2021-10-13 17:16:27,280 INFO [train.py:516] Training started
2021-10-13 17:16:27,280 INFO [train.py:517] {'exp_dir': PosixPath('tdnn_lstm_ctc-v2/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lr': 0.00025, 'feature_dim': 80, 'weight_decay': 0.0005, 'subsampling_factor': 3, 'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 10, 'reset_interval': 200, 'valid_interval': 1000, 'beam_size': 10, 'reduction': 'sum', 'use_double_scores': True, 'world_size': 1, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 20, 'start_epoch': 0, 'full_libri': True, 'feature_dir': PosixPath('data/fbank'), 'max_duration': 500, 'bucketing_sampler': True, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2}
2021-10-13 17:16:27,575 INFO [lexicon.py:113] Loading pre-compiled data/lang_phone/Linv.pt
2021-10-13 17:16:33,754 INFO [asr_datamodule.py:158] About to get train cuts
2021-10-13 17:16:33,755 INFO [asr_datamodule.py:319] About to get train cuts
2021-10-13 17:17:36,460 INFO [asr_datamodule.py:161] About to get Musan cuts
2021-10-13 17:17:37,645 INFO [asr_datamodule.py:164] About to create train dataset
2021-10-13 17:17:37,646 INFO [asr_datamodule.py:216] Using BucketingSampler.
2021-10-13 17:17:41,461 INFO [asr_datamodule.py:232] About to create train dataloader
2021-10-13 17:17:41,461 INFO [asr_datamodule.py:245] About to get dev cuts
2021-10-13 17:17:41,461 INFO [asr_datamodule.py:337] About to get dev cuts
2021-10-13 17:17:41,792 INFO [asr_datamodule.py:256] About to create dev dataset
2021-10-13 17:17:41,793 INFO [asr_datamodule.py:275] About to create dev dataloader
2021-10-13 17:17:46,074 INFO [train.py:451] Epoch 0, batch 0, batch avg loss 3.3296, total avg loss: 3.3296, batch size: 36
2021-10-13 17:17:51,172 INFO [train.py:451] Epoch 0, batch 10, batch avg loss 1.3829, total avg loss: 1.8175, batch size: 49
2021-10-13 17:17:55,940 INFO [train.py:451] Epoch 0, batch 20, batch avg loss 1.3589, total avg loss: 1.5660, batch size: 126
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2021-10-13 17:18:10,733 INFO [train.py:451] Epoch 0, batch 50, batch avg loss 1.2707, total avg loss: 1.3236, batch size: 41
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2021-10-13 17:18:25,014 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "964d9921-41f9-bf45-86b5-dd45d41a4093" will not be mixed in.
2021-10-13 17:18:25,261 INFO [train.py:451] Epoch 0, batch 80, batch avg loss 1.1616, total avg loss: 1.2545, batch size: 38
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2021-10-13 17:26:59,170 INFO [train.py:483] Epoch 0, valid loss 0.4640, best valid loss: 0.4640 best valid epoch: 0
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2021-10-13 17:35:51,138 INFO [train.py:483] Epoch 0, valid loss 0.4006, best valid loss: 0.4006 best valid epoch: 0
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2021-10-13 17:44:43,776 INFO [train.py:483] Epoch 0, valid loss 0.3666, best valid loss: 0.3666 best valid epoch: 0
2021-10-13 17:44:48,600 INFO [train.py:451] Epoch 0, batch 3010, batch avg loss 0.4936, total avg loss: 0.4977, batch size: 45
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2021-10-13 17:51:00,130 INFO [train.py:451] Epoch 0, batch 3760, batch avg loss 0.4357, total avg loss: 0.4717, batch size: 32
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2021-10-13 17:51:49,696 INFO [train.py:451] Epoch 0, batch 3860, batch avg loss 0.4779, total avg loss: 0.4708, batch size: 38
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2021-10-13 17:52:04,429 INFO [train.py:451] Epoch 0, batch 3890, batch avg loss 0.5016, total avg loss: 0.4675, batch size: 38
2021-10-13 17:52:09,259 INFO [train.py:451] Epoch 0, batch 3900, batch avg loss 0.4966, total avg loss: 0.4696, batch size: 57
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2021-10-13 17:52:19,549 INFO [train.py:451] Epoch 0, batch 3920, batch avg loss 0.4405, total avg loss: 0.4697, batch size: 32
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2021-10-13 17:52:34,483 INFO [train.py:451] Epoch 0, batch 3950, batch avg loss 0.4830, total avg loss: 0.4694, batch size: 42
