2023-02-19 00:52:17,563	32k	INFO	{'train': {'log_interval': 200, 'eval_interval': 1000, 'seed': 1234, 'epochs': 10000, 'learning_rate': 0.0001, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 6, 'fp16_run': False, 'lr_decay': 0.999875, 'segment_size': 17920, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0, 'use_sr': True, 'max_speclen': 384, 'port': '8001'}, 'data': {'training_files': 'filelists/train.txt', 'validation_files': 'filelists/val.txt', 'max_wav_value': 32768.0, 'sampling_rate': 32000, 'filter_length': 1280, 'hop_length': 320, 'win_length': 1280, 'n_mel_channels': 80, 'mel_fmin': 0.0, 'mel_fmax': None}, 'model': {'inter_channels': 192, 'hidden_channels': 192, 'filter_channels': 768, 'n_heads': 2, 'n_layers': 6, 'kernel_size': 3, 'p_dropout': 0.1, 'resblock': '1', 'resblock_kernel_sizes': [3, 7, 11], 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'upsample_rates': [10, 8, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4], 'n_layers_q': 3, 'use_spectral_norm': False, 'gin_channels': 256, 'ssl_dim': 256, 'n_speakers': 2}, 'spk': {'yuuka': 0}, 'model_dir': './logs\\32k'}
2023-02-19 00:52:17,563	32k	WARNING	K:\AI\so-vits-svc-32k is not a git repository, therefore hash value comparison will be ignored.
2023-02-19 00:52:22,439	32k	INFO	Loaded checkpoint './logs\32k\G_0.pth' (iteration 1)
2023-02-19 00:52:22,854	32k	INFO	Loaded checkpoint './logs\32k\D_0.pth' (iteration 1)
2023-02-19 00:52:30,954	32k	INFO	Train Epoch: 1 [0%]
2023-02-19 00:52:30,955	32k	INFO	[6.259579658508301, 2.384648084640503, 21.68582534790039, 51.37621307373047, 29.241214752197266, 0, 0.0001]
2023-02-19 00:52:36,208	32k	INFO	Saving model and optimizer state at iteration 1 to ./logs\32k\G_0.pth
2023-02-19 00:52:55,481	32k	INFO	Saving model and optimizer state at iteration 1 to ./logs\32k\D_0.pth
2023-02-19 00:53:15,383	32k	INFO	====> Epoch: 1
2023-02-19 00:53:35,194	32k	INFO	====> Epoch: 2
2023-02-19 00:53:54,475	32k	INFO	====> Epoch: 3
2023-02-19 00:54:14,074	32k	INFO	====> Epoch: 4
2023-02-19 00:54:33,774	32k	INFO	====> Epoch: 5
2023-02-19 00:54:53,292	32k	INFO	====> Epoch: 6
2023-02-19 00:55:12,950	32k	INFO	====> Epoch: 7
2023-02-19 00:55:32,408	32k	INFO	====> Epoch: 8
2023-02-19 00:55:51,786	32k	INFO	====> Epoch: 9
2023-02-19 00:56:11,304	32k	INFO	====> Epoch: 10
2023-02-19 00:56:23,833	32k	INFO	Train Epoch: 11 [53%]
2023-02-19 00:56:23,834	32k	INFO	[2.5820298194885254, 2.9517571926116943, 15.889677047729492, 22.983726501464844, 1.490109920501709, 200, 9.987507028906759e-05]
2023-02-19 00:56:31,013	32k	INFO	====> Epoch: 11
2023-02-19 00:56:50,430	32k	INFO	====> Epoch: 12
2023-02-19 00:57:09,908	32k	INFO	====> Epoch: 13
2023-02-19 00:57:29,328	32k	INFO	====> Epoch: 14
2023-02-19 00:57:48,792	32k	INFO	====> Epoch: 15
2023-02-19 00:58:08,199	32k	INFO	====> Epoch: 16
2023-02-19 00:58:27,628	32k	INFO	====> Epoch: 17
2023-02-19 00:58:47,154	32k	INFO	====> Epoch: 18
2023-02-19 00:59:06,558	32k	INFO	====> Epoch: 19
2023-02-19 00:59:26,031	32k	INFO	====> Epoch: 20
2023-02-19 00:59:45,505	32k	INFO	====> Epoch: 21
2023-02-19 00:59:50,417	32k	INFO	Train Epoch: 22 [5%]
2023-02-19 00:59:50,417	32k	INFO	[2.3022522926330566, 2.7468957901000977, 13.526983261108398, 18.26525115966797, 1.2778617143630981, 400, 9.973782786538036e-05]
2023-02-19 01:00:05,254	32k	INFO	====> Epoch: 22
2023-02-19 01:00:24,645	32k	INFO	====> Epoch: 23
2023-02-19 01:00:44,092	32k	INFO	====> Epoch: 24
2023-02-19 01:01:03,835	32k	INFO	====> Epoch: 25
2023-02-19 01:01:23,360	32k	INFO	====> Epoch: 26
2023-02-19 01:01:43,029	32k	INFO	====> Epoch: 27
2023-02-19 01:02:02,552	32k	INFO	====> Epoch: 28
2023-02-19 01:02:22,021	32k	INFO	====> Epoch: 29
2023-02-19 01:02:41,450	32k	INFO	====> Epoch: 30
2023-02-19 01:03:00,956	32k	INFO	====> Epoch: 31
2023-02-19 01:03:14,323	32k	INFO	Train Epoch: 32 [58%]
2023-02-19 01:03:14,323	32k	INFO	[2.2709786891937256, 2.5444483757019043, 16.769325256347656, 22.406368255615234, 1.2876263856887817, 600, 9.961322568533789e-05]
2023-02-19 01:03:20,673	32k	INFO	====> Epoch: 32
2023-02-19 01:03:40,138	32k	INFO	====> Epoch: 33
2023-02-19 01:03:59,566	32k	INFO	====> Epoch: 34
2023-02-19 01:04:19,064	32k	INFO	====> Epoch: 35
2023-02-19 01:04:38,491	32k	INFO	====> Epoch: 36
2023-02-19 01:04:57,953	32k	INFO	====> Epoch: 37
2023-02-19 01:05:17,368	32k	INFO	====> Epoch: 38
2023-02-19 01:05:36,826	32k	INFO	====> Epoch: 39
2023-02-19 01:05:56,268	32k	INFO	====> Epoch: 40
2023-02-19 01:06:15,805	32k	INFO	====> Epoch: 41
2023-02-19 01:06:35,572	32k	INFO	====> Epoch: 42
2023-02-19 01:06:41,408	32k	INFO	Train Epoch: 43 [11%]
2023-02-19 01:06:41,408	32k	INFO	[2.2252748012542725, 2.711310386657715, 18.655017852783203, 23.06989097595215, 0.9809578657150269, 800, 9.947634307304244e-05]
2023-02-19 01:06:55,421	32k	INFO	====> Epoch: 43
2023-02-19 01:07:15,100	32k	INFO	====> Epoch: 44
2023-02-19 01:07:34,525	32k	INFO	====> Epoch: 45
2023-02-19 09:58:01,753	32k	INFO	{'train': {'log_interval': 200, 'eval_interval': 1000, 'seed': 1234, 'epochs': 10000, 'learning_rate': 0.0001, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 6, 'fp16_run': False, 'lr_decay': 0.999875, 'segment_size': 17920, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0, 'use_sr': True, 'max_speclen': 384, 'port': '8001'}, 'data': {'training_files': 'filelists/train.txt', 'validation_files': 'filelists/val.txt', 'max_wav_value': 32768.0, 'sampling_rate': 32000, 'filter_length': 1280, 'hop_length': 320, 'win_length': 1280, 'n_mel_channels': 80, 'mel_fmin': 0.0, 'mel_fmax': None}, 'model': {'inter_channels': 192, 'hidden_channels': 192, 'filter_channels': 768, 'n_heads': 2, 'n_layers': 6, 'kernel_size': 3, 'p_dropout': 0.1, 'resblock': '1', 'resblock_kernel_sizes': [3, 7, 11], 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'upsample_rates': [10, 8, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4], 'n_layers_q': 3, 'use_spectral_norm': False, 'gin_channels': 256, 'ssl_dim': 256, 'n_speakers': 2}, 'spk': {'yuuka': 0}, 'model_dir': './logs\\32k'}
2023-02-19 09:58:01,753	32k	WARNING	K:\AI\so-vits-svc-32k is not a git repository, therefore hash value comparison will be ignored.
