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32k-resona/train.log

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+2023-02-08 23:42:36,711	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': {'resona': 0}, 'model_dir': './logs\\32k'}
+2023-02-08 23:43:22,288	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': {'resona': 0}, 'model_dir': './logs\\32k'}
+2023-02-08 23:48:45,714	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': 12, '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': {'resona': 0}, 'model_dir': './logs\\32k'}
+2023-02-08 23:48:54,959	32k	INFO	Loaded checkpoint './logs\32k\G_0.pth' (iteration 1)
+2023-02-08 23:48:58,405	32k	INFO	Loaded checkpoint './logs\32k\D_0.pth' (iteration 1)
+2023-02-08 23:58:19,270	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': 12, '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': {'resona': 0}, 'model_dir': './logs\\32k'}
+2023-02-08 23:58:24,560	32k	INFO	Loaded checkpoint './logs\32k\G_0.pth' (iteration 1)
+2023-02-08 23:58:24,968	32k	INFO	Loaded checkpoint './logs\32k\D_0.pth' (iteration 1)
+2023-02-08 23:59:37,724	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': {'resona': 0}, 'model_dir': './logs\\32k'}
+2023-02-08 23:59:42,440	32k	INFO	Loaded checkpoint './logs\32k\G_0.pth' (iteration 1)
+2023-02-08 23:59:42,831	32k	INFO	Loaded checkpoint './logs\32k\D_0.pth' (iteration 1)
+2023-02-09 00:00:09,549	32k	INFO	Train Epoch: 1 [0%]
+2023-02-09 00:00:09,550	32k	INFO	[2.2538223266601562, 2.6597347259521484, 11.763463020324707, 45.49671173095703, 11.207155227661133, 0, 0.0001]
+2023-02-09 00:00:15,641	32k	INFO	Saving model and optimizer state at iteration 1 to ./logs\32k\G_0.pth
+2023-02-09 00:00:34,164	32k	INFO	Saving model and optimizer state at iteration 1 to ./logs\32k\D_0.pth
+2023-02-09 00:01:33,109	32k	INFO	====> Epoch: 1
+2023-02-09 00:02:50,380	32k	INFO	====> Epoch: 2
+2023-02-09 00:04:06,903	32k	INFO	Train Epoch: 3 [99%]
+2023-02-09 00:04:06,904	32k	INFO	[2.4401040077209473, 2.5799942016601562, 9.811338424682617, 22.839824676513672, 1.509987711906433, 200, 9.99750015625e-05]
+2023-02-09 00:04:07,676	32k	INFO	====> Epoch: 3
+2023-02-09 00:05:24,690	32k	INFO	====> Epoch: 4
+2023-02-09 00:06:41,699	32k	INFO	====> Epoch: 5
+2023-02-09 00:07:57,478	32k	INFO	Train Epoch: 6 [97%]
+2023-02-09 00:07:57,479	32k	INFO	[2.500568389892578, 2.042257785797119, 10.643875122070312, 17.175233840942383, 1.2767752408981323, 400, 9.993751562304699e-05]
+2023-02-09 00:07:59,058	32k	INFO	====> Epoch: 6
+2023-02-09 00:09:16,169	32k	INFO	====> Epoch: 7
+2023-02-09 00:10:33,248	32k	INFO	====> Epoch: 8
+2023-02-09 00:11:48,262	32k	INFO	Train Epoch: 9 [96%]
+2023-02-09 00:11:48,262	32k	INFO	[2.4632694721221924, 2.3801445960998535, 6.719289779663086, 15.820771217346191, 1.1217436790466309, 600, 9.990004373906418e-05]
+2023-02-09 00:11:50,769	32k	INFO	====> Epoch: 9
+2023-02-09 00:13:08,802	32k	INFO	====> Epoch: 10
+2023-02-09 00:14:26,016	32k	INFO	====> Epoch: 11
+2023-02-09 00:15:40,270	32k	INFO	Train Epoch: 12 [94%]
+2023-02-09 00:15:40,271	32k	INFO	[2.455298662185669, 2.3402817249298096, 9.666932106018066, 20.384544372558594, 0.6758424043655396, 800, 9.986258590528146e-05]
+2023-02-09 00:15:43,517	32k	INFO	====> Epoch: 12
+2023-02-09 00:17:00,741	32k	INFO	====> Epoch: 13
+2023-02-09 00:18:18,179	32k	INFO	====> Epoch: 14
+2023-02-09 00:19:31,643	32k	INFO	Train Epoch: 15 [93%]
+2023-02-09 00:19:31,643	32k	INFO	[2.623318672180176, 2.104186534881592, 8.998331069946289, 18.40737533569336, 1.2988810539245605, 1000, 9.982514211643064e-05]
+2023-02-09 00:19:36,017	32k	INFO	Saving model and optimizer state at iteration 15 to ./logs\32k\G_1000.pth
+2023-02-09 00:19:56,035	32k	INFO	Saving model and optimizer state at iteration 15 to ./logs\32k\D_1000.pth
+2023-02-09 00:20:03,963	32k	INFO	====> Epoch: 15
+2023-02-09 00:21:21,250	32k	INFO	====> Epoch: 16
+2023-02-09 00:22:38,564	32k	INFO	====> Epoch: 17
+2023-02-09 00:23:51,362	32k	INFO	Train Epoch: 18 [91%]
+2023-02-09 00:23:51,362	32k	INFO	[2.5918359756469727, 2.205848217010498, 7.585756301879883, 19.934139251708984, 0.9796969294548035, 1200, 9.978771236724554e-05]
+2023-02-09 00:23:56,283	32k	INFO	====> Epoch: 18
+2023-02-09 00:25:13,666	32k	INFO	====> Epoch: 19
+2023-02-09 00:26:30,886	32k	INFO	====> Epoch: 20
+2023-02-09 00:27:42,775	32k	INFO	Train Epoch: 21 [90%]
+2023-02-09 00:27:42,775	32k	INFO	[2.3665695190429688, 2.329713821411133, 8.740349769592285, 18.66284942626953, 0.883953332901001, 1400, 9.975029665246193e-05]
+2023-02-09 00:27:48,506	32k	INFO	====> Epoch: 21
+2023-02-09 00:29:05,836	32k	INFO	====> Epoch: 22
+2023-02-09 00:30:23,087	32k	INFO	====> Epoch: 23
+2023-02-09 00:31:34,123	32k	INFO	Train Epoch: 24 [88%]
+2023-02-09 00:31:34,123	32k	INFO	[2.644050121307373, 2.4024436473846436, 3.807854652404785, 10.505730628967285, 0.7721247673034668, 1600, 9.971289496681757e-05]
+2023-02-09 00:31:40,666	32k	INFO	====> Epoch: 24
+2023-02-09 00:32:57,901	32k	INFO	====> Epoch: 25
+2023-02-09 00:34:15,203	32k	INFO	====> Epoch: 26
+2023-02-09 00:35:25,363	32k	INFO	Train Epoch: 27 [87%]
+2023-02-09 00:35:25,363	32k	INFO	[2.5081863403320312, 2.362135648727417, 10.08530044555664, 21.179523468017578, 1.0669621229171753, 1800, 9.967550730505221e-05]
+2023-02-09 00:35:32,734	32k	INFO	====> Epoch: 27
+2023-02-09 00:36:49,992	32k	INFO	====> Epoch: 28
+2023-02-09 00:38:07,250	32k	INFO	====> Epoch: 29
+2023-02-09 00:39:16,660	32k	INFO	Train Epoch: 30 [85%]
+2023-02-09 00:39:16,661	32k	INFO	[2.220442771911621, 2.801152229309082, 7.649630546569824, 14.329034805297852, 0.8535028696060181, 2000, 9.963813366190753e-05]
+2023-02-09 00:39:21,154	32k	INFO	Saving model and optimizer state at iteration 30 to ./logs\32k\G_2000.pth
+2023-02-09 00:39:37,843	32k	INFO	Saving model and optimizer state at iteration 30 to ./logs\32k\D_2000.pth
+2023-02-09 00:39:49,595	32k	INFO	====> Epoch: 30
+2023-02-09 00:41:06,937	32k	INFO	====> Epoch: 31
