2023-02-14 13:12:41,308	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': {'hiyo': 0}, 'model_dir': './logs\\32k'}
2023-02-14 13:12:58,959	32k	INFO	Loaded checkpoint './logs\32k\G_0.pth' (iteration 1)
2023-02-14 13:12:59,334	32k	INFO	Loaded checkpoint './logs\32k\D_0.pth' (iteration 1)
2023-02-14 13:13:25,423	32k	INFO	Train Epoch: 1 [0%]
2023-02-14 13:13:25,424	32k	INFO	[2.7184720039367676, 2.6034836769104004, 11.849114418029785, 47.374595642089844, 9.48661994934082, 0, 0.0001]
2023-02-14 13:13:30,918	32k	INFO	Saving model and optimizer state at iteration 1 to ./logs\32k\G_0.pth
2023-02-14 13:13:47,803	32k	INFO	Saving model and optimizer state at iteration 1 to ./logs\32k\D_0.pth
2023-02-14 13:15:02,651	32k	INFO	====> Epoch: 1
2023-02-14 13:16:34,036	32k	INFO	====> Epoch: 2
2023-02-14 13:17:26,475	32k	INFO	Train Epoch: 3 [44%]
2023-02-14 13:17:26,475	32k	INFO	[2.6585214138031006, 2.366513252258301, 10.955318450927734, 23.930110931396484, 1.0435259342193604, 200, 9.99750015625e-05]
2023-02-14 13:18:05,652	32k	INFO	====> Epoch: 3
2023-02-14 13:19:36,775	32k	INFO	====> Epoch: 4
2023-02-14 13:20:59,955	32k	INFO	Train Epoch: 5 [88%]
2023-02-14 13:20:59,955	32k	INFO	[2.501133441925049, 2.350158452987671, 9.644698143005371, 22.92068862915039, 0.7671791315078735, 400, 9.995000937421877e-05]
2023-02-14 13:21:08,211	32k	INFO	====> Epoch: 5
2023-02-14 13:22:39,553	32k	INFO	====> Epoch: 6
2023-02-14 13:24:10,878	32k	INFO	====> Epoch: 7
2023-02-14 13:24:54,687	32k	INFO	Train Epoch: 8 [32%]
2023-02-14 13:24:54,688	32k	INFO	[2.5303597450256348, 2.077411413192749, 12.605754852294922, 22.12200355529785, 1.3761812448501587, 600, 9.991253280566489e-05]
2023-02-14 13:25:42,491	32k	INFO	====> Epoch: 8
2023-02-14 13:27:13,843	32k	INFO	====> Epoch: 9
2023-02-14 13:28:28,606	32k	INFO	Train Epoch: 10 [76%]
2023-02-14 13:28:28,606	32k	INFO	[2.6250500679016113, 2.1927847862243652, 10.148731231689453, 20.529062271118164, 0.5864875316619873, 800, 9.98875562335968e-05]
2023-02-14 13:28:45,437	32k	INFO	====> Epoch: 10
2023-02-14 13:30:16,681	32k	INFO	====> Epoch: 11
2023-02-14 13:31:48,026	32k	INFO	====> Epoch: 12
2023-02-14 13:32:23,413	32k	INFO	Train Epoch: 13 [20%]
2023-02-14 13:32:23,413	32k	INFO	[2.4075894355773926, 2.307851791381836, 11.19032096862793, 22.16382598876953, 1.286623239517212, 1000, 9.98501030820433e-05]
2023-02-14 13:32:27,543	32k	INFO	Saving model and optimizer state at iteration 13 to ./logs\32k\G_1000.pth
2023-02-14 13:32:44,941	32k	INFO	Saving model and optimizer state at iteration 13 to ./logs\32k\D_1000.pth
2023-02-14 13:33:44,326	32k	INFO	====> Epoch: 13
2023-02-14 13:35:15,814	32k	INFO	====> Epoch: 14
2023-02-14 13:36:22,132	32k	INFO	Train Epoch: 15 [63%]
2023-02-14 13:36:22,132	32k	INFO	[2.5968217849731445, 2.164472818374634, 8.871170043945312, 20.146108627319336, 0.8499955534934998, 1200, 9.982514211643064e-05]
2023-02-14 13:36:47,621	32k	INFO	====> Epoch: 15
