ratio': 1.0}}, 'sensitive_data': {'name': 'fmnist', 'num_channels': 1, 'resolution': 28, 'n_classes': 10, 'train_path': 'dataset/fmnist/train_28.zip', 'test_path': 'dataset/fmnist/test_28.zip', 'fid_stats': 'dataset/fmnist/fid_stats_28.npz', 'train_num': 'val'}, 'model': {'ckpt': None, 'denoiser_name': 'edm', 'denoiser_network': 'song', 'ema_rate': 0.999, 'network': {'image_size': 28, 'num_in_channels': 1, 'num_out_channels': 1, 'label_dim': 10, 'attn_resolutions': [14], 'ch_mult': [2, 2]}, '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': 50, 's_min': 0.025, 's_max': 50, 'num_steps': 1000, 'tmin': 0.002, 'tmax': 80.0, 'rho': 7.0, 'guid_scale': 1.0}, 'sampler_acc': {'type': 'edm', 's_churn': 10, 's_min': 0.025, 's_max': 50, 'num_steps': 1000, '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/fmnist/fid_stats_28.npz'}, 'pretrain': {'log_dir': 'exp/dpdm/fmnist_28_eps1.0trainval-2024-10-23-23-32-54/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/fmnist_28_eps1.0trainval-2024-10-23-23-32-54/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': 32}}, 'gen': {'data_num': 60000, 'batch_size': 1000, 'log_dir': 'exp/dpdm/fmnist_28_eps1.0trainval-2024-10-23-23-32-54/gen'}, 'eval': {'batch_size': 1000}}
+INFO - dataset_loader.py - 2024-10-23 23:33:01,279 - delta is reset as 1.6657508770018431e-06
+INFO - dpsgd_diffusion.py - 2024-10-23 23:33:01,885 - Number of trainable parameters in model: 0
+INFO - dpsgd_diffusion.py - 2024-10-23 23:33:01,886 - Number of total epochs: 150
+INFO - dpsgd_diffusion.py - 2024-10-23 23:33:01,886 - Starting training at step 0
+INFO - dpsgd_diffusion.py - 2024-10-23 23:34:48,707 - Loss: 1.2114, step: 100
+INFO - dpsgd_diffusion.py - 2024-10-23 23:36:16,593 - Loss: 1.0908, step: 200
+INFO - dpsgd_diffusion.py - 2024-10-23 23:37:41,101 - Loss: 1.0491, step: 300
+INFO - dpsgd_diffusion.py - 2024-10-23 23:39:08,359 - Loss: 1.0457, step: 400
+INFO - dpsgd_diffusion.py - 2024-10-23 23:39:48,095 - Eps-value after 1 epochs: 0.1424
+INFO - dpsgd_diffusion.py - 2024-10-23 23:40:31,781 - Loss: 0.9765, step: 500
+INFO - dpsgd_diffusion.py - 2024-10-23 23:41:59,373 - Loss: 0.9684, step: 600
+INFO - dpsgd_diffusion.py - 2024-10-23 23:43:25,218 - Loss: 0.9495, step: 700
+INFO - dpsgd_diffusion.py - 2024-10-23 23:44:48,897 - Loss: 0.9435, step: 800
+INFO - dpsgd_diffusion.py - 2024-10-23 23:46:09,441 - Eps-value after 2 epochs: 0.1531
+INFO - dpsgd_diffusion.py - 2024-10-23 23:46:12,890 - Loss: 0.8979, step: 900
+INFO - dpsgd_diffusion.py - 2024-10-23 23:47:38,085 - Loss: 0.8570, step: 1000
+INFO - dpsgd_diffusion.py - 2024-10-23 23:49:03,186 - Loss: 0.8275, step: 1100
+INFO - dpsgd_diffusion.py - 2024-10-23 23:50:28,470 - Loss: 0.8093, step: 1200
+INFO - dpsgd_diffusion.py - 2024-10-23 23:52:15,498 - Loss: 0.7824, step: 1300
+INFO - dpsgd_diffusion.py - 2024-10-23 23:53:03,842 - Eps-value after 3 epochs: 0.1637
+INFO - dpsgd_diffusion.py - 2024-10-23 23:54:03,820 - Loss: 0.7427, step: 1400
+INFO - dpsgd_diffusion.py - 2024-10-23 23:55:52,227 - Loss: 0.7023, step: 1500
+INFO - dpsgd_diffusion.py - 2024-10-23 23:57:40,171 - Loss: 0.6886, step: 1600
+INFO - dpsgd_diffusion.py - 2024-10-23 23:59:27,129 - Loss: 0.6781, step: 1700
+INFO - dpsgd_diffusion.py - 2024-10-24 00:01:09,137 - Eps-value after 4 epochs: 0.1744
+INFO - dpsgd_diffusion.py - 2024-10-24 00:01:17,531 - Loss: 0.6238, step: 1800
+INFO - dpsgd_diffusion.py - 2024-10-24 00:03:04,562 - Loss: 0.6430, step: 1900
+INFO - dpsgd_diffusion.py - 2024-10-24 00:04:50,583 - Loss: 0.6068, step: 2000
+INFO - dpsgd_diffusion.py - 2024-10-24 00:04:50,689 - Saving snapshot checkpoint and sampling single batch at iteration 2000.
+WARNING - image.py - 2024-10-24 00:04:51,640 - 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 00:05:13,242 - FID at iteration 2000: 262.864115
+INFO - dpsgd_diffusion.py - 2024-10-24 00:06:59,095 - Loss: 0.6190, step: 2100
+INFO - dpsgd_diffusion.py - 2024-10-24 00:08:48,242 - Loss: 0.6335, step: 2200
+INFO - dpsgd_diffusion.py - 2024-10-24 00:09:29,392 - Eps-value after 5 epochs: 0.1851
+INFO - dpsgd_diffusion.py - 2024-10-24 00:10:31,995 - Loss: 0.5821, step: 2300
+INFO - dpsgd_diffusion.py - 2024-10-24 00:12:18,018 - Loss: 0.5699, step: 2400
+INFO - dpsgd_diffusion.py - 2024-10-24 00:14:03,867 - Loss: 0.5761, step: 2500
+INFO - dpsgd_diffusion.py - 2024-10-24 00:15:52,104 - Loss: 0.5217, step: 2600
+INFO - dpsgd_diffusion.py - 2024-10-24 00:17:24,583 - Eps-value after 6 epochs: 0.1957
+INFO - dpsgd_diffusion.py - 2024-10-24 00:17:36,856 - Loss: 0.5316, step: 2700
+INFO - dpsgd_diffusion.py - 2024-10-24 00:19:24,564 - Loss: 0.5782, step: 2800
+INFO - dpsgd_diffusion.py - 2024-10-24 00:21:13,525 - Loss: 0.5263, step: 2900
+INFO - dpsgd_diffusion.py - 2024-10-24 00:23:01,039 - Loss: 0.5663, step: 3000
+INFO - dpsgd_diffusion.py - 2024-10-24 00:24:45,274 - Loss: 0.5158, step: 3100
+INFO - dpsgd_diffusion.py - 2024-10-24 00:25:22,943 - Eps-value after 7 epochs: 0.2064
+INFO - dpsgd_diffusion.py - 2024-10-24 00:26:30,462 - Loss: 0.5003, step: 3200
+INFO - dpsgd_diffusion.py - 2024-10-24 00:28:19,798 - Loss: 0.5545, step: 3300
+INFO - dpsgd_diffusion.py - 2024-10-24 00:30:06,911 - Loss: 0.5066, step: 3400
+INFO - dpsgd_diffusion.py - 2024-10-24 00:31:55,268 - Loss: 0.4745, step: 3500
+INFO - dpsgd_diffusion.py - 2024-10-24 00:33:24,110 - Eps-value after 8 epochs: 0.2170
+INFO - dpsgd_diffusion.py - 2024-10-24 00:33:41,147 - Loss: 0.5118, step: 3600
+INFO - dpsgd_diffusion.py - 2024-10-24 00:35:28,471 - Loss: 0.5082, step: 3700
+INFO - dpsgd_diffusion.py - 2024-10-24 00:37:19,157 - Loss: 0.5003, step: 3800
+INFO - dpsgd_diffusion.py - 2024-10-24 00:39:06,149 - Loss: 0.4782, step: 3900
+INFO - dpsgd_diffusion.py - 2024-10-24 00:40:52,200 - Loss: 0.4797, step: 4000
+INFO - dpsgd_diffusion.py - 2024-10-24 00:40:52,213 - Saving snapshot checkpoint and sampling single batch at iteration 4000.
+WARNING - image.py - 2024-10-24 00:40:52,839 - 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 00:41:11,796 - FID at iteration 4000: 211.450741
+INFO - dpsgd_diffusion.py - 2024-10-24 00:41:46,743 - Eps-value after 9 epochs: 0.2277
+INFO - dpsgd_diffusion.py - 2024-10-24 00:42:57,289 - Loss: 0.4377, step: 4100
+INFO - dpsgd_diffusion.py - 2024-10-24 00:44:42,667 - Loss: 0.4686, step: 4200
+INFO - dpsgd_diffusion.py - 2024-10-24 00:46:28,588 - Loss: 0.4682, step: 4300
+INFO - dpsgd_diffusion.py - 2024-10-24 00:48:15,713 - Loss: 0.4620, step: 4400
+INFO - dpsgd_diffusion.py - 2024-10-24 00:49:39,794 - Eps-value after 10 epochs: 0.2383
+INFO - dpsgd_diffusion.py - 2024-10-24 00:50:00,758 - Loss: 0.4037, step: 4500
+INFO - dpsgd_diffusion.py - 2024-10-24 00:51:47,512 - Loss: 0.4425, step: 4600
+INFO - dpsgd_diffusion.py - 2024-10-24 00:53:31,833 - Loss: 0.4392, step: 4700
+INFO - dpsgd_diffusion.py - 2024-10-24 00:55:20,714 - Loss: 0.4953, step: 4800
+INFO - dpsgd_diffusion.py - 2024-10-24 00:57:07,551 - Loss: 0.4349, step: 4900
+INFO - dpsgd_diffusion.py - 2024-10-24 00:57:38,282 - Eps-value after 11 epochs: 0.2490
+INFO - dpsgd_diffusion.py - 2024-10-24 00:58:54,032 - Loss: 0.4027, step: 5000
+INFO - dpsgd_diffusion.py - 2024-10-24 01:00:38,933 - Loss: 0.4241, step: 5100
+INFO - dpsgd_diffusion.py - 2024-10-24 01:02:26,911 - Loss: 0.4252, step: 5200
+INFO - dpsgd_diffusion.py - 2024-10-24 01:04:15,838 - Loss: 0.4467, step: 5300
+INFO - dpsgd_diffusion.py - 2024-10-24 01:05:35,345 - Eps-value after 12 epochs: 0.2596
+INFO - dpsgd_diffusion.py - 2024-10-24 01:06:00,442 - Loss: 0.4000, step: 5400
+INFO - dpsgd_diffusion.py - 2024-10-24 01:07:47,508 - Loss: 0.4262, step: 5500
+INFO - dpsgd_diffusion.py - 2024-10-24 01:09:36,141 - Loss: 0.4192, step: 5600
+INFO - dpsgd_diffusion.py - 2024-10-24 01:11:24,098 - Loss: 0.4226, step: 5700
+INFO - dpsgd_diffusion.py - 2024-10-24 01:13:01,191 - Loss: 0.4439, step: 5800
+INFO - dpsgd_diffusion.py - 2024-10-24 01:13:21,461 - Eps-value after 13 epochs: 0.2703
+INFO - dpsgd_diffusion.py - 2024-10-24 01:14:26,244 - Loss: 0.3896, step: 5900
+INFO - dpsgd_diffusion.py - 2024-10-24 01:16:06,036 - Loss: 0.4288, step: 6000
+INFO - dpsgd_diffusion.py - 2024-10-24 01:16:06,317 - Saving snapshot checkpoint and sampling single batch at iteration 6000.
+WARNING - image.py - 2024-10-24 01:16:06,927 - 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 01:16:25,296 - FID at iteration 6000: 173.220066
+INFO - dpsgd_diffusion.py - 2024-10-24 01:18:05,207 - Loss: 0.3872, step: 6100
+INFO - dpsgd_diffusion.py - 2024-10-24 01:19:45,171 - Loss: 0.4121, step: 6200
+INFO - dpsgd_diffusion.py - 2024-10-24 01:21:00,545 - Eps-value after 14 epochs: 0.2810
+INFO - dpsgd_diffusion.py - 2024-10-24 01:21:30,326 - Loss: 0.3971, step: 6300
+INFO - dpsgd_diffusion.py - 2024-10-24 01:23:04,290 - Loss: 0.3882, step: 6400
+INFO - dpsgd_diffusion.py - 2024-10-24 01:24:29,323 - Loss: 0.3874, step: 6500
+INFO - dpsgd_diffusion.py - 2024-10-24 01:25:53,627 - Loss: 0.3823, step: 6600
+INFO - dpsgd_diffusion.py - 2024-10-24 01:27:19,656 - Loss: 0.3938, step: 6700
+INFO - dpsgd_diffusion.py - 2024-10-24 01:27:36,639 - Eps-value after 15 epochs: 0.2914
+INFO - dpsgd_diffusion.py - 2024-10-24 01:28:47,798 - Loss: 0.3913, step: 6800
+INFO - dpsgd_diffusion.py - 2024-10-24 01:30:18,590 - Loss: 0.3907, step: 6900
+INFO - dpsgd_diffusion.py - 2024-10-24 01:31:51,476 - Loss: 0.3737, step: 7000
+INFO - dpsgd_diffusion.py - 2024-10-24 01:33:24,450 - Loss: 0.3949, step: 7100
+INFO - dpsgd_diffusion.py - 2024-10-24 01:34:27,624 - Eps-value after 16 epochs: 0.3015
+INFO - dpsgd_diffusion.py - 2024-10-24 01:34:56,964 - Loss: 0.4044, step: 7200
+INFO - dpsgd_diffusion.py - 2024-10-24 01:36:30,648 - Loss: 0.4020, step: 7300
+INFO - dpsgd_diffusion.py - 2024-10-24 01:38:05,003 - Loss: 0.4271, step: 7400
+INFO - dpsgd_diffusion.py - 2024-10-24 01:39:39,851 - Loss: 0.3749, step: 7500
+INFO - dpsgd_diffusion.py - 2024-10-24 01:41:16,344 - Loss: 0.3698, step: 7600
+INFO - dpsgd_diffusion.py - 2024-10-24 01:41:31,420 - Eps-value after 17 epochs: 0.3113
+INFO - dpsgd_diffusion.py - 2024-10-24 01:42:51,722 - Loss: 0.3651, step: 7700
+INFO - dpsgd_diffusion.py - 2024-10-24 01:44:25,238 - Loss: 0.3634, step: 7800
+INFO - dpsgd_diffusion.py - 2024-10-24 01:46:00,722 - Loss: 0.3517, step: 7900
+INFO - dpsgd_diffusion.py - 2024-10-24 01:47:36,079 - Loss: 0.4044, step: 8000
+INFO - dpsgd_diffusion.py - 2024-10-24 01:47:36,097 - Saving snapshot checkpoint and sampling single batch at iteration 8000.
+WARNING - image.py - 2024-10-24 01:47:36,700 - 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 01:47:54,333 - FID at iteration 8000: 144.290407
+INFO - dpsgd_diffusion.py - 2024-10-24 01:48:53,888 - Eps-value after 18 epochs: 0.3208
+INFO - dpsgd_diffusion.py - 2024-10-24 01:49:27,326 - Loss: 0.4077, step: 8100
+INFO - dpsgd_diffusion.py - 2024-10-24 01:50:59,672 - Loss: 0.3837, step: 8200
+INFO - dpsgd_diffusion.py - 2024-10-24 01:52:33,458 - Loss: 0.4083, step: 8300
+INFO - dpsgd_diffusion.py - 2024-10-24 01:54:06,537 - Loss: 0.3785, step: 8400
+INFO - dpsgd_diffusion.py - 2024-10-24 01:55:38,891 - Loss: 0.3795, step: 8500
+INFO - dpsgd_diffusion.py - 2024-10-24 01:55:50,455 - Eps-value after 19 epochs: 0.3301
+INFO - dpsgd_diffusion.py - 2024-10-24 01:57:12,833 - Loss: 0.3562, step: 8600
+INFO - dpsgd_diffusion.py - 2024-10-24 01:58:46,581 - Loss: 0.3496, step: 8700
+INFO - dpsgd_diffusion.py - 2024-10-24 02:00:19,519 - Loss: 0.3746, step: 8800
+INFO - dpsgd_diffusion.py - 2024-10-24 02:01:52,118 - Loss: 0.3813, step: 8900
+INFO - dpsgd_diffusion.py - 2024-10-24 02:02:47,422 - Eps-value after 20 epochs: 0.3392
+INFO - dpsgd_diffusion.py - 2024-10-24 02:03:24,557 - Loss: 0.3795, step: 9000
+INFO - dpsgd_diffusion.py - 2024-10-24 02:04:59,091 - Loss: 0.3728, step: 9100
+INFO - dpsgd_diffusion.py - 2024-10-24 02:06:32,590 - Loss: 0.3585, step: 9200
+INFO - dpsgd_diffusion.py - 2024-10-24 02:08:06,164 - Loss: 0.3678, step: 9300
+INFO - dpsgd_diffusion.py - 2024-10-24 02:09:39,474 - Loss: 0.3466, step: 9400
+INFO - dpsgd_diffusion.py - 2024-10-24 02:09:46,871 - Eps-value after 21 epochs: 0.3481
+INFO - dpsgd_diffusion.py - 2024-10-24 02:11:12,017 - Loss: 0.3350, step: 9500
+INFO - dpsgd_diffusion.py - 2024-10-24 02:12:43,660 - Loss: 0.3716, step: 9600
+INFO - dpsgd_diffusion.py - 2024-10-24 02:14:17,340 - Loss: 0.3657, step: 9700
+INFO - dpsgd_diffusion.py - 2024-10-24 02:15:51,337 - Loss: 0.3656, step: 9800
+INFO - dpsgd_diffusion.py - 2024-10-24 02:16:42,277 - Eps-value after 22 epochs: 0.3567
+INFO - dpsgd_diffusion.py - 2024-10-24 02:17:22,852 - Loss: 0.3525, step: 9900
+INFO - dpsgd_diffusion.py - 2024-10-24 02:18:55,986 - Loss: 0.3879, step: 10000
+INFO - dpsgd_diffusion.py - 2024-10-24 02:18:56,041 - Saving snapshot checkpoint and sampling single batch at iteration 10000.