2023-02-19 09:58:06,761	32k	INFO	Loaded checkpoint './logs\32k\G_0.pth' (iteration 1)
2023-02-19 09:58:07,189	32k	INFO	Loaded checkpoint './logs\32k\D_0.pth' (iteration 1)
2023-02-19 09:58:15,314	32k	INFO	Train Epoch: 1 [0%]
2023-02-19 09:58:15,315	32k	INFO	[4.885988712310791, 2.5791049003601074, 20.521387100219727, 48.8116455078125, 23.21308135986328, 0, 0.0001]
2023-02-19 09:58:20,541	32k	INFO	Saving model and optimizer state at iteration 1 to ./logs\32k\G_0.pth
2023-02-19 09:58:38,703	32k	INFO	Saving model and optimizer state at iteration 1 to ./logs\32k\D_0.pth
2023-02-19 09:58:59,317	32k	INFO	====> Epoch: 1
2023-02-19 09:59:19,529	32k	INFO	====> Epoch: 2
2023-02-19 09:59:40,549	32k	INFO	====> Epoch: 3
2023-02-19 10:00:00,988	32k	INFO	====> Epoch: 4
2023-02-19 10:00:20,947	32k	INFO	====> Epoch: 5
2023-02-19 10:00:40,486	32k	INFO	====> Epoch: 6
2023-02-19 10:01:00,800	32k	INFO	====> Epoch: 7
2023-02-19 10:01:24,452	32k	INFO	====> Epoch: 8
2023-02-19 10:01:44,672	32k	INFO	====> Epoch: 9
2023-02-19 10:02:04,480	32k	INFO	====> Epoch: 10
2023-02-19 10:02:17,182	32k	INFO	Train Epoch: 11 [53%]
2023-02-19 10:02:17,183	32k	INFO	[2.7012827396392822, 2.8637466430664062, 15.74413013458252, 22.798664093017578, 1.4917662143707275, 200, 9.987507028906759e-05]
2023-02-19 10:02:24,886	32k	INFO	====> Epoch: 11
2023-02-19 10:02:44,970	32k	INFO	====> Epoch: 12
2023-02-19 10:03:05,381	32k	INFO	====> Epoch: 13
2023-02-19 10:03:27,787	32k	INFO	====> Epoch: 14
2023-02-19 10:03:48,128	32k	INFO	====> Epoch: 15
2023-02-19 10:04:09,326	32k	INFO	====> Epoch: 16
2023-02-19 10:04:30,931	32k	INFO	====> Epoch: 17
2023-02-19 10:04:50,506	32k	INFO	====> Epoch: 18
2023-02-19 10:05:10,053	32k	INFO	====> Epoch: 19
2023-02-19 10:05:29,550	32k	INFO	====> Epoch: 20
2023-02-19 10:05:49,075	32k	INFO	====> Epoch: 21
2023-02-19 10:05:54,186	32k	INFO	Train Epoch: 22 [5%]
2023-02-19 10:05:54,186	32k	INFO	[2.638063669204712, 2.1183853149414062, 12.34074878692627, 17.773197174072266, 1.2548296451568604, 400, 9.973782786538036e-05]
2023-02-19 10:06:11,523	32k	INFO	====> Epoch: 22
2023-02-19 10:06:31,785	32k	INFO	====> Epoch: 23
2023-02-19 10:06:51,275	32k	INFO	====> Epoch: 24
2023-02-19 10:07:10,746	32k	INFO	====> Epoch: 25
2023-02-19 10:07:30,179	32k	INFO	====> Epoch: 26
2023-02-19 10:07:49,638	32k	INFO	====> Epoch: 27
2023-02-19 10:08:09,150	32k	INFO	====> Epoch: 28
2023-02-19 10:08:28,647	32k	INFO	====> Epoch: 29
2023-02-19 10:08:48,090	32k	INFO	====> Epoch: 30
2023-02-19 10:09:07,588	32k	INFO	====> Epoch: 31
2023-02-19 10:09:20,991	32k	INFO	Train Epoch: 32 [58%]
2023-02-19 10:09:20,992	32k	INFO	[2.252321481704712, 2.6135807037353516, 16.764589309692383, 22.221206665039062, 1.2719413042068481, 600, 9.961322568533789e-05]
2023-02-19 10:09:27,359	32k	INFO	====> Epoch: 32
2023-02-19 10:09:46,836	32k	INFO	====> Epoch: 33
2023-02-19 10:10:06,405	32k	INFO	====> Epoch: 34
2023-02-19 10:10:25,867	32k	INFO	====> Epoch: 35
2023-02-19 10:10:45,391	32k	INFO	====> Epoch: 36
2023-02-19 10:11:04,848	32k	INFO	====> Epoch: 37
2023-02-19 10:11:24,345	32k	INFO	====> Epoch: 38
2023-02-19 10:11:43,820	32k	INFO	====> Epoch: 39
2023-02-19 10:12:03,363	32k	INFO	====> Epoch: 40
2023-02-19 10:12:22,862	32k	INFO	====> Epoch: 41
2023-02-19 10:12:42,390	32k	INFO	====> Epoch: 42
2023-02-19 10:12:48,119	32k	INFO	Train Epoch: 43 [11%]
2023-02-19 10:12:48,119	32k	INFO	[2.34789776802063, 2.904107093811035, 18.322242736816406, 23.1386661529541, 0.9494408369064331, 800, 9.947634307304244e-05]
2023-02-19 10:13:02,224	32k	INFO	====> Epoch: 43
2023-02-19 10:13:22,121	32k	INFO	====> Epoch: 44
2023-02-19 10:13:42,138	32k	INFO	====> Epoch: 45
2023-02-19 10:14:03,880	32k	INFO	====> Epoch: 46
2023-02-19 10:14:23,490	32k	INFO	====> Epoch: 47
2023-02-19 10:14:43,058	32k	INFO	====> Epoch: 48
2023-02-19 10:15:02,615	32k	INFO	====> Epoch: 49
2023-02-19 10:15:22,189	32k	INFO	====> Epoch: 50
2023-02-19 10:15:41,717	32k	INFO	====> Epoch: 51
2023-02-19 10:16:01,286	32k	INFO	====> Epoch: 52
2023-02-19 10:16:15,520	32k	INFO	Train Epoch: 53 [63%]
2023-02-19 10:16:15,521	32k	INFO	[2.2673277854919434, 2.6267404556274414, 14.748597145080566, 20.428760528564453, 0.9188843965530396, 1000, 9.935206756519513e-05]
2023-02-19 10:16:19,825	32k	INFO	Saving model and optimizer state at iteration 53 to ./logs\32k\G_1000.pth
2023-02-19 10:16:38,782	32k	INFO	Saving model and optimizer state at iteration 53 to ./logs\32k\D_1000.pth
2023-02-19 10:16:47,949	32k	INFO	====> Epoch: 53
2023-02-19 10:17:11,828	32k	INFO	====> Epoch: 54
2023-02-19 10:17:32,602	32k	INFO	====> Epoch: 55
2023-02-19 10:17:54,697	32k	INFO	====> Epoch: 56
2023-02-19 10:18:15,505	32k	INFO	====> Epoch: 57
2023-02-19 10:18:36,471	32k	INFO	====> Epoch: 58
2023-02-19 10:18:56,129	32k	INFO	====> Epoch: 59
2023-02-19 10:19:15,687	32k	INFO	====> Epoch: 60
2023-02-19 10:19:35,249	32k	INFO	====> Epoch: 61
2023-02-19 10:19:54,920	32k	INFO	====> Epoch: 62
2023-02-19 10:20:14,564	32k	INFO	====> Epoch: 63
2023-02-19 10:20:21,217	32k	INFO	Train Epoch: 64 [16%]
2023-02-19 10:20:21,217	32k	INFO	[2.6431660652160645, 2.1686131954193115, 11.168325424194336, 15.736719131469727, 0.8130778074264526, 1200, 9.921554382096622e-05]
2023-02-19 10:20:34,458	32k	INFO	====> Epoch: 64
2023-02-19 10:20:54,137	32k	INFO	====> Epoch: 65
2023-02-19 10:21:13,820	32k	INFO	====> Epoch: 66
2023-02-19 10:21:33,481	32k	INFO	====> Epoch: 67
2023-02-19 10:21:53,127	32k	INFO	====> Epoch: 68
2023-02-19 10:22:12,705	32k	INFO	====> Epoch: 69
2023-02-19 10:22:32,335	32k	INFO	====> Epoch: 70
2023-02-19 10:22:52,013	32k	INFO	====> Epoch: 71
2023-02-19 10:23:11,631	32k	INFO	====> Epoch: 72
2023-02-19 10:23:31,243	32k	INFO	====> Epoch: 73
2023-02-19 10:23:46,491	32k	INFO	Train Epoch: 74 [68%]
2023-02-19 10:23:46,491	32k	INFO	[2.523651361465454, 2.3198585510253906, 13.49683952331543, 19.15787124633789, 0.8243404626846313, 1400, 9.909159412887068e-05]
2023-02-19 10:23:51,274	32k	INFO	====> Epoch: 74
2023-02-19 10:24:10,913	32k	INFO	====> Epoch: 75
2023-02-19 10:24:30,500	32k	INFO	====> Epoch: 76
2023-02-19 10:24:50,118	32k	INFO	====> Epoch: 77
2023-02-19 10:25:09,788	32k	INFO	====> Epoch: 78
2023-02-19 10:25:29,434	32k	INFO	====> Epoch: 79
2023-02-19 10:25:49,013	32k	INFO	====> Epoch: 80
2023-02-19 10:26:08,630	32k	INFO	====> Epoch: 81
2023-02-19 10:26:28,322	32k	INFO	====> Epoch: 82
2023-02-19 10:26:47,986	32k	INFO	====> Epoch: 83
2023-02-19 10:27:07,609	32k	INFO	====> Epoch: 84
2023-02-19 10:27:15,126	32k	INFO	Train Epoch: 85 [21%]
2023-02-19 10:27:15,126	32k	INFO	[2.234614610671997, 2.673915386199951, 15.113679885864258, 20.68456268310547, 0.9050352573394775, 1600, 9.895542831185631e-05]
2023-02-19 10:27:27,568	32k	INFO	====> Epoch: 85
2023-02-19 10:27:47,162	32k	INFO	====> Epoch: 86
2023-02-19 10:28:06,803	32k	INFO	====> Epoch: 87
2023-02-19 10:28:26,491	32k	INFO	====> Epoch: 88
2023-02-19 10:28:46,205	32k	INFO	====> Epoch: 89
2023-02-19 10:29:05,923	32k	INFO	====> Epoch: 90
2023-02-19 10:29:25,543	32k	INFO	====> Epoch: 91
2023-02-19 10:29:45,199	32k	INFO	====> Epoch: 92
2023-02-19 10:30:04,872	32k	INFO	====> Epoch: 93
2023-02-19 10:30:24,573	32k	INFO	====> Epoch: 94
2023-02-19 10:30:40,658	32k	INFO	Train Epoch: 95 [74%]
2023-02-19 10:30:40,658	32k	INFO	[2.3925275802612305, 2.410752773284912, 16.29819679260254, 21.02007484436035, 1.3434288501739502, 1800, 9.883180358131438e-05]
2023-02-19 10:30:44,524	32k	INFO	====> Epoch: 95
2023-02-19 10:31:04,176	32k	INFO	====> Epoch: 96
2023-02-19 10:31:23,746	32k	INFO	====> Epoch: 97
2023-02-19 10:31:43,345	32k	INFO	====> Epoch: 98
2023-02-19 10:32:02,965	32k	INFO	====> Epoch: 99
2023-02-19 10:32:22,613	32k	INFO	====> Epoch: 100
2023-02-19 10:32:42,215	32k	INFO	====> Epoch: 101
2023-02-19 10:33:01,781	32k	INFO	====> Epoch: 102
2023-02-19 10:33:21,347	32k	INFO	====> Epoch: 103
2023-02-19 10:33:40,936	32k	INFO	====> Epoch: 104
2023-02-19 10:34:00,582	32k	INFO	====> Epoch: 105
2023-02-19 10:34:08,972	32k	INFO	Train Epoch: 106 [26%]
2023-02-19 10:34:08,972	32k	INFO	[2.4520838260650635, 2.415198564529419, 12.142949104309082, 16.184589385986328, 0.6698657274246216, 2000, 9.86959947531291e-05]