2021-10-13 17:52:39,436 INFO [train.py:451] Epoch 0, batch 3960, batch avg loss 0.4877, total avg loss: 0.4675, batch size: 73
2021-10-13 17:52:44,402 INFO [train.py:451] Epoch 0, batch 3970, batch avg loss 0.4946, total avg loss: 0.4673, batch size: 33
2021-10-13 17:52:48,970 INFO [train.py:451] Epoch 0, batch 3980, batch avg loss 0.4842, total avg loss: 0.4683, batch size: 72
2021-10-13 17:52:53,905 INFO [train.py:451] Epoch 0, batch 3990, batch avg loss 0.4057, total avg loss: 0.4679, batch size: 32
2021-10-13 17:52:58,782 INFO [train.py:451] Epoch 0, batch 4000, batch avg loss 0.4364, total avg loss: 0.4685, batch size: 32
2021-10-13 17:53:37,772 INFO [train.py:483] Epoch 0, valid loss 0.3450, best valid loss: 0.3450 best valid epoch: 0
2021-10-13 17:53:42,783 INFO [train.py:451] Epoch 0, batch 4010, batch avg loss 0.3959, total avg loss: 0.4555, batch size: 30
2021-10-13 17:53:47,575 INFO [train.py:451] Epoch 0, batch 4020, batch avg loss 0.4024, total avg loss: 0.4661, batch size: 31
2021-10-13 17:53:52,640 INFO [train.py:451] Epoch 0, batch 4030, batch avg loss 0.4397, total avg loss: 0.4609, batch size: 28
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2021-10-13 17:55:16,178 INFO [train.py:451] Epoch 0, batch 4200, batch avg loss 0.4518, total avg loss: 0.4569, batch size: 41
2021-10-13 17:55:21,229 INFO [train.py:451] Epoch 0, batch 4210, batch avg loss 0.4222, total avg loss: 0.4609, batch size: 34
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2021-10-13 17:55:30,909 INFO [train.py:451] Epoch 0, batch 4230, batch avg loss 0.4639, total avg loss: 0.4715, batch size: 49
2021-10-13 17:55:35,886 INFO [train.py:451] Epoch 0, batch 4240, batch avg loss 0.4533, total avg loss: 0.4669, batch size: 35
2021-10-13 17:55:40,810 INFO [train.py:451] Epoch 0, batch 4250, batch avg loss 0.4258, total avg loss: 0.4641, batch size: 31
2021-10-13 17:55:45,645 INFO [train.py:451] Epoch 0, batch 4260, batch avg loss 0.4785, total avg loss: 0.4627, batch size: 41
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2021-10-13 17:55:55,497 INFO [train.py:451] Epoch 0, batch 4280, batch avg loss 0.5017, total avg loss: 0.4600, batch size: 34
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2021-10-13 18:02:27,536 INFO [train.py:483] Epoch 0, valid loss 0.3279, best valid loss: 0.3279 best valid epoch: 0
2021-10-13 18:02:32,475 INFO [train.py:451] Epoch 0, batch 5010, batch avg loss 0.3625, total avg loss: 0.4352, batch size: 30
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2021-10-13 18:08:47,706 INFO [train.py:451] Epoch 0, batch 5770, batch avg loss 0.4784, total avg loss: 0.4351, batch size: 34
2021-10-13 18:08:52,897 INFO [train.py:451] Epoch 0, batch 5780, batch avg loss 0.4199, total avg loss: 0.4330, batch size: 34
2021-10-13 18:08:57,997 INFO [train.py:451] Epoch 0, batch 5790, batch avg loss 0.4606, total avg loss: 0.4326, batch size: 34
2021-10-13 18:09:03,013 INFO [train.py:451] Epoch 0, batch 5800, batch avg loss 0.4186, total avg loss: 0.4331, batch size: 34
2021-10-13 18:09:07,895 INFO [train.py:451] Epoch 0, batch 5810, batch avg loss 0.5024, total avg loss: 0.4408, batch size: 49
2021-10-13 18:09:12,946 INFO [train.py:451] Epoch 0, batch 5820, batch avg loss 0.4702, total avg loss: 0.4347, batch size: 35
2021-10-13 18:09:17,648 INFO [train.py:451] Epoch 0, batch 5830, batch avg loss 0.3758, total avg loss: 0.4316, batch size: 32
2021-10-13 18:09:22,469 INFO [train.py:451] Epoch 0, batch 5840, batch avg loss 0.3693, total avg loss: 0.4275, batch size: 31
2021-10-13 18:09:27,084 INFO [train.py:451] Epoch 0, batch 5850, batch avg loss 0.4198, total avg loss: 0.4308, batch size: 38
2021-10-13 18:09:32,059 INFO [train.py:451] Epoch 0, batch 5860, batch avg loss 0.3927, total avg loss: 0.4281, batch size: 31
2021-10-13 18:09:37,244 INFO [train.py:451] Epoch 0, batch 5870, batch avg loss 0.4131, total avg loss: 0.4257, batch size: 33
2021-10-13 18:09:42,228 INFO [train.py:451] Epoch 0, batch 5880, batch avg loss 0.4831, total avg loss: 0.4302, batch size: 34
2021-10-13 18:09:46,963 INFO [train.py:451] Epoch 0, batch 5890, batch avg loss 0.4575, total avg loss: 0.4297, batch size: 57