2023-02-14 13:38:19,098	32k	INFO	====> Epoch: 16
2023-02-14 13:39:50,485	32k	INFO	====> Epoch: 17
2023-02-14 13:40:17,300	32k	INFO	Train Epoch: 18 [7%]
2023-02-14 13:40:17,300	32k	INFO	[2.55147647857666, 2.1380293369293213, 11.658864974975586, 21.447202682495117, 1.0160374641418457, 1400, 9.978771236724554e-05]
2023-02-14 13:41:22,298	32k	INFO	====> Epoch: 18
2023-02-14 13:42:53,875	32k	INFO	====> Epoch: 19
2023-02-14 13:43:51,564	32k	INFO	Train Epoch: 20 [51%]
2023-02-14 13:43:51,565	32k	INFO	[2.5742850303649902, 2.244190216064453, 8.551454544067383, 19.180246353149414, 0.9120607376098633, 1600, 9.976276699833672e-05]
2023-02-14 13:44:25,617	32k	INFO	====> Epoch: 20
2023-02-14 13:45:57,188	32k	INFO	====> Epoch: 21
2023-02-14 13:47:25,925	32k	INFO	Train Epoch: 22 [95%]
2023-02-14 13:47:25,925	32k	INFO	[2.5543036460876465, 2.2138490676879883, 10.766397476196289, 21.196224212646484, 0.7341710329055786, 1800, 9.973782786538036e-05]
2023-02-14 13:47:29,068	32k	INFO	====> Epoch: 22
2023-02-14 13:49:00,647	32k	INFO	====> Epoch: 23
2023-02-14 13:50:32,139	32k	INFO	====> Epoch: 24
2023-02-14 13:51:21,238	32k	INFO	Train Epoch: 25 [39%]
2023-02-14 13:51:21,238	32k	INFO	[2.556088447570801, 2.2134010791778564, 11.56871223449707, 19.313758850097656, 1.033575177192688, 2000, 9.970043085494672e-05]
2023-02-14 13:51:25,444	32k	INFO	Saving model and optimizer state at iteration 25 to ./logs\32k\G_2000.pth
2023-02-14 13:51:42,644	32k	INFO	Saving model and optimizer state at iteration 25 to ./logs\32k\D_2000.pth
2023-02-14 13:52:28,601	32k	INFO	====> Epoch: 25
2023-02-14 13:54:00,018	32k	INFO	====> Epoch: 26
2023-02-14 13:55:20,005	32k	INFO	Train Epoch: 27 [83%]
2023-02-14 13:55:20,005	32k	INFO	[2.6195037364959717, 2.2152504920959473, 9.765068054199219, 19.97799301147461, 0.9908173680305481, 2200, 9.967550730505221e-05]
2023-02-14 13:55:31,723	32k	INFO	====> Epoch: 27
2023-02-14 13:57:03,227	32k	INFO	====> Epoch: 28
2023-02-14 13:58:38,892	32k	INFO	====> Epoch: 29
2023-02-14 13:59:19,484	32k	INFO	Train Epoch: 30 [27%]
2023-02-14 13:59:19,484	32k	INFO	[2.4715116024017334, 2.2915375232696533, 13.360817909240723, 22.131193161010742, 1.3168777227401733, 2400, 9.963813366190753e-05]
2023-02-14 14:00:10,655	32k	INFO	====> Epoch: 30
2023-02-14 14:01:42,081	32k	INFO	====> Epoch: 31
2023-02-14 14:02:53,596	32k	INFO	Train Epoch: 32 [71%]
2023-02-14 14:02:53,596	32k	INFO	[2.593963384628296, 2.0387449264526367, 9.328591346740723, 17.955137252807617, 0.30882203578948975, 2600, 9.961322568533789e-05]
2023-02-14 14:03:13,877	32k	INFO	====> Epoch: 32
2023-02-14 14:04:45,281	32k	INFO	====> Epoch: 33
2023-02-14 14:06:16,692	32k	INFO	====> Epoch: 34
2023-02-14 14:06:48,668	32k	INFO	Train Epoch: 35 [15%]
2023-02-14 14:06:48,669	32k	INFO	[2.5350048542022705, 1.9605062007904053, 7.99984884262085, 16.31661033630371, 0.5040841698646545, 2800, 9.957587539488128e-05]
2023-02-14 14:07:48,449	32k	INFO	====> Epoch: 35
2023-02-14 14:09:20,138	32k	INFO	====> Epoch: 36