+2023-02-09 00:42:24,214	32k	INFO	====> Epoch: 32
+2023-02-09 00:43:32,732	32k	INFO	Train Epoch: 33 [84%]
+2023-02-09 00:43:32,732	32k	INFO	[2.5671257972717285, 2.225325107574463, 6.000064849853516, 14.645927429199219, 0.91252601146698, 2200, 9.960077403212722e-05]
+2023-02-09 00:43:41,803	32k	INFO	====> Epoch: 33
+2023-02-09 00:44:59,115	32k	INFO	====> Epoch: 34
+2023-02-09 00:46:16,581	32k	INFO	====> Epoch: 35
+2023-02-09 00:47:24,262	32k	INFO	Train Epoch: 36 [82%]
+2023-02-09 00:47:24,262	32k	INFO	[2.566441059112549, 2.1268556118011475, 7.592073440551758, 18.499162673950195, 0.9556418657302856, 2400, 9.956342841045691e-05]
+2023-02-09 00:47:34,260	32k	INFO	====> Epoch: 36
+2023-02-09 00:48:51,449	32k	INFO	====> Epoch: 37
+2023-02-09 00:50:08,699	32k	INFO	====> Epoch: 38
+2023-02-09 00:51:15,505	32k	INFO	Train Epoch: 39 [81%]
+2023-02-09 00:51:15,505	32k	INFO	[2.479832410812378, 2.2814459800720215, 7.773660182952881, 18.745792388916016, 1.0438538789749146, 2600, 9.952609679164422e-05]
+2023-02-09 00:51:26,185	32k	INFO	====> Epoch: 39
+2023-02-09 00:52:43,425	32k	INFO	====> Epoch: 40
+2023-02-09 00:54:00,760	32k	INFO	====> Epoch: 41
+2023-02-09 00:55:06,807	32k	INFO	Train Epoch: 42 [79%]
+2023-02-09 00:55:06,808	32k	INFO	[2.484856367111206, 2.320491313934326, 10.475366592407227, 19.472793579101562, 1.3102898597717285, 2800, 9.948877917043875e-05]
+2023-02-09 00:55:18,363	32k	INFO	====> Epoch: 42
+2023-02-09 00:56:35,577	32k	INFO	====> Epoch: 43
+2023-02-09 00:57:52,857	32k	INFO	====> Epoch: 44
+2023-02-09 00:58:58,009	32k	INFO	Train Epoch: 45 [78%]
+2023-02-09 00:58:58,010	32k	INFO	[2.4101104736328125, 2.2083635330200195, 11.62160587310791, 18.154218673706055, 0.8082653880119324, 3000, 9.945147554159202e-05]
+2023-02-09 00:59:02,398	32k	INFO	Saving model and optimizer state at iteration 45 to ./logs\32k\G_3000.pth
+2023-02-09 00:59:20,855	32k	INFO	Saving model and optimizer state at iteration 45 to ./logs\32k\D_3000.pth
+2023-02-09 00:59:36,859	32k	INFO	====> Epoch: 45
+2023-02-09 01:00:54,227	32k	INFO	====> Epoch: 46
+2023-02-09 01:02:11,360	32k	INFO	====> Epoch: 47
+2023-02-09 01:03:15,636	32k	INFO	Train Epoch: 48 [76%]
+2023-02-09 01:03:15,636	32k	INFO	[2.5076825618743896, 2.1728971004486084, 8.223286628723145, 14.673565864562988, 0.8116975426673889, 3200, 9.941418589985758e-05]
+2023-02-09 01:03:28,785	32k	INFO	====> Epoch: 48
+2023-02-09 01:04:46,096	32k	INFO	====> Epoch: 49
+2023-02-09 01:06:03,441	32k	INFO	====> Epoch: 50
+2023-02-09 01:07:07,086	32k	INFO	Train Epoch: 51 [75%]
+2023-02-09 01:07:07,086	32k	INFO	[2.6912717819213867, 2.093996047973633, 7.675986289978027, 15.62138557434082, 1.2493226528167725, 3400, 9.937691023999092e-05]
+2023-02-09 01:07:21,073	32k	INFO	====> Epoch: 51
+2023-02-09 01:08:38,316	32k	INFO	====> Epoch: 52
+2023-02-09 01:09:55,540	32k	INFO	====> Epoch: 53
+2023-02-09 01:10:58,191	32k	INFO	Train Epoch: 54 [73%]
+2023-02-09 01:10:58,192	32k	INFO	[2.269256114959717, 2.731293201446533, 8.000404357910156, 14.131468772888184, 1.039566159248352, 3600, 9.933964855674948e-05]
+2023-02-09 01:11:13,014	32k	INFO	====> Epoch: 54
+2023-02-09 01:12:30,251	32k	INFO	====> Epoch: 55
+2023-02-09 01:13:47,584	32k	INFO	====> Epoch: 56
+2023-02-09 01:14:49,462	32k	INFO	Train Epoch: 57 [72%]
+2023-02-09 01:14:49,462	32k	INFO	[2.462212324142456, 2.380282402038574, 9.700272560119629, 19.343175888061523, 1.1376886367797852, 3800, 9.930240084489267e-05]
+2023-02-09 01:15:05,201	32k	INFO	====> Epoch: 57
+2023-02-09 01:16:22,533	32k	INFO	====> Epoch: 58
+2023-02-09 01:17:39,809	32k	INFO	====> Epoch: 59
+2023-02-09 01:18:40,881	32k	INFO	Train Epoch: 60 [70%]
+2023-02-09 01:18:40,882	32k	INFO	[2.4092752933502197, 2.3126306533813477, 11.08122730255127, 19.267513275146484, 0.5496549010276794, 4000, 9.926516709918191e-05]
+2023-02-09 01:18:45,348	32k	INFO	Saving model and optimizer state at iteration 60 to ./logs\32k\G_4000.pth
+2023-02-09 01:19:01,949	32k	INFO	Saving model and optimizer state at iteration 60 to ./logs\32k\D_4000.pth
+2023-02-09 01:19:22,256	32k	INFO	====> Epoch: 60
+2023-02-09 01:20:39,651	32k	INFO	====> Epoch: 61
+2023-02-09 01:21:57,031	32k	INFO	====> Epoch: 62
+2023-02-09 01:22:57,338	32k	INFO	Train Epoch: 63 [69%]
+2023-02-09 01:22:57,338	32k	INFO	[2.407628059387207, 2.4265987873077393, 9.841700553894043, 20.425884246826172, 1.1777186393737793, 4200, 9.922794731438052e-05]
+2023-02-09 01:23:14,639	32k	INFO	====> Epoch: 63
+2023-02-09 01:24:32,051	32k	INFO	====> Epoch: 64
+2023-02-09 01:25:49,407	32k	INFO	====> Epoch: 65
+2023-02-09 01:26:48,771	32k	INFO	Train Epoch: 66 [67%]
+2023-02-09 01:26:48,772	32k	INFO	[2.4743025302886963, 2.436379909515381, 9.201873779296875, 18.20071029663086, 0.933559000492096, 4400, 9.919074148525384e-05]
+2023-02-09 01:27:06,967	32k	INFO	====> Epoch: 66
+2023-02-09 01:28:24,205	32k	INFO	====> Epoch: 67
+2023-02-09 01:29:41,611	32k	INFO	====> Epoch: 68
+2023-02-09 01:30:55,639	32k	INFO	Train Epoch: 69 [66%]
+2023-02-09 01:30:55,640	32k	INFO	[2.476935863494873, 2.297214984893799, 11.784645080566406, 20.394569396972656, 0.9714463353157043, 4600, 9.915354960656915e-05]
+2023-02-09 01:31:14,621	32k	INFO	====> Epoch: 69
+2023-02-09 01:32:31,900	32k	INFO	====> Epoch: 70
+2023-02-09 01:33:49,165	32k	INFO	====> Epoch: 71
+2023-02-09 01:34:46,907	32k	INFO	Train Epoch: 72 [64%]
+2023-02-09 01:34:46,908	32k	INFO	[2.5771408081054688, 2.3849239349365234, 10.185483932495117, 17.841482162475586, 0.7261289358139038, 4800, 9.911637167309565e-05]
+2023-02-09 01:35:06,816	32k	INFO	====> Epoch: 72
+2023-02-09 01:36:24,001	32k	INFO	====> Epoch: 73
+2023-02-09 01:37:41,262	32k	INFO	====> Epoch: 74
+2023-02-09 01:38:38,250	32k	INFO	Train Epoch: 75 [63%]
+2023-02-09 01:38:38,250	32k	INFO	[2.465418815612793, 2.232435941696167, 10.514191627502441, 20.439416885375977, 1.0744853019714355, 5000, 9.907920767960457e-05]