2021-10-13 18:09:51,827 INFO [train.py:451] Epoch 0, batch 5900, batch avg loss 0.4383, total avg loss: 0.4339, batch size: 42
2021-10-13 18:09:56,602 INFO [train.py:451] Epoch 0, batch 5910, batch avg loss 0.4190, total avg loss: 0.4349, batch size: 38
2021-10-13 18:10:01,631 INFO [train.py:451] Epoch 0, batch 5920, batch avg loss 0.3807, total avg loss: 0.4343, batch size: 35
2021-10-13 18:10:06,487 INFO [train.py:451] Epoch 0, batch 5930, batch avg loss 0.4167, total avg loss: 0.4349, batch size: 31
2021-10-13 18:10:11,476 INFO [train.py:451] Epoch 0, batch 5940, batch avg loss 0.3860, total avg loss: 0.4350, batch size: 35
2021-10-13 18:10:16,453 INFO [train.py:451] Epoch 0, batch 5950, batch avg loss 0.3834, total avg loss: 0.4346, batch size: 27
2021-10-13 18:10:21,468 INFO [train.py:451] Epoch 0, batch 5960, batch avg loss 0.3731, total avg loss: 0.4344, batch size: 31
2021-10-13 18:10:26,398 INFO [train.py:451] Epoch 0, batch 5970, batch avg loss 0.4620, total avg loss: 0.4340, batch size: 35
2021-10-13 18:10:31,353 INFO [train.py:451] Epoch 0, batch 5980, batch avg loss 0.4475, total avg loss: 0.4349, batch size: 39
2021-10-13 18:10:36,339 INFO [train.py:451] Epoch 0, batch 5990, batch avg loss 0.4396, total avg loss: 0.4349, batch size: 38
2021-10-13 18:10:41,309 INFO [train.py:451] Epoch 0, batch 6000, batch avg loss 0.3823, total avg loss: 0.4335, batch size: 32
2021-10-13 18:11:22,757 INFO [train.py:483] Epoch 0, valid loss 0.3152, best valid loss: 0.3152 best valid epoch: 0
2021-10-13 18:11:27,823 INFO [train.py:451] Epoch 0, batch 6010, batch avg loss 0.4038, total avg loss: 0.4349, batch size: 33
2021-10-13 18:11:32,741 INFO [train.py:451] Epoch 0, batch 6020, batch avg loss 0.4109, total avg loss: 0.4393, batch size: 35
2021-10-13 18:11:37,557 INFO [train.py:451] Epoch 0, batch 6030, batch avg loss 0.4852, total avg loss: 0.4376, batch size: 130
2021-10-13 18:11:42,339 INFO [train.py:451] Epoch 0, batch 6040, batch avg loss 0.4809, total avg loss: 0.4426, batch size: 49
2021-10-13 18:11:47,420 INFO [train.py:451] Epoch 0, batch 6050, batch avg loss 0.4071, total avg loss: 0.4360, batch size: 34
2021-10-13 18:11:52,392 INFO [train.py:451] Epoch 0, batch 6060, batch avg loss 0.3570, total avg loss: 0.4328, batch size: 29
2021-10-13 18:11:57,138 INFO [train.py:451] Epoch 0, batch 6070, batch avg loss 0.4888, total avg loss: 0.4356, batch size: 74
2021-10-13 18:12:01,958 INFO [train.py:451] Epoch 0, batch 6080, batch avg loss 0.3624, total avg loss: 0.4372, batch size: 30
2021-10-13 18:12:07,125 INFO [train.py:451] Epoch 0, batch 6090, batch avg loss 0.3361, total avg loss: 0.4366, batch size: 27
2021-10-13 18:12:12,004 INFO [train.py:451] Epoch 0, batch 6100, batch avg loss 0.3126, total avg loss: 0.4350, batch size: 30
2021-10-13 18:12:16,877 INFO [train.py:451] Epoch 0, batch 6110, batch avg loss 0.3736, total avg loss: 0.4335, batch size: 29
2021-10-13 18:12:21,835 INFO [train.py:451] Epoch 0, batch 6120, batch avg loss 0.3539, total avg loss: 0.4324, batch size: 28
2021-10-13 18:12:26,796 INFO [train.py:451] Epoch 0, batch 6130, batch avg loss 0.4370, total avg loss: 0.4339, batch size: 33
2021-10-13 18:12:31,976 INFO [train.py:451] Epoch 0, batch 6140, batch avg loss 0.4978, total avg loss: 0.4327, batch size: 41
2021-10-13 18:12:36,982 INFO [train.py:451] Epoch 0, batch 6150, batch avg loss 0.3604, total avg loss: 0.4332, batch size: 29
2021-10-13 18:12:41,931 INFO [train.py:451] Epoch 0, batch 6160, batch avg loss 0.3897, total avg loss: 0.4327, batch size: 30
2021-10-13 18:12:46,885 INFO [train.py:451] Epoch 0, batch 6170, batch avg loss 0.3972, total avg loss: 0.4325, batch size: 29
2021-10-13 18:12:51,920 INFO [train.py:451] Epoch 0, batch 6180, batch avg loss 0.4618, total avg loss: 0.4318, batch size: 74
2021-10-13 18:12:56,832 INFO [train.py:451] Epoch 0, batch 6190, batch avg loss 0.4178, total avg loss: 0.4319, batch size: 45
2021-10-13 18:13:01,678 INFO [train.py:451] Epoch 0, batch 6200, batch avg loss 0.4877, total avg loss: 0.4331, batch size: 39