2023-02-14 14:10:23,083	32k	INFO	Train Epoch: 37 [59%]
2023-02-14 14:10:23,083	32k	INFO	[2.4650959968566895, 2.2761664390563965, 9.025921821594238, 19.93177604675293, 0.5862252712249756, 3000, 9.95509829819056e-05]
2023-02-14 14:10:27,302	32k	INFO	Saving model and optimizer state at iteration 37 to ./logs\32k\G_3000.pth
2023-02-14 14:10:44,327	32k	INFO	Saving model and optimizer state at iteration 37 to ./logs\32k\D_3000.pth
2023-02-14 14:11:16,616	32k	INFO	====> Epoch: 37
2023-02-14 14:12:50,532	32k	INFO	====> Epoch: 38
2023-02-14 14:14:23,408	32k	INFO	====> Epoch: 39
2023-02-14 14:14:46,793	32k	INFO	Train Epoch: 40 [2%]
2023-02-14 14:14:46,794	32k	INFO	[2.3914408683776855, 2.3529629707336426, 12.639781951904297, 19.53929901123047, 0.5159912109375, 3200, 9.951365602954526e-05]
2023-02-14 14:15:55,371	32k	INFO	====> Epoch: 40
2023-02-14 14:17:27,123	32k	INFO	====> Epoch: 41
2023-02-14 14:18:21,616	32k	INFO	Train Epoch: 42 [46%]
2023-02-14 14:18:21,616	32k	INFO	[2.4428353309631348, 2.258873462677002, 10.040135383605957, 17.569446563720703, 0.9567443132400513, 3400, 9.948877917043875e-05]
2023-02-14 14:18:59,327	32k	INFO	====> Epoch: 42
2023-02-14 14:20:31,127	32k	INFO	====> Epoch: 43
2023-02-14 14:21:56,538	32k	INFO	Train Epoch: 44 [90%]
2023-02-14 14:21:56,539	32k	INFO	[2.3611392974853516, 2.405252456665039, 12.823965072631836, 20.826034545898438, 0.8344462513923645, 3600, 9.94639085301583e-05]
2023-02-14 14:22:03,119	32k	INFO	====> Epoch: 44
2023-02-14 14:23:34,741	32k	INFO	====> Epoch: 45
2023-02-14 14:25:06,470	32k	INFO	====> Epoch: 46
2023-02-14 14:25:52,327	32k	INFO	Train Epoch: 47 [34%]
2023-02-14 14:25:52,327	32k	INFO	[2.4644553661346436, 2.2366631031036377, 11.37562370300293, 20.556499481201172, 0.9424434304237366, 3800, 9.942661422663591e-05]
2023-02-14 14:26:38,621	32k	INFO	====> Epoch: 47
2023-02-14 14:28:10,337	32k	INFO	====> Epoch: 48
2023-02-14 14:29:27,206	32k	INFO	Train Epoch: 49 [78%]
2023-02-14 14:29:27,206	32k	INFO	[2.5212016105651855, 2.3413655757904053, 10.579129219055176, 19.582210540771484, 0.94678795337677, 4000, 9.940175912662009e-05]
2023-02-14 14:29:31,315	32k	INFO	Saving model and optimizer state at iteration 49 to ./logs\32k\G_4000.pth
2023-02-14 14:29:48,727	32k	INFO	Saving model and optimizer state at iteration 49 to ./logs\32k\D_4000.pth
2023-02-14 14:30:07,524	32k	INFO	====> Epoch: 49
2023-02-14 14:31:39,485	32k	INFO	====> Epoch: 50
2023-02-14 14:33:11,385	32k	INFO	====> Epoch: 51
2023-02-14 14:33:48,633	32k	INFO	Train Epoch: 52 [22%]
2023-02-14 14:33:48,634	32k	INFO	[2.2625677585601807, 2.5999932289123535, 16.192663192749023, 21.976665496826172, 0.7557628750801086, 4200, 9.936448812621091e-05]
2023-02-14 14:34:43,699	32k	INFO	====> Epoch: 52
2023-02-14 14:36:15,604	32k	INFO	====> Epoch: 53
2023-02-14 14:37:23,825	32k	INFO	Train Epoch: 54 [66%]
2023-02-14 14:37:23,825	32k	INFO	[2.500643730163574, 2.2777493000030518, 8.213461875915527, 17.501588821411133, 0.7716516256332397, 4400, 9.933964855674948e-05]