+WARNING - image.py - 2024-10-24 02:18:56,697 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 02:19:14,326 - FID at iteration 10000: 127.588882
+INFO - dpsgd_diffusion.py - 2024-10-24 02:20:48,553 - Loss: 0.3529, step: 10100
+INFO - dpsgd_diffusion.py - 2024-10-24 02:22:20,293 - Loss: 0.3564, step: 10200
+INFO - dpsgd_diffusion.py - 2024-10-24 02:23:56,021 - Loss: 0.3324, step: 10300
+INFO - dpsgd_diffusion.py - 2024-10-24 02:23:59,631 - Eps-value after 23 epochs: 0.3652
+INFO - dpsgd_diffusion.py - 2024-10-24 02:25:28,118 - Loss: 0.3759, step: 10400
+INFO - dpsgd_diffusion.py - 2024-10-24 02:27:00,523 - Loss: 0.3411, step: 10500
+INFO - dpsgd_diffusion.py - 2024-10-24 02:28:33,268 - Loss: 0.3589, step: 10600
+INFO - dpsgd_diffusion.py - 2024-10-24 02:30:07,879 - Loss: 0.3397, step: 10700
+INFO - dpsgd_diffusion.py - 2024-10-24 02:30:56,239 - Eps-value after 24 epochs: 0.3735
+INFO - dpsgd_diffusion.py - 2024-10-24 02:31:41,187 - Loss: 0.3332, step: 10800
+INFO - dpsgd_diffusion.py - 2024-10-24 02:33:13,819 - Loss: 0.3181, step: 10900
+INFO - dpsgd_diffusion.py - 2024-10-24 02:34:46,630 - Loss: 0.3836, step: 11000
+INFO - dpsgd_diffusion.py - 2024-10-24 02:36:20,236 - Loss: 0.3195, step: 11100
+INFO - dpsgd_diffusion.py - 2024-10-24 02:37:51,697 - Loss: 0.3320, step: 11200
+INFO - dpsgd_diffusion.py - 2024-10-24 02:37:51,713 - Eps-value after 25 epochs: 0.3817
+INFO - dpsgd_diffusion.py - 2024-10-24 02:39:23,986 - Loss: 0.3576, step: 11300
+INFO - dpsgd_diffusion.py - 2024-10-24 02:40:56,778 - Loss: 0.3450, step: 11400
+INFO - dpsgd_diffusion.py - 2024-10-24 02:42:29,334 - Loss: 0.3473, step: 11500
+INFO - dpsgd_diffusion.py - 2024-10-24 02:44:02,090 - Loss: 0.3488, step: 11600
+INFO - dpsgd_diffusion.py - 2024-10-24 02:44:47,048 - Eps-value after 26 epochs: 0.3897
+INFO - dpsgd_diffusion.py - 2024-10-24 02:45:35,337 - Loss: 0.3859, step: 11700
+INFO - dpsgd_diffusion.py - 2024-10-24 02:47:08,689 - Loss: 0.3610, step: 11800
+INFO - dpsgd_diffusion.py - 2024-10-24 02:48:42,539 - Loss: 0.3534, step: 11900
+INFO - dpsgd_diffusion.py - 2024-10-24 02:50:16,388 - Loss: 0.3594, step: 12000
+INFO - dpsgd_diffusion.py - 2024-10-24 02:50:16,404 - Saving snapshot checkpoint and sampling single batch at iteration 12000.
+WARNING - image.py - 2024-10-24 02:50:17,062 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 02:50:34,670 - FID at iteration 12000: 115.405550
+INFO - dpsgd_diffusion.py - 2024-10-24 02:52:03,262 - Eps-value after 27 epochs: 0.3976
+INFO - dpsgd_diffusion.py - 2024-10-24 02:52:06,832 - Loss: 0.3342, step: 12100
+INFO - dpsgd_diffusion.py - 2024-10-24 02:53:39,415 - Loss: 0.3899, step: 12200
+INFO - dpsgd_diffusion.py - 2024-10-24 02:55:13,411 - Loss: 0.3389, step: 12300
+INFO - dpsgd_diffusion.py - 2024-10-24 02:56:46,336 - Loss: 0.3621, step: 12400
+INFO - dpsgd_diffusion.py - 2024-10-24 02:58:19,050 - Loss: 0.3961, step: 12500
+INFO - dpsgd_diffusion.py - 2024-10-24 02:59:00,857 - Eps-value after 28 epochs: 0.4053
+INFO - dpsgd_diffusion.py - 2024-10-24 02:59:52,180 - Loss: 0.3784, step: 12600
+INFO - dpsgd_diffusion.py - 2024-10-24 03:01:24,555 - Loss: 0.3290, step: 12700
+INFO - dpsgd_diffusion.py - 2024-10-24 03:02:58,357 - Loss: 0.3121, step: 12800
+INFO - dpsgd_diffusion.py - 2024-10-24 03:04:30,089 - Loss: 0.3416, step: 12900
+INFO - dpsgd_diffusion.py - 2024-10-24 03:05:56,032 - Eps-value after 29 epochs: 0.4129
+INFO - dpsgd_diffusion.py - 2024-10-24 03:06:03,326 - Loss: 0.3361, step: 13000
+INFO - dpsgd_diffusion.py - 2024-10-24 03:07:35,535 - Loss: 0.3858, step: 13100
+INFO - dpsgd_diffusion.py - 2024-10-24 03:09:08,463 - Loss: 0.3559, step: 13200
+INFO - dpsgd_diffusion.py - 2024-10-24 03:10:42,247 - Loss: 0.3463, step: 13300
+INFO - dpsgd_diffusion.py - 2024-10-24 03:12:15,402 - Loss: 0.3212, step: 13400
+INFO - dpsgd_diffusion.py - 2024-10-24 03:12:53,020 - Eps-value after 30 epochs: 0.4204
+INFO - dpsgd_diffusion.py - 2024-10-24 03:13:49,961 - Loss: 0.3113, step: 13500
+INFO - dpsgd_diffusion.py - 2024-10-24 03:15:23,184 - Loss: 0.3613, step: 13600
+INFO - dpsgd_diffusion.py - 2024-10-24 03:16:46,054 - Loss: 0.3506, step: 13700
+INFO - dpsgd_diffusion.py - 2024-10-24 03:18:11,278 - Loss: 0.3458, step: 13800
+INFO - dpsgd_diffusion.py - 2024-10-24 03:19:26,441 - Eps-value after 31 epochs: 0.4278
+INFO - dpsgd_diffusion.py - 2024-10-24 03:19:36,312 - Loss: 0.3377, step: 13900
+INFO - dpsgd_diffusion.py - 2024-10-24 03:20:58,879 - Loss: 0.3223, step: 14000
+INFO - dpsgd_diffusion.py - 2024-10-24 03:20:58,894 - Saving snapshot checkpoint and sampling single batch at iteration 14000.
+WARNING - image.py - 2024-10-24 03:20:59,498 - 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 03:21:16,143 - FID at iteration 14000: 108.085139
+INFO - dpsgd_diffusion.py - 2024-10-24 03:22:41,024 - Loss: 0.3293, step: 14100
+INFO - dpsgd_diffusion.py - 2024-10-24 03:24:05,097 - Loss: 0.3366, step: 14200
+INFO - dpsgd_diffusion.py - 2024-10-24 03:25:29,285 - Loss: 0.3383, step: 14300
+INFO - dpsgd_diffusion.py - 2024-10-24 03:25:59,803 - Eps-value after 32 epochs: 0.4350
+INFO - dpsgd_diffusion.py - 2024-10-24 03:26:54,232 - Loss: 0.3277, step: 14400
+INFO - dpsgd_diffusion.py - 2024-10-24 03:28:18,574 - Loss: 0.3384, step: 14500
+INFO - dpsgd_diffusion.py - 2024-10-24 03:29:42,402 - Loss: 0.3132, step: 14600
+INFO - dpsgd_diffusion.py - 2024-10-24 03:31:05,606 - Loss: 0.3188, step: 14700
+INFO - dpsgd_diffusion.py - 2024-10-24 03:32:14,882 - Eps-value after 33 epochs: 0.4421
+INFO - dpsgd_diffusion.py - 2024-10-24 03:32:28,463 - Loss: 0.3477, step: 14800
+INFO - dpsgd_diffusion.py - 2024-10-24 03:33:51,547 - Loss: 0.3471, step: 14900
+INFO - dpsgd_diffusion.py - 2024-10-24 03:35:15,266 - Loss: 0.3385, step: 15000
+INFO - dpsgd_diffusion.py - 2024-10-24 03:36:40,637 - Loss: 0.3364, step: 15100
+INFO - dpsgd_diffusion.py - 2024-10-24 03:38:04,966 - Loss: 0.3088, step: 15200
+INFO - dpsgd_diffusion.py - 2024-10-24 03:38:32,691 - Eps-value after 34 epochs: 0.4492
+INFO - dpsgd_diffusion.py - 2024-10-24 03:39:29,923 - Loss: 0.3223, step: 15300
+INFO - dpsgd_diffusion.py - 2024-10-24 03:40:56,886 - Loss: 0.3211, step: 15400
+INFO - dpsgd_diffusion.py - 2024-10-24 03:42:20,171 - Loss: 0.3097, step: 15500
+INFO - dpsgd_diffusion.py - 2024-10-24 03:43:42,926 - Loss: 0.3441, step: 15600
+INFO - dpsgd_diffusion.py - 2024-10-24 03:44:49,589 - Eps-value after 35 epochs: 0.4561
+INFO - dpsgd_diffusion.py - 2024-10-24 03:45:05,980 - Loss: 0.3474, step: 15700
+INFO - dpsgd_diffusion.py - 2024-10-24 03:46:31,058 - Loss: 0.3321, step: 15800
+INFO - dpsgd_diffusion.py - 2024-10-24 03:47:53,804 - Loss: 0.3503, step: 15900
+INFO - dpsgd_diffusion.py - 2024-10-24 03:49:18,776 - Loss: 0.3293, step: 16000
+INFO - dpsgd_diffusion.py - 2024-10-24 03:49:18,795 - Saving snapshot checkpoint and sampling single batch at iteration 16000.
+WARNING - image.py - 2024-10-24 03:49:19,414 - 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 03:49:36,529 - FID at iteration 16000: 102.996280
+INFO - dpsgd_diffusion.py - 2024-10-24 03:51:01,270 - Loss: 0.3437, step: 16100
+INFO - dpsgd_diffusion.py - 2024-10-24 03:51:25,728 - Eps-value after 36 epochs: 0.4630
+INFO - dpsgd_diffusion.py - 2024-10-24 03:52:25,998 - Loss: 0.3162, step: 16200
+INFO - dpsgd_diffusion.py - 2024-10-24 03:53:49,843 - Loss: 0.3222, step: 16300
+INFO - dpsgd_diffusion.py - 2024-10-24 03:55:13,857 - Loss: 0.3420, step: 16400
+INFO - dpsgd_diffusion.py - 2024-10-24 03:56:37,298 - Loss: 0.3341, step: 16500
+INFO - dpsgd_diffusion.py - 2024-10-24 03:57:42,246 - Eps-value after 37 epochs: 0.4698
+INFO - dpsgd_diffusion.py - 2024-10-24 03:58:01,996 - Loss: 0.3349, step: 16600
+INFO - dpsgd_diffusion.py - 2024-10-24 03:59:25,826 - Loss: 0.3040, step: 16700
+INFO - dpsgd_diffusion.py - 2024-10-24 04:00:49,435 - Loss: 0.3270, step: 16800
+INFO - dpsgd_diffusion.py - 2024-10-24 04:02:14,936 - Loss: 0.3439, step: 16900
+INFO - dpsgd_diffusion.py - 2024-10-24 04:03:40,094 - Loss: 0.3348, step: 17000
+INFO - dpsgd_diffusion.py - 2024-10-24 04:04:00,678 - Eps-value after 38 epochs: 0.4765
+INFO - dpsgd_diffusion.py - 2024-10-24 04:05:03,927 - Loss: 0.3396, step: 17100
+INFO - dpsgd_diffusion.py - 2024-10-24 04:06:28,706 - Loss: 0.3485, step: 17200
+INFO - dpsgd_diffusion.py - 2024-10-24 04:07:53,778 - Loss: 0.3210, step: 17300
+INFO - dpsgd_diffusion.py - 2024-10-24 04:09:18,196 - Loss: 0.3534, step: 17400
+INFO - dpsgd_diffusion.py - 2024-10-24 04:10:18,128 - Eps-value after 39 epochs: 0.4831
+INFO - dpsgd_diffusion.py - 2024-10-24 04:10:41,411 - Loss: 0.2911, step: 17500
+INFO - dpsgd_diffusion.py - 2024-10-24 04:12:05,574 - Loss: 0.3402, step: 17600
+INFO - dpsgd_diffusion.py - 2024-10-24 04:13:30,553 - Loss: 0.3196, step: 17700
+INFO - dpsgd_diffusion.py - 2024-10-24 04:14:54,398 - Loss: 0.2957, step: 17800
+INFO - dpsgd_diffusion.py - 2024-10-24 04:16:17,800 - Loss: 0.3107, step: 17900
+INFO - dpsgd_diffusion.py - 2024-10-24 04:16:33,929 - Eps-value after 40 epochs: 0.4896
+INFO - dpsgd_diffusion.py - 2024-10-24 04:17:42,017 - Loss: 0.3090, step: 18000
+INFO - dpsgd_diffusion.py - 2024-10-24 04:17:42,064 - Saving snapshot checkpoint and sampling single batch at iteration 18000.
+WARNING - image.py - 2024-10-24 04:17:42,690 - 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 04:17:59,257 - FID at iteration 18000: 99.254667
+INFO - dpsgd_diffusion.py - 2024-10-24 04:19:23,087 - Loss: 0.3203, step: 18100
+INFO - dpsgd_diffusion.py - 2024-10-24 04:20:49,156 - Loss: 0.3270, step: 18200
+INFO - dpsgd_diffusion.py - 2024-10-24 04:22:13,693 - Loss: 0.3272, step: 18300
+INFO - dpsgd_diffusion.py - 2024-10-24 04:23:10,952 - Eps-value after 41 epochs: 0.4961
+INFO - dpsgd_diffusion.py - 2024-10-24 04:23:38,011 - Loss: 0.3084, step: 18400
+INFO - dpsgd_diffusion.py - 2024-10-24 04:25:01,474 - Loss: 0.3068, step: 18500
+INFO - dpsgd_diffusion.py - 2024-10-24 04:26:26,421 - Loss: 0.3191, step: 18600
+INFO - dpsgd_diffusion.py - 2024-10-24 04:27:52,106 - Loss: 0.3103, step: 18700
+INFO - dpsgd_diffusion.py - 2024-10-24 04:29:17,612 - Loss: 0.3399, step: 18800
+INFO - dpsgd_diffusion.py - 2024-10-24 04:29:30,924 - Eps-value after 42 epochs: 0.5024
+INFO - dpsgd_diffusion.py - 2024-10-24 04:30:41,883 - Loss: 0.3268, step: 18900
+INFO - dpsgd_diffusion.py - 2024-10-24 04:32:06,569 - Loss: 0.3204, step: 19000
+INFO - dpsgd_diffusion.py - 2024-10-24 04:33:30,863 - Loss: 0.3309, step: 19100
+INFO - dpsgd_diffusion.py - 2024-10-24 04:34:53,812 - Loss: 0.3405, step: 19200
+INFO - dpsgd_diffusion.py - 2024-10-24 04:35:47,611 - Eps-value after 43 epochs: 0.5088
+INFO - dpsgd_diffusion.py - 2024-10-24 04:36:17,730 - Loss: 0.3068, step: 19300
+INFO - dpsgd_diffusion.py - 2024-10-24 04:37:42,006 - Loss: 0.3019, step: 19400
+INFO - dpsgd_diffusion.py - 2024-10-24 04:39:05,259 - Loss: 0.3390, step: 19500
+INFO - dpsgd_diffusion.py - 2024-10-24 04:40:30,928 - Loss: 0.3533, step: 19600
+INFO - dpsgd_diffusion.py - 2024-10-24 04:41:56,477 - Loss: 0.3215, step: 19700
+INFO - dpsgd_diffusion.py - 2024-10-24 04:42:06,604 - Eps-value after 44 epochs: 0.5150
+INFO - dpsgd_diffusion.py - 2024-10-24 04:43:23,567 - Loss: 0.3388, step: 19800
+INFO - dpsgd_diffusion.py - 2024-10-24 04:44:47,968 - Loss: 0.3328, step: 19900
+INFO - dpsgd_diffusion.py - 2024-10-24 04:46:13,159 - Loss: 0.3249, step: 20000
+INFO - dpsgd_diffusion.py - 2024-10-24 04:46:13,178 - Saving snapshot checkpoint and sampling single batch at iteration 20000.