2021-10-13 18:13:06,550 INFO [train.py:451] Epoch 0, batch 6210, batch avg loss 0.4464, total avg loss: 0.4372, batch size: 35
2021-10-13 18:13:11,668 INFO [train.py:451] Epoch 0, batch 6220, batch avg loss 0.3744, total avg loss: 0.4154, batch size: 29
2021-10-13 18:13:16,561 INFO [train.py:451] Epoch 0, batch 6230, batch avg loss 0.4397, total avg loss: 0.4258, batch size: 36
2021-10-13 18:13:21,245 INFO [train.py:451] Epoch 0, batch 6240, batch avg loss 0.3534, total avg loss: 0.4270, batch size: 27
2021-10-13 18:13:26,154 INFO [train.py:451] Epoch 0, batch 6250, batch avg loss 0.4039, total avg loss: 0.4250, batch size: 30
2021-10-13 18:13:31,267 INFO [train.py:451] Epoch 0, batch 6260, batch avg loss 0.4900, total avg loss: 0.4264, batch size: 31
2021-10-13 18:13:36,133 INFO [train.py:451] Epoch 0, batch 6270, batch avg loss 0.4439, total avg loss: 0.4304, batch size: 35
2021-10-13 18:13:40,934 INFO [train.py:451] Epoch 0, batch 6280, batch avg loss 0.4365, total avg loss: 0.4324, batch size: 38
2021-10-13 18:13:45,901 INFO [train.py:451] Epoch 0, batch 6290, batch avg loss 0.4717, total avg loss: 0.4299, batch size: 38
2021-10-13 18:13:50,904 INFO [train.py:451] Epoch 0, batch 6300, batch avg loss 0.3644, total avg loss: 0.4292, batch size: 33
2021-10-13 18:13:55,964 INFO [train.py:451] Epoch 0, batch 6310, batch avg loss 0.4340, total avg loss: 0.4299, batch size: 34
2021-10-13 18:14:01,149 INFO [train.py:451] Epoch 0, batch 6320, batch avg loss 0.4077, total avg loss: 0.4282, batch size: 34
2021-10-13 18:14:06,404 INFO [train.py:451] Epoch 0, batch 6330, batch avg loss 0.3964, total avg loss: 0.4265, batch size: 31
2021-10-13 18:14:11,486 INFO [train.py:451] Epoch 0, batch 6340, batch avg loss 0.4142, total avg loss: 0.4261, batch size: 35
2021-10-13 18:14:16,654 INFO [train.py:451] Epoch 0, batch 6350, batch avg loss 0.4409, total avg loss: 0.4255, batch size: 30
2021-10-13 18:14:21,847 INFO [train.py:451] Epoch 0, batch 6360, batch avg loss 0.4750, total avg loss: 0.4245, batch size: 42
2021-10-13 18:14:26,785 INFO [train.py:451] Epoch 0, batch 6370, batch avg loss 0.4318, total avg loss: 0.4258, batch size: 73
2021-10-13 18:14:31,809 INFO [train.py:451] Epoch 0, batch 6380, batch avg loss 0.4010, total avg loss: 0.4255, batch size: 35
2021-10-13 18:14:36,776 INFO [train.py:451] Epoch 0, batch 6390, batch avg loss 0.4719, total avg loss: 0.4250, batch size: 49
2021-10-13 18:14:41,799 INFO [train.py:451] Epoch 0, batch 6400, batch avg loss 0.5216, total avg loss: 0.4258, batch size: 38
2021-10-13 18:14:46,888 INFO [train.py:451] Epoch 0, batch 6410, batch avg loss 0.4432, total avg loss: 0.4281, batch size: 33
2021-10-13 18:14:51,801 INFO [train.py:451] Epoch 0, batch 6420, batch avg loss 0.5026, total avg loss: 0.4200, batch size: 56
2021-10-13 18:14:56,579 INFO [train.py:451] Epoch 0, batch 6430, batch avg loss 0.4629, total avg loss: 0.4256, batch size: 38
2021-10-13 18:15:01,432 INFO [train.py:451] Epoch 0, batch 6440, batch avg loss 0.3245, total avg loss: 0.4241, batch size: 32
2021-10-13 18:15:06,430 INFO [train.py:451] Epoch 0, batch 6450, batch avg loss 0.4033, total avg loss: 0.4257, batch size: 33
2021-10-13 18:15:11,367 INFO [train.py:451] Epoch 0, batch 6460, batch avg loss 0.4122, total avg loss: 0.4245, batch size: 29
2021-10-13 18:15:16,410 INFO [train.py:451] Epoch 0, batch 6470, batch avg loss 0.4204, total avg loss: 0.4252, batch size: 29
2021-10-13 18:15:21,301 INFO [train.py:451] Epoch 0, batch 6480, batch avg loss 0.4388, total avg loss: 0.4259, batch size: 49
2021-10-13 18:15:26,282 INFO [train.py:451] Epoch 0, batch 6490, batch avg loss 0.5014, total avg loss: 0.4253, batch size: 39
2021-10-13 18:15:31,380 INFO [train.py:451] Epoch 0, batch 6500, batch avg loss 0.3692, total avg loss: 0.4230, batch size: 31
2021-10-13 18:15:36,355 INFO [train.py:451] Epoch 0, batch 6510, batch avg loss 0.3807, total avg loss: 0.4212, batch size: 30
2021-10-13 18:15:41,165 INFO [train.py:451] Epoch 0, batch 6520, batch avg loss 0.3654, total avg loss: 0.4224, batch size: 29
2021-10-13 18:15:46,056 INFO [train.py:451] Epoch 0, batch 6530, batch avg loss 0.4830, total avg loss: 0.4246, batch size: 36