2023-02-14 14:37:47,647	32k	INFO	====> Epoch: 54
2023-02-14 14:39:19,631	32k	INFO	====> Epoch: 55
2023-02-14 14:40:51,967	32k	INFO	====> Epoch: 56
2023-02-14 14:41:20,508	32k	INFO	Train Epoch: 57 [10%]
2023-02-14 14:41:20,508	32k	INFO	[2.4037282466888428, 2.369466543197632, 11.0797758102417, 18.995946884155273, 0.6406805515289307, 4600, 9.930240084489267e-05]
2023-02-14 14:42:23,986	32k	INFO	====> Epoch: 57
2023-02-14 14:43:56,023	32k	INFO	====> Epoch: 58
2023-02-14 14:44:55,964	32k	INFO	Train Epoch: 59 [54%]
2023-02-14 14:44:55,965	32k	INFO	[2.243375778198242, 2.4461309909820557, 15.596379280090332, 22.729717254638672, 0.9244585037231445, 4800, 9.927757679628145e-05]
2023-02-14 14:45:28,557	32k	INFO	====> Epoch: 59
2023-02-14 14:47:00,367	32k	INFO	====> Epoch: 60
2023-02-14 14:48:31,104	32k	INFO	Train Epoch: 61 [98%]
2023-02-14 14:48:31,105	32k	INFO	[2.5503604412078857, 2.111229658126831, 8.807084083557129, 16.10047721862793, 1.107606053352356, 5000, 9.92527589532945e-05]
2023-02-14 14:48:35,266	32k	INFO	Saving model and optimizer state at iteration 61 to ./logs\32k\G_5000.pth
2023-02-14 14:48:51,439	32k	INFO	Saving model and optimizer state at iteration 61 to ./logs\32k\D_5000.pth
2023-02-14 14:48:56,220	32k	INFO	====> Epoch: 61
2023-02-14 14:50:27,858	32k	INFO	====> Epoch: 62
2023-02-14 14:51:59,496	32k	INFO	====> Epoch: 63
2023-02-14 14:52:50,498	32k	INFO	Train Epoch: 64 [41%]
2023-02-14 14:52:50,498	32k	INFO	[2.603325128555298, 2.3429884910583496, 11.47877025604248, 19.95724868774414, 0.4535157382488251, 5200, 9.921554382096622e-05]
2023-02-14 14:53:31,542	32k	INFO	====> Epoch: 64
2023-02-14 14:55:03,278	32k	INFO	====> Epoch: 65
2023-02-14 14:56:25,149	32k	INFO	Train Epoch: 66 [85%]
2023-02-14 14:56:25,149	32k	INFO	[2.548145055770874, 2.1794002056121826, 8.618596076965332, 18.201990127563477, 0.42253807187080383, 5400, 9.919074148525384e-05]
2023-02-14 14:56:35,169	32k	INFO	====> Epoch: 66
2023-02-14 14:58:06,861	32k	INFO	====> Epoch: 67
2023-02-14 14:59:38,893	32k	INFO	====> Epoch: 68
2023-02-14 15:00:21,295	32k	INFO	Train Epoch: 69 [29%]
2023-02-14 15:00:21,295	32k	INFO	[2.462594985961914, 2.514474868774414, 8.234503746032715, 16.56818962097168, 0.3946729898452759, 5600, 9.915354960656915e-05]
2023-02-14 15:01:11,023	32k	INFO	====> Epoch: 69
2023-02-14 15:02:42,743	32k	INFO	====> Epoch: 70
2023-02-14 15:03:56,160	32k	INFO	Train Epoch: 71 [73%]
2023-02-14 15:03:56,161	32k	INFO	[2.3674912452697754, 2.400963306427002, 11.68331241607666, 21.614397048950195, 0.8642704486846924, 5800, 9.912876276844171e-05]
2023-02-14 15:04:14,926	32k	INFO	====> Epoch: 71
2023-02-14 15:05:46,720	32k	INFO	====> Epoch: 72
2023-02-14 15:07:18,452	32k	INFO	====> Epoch: 73
2023-02-14 15:07:52,317	32k	INFO	Train Epoch: 74 [17%]
2023-02-14 15:07:52,318	32k	INFO	[2.3848366737365723, 2.3195393085479736, 11.794806480407715, 20.811321258544922, 0.9752936959266663, 6000, 9.909159412887068e-05]