2023-02-19 10:34:13,205	32k	INFO	Saving model and optimizer state at iteration 106 to ./logs\32k\G_2000.pth
2023-02-19 10:34:31,330	32k	INFO	Saving model and optimizer state at iteration 106 to ./logs\32k\D_2000.pth
2023-02-19 10:34:46,550	32k	INFO	====> Epoch: 106
2023-02-19 10:35:06,128	32k	INFO	====> Epoch: 107
2023-02-19 10:35:25,770	32k	INFO	====> Epoch: 108
2023-02-19 10:35:45,316	32k	INFO	====> Epoch: 109
2023-02-19 10:36:04,886	32k	INFO	====> Epoch: 110
2023-02-19 10:36:24,494	32k	INFO	====> Epoch: 111
2023-02-19 10:36:44,060	32k	INFO	====> Epoch: 112
2023-02-19 10:37:03,612	32k	INFO	====> Epoch: 113
2023-02-19 10:37:23,126	32k	INFO	====> Epoch: 114
2023-02-19 10:37:42,791	32k	INFO	====> Epoch: 115
2023-02-19 10:37:59,739	32k	INFO	Train Epoch: 116 [79%]
2023-02-19 10:37:59,740	32k	INFO	[2.173581600189209, 2.597672939300537, 19.673391342163086, 20.529300689697266, 0.6353464722633362, 2200, 9.857269413218213e-05]
2023-02-19 10:38:02,696	32k	INFO	====> Epoch: 116
2023-02-19 10:38:22,357	32k	INFO	====> Epoch: 117
2023-02-19 10:38:42,132	32k	INFO	====> Epoch: 118
2023-02-19 10:39:01,840	32k	INFO	====> Epoch: 119
2023-02-19 10:39:21,476	32k	INFO	====> Epoch: 120
2023-02-19 10:39:41,098	32k	INFO	====> Epoch: 121
2023-02-19 10:40:00,770	32k	INFO	====> Epoch: 122
2023-02-19 10:40:20,435	32k	INFO	====> Epoch: 123
2023-02-19 10:40:40,053	32k	INFO	====> Epoch: 124
2023-02-19 10:40:59,685	32k	INFO	====> Epoch: 125
2023-02-19 10:41:19,254	32k	INFO	====> Epoch: 126
2023-02-19 10:41:28,468	32k	INFO	Train Epoch: 127 [32%]
2023-02-19 10:41:28,468	32k	INFO	[2.2418031692504883, 2.729581356048584, 17.634613037109375, 18.02391815185547, 0.39771148562431335, 2400, 9.84372413569007e-05]
2023-02-19 10:41:39,158	32k	INFO	====> Epoch: 127
2023-02-19 10:41:58,736	32k	INFO	====> Epoch: 128
2023-02-19 10:42:18,349	32k	INFO	====> Epoch: 129
2023-02-19 10:42:37,898	32k	INFO	====> Epoch: 130
2023-02-19 10:42:57,642	32k	INFO	====> Epoch: 131
2023-02-19 10:43:17,176	32k	INFO	====> Epoch: 132
2023-02-19 10:43:36,760	32k	INFO	====> Epoch: 133
2023-02-19 10:43:56,375	32k	INFO	====> Epoch: 134
2023-02-19 10:44:15,877	32k	INFO	====> Epoch: 135
2023-02-19 10:44:35,387	32k	INFO	====> Epoch: 136
2023-02-19 10:44:53,146	32k	INFO	Train Epoch: 137 [84%]
2023-02-19 10:44:53,146	32k	INFO	[1.9678010940551758, 2.595189094543457, 15.501832962036133, 17.879207611083984, 0.4402991235256195, 2600, 9.831426399582366e-05]
2023-02-19 10:44:55,266	32k	INFO	====> Epoch: 137
2023-02-19 10:45:14,872	32k	INFO	====> Epoch: 138
2023-02-19 10:45:34,425	32k	INFO	====> Epoch: 139
2023-02-19 10:45:53,939	32k	INFO	====> Epoch: 140
2023-02-19 10:46:13,513	32k	INFO	====> Epoch: 141
2023-02-19 10:46:33,087	32k	INFO	====> Epoch: 142
2023-02-19 10:46:52,703	32k	INFO	====> Epoch: 143
2023-02-19 10:47:12,387	32k	INFO	====> Epoch: 144
2023-02-19 10:47:31,975	32k	INFO	====> Epoch: 145
2023-02-19 10:47:51,629	32k	INFO	====> Epoch: 146
2023-02-19 10:48:11,263	32k	INFO	====> Epoch: 147
2023-02-19 10:48:21,336	32k	INFO	Train Epoch: 148 [37%]
2023-02-19 10:48:21,336	32k	INFO	[2.5357589721679688, 2.358914613723755, 13.429749488830566, 18.84111785888672, 1.1937034130096436, 2800, 9.817916633997459e-05]
2023-02-19 10:48:31,130	32k	INFO	====> Epoch: 148
2023-02-19 10:48:50,747	32k	INFO	====> Epoch: 149
2023-02-19 10:49:10,411	32k	INFO	====> Epoch: 150
2023-02-19 10:49:29,989	32k	INFO	====> Epoch: 151
2023-02-19 10:49:49,583	32k	INFO	====> Epoch: 152
2023-02-19 10:50:09,163	32k	INFO	====> Epoch: 153
2023-02-19 10:50:28,800	32k	INFO	====> Epoch: 154
2023-02-19 10:50:48,404	32k	INFO	====> Epoch: 155
2023-02-19 10:51:08,009	32k	INFO	====> Epoch: 156
2023-02-19 10:51:27,600	32k	INFO	====> Epoch: 157
2023-02-19 10:51:46,235	32k	INFO	Train Epoch: 158 [89%]
2023-02-19 10:51:46,236	32k	INFO	[2.284935712814331, 2.6029856204986572, 16.011905670166016, 19.070634841918945, 0.9385358095169067, 3000, 9.80565113912702e-05]
2023-02-19 10:51:50,428	32k	INFO	Saving model and optimizer state at iteration 158 to ./logs\32k\G_3000.pth
2023-02-19 10:52:08,420	32k	INFO	Saving model and optimizer state at iteration 158 to ./logs\32k\D_3000.pth
2023-02-19 10:52:13,226	32k	INFO	====> Epoch: 158
2023-02-19 10:52:32,753	32k	INFO	====> Epoch: 159
2023-02-19 10:52:52,288	32k	INFO	====> Epoch: 160
2023-02-19 10:53:11,815	32k	INFO	====> Epoch: 161
2023-02-19 10:53:31,531	32k	INFO	====> Epoch: 162
2023-02-19 10:53:51,096	32k	INFO	====> Epoch: 163
2023-02-19 10:54:10,786	32k	INFO	====> Epoch: 164
2023-02-19 10:54:30,334	32k	INFO	====> Epoch: 165
2023-02-19 10:54:49,856	32k	INFO	====> Epoch: 166
2023-02-19 10:55:09,469	32k	INFO	====> Epoch: 167
2023-02-19 10:55:29,052	32k	INFO	====> Epoch: 168
2023-02-19 10:55:39,985	32k	INFO	Train Epoch: 169 [42%]
2023-02-19 10:55:39,985	32k	INFO	[2.055294990539551, 2.645547389984131, 18.572147369384766, 21.72458839416504, 0.7422290444374084, 3200, 9.792176792382932e-05]
2023-02-19 10:55:48,948	32k	INFO	====> Epoch: 169
2023-02-19 10:56:08,592	32k	INFO	====> Epoch: 170
2023-02-19 10:56:28,191	32k	INFO	====> Epoch: 171
2023-02-19 10:56:47,791	32k	INFO	====> Epoch: 172
2023-02-19 10:57:07,401	32k	INFO	====> Epoch: 173
2023-02-19 10:57:26,913	32k	INFO	====> Epoch: 174
2023-02-19 10:57:46,480	32k	INFO	====> Epoch: 175
2023-02-19 10:58:06,125	32k	INFO	====> Epoch: 176
2023-02-19 10:58:25,833	32k	INFO	====> Epoch: 177
2023-02-19 10:58:45,438	32k	INFO	====> Epoch: 178
2023-02-19 10:59:04,582	32k	INFO	Train Epoch: 179 [95%]
2023-02-19 10:59:04,583	32k	INFO	[2.4237661361694336, 2.700900077819824, 12.830799102783203, 15.212872505187988, 0.817108690738678, 3400, 9.779943454222217e-05]
2023-02-19 10:59:05,360	32k	INFO	====> Epoch: 179
2023-02-19 10:59:24,965	32k	INFO	====> Epoch: 180
2023-02-19 10:59:44,593	32k	INFO	====> Epoch: 181
2023-02-19 11:00:04,235	32k	INFO	====> Epoch: 182
2023-02-19 11:00:23,857	32k	INFO	====> Epoch: 183
2023-02-19 11:00:43,474	32k	INFO	====> Epoch: 184
2023-02-19 11:01:03,074	32k	INFO	====> Epoch: 185
2023-02-19 11:01:22,689	32k	INFO	====> Epoch: 186
2023-02-19 11:01:42,329	32k	INFO	====> Epoch: 187
2023-02-19 11:02:01,887	32k	INFO	====> Epoch: 188
2023-02-19 11:02:21,485	32k	INFO	====> Epoch: 189
2023-02-19 11:02:33,363	32k	INFO	Train Epoch: 190 [47%]
2023-02-19 11:02:33,363	32k	INFO	[1.858404278755188, 3.373487949371338, 13.83063793182373, 16.426828384399414, 0.5386475324630737, 3600, 9.766504433460612e-05]
2023-02-19 11:02:41,573	32k	INFO	====> Epoch: 190
2023-02-19 11:03:01,193	32k	INFO	====> Epoch: 191
2023-02-19 11:03:20,789	32k	INFO	====> Epoch: 192
2023-02-19 11:03:40,453	32k	INFO	====> Epoch: 193
2023-02-19 11:04:00,059	32k	INFO	====> Epoch: 194
2023-02-19 11:04:19,651	32k	INFO	====> Epoch: 195
2023-02-19 11:04:39,248	32k	INFO	====> Epoch: 196
2023-02-19 11:04:58,894	32k	INFO	====> Epoch: 197
2023-02-19 11:05:18,520	32k	INFO	====> Epoch: 198
2023-02-19 11:05:38,110	32k	INFO	====> Epoch: 199
2023-02-19 11:05:57,738	32k	INFO	====> Epoch: 200
2023-02-19 11:06:01,790	32k	INFO	Train Epoch: 201 [0%]
2023-02-19 11:06:01,790	32k	INFO	[2.286409854888916, 2.695997714996338, 20.394229888916016, 19.332231521606445, 0.7787021994590759, 3800, 9.753083879807726e-05]
2023-02-19 11:06:17,635	32k	INFO	====> Epoch: 201
2023-02-19 11:06:37,273	32k	INFO	====> Epoch: 202
2023-02-19 11:06:56,889	32k	INFO	====> Epoch: 203
2023-02-19 11:07:16,485	32k	INFO	====> Epoch: 204
2023-02-19 11:07:36,099	32k	INFO	====> Epoch: 205
2023-02-19 11:07:55,728	32k	INFO	====> Epoch: 206
2023-02-19 11:08:15,343	32k	INFO	====> Epoch: 207
2023-02-19 11:08:34,891	32k	INFO	====> Epoch: 208
2023-02-19 11:08:54,540	32k	INFO	====> Epoch: 209
2023-02-19 11:09:14,170	32k	INFO	====> Epoch: 210
2023-02-19 11:09:26,797	32k	INFO	Train Epoch: 211 [53%]
2023-02-19 11:09:26,798	32k	INFO	[2.2917189598083496, 2.7440385818481445, 15.518340110778809, 16.26446533203125, 0.6934877038002014, 4000, 9.740899380309685e-05]
2023-02-19 11:09:31,011	32k	INFO	Saving model and optimizer state at iteration 211 to ./logs\32k\G_4000.pth