+WARNING - image.py - 2024-10-24 04:46:13,793 - 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 04:46:30,337 - FID at iteration 20000: 95.236627
+INFO - dpsgd_diffusion.py - 2024-10-24 04:47:55,156 - Loss: 0.3271, step: 20100
+INFO - dpsgd_diffusion.py - 2024-10-24 04:48:45,931 - Eps-value after 45 epochs: 0.5212
+INFO - dpsgd_diffusion.py - 2024-10-24 04:49:18,668 - Loss: 0.2998, step: 20200
+INFO - dpsgd_diffusion.py - 2024-10-24 04:50:43,718 - Loss: 0.3284, step: 20300
+INFO - dpsgd_diffusion.py - 2024-10-24 04:52:05,900 - Loss: 0.3166, step: 20400
+INFO - dpsgd_diffusion.py - 2024-10-24 04:53:28,441 - Loss: 0.2921, step: 20500
+INFO - dpsgd_diffusion.py - 2024-10-24 04:54:54,082 - Loss: 0.3201, step: 20600
+INFO - dpsgd_diffusion.py - 2024-10-24 04:55:01,040 - Eps-value after 46 epochs: 0.5273
+INFO - dpsgd_diffusion.py - 2024-10-24 04:56:18,863 - Loss: 0.3217, step: 20700
+INFO - dpsgd_diffusion.py - 2024-10-24 04:57:42,617 - Loss: 0.3196, step: 20800
+INFO - dpsgd_diffusion.py - 2024-10-24 04:59:04,981 - Loss: 0.3349, step: 20900
+INFO - dpsgd_diffusion.py - 2024-10-24 05:00:29,990 - Loss: 0.3161, step: 21000
+INFO - dpsgd_diffusion.py - 2024-10-24 05:01:17,092 - Eps-value after 47 epochs: 0.5333
+INFO - dpsgd_diffusion.py - 2024-10-24 05:01:55,345 - Loss: 0.3126, step: 21100
+INFO - dpsgd_diffusion.py - 2024-10-24 05:03:19,246 - Loss: 0.3197, step: 21200
+INFO - dpsgd_diffusion.py - 2024-10-24 05:04:42,833 - Loss: 0.2972, step: 21300
+INFO - dpsgd_diffusion.py - 2024-10-24 05:06:05,799 - Loss: 0.3438, step: 21400
+INFO - dpsgd_diffusion.py - 2024-10-24 05:07:29,609 - Loss: 0.3314, step: 21500
+INFO - dpsgd_diffusion.py - 2024-10-24 05:07:32,974 - Eps-value after 48 epochs: 0.5394
+INFO - dpsgd_diffusion.py - 2024-10-24 05:08:55,168 - Loss: 0.3129, step: 21600
+INFO - dpsgd_diffusion.py - 2024-10-24 05:10:18,677 - Loss: 0.3138, step: 21700
+INFO - dpsgd_diffusion.py - 2024-10-24 05:11:42,409 - Loss: 0.3167, step: 21800
+INFO - dpsgd_diffusion.py - 2024-10-24 05:13:07,425 - Loss: 0.3292, step: 21900
+INFO - dpsgd_diffusion.py - 2024-10-24 05:13:50,476 - Eps-value after 49 epochs: 0.5453
+INFO - dpsgd_diffusion.py - 2024-10-24 05:14:31,169 - Loss: 0.3335, step: 22000
+INFO - dpsgd_diffusion.py - 2024-10-24 05:14:31,185 - Saving snapshot checkpoint and sampling single batch at iteration 22000.
+WARNING - image.py - 2024-10-24 05:14:31,805 - 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 05:14:48,431 - FID at iteration 22000: 92.070179
+INFO - dpsgd_diffusion.py - 2024-10-24 05:16:14,023 - Loss: 0.2995, step: 22100
+INFO - dpsgd_diffusion.py - 2024-10-24 05:17:39,925 - Loss: 0.3798, step: 22200
+INFO - dpsgd_diffusion.py - 2024-10-24 05:19:04,568 - Loss: 0.3145, step: 22300
+INFO - dpsgd_diffusion.py - 2024-10-24 05:20:28,395 - Loss: 0.3314, step: 22400
+INFO - dpsgd_diffusion.py - 2024-10-24 05:20:28,410 - Eps-value after 50 epochs: 0.5512
+INFO - dpsgd_diffusion.py - 2024-10-24 05:21:53,780 - Loss: 0.3201, step: 22500
+INFO - dpsgd_diffusion.py - 2024-10-24 05:23:19,983 - Loss: 0.2965, step: 22600
+INFO - dpsgd_diffusion.py - 2024-10-24 05:24:45,515 - Loss: 0.3347, step: 22700
+INFO - dpsgd_diffusion.py - 2024-10-24 05:26:10,622 - Loss: 0.3395, step: 22800
+INFO - dpsgd_diffusion.py - 2024-10-24 05:26:50,892 - Eps-value after 51 epochs: 0.5570
+INFO - dpsgd_diffusion.py - 2024-10-24 05:27:34,000 - Loss: 0.3012, step: 22900
+INFO - dpsgd_diffusion.py - 2024-10-24 05:28:59,016 - Loss: 0.3432, step: 23000
+INFO - dpsgd_diffusion.py - 2024-10-24 05:30:23,653 - Loss: 0.3144, step: 23100
+INFO - dpsgd_diffusion.py - 2024-10-24 05:31:47,072 - Loss: 0.3209, step: 23200
+INFO - dpsgd_diffusion.py - 2024-10-24 05:33:06,962 - Eps-value after 52 epochs: 0.5628
+INFO - dpsgd_diffusion.py - 2024-10-24 05:33:10,227 - Loss: 0.3427, step: 23300
+INFO - dpsgd_diffusion.py - 2024-10-24 05:34:33,805 - Loss: 0.3124, step: 23400
+INFO - dpsgd_diffusion.py - 2024-10-24 05:36:00,129 - Loss: 0.3337, step: 23500
+INFO - dpsgd_diffusion.py - 2024-10-24 05:37:24,855 - Loss: 0.3021, step: 23600
+INFO - dpsgd_diffusion.py - 2024-10-24 05:38:49,058 - Loss: 0.3536, step: 23700
+INFO - dpsgd_diffusion.py - 2024-10-24 05:39:25,056 - Eps-value after 53 epochs: 0.5685
+INFO - dpsgd_diffusion.py - 2024-10-24 05:40:12,301 - Loss: 0.3395, step: 23800
+INFO - dpsgd_diffusion.py - 2024-10-24 05:41:37,789 - Loss: 0.3214, step: 23900
+INFO - dpsgd_diffusion.py - 2024-10-24 05:43:02,633 - Loss: 0.3374, step: 24000
+INFO - dpsgd_diffusion.py - 2024-10-24 05:43:02,651 - Saving snapshot checkpoint and sampling single batch at iteration 24000.
+WARNING - image.py - 2024-10-24 05:43:03,272 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 05:43:19,854 - FID at iteration 24000: 90.595262
+INFO - dpsgd_diffusion.py - 2024-10-24 05:44:44,568 - Loss: 0.3242, step: 24100
+INFO - dpsgd_diffusion.py - 2024-10-24 05:46:03,274 - Eps-value after 54 epochs: 0.5742
+INFO - dpsgd_diffusion.py - 2024-10-24 05:46:09,885 - Loss: 0.3291, step: 24200
+INFO - dpsgd_diffusion.py - 2024-10-24 05:47:33,695 - Loss: 0.3205, step: 24300
+INFO - dpsgd_diffusion.py - 2024-10-24 05:48:58,534 - Loss: 0.2978, step: 24400
+INFO - dpsgd_diffusion.py - 2024-10-24 05:50:23,325 - Loss: 0.3308, step: 24500
+INFO - dpsgd_diffusion.py - 2024-10-24 05:51:48,340 - Loss: 0.3305, step: 24600
+INFO - dpsgd_diffusion.py - 2024-10-24 05:52:22,303 - Eps-value after 55 epochs: 0.5798
+INFO - dpsgd_diffusion.py - 2024-10-24 05:53:12,833 - Loss: 0.3469, step: 24700
+INFO - dpsgd_diffusion.py - 2024-10-24 05:54:38,051 - Loss: 0.2894, step: 24800
+INFO - dpsgd_diffusion.py - 2024-10-24 05:56:03,998 - Loss: 0.3275, step: 24900
+INFO - dpsgd_diffusion.py - 2024-10-24 05:57:28,283 - Loss: 0.3469, step: 25000
+INFO - dpsgd_diffusion.py - 2024-10-24 05:58:41,611 - Eps-value after 56 epochs: 0.5854
+INFO - dpsgd_diffusion.py - 2024-10-24 05:58:51,625 - Loss: 0.3311, step: 25100
+INFO - dpsgd_diffusion.py - 2024-10-24 06:00:14,591 - Loss: 0.3420, step: 25200
+INFO - dpsgd_diffusion.py - 2024-10-24 06:01:40,525 - Loss: 0.3010, step: 25300
+INFO - dpsgd_diffusion.py - 2024-10-24 06:03:05,432 - Loss: 0.3258, step: 25400
+INFO - dpsgd_diffusion.py - 2024-10-24 06:04:29,078 - Loss: 0.3298, step: 25500
+INFO - dpsgd_diffusion.py - 2024-10-24 06:04:59,232 - Eps-value after 57 epochs: 0.5909
+INFO - dpsgd_diffusion.py - 2024-10-24 06:05:52,771 - Loss: 0.3353, step: 25600
+INFO - dpsgd_diffusion.py - 2024-10-24 06:07:16,989 - Loss: 0.3121, step: 25700
+INFO - dpsgd_diffusion.py - 2024-10-24 06:08:41,846 - Loss: 0.2932, step: 25800
+INFO - dpsgd_diffusion.py - 2024-10-24 06:10:06,408 - Loss: 0.3239, step: 25900
+INFO - dpsgd_diffusion.py - 2024-10-24 06:11:17,275 - Eps-value after 58 epochs: 0.5964
+INFO - dpsgd_diffusion.py - 2024-10-24 06:11:30,602 - Loss: 0.3057, step: 26000
+INFO - dpsgd_diffusion.py - 2024-10-24 06:11:30,652 - Saving snapshot checkpoint and sampling single batch at iteration 26000.
+WARNING - image.py - 2024-10-24 06:11:31,282 - 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 06:11:47,698 - FID at iteration 26000: 86.496187
+INFO - dpsgd_diffusion.py - 2024-10-24 06:13:10,782 - Loss: 0.3296, step: 26100
+INFO - dpsgd_diffusion.py - 2024-10-24 06:14:34,728 - Loss: 0.3005, step: 26200
+INFO - dpsgd_diffusion.py - 2024-10-24 06:15:58,571 - Loss: 0.3202, step: 26300
+INFO - dpsgd_diffusion.py - 2024-10-24 06:17:22,914 - Loss: 0.3022, step: 26400
+INFO - dpsgd_diffusion.py - 2024-10-24 06:17:49,486 - Eps-value after 59 epochs: 0.6019
+INFO - dpsgd_diffusion.py - 2024-10-24 06:18:47,328 - Loss: 0.3261, step: 26500
+INFO - dpsgd_diffusion.py - 2024-10-24 06:20:13,914 - Loss: 0.3233, step: 26600
+INFO - dpsgd_diffusion.py - 2024-10-24 06:21:37,080 - Loss: 0.3274, step: 26700
+INFO - dpsgd_diffusion.py - 2024-10-24 06:23:01,365 - Loss: 0.3204, step: 26800
+INFO - dpsgd_diffusion.py - 2024-10-24 06:24:08,522 - Eps-value after 60 epochs: 0.6072
+INFO - dpsgd_diffusion.py - 2024-10-24 06:24:25,413 - Loss: 0.3039, step: 26900
+INFO - dpsgd_diffusion.py - 2024-10-24 06:25:49,039 - Loss: 0.3511, step: 27000
+INFO - dpsgd_diffusion.py - 2024-10-24 06:27:15,150 - Loss: 0.3181, step: 27100
+INFO - dpsgd_diffusion.py - 2024-10-24 06:28:38,608 - Loss: 0.2928, step: 27200
+INFO - dpsgd_diffusion.py - 2024-10-24 06:30:03,414 - Loss: 0.3259, step: 27300
+INFO - dpsgd_diffusion.py - 2024-10-24 06:30:26,534 - Eps-value after 61 epochs: 0.6126
+INFO - dpsgd_diffusion.py - 2024-10-24 06:31:27,112 - Loss: 0.3128, step: 27400
+INFO - dpsgd_diffusion.py - 2024-10-24 06:32:50,668 - Loss: 0.3165, step: 27500
+INFO - dpsgd_diffusion.py - 2024-10-24 06:34:15,390 - Loss: 0.3072, step: 27600
+INFO - dpsgd_diffusion.py - 2024-10-24 06:35:40,672 - Loss: 0.3208, step: 27700
+INFO - dpsgd_diffusion.py - 2024-10-24 06:36:44,830 - Eps-value after 62 epochs: 0.6180
+INFO - dpsgd_diffusion.py - 2024-10-24 06:37:04,819 - Loss: 0.3535, step: 27800
+INFO - dpsgd_diffusion.py - 2024-10-24 06:38:28,832 - Loss: 0.3075, step: 27900
+INFO - dpsgd_diffusion.py - 2024-10-24 06:39:51,336 - Loss: 0.3151, step: 28000
+INFO - dpsgd_diffusion.py - 2024-10-24 06:39:51,355 - Saving snapshot checkpoint and sampling single batch at iteration 28000.
+WARNING - image.py - 2024-10-24 06:39:51,984 - 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 06:40:08,447 - FID at iteration 28000: 85.912867
+INFO - dpsgd_diffusion.py - 2024-10-24 06:41:34,532 - Loss: 0.3288, step: 28100
+INFO - dpsgd_diffusion.py - 2024-10-24 06:42:59,546 - Loss: 0.3656, step: 28200
+INFO - dpsgd_diffusion.py - 2024-10-24 06:43:20,010 - Eps-value after 63 epochs: 0.6233
+INFO - dpsgd_diffusion.py - 2024-10-24 06:44:25,581 - Loss: 0.3136, step: 28300
+INFO - dpsgd_diffusion.py - 2024-10-24 06:45:47,981 - Loss: 0.3057, step: 28400
+INFO - dpsgd_diffusion.py - 2024-10-24 06:47:11,436 - Loss: 0.3226, step: 28500
+INFO - dpsgd_diffusion.py - 2024-10-24 06:48:37,631 - Loss: 0.2904, step: 28600
+INFO - dpsgd_diffusion.py - 2024-10-24 06:49:39,265 - Eps-value after 64 epochs: 0.6285
+INFO - dpsgd_diffusion.py - 2024-10-24 06:50:03,013 - Loss: 0.3040, step: 28700
+INFO - dpsgd_diffusion.py - 2024-10-24 06:51:26,092 - Loss: 0.3273, step: 28800
+INFO - dpsgd_diffusion.py - 2024-10-24 06:52:49,630 - Loss: 0.3017, step: 28900
+INFO - dpsgd_diffusion.py - 2024-10-24 06:54:14,780 - Loss: 0.3047, step: 29000
+INFO - dpsgd_diffusion.py - 2024-10-24 06:55:38,284 - Loss: 0.3126, step: 29100
+INFO - dpsgd_diffusion.py - 2024-10-24 06:55:55,609 - Eps-value after 65 epochs: 0.6336
+INFO - dpsgd_diffusion.py - 2024-10-24 06:57:02,925 - Loss: 0.2909, step: 29200
+INFO - dpsgd_diffusion.py - 2024-10-24 06:58:26,586 - Loss: 0.3211, step: 29300
+INFO - dpsgd_diffusion.py - 2024-10-24 06:59:50,627 - Loss: 0.3359, step: 29400
+INFO - dpsgd_diffusion.py - 2024-10-24 07:01:13,327 - Loss: 0.3148, step: 29500
+INFO - dpsgd_diffusion.py - 2024-10-24 07:02:10,216 - Eps-value after 66 epochs: 0.6388
+INFO - dpsgd_diffusion.py - 2024-10-24 07:02:37,204 - Loss: 0.2985, step: 29600
+INFO - dpsgd_diffusion.py - 2024-10-24 07:04:03,060 - Loss: 0.2912, step: 29700
+INFO - dpsgd_diffusion.py - 2024-10-24 07:05:26,782 - Loss: 0.3298, step: 29800
+INFO - dpsgd_diffusion.py - 2024-10-24 07:06:51,049 - Loss: 0.2947, step: 29900
+INFO - dpsgd_diffusion.py - 2024-10-24 07:08:13,198 - Loss: 0.3140, step: 30000
+INFO - dpsgd_diffusion.py - 2024-10-24 07:08:13,249 - Saving snapshot checkpoint and sampling single batch at iteration 30000.