2023-02-14 15:07:56,403	32k	INFO	Saving model and optimizer state at iteration 74 to ./logs\32k\G_6000.pth
2023-02-14 15:08:11,990	32k	INFO	Saving model and optimizer state at iteration 74 to ./logs\32k\D_6000.pth
2023-02-14 15:09:13,833	32k	INFO	====> Epoch: 74
2023-02-14 15:10:45,810	32k	INFO	====> Epoch: 75
2023-02-14 15:11:50,593	32k	INFO	Train Epoch: 76 [61%]
2023-02-14 15:11:50,594	32k	INFO	[2.413219451904297, 2.3621764183044434, 11.660679817199707, 19.284757614135742, 0.46086761355400085, 6200, 9.906682277864462e-05]
2023-02-14 15:12:17,911	32k	INFO	====> Epoch: 76
2023-02-14 15:13:49,709	32k	INFO	====> Epoch: 77
2023-02-14 15:15:21,342	32k	INFO	====> Epoch: 78
2023-02-14 15:15:46,525	32k	INFO	Train Epoch: 79 [5%]
2023-02-14 15:15:46,526	32k	INFO	[2.4900527000427246, 2.1775360107421875, 11.207487106323242, 18.59693717956543, 1.0485306978225708, 6400, 9.902967736366644e-05]
2023-02-14 15:16:53,382	32k	INFO	====> Epoch: 79
2023-02-14 15:18:25,180	32k	INFO	====> Epoch: 80
2023-02-14 15:19:21,341	32k	INFO	Train Epoch: 81 [49%]
2023-02-14 15:19:21,342	32k	INFO	[2.4466681480407715, 2.321159839630127, 10.79179573059082, 18.92087173461914, 0.9803774356842041, 6600, 9.900492149166423e-05]
2023-02-14 15:19:57,171	32k	INFO	====> Epoch: 81
2023-02-14 15:21:28,987	32k	INFO	====> Epoch: 82
2023-02-14 15:22:56,286	32k	INFO	Train Epoch: 83 [93%]
2023-02-14 15:22:56,287	32k	INFO	[2.425461530685425, 2.4654641151428223, 11.531881332397461, 20.07356071472168, 1.0066664218902588, 6800, 9.89801718082432e-05]
2023-02-14 15:23:01,149	32k	INFO	====> Epoch: 83
2023-02-14 15:24:32,889	32k	INFO	====> Epoch: 84
2023-02-14 15:26:04,666	32k	INFO	====> Epoch: 85
2023-02-14 15:26:52,217	32k	INFO	Train Epoch: 86 [37%]
2023-02-14 15:26:52,217	32k	INFO	[2.526332378387451, 2.203555107116699, 11.341078758239746, 20.046627044677734, 1.3592050075531006, 7000, 9.894305888331732e-05]
2023-02-14 15:26:56,417	32k	INFO	Saving model and optimizer state at iteration 86 to ./logs\32k\G_7000.pth
2023-02-14 15:27:14,398	32k	INFO	Saving model and optimizer state at iteration 86 to ./logs\32k\D_7000.pth
2023-02-14 15:28:02,287	32k	INFO	====> Epoch: 86
2023-02-14 15:29:33,965	32k	INFO	====> Epoch: 87
2023-02-14 15:30:52,509	32k	INFO	Train Epoch: 88 [80%]
2023-02-14 15:30:52,509	32k	INFO	[2.5021166801452637, 2.4259166717529297, 8.362788200378418, 17.521188735961914, 1.4112741947174072, 7200, 9.891832466458178e-05]
2023-02-14 15:31:05,995	32k	INFO	====> Epoch: 88
2023-02-14 15:32:37,695	32k	INFO	====> Epoch: 89
2023-02-14 15:34:09,432	32k	INFO	====> Epoch: 90
2023-02-14 15:34:48,347	32k	INFO	Train Epoch: 91 [24%]
2023-02-14 15:34:48,347	32k	INFO	[2.2584304809570312, 2.37516188621521, 11.344868659973145, 20.501863479614258, 0.8927188515663147, 7400, 9.888123492943583e-05]
2023-02-14 15:35:41,516	32k	INFO	====> Epoch: 91
2023-02-14 15:37:13,309	32k	INFO	====> Epoch: 92