+2023-02-09 01:38:42,755	32k	INFO	Saving model and optimizer state at iteration 75 to ./logs\32k\G_5000.pth
+2023-02-09 01:38:58,887	32k	INFO	Saving model and optimizer state at iteration 75 to ./logs\32k\D_5000.pth
+2023-02-09 01:39:23,174	32k	INFO	====> Epoch: 75
+2023-02-09 01:40:40,570	32k	INFO	====> Epoch: 76
+2023-02-09 01:41:58,063	32k	INFO	====> Epoch: 77
+2023-02-09 01:42:54,165	32k	INFO	Train Epoch: 78 [61%]
+2023-02-09 01:42:54,165	32k	INFO	[2.6826303005218506, 2.095520496368408, 3.6221730709075928, 12.267330169677734, 0.8276277184486389, 5200, 9.904205762086905e-05]
+2023-02-09 01:43:15,721	32k	INFO	====> Epoch: 78
+2023-02-09 01:44:33,155	32k	INFO	====> Epoch: 79
+2023-02-09 01:45:50,627	32k	INFO	====> Epoch: 80
+2023-02-09 01:46:45,893	32k	INFO	Train Epoch: 81 [60%]
+2023-02-09 01:46:45,893	32k	INFO	[2.520723342895508, 2.3147475719451904, 9.123695373535156, 19.63064956665039, 0.5058915019035339, 5400, 9.900492149166423e-05]
+2023-02-09 01:47:08,201	32k	INFO	====> Epoch: 81
+2023-02-09 01:48:25,508	32k	INFO	====> Epoch: 82
+2023-02-09 01:49:42,705	32k	INFO	====> Epoch: 83
+2023-02-09 01:50:37,129	32k	INFO	Train Epoch: 84 [58%]
+2023-02-09 01:50:37,129	32k	INFO	[2.2849955558776855, 2.513012409210205, 12.865352630615234, 19.767900466918945, 0.9678688049316406, 5600, 9.896779928676716e-05]
+2023-02-09 01:51:00,273	32k	INFO	====> Epoch: 84
+2023-02-09 01:52:17,483	32k	INFO	====> Epoch: 85
+2023-02-09 01:53:34,853	32k	INFO	====> Epoch: 86
+2023-02-09 01:54:28,632	32k	INFO	Train Epoch: 87 [57%]
+2023-02-09 01:54:28,633	32k	INFO	[2.230534553527832, 2.553818464279175, 10.746828079223633, 17.61080551147461, 0.6103332042694092, 5800, 9.89306910009569e-05]
+2023-02-09 01:54:52,593	32k	INFO	====> Epoch: 87
+2023-02-09 01:56:09,924	32k	INFO	====> Epoch: 88
+2023-02-09 01:57:27,133	32k	INFO	====> Epoch: 89
+2023-02-09 01:58:19,773	32k	INFO	Train Epoch: 90 [55%]
+2023-02-09 01:58:19,773	32k	INFO	[2.9170384407043457, 1.8074010610580444, 6.252444267272949, 11.759191513061523, 0.5820555090904236, 6000, 9.889359662901445e-05]
+2023-02-09 01:58:24,216	32k	INFO	Saving model and optimizer state at iteration 90 to ./logs\32k\G_6000.pth
+2023-02-09 01:58:41,937	32k	INFO	Saving model and optimizer state at iteration 90 to ./logs\32k\D_6000.pth
+2023-02-09 01:59:10,016	32k	INFO	====> Epoch: 90
+2023-02-09 02:00:27,480	32k	INFO	====> Epoch: 91
+2023-02-09 02:01:44,927	32k	INFO	====> Epoch: 92
+2023-02-09 02:02:36,973	32k	INFO	Train Epoch: 93 [54%]
+2023-02-09 02:02:36,974	32k	INFO	[2.248124837875366, 2.691680908203125, 9.031513214111328, 17.063570022583008, 0.8340508937835693, 6200, 9.885651616572276e-05]
+2023-02-09 02:03:02,579	32k	INFO	====> Epoch: 93
+2023-02-09 02:04:19,880	32k	INFO	====> Epoch: 94
+2023-02-09 02:05:37,183	32k	INFO	====> Epoch: 95
+2023-02-09 02:06:28,292	32k	INFO	Train Epoch: 96 [52%]
+2023-02-09 02:06:28,292	32k	INFO	[2.7041618824005127, 2.0895659923553467, 6.933722019195557, 14.279449462890625, 0.9478716254234314, 6400, 9.881944960586671e-05]
+2023-02-09 02:06:54,691	32k	INFO	====> Epoch: 96
+2023-02-09 02:08:11,826	32k	INFO	====> Epoch: 97
+2023-02-09 02:09:29,228	32k	INFO	====> Epoch: 98
+2023-02-09 02:10:19,605	32k	INFO	Train Epoch: 99 [51%]
+2023-02-09 02:10:19,605	32k	INFO	[2.465763568878174, 2.286102294921875, 8.612666130065918, 17.839487075805664, 0.8176154494285583, 6600, 9.87823969442332e-05]
+2023-02-09 02:10:46,982	32k	INFO	====> Epoch: 99
+2023-02-09 02:12:04,533	32k	INFO	====> Epoch: 100
+2023-02-09 02:13:21,854	32k	INFO	====> Epoch: 101
+2023-02-09 02:14:11,250	32k	INFO	Train Epoch: 102 [49%]
+2023-02-09 02:14:11,250	32k	INFO	[2.325270652770996, 2.503894567489624, 13.427793502807617, 18.58626937866211, 1.1115485429763794, 6800, 9.874535817561101e-05]
+2023-02-09 02:14:39,395	32k	INFO	====> Epoch: 102
+2023-02-09 02:15:56,665	32k	INFO	====> Epoch: 103
+2023-02-09 02:17:13,783	32k	INFO	====> Epoch: 104
+2023-02-09 02:18:02,422	32k	INFO	Train Epoch: 105 [48%]
+2023-02-09 02:18:02,422	32k	INFO	[2.5144426822662354, 2.262126922607422, 9.593130111694336, 19.810073852539062, 0.9265149235725403, 7000, 9.870833329479095e-05]
+2023-02-09 02:18:06,802	32k	INFO	Saving model and optimizer state at iteration 105 to ./logs\32k\G_7000.pth
+2023-02-09 02:18:26,842	32k	INFO	Saving model and optimizer state at iteration 105 to ./logs\32k\D_7000.pth
+2023-02-09 02:18:59,625	32k	INFO	====> Epoch: 105
+2023-02-09 02:20:16,918	32k	INFO	====> Epoch: 106
+2023-02-09 02:21:34,168	32k	INFO	====> Epoch: 107
+2023-02-09 02:22:22,051	32k	INFO	Train Epoch: 108 [46%]
+2023-02-09 02:22:22,051	32k	INFO	[2.5437726974487305, 1.968860387802124, 8.248473167419434, 15.724898338317871, 0.8549317717552185, 7200, 9.867132229656573e-05]
+2023-02-09 02:22:51,735	32k	INFO	====> Epoch: 108
+2023-02-09 02:24:08,913	32k	INFO	====> Epoch: 109
+2023-02-09 02:25:26,159	32k	INFO	====> Epoch: 110
+2023-02-09 02:26:13,089	32k	INFO	Train Epoch: 111 [45%]
+2023-02-09 02:26:13,090	32k	INFO	[2.4943034648895264, 2.1690049171447754, 8.534195899963379, 18.047616958618164, 0.6213272213935852, 7400, 9.863432517573002e-05]
+2023-02-09 02:26:43,675	32k	INFO	====> Epoch: 111
+2023-02-09 02:28:00,862	32k	INFO	====> Epoch: 112
+2023-02-09 02:29:18,116	32k	INFO	====> Epoch: 113
+2023-02-09 02:30:04,341	32k	INFO	Train Epoch: 114 [43%]
+2023-02-09 02:30:04,342	32k	INFO	[2.590273141860962, 2.244678497314453, 8.331713676452637, 17.773584365844727, 0.4851672947406769, 7600, 9.859734192708044e-05]
+2023-02-09 02:30:35,745	32k	INFO	====> Epoch: 114
+2023-02-09 02:31:52,967	32k	INFO	====> Epoch: 115
+2023-02-09 02:33:10,215	32k	INFO	====> Epoch: 116
+2023-02-09 02:33:55,596	32k	INFO	Train Epoch: 117 [42%]
+2023-02-09 02:33:55,597	32k	INFO	[2.43920636177063, 2.299633026123047, 10.326704978942871, 19.797151565551758, 0.7202500104904175, 7800, 9.85603725454156e-05]
+2023-02-09 02:34:27,882	32k	INFO	====> Epoch: 117
+2023-02-09 02:35:45,221	32k	INFO	====> Epoch: 118
+2023-02-09 02:37:02,373	32k	INFO	====> Epoch: 119