2021-10-13 18:15:50,998 INFO [train.py:451] Epoch 0, batch 6540, batch avg loss 0.3936, total avg loss: 0.4230, batch size: 30
2021-10-13 18:15:55,706 INFO [train.py:451] Epoch 0, batch 6550, batch avg loss 0.3583, total avg loss: 0.4246, batch size: 28
2021-10-13 18:16:00,717 INFO [train.py:451] Epoch 0, batch 6560, batch avg loss 0.4076, total avg loss: 0.4247, batch size: 31
2021-10-13 18:16:05,768 INFO [train.py:451] Epoch 0, batch 6570, batch avg loss 0.3448, total avg loss: 0.4245, batch size: 32
2021-10-13 18:16:10,717 INFO [train.py:451] Epoch 0, batch 6580, batch avg loss 0.4660, total avg loss: 0.4256, batch size: 34
2021-10-13 18:16:15,906 INFO [train.py:451] Epoch 0, batch 6590, batch avg loss 0.4860, total avg loss: 0.4259, batch size: 35
2021-10-13 18:16:20,981 INFO [train.py:451] Epoch 0, batch 6600, batch avg loss 0.4384, total avg loss: 0.4255, batch size: 32
2021-10-13 18:16:25,909 INFO [train.py:451] Epoch 0, batch 6610, batch avg loss 0.5246, total avg loss: 0.4244, batch size: 73
2021-10-13 18:16:30,812 INFO [train.py:451] Epoch 0, batch 6620, batch avg loss 0.3655, total avg loss: 0.4219, batch size: 32
2021-10-13 18:16:35,684 INFO [train.py:451] Epoch 0, batch 6630, batch avg loss 0.4447, total avg loss: 0.4141, batch size: 41
2021-10-13 18:16:40,627 INFO [train.py:451] Epoch 0, batch 6640, batch avg loss 0.4660, total avg loss: 0.4117, batch size: 45
2021-10-13 18:16:45,729 INFO [train.py:451] Epoch 0, batch 6650, batch avg loss 0.4196, total avg loss: 0.4121, batch size: 28
2021-10-13 18:16:50,635 INFO [train.py:451] Epoch 0, batch 6660, batch avg loss 0.5498, total avg loss: 0.4138, batch size: 42
2021-10-13 18:16:55,461 INFO [train.py:451] Epoch 0, batch 6670, batch avg loss 0.4127, total avg loss: 0.4161, batch size: 29
2021-10-13 18:17:00,270 INFO [train.py:451] Epoch 0, batch 6680, batch avg loss 0.4097, total avg loss: 0.4148, batch size: 35
2021-10-13 18:17:05,387 INFO [train.py:451] Epoch 0, batch 6690, batch avg loss 0.4289, total avg loss: 0.4161, batch size: 35
2021-10-13 18:17:10,340 INFO [train.py:451] Epoch 0, batch 6700, batch avg loss 0.3618, total avg loss: 0.4177, batch size: 33
2021-10-13 18:17:15,294 INFO [train.py:451] Epoch 0, batch 6710, batch avg loss 0.3957, total avg loss: 0.4173, batch size: 38
2021-10-13 18:17:20,119 INFO [train.py:451] Epoch 0, batch 6720, batch avg loss 0.4296, total avg loss: 0.4174, batch size: 42
2021-10-13 18:17:25,134 INFO [train.py:451] Epoch 0, batch 6730, batch avg loss 0.4716, total avg loss: 0.4190, batch size: 34
2021-10-13 18:17:30,237 INFO [train.py:451] Epoch 0, batch 6740, batch avg loss 0.4306, total avg loss: 0.4192, batch size: 27
2021-10-13 18:17:33,461 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "0c811055-8d43-54bc-d3bb-3ab20b16c5eb" will not be mixed in.
2021-10-13 18:17:35,388 INFO [train.py:451] Epoch 0, batch 6750, batch avg loss 0.4240, total avg loss: 0.4193, batch size: 41
2021-10-13 18:17:40,576 INFO [train.py:451] Epoch 0, batch 6760, batch avg loss 0.4893, total avg loss: 0.4191, batch size: 32
2021-10-13 18:17:45,397 INFO [train.py:451] Epoch 0, batch 6770, batch avg loss 0.4390, total avg loss: 0.4194, batch size: 41
2021-10-13 18:17:50,325 INFO [train.py:451] Epoch 0, batch 6780, batch avg loss 0.5068, total avg loss: 0.4196, batch size: 137
2021-10-13 18:17:55,311 INFO [train.py:451] Epoch 0, batch 6790, batch avg loss 0.5049, total avg loss: 0.4202, batch size: 56
2021-10-13 18:18:00,205 INFO [train.py:451] Epoch 0, batch 6800, batch avg loss 0.4387, total avg loss: 0.4216, batch size: 38
2021-10-13 18:18:05,109 INFO [train.py:451] Epoch 0, batch 6810, batch avg loss 0.5021, total avg loss: 0.4557, batch size: 133
2021-10-13 18:18:09,995 INFO [train.py:451] Epoch 0, batch 6820, batch avg loss 0.4388, total avg loss: 0.4394, batch size: 33
2021-10-13 18:18:15,101 INFO [train.py:451] Epoch 0, batch 6830, batch avg loss 0.3631, total avg loss: 0.4264, batch size: 34
2021-10-13 18:18:19,998 INFO [train.py:451] Epoch 0, batch 6840, batch avg loss 0.3255, total avg loss: 0.4194, batch size: 34