+WARNING - image.py - 2024-10-24 07:08:13,882 - 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 07:08:30,357 - FID at iteration 30000: 82.544921
+INFO - dpsgd_diffusion.py - 2024-10-24 07:08:43,652 - Eps-value after 67 epochs: 0.6440
+INFO - dpsgd_diffusion.py - 2024-10-24 07:09:54,228 - Loss: 0.2909, step: 30100
+INFO - dpsgd_diffusion.py - 2024-10-24 07:11:18,758 - Loss: 0.2925, step: 30200
+INFO - dpsgd_diffusion.py - 2024-10-24 07:12:43,911 - Loss: 0.2911, step: 30300
+INFO - dpsgd_diffusion.py - 2024-10-24 07:14:08,153 - Loss: 0.2956, step: 30400
+INFO - dpsgd_diffusion.py - 2024-10-24 07:15:02,301 - Eps-value after 68 epochs: 0.6491
+INFO - dpsgd_diffusion.py - 2024-10-24 07:15:32,116 - Loss: 0.3071, step: 30500
+INFO - dpsgd_diffusion.py - 2024-10-24 07:16:56,308 - Loss: 0.3304, step: 30600
+INFO - dpsgd_diffusion.py - 2024-10-24 07:18:21,766 - Loss: 0.3145, step: 30700
+INFO - dpsgd_diffusion.py - 2024-10-24 07:19:46,748 - Loss: 0.3032, step: 30800
+INFO - dpsgd_diffusion.py - 2024-10-24 07:21:12,570 - Loss: 0.3300, step: 30900
+INFO - dpsgd_diffusion.py - 2024-10-24 07:21:22,861 - Eps-value after 69 epochs: 0.6541
+INFO - dpsgd_diffusion.py - 2024-10-24 07:22:36,805 - Loss: 0.3358, step: 31000
+INFO - dpsgd_diffusion.py - 2024-10-24 07:24:01,257 - Loss: 0.2995, step: 31100
+INFO - dpsgd_diffusion.py - 2024-10-24 07:25:25,149 - Loss: 0.3081, step: 31200
+INFO - dpsgd_diffusion.py - 2024-10-24 07:26:48,604 - Loss: 0.2861, step: 31300
+INFO - dpsgd_diffusion.py - 2024-10-24 07:27:38,087 - Eps-value after 70 epochs: 0.6591
+INFO - dpsgd_diffusion.py - 2024-10-24 07:28:11,279 - Loss: 0.3046, step: 31400
+INFO - dpsgd_diffusion.py - 2024-10-24 07:29:35,575 - Loss: 0.2770, step: 31500
+INFO - dpsgd_diffusion.py - 2024-10-24 07:31:00,030 - Loss: 0.3076, step: 31600
+INFO - dpsgd_diffusion.py - 2024-10-24 07:32:24,242 - Loss: 0.3244, step: 31700
+INFO - dpsgd_diffusion.py - 2024-10-24 07:33:48,772 - Loss: 0.2908, step: 31800
+INFO - dpsgd_diffusion.py - 2024-10-24 07:33:55,642 - Eps-value after 71 epochs: 0.6642
+INFO - dpsgd_diffusion.py - 2024-10-24 07:35:13,573 - Loss: 0.2898, step: 31900
+INFO - dpsgd_diffusion.py - 2024-10-24 07:36:37,615 - Loss: 0.3051, step: 32000
+INFO - dpsgd_diffusion.py - 2024-10-24 07:36:37,632 - Saving snapshot checkpoint and sampling single batch at iteration 32000.
+WARNING - image.py - 2024-10-24 07:36:38,251 - 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 07:36:54,703 - FID at iteration 32000: 79.718256
+INFO - dpsgd_diffusion.py - 2024-10-24 07:38:19,706 - Loss: 0.2746, step: 32100
+INFO - dpsgd_diffusion.py - 2024-10-24 07:39:45,365 - Loss: 0.3061, step: 32200
+INFO - dpsgd_diffusion.py - 2024-10-24 07:40:32,592 - Eps-value after 72 epochs: 0.6692
+INFO - dpsgd_diffusion.py - 2024-10-24 07:41:10,547 - Loss: 0.3096, step: 32300
+INFO - dpsgd_diffusion.py - 2024-10-24 07:42:34,458 - Loss: 0.3009, step: 32400
+INFO - dpsgd_diffusion.py - 2024-10-24 07:43:57,915 - Loss: 0.2828, step: 32500
+INFO - dpsgd_diffusion.py - 2024-10-24 07:45:20,780 - Loss: 0.3176, step: 32600
+INFO - dpsgd_diffusion.py - 2024-10-24 07:46:43,992 - Loss: 0.3102, step: 32700
+INFO - dpsgd_diffusion.py - 2024-10-24 07:46:47,231 - Eps-value after 73 epochs: 0.6741
+INFO - dpsgd_diffusion.py - 2024-10-24 07:48:07,664 - Loss: 0.2825, step: 32800
+INFO - dpsgd_diffusion.py - 2024-10-24 07:49:32,880 - Loss: 0.3109, step: 32900
+INFO - dpsgd_diffusion.py - 2024-10-24 07:50:56,952 - Loss: 0.3438, step: 33000
+INFO - dpsgd_diffusion.py - 2024-10-24 07:52:20,004 - Loss: 0.3365, step: 33100
+INFO - dpsgd_diffusion.py - 2024-10-24 07:53:03,900 - Eps-value after 74 epochs: 0.6789
+INFO - dpsgd_diffusion.py - 2024-10-24 07:53:44,117 - Loss: 0.3160, step: 33200
+INFO - dpsgd_diffusion.py - 2024-10-24 07:55:08,458 - Loss: 0.3418, step: 33300
+INFO - dpsgd_diffusion.py - 2024-10-24 07:56:32,003 - Loss: 0.3360, step: 33400
+INFO - dpsgd_diffusion.py - 2024-10-24 07:57:55,712 - Loss: 0.2970, step: 33500
+INFO - dpsgd_diffusion.py - 2024-10-24 07:59:19,724 - Loss: 0.3187, step: 33600
+INFO - dpsgd_diffusion.py - 2024-10-24 07:59:19,742 - Eps-value after 75 epochs: 0.6838
+INFO - dpsgd_diffusion.py - 2024-10-24 08:00:44,505 - Loss: 0.3059, step: 33700
+INFO - dpsgd_diffusion.py - 2024-10-24 08:02:08,156 - Loss: 0.3059, step: 33800
+INFO - dpsgd_diffusion.py - 2024-10-24 08:03:32,733 - Loss: 0.2804, step: 33900
+INFO - dpsgd_diffusion.py - 2024-10-24 08:04:56,284 - Loss: 0.3023, step: 34000
+INFO - dpsgd_diffusion.py - 2024-10-24 08:04:56,311 - Saving snapshot checkpoint and sampling single batch at iteration 34000.
+WARNING - image.py - 2024-10-24 08:04:56,954 - 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 08:05:13,410 - FID at iteration 34000: 77.798635
+INFO - dpsgd_diffusion.py - 2024-10-24 08:05:53,481 - Eps-value after 76 epochs: 0.6886
+INFO - dpsgd_diffusion.py - 2024-10-24 08:06:38,274 - Loss: 0.3041, step: 34100
+INFO - dpsgd_diffusion.py - 2024-10-24 08:08:02,804 - Loss: 0.3073, step: 34200
+INFO - dpsgd_diffusion.py - 2024-10-24 08:09:25,786 - Loss: 0.2757, step: 34300
+INFO - dpsgd_diffusion.py - 2024-10-24 08:10:49,241 - Loss: 0.2922, step: 34400
+INFO - dpsgd_diffusion.py - 2024-10-24 08:12:09,521 - Eps-value after 77 epochs: 0.6935
+INFO - dpsgd_diffusion.py - 2024-10-24 08:12:12,909 - Loss: 0.2973, step: 34500
+INFO - dpsgd_diffusion.py - 2024-10-24 08:13:37,188 - Loss: 0.2688, step: 34600
+INFO - dpsgd_diffusion.py - 2024-10-24 08:15:00,853 - Loss: 0.2815, step: 34700
+INFO - dpsgd_diffusion.py - 2024-10-24 08:16:25,838 - Loss: 0.2715, step: 34800
+INFO - dpsgd_diffusion.py - 2024-10-24 08:17:52,090 - Loss: 0.3000, step: 34900
+INFO - dpsgd_diffusion.py - 2024-10-24 08:18:29,243 - Eps-value after 78 epochs: 0.6984
+INFO - dpsgd_diffusion.py - 2024-10-24 08:19:15,600 - Loss: 0.3090, step: 35000
+INFO - dpsgd_diffusion.py - 2024-10-24 08:20:38,353 - Loss: 0.3240, step: 35100
+INFO - dpsgd_diffusion.py - 2024-10-24 08:22:01,670 - Loss: 0.3171, step: 35200
+INFO - dpsgd_diffusion.py - 2024-10-24 08:23:23,569 - Loss: 0.2578, step: 35300
+INFO - dpsgd_diffusion.py - 2024-10-24 08:24:41,157 - Eps-value after 79 epochs: 0.7030
+INFO - dpsgd_diffusion.py - 2024-10-24 08:24:47,792 - Loss: 0.3131, step: 35400
+INFO - dpsgd_diffusion.py - 2024-10-24 08:26:11,682 - Loss: 0.3097, step: 35500
+INFO - dpsgd_diffusion.py - 2024-10-24 08:27:35,139 - Loss: 0.3051, step: 35600
+INFO - dpsgd_diffusion.py - 2024-10-24 08:28:58,045 - Loss: 0.3035, step: 35700
+INFO - dpsgd_diffusion.py - 2024-10-24 08:30:21,550 - Loss: 0.3471, step: 35800
+INFO - dpsgd_diffusion.py - 2024-10-24 08:30:55,266 - Eps-value after 80 epochs: 0.7077
+INFO - dpsgd_diffusion.py - 2024-10-24 08:31:45,271 - Loss: 0.3043, step: 35900
+INFO - dpsgd_diffusion.py - 2024-10-24 08:33:09,323 - Loss: 0.3174, step: 36000
+INFO - dpsgd_diffusion.py - 2024-10-24 08:33:09,339 - Saving snapshot checkpoint and sampling single batch at iteration 36000.
+WARNING - image.py - 2024-10-24 08:33:09,941 - 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 08:33:26,429 - FID at iteration 36000: 77.246991
+INFO - dpsgd_diffusion.py - 2024-10-24 08:34:49,626 - Loss: 0.3027, step: 36100
+INFO - dpsgd_diffusion.py - 2024-10-24 08:36:14,979 - Loss: 0.3111, step: 36200
+INFO - dpsgd_diffusion.py - 2024-10-24 08:37:28,519 - Eps-value after 81 epochs: 0.7124
+INFO - dpsgd_diffusion.py - 2024-10-24 08:37:38,533 - Loss: 0.2754, step: 36300
+INFO - dpsgd_diffusion.py - 2024-10-24 08:39:03,266 - Loss: 0.3039, step: 36400
+INFO - dpsgd_diffusion.py - 2024-10-24 08:40:28,179 - Loss: 0.3262, step: 36500
+INFO - dpsgd_diffusion.py - 2024-10-24 08:41:52,913 - Loss: 0.3137, step: 36600
+INFO - dpsgd_diffusion.py - 2024-10-24 08:43:17,408 - Loss: 0.2676, step: 36700
+INFO - dpsgd_diffusion.py - 2024-10-24 08:43:47,360 - Eps-value after 82 epochs: 0.7171
+INFO - dpsgd_diffusion.py - 2024-10-24 08:44:40,760 - Loss: 0.3147, step: 36800
+INFO - dpsgd_diffusion.py - 2024-10-24 08:46:07,209 - Loss: 0.2869, step: 36900
+INFO - dpsgd_diffusion.py - 2024-10-24 08:47:31,151 - Loss: 0.2990, step: 37000
+INFO - dpsgd_diffusion.py - 2024-10-24 08:48:54,990 - Loss: 0.3209, step: 37100
+INFO - dpsgd_diffusion.py - 2024-10-24 08:50:06,075 - Eps-value after 83 epochs: 0.7218
+INFO - dpsgd_diffusion.py - 2024-10-24 08:50:19,552 - Loss: 0.3233, step: 37200
+INFO - dpsgd_diffusion.py - 2024-10-24 08:51:44,906 - Loss: 0.3230, step: 37300
+INFO - dpsgd_diffusion.py - 2024-10-24 08:53:10,717 - Loss: 0.3154, step: 37400
+INFO - dpsgd_diffusion.py - 2024-10-24 08:54:32,878 - Loss: 0.2812, step: 37500
+INFO - dpsgd_diffusion.py - 2024-10-24 08:55:57,177 - Loss: 0.2876, step: 37600
+INFO - dpsgd_diffusion.py - 2024-10-24 08:56:23,596 - Eps-value after 84 epochs: 0.7265
+INFO - dpsgd_diffusion.py - 2024-10-24 08:57:20,783 - Loss: 0.3110, step: 37700
+INFO - dpsgd_diffusion.py - 2024-10-24 08:58:43,316 - Loss: 0.2929, step: 37800
+INFO - dpsgd_diffusion.py - 2024-10-24 09:00:05,584 - Loss: 0.2813, step: 37900
+INFO - dpsgd_diffusion.py - 2024-10-24 09:01:31,178 - Loss: 0.3138, step: 38000
+INFO - dpsgd_diffusion.py - 2024-10-24 09:01:31,222 - Saving snapshot checkpoint and sampling single batch at iteration 38000.
+WARNING - image.py - 2024-10-24 09:01:31,839 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 09:01:48,244 - FID at iteration 38000: 76.956866
+INFO - dpsgd_diffusion.py - 2024-10-24 09:02:56,087 - Eps-value after 85 epochs: 0.7310
+INFO - dpsgd_diffusion.py - 2024-10-24 09:03:13,042 - Loss: 0.3344, step: 38100
+INFO - dpsgd_diffusion.py - 2024-10-24 09:04:38,163 - Loss: 0.3167, step: 38200
+INFO - dpsgd_diffusion.py - 2024-10-24 09:06:02,646 - Loss: 0.3044, step: 38300
+INFO - dpsgd_diffusion.py - 2024-10-24 09:07:26,267 - Loss: 0.3127, step: 38400
+INFO - dpsgd_diffusion.py - 2024-10-24 09:08:51,281 - Loss: 0.3017, step: 38500
+INFO - dpsgd_diffusion.py - 2024-10-24 09:09:15,326 - Eps-value after 86 epochs: 0.7355
+INFO - dpsgd_diffusion.py - 2024-10-24 09:10:15,503 - Loss: 0.3201, step: 38600
+INFO - dpsgd_diffusion.py - 2024-10-24 09:11:40,537 - Loss: 0.2977, step: 38700
+INFO - dpsgd_diffusion.py - 2024-10-24 09:13:04,153 - Loss: 0.2816, step: 38800
+INFO - dpsgd_diffusion.py - 2024-10-24 09:14:27,383 - Loss: 0.2905, step: 38900
+INFO - dpsgd_diffusion.py - 2024-10-24 09:15:31,537 - Eps-value after 87 epochs: 0.7401
+INFO - dpsgd_diffusion.py - 2024-10-24 09:15:51,673 - Loss: 0.2815, step: 39000
+INFO - dpsgd_diffusion.py - 2024-10-24 09:17:16,797 - Loss: 0.3172, step: 39100
+INFO - dpsgd_diffusion.py - 2024-10-24 09:18:39,202 - Loss: 0.3156, step: 39200
+INFO - dpsgd_diffusion.py - 2024-10-24 09:20:03,606 - Loss: 0.2982, step: 39300
+INFO - dpsgd_diffusion.py - 2024-10-24 09:21:27,779 - Loss: 0.2956, step: 39400
+INFO - dpsgd_diffusion.py - 2024-10-24 09:21:47,852 - Eps-value after 88 epochs: 0.7446
+INFO - dpsgd_diffusion.py - 2024-10-24 09:22:52,175 - Loss: 0.2776, step: 39500
+INFO - dpsgd_diffusion.py - 2024-10-24 09:24:15,052 - Loss: 0.2879, step: 39600
+INFO - dpsgd_diffusion.py - 2024-10-24 09:25:37,921 - Loss: 0.2785, step: 39700
+INFO - dpsgd_diffusion.py - 2024-10-24 09:27:01,828 - Loss: 0.2900, step: 39800
+INFO - dpsgd_diffusion.py - 2024-10-24 09:28:01,336 - Eps-value after 89 epochs: 0.7491
+INFO - dpsgd_diffusion.py - 2024-10-24 09:28:24,013 - Loss: 0.2763, step: 39900
+INFO - dpsgd_diffusion.py - 2024-10-24 09:29:49,475 - Loss: 0.2949, step: 40000
+INFO - dpsgd_diffusion.py - 2024-10-24 09:29:49,493 - Saving snapshot checkpoint and sampling single batch at iteration 40000.