+2023-02-09 02:37:46,886	32k	INFO	Train Epoch: 120 [40%]
+2023-02-09 02:37:46,887	32k	INFO	[2.7452502250671387, 1.881577968597412, 6.52833890914917, 14.799930572509766, 0.5330202579498291, 8000, 9.8523417025536e-05]
+2023-02-09 02:37:51,352	32k	INFO	Saving model and optimizer state at iteration 120 to ./logs\32k\G_8000.pth
+2023-02-09 02:38:05,481	32k	INFO	Saving model and optimizer state at iteration 120 to ./logs\32k\D_8000.pth
+2023-02-09 02:38:41,822	32k	INFO	====> Epoch: 120
+2023-02-09 02:39:59,271	32k	INFO	====> Epoch: 121
+2023-02-09 02:41:16,709	32k	INFO	====> Epoch: 122
+2023-02-09 02:42:00,437	32k	INFO	Train Epoch: 123 [39%]
+2023-02-09 02:42:00,437	32k	INFO	[2.5362741947174072, 2.0902092456817627, 8.579750061035156, 16.44462776184082, 0.640954852104187, 8200, 9.848647536224416e-05]
+2023-02-09 02:42:34,336	32k	INFO	====> Epoch: 123
+2023-02-09 02:43:51,542	32k	INFO	====> Epoch: 124
+2023-02-09 02:45:08,701	32k	INFO	====> Epoch: 125
+2023-02-09 02:45:51,519	32k	INFO	Train Epoch: 126 [37%]
+2023-02-09 02:45:51,519	32k	INFO	[2.5620243549346924, 2.1529924869537354, 6.262371063232422, 14.607771873474121, 0.6447804570198059, 8400, 9.84495475503445e-05]
+2023-02-09 02:46:26,225	32k	INFO	====> Epoch: 126
+2023-02-09 02:47:43,549	32k	INFO	====> Epoch: 127
+2023-02-09 02:49:00,851	32k	INFO	====> Epoch: 128
+2023-02-09 02:49:43,071	32k	INFO	Train Epoch: 129 [36%]
+2023-02-09 02:49:43,072	32k	INFO	[2.6855649948120117, 2.093135356903076, 8.865744590759277, 14.999298095703125, 0.4640387296676636, 8600, 9.841263358464336e-05]
+2023-02-09 02:50:18,555	32k	INFO	====> Epoch: 129
+2023-02-09 02:51:35,826	32k	INFO	====> Epoch: 130
+2023-02-09 02:52:53,221	32k	INFO	====> Epoch: 131
+2023-02-09 02:53:34,341	32k	INFO	Train Epoch: 132 [34%]
+2023-02-09 02:53:34,342	32k	INFO	[2.45322322845459, 2.1377835273742676, 7.04033899307251, 14.747097969055176, 0.6762568950653076, 8800, 9.837573345994909e-05]
+2023-02-09 02:54:10,739	32k	INFO	====> Epoch: 132
+2023-02-09 02:55:27,964	32k	INFO	====> Epoch: 133
+2023-02-09 02:56:45,156	32k	INFO	====> Epoch: 134
+2023-02-09 02:57:25,569	32k	INFO	Train Epoch: 135 [33%]
+2023-02-09 02:57:25,569	32k	INFO	[2.516730308532715, 2.3212270736694336, 9.098499298095703, 18.753353118896484, 0.8016514182090759, 9000, 9.833884717107196e-05]
+2023-02-09 02:57:30,052	32k	INFO	Saving model and optimizer state at iteration 135 to ./logs\32k\G_9000.pth
+2023-02-09 02:57:49,313	32k	INFO	Saving model and optimizer state at iteration 135 to ./logs\32k\D_9000.pth
+2023-02-09 02:58:30,142	32k	INFO	====> Epoch: 135
+2023-02-09 02:59:47,578	32k	INFO	====> Epoch: 136
+2023-02-09 03:01:04,989	32k	INFO	====> Epoch: 137
+2023-02-09 03:01:44,604	32k	INFO	Train Epoch: 138 [31%]
+2023-02-09 03:01:44,604	32k	INFO	[2.5661885738372803, 2.1553306579589844, 12.048609733581543, 19.532424926757812, 1.2839446067810059, 9200, 9.830197471282419e-05]
+2023-02-09 03:02:22,615	32k	INFO	====> Epoch: 138
+2023-02-09 03:03:39,975	32k	INFO	====> Epoch: 139
+2023-02-09 03:04:57,273	32k	INFO	====> Epoch: 140
+2023-02-09 03:05:35,900	32k	INFO	Train Epoch: 141 [30%]
+2023-02-09 03:05:35,901	32k	INFO	[2.4171464443206787, 2.1831154823303223, 7.9948649406433105, 16.37220001220703, 0.44416582584381104, 9400, 9.826511608001993e-05]
+2023-02-09 03:06:14,772	32k	INFO	====> Epoch: 141
+2023-02-09 03:07:32,122	32k	INFO	====> Epoch: 142
+2023-02-09 03:08:49,280	32k	INFO	====> Epoch: 143
+2023-02-09 03:09:27,103	32k	INFO	Train Epoch: 144 [28%]
+2023-02-09 03:09:27,104	32k	INFO	[2.356396436691284, 2.559436798095703, 10.186924934387207, 18.508615493774414, 0.9834058284759521, 9600, 9.822827126747529e-05]
+2023-02-09 03:10:06,785	32k	INFO	====> Epoch: 144
+2023-02-09 03:11:24,022	32k	INFO	====> Epoch: 145
+2023-02-09 03:12:41,266	32k	INFO	====> Epoch: 146
+2023-02-09 03:13:18,484	32k	INFO	Train Epoch: 147 [27%]
+2023-02-09 03:13:18,485	32k	INFO	[2.5275373458862305, 2.34236478805542, 11.132747650146484, 20.919368743896484, 0.8620054721832275, 9800, 9.819144027000834e-05]
+2023-02-09 03:13:58,998	32k	INFO	====> Epoch: 147
+2023-02-09 03:15:16,211	32k	INFO	====> Epoch: 148
+2023-02-09 03:16:33,518	32k	INFO	====> Epoch: 149
+2023-02-09 03:17:09,655	32k	INFO	Train Epoch: 150 [25%]
+2023-02-09 03:17:09,655	32k	INFO	[2.490751028060913, 2.18687105178833, 8.969452857971191, 16.82117462158203, 0.8959197998046875, 10000, 9.815462308243906e-05]
+2023-02-09 03:17:14,164	32k	INFO	Saving model and optimizer state at iteration 150 to ./logs\32k\G_10000.pth
+2023-02-09 03:17:34,194	32k	INFO	Saving model and optimizer state at iteration 150 to ./logs\32k\D_10000.pth
+2023-02-09 03:18:19,227	32k	INFO	====> Epoch: 150
+2023-02-09 03:19:36,776	32k	INFO	====> Epoch: 151
+2023-02-09 03:20:54,200	32k	INFO	====> Epoch: 152
+2023-02-09 03:21:29,735	32k	INFO	Train Epoch: 153 [24%]
+2023-02-09 03:21:29,735	32k	INFO	[2.548633575439453, 2.158717393875122, 10.489928245544434, 18.32561683654785, 1.113476276397705, 10200, 9.811781969958938e-05]
+2023-02-09 03:22:11,964	32k	INFO	====> Epoch: 153
+2023-02-09 03:23:29,243	32k	INFO	====> Epoch: 154
+2023-02-09 03:24:46,521	32k	INFO	====> Epoch: 155
+2023-02-09 03:25:21,203	32k	INFO	Train Epoch: 156 [22%]
+2023-02-09 03:25:21,204	32k	INFO	[2.6085586547851562, 2.2196943759918213, 6.367833137512207, 16.077957153320312, 1.0498080253601074, 10400, 9.808103011628319e-05]
+2023-02-09 03:26:04,324	32k	INFO	====> Epoch: 156
+2023-02-09 03:27:21,640	32k	INFO	====> Epoch: 157
+2023-02-09 03:28:38,959	32k	INFO	====> Epoch: 158
+2023-02-09 03:29:12,697	32k	INFO	Train Epoch: 159 [21%]
+2023-02-09 03:29:12,697	32k	INFO	[2.4405250549316406, 2.2709829807281494, 9.20801830291748, 17.569778442382812, 0.8504314422607422, 10600, 9.804425432734629e-05]
+2023-02-09 03:29:56,480	32k	INFO	====> Epoch: 159
+2023-02-09 03:31:13,840	32k	INFO	====> Epoch: 160
+2023-02-09 03:32:31,264	32k	INFO	====> Epoch: 161
+2023-02-09 03:33:04,232	32k	INFO	Train Epoch: 162 [19%]