2023-02-19 11:09:51,469	32k	INFO	Saving model and optimizer state at iteration 211 to ./logs\32k\D_4000.pth
2023-02-19 11:10:02,495	32k	INFO	====> Epoch: 211
2023-02-19 11:10:22,019	32k	INFO	====> Epoch: 212
2023-02-19 11:10:41,554	32k	INFO	====> Epoch: 213
2023-02-19 11:11:01,073	32k	INFO	====> Epoch: 214
2023-02-19 11:11:20,612	32k	INFO	====> Epoch: 215
2023-02-19 11:11:40,229	32k	INFO	====> Epoch: 216
2023-02-19 11:11:59,823	32k	INFO	====> Epoch: 217
2023-02-19 11:12:19,432	32k	INFO	====> Epoch: 218
2023-02-19 11:12:38,982	32k	INFO	====> Epoch: 219
2023-02-19 11:12:58,555	32k	INFO	====> Epoch: 220
2023-02-19 11:13:18,155	32k	INFO	====> Epoch: 221
2023-02-19 11:13:23,102	32k	INFO	Train Epoch: 222 [5%]
2023-02-19 11:13:23,102	32k	INFO	[2.2692172527313232, 2.6678965091705322, 17.69780731201172, 20.136995315551758, 0.6864567995071411, 4200, 9.727514011608789e-05]
2023-02-19 11:13:38,082	32k	INFO	====> Epoch: 222
2023-02-19 11:13:57,751	32k	INFO	====> Epoch: 223
2023-02-19 11:14:17,317	32k	INFO	====> Epoch: 224
2023-02-19 11:14:36,905	32k	INFO	====> Epoch: 225
2023-02-19 11:14:56,538	32k	INFO	====> Epoch: 226
2023-02-19 11:15:16,180	32k	INFO	====> Epoch: 227
2023-02-19 11:15:35,773	32k	INFO	====> Epoch: 228
2023-02-19 11:15:55,427	32k	INFO	====> Epoch: 229
2023-02-19 11:16:15,006	32k	INFO	====> Epoch: 230
2023-02-19 11:16:34,619	32k	INFO	====> Epoch: 231
2023-02-19 11:16:48,169	32k	INFO	Train Epoch: 232 [58%]
2023-02-19 11:16:48,170	32k	INFO	[2.2375569343566895, 2.629099130630493, 15.8519868850708, 17.997573852539062, 0.4304458200931549, 4400, 9.715361456473177e-05]
2023-02-19 11:16:54,566	32k	INFO	====> Epoch: 232
2023-02-19 11:17:14,203	32k	INFO	====> Epoch: 233
2023-02-19 11:17:33,790	32k	INFO	====> Epoch: 234
2023-02-19 11:17:53,406	32k	INFO	====> Epoch: 235
2023-02-19 11:18:13,072	32k	INFO	====> Epoch: 236
2023-02-19 11:18:32,696	32k	INFO	====> Epoch: 237
2023-02-19 11:18:52,331	32k	INFO	====> Epoch: 238
2023-02-19 11:19:11,901	32k	INFO	====> Epoch: 239
2023-02-19 11:19:31,520	32k	INFO	====> Epoch: 240
2023-02-19 11:19:51,132	32k	INFO	====> Epoch: 241
2023-02-19 11:20:10,731	32k	INFO	====> Epoch: 242
2023-02-19 11:20:16,470	32k	INFO	Train Epoch: 243 [11%]
2023-02-19 11:20:16,470	32k	INFO	[2.1481809616088867, 2.5824615955352783, 14.348868370056152, 17.293001174926758, 0.6088849902153015, 4600, 9.702011180479129e-05]
2023-02-19 11:20:30,580	32k	INFO	====> Epoch: 243
2023-02-19 11:20:50,191	32k	INFO	====> Epoch: 244
2023-02-19 11:21:09,779	32k	INFO	====> Epoch: 245
2023-02-19 11:21:29,444	32k	INFO	====> Epoch: 246
2023-02-19 11:21:49,014	32k	INFO	====> Epoch: 247
2023-02-19 11:22:08,589	32k	INFO	====> Epoch: 248
2023-02-19 11:22:28,235	32k	INFO	====> Epoch: 249
2023-02-19 11:22:47,886	32k	INFO	====> Epoch: 250
2023-02-19 11:23:07,478	32k	INFO	====> Epoch: 251
2023-02-19 11:23:27,108	32k	INFO	====> Epoch: 252
2023-02-19 11:23:41,516	32k	INFO	Train Epoch: 253 [63%]
2023-02-19 11:23:41,516	32k	INFO	[2.2075626850128174, 2.759037733078003, 16.229446411132812, 19.35388946533203, 0.7824491858482361, 4800, 9.689890485956725e-05]
2023-02-19 11:23:47,056	32k	INFO	====> Epoch: 253
2023-02-19 11:24:06,652	32k	INFO	====> Epoch: 254
2023-02-19 11:24:26,228	32k	INFO	====> Epoch: 255
2023-02-19 11:24:45,885	32k	INFO	====> Epoch: 256
2023-02-19 11:25:05,450	32k	INFO	====> Epoch: 257
2023-02-19 11:25:25,062	32k	INFO	====> Epoch: 258
2023-02-19 11:25:44,680	32k	INFO	====> Epoch: 259
2023-02-19 11:26:04,299	32k	INFO	====> Epoch: 260
2023-02-19 11:26:23,879	32k	INFO	====> Epoch: 261
2023-02-19 11:26:43,517	32k	INFO	====> Epoch: 262
2023-02-19 11:27:03,127	32k	INFO	====> Epoch: 263
2023-02-19 11:27:09,821	32k	INFO	Train Epoch: 264 [16%]
2023-02-19 11:27:09,821	32k	INFO	[2.3156111240386963, 2.653826951980591, 14.277311325073242, 16.898771286010742, 0.792119026184082, 5000, 9.676575210666227e-05]
2023-02-19 11:27:14,004	32k	INFO	Saving model and optimizer state at iteration 264 to ./logs\32k\G_5000.pth
2023-02-19 11:27:31,625	32k	INFO	Saving model and optimizer state at iteration 264 to ./logs\32k\D_5000.pth
2023-02-19 11:27:48,527	32k	INFO	====> Epoch: 264
2023-02-19 11:28:08,128	32k	INFO	====> Epoch: 265
2023-02-19 11:28:27,718	32k	INFO	====> Epoch: 266
2023-02-19 11:28:47,282	32k	INFO	====> Epoch: 267
2023-02-19 11:29:06,849	32k	INFO	====> Epoch: 268
2023-02-19 11:29:26,398	32k	INFO	====> Epoch: 269
2023-02-19 11:29:45,966	32k	INFO	====> Epoch: 270
2023-02-19 11:30:05,588	32k	INFO	====> Epoch: 271
2023-02-19 11:30:25,210	32k	INFO	====> Epoch: 272
2023-02-19 11:30:44,805	32k	INFO	====> Epoch: 273
2023-02-19 11:31:00,163	32k	INFO	Train Epoch: 274 [68%]
2023-02-19 11:31:00,163	32k	INFO	[2.5202348232269287, 2.6853830814361572, 12.198919296264648, 17.470657348632812, 1.010284423828125, 5200, 9.664486293227385e-05]
2023-02-19 11:31:04,871	32k	INFO	====> Epoch: 274
2023-02-19 11:31:24,524	32k	INFO	====> Epoch: 275
2023-02-19 11:31:44,192	32k	INFO	====> Epoch: 276
2023-02-19 11:32:03,771	32k	INFO	====> Epoch: 277
2023-02-19 11:32:23,441	32k	INFO	====> Epoch: 278
2023-02-19 11:32:43,028	32k	INFO	====> Epoch: 279
2023-02-19 11:33:02,669	32k	INFO	====> Epoch: 280
2023-02-19 11:33:22,327	32k	INFO	====> Epoch: 281
2023-02-19 11:33:41,904	32k	INFO	====> Epoch: 282
2023-02-19 11:34:01,510	32k	INFO	====> Epoch: 283
2023-02-19 11:34:21,149	32k	INFO	====> Epoch: 284
2023-02-19 11:34:28,645	32k	INFO	Train Epoch: 285 [21%]
2023-02-19 11:34:28,645	32k	INFO	[2.1849722862243652, 2.6742019653320312, 17.45437240600586, 19.302379608154297, 0.47049564123153687, 5400, 9.651205926878348e-05]
2023-02-19 11:34:41,074	32k	INFO	====> Epoch: 285
2023-02-19 11:35:00,663	32k	INFO	====> Epoch: 286
2023-02-19 11:35:20,204	32k	INFO	====> Epoch: 287
2023-02-19 11:35:39,792	32k	INFO	====> Epoch: 288
2023-02-19 11:35:59,412	32k	INFO	====> Epoch: 289
2023-02-19 11:36:19,030	32k	INFO	====> Epoch: 290
2023-02-19 11:36:38,610	32k	INFO	====> Epoch: 291
2023-02-19 11:36:58,221	32k	INFO	====> Epoch: 292
2023-02-19 11:37:17,873	32k	INFO	====> Epoch: 293
2023-02-19 11:37:37,744	32k	INFO	====> Epoch: 294
2023-02-19 11:37:53,973	32k	INFO	Train Epoch: 295 [74%]
2023-02-19 11:37:53,973	32k	INFO	[2.275301933288574, 2.737536907196045, 16.912981033325195, 20.602113723754883, 1.1094117164611816, 5600, 9.639148703212408e-05]
2023-02-19 11:37:57,829	32k	INFO	====> Epoch: 295
2023-02-19 11:38:17,495	32k	INFO	====> Epoch: 296
2023-02-19 11:38:37,068	32k	INFO	====> Epoch: 297
2023-02-19 11:38:56,622	32k	INFO	====> Epoch: 298
2023-02-19 11:39:16,278	32k	INFO	====> Epoch: 299
2023-02-19 11:39:35,894	32k	INFO	====> Epoch: 300
2023-02-19 11:39:55,537	32k	INFO	====> Epoch: 301
2023-02-19 11:40:15,105	32k	INFO	====> Epoch: 302
2023-02-19 11:40:34,680	32k	INFO	====> Epoch: 303
2023-02-19 11:40:54,337	32k	INFO	====> Epoch: 304
2023-02-19 11:41:13,926	32k	INFO	====> Epoch: 305
2023-02-19 11:41:22,265	32k	INFO	Train Epoch: 306 [26%]
2023-02-19 11:41:22,266	32k	INFO	[2.1130294799804688, 2.605156660079956, 13.737504005432129, 18.2040958404541, 0.6517429351806641, 5800, 9.625903154283315e-05]
2023-02-19 11:41:33,904	32k	INFO	====> Epoch: 306
2023-02-19 11:41:53,551	32k	INFO	====> Epoch: 307
2023-02-19 11:42:13,197	32k	INFO	====> Epoch: 308
2023-02-19 11:42:32,740	32k	INFO	====> Epoch: 309
2023-02-19 11:42:52,382	32k	INFO	====> Epoch: 310
2023-02-19 11:43:12,004	32k	INFO	====> Epoch: 311
2023-02-19 11:43:31,599	32k	INFO	====> Epoch: 312
2023-02-19 11:43:51,218	32k	INFO	====> Epoch: 313
2023-02-19 11:44:10,806	32k	INFO	====> Epoch: 314
2023-02-19 11:44:30,411	32k	INFO	====> Epoch: 315
2023-02-19 11:44:47,303	32k	INFO	Train Epoch: 316 [79%]
2023-02-19 11:44:47,303	32k	INFO	[1.8626766204833984, 2.7689990997314453, 22.44615364074707, 18.596677780151367, 0.9108448028564453, 6000, 9.613877541298036e-05]
2023-02-19 11:44:51,515	32k	INFO	Saving model and optimizer state at iteration 316 to ./logs\32k\G_6000.pth
2023-02-19 11:45:09,459	32k	INFO	Saving model and optimizer state at iteration 316 to ./logs\32k\D_6000.pth