+WARNING - image.py - 2024-10-24 09:29:50,101 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 09:30:06,535 - FID at iteration 40000: 75.604872
+INFO - dpsgd_diffusion.py - 2024-10-24 09:31:32,107 - Loss: 0.2645, step: 40100
+INFO - dpsgd_diffusion.py - 2024-10-24 09:32:57,002 - Loss: 0.3097, step: 40200
+INFO - dpsgd_diffusion.py - 2024-10-24 09:34:20,097 - Loss: 0.3076, step: 40300
+INFO - dpsgd_diffusion.py - 2024-10-24 09:34:36,586 - Eps-value after 90 epochs: 0.7536
+INFO - dpsgd_diffusion.py - 2024-10-24 09:35:43,793 - Loss: 0.3113, step: 40400
+INFO - dpsgd_diffusion.py - 2024-10-24 09:37:06,936 - Loss: 0.3214, step: 40500
+INFO - dpsgd_diffusion.py - 2024-10-24 09:38:29,806 - Loss: 0.2886, step: 40600
+INFO - dpsgd_diffusion.py - 2024-10-24 09:39:52,893 - Loss: 0.3138, step: 40700
+INFO - dpsgd_diffusion.py - 2024-10-24 09:40:49,906 - Eps-value after 91 epochs: 0.7581
+INFO - dpsgd_diffusion.py - 2024-10-24 09:41:16,775 - Loss: 0.3074, step: 40800
+INFO - dpsgd_diffusion.py - 2024-10-24 09:42:39,846 - Loss: 0.2846, step: 40900
+INFO - dpsgd_diffusion.py - 2024-10-24 09:44:04,763 - Loss: 0.3070, step: 41000
+INFO - dpsgd_diffusion.py - 2024-10-24 09:45:28,639 - Loss: 0.3011, step: 41100
+INFO - dpsgd_diffusion.py - 2024-10-24 09:46:53,120 - Loss: 0.3200, step: 41200
+INFO - dpsgd_diffusion.py - 2024-10-24 09:47:06,453 - Eps-value after 92 epochs: 0.7626
+INFO - dpsgd_diffusion.py - 2024-10-24 09:48:16,220 - Loss: 0.3225, step: 41300
+INFO - dpsgd_diffusion.py - 2024-10-24 09:49:40,314 - Loss: 0.3319, step: 41400
+INFO - dpsgd_diffusion.py - 2024-10-24 09:51:04,371 - Loss: 0.3016, step: 41500
+INFO - dpsgd_diffusion.py - 2024-10-24 09:52:27,936 - Loss: 0.2891, step: 41600
+INFO - dpsgd_diffusion.py - 2024-10-24 09:53:21,203 - Eps-value after 93 epochs: 0.7669
+INFO - dpsgd_diffusion.py - 2024-10-24 09:53:51,396 - Loss: 0.2698, step: 41700
+INFO - dpsgd_diffusion.py - 2024-10-24 09:55:15,454 - Loss: 0.2937, step: 41800
+INFO - dpsgd_diffusion.py - 2024-10-24 09:56:38,622 - Loss: 0.3257, step: 41900
+INFO - dpsgd_diffusion.py - 2024-10-24 09:58:03,031 - Loss: 0.3244, step: 42000
+INFO - dpsgd_diffusion.py - 2024-10-24 09:58:03,057 - Saving snapshot checkpoint and sampling single batch at iteration 42000.
+WARNING - image.py - 2024-10-24 09:58:03,664 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 09:58:20,081 - FID at iteration 42000: 75.296796
+INFO - dpsgd_diffusion.py - 2024-10-24 09:59:44,004 - Loss: 0.3013, step: 42100
+INFO - dpsgd_diffusion.py - 2024-10-24 09:59:53,797 - Eps-value after 94 epochs: 0.7713
+INFO - dpsgd_diffusion.py - 2024-10-24 10:01:08,515 - Loss: 0.3120, step: 42200
+INFO - dpsgd_diffusion.py - 2024-10-24 10:02:31,726 - Loss: 0.2925, step: 42300
+INFO - dpsgd_diffusion.py - 2024-10-24 10:03:54,603 - Loss: 0.3007, step: 42400
+INFO - dpsgd_diffusion.py - 2024-10-24 10:05:17,751 - Loss: 0.2970, step: 42500
+INFO - dpsgd_diffusion.py - 2024-10-24 10:06:07,555 - Eps-value after 95 epochs: 0.7756
+INFO - dpsgd_diffusion.py - 2024-10-24 10:06:40,173 - Loss: 0.2681, step: 42600
+INFO - dpsgd_diffusion.py - 2024-10-24 10:08:03,351 - Loss: 0.2886, step: 42700
+INFO - dpsgd_diffusion.py - 2024-10-24 10:09:26,706 - Loss: 0.2661, step: 42800
+INFO - dpsgd_diffusion.py - 2024-10-24 10:10:50,423 - Loss: 0.3133, step: 42900
+INFO - dpsgd_diffusion.py - 2024-10-24 10:12:14,293 - Loss: 0.3097, step: 43000
+INFO - dpsgd_diffusion.py - 2024-10-24 10:12:20,937 - Eps-value after 96 epochs: 0.7799
+INFO - dpsgd_diffusion.py - 2024-10-24 10:13:38,510 - Loss: 0.2881, step: 43100
+INFO - dpsgd_diffusion.py - 2024-10-24 10:15:03,721 - Loss: 0.2835, step: 43200
+INFO - dpsgd_diffusion.py - 2024-10-24 10:16:28,822 - Loss: 0.2775, step: 43300
+INFO - dpsgd_diffusion.py - 2024-10-24 10:18:01,274 - Loss: 0.2927, step: 43400
+INFO - dpsgd_diffusion.py - 2024-10-24 10:18:47,737 - Eps-value after 97 epochs: 0.7843
+INFO - dpsgd_diffusion.py - 2024-10-24 10:19:24,526 - Loss: 0.3184, step: 43500
+INFO - dpsgd_diffusion.py - 2024-10-24 10:20:47,908 - Loss: 0.3040, step: 43600
+INFO - dpsgd_diffusion.py - 2024-10-24 10:22:11,454 - Loss: 0.2796, step: 43700
+INFO - dpsgd_diffusion.py - 2024-10-24 10:23:34,973 - Loss: 0.2829, step: 43800
+INFO - dpsgd_diffusion.py - 2024-10-24 10:24:58,294 - Loss: 0.2715, step: 43900
+INFO - dpsgd_diffusion.py - 2024-10-24 10:25:01,558 - Eps-value after 98 epochs: 0.7886
+INFO - dpsgd_diffusion.py - 2024-10-24 10:26:22,461 - Loss: 0.2957, step: 44000
+INFO - dpsgd_diffusion.py - 2024-10-24 10:26:22,480 - Saving snapshot checkpoint and sampling single batch at iteration 44000.
+WARNING - image.py - 2024-10-24 10:26:23,094 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 10:26:39,456 - FID at iteration 44000: 72.807880
+INFO - dpsgd_diffusion.py - 2024-10-24 10:28:01,958 - Loss: 0.2840, step: 44100
+INFO - dpsgd_diffusion.py - 2024-10-24 10:29:24,126 - Loss: 0.2925, step: 44200
+INFO - dpsgd_diffusion.py - 2024-10-24 10:30:47,939 - Loss: 0.2824, step: 44300
+INFO - dpsgd_diffusion.py - 2024-10-24 10:31:31,092 - Eps-value after 99 epochs: 0.7930
+INFO - dpsgd_diffusion.py - 2024-10-24 10:32:12,142 - Loss: 0.2905, step: 44400
+INFO - dpsgd_diffusion.py - 2024-10-24 10:33:37,254 - Loss: 0.3425, step: 44500
+INFO - dpsgd_diffusion.py - 2024-10-24 10:35:01,894 - Loss: 0.2800, step: 44600
+INFO - dpsgd_diffusion.py - 2024-10-24 10:36:25,043 - Loss: 0.2998, step: 44700
+INFO - dpsgd_diffusion.py - 2024-10-24 10:37:48,836 - Loss: 0.2845, step: 44800
+INFO - dpsgd_diffusion.py - 2024-10-24 10:37:48,852 - Eps-value after 100 epochs: 0.7973
+INFO - dpsgd_diffusion.py - 2024-10-24 10:39:11,846 - Loss: 0.2984, step: 44900
+INFO - dpsgd_diffusion.py - 2024-10-24 10:40:39,758 - Loss: 0.3031, step: 45000
+INFO - dpsgd_diffusion.py - 2024-10-24 10:42:03,002 - Loss: 0.2739, step: 45100
+INFO - dpsgd_diffusion.py - 2024-10-24 10:43:27,018 - Loss: 0.2814, step: 45200
+INFO - dpsgd_diffusion.py - 2024-10-24 10:44:07,698 - Eps-value after 101 epochs: 0.8014
+INFO - dpsgd_diffusion.py - 2024-10-24 10:44:51,090 - Loss: 0.2779, step: 45300
+INFO - dpsgd_diffusion.py - 2024-10-24 10:46:13,722 - Loss: 0.2785, step: 45400
+INFO - dpsgd_diffusion.py - 2024-10-24 10:47:39,552 - Loss: 0.2851, step: 45500
+INFO - dpsgd_diffusion.py - 2024-10-24 10:49:02,199 - Loss: 0.2982, step: 45600
+INFO - dpsgd_diffusion.py - 2024-10-24 10:50:24,418 - Eps-value after 102 epochs: 0.8056
+INFO - dpsgd_diffusion.py - 2024-10-24 10:50:27,780 - Loss: 0.3017, step: 45700
+INFO - dpsgd_diffusion.py - 2024-10-24 10:51:49,949 - Loss: 0.2953, step: 45800
+INFO - dpsgd_diffusion.py - 2024-10-24 10:53:14,714 - Loss: 0.2910, step: 45900
+INFO - dpsgd_diffusion.py - 2024-10-24 10:54:39,134 - Loss: 0.2735, step: 46000
+INFO - dpsgd_diffusion.py - 2024-10-24 10:54:39,147 - Saving snapshot checkpoint and sampling single batch at iteration 46000.
+WARNING - image.py - 2024-10-24 10:54:39,780 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 10:54:56,145 - FID at iteration 46000: 72.356307
+INFO - dpsgd_diffusion.py - 2024-10-24 10:56:21,117 - Loss: 0.3314, step: 46100
+INFO - dpsgd_diffusion.py - 2024-10-24 10:56:57,530 - Eps-value after 103 epochs: 0.8098
+INFO - dpsgd_diffusion.py - 2024-10-24 10:57:44,199 - Loss: 0.2953, step: 46200
+INFO - dpsgd_diffusion.py - 2024-10-24 10:59:07,562 - Loss: 0.2690, step: 46300
+INFO - dpsgd_diffusion.py - 2024-10-24 11:00:32,719 - Loss: 0.2869, step: 46400
+INFO - dpsgd_diffusion.py - 2024-10-24 11:01:59,229 - Loss: 0.3150, step: 46500
+INFO - dpsgd_diffusion.py - 2024-10-24 11:03:15,520 - Eps-value after 104 epochs: 0.8140
+INFO - dpsgd_diffusion.py - 2024-10-24 11:03:22,275 - Loss: 0.2686, step: 46600
+INFO - dpsgd_diffusion.py - 2024-10-24 11:04:46,502 - Loss: 0.2795, step: 46700
+INFO - dpsgd_diffusion.py - 2024-10-24 11:06:09,849 - Loss: 0.3154, step: 46800
+INFO - dpsgd_diffusion.py - 2024-10-24 11:07:32,936 - Loss: 0.3188, step: 46900
+INFO - dpsgd_diffusion.py - 2024-10-24 11:08:55,156 - Loss: 0.2734, step: 47000
+INFO - dpsgd_diffusion.py - 2024-10-24 11:09:28,253 - Eps-value after 105 epochs: 0.8182
+INFO - dpsgd_diffusion.py - 2024-10-24 11:10:17,354 - Loss: 0.2698, step: 47100
+INFO - dpsgd_diffusion.py - 2024-10-24 11:11:41,444 - Loss: 0.3273, step: 47200
+INFO - dpsgd_diffusion.py - 2024-10-24 11:13:03,591 - Loss: 0.3114, step: 47300
+INFO - dpsgd_diffusion.py - 2024-10-24 11:14:26,925 - Loss: 0.2890, step: 47400
+INFO - dpsgd_diffusion.py - 2024-10-24 11:15:40,712 - Eps-value after 106 epochs: 0.8223
+INFO - dpsgd_diffusion.py - 2024-10-24 11:15:50,771 - Loss: 0.2809, step: 47500
+INFO - dpsgd_diffusion.py - 2024-10-24 11:17:15,018 - Loss: 0.3063, step: 47600
+INFO - dpsgd_diffusion.py - 2024-10-24 11:18:38,130 - Loss: 0.2848, step: 47700
+INFO - dpsgd_diffusion.py - 2024-10-24 11:20:02,571 - Loss: 0.2967, step: 47800
+INFO - dpsgd_diffusion.py - 2024-10-24 11:21:25,545 - Loss: 0.3167, step: 47900
+INFO - dpsgd_diffusion.py - 2024-10-24 11:21:56,106 - Eps-value after 107 epochs: 0.8265
+INFO - dpsgd_diffusion.py - 2024-10-24 11:22:47,947 - Loss: 0.3182, step: 48000
+INFO - dpsgd_diffusion.py - 2024-10-24 11:22:47,967 - Saving snapshot checkpoint and sampling single batch at iteration 48000.
+WARNING - image.py - 2024-10-24 11:22:48,593 - 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 11:23:05,081 - FID at iteration 48000: 71.567444
+INFO - dpsgd_diffusion.py - 2024-10-24 11:24:29,158 - Loss: 0.3048, step: 48100
+INFO - dpsgd_diffusion.py - 2024-10-24 11:25:52,478 - Loss: 0.3184, step: 48200
+INFO - dpsgd_diffusion.py - 2024-10-24 11:27:16,481 - Loss: 0.2817, step: 48300
+INFO - dpsgd_diffusion.py - 2024-10-24 11:28:26,282 - Eps-value after 108 epochs: 0.8307
+INFO - dpsgd_diffusion.py - 2024-10-24 11:28:39,792 - Loss: 0.3127, step: 48400
+INFO - dpsgd_diffusion.py - 2024-10-24 11:30:03,138 - Loss: 0.2779, step: 48500
+INFO - dpsgd_diffusion.py - 2024-10-24 11:31:27,236 - Loss: 0.3181, step: 48600
+INFO - dpsgd_diffusion.py - 2024-10-24 11:32:50,569 - Loss: 0.2714, step: 48700
+INFO - dpsgd_diffusion.py - 2024-10-24 11:34:14,460 - Loss: 0.2982, step: 48800
+INFO - dpsgd_diffusion.py - 2024-10-24 11:34:40,849 - Eps-value after 109 epochs: 0.8348
+INFO - dpsgd_diffusion.py - 2024-10-24 11:35:36,662 - Loss: 0.2924, step: 48900
+INFO - dpsgd_diffusion.py - 2024-10-24 11:37:01,987 - Loss: 0.3565, step: 49000
+INFO - dpsgd_diffusion.py - 2024-10-24 11:38:25,555 - Loss: 0.2669, step: 49100
+INFO - dpsgd_diffusion.py - 2024-10-24 11:39:49,606 - Loss: 0.2709, step: 49200
+INFO - dpsgd_diffusion.py - 2024-10-24 11:40:56,048 - Eps-value after 110 epochs: 0.8388
+INFO - dpsgd_diffusion.py - 2024-10-24 11:41:13,005 - Loss: 0.3082, step: 49300
+INFO - dpsgd_diffusion.py - 2024-10-24 11:42:37,758 - Loss: 0.2896, step: 49400
+INFO - dpsgd_diffusion.py - 2024-10-24 11:44:02,238 - Loss: 0.2861, step: 49500
+INFO - dpsgd_diffusion.py - 2024-10-24 11:45:25,637 - Loss: 0.3033, step: 49600
+INFO - dpsgd_diffusion.py - 2024-10-24 11:46:48,888 - Loss: 0.3028, step: 49700
+INFO - dpsgd_diffusion.py - 2024-10-24 11:47:11,765 - Eps-value after 111 epochs: 0.8428
+INFO - dpsgd_diffusion.py - 2024-10-24 11:48:12,000 - Loss: 0.3285, step: 49800
+INFO - dpsgd_diffusion.py - 2024-10-24 11:49:35,823 - Loss: 0.3057, step: 49900
+INFO - dpsgd_diffusion.py - 2024-10-24 11:51:00,388 - Loss: 0.3012, step: 50000
+INFO - dpsgd_diffusion.py - 2024-10-24 11:51:00,410 - Saving snapshot checkpoint and sampling single batch at iteration 50000.