+2023-02-09 03:33:04,232	32k	INFO	[2.257436752319336, 2.3079981803894043, 7.7498016357421875, 11.477513313293457, 0.9249159693717957, 10800, 9.800749232760646e-05]
+2023-02-09 03:33:48,914	32k	INFO	====> Epoch: 162
+2023-02-09 03:35:06,402	32k	INFO	====> Epoch: 163
+2023-02-09 03:36:23,790	32k	INFO	====> Epoch: 164
+2023-02-09 03:36:56,013	32k	INFO	Train Epoch: 165 [18%]
+2023-02-09 03:36:56,014	32k	INFO	[2.8059158325195312, 2.351126194000244, 6.358356952667236, 17.166919708251953, 0.9102870225906372, 11000, 9.797074411189339e-05]
+2023-02-09 03:37:00,483	32k	INFO	Saving model and optimizer state at iteration 165 to ./logs\32k\G_11000.pth
+2023-02-09 03:37:19,672	32k	INFO	Saving model and optimizer state at iteration 165 to ./logs\32k\D_11000.pth
+2023-02-09 03:38:09,015	32k	INFO	====> Epoch: 165
+2023-02-09 03:39:26,362	32k	INFO	====> Epoch: 166
+2023-02-09 03:40:43,704	32k	INFO	====> Epoch: 167
+2023-02-09 03:41:15,021	32k	INFO	Train Epoch: 168 [16%]
+2023-02-09 03:41:15,021	32k	INFO	[2.475376844406128, 2.1227407455444336, 12.1411771774292, 18.27838897705078, 0.7968288064002991, 11200, 9.79340096750387e-05]
+2023-02-09 03:42:01,310	32k	INFO	====> Epoch: 168
+2023-02-09 03:43:18,712	32k	INFO	====> Epoch: 169
+2023-02-09 03:44:36,075	32k	INFO	====> Epoch: 170
+2023-02-09 03:45:06,554	32k	INFO	Train Epoch: 171 [15%]
+2023-02-09 03:45:06,554	32k	INFO	[2.3927536010742188, 2.2263312339782715, 12.99544620513916, 19.178787231445312, 0.8865859508514404, 11400, 9.789728901187598e-05]
+2023-02-09 03:45:53,638	32k	INFO	====> Epoch: 171
+2023-02-09 03:47:10,832	32k	INFO	====> Epoch: 172
+2023-02-09 03:48:28,090	32k	INFO	====> Epoch: 173
+2023-02-09 03:48:57,840	32k	INFO	Train Epoch: 174 [13%]
+2023-02-09 03:48:57,841	32k	INFO	[2.629887580871582, 1.9182734489440918, 8.588482856750488, 16.633516311645508, 0.8498013019561768, 11600, 9.786058211724074e-05]
+2023-02-09 03:49:45,780	32k	INFO	====> Epoch: 174
+2023-02-09 03:51:03,165	32k	INFO	====> Epoch: 175
+2023-02-09 03:52:20,366	32k	INFO	====> Epoch: 176
+2023-02-09 03:52:48,998	32k	INFO	Train Epoch: 177 [12%]
+2023-02-09 03:52:48,999	32k	INFO	[2.5986101627349854, 2.2137248516082764, 8.166472434997559, 14.432415962219238, 0.9092381596565247, 11800, 9.782388898597041e-05]
+2023-02-09 03:53:37,879	32k	INFO	====> Epoch: 177
+2023-02-09 03:54:55,253	32k	INFO	====> Epoch: 178
+2023-02-09 03:56:12,342	32k	INFO	====> Epoch: 179
+2023-02-09 03:56:40,201	32k	INFO	Train Epoch: 180 [10%]
+2023-02-09 03:56:40,202	32k	INFO	[2.377601146697998, 2.379459857940674, 10.841035842895508, 17.28376007080078, 1.0782395601272583, 12000, 9.778720961290439e-05]
+2023-02-09 03:56:44,727	32k	INFO	Saving model and optimizer state at iteration 180 to ./logs\32k\G_12000.pth
+2023-02-09 03:57:02,672	32k	INFO	Saving model and optimizer state at iteration 180 to ./logs\32k\D_12000.pth
+2023-02-09 03:57:55,590	32k	INFO	====> Epoch: 180
+2023-02-09 03:59:12,901	32k	INFO	====> Epoch: 181
+2023-02-09 04:00:30,140	32k	INFO	====> Epoch: 182
+2023-02-09 04:00:57,262	32k	INFO	Train Epoch: 183 [9%]
+2023-02-09 04:00:57,263	32k	INFO	[2.599541664123535, 2.0492987632751465, 7.433438301086426, 15.581904411315918, 0.7799375653266907, 12200, 9.7750543992884e-05]
+2023-02-09 04:01:47,831	32k	INFO	====> Epoch: 183
+2023-02-09 04:03:05,064	32k	INFO	====> Epoch: 184
+2023-02-09 04:04:22,336	32k	INFO	====> Epoch: 185
+2023-02-09 04:04:48,629	32k	INFO	Train Epoch: 186 [7%]
+2023-02-09 04:04:48,629	32k	INFO	[2.708937406539917, 2.195622682571411, 8.098736763000488, 16.33951187133789, 1.0215028524398804, 12400, 9.771389212075249e-05]
+2023-02-09 04:05:39,876	32k	INFO	====> Epoch: 186
+2023-02-09 04:06:57,132	32k	INFO	====> Epoch: 187
+2023-02-09 04:08:14,442	32k	INFO	====> Epoch: 188
+2023-02-09 04:08:39,906	32k	INFO	Train Epoch: 189 [6%]
+2023-02-09 04:08:39,907	32k	INFO	[2.543393135070801, 2.578526258468628, 9.853076934814453, 19.175247192382812, 0.9220128655433655, 12600, 9.767725399135504e-05]
+2023-02-09 04:09:31,978	32k	INFO	====> Epoch: 189
+2023-02-09 04:10:49,213	32k	INFO	====> Epoch: 190
+2023-02-09 04:12:06,344	32k	INFO	====> Epoch: 191
+2023-02-09 04:12:31,054	32k	INFO	Train Epoch: 192 [4%]
+2023-02-09 04:12:31,055	32k	INFO	[2.5891571044921875, 2.2922167778015137, 9.008637428283691, 15.618788719177246, 0.6253546476364136, 12800, 9.764062959953878e-05]
+2023-02-09 04:13:24,094	32k	INFO	====> Epoch: 192
+2023-02-09 04:14:41,399	32k	INFO	====> Epoch: 193
+2023-02-09 04:15:58,594	32k	INFO	====> Epoch: 194
+2023-02-09 04:16:22,307	32k	INFO	Train Epoch: 195 [3%]
+2023-02-09 04:16:22,307	32k	INFO	[2.571204900741577, 2.3370144367218018, 11.431970596313477, 19.423154830932617, 0.8416497707366943, 13000, 9.760401894015275e-05]
+2023-02-09 04:16:26,662	32k	INFO	Saving model and optimizer state at iteration 195 to ./logs\32k\G_13000.pth
+2023-02-09 04:16:45,402	32k	INFO	Saving model and optimizer state at iteration 195 to ./logs\32k\D_13000.pth
+2023-02-09 04:17:42,745	32k	INFO	====> Epoch: 195
+2023-02-09 04:19:00,170	32k	INFO	====> Epoch: 196
+2023-02-09 04:20:17,357	32k	INFO	====> Epoch: 197
+2023-02-09 04:20:40,493	32k	INFO	Train Epoch: 198 [1%]
+2023-02-09 04:20:40,493	32k	INFO	[2.6889920234680176, 2.216010332107544, 9.279974937438965, 17.859813690185547, 0.6048229932785034, 13200, 9.756742200804793e-05]
+2023-02-09 04:21:35,077	32k	INFO	====> Epoch: 198
+2023-02-09 04:22:52,432	32k	INFO	====> Epoch: 199
+2023-02-09 04:24:09,676	32k	INFO	====> Epoch: 200
+2023-02-09 04:24:31,716	32k	INFO	Train Epoch: 201 [0%]
+2023-02-09 04:24:31,716	32k	INFO	[2.5305514335632324, 2.058469772338867, 7.646169662475586, 16.716352462768555, 1.0848625898361206, 13400, 9.753083879807726e-05]
+2023-02-09 04:25:27,141	32k	INFO	====> Epoch: 201
+2023-02-09 04:26:44,453	32k	INFO	====> Epoch: 202
+2023-02-09 04:28:01,216	32k	INFO	Train Epoch: 203 [99%]
+2023-02-09 04:28:01,217	32k	INFO	[2.600423812866211, 2.4881081581115723, 8.454794883728027, 15.183467864990234, 0.6778528690338135, 13600, 9.750645761229709e-05]