2023-02-14 15:38:23,255	32k	INFO	Train Epoch: 93 [68%]
2023-02-14 15:38:23,256	32k	INFO	[2.407334327697754, 2.4313995838165283, 5.9084062576293945, 14.895120620727539, 0.34809207916259766, 7600, 9.885651616572276e-05]
2023-02-14 15:38:45,350	32k	INFO	====> Epoch: 93
2023-02-14 15:40:17,151	32k	INFO	====> Epoch: 94
2023-02-14 15:41:48,881	32k	INFO	====> Epoch: 95
2023-02-14 15:42:19,249	32k	INFO	Train Epoch: 96 [12%]
2023-02-14 15:42:19,250	32k	INFO	[2.475175142288208, 2.287485361099243, 12.157411575317383, 19.902236938476562, 0.8755276799201965, 7800, 9.881944960586671e-05]
2023-02-14 15:43:20,970	32k	INFO	====> Epoch: 96
2023-02-14 15:44:52,717	32k	INFO	====> Epoch: 97
2023-02-14 15:45:54,039	32k	INFO	Train Epoch: 98 [56%]
2023-02-14 15:45:54,040	32k	INFO	[2.4083688259124756, 2.3332595825195312, 8.003752708435059, 16.818313598632812, 0.21219749748706818, 8000, 9.879474628751914e-05]
2023-02-14 15:45:58,128	32k	INFO	Saving model and optimizer state at iteration 98 to ./logs\32k\G_8000.pth
2023-02-14 15:46:17,546	32k	INFO	Saving model and optimizer state at iteration 98 to ./logs\32k\D_8000.pth
2023-02-14 15:46:51,611	32k	INFO	====> Epoch: 98
2023-02-14 15:48:23,605	32k	INFO	====> Epoch: 99
2023-02-14 15:49:55,516	32k	INFO	====> Epoch: 100
2023-02-14 15:50:17,192	32k	INFO	Train Epoch: 101 [0%]
2023-02-14 15:50:17,193	32k	INFO	[2.4577560424804688, 2.4049196243286133, 9.910334587097168, 17.469593048095703, 1.0077900886535645, 8200, 9.875770288847208e-05]
2023-02-14 15:51:27,581	32k	INFO	====> Epoch: 101
2023-02-14 15:52:59,453	32k	INFO	====> Epoch: 102
2023-02-14 15:53:52,351	32k	INFO	Train Epoch: 103 [44%]
2023-02-14 15:53:52,352	32k	INFO	[2.3302505016326904, 2.4835445880889893, 12.40600299835205, 19.35659408569336, 1.1815543174743652, 8400, 9.873301500583906e-05]
2023-02-14 15:54:31,690	32k	INFO	====> Epoch: 103
2023-02-14 15:56:03,804	32k	INFO	====> Epoch: 104
2023-02-14 15:57:27,545	32k	INFO	Train Epoch: 105 [88%]
2023-02-14 15:57:27,546	32k	INFO	[2.442326068878174, 2.3513882160186768, 8.61225700378418, 19.59931755065918, 0.6629721522331238, 8600, 9.870833329479095e-05]
2023-02-14 15:57:35,926	32k	INFO	====> Epoch: 105
2023-02-14 15:59:07,717	32k	INFO	====> Epoch: 106
2023-02-14 16:00:39,510	32k	INFO	====> Epoch: 107
2023-02-14 16:01:23,648	32k	INFO	Train Epoch: 108 [32%]
2023-02-14 16:01:23,649	32k	INFO	[2.329326629638672, 2.2350986003875732, 12.052788734436035, 20.033588409423828, 0.4254380762577057, 8800, 9.867132229656573e-05]
2023-02-14 16:02:11,617	32k	INFO	====> Epoch: 108
2023-02-14 16:03:43,324	32k	INFO	====> Epoch: 109
2023-02-14 16:04:58,449	32k	INFO	Train Epoch: 110 [76%]
2023-02-14 16:04:58,449	32k	INFO	[2.2845022678375244, 2.4646148681640625, 13.254862785339355, 20.803882598876953, 0.39776870608329773, 9000, 9.864665600773098e-05]
2023-02-14 16:05:02,554	32k	INFO	Saving model and optimizer state at iteration 110 to ./logs\32k\G_9000.pth