+WARNING - image.py - 2024-10-24 11:51:01,016 - 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 11:51:17,389 - FID at iteration 50000: 71.454051
+INFO - dpsgd_diffusion.py - 2024-10-24 11:52:41,481 - Loss: 0.3106, step: 50100
+INFO - dpsgd_diffusion.py - 2024-10-24 11:53:45,195 - Eps-value after 112 epochs: 0.8469
+INFO - dpsgd_diffusion.py - 2024-10-24 11:54:05,602 - Loss: 0.3085, step: 50200
+INFO - dpsgd_diffusion.py - 2024-10-24 11:55:29,083 - Loss: 0.2764, step: 50300
+INFO - dpsgd_diffusion.py - 2024-10-24 11:56:54,347 - Loss: 0.2836, step: 50400
+INFO - dpsgd_diffusion.py - 2024-10-24 11:58:18,896 - Loss: 0.2824, step: 50500
+INFO - dpsgd_diffusion.py - 2024-10-24 11:59:42,371 - Loss: 0.2906, step: 50600
+INFO - dpsgd_diffusion.py - 2024-10-24 12:00:02,697 - Eps-value after 113 epochs: 0.8509
+INFO - dpsgd_diffusion.py - 2024-10-24 12:01:05,849 - Loss: 0.3008, step: 50700
+INFO - dpsgd_diffusion.py - 2024-10-24 12:02:29,835 - Loss: 0.2841, step: 50800
+INFO - dpsgd_diffusion.py - 2024-10-24 12:03:52,259 - Loss: 0.2923, step: 50900
+INFO - dpsgd_diffusion.py - 2024-10-24 12:05:15,881 - Loss: 0.3000, step: 51000
+INFO - dpsgd_diffusion.py - 2024-10-24 12:06:17,416 - Eps-value after 114 epochs: 0.8549
+INFO - dpsgd_diffusion.py - 2024-10-24 12:06:41,394 - Loss: 0.2789, step: 51100
+INFO - dpsgd_diffusion.py - 2024-10-24 12:08:04,758 - Loss: 0.2883, step: 51200
+INFO - dpsgd_diffusion.py - 2024-10-24 12:09:28,421 - Loss: 0.2945, step: 51300
+INFO - dpsgd_diffusion.py - 2024-10-24 12:10:52,509 - Loss: 0.2830, step: 51400
+INFO - dpsgd_diffusion.py - 2024-10-24 12:12:15,816 - Loss: 0.2819, step: 51500
+INFO - dpsgd_diffusion.py - 2024-10-24 12:12:32,813 - Eps-value after 115 epochs: 0.8589
+INFO - dpsgd_diffusion.py - 2024-10-24 12:13:40,640 - Loss: 0.3085, step: 51600
+INFO - dpsgd_diffusion.py - 2024-10-24 12:15:02,931 - Loss: 0.2638, step: 51700
+INFO - dpsgd_diffusion.py - 2024-10-24 12:16:25,961 - Loss: 0.2896, step: 51800
+INFO - dpsgd_diffusion.py - 2024-10-24 12:17:50,761 - Loss: 0.3109, step: 51900
+INFO - dpsgd_diffusion.py - 2024-10-24 12:18:47,886 - Eps-value after 116 epochs: 0.8629
+INFO - dpsgd_diffusion.py - 2024-10-24 12:19:14,610 - Loss: 0.3130, step: 52000
+INFO - dpsgd_diffusion.py - 2024-10-24 12:19:14,628 - Saving snapshot checkpoint and sampling single batch at iteration 52000.
+WARNING - image.py - 2024-10-24 12:19:15,237 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 12:19:31,625 - FID at iteration 52000: 71.557133
+INFO - dpsgd_diffusion.py - 2024-10-24 12:20:55,780 - Loss: 0.2885, step: 52100
+INFO - dpsgd_diffusion.py - 2024-10-24 12:22:18,958 - Loss: 0.2898, step: 52200
+INFO - dpsgd_diffusion.py - 2024-10-24 12:23:43,356 - Loss: 0.3150, step: 52300
+INFO - dpsgd_diffusion.py - 2024-10-24 12:25:06,735 - Loss: 0.2896, step: 52400
+INFO - dpsgd_diffusion.py - 2024-10-24 12:25:19,704 - Eps-value after 117 epochs: 0.8669
+INFO - dpsgd_diffusion.py - 2024-10-24 12:26:31,007 - Loss: 0.2899, step: 52500
+INFO - dpsgd_diffusion.py - 2024-10-24 12:27:56,820 - Loss: 0.3507, step: 52600
+INFO - dpsgd_diffusion.py - 2024-10-24 12:29:21,045 - Loss: 0.2911, step: 52700
+INFO - dpsgd_diffusion.py - 2024-10-24 12:30:44,250 - Loss: 0.2775, step: 52800
+INFO - dpsgd_diffusion.py - 2024-10-24 12:31:38,439 - Eps-value after 118 epochs: 0.8709
+INFO - dpsgd_diffusion.py - 2024-10-24 12:32:08,727 - Loss: 0.2847, step: 52900
+INFO - dpsgd_diffusion.py - 2024-10-24 12:33:32,320 - Loss: 0.2797, step: 53000
+INFO - dpsgd_diffusion.py - 2024-10-24 12:34:56,437 - Loss: 0.2946, step: 53100
+INFO - dpsgd_diffusion.py - 2024-10-24 12:36:20,401 - Loss: 0.2872, step: 53200
+INFO - dpsgd_diffusion.py - 2024-10-24 12:37:44,467 - Loss: 0.3310, step: 53300
+INFO - dpsgd_diffusion.py - 2024-10-24 12:37:54,519 - Eps-value after 119 epochs: 0.8749
+INFO - dpsgd_diffusion.py - 2024-10-24 12:39:09,001 - Loss: 0.3468, step: 53400
+INFO - dpsgd_diffusion.py - 2024-10-24 12:40:33,555 - Loss: 0.3012, step: 53500
+INFO - dpsgd_diffusion.py - 2024-10-24 12:41:58,801 - Loss: 0.2630, step: 53600
+INFO - dpsgd_diffusion.py - 2024-10-24 12:43:22,772 - Loss: 0.2734, step: 53700
+INFO - dpsgd_diffusion.py - 2024-10-24 12:44:13,907 - Eps-value after 120 epochs: 0.8788
+INFO - dpsgd_diffusion.py - 2024-10-24 12:44:47,589 - Loss: 0.2829, step: 53800
+INFO - dpsgd_diffusion.py - 2024-10-24 12:46:12,889 - Loss: 0.2882, step: 53900
+INFO - dpsgd_diffusion.py - 2024-10-24 12:47:36,723 - Loss: 0.2759, step: 54000
+INFO - dpsgd_diffusion.py - 2024-10-24 12:47:36,798 - Saving snapshot checkpoint and sampling single batch at iteration 54000.
+WARNING - image.py - 2024-10-24 12:47:37,421 - 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 12:47:54,157 - FID at iteration 54000: 72.074276
+INFO - dpsgd_diffusion.py - 2024-10-24 12:49:20,176 - Loss: 0.2721, step: 54100
+INFO - dpsgd_diffusion.py - 2024-10-24 12:50:44,845 - Loss: 0.3157, step: 54200
+INFO - dpsgd_diffusion.py - 2024-10-24 12:50:51,587 - Eps-value after 121 epochs: 0.8826
+INFO - dpsgd_diffusion.py - 2024-10-24 12:52:08,960 - Loss: 0.3118, step: 54300
+INFO - dpsgd_diffusion.py - 2024-10-24 12:53:33,023 - Loss: 0.3028, step: 54400
+INFO - dpsgd_diffusion.py - 2024-10-24 12:54:58,264 - Loss: 0.2878, step: 54500
+INFO - dpsgd_diffusion.py - 2024-10-24 12:56:22,458 - Loss: 0.2858, step: 54600
+INFO - dpsgd_diffusion.py - 2024-10-24 12:57:08,947 - Eps-value after 122 epochs: 0.8865
+INFO - dpsgd_diffusion.py - 2024-10-24 12:57:45,954 - Loss: 0.2761, step: 54700
+INFO - dpsgd_diffusion.py - 2024-10-24 12:59:09,618 - Loss: 0.2814, step: 54800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:00:34,565 - Loss: 0.2902, step: 54900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:01:58,152 - Loss: 0.2855, step: 55000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:03:24,789 - Loss: 0.2696, step: 55100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:03:28,167 - Eps-value after 123 epochs: 0.8903
+INFO - dpsgd_diffusion.py - 2024-10-24 13:04:50,086 - Loss: 0.2699, step: 55200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:06:14,350 - Loss: 0.2961, step: 55300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:07:39,231 - Loss: 0.2961, step: 55400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:09:03,798 - Loss: 0.2927, step: 55500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:09:47,319 - Eps-value after 124 epochs: 0.8942
+INFO - dpsgd_diffusion.py - 2024-10-24 13:10:27,769 - Loss: 0.2911, step: 55600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:11:53,463 - Loss: 0.3170, step: 55700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:13:17,316 - Loss: 0.3049, step: 55800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:14:42,306 - Loss: 0.2769, step: 55900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:16:09,416 - Loss: 0.2885, step: 56000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:16:09,434 - Eps-value after 125 epochs: 0.8980
+INFO - dpsgd_diffusion.py - 2024-10-24 13:16:09,446 - Saving snapshot checkpoint and sampling single batch at iteration 56000.
+WARNING - image.py - 2024-10-24 13:16:10,064 - 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 13:16:26,805 - FID at iteration 56000: 71.952707
+INFO - dpsgd_diffusion.py - 2024-10-24 13:17:50,505 - Loss: 0.2809, step: 56100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:19:15,135 - Loss: 0.2895, step: 56200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:20:38,941 - Loss: 0.3041, step: 56300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:22:02,479 - Loss: 0.2987, step: 56400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:22:42,614 - Eps-value after 126 epochs: 0.9018
+INFO - dpsgd_diffusion.py - 2024-10-24 13:23:25,084 - Loss: 0.2795, step: 56500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:24:49,505 - Loss: 0.2962, step: 56600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:26:14,165 - Loss: 0.2955, step: 56700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:27:37,263 - Loss: 0.3018, step: 56800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:28:56,441 - Eps-value after 127 epochs: 0.9057
+INFO - dpsgd_diffusion.py - 2024-10-24 13:28:59,867 - Loss: 0.2761, step: 56900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:30:23,485 - Loss: 0.2883, step: 57000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:31:48,168 - Loss: 0.3160, step: 57100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:33:11,951 - Loss: 0.2730, step: 57200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:34:35,792 - Loss: 0.2931, step: 57300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:35:13,032 - Eps-value after 128 epochs: 0.9095
+INFO - dpsgd_diffusion.py - 2024-10-24 13:35:59,906 - Loss: 0.2897, step: 57400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:37:25,029 - Loss: 0.2838, step: 57500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:38:49,624 - Loss: 0.3017, step: 57600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:40:13,399 - Loss: 0.2915, step: 57700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:41:29,836 - Eps-value after 129 epochs: 0.9134
+INFO - dpsgd_diffusion.py - 2024-10-24 13:41:36,819 - Loss: 0.2785, step: 57800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:43:01,220 - Loss: 0.2679, step: 57900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:44:25,038 - Loss: 0.3086, step: 58000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:44:25,052 - Saving snapshot checkpoint and sampling single batch at iteration 58000.
+WARNING - image.py - 2024-10-24 13:44:25,649 - 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 13:44:42,495 - FID at iteration 58000: 68.870662
+INFO - dpsgd_diffusion.py - 2024-10-24 13:46:07,419 - Loss: 0.2964, step: 58100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:47:31,045 - Loss: 0.2914, step: 58200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:48:05,084 - Eps-value after 130 epochs: 0.9172
+INFO - dpsgd_diffusion.py - 2024-10-24 13:48:56,806 - Loss: 0.3019, step: 58300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:50:19,922 - Loss: 0.2813, step: 58400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:51:44,217 - Loss: 0.2669, step: 58500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:53:07,433 - Loss: 0.3012, step: 58600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:54:20,275 - Eps-value after 131 epochs: 0.9211
+INFO - dpsgd_diffusion.py - 2024-10-24 13:54:30,728 - Loss: 0.2903, step: 58700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:55:55,926 - Loss: 0.2892, step: 58800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:57:20,401 - Loss: 0.2698, step: 58900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:58:44,215 - Loss: 0.3161, step: 59000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:00:10,976 - Loss: 0.2499, step: 59100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:00:40,785 - Eps-value after 132 epochs: 0.9248
+INFO - dpsgd_diffusion.py - 2024-10-24 14:01:34,887 - Loss: 0.2808, step: 59200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:02:58,368 - Loss: 0.2749, step: 59300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:04:25,107 - Loss: 0.3251, step: 59400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:05:48,953 - Loss: 0.2854, step: 59500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:07:00,217 - Eps-value after 133 epochs: 0.9284
+INFO - dpsgd_diffusion.py - 2024-10-24 14:07:14,149 - Loss: 0.3065, step: 59600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:08:39,637 - Loss: 0.2883, step: 59700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:10:04,372 - Loss: 0.2680, step: 59800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:11:28,055 - Loss: 0.2794, step: 59900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:12:53,223 - Loss: 0.2709, step: 60000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:12:53,239 - Saving snapshot checkpoint and sampling single batch at iteration 60000.
+WARNING - image.py - 2024-10-24 14:12:53,864 - 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 14:13:10,712 - FID at iteration 60000: 68.922031
+INFO - dpsgd_diffusion.py - 2024-10-24 14:13:38,934 - Eps-value after 134 epochs: 0.9321
+INFO - dpsgd_diffusion.py - 2024-10-24 14:14:37,275 - Loss: 0.2757, step: 60100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:16:01,354 - Loss: 0.2804, step: 60200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:17:26,546 - Loss: 0.2922, step: 60300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:18:51,472 - Loss: 0.2837, step: 60400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:19:58,849 - Eps-value after 135 epochs: 0.9358
+INFO - dpsgd_diffusion.py - 2024-10-24 14:20:16,505 - Loss: 0.2918, step: 60500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:21:40,618 - Loss: 0.2445, step: 60600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:23:05,297 - Loss: 0.2681, step: 60700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:24:29,368 - Loss: 0.2805, step: 60800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:25:52,659 - Loss: 0.2939, step: 60900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:26:17,221 - Eps-value after 136 epochs: 0.9394
+INFO - dpsgd_diffusion.py - 2024-10-24 14:27:17,322 - Loss: 0.2938, step: 61000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:28:41,571 - Loss: 0.2861, step: 61100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:30:05,062 - Loss: 0.3166, step: 61200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:31:29,230 - Loss: 0.2938, step: 61300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:32:33,535 - Eps-value after 137 epochs: 0.9431
+INFO - dpsgd_diffusion.py - 2024-10-24 14:32:53,955 - Loss: 0.3054, step: 61400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:34:17,999 - Loss: 0.2969, step: 61500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:35:42,123 - Loss: 0.3135, step: 61600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:37:06,733 - Loss: 0.2931, step: 61700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:38:31,184 - Loss: 0.2842, step: 61800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:38:51,336 - Eps-value after 138 epochs: 0.9468
+INFO - dpsgd_diffusion.py - 2024-10-24 14:39:55,826 - Loss: 0.2721, step: 61900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:41:21,083 - Loss: 0.2737, step: 62000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:41:21,114 - Saving snapshot checkpoint and sampling single batch at iteration 62000.
+WARNING - image.py - 2024-10-24 14:41:21,733 - 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 14:41:38,376 - FID at iteration 62000: 67.919700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:43:02,409 - Loss: 0.3263, step: 62100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:44:26,268 - Loss: 0.2925, step: 62200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:45:26,640 - Eps-value after 139 epochs: 0.9505
+INFO - dpsgd_diffusion.py - 2024-10-24 14:45:49,342 - Loss: 0.2814, step: 62300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:47:14,696 - Loss: 0.2872, step: 62400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:48:39,838 - Loss: 0.2940, step: 62500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:50:02,909 - Loss: 0.2698, step: 62600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:51:26,196 - Loss: 0.3072, step: 62700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:51:42,768 - Eps-value after 140 epochs: 0.9541
+INFO - dpsgd_diffusion.py - 2024-10-24 14:52:50,312 - Loss: 0.2972, step: 62800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:54:13,253 - Loss: 0.2868, step: 62900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:55:37,201 - Loss: 0.3112, step: 63000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:57:00,605 - Loss: 0.2717, step: 63100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:57:57,156 - Eps-value after 141 epochs: 0.9578
+INFO - dpsgd_diffusion.py - 2024-10-24 14:58:23,913 - Loss: 0.2860, step: 63200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:59:47,885 - Loss: 0.3140, step: 63300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:01:13,235 - Loss: 0.2831, step: 63400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:02:37,711 - Loss: 0.2845, step: 63500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:04:01,630 - Loss: 0.2799, step: 63600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:04:15,426 - Eps-value after 142 epochs: 0.9615
+INFO - dpsgd_diffusion.py - 2024-10-24 15:05:24,504 - Loss: 0.2946, step: 63700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:06:48,130 - Loss: 0.2839, step: 63800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:08:12,025 - Loss: 0.2790, step: 63900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:09:35,868 - Loss: 0.2721, step: 64000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:09:35,885 - Saving snapshot checkpoint and sampling single batch at iteration 64000.