2021-10-13 18:18:24,749 INFO [train.py:451] Epoch 0, batch 6850, batch avg loss 0.4169, total avg loss: 0.4256, batch size: 39
2021-10-13 18:18:29,659 INFO [train.py:451] Epoch 0, batch 6860, batch avg loss 0.4357, total avg loss: 0.4235, batch size: 38
2021-10-13 18:18:34,534 INFO [train.py:451] Epoch 0, batch 6870, batch avg loss 0.3598, total avg loss: 0.4236, batch size: 32
2021-10-13 18:18:39,395 INFO [train.py:451] Epoch 0, batch 6880, batch avg loss 0.4384, total avg loss: 0.4247, batch size: 73
2021-10-13 18:18:44,413 INFO [train.py:451] Epoch 0, batch 6890, batch avg loss 0.4550, total avg loss: 0.4228, batch size: 35
2021-10-13 18:18:49,540 INFO [train.py:451] Epoch 0, batch 6900, batch avg loss 0.4121, total avg loss: 0.4202, batch size: 29
2021-10-13 18:18:54,498 INFO [train.py:451] Epoch 0, batch 6910, batch avg loss 0.5688, total avg loss: 0.4214, batch size: 127
2021-10-13 18:18:59,269 INFO [train.py:451] Epoch 0, batch 6920, batch avg loss 0.4401, total avg loss: 0.4232, batch size: 49
2021-10-13 18:19:04,208 INFO [train.py:451] Epoch 0, batch 6930, batch avg loss 0.4536, total avg loss: 0.4245, batch size: 27
2021-10-13 18:19:09,278 INFO [train.py:451] Epoch 0, batch 6940, batch avg loss 0.5241, total avg loss: 0.4231, batch size: 130
2021-10-13 18:19:14,279 INFO [train.py:451] Epoch 0, batch 6950, batch avg loss 0.3988, total avg loss: 0.4220, batch size: 36
2021-10-13 18:19:19,223 INFO [train.py:451] Epoch 0, batch 6960, batch avg loss 0.4141, total avg loss: 0.4219, batch size: 31
2021-10-13 18:19:24,143 INFO [train.py:451] Epoch 0, batch 6970, batch avg loss 0.4603, total avg loss: 0.4222, batch size: 56
2021-10-13 18:19:28,944 INFO [train.py:451] Epoch 0, batch 6980, batch avg loss 0.4175, total avg loss: 0.4221, batch size: 39
2021-10-13 18:19:33,842 INFO [train.py:451] Epoch 0, batch 6990, batch avg loss 0.3712, total avg loss: 0.4209, batch size: 45
2021-10-13 18:19:38,749 INFO [train.py:451] Epoch 0, batch 7000, batch avg loss 0.4225, total avg loss: 0.4208, batch size: 36
2021-10-13 18:20:20,951 INFO [train.py:483] Epoch 0, valid loss 0.3007, best valid loss: 0.3007 best valid epoch: 0
2021-10-13 18:20:25,762 INFO [train.py:451] Epoch 0, batch 7010, batch avg loss 0.3920, total avg loss: 0.4221, batch size: 45
2021-10-13 18:20:30,654 INFO [train.py:451] Epoch 0, batch 7020, batch avg loss 0.3446, total avg loss: 0.4167, batch size: 31
2021-10-13 18:20:35,677 INFO [train.py:451] Epoch 0, batch 7030, batch avg loss 0.4513, total avg loss: 0.4137, batch size: 31
2021-10-13 18:20:40,733 INFO [train.py:451] Epoch 0, batch 7040, batch avg loss 0.4307, total avg loss: 0.4105, batch size: 31
2021-10-13 18:20:45,696 INFO [train.py:451] Epoch 0, batch 7050, batch avg loss 0.4401, total avg loss: 0.4129, batch size: 33
2021-10-13 18:20:50,697 INFO [train.py:451] Epoch 0, batch 7060, batch avg loss 0.4204, total avg loss: 0.4131, batch size: 36
2021-10-13 18:20:55,572 INFO [train.py:451] Epoch 0, batch 7070, batch avg loss 0.3839, total avg loss: 0.4142, batch size: 35
2021-10-13 18:21:00,435 INFO [train.py:451] Epoch 0, batch 7080, batch avg loss 0.4702, total avg loss: 0.4182, batch size: 33
2021-10-13 18:21:05,027 INFO [train.py:451] Epoch 0, batch 7090, batch avg loss 0.5669, total avg loss: 0.4216, batch size: 129
2021-10-13 18:21:09,894 INFO [train.py:451] Epoch 0, batch 7100, batch avg loss 0.4233, total avg loss: 0.4221, batch size: 38
2021-10-13 18:21:14,889 INFO [train.py:451] Epoch 0, batch 7110, batch avg loss 0.4400, total avg loss: 0.4239, batch size: 45
2021-10-13 18:21:19,862 INFO [train.py:451] Epoch 0, batch 7120, batch avg loss 0.4442, total avg loss: 0.4245, batch size: 29
2021-10-13 18:21:24,934 INFO [train.py:451] Epoch 0, batch 7130, batch avg loss 0.3589, total avg loss: 0.4247, batch size: 27
2021-10-13 18:21:29,822 INFO [train.py:451] Epoch 0, batch 7140, batch avg loss 0.4366, total avg loss: 0.4251, batch size: 45
2021-10-13 18:21:34,555 INFO [train.py:451] Epoch 0, batch 7150, batch avg loss 0.3719, total avg loss: 0.4263, batch size: 30
2021-10-13 18:21:39,466 INFO [train.py:451] Epoch 0, batch 7160, batch avg loss 0.4926, total avg loss: 0.4254, batch size: 36