+2023-02-09 04:28:01,979	32k	INFO	====> Epoch: 203
+2023-02-09 04:29:19,697	32k	INFO	====> Epoch: 204
+2023-02-09 04:30:36,964	32k	INFO	====> Epoch: 205
+2023-02-09 04:31:52,908	32k	INFO	Train Epoch: 206 [97%]
+2023-02-09 04:31:52,908	32k	INFO	[2.2509708404541016, 2.403043270111084, 12.252212524414062, 18.043561935424805, 0.5615130662918091, 13800, 9.746989726111722e-05]
+2023-02-09 04:31:54,581	32k	INFO	====> Epoch: 206
+2023-02-09 04:33:11,675	32k	INFO	====> Epoch: 207
+2023-02-09 04:34:28,928	32k	INFO	====> Epoch: 208
+2023-02-09 04:35:44,062	32k	INFO	Train Epoch: 209 [96%]
+2023-02-09 04:35:44,062	32k	INFO	[2.617187261581421, 2.0524563789367676, 7.7549052238464355, 16.08784294128418, 0.8999805450439453, 14000, 9.743335061835535e-05]
+2023-02-09 04:35:48,541	32k	INFO	Saving model and optimizer state at iteration 209 to ./logs\32k\G_14000.pth
+2023-02-09 04:36:07,364	32k	INFO	Saving model and optimizer state at iteration 209 to ./logs\32k\D_14000.pth
+2023-02-09 04:36:13,209	32k	INFO	====> Epoch: 209
+2023-02-09 04:37:30,391	32k	INFO	====> Epoch: 210
+2023-02-09 04:38:48,344	32k	INFO	====> Epoch: 211
+2023-02-09 04:40:03,265	32k	INFO	Train Epoch: 212 [94%]
+2023-02-09 04:40:03,266	32k	INFO	[2.545370578765869, 2.182567596435547, 11.445052146911621, 18.725894927978516, 1.0447421073913574, 14200, 9.739681767887146e-05]
+2023-02-09 04:40:06,510	32k	INFO	====> Epoch: 212
+2023-02-09 04:41:23,719	32k	INFO	====> Epoch: 213
+2023-02-09 04:42:40,924	32k	INFO	====> Epoch: 214
+2023-02-09 04:43:54,429	32k	INFO	Train Epoch: 215 [93%]
+2023-02-09 04:43:54,429	32k	INFO	[2.492203950881958, 2.0672144889831543, 7.254634380340576, 17.217519760131836, 1.2190320491790771, 14400, 9.736029843752747e-05]
+2023-02-09 04:43:58,496	32k	INFO	====> Epoch: 215
+2023-02-09 04:45:16,448	32k	INFO	====> Epoch: 216
+2023-02-09 04:46:33,534	32k	INFO	====> Epoch: 217
+2023-02-09 04:47:46,895	32k	INFO	Train Epoch: 218 [91%]
+2023-02-09 04:47:46,895	32k	INFO	[2.3578548431396484, 2.3237144947052, 9.533674240112305, 16.62578773498535, 0.6960291862487793, 14600, 9.732379288918723e-05]
+2023-02-09 04:47:51,775	32k	INFO	====> Epoch: 218
+2023-02-09 04:49:09,870	32k	INFO	====> Epoch: 219
+2023-02-09 04:50:27,058	32k	INFO	====> Epoch: 220
+2023-02-09 04:51:38,829	32k	INFO	Train Epoch: 221 [90%]
+2023-02-09 04:51:38,829	32k	INFO	[2.4930238723754883, 2.0849337577819824, 10.19448471069336, 19.00885772705078, 0.8905020952224731, 14800, 9.728730102871649e-05]
+2023-02-09 04:51:44,567	32k	INFO	====> Epoch: 221
+2023-02-09 04:53:01,748	32k	INFO	====> Epoch: 222
+2023-02-09 04:54:18,922	32k	INFO	====> Epoch: 223
+2023-02-09 04:55:29,903	32k	INFO	Train Epoch: 224 [88%]
+2023-02-09 04:55:29,903	32k	INFO	[2.5622823238372803, 2.385986566543579, 8.60080623626709, 15.33399486541748, 0.5517116189002991, 15000, 9.725082285098293e-05]
+2023-02-09 04:55:34,236	32k	INFO	Saving model and optimizer state at iteration 224 to ./logs\32k\G_15000.pth
+2023-02-09 04:55:51,622	32k	INFO	Saving model and optimizer state at iteration 224 to ./logs\32k\D_15000.pth
+2023-02-09 04:56:01,949	32k	INFO	====> Epoch: 224
+2023-02-09 04:57:20,182	32k	INFO	====> Epoch: 225
+2023-02-09 04:58:38,052	32k	INFO	====> Epoch: 226
+2023-02-09 04:59:48,934	32k	INFO	Train Epoch: 227 [87%]
+2023-02-09 04:59:48,934	32k	INFO	[2.699136972427368, 2.1048390865325928, 7.052115440368652, 13.004521369934082, 0.8088562488555908, 15200, 9.721435835085619e-05]
+2023-02-09 04:59:56,318	32k	INFO	====> Epoch: 227
+2023-02-09 05:01:14,332	32k	INFO	====> Epoch: 228
+2023-02-09 05:02:32,242	32k	INFO	====> Epoch: 229
+2023-02-09 05:03:41,538	32k	INFO	Train Epoch: 230 [85%]
+2023-02-09 05:03:41,538	32k	INFO	[2.5119049549102783, 2.255728244781494, 10.959632873535156, 17.58148765563965, 0.4408050775527954, 15400, 9.717790752320778e-05]
+2023-02-09 05:03:49,736	32k	INFO	====> Epoch: 230
+2023-02-09 05:05:07,018	32k	INFO	====> Epoch: 231
+2023-02-09 05:06:24,410	32k	INFO	====> Epoch: 232
+2023-02-09 05:07:32,817	32k	INFO	Train Epoch: 233 [84%]
+2023-02-09 05:07:32,817	32k	INFO	[2.4837000370025635, 2.74263858795166, 8.370596885681152, 18.025814056396484, 0.5485965609550476, 15600, 9.714147036291117e-05]
+2023-02-09 05:07:41,860	32k	INFO	====> Epoch: 233
+2023-02-09 05:08:59,157	32k	INFO	====> Epoch: 234
+2023-02-09 05:10:16,236	32k	INFO	====> Epoch: 235
+2023-02-09 05:11:23,813	32k	INFO	Train Epoch: 236 [82%]
+2023-02-09 05:11:23,814	32k	INFO	[2.615351915359497, 2.0890252590179443, 7.356450080871582, 16.96343994140625, 0.756279706954956, 15800, 9.710504686484176e-05]
+2023-02-09 05:11:33,681	32k	INFO	====> Epoch: 236
+2023-02-09 05:12:51,784	32k	INFO	====> Epoch: 237
+2023-02-09 05:14:09,960	32k	INFO	====> Epoch: 238
+2023-02-09 05:15:16,775	32k	INFO	Train Epoch: 239 [81%]
+2023-02-09 05:15:16,775	32k	INFO	[2.424504518508911, 2.7784013748168945, 9.054712295532227, 16.200336456298828, 0.7321522831916809, 16000, 9.706863702387684e-05]
+2023-02-09 05:15:21,217	32k	INFO	Saving model and optimizer state at iteration 239 to ./logs\32k\G_16000.pth
+2023-02-09 05:15:38,638	32k	INFO	Saving model and optimizer state at iteration 239 to ./logs\32k\D_16000.pth
+2023-02-09 05:15:52,955	32k	INFO	====> Epoch: 239
+2023-02-09 05:17:11,161	32k	INFO	====> Epoch: 240
+2023-02-09 05:18:29,322	32k	INFO	====> Epoch: 241
+2023-02-09 05:19:35,475	32k	INFO	Train Epoch: 242 [79%]
+2023-02-09 05:19:35,476	32k	INFO	[2.4205849170684814, 2.218247413635254, 9.974660873413086, 17.676557540893555, 0.6420414447784424, 16200, 9.703224083489565e-05]
+2023-02-09 05:19:47,005	32k	INFO	====> Epoch: 242
+2023-02-09 05:21:04,968	32k	INFO	====> Epoch: 243
+2023-02-09 05:22:22,082	32k	INFO	====> Epoch: 244
+2023-02-09 05:23:27,331	32k	INFO	Train Epoch: 245 [78%]
+2023-02-09 05:23:27,332	32k	INFO	[2.514897584915161, 2.4238204956054688, 9.19957447052002, 17.169466018676758, 0.6433432698249817, 16400, 9.699585829277933e-05]