+WARNING - image.py - 2024-10-24 15:09:36,516 - 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 15:09:53,189 - FID at iteration 64000: 66.494962
+INFO - dpsgd_diffusion.py - 2024-10-24 15:10:47,946 - Eps-value after 143 epochs: 0.9652
+INFO - dpsgd_diffusion.py - 2024-10-24 15:11:18,382 - Loss: 0.2614, step: 64100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:12:44,319 - Loss: 0.2928, step: 64200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:14:09,520 - Loss: 0.2900, step: 64300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:15:33,698 - Loss: 0.2979, step: 64400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:16:58,101 - Loss: 0.2853, step: 64500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:17:08,501 - Eps-value after 144 epochs: 0.9688
+INFO - dpsgd_diffusion.py - 2024-10-24 15:18:22,764 - Loss: 0.2846, step: 64600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:19:47,913 - Loss: 0.2870, step: 64700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:21:12,330 - Loss: 0.2806, step: 64800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:22:36,439 - Loss: 0.3015, step: 64900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:23:26,335 - Eps-value after 145 epochs: 0.9725
+INFO - dpsgd_diffusion.py - 2024-10-24 15:24:00,736 - Loss: 0.2684, step: 65000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:25:25,728 - Loss: 0.2826, step: 65100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:26:49,170 - Loss: 0.2826, step: 65200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:28:14,441 - Loss: 0.2778, step: 65300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:29:38,639 - Loss: 0.2584, step: 65400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:29:45,173 - Eps-value after 146 epochs: 0.9760
+INFO - dpsgd_diffusion.py - 2024-10-24 15:31:03,337 - Loss: 0.2792, step: 65500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:32:28,123 - Loss: 0.2740, step: 65600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:33:54,059 - Loss: 0.2938, step: 65700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:35:18,687 - Loss: 0.2550, step: 65800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:36:05,724 - Eps-value after 147 epochs: 0.9795
+INFO - dpsgd_diffusion.py - 2024-10-24 15:36:43,403 - Loss: 0.2648, step: 65900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:38:08,619 - Loss: 0.2782, step: 66000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:38:08,660 - Saving snapshot checkpoint and sampling single batch at iteration 66000.
+WARNING - image.py - 2024-10-24 15:38:09,276 - 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 15:38:25,948 - FID at iteration 66000: 66.429036
+INFO - dpsgd_diffusion.py - 2024-10-24 15:39:50,723 - Loss: 0.3051, step: 66100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:41:15,322 - Loss: 0.2948, step: 66200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:42:39,631 - Loss: 0.2628, step: 66300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:42:43,142 - Eps-value after 148 epochs: 0.9830
+INFO - dpsgd_diffusion.py - 2024-10-24 15:44:02,951 - Loss: 0.2783, step: 66400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:45:26,839 - Loss: 0.2574, step: 66500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:46:51,764 - Loss: 0.3062, step: 66600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:48:16,667 - Loss: 0.2672, step: 66700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:49:01,058 - Eps-value after 149 epochs: 0.9865
+INFO - dpsgd_diffusion.py - 2024-10-24 15:49:41,910 - Loss: 0.2628, step: 66800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:51:06,749 - Loss: 0.3051, step: 66900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:52:29,966 - Loss: 0.2843, step: 67000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:53:52,505 - Loss: 0.2847, step: 67100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:55:17,427 - Loss: 0.2750, step: 67200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:55:17,443 - Eps-value after 150 epochs: 0.9900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:55:17,763 - Saving final checkpoint.
+INFO - dpsgd_diffusion.py - 2024-10-24 15:55:17,766 - start to generate 60000 samples
+INFO - dpsgd_diffusion.py - 2024-10-24 17:07:13,981 - Generation Finished!
+INFO - dataset_loader.py - 2024-10-24 17:20:35,140 - delta is reset as 1.6657508770018431e-06
+INFO - evaluator.py - 2024-10-24 17:21:21,194 - Epoch: 0 Train acc: 56.28363636363637 Val acc: 68.32000000000001 Test acc68.65; Train loss: 0.0044400550452145665 Val loss: 0.0010257497072219848
+INFO - evaluator.py - 2024-10-24 17:21:44,252 - Epoch: 1 Train acc: 81.07818181818182 Val acc: 71.36 Test acc71.00999999999999; Train loss: 0.0019084283357316796 Val loss: 0.0010729053258895873
+INFO - evaluator.py - 2024-10-24 17:22:07,436 - Epoch: 2 Train acc: 84.4090909090909 Val acc: 69.98 Test acc69.64; Train loss: 0.00158005214008418 Val loss: 0.0011138663291931153
+INFO - evaluator.py - 2024-10-24 17:22:30,523 - Epoch: 3 Train acc: 86.58 Val acc: 72.82 Test acc72.47; Train loss: 0.0013754799436439167 Val loss: 0.0010196246862411499
+INFO - evaluator.py - 2024-10-24 17:22:53,633 - Epoch: 4 Train acc: 88.51818181818182 Val acc: 18.54 Test acc18.69; Train loss: 0.0011921325838023967 Val loss: 0.008120862579345703
+INFO - evaluator.py - 2024-10-24 17:23:16,782 - Epoch: 5 Train acc: 90.71272727272728 Val acc: 26.06 Test acc25.619999999999997; Train loss: 0.0009818712391636588 Val loss: 0.005277646255493164
+INFO - evaluator.py - 2024-10-24 17:23:39,440 - Epoch: 6 Train acc: 91.84727272727272 Val acc: 24.560000000000002 Test acc23.98; Train loss: 0.0008754865624687889 Val loss: 0.004283989906311035
+INFO - evaluator.py - 2024-10-24 17:24:02,136 - Epoch: 7 Train acc: 92.75818181818181 Val acc: 36.04 Test acc35.39; Train loss: 0.0007787070458585565 Val loss: 0.0030379226684570312
+INFO - evaluator.py - 2024-10-24 17:24:24,053 - Epoch: 8 Train acc: 93.65454545454546 Val acc: 60.160000000000004 Test acc59.41; Train loss: 0.0006881837300278924 Val loss: 0.0017021629810333252
+INFO - evaluator.py - 2024-10-24 17:24:46,180 - Epoch: 9 Train acc: 94.63090909090909 Val acc: 42.24 Test acc42.66; Train loss: 0.0005791870737617667 Val loss: 0.0035584734916687013
+INFO - evaluator.py - 2024-10-24 17:25:08,052 - Epoch: 10 Train acc: 95.36181818181818 Val acc: 50.480000000000004 Test acc50.51; Train loss: 0.0005152460816231641 Val loss: 0.0034040340423583983
+INFO - evaluator.py - 2024-10-24 17:25:29,993 - Epoch: 11 Train acc: 96.2690909090909 Val acc: 56.04 Test acc56.44; Train loss: 0.0004100993405011567 Val loss: 0.0020662573099136353
+INFO - evaluator.py - 2024-10-24 17:25:53,126 - Epoch: 12 Train acc: 96.52909090909091 Val acc: 22.040000000000003 Test acc21.22; Train loss: 0.0003761432082138278 Val loss: 0.008977201080322265
+INFO - evaluator.py - 2024-10-24 17:26:15,170 - Epoch: 13 Train acc: 96.78 Val acc: 52.16 Test acc51.629999999999995; Train loss: 0.00036115452979098667 Val loss: 0.0024284934520721435
+INFO - evaluator.py - 2024-10-24 17:26:38,488 - Epoch: 14 Train acc: 96.96727272727273 Val acc: 44.56 Test acc44.18; Train loss: 0.000336317809230902 Val loss: 0.003138593769073486
+INFO - evaluator.py - 2024-10-24 17:27:00,795 - Epoch: 15 Train acc: 97.27636363636364 Val acc: 19.6 Test acc19.27; Train loss: 0.0002976528627289967 Val loss: 0.012609846878051757
+INFO - evaluator.py - 2024-10-24 17:27:22,551 - Epoch: 16 Train acc: 97.50909090909092 Val acc: 60.760000000000005 Test acc60.18; Train loss: 0.0002736533667214892 Val loss: 0.001710721969604492
+INFO - evaluator.py - 2024-10-24 17:27:44,322 - Epoch: 17 Train acc: 97.72545454545455 Val acc: 19.88 Test acc19.42; Train loss: 0.0002494397827847437 Val loss: 0.007724240970611572
+INFO - evaluator.py - 2024-10-24 17:28:06,010 - Epoch: 18 Train acc: 98.02181818181818 Val acc: 23.84 Test acc23.080000000000002; Train loss: 0.00021902779278091408 Val loss: 0.011441983795166016
+INFO - evaluator.py - 2024-10-24 17:28:29,038 - Epoch: 19 Train acc: 98.02 Val acc: 20.86 Test acc20.44; Train loss: 0.00022189362560483543 Val loss: 0.008089746570587157
+INFO - evaluator.py - 2024-10-24 17:28:51,303 - Epoch: 20 Train acc: 99.23818181818181 Val acc: 71.41999999999999 Test acc70.73; Train loss: 9.028445174070922e-05 Val loss: 0.0016600444316864013
+INFO - evaluator.py - 2024-10-24 17:29:13,480 - Epoch: 21 Train acc: 99.50545454545454 Val acc: 71.34 Test acc71.28999999999999; Train loss: 6.037019111046737e-05 Val loss: 0.0021944696426391603
+INFO - evaluator.py - 2024-10-24 17:29:35,628 - Epoch: 22 Train acc: 99.51454545454546 Val acc: 71.6 Test acc71.67; Train loss: 5.549039980819957e-05 Val loss: 0.002501584196090698
+INFO - evaluator.py - 2024-10-24 17:29:58,483 - Epoch: 23 Train acc: 99.66909090909091 Val acc: 64.44 Test acc64.87; Train loss: 4.104064588371495e-05 Val loss: 0.0041476011276245115
+INFO - evaluator.py - 2024-10-24 17:30:20,542 - Epoch: 24 Train acc: 99.66545454545455 Val acc: 68.42 Test acc69.31; Train loss: 3.935757931216027e-05 Val loss: 0.0032319727420806886
+INFO - evaluator.py - 2024-10-24 17:30:42,520 - Epoch: 25 Train acc: 99.69272727272728 Val acc: 67.54 Test acc68.26; Train loss: 3.570752448868006e-05 Val loss: 0.003959121322631836
+INFO - evaluator.py - 2024-10-24 17:31:05,323 - Epoch: 26 Train acc: 99.76181818181819 Val acc: 66.72 Test acc67.72; Train loss: 3.0562646615064955e-05 Val loss: 0.0043720166206359865
+INFO - evaluator.py - 2024-10-24 17:31:28,032 - Epoch: 27 Train acc: 99.73636363636363 Val acc: 69.64 Test acc70.17; Train loss: 3.091647860763425e-05 Val loss: 0.0034600093364715576
+INFO - evaluator.py - 2024-10-24 17:31:51,240 - Epoch: 28 Train acc: 99.7309090909091 Val acc: 65.78 Test acc65.71000000000001; Train loss: 3.031191910585304e-05 Val loss: 0.004032142400741577
+INFO - evaluator.py - 2024-10-24 17:32:13,801 - Epoch: 29 Train acc: 99.59636363636363 Val acc: 69.19999999999999 Test acc69.87; Train loss: 4.113815856610679e-05 Val loss: 0.004079467248916626
+INFO - evaluator.py - 2024-10-24 17:32:36,515 - Epoch: 30 Train acc: 99.75272727272727 Val acc: 59.14 Test acc59.309999999999995; Train loss: 2.6453457564110234e-05 Val loss: 0.006422572231292724
+INFO - evaluator.py - 2024-10-24 17:32:58,117 - Epoch: 31 Train acc: 99.68545454545455 Val acc: 75.06 Test acc74.02; Train loss: 3.5609031150075186e-05 Val loss: 0.0025529363632202148
+INFO - evaluator.py - 2024-10-24 17:33:20,635 - Epoch: 32 Train acc: 99.70545454545454 Val acc: 69.24 Test acc69.22; Train loss: 3.684069892677309e-05 Val loss: 0.003889526653289795
+INFO - evaluator.py - 2024-10-24 17:33:44,205 - Epoch: 33 Train acc: 99.78545454545454 Val acc: 69.82000000000001 Test acc70.22; Train loss: 2.3929574712581762e-05 Val loss: 0.0035607677936553956
+INFO - evaluator.py - 2024-10-24 17:34:06,176 - Epoch: 34 Train acc: 99.70181818181818 Val acc: 68.16 Test acc69.01; Train loss: 3.362916723708622e-05 Val loss: 0.0040038283348083495
+INFO - evaluator.py - 2024-10-24 17:34:29,300 - Epoch: 35 Train acc: 99.74181818181819 Val acc: 67.5 Test acc66.86; Train loss: 2.8508952990258958e-05 Val loss: 0.004581573963165283
+INFO - evaluator.py - 2024-10-24 17:34:52,099 - Epoch: 36 Train acc: 99.81090909090909 Val acc: 61.739999999999995 Test acc60.49; Train loss: 2.091490070055112e-05 Val loss: 0.004830532646179199
+INFO - evaluator.py - 2024-10-24 17:35:14,904 - Epoch: 37 Train acc: 99.72181818181818 Val acc: 73.18 Test acc72.36; Train loss: 2.9380758193490857e-05 Val loss: 0.0031535443782806398
+INFO - evaluator.py - 2024-10-24 17:35:38,032 - Epoch: 38 Train acc: 99.75818181818182 Val acc: 69.67999999999999 Test acc69.15; Train loss: 2.6162831790597095e-05 Val loss: 0.0037209397315979002
+INFO - evaluator.py - 2024-10-24 17:36:00,945 - Epoch: 39 Train acc: 99.78363636363636 Val acc: 71.78 Test acc71.81; Train loss: 2.472728601549699e-05 Val loss: 0.003475987243652344
+INFO - evaluator.py - 2024-10-24 17:36:23,983 - Epoch: 40 Train acc: 99.91636363636364 Val acc: 73.61999999999999 Test acc73.42; Train loss: 9.770247699833073e-06 Val loss: 0.0032352622032165526
+INFO - evaluator.py - 2024-10-24 17:36:45,871 - Epoch: 41 Train acc: 99.98363636363636 Val acc: 73.98 Test acc73.64; Train loss: 3.8353082069079395e-06 Val loss: 0.0033005467891693116
+INFO - evaluator.py - 2024-10-24 17:37:08,576 - Epoch: 42 Train acc: 99.98181818181818 Val acc: 72.18 Test acc72.02; Train loss: 3.0712271098640154e-06 Val loss: 0.003839799499511719
+INFO - evaluator.py - 2024-10-24 17:37:30,328 - Epoch: 43 Train acc: 99.99454545454546 Val acc: 72.7 Test acc72.61; Train loss: 2.0593864334924463e-06 Val loss: 0.003803525161743164
+INFO - evaluator.py - 2024-10-24 17:37:53,057 - Epoch: 44 Train acc: 99.99818181818182 Val acc: 72.61999999999999 Test acc72.86; Train loss: 1.7760899919349785e-06 Val loss: 0.0038269147872924806
+INFO - evaluator.py - 2024-10-24 17:38:15,651 - Epoch: 45 Train acc: 99.99818181818182 Val acc: 72.48 Test acc72.41; Train loss: 1.5944127336049199e-06 Val loss: 0.003919069242477417
+INFO - evaluator.py - 2024-10-24 17:38:38,611 - Epoch: 46 Train acc: 99.99454545454546 Val acc: 71.08 Test acc71.2; Train loss: 1.7727705500874435e-06 Val loss: 0.004376184558868408
+INFO - evaluator.py - 2024-10-24 17:39:01,482 - Epoch: 47 Train acc: 100.0 Val acc: 69.39999999999999 Test acc69.84; Train loss: 1.1186579455335793e-06 Val loss: 0.004715028476715088
+INFO - evaluator.py - 2024-10-24 17:39:23,300 - Epoch: 48 Train acc: 100.0 Val acc: 69.72 Test acc70.17999999999999; Train loss: 9.25847671169322e-07 Val loss: 0.00465433521270752
+INFO - evaluator.py - 2024-10-24 17:39:45,582 - Epoch: 49 Train acc: 99.99818181818182 Val acc: 67.88 Test acc68.55; Train loss: 8.412618353428446e-07 Val loss: 0.004982828617095947
+INFO - evaluator.py - 2024-10-24 17:39:45,592 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from resnet is 75.06 and 74.02
+INFO - evaluator.py - 2024-10-24 17:39:45,592 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from resnet is 75.06 and 74.02
+INFO - evaluator.py - 2024-10-24 17:39:45,592 - The best acc test dataset from resnet is 74.02
+INFO - evaluator.py - 2024-10-24 17:40:12,838 - Epoch: 0 Train acc: 76.74363636363636 Val acc: 73.88 Test acc74.44; Train loss: 0.0023641403561288662 Val loss: 0.0008830383896827698