2021-10-13 18:21:44,415 INFO [train.py:451] Epoch 0, batch 7170, batch avg loss 0.5028, total avg loss: 0.4244, batch size: 73
2021-10-13 18:21:49,376 INFO [train.py:451] Epoch 0, batch 7180, batch avg loss 0.3594, total avg loss: 0.4217, batch size: 30
2021-10-13 18:21:54,299 INFO [train.py:451] Epoch 0, batch 7190, batch avg loss 0.3882, total avg loss: 0.4202, batch size: 31
2021-10-13 18:21:59,373 INFO [train.py:451] Epoch 0, batch 7200, batch avg loss 0.4627, total avg loss: 0.4202, batch size: 38
2021-10-13 18:22:04,352 INFO [train.py:451] Epoch 0, batch 7210, batch avg loss 0.3965, total avg loss: 0.4309, batch size: 34
2021-10-13 18:22:09,231 INFO [train.py:451] Epoch 0, batch 7220, batch avg loss 0.3463, total avg loss: 0.4178, batch size: 27
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2021-10-13 18:29:14,876 INFO [train.py:483] Epoch 0, valid loss 0.2949, best valid loss: 0.2949 best valid epoch: 0
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2021-10-13 18:35:05,562 INFO [train.py:451] Epoch 0, batch 8700, batch avg loss 0.3816, total avg loss: 0.3982, batch size: 36
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2021-10-13 18:35:15,191 INFO [train.py:451] Epoch 0, batch 8720, batch avg loss 0.3610, total avg loss: 0.3987, batch size: 36
2021-10-13 18:35:20,122 INFO [train.py:451] Epoch 0, batch 8730, batch avg loss 0.3661, total avg loss: 0.3982, batch size: 27
2021-10-13 18:35:25,069 INFO [train.py:451] Epoch 0, batch 8740, batch avg loss 0.3771, total avg loss: 0.3981, batch size: 42
2021-10-13 18:35:29,866 INFO [train.py:451] Epoch 0, batch 8750, batch avg loss 0.3285, total avg loss: 0.3990, batch size: 27
2021-10-13 18:35:34,751 INFO [train.py:451] Epoch 0, batch 8760, batch avg loss 0.4133, total avg loss: 0.4001, batch size: 33
2021-10-13 18:35:39,682 INFO [train.py:451] Epoch 0, batch 8770, batch avg loss 0.4027, total avg loss: 0.4013, batch size: 36
2021-10-13 18:35:44,710 INFO [train.py:451] Epoch 0, batch 8780, batch avg loss 0.4392, total avg loss: 0.4017, batch size: 41
2021-10-13 18:35:49,686 INFO [train.py:451] Epoch 0, batch 8790, batch avg loss 0.3814, total avg loss: 0.4007, batch size: 34
2021-10-13 18:35:54,633 INFO [train.py:451] Epoch 0, batch 8800, batch avg loss 0.3579, total avg loss: 0.4005, batch size: 30
2021-10-13 18:35:59,709 INFO [train.py:451] Epoch 0, batch 8810, batch avg loss 0.3714, total avg loss: 0.3699, batch size: 31
2021-10-13 18:36:04,601 INFO [train.py:451] Epoch 0, batch 8820, batch avg loss 0.4837, total avg loss: 0.3914, batch size: 131
2021-10-13 18:36:09,663 INFO [train.py:451] Epoch 0, batch 8830, batch avg loss 0.4798, total avg loss: 0.3963, batch size: 56
2021-10-13 18:36:14,749 INFO [train.py:451] Epoch 0, batch 8840, batch avg loss 0.4732, total avg loss: 0.3974, batch size: 34
2021-10-13 18:36:16,386 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "3a84ed92-d27e-f53b-1a03-b98ccb861448" will not be mixed in.
2021-10-13 18:36:19,765 INFO [train.py:451] Epoch 0, batch 8850, batch avg loss 0.3128, total avg loss: 0.3932, batch size: 32
2021-10-13 18:36:24,692 INFO [train.py:451] Epoch 0, batch 8860, batch avg loss 0.3232, total avg loss: 0.3931, batch size: 29
2021-10-13 18:36:29,396 INFO [train.py:451] Epoch 0, batch 8870, batch avg loss 0.4429, total avg loss: 0.3995, batch size: 49
2021-10-13 18:36:34,252 INFO [train.py:451] Epoch 0, batch 8880, batch avg loss 0.3725, total avg loss: 0.4005, batch size: 34
2021-10-13 18:36:39,151 INFO [train.py:451] Epoch 0, batch 8890, batch avg loss 0.4616, total avg loss: 0.4014, batch size: 37
2021-10-13 18:36:44,283 INFO [train.py:451] Epoch 0, batch 8900, batch avg loss 0.4453, total avg loss: 0.3990, batch size: 45
2021-10-13 18:36:49,259 INFO [train.py:451] Epoch 0, batch 8910, batch avg loss 0.4761, total avg loss: 0.3987, batch size: 36
2021-10-13 18:36:54,246 INFO [train.py:451] Epoch 0, batch 8920, batch avg loss 0.3069, total avg loss: 0.3984, batch size: 31
2021-10-13 18:36:58,975 INFO [train.py:451] Epoch 0, batch 8930, batch avg loss 0.3174, total avg loss: 0.3988, batch size: 32