2023-02-19 11:45:15,966	32k	INFO	====> Epoch: 316
2023-02-19 11:45:35,566	32k	INFO	====> Epoch: 317
2023-02-19 11:45:55,162	32k	INFO	====> Epoch: 318
2023-02-19 11:46:14,676	32k	INFO	====> Epoch: 319
2023-02-19 11:46:34,275	32k	INFO	====> Epoch: 320
2023-02-19 11:46:53,846	32k	INFO	====> Epoch: 321
2023-02-19 11:47:13,481	32k	INFO	====> Epoch: 322
2023-02-19 11:47:33,118	32k	INFO	====> Epoch: 323
2023-02-19 11:47:52,781	32k	INFO	====> Epoch: 324
2023-02-19 11:48:12,412	32k	INFO	====> Epoch: 325
2023-02-19 11:48:31,983	32k	INFO	====> Epoch: 326
2023-02-19 11:48:41,283	32k	INFO	Train Epoch: 327 [32%]
2023-02-19 11:48:41,283	32k	INFO	[2.134446620941162, 2.5606751441955566, 17.715618133544922, 18.039941787719727, 0.7827563285827637, 6200, 9.600666718507311e-05]
2023-02-19 11:48:51,957	32k	INFO	====> Epoch: 327
2023-02-19 11:49:11,559	32k	INFO	====> Epoch: 328
2023-02-19 11:49:31,233	32k	INFO	====> Epoch: 329
2023-02-19 11:49:50,840	32k	INFO	====> Epoch: 330
2023-02-19 11:50:10,505	32k	INFO	====> Epoch: 331
2023-02-19 11:50:30,086	32k	INFO	====> Epoch: 332
2023-02-19 11:50:49,767	32k	INFO	====> Epoch: 333
2023-02-19 11:51:09,467	32k	INFO	====> Epoch: 334
2023-02-19 11:51:29,060	32k	INFO	====> Epoch: 335
2023-02-19 11:51:48,709	32k	INFO	====> Epoch: 336
2023-02-19 11:52:06,472	32k	INFO	Train Epoch: 337 [84%]
2023-02-19 11:52:06,472	32k	INFO	[2.3614232540130615, 2.127523183822632, 13.096696853637695, 15.430811882019043, 1.0329926013946533, 6400, 9.588672633328296e-05]
2023-02-19 11:52:08,588	32k	INFO	====> Epoch: 337
2023-02-19 11:52:28,158	32k	INFO	====> Epoch: 338
2023-02-19 11:52:47,762	32k	INFO	====> Epoch: 339
2023-02-19 11:53:07,351	32k	INFO	====> Epoch: 340
2023-02-19 11:53:26,929	32k	INFO	====> Epoch: 341
2023-02-19 11:53:46,569	32k	INFO	====> Epoch: 342
2023-02-19 11:54:06,210	32k	INFO	====> Epoch: 343
2023-02-19 11:54:25,837	32k	INFO	====> Epoch: 344
2023-02-19 11:54:45,483	32k	INFO	====> Epoch: 345
2023-02-19 11:55:05,098	32k	INFO	====> Epoch: 346
2023-02-19 11:55:24,762	32k	INFO	====> Epoch: 347
2023-02-19 11:55:34,848	32k	INFO	Train Epoch: 348 [37%]
2023-02-19 11:55:34,849	32k	INFO	[2.5325193405151367, 2.0467236042022705, 9.755393028259277, 12.007466316223145, 0.2873714864253998, 6600, 9.575496445633683e-05]
2023-02-19 11:55:44,674	32k	INFO	====> Epoch: 348
2023-02-19 11:56:04,439	32k	INFO	====> Epoch: 349
2023-02-19 11:56:24,075	32k	INFO	====> Epoch: 350
2023-02-19 11:56:43,652	32k	INFO	====> Epoch: 351
2023-02-19 11:57:03,215	32k	INFO	====> Epoch: 352
2023-02-19 11:57:22,843	32k	INFO	====> Epoch: 353
2023-02-19 11:57:42,508	32k	INFO	====> Epoch: 354
2023-02-19 11:58:02,106	32k	INFO	====> Epoch: 355
2023-02-19 11:58:21,710	32k	INFO	====> Epoch: 356
2023-02-19 11:58:41,438	32k	INFO	====> Epoch: 357
2023-02-19 11:59:00,100	32k	INFO	Train Epoch: 358 [89%]
2023-02-19 11:59:00,101	32k	INFO	[2.309570074081421, 2.5850276947021484, 13.4173583984375, 16.9825382232666, 0.4784981906414032, 6800, 9.56353380560381e-05]
2023-02-19 11:59:01,366	32k	INFO	====> Epoch: 358
2023-02-19 11:59:20,928	32k	INFO	====> Epoch: 359
2023-02-19 11:59:40,589	32k	INFO	====> Epoch: 360
2023-02-19 12:00:00,179	32k	INFO	====> Epoch: 361
2023-02-19 12:00:19,818	32k	INFO	====> Epoch: 362
2023-02-19 12:00:39,433	32k	INFO	====> Epoch: 363
2023-02-19 12:00:59,077	32k	INFO	====> Epoch: 364
2023-02-19 12:01:18,665	32k	INFO	====> Epoch: 365
2023-02-19 12:01:38,287	32k	INFO	====> Epoch: 366
2023-02-19 12:01:57,919	32k	INFO	====> Epoch: 367
2023-02-19 12:02:17,535	32k	INFO	====> Epoch: 368
2023-02-19 12:02:28,494	32k	INFO	Train Epoch: 369 [42%]
2023-02-19 12:02:28,494	32k	INFO	[2.1438865661621094, 2.569798231124878, 18.708538055419922, 19.017616271972656, 0.3679341971874237, 7000, 9.550392162201736e-05]
2023-02-19 12:02:32,717	32k	INFO	Saving model and optimizer state at iteration 369 to ./logs\32k\G_7000.pth
2023-02-19 12:02:50,778	32k	INFO	Saving model and optimizer state at iteration 369 to ./logs\32k\D_7000.pth
2023-02-19 12:03:03,651	32k	INFO	====> Epoch: 369
2023-02-19 12:03:23,235	32k	INFO	====> Epoch: 370
2023-02-19 12:03:42,803	32k	INFO	====> Epoch: 371
2023-02-19 12:04:02,411	32k	INFO	====> Epoch: 372
2023-02-19 12:04:21,953	32k	INFO	====> Epoch: 373
2023-02-19 12:04:41,504	32k	INFO	====> Epoch: 374
2023-02-19 12:05:01,114	32k	INFO	====> Epoch: 375
2023-02-19 12:05:20,776	32k	INFO	====> Epoch: 376
2023-02-19 12:05:40,345	32k	INFO	====> Epoch: 377
2023-02-19 12:05:59,933	32k	INFO	====> Epoch: 378
2023-02-19 12:06:19,091	32k	INFO	Train Epoch: 379 [95%]
2023-02-19 12:06:19,091	32k	INFO	[2.0112531185150146, 3.151000499725342, 21.729772567749023, 17.626426696777344, 0.05288940668106079, 7200, 9.538460884880585e-05]
2023-02-19 12:06:19,867	32k	INFO	====> Epoch: 379
2023-02-19 12:06:39,460	32k	INFO	====> Epoch: 380
2023-02-19 12:06:59,053	32k	INFO	====> Epoch: 381
2023-02-19 12:07:18,709	32k	INFO	====> Epoch: 382
2023-02-19 12:07:38,341	32k	INFO	====> Epoch: 383
2023-02-19 12:07:57,934	32k	INFO	====> Epoch: 384
2023-02-19 12:08:17,553	32k	INFO	====> Epoch: 385
2023-02-19 12:08:37,236	32k	INFO	====> Epoch: 386
2023-02-19 12:08:56,799	32k	INFO	====> Epoch: 387
2023-02-19 12:09:16,406	32k	INFO	====> Epoch: 388
2023-02-19 12:09:36,019	32k	INFO	====> Epoch: 389
2023-02-19 12:09:47,851	32k	INFO	Train Epoch: 390 [47%]
2023-02-19 12:09:47,851	32k	INFO	[2.3823697566986084, 2.554415702819824, 13.744731903076172, 18.62793731689453, 0.7019649147987366, 7400, 9.525353695205543e-05]
2023-02-19 12:09:55,957	32k	INFO	====> Epoch: 390
2023-02-19 12:10:15,602	32k	INFO	====> Epoch: 391
2023-02-19 12:10:35,217	32k	INFO	====> Epoch: 392
2023-02-19 12:10:54,874	32k	INFO	====> Epoch: 393
2023-02-19 12:11:14,510	32k	INFO	====> Epoch: 394
2023-02-19 12:11:34,263	32k	INFO	====> Epoch: 395
2023-02-19 12:11:53,845	32k	INFO	====> Epoch: 396
2023-02-19 12:12:13,473	32k	INFO	====> Epoch: 397
2023-02-19 12:12:33,092	32k	INFO	====> Epoch: 398
2023-02-19 12:12:52,723	32k	INFO	====> Epoch: 399
2023-02-19 12:13:12,340	32k	INFO	====> Epoch: 400
2023-02-19 12:13:16,360	32k	INFO	Train Epoch: 401 [0%]
2023-02-19 12:13:16,361	32k	INFO	[2.060042381286621, 2.767242670059204, 16.21058464050293, 16.564598083496094, 0.580163836479187, 7600, 9.512264516656537e-05]
2023-02-19 12:13:32,189	32k	INFO	====> Epoch: 401
2023-02-19 12:13:51,798	32k	INFO	====> Epoch: 402
2023-02-19 12:14:11,417	32k	INFO	====> Epoch: 403
2023-02-19 12:14:31,034	32k	INFO	====> Epoch: 404
2023-02-19 12:14:50,628	32k	INFO	====> Epoch: 405
2023-02-19 12:15:10,268	32k	INFO	====> Epoch: 406
2023-02-19 12:15:29,856	32k	INFO	====> Epoch: 407
2023-02-19 12:15:49,473	32k	INFO	====> Epoch: 408
2023-02-19 12:16:09,070	32k	INFO	====> Epoch: 409
2023-02-19 12:16:28,647	32k	INFO	====> Epoch: 410
2023-02-19 12:16:41,291	32k	INFO	Train Epoch: 411 [53%]
2023-02-19 12:16:41,291	32k	INFO	[2.276326894760132, 2.791963815689087, 16.988666534423828, 16.02008819580078, 1.0126944780349731, 7800, 9.500380872092753e-05]
2023-02-19 12:16:48,539	32k	INFO	====> Epoch: 411
2023-02-19 12:17:08,149	32k	INFO	====> Epoch: 412
2023-02-19 12:17:27,812	32k	INFO	====> Epoch: 413
2023-02-19 12:17:47,415	32k	INFO	====> Epoch: 414
2023-02-19 12:18:07,018	32k	INFO	====> Epoch: 415
2023-02-19 12:18:26,626	32k	INFO	====> Epoch: 416
2023-02-19 12:18:46,268	32k	INFO	====> Epoch: 417
2023-02-19 12:19:05,865	32k	INFO	====> Epoch: 418
2023-02-19 12:19:25,510	32k	INFO	====> Epoch: 419
2023-02-19 12:19:45,050	32k	INFO	====> Epoch: 420
2023-02-19 12:20:04,641	32k	INFO	====> Epoch: 421
2023-02-19 12:20:09,552	32k	INFO	Train Epoch: 422 [5%]
2023-02-19 12:20:09,552	32k	INFO	[1.8605238199234009, 2.7951276302337646, 22.017553329467773, 20.881940841674805, 0.5991621017456055, 8000, 9.487326009722552e-05]
2023-02-19 12:20:13,861	32k	INFO	Saving model and optimizer state at iteration 422 to ./logs\32k\G_8000.pth
2023-02-19 12:20:30,750	32k	INFO	Saving model and optimizer state at iteration 422 to ./logs\32k\D_8000.pth
2023-02-19 12:20:49,367	32k	INFO	====> Epoch: 422
2023-02-19 12:21:09,253	32k	INFO	====> Epoch: 423