+INFO - evaluator.py - 2024-10-24 17:40:39,214 - Epoch: 1 Train acc: 84.99818181818182 Val acc: 73.42 Test acc74.03; Train loss: 0.0015382649405436082 Val loss: 0.0010098230719566344
+INFO - evaluator.py - 2024-10-24 17:41:05,474 - Epoch: 2 Train acc: 87.66909090909091 Val acc: 74.14 Test acc74.45; Train loss: 0.0012739189221100374 Val loss: 0.0009200613141059875
+INFO - evaluator.py - 2024-10-24 17:41:31,786 - Epoch: 3 Train acc: 89.14 Val acc: 74.44 Test acc75.02; Train loss: 0.001131229330463843 Val loss: 0.0010530741691589355
+INFO - evaluator.py - 2024-10-24 17:41:58,275 - Epoch: 4 Train acc: 90.62363636363636 Val acc: 73.76 Test acc73.6; Train loss: 0.0009781637224284085 Val loss: 0.0010377041220664977
+INFO - evaluator.py - 2024-10-24 17:42:24,669 - Epoch: 5 Train acc: 91.96909090909091 Val acc: 67.64 Test acc68.81; Train loss: 0.0008544048946012151 Val loss: 0.0018464314699172973
+INFO - evaluator.py - 2024-10-24 17:42:51,205 - Epoch: 6 Train acc: 93.18363636363637 Val acc: 62.419999999999995 Test acc64.28; Train loss: 0.0007344859608195045 Val loss: 0.002148126983642578
+INFO - evaluator.py - 2024-10-24 17:43:17,517 - Epoch: 7 Train acc: 94.48727272727272 Val acc: 70.08 Test acc70.98; Train loss: 0.0005914792075753212 Val loss: 0.001405067229270935
+INFO - evaluator.py - 2024-10-24 17:43:43,719 - Epoch: 8 Train acc: 95.37636363636364 Val acc: 42.68 Test acc43.95; Train loss: 0.0005077399940653281 Val loss: 0.00441994514465332
+INFO - evaluator.py - 2024-10-24 17:44:10,139 - Epoch: 9 Train acc: 95.97454545454546 Val acc: 71.12 Test acc71.64; Train loss: 0.00044302842048081487 Val loss: 0.001309081530570984
+INFO - evaluator.py - 2024-10-24 17:44:36,422 - Epoch: 10 Train acc: 96.81090909090909 Val acc: 62.339999999999996 Test acc62.43; Train loss: 0.0003516449419950897 Val loss: 0.001873175549507141
+INFO - evaluator.py - 2024-10-24 17:45:02,917 - Epoch: 11 Train acc: 97.14181818181818 Val acc: 50.5 Test acc50.42; Train loss: 0.00030912642624567857 Val loss: 0.002763956356048584
+INFO - evaluator.py - 2024-10-24 17:45:29,053 - Epoch: 12 Train acc: 97.53636363636363 Val acc: 67.62 Test acc67.47999999999999; Train loss: 0.0002719770169393583 Val loss: 0.0015297585010528565
+INFO - evaluator.py - 2024-10-24 17:45:55,431 - Epoch: 13 Train acc: 97.81272727272727 Val acc: 69.74000000000001 Test acc69.25; Train loss: 0.00024139598685909402 Val loss: 0.0014949249029159546
+INFO - evaluator.py - 2024-10-24 17:46:21,618 - Epoch: 14 Train acc: 97.97272727272728 Val acc: 71.41999999999999 Test acc71.17999999999999; Train loss: 0.0002157087870280851 Val loss: 0.001564763069152832
+INFO - evaluator.py - 2024-10-24 17:46:47,862 - Epoch: 15 Train acc: 98.28727272727272 Val acc: 71.67999999999999 Test acc70.78999999999999; Train loss: 0.00019106769060546702 Val loss: 0.0015325630187988282
+INFO - evaluator.py - 2024-10-24 17:47:14,009 - Epoch: 16 Train acc: 98.37272727272726 Val acc: 74.4 Test acc73.88; Train loss: 0.00017544010904702273 Val loss: 0.001340116000175476
+INFO - evaluator.py - 2024-10-24 17:47:40,183 - Epoch: 17 Train acc: 98.76727272727273 Val acc: 43.4 Test acc43.480000000000004; Train loss: 0.00014191976209086452 Val loss: 0.0043270848274230955
+INFO - evaluator.py - 2024-10-24 17:48:06,629 - Epoch: 18 Train acc: 98.52909090909091 Val acc: 74.22 Test acc74.00999999999999; Train loss: 0.00015937827530909668 Val loss: 0.0015238825559616088
+INFO - evaluator.py - 2024-10-24 17:48:32,817 - Epoch: 19 Train acc: 98.72181818181818 Val acc: 63.9 Test acc62.55; Train loss: 0.00013554202922704545 Val loss: 0.002096731162071228
+INFO - evaluator.py - 2024-10-24 17:48:59,192 - Epoch: 20 Train acc: 99.44363636363637 Val acc: 68.96 Test acc67.81; Train loss: 5.943257921925661e-05 Val loss: 0.0020904414653778076
+INFO - evaluator.py - 2024-10-24 17:49:25,241 - Epoch: 21 Train acc: 99.68727272727273 Val acc: 70.0 Test acc69.12; Train loss: 3.788270370357416e-05 Val loss: 0.002342355680465698
+INFO - evaluator.py - 2024-10-24 17:49:51,465 - Epoch: 22 Train acc: 99.72727272727273 Val acc: 72.64 Test acc71.73; Train loss: 3.140487584327771e-05 Val loss: 0.002193358612060547
+INFO - evaluator.py - 2024-10-24 17:50:17,604 - Epoch: 23 Train acc: 99.71454545454546 Val acc: 75.46000000000001 Test acc75.28; Train loss: 2.965798468053849e-05 Val loss: 0.0018983101367950439
+INFO - evaluator.py - 2024-10-24 17:50:43,837 - Epoch: 24 Train acc: 99.77636363636364 Val acc: 76.06 Test acc76.58; Train loss: 2.5726140969411726e-05 Val loss: 0.0017406086444854737
+INFO - evaluator.py - 2024-10-24 17:51:10,289 - Epoch: 25 Train acc: 99.80545454545454 Val acc: 73.28 Test acc73.16; Train loss: 2.378732203453017e-05 Val loss: 0.002214046001434326
+INFO - evaluator.py - 2024-10-24 17:51:36,415 - Epoch: 26 Train acc: 99.77090909090909 Val acc: 75.94 Test acc75.92999999999999; Train loss: 2.5900819664291867e-05 Val loss: 0.001946228051185608
+INFO - evaluator.py - 2024-10-24 17:52:02,571 - Epoch: 27 Train acc: 99.81636363636363 Val acc: 75.82 Test acc75.67; Train loss: 2.205900895007124e-05 Val loss: 0.0020680343151092528
+INFO - evaluator.py - 2024-10-24 17:52:28,573 - Epoch: 28 Train acc: 99.81272727272727 Val acc: 74.2 Test acc75.44; Train loss: 2.2069452880532482e-05 Val loss: 0.002263546085357666
+INFO - evaluator.py - 2024-10-24 17:52:54,787 - Epoch: 29 Train acc: 99.8 Val acc: 74.78 Test acc75.49; Train loss: 2.2585022320378233e-05 Val loss: 0.0021951653003692627
+INFO - evaluator.py - 2024-10-24 17:53:20,834 - Epoch: 30 Train acc: 99.79090909090908 Val acc: 73.14 Test acc74.22999999999999; Train loss: 2.257527583702044e-05 Val loss: 0.0025620187759399413
+INFO - evaluator.py - 2024-10-24 17:53:46,942 - Epoch: 31 Train acc: 99.81818181818181 Val acc: 74.14 Test acc74.48; Train loss: 2.0390867940891026e-05 Val loss: 0.002507220649719238
+INFO - evaluator.py - 2024-10-24 17:54:13,168 - Epoch: 32 Train acc: 99.79636363636364 Val acc: 76.4 Test acc76.27000000000001; Train loss: 2.1092253270961174e-05 Val loss: 0.0022491948127746583
+INFO - evaluator.py - 2024-10-24 17:54:39,133 - Epoch: 33 Train acc: 99.86181818181818 Val acc: 75.33999999999999 Test acc75.84; Train loss: 1.7847283760784195e-05 Val loss: 0.002278559875488281
+INFO - evaluator.py - 2024-10-24 17:55:05,153 - Epoch: 34 Train acc: 99.7890909090909 Val acc: 73.72 Test acc74.61; Train loss: 2.126254428056365e-05 Val loss: 0.0027609023571014404
+INFO - evaluator.py - 2024-10-24 17:55:31,181 - Epoch: 35 Train acc: 99.83636363636363 Val acc: 75.76 Test acc76.03999999999999; Train loss: 1.8096360012449706e-05 Val loss: 0.0024663201332092285
+INFO - evaluator.py - 2024-10-24 17:55:57,188 - Epoch: 36 Train acc: 99.8709090909091 Val acc: 72.8 Test acc73.68; Train loss: 1.5779966860215858e-05 Val loss: 0.002861015796661377
+INFO - evaluator.py - 2024-10-24 17:56:23,230 - Epoch: 37 Train acc: 99.81272727272727 Val acc: 74.48 Test acc75.12; Train loss: 2.0913373214981138e-05 Val loss: 0.0025450043678283693
+INFO - evaluator.py - 2024-10-24 17:56:49,282 - Epoch: 38 Train acc: 99.85818181818182 Val acc: 74.66000000000001 Test acc74.83999999999999; Train loss: 1.634656593682435e-05 Val loss: 0.00247074613571167
+INFO - evaluator.py - 2024-10-24 17:57:15,421 - Epoch: 39 Train acc: 99.85272727272726 Val acc: 73.74000000000001 Test acc73.45; Train loss: 1.5326940542383287e-05 Val loss: 0.0028863544464111327
+INFO - evaluator.py - 2024-10-24 17:57:41,445 - Epoch: 40 Train acc: 99.90909090909092 Val acc: 76.66 Test acc76.36; Train loss: 1.1153497024563628e-05 Val loss: 0.002483533239364624
+INFO - evaluator.py - 2024-10-24 17:58:07,415 - Epoch: 41 Train acc: 99.9309090909091 Val acc: 76.46 Test acc76.29; Train loss: 6.776571252174274e-06 Val loss: 0.0024695645332336426
+INFO - evaluator.py - 2024-10-24 17:58:33,324 - Epoch: 42 Train acc: 99.94727272727273 Val acc: 75.86 Test acc75.99000000000001; Train loss: 5.664306893562422e-06 Val loss: 0.002418782329559326
+INFO - evaluator.py - 2024-10-24 17:58:59,345 - Epoch: 43 Train acc: 99.95818181818181 Val acc: 75.82 Test acc76.3; Train loss: 5.3682247454244965e-06 Val loss: 0.0025148394584655763
+INFO - evaluator.py - 2024-10-24 17:59:25,333 - Epoch: 44 Train acc: 99.95454545454545 Val acc: 74.88 Test acc75.39; Train loss: 5.4173841805591525e-06 Val loss: 0.0026156550884246827
+INFO - evaluator.py - 2024-10-24 17:59:51,357 - Epoch: 45 Train acc: 99.97454545454545 Val acc: 74.98 Test acc75.57000000000001; Train loss: 3.8088054238125385e-06 Val loss: 0.0026035645008087157
+INFO - evaluator.py - 2024-10-24 18:00:17,469 - Epoch: 46 Train acc: 99.95636363636363 Val acc: 75.48 Test acc76.03; Train loss: 4.861139432118348e-06 Val loss: 0.0025271445751190186
+INFO - evaluator.py - 2024-10-24 18:00:43,389 - Epoch: 47 Train acc: 99.97636363636364 Val acc: 74.8 Test acc75.1; Train loss: 3.5725472624686847e-06 Val loss: 0.0025985263347625734
+INFO - evaluator.py - 2024-10-24 18:01:09,399 - Epoch: 48 Train acc: 99.98181818181818 Val acc: 74.38 Test acc74.78; Train loss: 3.142465137220411e-06 Val loss: 0.0026530692100524902
+INFO - evaluator.py - 2024-10-24 18:01:35,360 - Epoch: 49 Train acc: 99.95818181818181 Val acc: 74.62 Test acc75.0; Train loss: 4.777103683888775e-06 Val loss: 0.0026517826557159426
+INFO - evaluator.py - 2024-10-24 18:01:35,365 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from wrn is 76.66 and 76.36
+INFO - evaluator.py - 2024-10-24 18:01:35,365 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from wrn is 76.66 and 76.36
+INFO - evaluator.py - 2024-10-24 18:01:35,365 - The best acc test dataset from wrn is 76.58
+INFO - evaluator.py - 2024-10-24 18:03:17,012 - Epoch: 0 Train acc: 69.09272727272727 Val acc: 70.39999999999999 Test acc69.89; Train loss: 0.0037960954519835385 Val loss: 0.0011546549558639526
+INFO - evaluator.py - 2024-10-24 18:04:58,039 - Epoch: 1 Train acc: 84.47272727272728 Val acc: 73.78 Test acc73.65; Train loss: 0.0016009422984990206 Val loss: 0.0011431578874588012
+INFO - evaluator.py - 2024-10-24 18:06:39,018 - Epoch: 2 Train acc: 89.3509090909091 Val acc: 71.06 Test acc71.0; Train loss: 0.001135951396281069 Val loss: 0.0013852068901062011
+INFO - evaluator.py - 2024-10-24 18:08:20,166 - Epoch: 3 Train acc: 92.47090909090909 Val acc: 73.83999999999999 Test acc73.85000000000001; Train loss: 0.0008057965958660299 Val loss: 0.0013047876596450805
+INFO - evaluator.py - 2024-10-24 18:10:01,139 - Epoch: 4 Train acc: 94.80545454545455 Val acc: 59.08 Test acc59.39; Train loss: 0.0005835436272350224 Val loss: 0.0030878276348114014
+INFO - evaluator.py - 2024-10-24 18:11:42,078 - Epoch: 5 Train acc: 95.91636363636363 Val acc: 72.44 Test acc72.35000000000001; Train loss: 0.00045254616236144846 Val loss: 0.0014768490076065063
+INFO - evaluator.py - 2024-10-24 18:13:23,100 - Epoch: 6 Train acc: 96.78545454545454 Val acc: 70.92 Test acc71.38; Train loss: 0.00036101146977056154 Val loss: 0.0016038918018341064
+INFO - evaluator.py - 2024-10-24 18:15:04,183 - Epoch: 7 Train acc: 97.08909090909091 Val acc: 71.22 Test acc71.26; Train loss: 0.0003223379533737898 Val loss: 0.0016137248754501342
+INFO - evaluator.py - 2024-10-24 18:16:45,147 - Epoch: 8 Train acc: 97.68181818181819 Val acc: 48.52 Test acc49.33; Train loss: 0.00025691057286140594 Val loss: 0.003705125045776367
+INFO - evaluator.py - 2024-10-24 18:18:26,123 - Epoch: 9 Train acc: 97.97818181818182 Val acc: 63.78 Test acc64.09; Train loss: 0.0002256866049360145 Val loss: 0.0022908727169036864
+INFO - evaluator.py - 2024-10-24 18:20:07,177 - Epoch: 10 Train acc: 97.97636363636364 Val acc: 68.5 Test acc69.47; Train loss: 0.00022853929214179515 Val loss: 0.002037970995903015
+INFO - evaluator.py - 2024-10-24 18:21:48,108 - Epoch: 11 Train acc: 98.46363636363637 Val acc: 60.8 Test acc60.919999999999995; Train loss: 0.00016644935669716108 Val loss: 0.003791252374649048
+INFO - evaluator.py - 2024-10-24 18:23:29,047 - Epoch: 12 Train acc: 98.52363636363637 Val acc: 13.16 Test acc12.98; Train loss: 0.00016338031413033605 Val loss: 0.011318651580810546
+INFO - evaluator.py - 2024-10-24 18:25:10,006 - Epoch: 13 Train acc: 98.60727272727273 Val acc: 65.68 Test acc66.02; Train loss: 0.00015421677309681068 Val loss: 0.0022224844455718995
+INFO - evaluator.py - 2024-10-24 18:26:50,914 - Epoch: 14 Train acc: 98.65454545454546 Val acc: 45.14 Test acc45.2; Train loss: 0.00014203658486631785 Val loss: 0.0045185576438903806
+INFO - evaluator.py - 2024-10-24 18:28:31,785 - Epoch: 15 Train acc: 98.77818181818182 Val acc: 25.8 Test acc25.569999999999997; Train loss: 0.00013063426922837443 Val loss: 0.006471778297424316
+INFO - evaluator.py - 2024-10-24 18:30:12,667 - Epoch: 16 Train acc: 98.84363636363636 Val acc: 33.300000000000004 Test acc33.18; Train loss: 0.00012611072345806117 Val loss: 0.00718333330154419
+INFO - evaluator.py - 2024-10-24 18:31:53,696 - Epoch: 17 Train acc: 99.02363636363636 Val acc: 29.099999999999998 Test acc29.599999999999998; Train loss: 0.00011261145817539232 Val loss: 0.012101616668701172
+INFO - evaluator.py - 2024-10-24 18:33:34,624 - Epoch: 18 Train acc: 99.25272727272727 Val acc: 17.02 Test acc17.05; Train loss: 8.081494339911098e-05 Val loss: 0.012222080612182617
+INFO - evaluator.py - 2024-10-24 18:35:15,553 - Epoch: 19 Train acc: 99.15818181818182 Val acc: 21.82 Test acc21.77; Train loss: 9.425737402952192e-05 Val loss: 0.010634214401245116
+INFO - evaluator.py - 2024-10-24 18:36:56,483 - Epoch: 20 Train acc: 99.86909090909091 Val acc: 64.88000000000001 Test acc65.86; Train loss: 1.6689149115700276e-05 Val loss: 0.002796035718917847
+INFO - evaluator.py - 2024-10-24 18:38:37,353 - Epoch: 21 Train acc: 99.99454545454546 Val acc: 72.18 Test acc72.22; Train loss: 4.4335755974647e-06 Val loss: 0.0022180062294006346
+INFO - evaluator.py - 2024-10-24 18:40:18,204 - Epoch: 22 Train acc: 99.99818181818182 Val acc: 71.88 Test acc71.83; Train loss: 2.6421444776298647e-06 Val loss: 0.0024295718669891356
+INFO - evaluator.py - 2024-10-24 18:41:59,007 - Epoch: 23 Train acc: 100.0 Val acc: 71.64 Test acc71.61999999999999; Train loss: 1.747014181058727e-06 Val loss: 0.0027033145904541015
+INFO - evaluator.py - 2024-10-24 18:43:40,083 - Epoch: 24 Train acc: 100.0 Val acc: 72.66 Test acc72.25; Train loss: 1.1553742506790017e-06 Val loss: 0.002744242000579834
+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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