+2023-02-09 05:23:39,660	32k	INFO	====> Epoch: 245
+2023-02-09 05:24:56,840	32k	INFO	====> Epoch: 246
+2023-02-09 05:26:14,893	32k	INFO	====> Epoch: 247
+2023-02-09 05:27:19,222	32k	INFO	Train Epoch: 248 [76%]
+2023-02-09 05:27:19,223	32k	INFO	[2.485236406326294, 2.0981593132019043, 8.128880500793457, 17.695823669433594, 0.830685555934906, 16600, 9.695948939241093e-05]
+2023-02-09 05:27:32,499	32k	INFO	====> Epoch: 248
+2023-02-09 05:28:49,748	32k	INFO	====> Epoch: 249
+2023-02-09 05:30:07,019	32k	INFO	====> Epoch: 250
+2023-02-09 05:31:10,400	32k	INFO	Train Epoch: 251 [75%]
+2023-02-09 05:31:10,400	32k	INFO	[2.519704580307007, 2.247236490249634, 8.27247428894043, 16.95021629333496, 0.7045202255249023, 16800, 9.692313412867544e-05]
+2023-02-09 05:31:24,372	32k	INFO	====> Epoch: 251
+2023-02-09 05:32:41,518	32k	INFO	====> Epoch: 252
+2023-02-09 05:33:58,597	32k	INFO	====> Epoch: 253
+2023-02-09 05:35:01,233	32k	INFO	Train Epoch: 254 [73%]
+2023-02-09 05:35:01,233	32k	INFO	[2.723464012145996, 2.004565477371216, 8.685633659362793, 17.136409759521484, 0.7529882788658142, 17000, 9.68867924964598e-05]
+2023-02-09 05:35:05,562	32k	INFO	Saving model and optimizer state at iteration 254 to ./logs\32k\G_17000.pth
+2023-02-09 05:35:21,651	32k	INFO	Saving model and optimizer state at iteration 254 to ./logs\32k\D_17000.pth
+2023-02-09 05:35:40,285	32k	INFO	====> Epoch: 254
+2023-02-09 05:36:58,360	32k	INFO	====> Epoch: 255
+2023-02-09 05:38:16,245	32k	INFO	====> Epoch: 256
+2023-02-09 05:39:18,827	32k	INFO	Train Epoch: 257 [72%]
+2023-02-09 05:39:18,828	32k	INFO	[2.5846078395843506, 2.0677099227905273, 7.601481914520264, 16.82730484008789, 0.46195539832115173, 17200, 9.685046449065278e-05]
+2023-02-09 05:39:34,463	32k	INFO	====> Epoch: 257
+2023-02-09 05:40:52,484	32k	INFO	====> Epoch: 258
+2023-02-09 05:42:09,468	32k	INFO	====> Epoch: 259
+2023-02-09 05:43:10,293	32k	INFO	Train Epoch: 260 [70%]
+2023-02-09 05:43:10,294	32k	INFO	[2.612074375152588, 2.061795234680176, 9.713607788085938, 17.434011459350586, 1.13643217086792, 17400, 9.681415010614512e-05]
+2023-02-09 05:43:26,783	32k	INFO	====> Epoch: 260
+2023-02-09 05:44:43,852	32k	INFO	====> Epoch: 261
+2023-02-09 05:46:00,940	32k	INFO	====> Epoch: 262
+2023-02-09 05:47:00,945	32k	INFO	Train Epoch: 263 [69%]
+2023-02-09 05:47:00,945	32k	INFO	[2.5661118030548096, 2.090351104736328, 10.91147232055664, 18.185489654541016, 0.6497505903244019, 17600, 9.67778493378295e-05]
+2023-02-09 05:47:18,327	32k	INFO	====> Epoch: 263
+2023-02-09 05:48:35,339	32k	INFO	====> Epoch: 264
+2023-02-09 05:49:52,519	32k	INFO	====> Epoch: 265
+2023-02-09 05:50:51,655	32k	INFO	Train Epoch: 266 [67%]
+2023-02-09 05:50:51,656	32k	INFO	[2.441783905029297, 2.2755415439605713, 8.574409484863281, 17.144365310668945, 1.0087649822235107, 17800, 9.674156218060047e-05]
+2023-02-09 05:51:09,757	32k	INFO	====> Epoch: 266
+2023-02-09 05:52:26,654	32k	INFO	====> Epoch: 267
+2023-02-09 05:53:43,656	32k	INFO	====> Epoch: 268
+2023-02-09 05:54:42,178	32k	INFO	Train Epoch: 269 [66%]
+2023-02-09 05:54:42,178	32k	INFO	[2.4041616916656494, 2.2542026042938232, 10.453770637512207, 16.896394729614258, 0.874467670917511, 18000, 9.670528862935451e-05]
+2023-02-09 05:54:46,567	32k	INFO	Saving model and optimizer state at iteration 269 to ./logs\32k\G_18000.pth
+2023-02-09 05:55:05,522	32k	INFO	Saving model and optimizer state at iteration 269 to ./logs\32k\D_18000.pth
+2023-02-09 05:55:28,321	32k	INFO	====> Epoch: 269
+2023-02-09 05:56:46,319	32k	INFO	====> Epoch: 270
+2023-02-09 05:58:04,206	32k	INFO	====> Epoch: 271
+2023-02-09 05:59:02,672	32k	INFO	Train Epoch: 272 [64%]
+2023-02-09 05:59:02,672	32k	INFO	[2.449748992919922, 2.5339150428771973, 10.715926170349121, 18.00714683532715, 0.7727646231651306, 18200, 9.666902867899003e-05]
+2023-02-09 05:59:22,434	32k	INFO	====> Epoch: 272
+2023-02-09 06:00:40,362	32k	INFO	====> Epoch: 273
+2023-02-09 06:01:58,348	32k	INFO	====> Epoch: 274
+2023-02-09 06:02:55,204	32k	INFO	Train Epoch: 275 [63%]
+2023-02-09 06:02:55,204	32k	INFO	[2.5664258003234863, 2.2539126873016357, 8.40420913696289, 19.330455780029297, 1.2545685768127441, 18400, 9.663278232440732e-05]
+2023-02-09 06:03:15,780	32k	INFO	====> Epoch: 275
+2023-02-09 06:04:33,087	32k	INFO	====> Epoch: 276
+2023-02-09 06:05:50,254	32k	INFO	====> Epoch: 277
+2023-02-09 06:06:46,232	32k	INFO	Train Epoch: 278 [61%]
+2023-02-09 06:06:46,233	32k	INFO	[2.5849533081054688, 2.1905155181884766, 7.024458885192871, 16.025548934936523, 0.6339879035949707, 18600, 9.659654956050859e-05]
+2023-02-09 06:07:07,698	32k	INFO	====> Epoch: 278
+2023-02-09 06:08:24,695	32k	INFO	====> Epoch: 279
+2023-02-09 06:09:41,686	32k	INFO	====> Epoch: 280
+2023-02-09 06:10:36,725	32k	INFO	Train Epoch: 281 [60%]
+2023-02-09 06:10:36,725	32k	INFO	[2.621020793914795, 1.9545187950134277, 8.0020112991333, 14.753083229064941, 0.5816468000411987, 18800, 9.656033038219798e-05]
+2023-02-09 06:10:58,970	32k	INFO	====> Epoch: 281
+2023-02-09 06:12:16,092	32k	INFO	====> Epoch: 282
+2023-02-09 06:13:34,226	32k	INFO	====> Epoch: 283
+2023-02-09 06:14:29,367	32k	INFO	Train Epoch: 284 [58%]
+2023-02-09 06:14:29,367	32k	INFO	[2.5978457927703857, 2.0676093101501465, 10.083847045898438, 15.90844440460205, 0.649118959903717, 19000, 9.652412478438153e-05]
+2023-02-09 06:14:34,626	32k	INFO	Saving model and optimizer state at iteration 284 to ./logs\32k\G_19000.pth
+2023-02-09 06:14:53,784	32k	INFO	Saving model and optimizer state at iteration 284 to ./logs\32k\D_19000.pth
+2023-02-09 06:15:20,487	32k	INFO	====> Epoch: 284
+2023-02-09 06:16:38,462	32k	INFO	====> Epoch: 285
+2023-02-09 06:17:56,373	32k	INFO	====> Epoch: 286
+2023-02-09 06:18:49,853	32k	INFO	Train Epoch: 287 [57%]
+2023-02-09 06:18:49,853	32k	INFO	[2.6181414127349854, 2.086658477783203, 7.769612789154053, 13.018095970153809, 0.7441149950027466, 19200, 9.64879327619672e-05]