2023-02-19 12:21:29,092	32k	INFO	====> Epoch: 424
2023-02-19 12:21:48,850	32k	INFO	====> Epoch: 425
2023-02-19 12:22:08,448	32k	INFO	====> Epoch: 426
2023-02-19 12:22:28,085	32k	INFO	====> Epoch: 427
2023-02-19 12:22:47,686	32k	INFO	====> Epoch: 428
2023-02-19 12:23:07,278	32k	INFO	====> Epoch: 429
2023-02-19 12:23:27,118	32k	INFO	====> Epoch: 430
2023-02-19 12:23:46,825	32k	INFO	====> Epoch: 431
2023-02-19 12:24:00,324	32k	INFO	Train Epoch: 432 [58%]
2023-02-19 12:24:00,325	32k	INFO	[2.2748827934265137, 2.393998861312866, 16.518442153930664, 15.991931915283203, 0.6844744086265564, 8200, 9.475473520763392e-05]
2023-02-19 12:24:06,743	32k	INFO	====> Epoch: 432
2023-02-19 12:24:26,417	32k	INFO	====> Epoch: 433
2023-02-19 12:24:46,429	32k	INFO	====> Epoch: 434
2023-02-19 12:25:06,352	32k	INFO	====> Epoch: 435
2023-02-19 12:25:26,166	32k	INFO	====> Epoch: 436
2023-02-19 12:25:45,962	32k	INFO	====> Epoch: 437
2023-02-19 12:26:05,572	32k	INFO	====> Epoch: 438
2023-02-19 12:26:25,223	32k	INFO	====> Epoch: 439
2023-02-19 12:26:44,804	32k	INFO	====> Epoch: 440
2023-02-19 12:27:04,427	32k	INFO	====> Epoch: 441
2023-02-19 12:27:23,994	32k	INFO	====> Epoch: 442
2023-02-19 12:27:29,761	32k	INFO	Train Epoch: 443 [11%]
2023-02-19 12:27:29,761	32k	INFO	[2.212078332901001, 2.7991578578948975, 14.290653228759766, 16.723142623901367, 0.758256733417511, 8400, 9.46245288460454e-05]
2023-02-19 12:27:43,980	32k	INFO	====> Epoch: 443
2023-02-19 12:28:03,844	32k	INFO	====> Epoch: 444
2023-02-19 12:28:23,653	32k	INFO	====> Epoch: 445
2023-02-19 12:28:43,304	32k	INFO	====> Epoch: 446
2023-02-19 12:29:03,085	32k	INFO	====> Epoch: 447
2023-02-19 12:29:22,793	32k	INFO	====> Epoch: 448
2023-02-19 12:29:42,697	32k	INFO	====> Epoch: 449
2023-02-19 12:30:02,286	32k	INFO	====> Epoch: 450
2023-02-19 12:30:21,939	32k	INFO	====> Epoch: 451
2023-02-19 12:30:41,561	32k	INFO	====> Epoch: 452
2023-02-19 12:30:55,959	32k	INFO	Train Epoch: 453 [63%]
2023-02-19 12:30:55,960	32k	INFO	[2.3873302936553955, 2.6133875846862793, 14.57663631439209, 17.228395462036133, 0.6703507900238037, 8600, 9.450631469568687e-05]
2023-02-19 12:31:01,484	32k	INFO	====> Epoch: 453
2023-02-19 12:31:21,352	32k	INFO	====> Epoch: 454
2023-02-19 12:31:41,004	32k	INFO	====> Epoch: 455
2023-02-19 12:32:00,663	32k	INFO	====> Epoch: 456
2023-02-19 12:32:20,262	32k	INFO	====> Epoch: 457
2023-02-19 12:32:39,921	32k	INFO	====> Epoch: 458
2023-02-19 12:32:59,815	32k	INFO	====> Epoch: 459
2023-02-19 12:33:20,886	32k	INFO	====> Epoch: 460
2023-02-19 12:33:40,723	32k	INFO	====> Epoch: 461
2023-02-19 12:34:00,374	32k	INFO	====> Epoch: 462
2023-02-19 12:34:20,007	32k	INFO	====> Epoch: 463
2023-02-19 12:34:37,407	32k	INFO	Train Epoch: 464 [16%]
2023-02-19 12:34:37,407	32k	INFO	[3.0064706802368164, 2.5415701866149902, 9.37286376953125, 13.644079208374023, 0.7945896983146667, 8800, 9.437644969889592e-05]
2023-02-19 12:34:50,681	32k	INFO	====> Epoch: 464
2023-02-19 12:35:10,328	32k	INFO	====> Epoch: 465
2023-02-19 12:35:29,951	32k	INFO	====> Epoch: 466
2023-02-19 12:35:49,602	32k	INFO	====> Epoch: 467
2023-02-19 12:36:09,383	32k	INFO	====> Epoch: 468
2023-02-19 12:36:29,051	32k	INFO	====> Epoch: 469
2023-02-19 12:36:48,693	32k	INFO	====> Epoch: 470
2023-02-19 12:37:08,307	32k	INFO	====> Epoch: 471
2023-02-19 12:37:27,980	32k	INFO	====> Epoch: 472
2023-02-19 12:37:47,600	32k	INFO	====> Epoch: 473
2023-02-19 12:38:02,872	32k	INFO	Train Epoch: 474 [68%]
2023-02-19 12:38:02,873	32k	INFO	[2.2592387199401855, 2.68511962890625, 14.775422096252441, 18.476463317871094, 0.7293793559074402, 9000, 9.425854547309881e-05]
2023-02-19 12:38:07,146	32k	INFO	Saving model and optimizer state at iteration 474 to ./logs\32k\G_9000.pth
2023-02-19 12:38:23,693	32k	INFO	Saving model and optimizer state at iteration 474 to ./logs\32k\D_9000.pth
2023-02-19 12:38:31,718	32k	INFO	====> Epoch: 474
2023-02-19 12:38:51,663	32k	INFO	====> Epoch: 475
2023-02-19 12:39:11,421	32k	INFO	====> Epoch: 476
2023-02-19 12:39:31,207	32k	INFO	====> Epoch: 477
2023-02-19 12:39:50,792	32k	INFO	====> Epoch: 478
2023-02-19 12:40:10,453	32k	INFO	====> Epoch: 479
2023-02-19 12:40:30,150	32k	INFO	====> Epoch: 480
2023-02-19 12:40:49,778	32k	INFO	====> Epoch: 481
2023-02-19 12:41:09,416	32k	INFO	====> Epoch: 482
2023-02-19 12:41:29,039	32k	INFO	====> Epoch: 483
2023-02-19 12:41:48,739	32k	INFO	====> Epoch: 484
2023-02-19 12:41:56,274	32k	INFO	Train Epoch: 485 [21%]
2023-02-19 12:41:56,274	32k	INFO	[2.0364489555358887, 2.666236400604248, 15.535277366638184, 17.430566787719727, 0.9094477891921997, 9200, 9.412902094614211e-05]
2023-02-19 12:42:08,707	32k	INFO	====> Epoch: 485
2023-02-19 12:42:28,424	32k	INFO	====> Epoch: 486
2023-02-19 12:42:48,137	32k	INFO	====> Epoch: 487
2023-02-19 12:43:07,787	32k	INFO	====> Epoch: 488
2023-02-19 12:43:27,391	32k	INFO	====> Epoch: 489
2023-02-19 12:43:47,060	32k	INFO	====> Epoch: 490
2023-02-19 12:44:06,721	32k	INFO	====> Epoch: 491
2023-02-19 12:44:26,376	32k	INFO	====> Epoch: 492
2023-02-19 12:44:46,053	32k	INFO	====> Epoch: 493
2023-02-19 12:45:05,716	32k	INFO	====> Epoch: 494
2023-02-19 12:45:21,792	32k	INFO	Train Epoch: 495 [74%]
2023-02-19 12:45:21,793	32k	INFO	[2.3733603954315186, 2.47238826751709, 12.350341796875, 17.111530303955078, 0.7110298275947571, 9400, 9.401142583237059e-05]
2023-02-19 12:45:25,630	32k	INFO	====> Epoch: 495
2023-02-19 12:45:45,272	32k	INFO	====> Epoch: 496
2023-02-19 12:46:04,842	32k	INFO	====> Epoch: 497
2023-02-19 12:46:24,549	32k	INFO	====> Epoch: 498
2023-02-19 12:46:44,160	32k	INFO	====> Epoch: 499
2023-02-19 12:47:03,818	32k	INFO	====> Epoch: 500
2023-02-19 12:47:23,460	32k	INFO	====> Epoch: 501
2023-02-19 12:47:43,079	32k	INFO	====> Epoch: 502
2023-02-19 12:48:02,692	32k	INFO	====> Epoch: 503
2023-02-19 12:48:22,357	32k	INFO	====> Epoch: 504
2023-02-19 12:48:41,987	32k	INFO	====> Epoch: 505
2023-02-19 12:48:50,352	32k	INFO	Train Epoch: 506 [26%]
2023-02-19 12:48:50,353	32k	INFO	[2.4896092414855957, 2.6762804985046387, 7.441979885101318, 12.384513854980469, 0.8221025466918945, 9600, 9.388224088263103e-05]
2023-02-19 12:49:01,920	32k	INFO	====> Epoch: 506
2023-02-19 12:49:21,631	32k	INFO	====> Epoch: 507
2023-02-19 12:49:41,235	32k	INFO	====> Epoch: 508
2023-02-19 12:50:00,878	32k	INFO	====> Epoch: 509
2023-02-19 12:50:20,529	32k	INFO	====> Epoch: 510
2023-02-19 12:50:40,218	32k	INFO	====> Epoch: 511
2023-02-19 12:50:59,886	32k	INFO	====> Epoch: 512
2023-02-19 12:51:19,546	32k	INFO	====> Epoch: 513
2023-02-19 12:51:39,163	32k	INFO	====> Epoch: 514
2023-02-19 12:51:58,821	32k	INFO	====> Epoch: 515
2023-02-19 12:52:15,760	32k	INFO	Train Epoch: 516 [79%]
2023-02-19 12:52:15,761	32k	INFO	[1.8880212306976318, 2.990048885345459, 18.54258918762207, 15.826509475708008, 0.7649766206741333, 9800, 9.376495407047951e-05]
2023-02-19 12:52:18,707	32k	INFO	====> Epoch: 516
2023-02-19 12:52:38,388	32k	INFO	====> Epoch: 517
2023-02-19 12:52:58,019	32k	INFO	====> Epoch: 518
2023-02-19 12:53:17,689	32k	INFO	====> Epoch: 519
2023-02-19 12:53:37,358	32k	INFO	====> Epoch: 520
2023-02-19 12:53:56,945	32k	INFO	====> Epoch: 521
2023-02-19 12:54:16,610	32k	INFO	====> Epoch: 522
2023-02-19 12:54:36,229	32k	INFO	====> Epoch: 523
2023-02-19 12:54:55,834	32k	INFO	====> Epoch: 524
2023-02-19 12:55:15,475	32k	INFO	====> Epoch: 525
2023-02-19 12:55:35,099	32k	INFO	====> Epoch: 526
2023-02-19 12:55:44,384	32k	INFO	Train Epoch: 527 [32%]
2023-02-19 12:55:44,385	32k	INFO	[2.205662727355957, 2.867584705352783, 16.78660774230957, 16.71090316772461, 0.8774336576461792, 10000, 9.36361078076803e-05]
2023-02-19 12:55:48,586	32k	INFO	Saving model and optimizer state at iteration 527 to ./logs\32k\G_10000.pth
2023-02-19 12:56:06,798	32k	INFO	Saving model and optimizer state at iteration 527 to ./logs\32k\D_10000.pth
2023-02-19 12:56:21,401	32k	INFO	====> Epoch: 527
2023-02-19 12:56:41,345	32k	INFO	====> Epoch: 528
2023-02-19 12:57:00,917	32k	INFO	====> Epoch: 529
2023-02-19 12:57:20,811	32k	INFO	====> Epoch: 530
2023-02-19 12:57:40,435	32k	INFO	====> Epoch: 531
