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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
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