2023-02-14 16:05:20,872	32k	INFO	Saving model and optimizer state at iteration 110 to ./logs\32k\D_9000.pth
2023-02-14 16:05:41,237	32k	INFO	====> Epoch: 110
2023-02-14 16:07:12,826	32k	INFO	====> Epoch: 111
2023-02-14 16:08:44,738	32k	INFO	====> Epoch: 112
2023-02-14 16:09:20,227	32k	INFO	Train Epoch: 113 [20%]
2023-02-14 16:09:20,228	32k	INFO	[2.4082977771759033, 2.255082845687866, 10.537095069885254, 17.689083099365234, 0.7687245607376099, 9200, 9.86096681355974e-05]
2023-02-14 16:10:16,749	32k	INFO	====> Epoch: 113
2023-02-14 16:11:48,707	32k	INFO	====> Epoch: 114
2023-02-14 16:12:55,268	32k	INFO	Train Epoch: 115 [63%]
2023-02-14 16:12:55,268	32k	INFO	[2.531501293182373, 2.146554946899414, 8.033232688903809, 17.08489990234375, 0.7147048115730286, 9400, 9.858501725933955e-05]
2023-02-14 16:13:20,854	32k	INFO	====> Epoch: 115
2023-02-14 16:14:52,750	32k	INFO	====> Epoch: 116
2023-02-14 16:16:24,652	32k	INFO	====> Epoch: 117
2023-02-14 16:16:51,576	32k	INFO	Train Epoch: 118 [7%]
2023-02-14 16:16:51,576	32k	INFO	[2.305955648422241, 2.5261385440826416, 11.671979904174805, 17.262502670288086, 0.9132139682769775, 9600, 9.854805249884741e-05]
2023-02-14 16:17:56,852	32k	INFO	====> Epoch: 118
2023-02-14 16:19:28,743	32k	INFO	====> Epoch: 119
2023-02-14 16:20:26,662	32k	INFO	Train Epoch: 120 [51%]
2023-02-14 16:20:26,663	32k	INFO	[2.406419038772583, 2.3848094940185547, 12.830516815185547, 19.702857971191406, 0.9984562397003174, 9800, 9.8523417025536e-05]
2023-02-14 16:21:00,932	32k	INFO	====> Epoch: 120
2023-02-14 16:22:32,586	32k	INFO	====> Epoch: 121
2023-02-14 16:24:01,646	32k	INFO	Train Epoch: 122 [95%]
2023-02-14 16:24:01,646	32k	INFO	[2.6088125705718994, 2.3717613220214844, 7.807750225067139, 17.539306640625, 0.6654758453369141, 10000, 9.8498787710708e-05]
2023-02-14 16:24:05,762	32k	INFO	Saving model and optimizer state at iteration 122 to ./logs\32k\G_10000.pth
2023-02-14 16:24:21,684	32k	INFO	Saving model and optimizer state at iteration 122 to ./logs\32k\D_10000.pth
2023-02-14 16:24:28,063	32k	INFO	====> Epoch: 122
2023-02-14 16:26:00,976	32k	INFO	====> Epoch: 123
2023-02-14 16:27:37,900	32k	INFO	====> Epoch: 124
2023-02-14 16:28:33,348	32k	INFO	Train Epoch: 125 [39%]
2023-02-14 16:28:33,348	32k	INFO	[2.4001426696777344, 2.470517158508301, 13.341828346252441, 17.714656829833984, 0.679930567741394, 10200, 9.846185528225477e-05]
2023-02-14 16:29:19,086	32k	INFO	====> Epoch: 125
2023-02-14 16:30:59,664	32k	INFO	====> Epoch: 126
2023-02-14 16:32:28,289	32k	INFO	Train Epoch: 127 [83%]
2023-02-14 16:32:28,290	32k	INFO	[2.491032600402832, 2.2914695739746094, 10.223406791687012, 18.337860107421875, 0.9281787872314453, 10400, 9.84372413569007e-05]
2023-02-14 16:32:41,455	32k	INFO	====> Epoch: 127
2023-02-14 16:34:22,334	32k	INFO	====> Epoch: 128
2023-02-14 16:36:01,890	32k	INFO	====> Epoch: 129
2023-02-14 16:36:43,266	32k	INFO	Train Epoch: 130 [27%]
2023-02-14 16:36:43,266	32k	INFO	[2.5348987579345703, 2.291205883026123, 14.648847579956055, 19.59044647216797, 0.8180601596832275, 10600, 9.840033200544528e-05]