2023-02-19 12:58:00,053	32k	INFO	====> Epoch: 532
2023-02-19 12:58:19,745	32k	INFO	====> Epoch: 533
2023-02-19 12:58:39,609	32k	INFO	====> Epoch: 534
2023-02-19 12:58:59,368	32k	INFO	====> Epoch: 535
2023-02-19 12:59:18,968	32k	INFO	====> Epoch: 536
2023-02-19 12:59:36,810	32k	INFO	Train Epoch: 537 [84%]
2023-02-19 12:59:36,811	32k	INFO	[2.1262154579162598, 2.892063856124878, 15.072270393371582, 16.91364288330078, 0.7413275837898254, 10200, 9.351912848886779e-05]
2023-02-19 12:59:38,927	32k	INFO	====> Epoch: 537
2023-02-19 12:59:58,556	32k	INFO	====> Epoch: 538
2023-02-19 13:00:18,246	32k	INFO	====> Epoch: 539
2023-02-19 13:00:38,193	32k	INFO	====> Epoch: 540
2023-02-19 13:00:57,974	32k	INFO	====> Epoch: 541
2023-02-19 13:01:17,641	32k	INFO	====> Epoch: 542
2023-02-19 13:01:37,271	32k	INFO	====> Epoch: 543
2023-02-19 13:01:56,865	32k	INFO	====> Epoch: 544
2023-02-19 13:02:16,505	32k	INFO	====> Epoch: 545
2023-02-19 13:02:36,125	32k	INFO	====> Epoch: 546
2023-02-19 13:02:55,699	32k	INFO	====> Epoch: 547
2023-02-19 13:03:05,776	32k	INFO	Train Epoch: 548 [37%]
2023-02-19 13:03:05,776	32k	INFO	[2.2443082332611084, 2.77612566947937, 14.530040740966797, 16.31207275390625, 0.7399520874023438, 10400, 9.339062002506615e-05]
2023-02-19 13:03:15,650	32k	INFO	====> Epoch: 548
2023-02-19 13:03:35,253	32k	INFO	====> Epoch: 549
2023-02-19 13:03:54,904	32k	INFO	====> Epoch: 550
2023-02-19 13:04:14,500	32k	INFO	====> Epoch: 551
2023-02-19 13:04:34,189	32k	INFO	====> Epoch: 552
2023-02-19 13:04:53,801	32k	INFO	====> Epoch: 553
2023-02-19 13:05:13,431	32k	INFO	====> Epoch: 554
2023-02-19 13:05:33,057	32k	INFO	====> Epoch: 555
2023-02-19 13:05:52,677	32k	INFO	====> Epoch: 556
2023-02-19 13:06:12,273	32k	INFO	====> Epoch: 557
2023-02-19 13:06:30,977	32k	INFO	Train Epoch: 558 [89%]
2023-02-19 13:06:30,978	32k	INFO	[1.9971816539764404, 2.9987430572509766, 16.42749786376953, 17.72498893737793, 0.7169369459152222, 10600, 9.327394739343082e-05]
2023-02-19 13:06:32,240	32k	INFO	====> Epoch: 558
2023-02-19 13:06:51,888	32k	INFO	====> Epoch: 559
2023-02-19 13:07:11,500	32k	INFO	====> Epoch: 560
2023-02-19 13:07:31,432	32k	INFO	====> Epoch: 561
2023-02-19 13:07:51,063	32k	INFO	====> Epoch: 562
2023-02-19 13:08:10,683	32k	INFO	====> Epoch: 563
2023-02-19 13:08:30,306	32k	INFO	====> Epoch: 564
2023-02-19 13:08:49,925	32k	INFO	====> Epoch: 565
2023-02-19 13:09:09,599	32k	INFO	====> Epoch: 566
2023-02-19 13:09:29,187	32k	INFO	====> Epoch: 567
2023-02-19 13:09:48,800	32k	INFO	====> Epoch: 568
2023-02-19 13:09:59,768	32k	INFO	Train Epoch: 569 [42%]
2023-02-19 13:09:59,768	32k	INFO	[2.171602725982666, 2.590355157852173, 13.851484298706055, 15.077618598937988, 0.7326598167419434, 10800, 9.314577584301187e-05]
2023-02-19 13:10:08,772	32k	INFO	====> Epoch: 569
2023-02-19 13:10:28,413	32k	INFO	====> Epoch: 570
2023-02-19 13:10:48,029	32k	INFO	====> Epoch: 571
2023-02-19 13:11:07,762	32k	INFO	====> Epoch: 572
2023-02-19 13:11:27,359	32k	INFO	====> Epoch: 573
2023-02-19 13:11:46,968	32k	INFO	====> Epoch: 574
2023-02-19 13:12:06,652	32k	INFO	====> Epoch: 575
2023-02-19 13:12:26,301	32k	INFO	====> Epoch: 576
2023-02-19 13:12:45,960	32k	INFO	====> Epoch: 577
2023-02-19 13:13:05,579	32k	INFO	====> Epoch: 578
2023-02-19 13:13:24,791	32k	INFO	Train Epoch: 579 [95%]
2023-02-19 13:13:24,792	32k	INFO	[2.4127438068389893, 2.4390709400177, 18.234426498413086, 15.20917797088623, 0.9935965538024902, 11000, 9.302940909450543e-05]
2023-02-19 13:13:29,089	32k	INFO	Saving model and optimizer state at iteration 579 to ./logs\32k\G_11000.pth
2023-02-19 13:13:46,080	32k	INFO	Saving model and optimizer state at iteration 579 to ./logs\32k\D_11000.pth
2023-02-19 13:13:50,303	32k	INFO	====> Epoch: 579
2023-02-19 13:14:10,200	32k	INFO	====> Epoch: 580
2023-02-19 13:14:29,994	32k	INFO	====> Epoch: 581
2023-02-19 13:14:49,886	32k	INFO	====> Epoch: 582
2023-02-19 13:15:09,476	32k	INFO	====> Epoch: 583
2023-02-19 13:15:29,373	32k	INFO	====> Epoch: 584
2023-02-19 13:15:49,189	32k	INFO	====> Epoch: 585
2023-02-19 13:16:09,053	32k	INFO	====> Epoch: 586
2023-02-19 13:16:28,930	32k	INFO	====> Epoch: 587
2023-02-19 13:16:48,676	32k	INFO	====> Epoch: 588
2023-02-19 13:17:08,497	32k	INFO	====> Epoch: 589
2023-02-19 13:17:20,344	32k	INFO	Train Epoch: 590 [47%]
2023-02-19 13:17:20,345	32k	INFO	[2.5262322425842285, 2.621953010559082, 13.513108253479004, 15.703678131103516, 0.5160157680511475, 11200, 9.29015735741762e-05]
2023-02-19 13:17:28,485	32k	INFO	====> Epoch: 590
2023-02-19 13:17:48,164	32k	INFO	====> Epoch: 591
2023-02-19 13:18:07,959	32k	INFO	====> Epoch: 592
2023-02-19 13:18:27,653	32k	INFO	====> Epoch: 593
2023-02-19 13:18:47,325	32k	INFO	====> Epoch: 594
2023-02-19 13:19:06,920	32k	INFO	====> Epoch: 595
2023-02-19 13:19:26,787	32k	INFO	====> Epoch: 596
2023-02-19 13:19:46,492	32k	INFO	====> Epoch: 597
2023-02-19 13:20:06,128	32k	INFO	====> Epoch: 598
2023-02-19 13:20:25,792	32k	INFO	====> Epoch: 599
2023-02-19 13:20:45,413	32k	INFO	====> Epoch: 600
2023-02-19 13:20:49,465	32k	INFO	Train Epoch: 601 [0%]
2023-02-19 13:20:49,465	32k	INFO	[2.056851625442505, 2.9963862895965576, 17.283754348754883, 17.02275848388672, 0.9403309226036072, 11400, 9.277391371786995e-05]
2023-02-19 13:21:05,470	32k	INFO	====> Epoch: 601
2023-02-19 13:21:25,118	32k	INFO	====> Epoch: 602
2023-02-19 13:21:44,922	32k	INFO	====> Epoch: 603
2023-02-19 13:22:04,542	32k	INFO	====> Epoch: 604
2023-02-19 13:22:24,116	32k	INFO	====> Epoch: 605
2023-02-19 13:22:43,845	32k	INFO	====> Epoch: 606
2023-02-19 13:23:03,995	32k	INFO	====> Epoch: 607
2023-02-19 13:23:24,240	32k	INFO	====> Epoch: 608
2023-02-19 13:23:44,685	32k	INFO	====> Epoch: 609
2023-02-19 13:24:06,943	32k	INFO	====> Epoch: 610
2023-02-19 13:24:21,160	32k	INFO	Train Epoch: 611 [53%]
2023-02-19 13:24:21,161	32k	INFO	[2.046140193939209, 3.0505690574645996, 18.795839309692383, 19.634422302246094, 0.7730445265769958, 11600, 9.265801153564152e-05]
2023-02-19 13:24:28,568	32k	INFO	====> Epoch: 611
2023-02-19 13:24:48,706	32k	INFO	====> Epoch: 612
2023-02-19 13:25:08,885	32k	INFO	====> Epoch: 613
2023-02-19 13:25:28,992	32k	INFO	====> Epoch: 614
2023-02-19 13:25:48,957	32k	INFO	====> Epoch: 615
2023-02-19 13:26:08,955	32k	INFO	====> Epoch: 616
2023-02-19 13:26:28,851	32k	INFO	====> Epoch: 617
2023-02-19 13:26:48,849	32k	INFO	====> Epoch: 618
2023-02-19 13:27:08,836	32k	INFO	====> Epoch: 619
2023-02-19 13:27:28,912	32k	INFO	====> Epoch: 620
2023-02-19 13:27:49,369	32k	INFO	====> Epoch: 621
2023-02-19 13:27:54,743	32k	INFO	Train Epoch: 622 [5%]
2023-02-19 13:27:54,744	32k	INFO	[2.1044559478759766, 2.9037117958068848, 18.362964630126953, 17.604398727416992, 0.6811983585357666, 11800, 9.25306863679056e-05]
2023-02-19 13:28:10,067	32k	INFO	====> Epoch: 622
2023-02-19 13:28:30,230	32k	INFO	====> Epoch: 623
2023-02-19 13:28:50,259	32k	INFO	====> Epoch: 624
2023-02-19 13:29:10,185	32k	INFO	====> Epoch: 625
2023-02-19 13:29:30,067	32k	INFO	====> Epoch: 626
2023-02-19 13:29:50,074	32k	INFO	====> Epoch: 627
2023-02-19 13:30:10,092	32k	INFO	====> Epoch: 628
2023-02-19 13:30:30,071	32k	INFO	====> Epoch: 629
2023-02-19 13:30:50,611	32k	INFO	====> Epoch: 630
2023-02-19 13:31:10,685	32k	INFO	====> Epoch: 631
2023-02-19 13:31:24,362	32k	INFO	Train Epoch: 632 [58%]
2023-02-19 13:31:24,362	32k	INFO	[2.0907158851623535, 2.8510940074920654, 16.303882598876953, 16.795988082885742, 0.6851125359535217, 12000, 9.24150880489024e-05]
2023-02-19 13:31:28,856	32k	INFO	Saving model and optimizer state at iteration 632 to ./logs\32k\G_12000.pth
2023-02-19 13:31:46,760	32k	INFO	Saving model and optimizer state at iteration 632 to ./logs\32k\D_12000.pth
2023-02-19 13:31:56,955	32k	INFO	====> Epoch: 632
2023-02-19 13:32:17,156	32k	INFO	====> Epoch: 633
2023-02-19 13:32:37,149	32k	INFO	====> Epoch: 634
2023-02-19 13:32:56,970	32k	INFO	====> Epoch: 635
2023-02-19 13:33:16,861	32k	INFO	====> Epoch: 636
2023-02-19 13:33:36,793	32k	INFO	====> Epoch: 637
2023-02-19 13:33:56,654	32k	INFO	====> Epoch: 638
2023-02-19 13:34:16,576	32k	INFO	====> Epoch: 639