+2023-02-09 06:19:13,818	32k	INFO	====> Epoch: 287
+2023-02-09 06:20:30,767	32k	INFO	====> Epoch: 288
+2023-02-09 06:21:47,755	32k	INFO	====> Epoch: 289
+2023-02-09 06:22:41,198	32k	INFO	Train Epoch: 290 [55%]
+2023-02-09 06:22:41,198	32k	INFO	[2.5569255352020264, 2.232271671295166, 9.287805557250977, 17.64458656311035, 0.9140328168869019, 19400, 9.645175430986486e-05]
+2023-02-09 06:23:05,925	32k	INFO	====> Epoch: 290
+2023-02-09 06:24:23,877	32k	INFO	====> Epoch: 291
+2023-02-09 06:25:41,896	32k	INFO	====> Epoch: 292
+2023-02-09 06:26:34,600	32k	INFO	Train Epoch: 293 [54%]
+2023-02-09 06:26:34,600	32k	INFO	[2.3312571048736572, 2.471904993057251, 8.659703254699707, 14.936263084411621, 0.5374557971954346, 19600, 9.641558942298625e-05]
+2023-02-09 06:27:00,329	32k	INFO	====> Epoch: 293
+2023-02-09 06:28:17,379	32k	INFO	====> Epoch: 294
+2023-02-09 06:29:34,319	32k	INFO	====> Epoch: 295
+2023-02-09 06:30:25,273	32k	INFO	Train Epoch: 296 [52%]
+2023-02-09 06:30:25,273	32k	INFO	[2.580073118209839, 2.0791285037994385, 6.087092876434326, 15.868849754333496, 0.6256889700889587, 19800, 9.637943809624507e-05]
+2023-02-09 06:30:51,672	32k	INFO	====> Epoch: 296
+2023-02-09 06:32:08,760	32k	INFO	====> Epoch: 297
+2023-02-09 06:33:25,874	32k	INFO	====> Epoch: 298
+2023-02-09 06:34:16,143	32k	INFO	Train Epoch: 299 [51%]
+2023-02-09 06:34:16,143	32k	INFO	[2.7276787757873535, 2.1408498287200928, 7.651435852050781, 13.118131637573242, 0.8090221881866455, 20000, 9.634330032455689e-05]
+2023-02-09 06:34:20,490	32k	INFO	Saving model and optimizer state at iteration 299 to ./logs\32k\G_20000.pth
+2023-02-09 06:34:38,151	32k	INFO	Saving model and optimizer state at iteration 299 to ./logs\32k\D_20000.pth
+2023-02-09 06:35:09,071	32k	INFO	====> Epoch: 299
+2023-02-09 06:36:26,919	32k	INFO	====> Epoch: 300
+2023-02-09 06:37:44,715	32k	INFO	====> Epoch: 301
+2023-02-09 06:38:34,897	32k	INFO	Train Epoch: 302 [49%]
+2023-02-09 06:38:34,897	32k	INFO	[2.3907358646392822, 2.586805820465088, 12.788217544555664, 17.23185157775879, 0.9444102048873901, 20200, 9.63071761028392e-05]
+2023-02-09 06:39:02,960	32k	INFO	====> Epoch: 302
+2023-02-09 06:40:19,965	32k	INFO	====> Epoch: 303
+2023-02-09 06:41:36,910	32k	INFO	====> Epoch: 304
+2023-02-09 06:42:25,288	32k	INFO	Train Epoch: 305 [48%]
+2023-02-09 06:42:25,288	32k	INFO	[2.543513059616089, 2.2357592582702637, 11.66889476776123, 17.664663314819336, 0.9346078038215637, 20400, 9.627106542601141e-05]
+2023-02-09 06:42:54,185	32k	INFO	====> Epoch: 305
+2023-02-09 06:44:11,243	32k	INFO	====> Epoch: 306
+2023-02-09 06:45:28,529	32k	INFO	====> Epoch: 307
+2023-02-09 06:46:16,141	32k	INFO	Train Epoch: 308 [46%]
+2023-02-09 06:46:16,142	32k	INFO	[2.2425007820129395, 2.718269109725952, 10.438041687011719, 14.429862022399902, 0.450414776802063, 20600, 9.62349682889948e-05]
+2023-02-09 06:46:45,885	32k	INFO	====> Epoch: 308
+2023-02-09 06:48:02,967	32k	INFO	====> Epoch: 309
+2023-02-09 06:49:20,096	32k	INFO	====> Epoch: 310
+2023-02-09 06:50:06,904	32k	INFO	Train Epoch: 311 [45%]
+2023-02-09 06:50:06,904	32k	INFO	[2.511903762817383, 2.2772610187530518, 7.602670192718506, 16.86819839477539, 0.4354688823223114, 20800, 9.619888468671259e-05]
+2023-02-09 06:50:37,485	32k	INFO	====> Epoch: 311
+2023-02-09 06:51:54,650	32k	INFO	====> Epoch: 312
+2023-02-09 06:53:11,706	32k	INFO	====> Epoch: 313
+2023-02-09 06:53:57,594	32k	INFO	Train Epoch: 314 [43%]
+2023-02-09 06:53:57,595	32k	INFO	[2.548445463180542, 2.205153226852417, 9.582110404968262, 17.887943267822266, 0.8334051966667175, 21000, 9.61628146140899e-05]
+2023-02-09 06:54:01,984	32k	INFO	Saving model and optimizer state at iteration 314 to ./logs\32k\G_21000.pth
+2023-02-09 06:54:19,329	32k	INFO	Saving model and optimizer state at iteration 314 to ./logs\32k\D_21000.pth
+2023-02-09 06:54:54,644	32k	INFO	====> Epoch: 314
+2023-02-09 06:56:12,706	32k	INFO	====> Epoch: 315
+2023-02-09 06:57:30,625	32k	INFO	====> Epoch: 316
+2023-02-09 06:58:16,770	32k	INFO	Train Epoch: 317 [42%]
+2023-02-09 06:58:16,770	32k	INFO	[2.5582966804504395, 2.0421464443206787, 9.006149291992188, 15.637856483459473, 1.0011146068572998, 21200, 9.612675806605373e-05]
+2023-02-09 06:58:48,980	32k	INFO	====> Epoch: 317
+2023-02-09 07:00:06,236	32k	INFO	====> Epoch: 318
+2023-02-09 07:01:23,359	32k	INFO	====> Epoch: 319
+2023-02-09 07:02:08,658	32k	INFO	Train Epoch: 320 [40%]
+2023-02-09 07:02:08,658	32k	INFO	[2.39801287651062, 2.3436264991760254, 10.229707717895508, 19.279922485351562, 0.727281391620636, 21400, 9.609071503753299e-05]
+2023-02-09 07:02:41,674	32k	INFO	====> Epoch: 320
+2023-02-09 07:03:59,633	32k	INFO	====> Epoch: 321
+2023-02-09 07:05:17,720	32k	INFO	====> Epoch: 322
+2023-02-09 07:06:01,216	32k	INFO	Train Epoch: 323 [39%]
+2023-02-09 07:06:01,216	32k	INFO	[2.6565518379211426, 2.263072967529297, 6.5126566886901855, 15.140740394592285, 0.8894218802452087, 21600, 9.60546855234585e-05]
+2023-02-09 07:06:35,104	32k	INFO	====> Epoch: 323
+2023-02-09 07:07:52,224	32k	INFO	====> Epoch: 324
+2023-02-09 07:09:09,258	32k	INFO	====> Epoch: 325
+2023-02-09 07:09:51,895	32k	INFO	Train Epoch: 326 [37%]
+2023-02-09 07:09:51,896	32k	INFO	[2.869607448577881, 1.8302068710327148, 5.35368013381958, 10.451361656188965, 0.7921611070632935, 21800, 9.601866951876297e-05]
+2023-02-09 07:10:26,577	32k	INFO	====> Epoch: 326
+2023-02-09 07:11:43,674	32k	INFO	====> Epoch: 327
+2023-02-09 07:13:00,883	32k	INFO	====> Epoch: 328
+2023-02-09 07:13:42,678	32k	INFO	Train Epoch: 329 [36%]
+2023-02-09 07:13:42,678	32k	INFO	[2.542959451675415, 2.098376512527466, 9.891724586486816, 15.331761360168457, 0.7128269076347351, 22000, 9.5982667018381e-05]
+2023-02-09 07:13:47,172	32k	INFO	Saving model and optimizer state at iteration 329 to ./logs\32k\G_22000.pth
+2023-02-09 07:14:04,902	32k	INFO	Saving model and optimizer state at iteration 329 to ./logs\32k\D_22000.pth
+2023-02-09 07:14:44,184	32k	INFO	====> Epoch: 329