2023-02-14 16:37:34,913	32k	INFO	====> Epoch: 130
2023-02-14 16:39:06,291	32k	INFO	====> Epoch: 131
2023-02-14 16:40:17,727	32k	INFO	Train Epoch: 132 [71%]
2023-02-14 16:40:17,727	32k	INFO	[2.404866933822632, 2.1770548820495605, 9.80937671661377, 16.411579132080078, 0.1555909514427185, 10800, 9.837573345994909e-05]
2023-02-14 16:40:37,992	32k	INFO	====> Epoch: 132
2023-02-14 16:42:09,446	32k	INFO	====> Epoch: 133
2023-02-14 16:43:42,676	32k	INFO	====> Epoch: 134
2023-02-14 16:44:16,320	32k	INFO	Train Epoch: 135 [15%]
2023-02-14 16:44:16,320	32k	INFO	[2.379476547241211, 2.3253965377807617, 12.204051971435547, 18.108963012695312, 0.6560300588607788, 11000, 9.833884717107196e-05]
2023-02-14 16:44:20,463	32k	INFO	Saving model and optimizer state at iteration 135 to ./logs\32k\G_11000.pth
2023-02-14 16:44:40,760	32k	INFO	Saving model and optimizer state at iteration 135 to ./logs\32k\D_11000.pth
2023-02-14 16:45:43,602	32k	INFO	====> Epoch: 135
2023-02-14 16:47:14,892	32k	INFO	====> Epoch: 136
2023-02-14 16:48:17,804	32k	INFO	Train Epoch: 137 [59%]
2023-02-14 16:48:17,804	32k	INFO	[2.182915210723877, 2.490650177001953, 13.673564910888672, 19.964622497558594, 0.8504718542098999, 11200, 9.831426399582366e-05]
2023-02-14 16:48:46,586	32k	INFO	====> Epoch: 137
2023-02-14 16:50:17,982	32k	INFO	====> Epoch: 138
2023-02-14 16:51:49,652	32k	INFO	====> Epoch: 139
2023-02-14 16:52:13,059	32k	INFO	Train Epoch: 140 [2%]
2023-02-14 16:52:13,059	32k	INFO	[2.4849462509155273, 2.2978439331054688, 10.085766792297363, 16.271583557128906, 0.9613367915153503, 11400, 9.827740075511432e-05]
2023-02-14 16:53:21,336	32k	INFO	====> Epoch: 140
2023-02-14 16:54:52,874	32k	INFO	====> Epoch: 141
2023-02-14 16:55:48,996	32k	INFO	Train Epoch: 142 [46%]
2023-02-14 16:55:48,996	32k	INFO	[2.419895887374878, 2.231900215148926, 9.550468444824219, 15.956775665283203, 0.6037944555282593, 11600, 9.825283294050992e-05]
2023-02-14 16:56:34,619	32k	INFO	====> Epoch: 142
2023-02-14 16:58:12,965	32k	INFO	====> Epoch: 143
2023-02-14 16:59:51,011	32k	INFO	Train Epoch: 144 [90%]
2023-02-14 16:59:51,012	32k	INFO	[2.526609182357788, 2.2438442707061768, 9.708596229553223, 18.94228744506836, 0.6018266081809998, 11800, 9.822827126747529e-05]
2023-02-14 16:59:58,459	32k	INFO	====> Epoch: 144
2023-02-14 17:01:41,567	32k	INFO	====> Epoch: 145
2023-02-14 17:03:19,851	32k	INFO	====> Epoch: 146
2023-02-14 17:04:09,172	32k	INFO	Train Epoch: 147 [34%]
2023-02-14 17:04:09,173	32k	INFO	[2.1274771690368652, 2.403050661087036, 14.53994083404541, 20.740161895751953, 0.8522300720214844, 12000, 9.819144027000834e-05]
2023-02-14 17:04:13,341	32k	INFO	Saving model and optimizer state at iteration 147 to ./logs\32k\G_12000.pth
2023-02-14 17:04:30,263	32k	INFO	Saving model and optimizer state at iteration 147 to ./logs\32k\D_12000.pth
2023-02-14 17:05:36,434	32k	INFO	====> Epoch: 147