2023-02-19 13:34:36,445	32k	INFO	====> Epoch: 640
2023-02-19 13:34:56,252	32k	INFO	====> Epoch: 641
2023-02-19 13:35:16,142	32k	INFO	====> Epoch: 642
2023-02-19 13:35:21,999	32k	INFO	Train Epoch: 643 [11%]
2023-02-19 13:35:21,999	32k	INFO	[2.3126425743103027, 3.3445053100585938, 15.56027889251709, 17.644582748413086, 0.5389391779899597, 12200, 9.228809669227663e-05]
2023-02-19 13:35:36,442	32k	INFO	====> Epoch: 643
2023-02-19 13:35:56,329	32k	INFO	====> Epoch: 644
2023-02-19 13:36:16,285	32k	INFO	====> Epoch: 645
2023-02-19 13:36:36,225	32k	INFO	====> Epoch: 646
2023-02-19 13:36:56,109	32k	INFO	====> Epoch: 647
2023-02-19 13:37:15,986	32k	INFO	====> Epoch: 648
2023-02-19 13:37:36,535	32k	INFO	====> Epoch: 649
2023-02-19 13:37:58,438	32k	INFO	====> Epoch: 650
2023-02-19 13:38:20,631	32k	INFO	====> Epoch: 651
2023-02-19 13:38:48,478	32k	INFO	====> Epoch: 652
2023-02-19 13:39:15,549	32k	INFO	Train Epoch: 653 [63%]
2023-02-19 13:39:15,549	32k	INFO	[2.3998544216156006, 2.5205063819885254, 13.50827693939209, 16.829593658447266, 0.518319845199585, 12400, 9.217280143985396e-05]
2023-02-19 13:39:23,399	32k	INFO	====> Epoch: 653
2023-02-19 13:39:44,062	32k	INFO	====> Epoch: 654
2023-02-19 13:40:04,509	32k	INFO	====> Epoch: 655
2023-02-19 13:40:24,966	32k	INFO	====> Epoch: 656
2023-02-19 13:40:46,616	32k	INFO	====> Epoch: 657
2023-02-19 13:41:06,997	32k	INFO	====> Epoch: 658
2023-02-19 13:41:27,230	32k	INFO	====> Epoch: 659
2023-02-19 13:41:47,556	32k	INFO	====> Epoch: 660
2023-02-19 13:42:07,868	32k	INFO	====> Epoch: 661
2023-02-19 13:42:28,224	32k	INFO	====> Epoch: 662
2023-02-19 13:42:48,842	32k	INFO	====> Epoch: 663
2023-02-19 13:42:55,986	32k	INFO	Train Epoch: 664 [16%]
2023-02-19 13:42:55,987	32k	INFO	[2.1531105041503906, 2.9205827713012695, 12.979509353637695, 14.956928253173828, 1.0134525299072266, 12600, 9.204614301917867e-05]
2023-02-19 13:43:09,755	32k	INFO	====> Epoch: 664
2023-02-19 13:43:30,293	32k	INFO	====> Epoch: 665
2023-02-19 13:43:50,838	32k	INFO	====> Epoch: 666
2023-02-19 13:44:11,454	32k	INFO	====> Epoch: 667
2023-02-19 13:44:32,018	32k	INFO	====> Epoch: 668
2023-02-19 13:44:52,353	32k	INFO	====> Epoch: 669
2023-02-19 13:45:12,710	32k	INFO	====> Epoch: 670
2023-02-19 13:45:32,939	32k	INFO	====> Epoch: 671
2023-02-19 13:45:53,246	32k	INFO	====> Epoch: 672
2023-02-19 13:46:13,398	32k	INFO	====> Epoch: 673
2023-02-19 13:46:29,187	32k	INFO	Train Epoch: 674 [68%]
2023-02-19 13:46:29,187	32k	INFO	[2.120356321334839, 2.7157726287841797, 13.795363426208496, 18.83095932006836, 0.682133138179779, 12800, 9.193115003878036e-05]
2023-02-19 13:46:34,017	32k	INFO	====> Epoch: 674
2023-02-19 13:46:54,188	32k	INFO	====> Epoch: 675
2023-02-19 13:47:14,233	32k	INFO	====> Epoch: 676
2023-02-19 13:47:34,315	32k	INFO	====> Epoch: 677
2023-02-19 13:47:54,390	32k	INFO	====> Epoch: 678
2023-02-19 13:48:14,419	32k	INFO	====> Epoch: 679
2023-02-19 13:48:34,434	32k	INFO	====> Epoch: 680
2023-02-19 13:48:54,500	32k	INFO	====> Epoch: 681
2023-02-19 13:49:14,535	32k	INFO	====> Epoch: 682
2023-02-19 13:49:34,589	32k	INFO	====> Epoch: 683
2023-02-19 13:49:54,633	32k	INFO	====> Epoch: 684
2023-02-19 13:50:02,241	32k	INFO	Train Epoch: 685 [21%]
2023-02-19 13:50:02,241	32k	INFO	[1.9549205303192139, 2.509674549102783, 15.415645599365234, 16.405630111694336, 0.5037123560905457, 13000, 9.180482368119022e-05]
2023-02-19 13:50:06,487	32k	INFO	Saving model and optimizer state at iteration 685 to ./logs\32k\G_13000.pth
2023-02-19 13:50:24,993	32k	INFO	Saving model and optimizer state at iteration 685 to ./logs\32k\D_13000.pth
2023-02-19 13:50:41,214	32k	INFO	====> Epoch: 685
2023-02-19 13:51:01,502	32k	INFO	====> Epoch: 686
2023-02-19 13:51:21,512	32k	INFO	====> Epoch: 687
2023-02-19 13:51:41,753	32k	INFO	====> Epoch: 688
2023-02-19 13:52:01,822	32k	INFO	====> Epoch: 689
2023-02-19 13:52:21,926	32k	INFO	====> Epoch: 690
2023-02-19 13:52:41,986	32k	INFO	====> Epoch: 691
2023-02-19 13:53:02,270	32k	INFO	====> Epoch: 692
2023-02-19 13:53:22,470	32k	INFO	====> Epoch: 693
2023-02-19 13:53:42,473	32k	INFO	====> Epoch: 694
2023-02-19 13:53:58,964	32k	INFO	Train Epoch: 695 [74%]
2023-02-19 13:53:58,964	32k	INFO	[2.0492773056030273, 2.9210152626037598, 20.51807403564453, 21.43520164489746, 1.142478108406067, 13200, 9.169013218034329e-05]
2023-02-19 13:54:02,863	32k	INFO	====> Epoch: 695
2023-02-19 13:54:22,924	32k	INFO	====> Epoch: 696
2023-02-19 13:54:42,972	32k	INFO	====> Epoch: 697
2023-02-19 13:55:03,260	32k	INFO	====> Epoch: 698
2023-02-19 13:55:23,335	32k	INFO	====> Epoch: 699
2023-02-19 13:55:43,367	32k	INFO	====> Epoch: 700
2023-02-19 13:56:03,452	32k	INFO	====> Epoch: 701
2023-02-19 13:56:23,507	32k	INFO	====> Epoch: 702
2023-02-19 13:56:43,553	32k	INFO	====> Epoch: 703
2023-02-19 13:57:03,890	32k	INFO	====> Epoch: 704
2023-02-19 13:57:24,195	32k	INFO	====> Epoch: 705
2023-02-19 13:57:32,664	32k	INFO	Train Epoch: 706 [26%]
2023-02-19 13:57:32,664	32k	INFO	[2.137279987335205, 2.8736164569854736, 16.14851188659668, 14.964777946472168, 0.5104328393936157, 13400, 9.156413701526141e-05]
2023-02-19 13:57:44,470	32k	INFO	====> Epoch: 706
2023-02-19 13:58:04,489	32k	INFO	====> Epoch: 707
2023-02-19 13:58:24,565	32k	INFO	====> Epoch: 708
2023-02-19 13:58:44,632	32k	INFO	====> Epoch: 709
2023-02-19 13:59:04,804	32k	INFO	====> Epoch: 710
2023-02-19 13:59:24,867	32k	INFO	====> Epoch: 711
2023-02-19 13:59:45,104	32k	INFO	====> Epoch: 712
2023-02-19 14:00:05,190	32k	INFO	====> Epoch: 713
2023-02-19 14:00:25,258	32k	INFO	====> Epoch: 714
2023-02-19 14:00:45,336	32k	INFO	====> Epoch: 715
2023-02-19 14:01:02,624	32k	INFO	Train Epoch: 716 [79%]
2023-02-19 14:01:02,624	32k	INFO	[1.762975811958313, 2.899984359741211, 21.13072967529297, 15.85500717163086, 1.2023099660873413, 13600, 9.144974620357048e-05]
2023-02-19 14:01:05,730	32k	INFO	====> Epoch: 716
2023-02-19 14:01:25,807	32k	INFO	====> Epoch: 717
2023-02-19 14:01:45,813	32k	INFO	====> Epoch: 718
2023-02-19 14:02:05,878	32k	INFO	====> Epoch: 719
2023-02-19 14:02:26,003	32k	INFO	====> Epoch: 720
2023-02-19 14:02:46,038	32k	INFO	====> Epoch: 721
2023-02-19 14:03:06,130	32k	INFO	====> Epoch: 722
2023-02-19 14:03:26,223	32k	INFO	====> Epoch: 723
2023-02-19 14:03:46,269	32k	INFO	====> Epoch: 724
2023-02-19 14:04:06,294	32k	INFO	====> Epoch: 725
2023-02-19 14:04:26,438	32k	INFO	====> Epoch: 726
2023-02-19 14:04:35,838	32k	INFO	Train Epoch: 727 [32%]
2023-02-19 14:04:35,839	32k	INFO	[2.231358051300049, 2.4955027103424072, 12.61548137664795, 13.37820816040039, 0.7114076614379883, 13800, 9.132408136270243e-05]
2023-02-19 14:04:46,835	32k	INFO	====> Epoch: 727
2023-02-19 14:05:07,009	32k	INFO	====> Epoch: 728
2023-02-19 14:05:27,022	32k	INFO	====> Epoch: 729
2023-02-19 14:05:47,131	32k	INFO	====> Epoch: 730
2023-02-19 14:06:07,140	32k	INFO	====> Epoch: 731
2023-02-19 14:06:27,225	32k	INFO	====> Epoch: 732
2023-02-19 14:06:47,266	32k	INFO	====> Epoch: 733
2023-02-19 14:07:07,365	32k	INFO	====> Epoch: 734
2023-02-19 14:07:27,419	32k	INFO	====> Epoch: 735
2023-02-19 14:07:47,463	32k	INFO	====> Epoch: 736
2023-02-19 14:08:05,749	32k	INFO	Train Epoch: 737 [84%]
2023-02-19 14:08:05,750	32k	INFO	[2.0271754264831543, 2.876140594482422, 14.655152320861816, 14.280223846435547, 0.5791776180267334, 14000, 9.120999045184433e-05]
2023-02-19 14:08:10,006	32k	INFO	Saving model and optimizer state at iteration 737 to ./logs\32k\G_14000.pth
2023-02-19 14:08:26,063	32k	INFO	Saving model and optimizer state at iteration 737 to ./logs\32k\D_14000.pth
2023-02-19 14:08:31,910	32k	INFO	====> Epoch: 737
2023-02-19 14:08:52,271	32k	INFO	====> Epoch: 738
2023-02-19 14:09:12,223	32k	INFO	====> Epoch: 739
2023-02-19 14:09:32,201	32k	INFO	====> Epoch: 740
2023-02-19 14:09:52,186	32k	INFO	====> Epoch: 741
2023-02-19 14:10:12,190	32k	INFO	====> Epoch: 742
2023-02-19 14:10:32,165	32k	INFO	====> Epoch: 743
2023-02-19 14:10:52,419	32k	INFO	====> Epoch: 744
2023-02-19 14:11:12,589	32k	INFO	====> Epoch: 745
2023-02-19 14:11:32,627	32k	INFO	====> Epoch: 746
2023-02-19 14:11:52,686	32k	INFO	====> Epoch: 747