2021-10-13 18:37:03,797 INFO [train.py:451] Epoch 0, batch 8940, batch avg loss 0.3486, total avg loss: 0.3985, batch size: 33
2021-10-13 18:37:08,872 INFO [train.py:451] Epoch 0, batch 8950, batch avg loss 0.3039, total avg loss: 0.3979, batch size: 28
2021-10-13 18:37:13,891 INFO [train.py:451] Epoch 0, batch 8960, batch avg loss 0.3956, total avg loss: 0.3973, batch size: 29
2021-10-13 18:37:18,745 INFO [train.py:451] Epoch 0, batch 8970, batch avg loss 0.4009, total avg loss: 0.3989, batch size: 39
2021-10-13 18:37:23,706 INFO [train.py:451] Epoch 0, batch 8980, batch avg loss 0.3353, total avg loss: 0.3982, batch size: 29
2021-10-13 18:37:28,692 INFO [train.py:451] Epoch 0, batch 8990, batch avg loss 0.3426, total avg loss: 0.3973, batch size: 32
2021-10-13 18:37:33,511 INFO [train.py:451] Epoch 0, batch 9000, batch avg loss 0.5234, total avg loss: 0.3970, batch size: 129
2021-10-13 18:38:13,423 INFO [train.py:483] Epoch 0, valid loss 0.2846, best valid loss: 0.2846 best valid epoch: 0
2021-10-13 18:38:18,399 INFO [train.py:451] Epoch 0, batch 9010, batch avg loss 0.3715, total avg loss: 0.3848, batch size: 32
2021-10-13 18:38:23,367 INFO [train.py:451] Epoch 0, batch 9020, batch avg loss 0.3892, total avg loss: 0.3910, batch size: 36
2021-10-13 18:38:28,483 INFO [train.py:451] Epoch 0, batch 9030, batch avg loss 0.3309, total avg loss: 0.3825, batch size: 30
2021-10-13 18:38:33,327 INFO [train.py:451] Epoch 0, batch 9040, batch avg loss 0.3881, total avg loss: 0.3882, batch size: 38
2021-10-13 18:38:38,428 INFO [train.py:451] Epoch 0, batch 9050, batch avg loss 0.3734, total avg loss: 0.3894, batch size: 57
2021-10-13 18:38:43,440 INFO [train.py:451] Epoch 0, batch 9060, batch avg loss 0.3945, total avg loss: 0.3909, batch size: 35
2021-10-13 18:38:48,317 INFO [train.py:451] Epoch 0, batch 9070, batch avg loss 0.3818, total avg loss: 0.3924, batch size: 33
2021-10-13 18:38:53,190 INFO [train.py:451] Epoch 0, batch 9080, batch avg loss 0.3546, total avg loss: 0.3923, batch size: 31
2021-10-13 18:38:58,111 INFO [train.py:451] Epoch 0, batch 9090, batch avg loss 0.4132, total avg loss: 0.3909, batch size: 30
2021-10-13 18:39:03,033 INFO [train.py:451] Epoch 0, batch 9100, batch avg loss 0.3359, total avg loss: 0.3913, batch size: 31
2021-10-13 18:39:07,976 INFO [train.py:451] Epoch 0, batch 9110, batch avg loss 0.4064, total avg loss: 0.3903, batch size: 36
2021-10-13 18:39:12,893 INFO [train.py:451] Epoch 0, batch 9120, batch avg loss 0.4170, total avg loss: 0.3909, batch size: 34
2021-10-13 18:39:17,706 INFO [train.py:451] Epoch 0, batch 9130, batch avg loss 0.4335, total avg loss: 0.3908, batch size: 41
2021-10-13 18:39:22,613 INFO [train.py:451] Epoch 0, batch 9140, batch avg loss 0.3344, total avg loss: 0.3912, batch size: 29
2021-10-13 18:39:27,466 INFO [train.py:451] Epoch 0, batch 9150, batch avg loss 0.4368, total avg loss: 0.3907, batch size: 73
2021-10-13 18:39:32,355 INFO [train.py:451] Epoch 0, batch 9160, batch avg loss 0.4155, total avg loss: 0.3914, batch size: 35
2021-10-13 18:39:37,220 INFO [train.py:451] Epoch 0, batch 9170, batch avg loss 0.4083, total avg loss: 0.3907, batch size: 37
2021-10-13 18:39:42,209 INFO [train.py:451] Epoch 0, batch 9180, batch avg loss 0.4599, total avg loss: 0.3909, batch size: 34
2021-10-13 18:39:47,038 INFO [train.py:451] Epoch 0, batch 9190, batch avg loss 0.3744, total avg loss: 0.3919, batch size: 31
2021-10-13 18:39:51,771 INFO [train.py:451] Epoch 0, batch 9200, batch avg loss 0.4533, total avg loss: 0.3931, batch size: 49
2021-10-13 18:39:56,741 INFO [train.py:451] Epoch 0, batch 9210, batch avg loss 0.3423, total avg loss: 0.3933, batch size: 30
2021-10-13 18:40:01,532 INFO [train.py:451] Epoch 0, batch 9220, batch avg loss 0.5195, total avg loss: 0.4066, batch size: 130
2021-10-13 18:40:06,374 INFO [train.py:451] Epoch 0, batch 9230, batch avg loss 0.3105, total avg loss: 0.3948, batch size: 29
2021-10-13 18:40:11,214 INFO [train.py:451] Epoch 0, batch 9240, batch avg loss 0.4456, total avg loss: 0.3917, batch size: 32
2021-10-13 18:40:16,115 INFO [train.py:451] Epoch 0, batch 9250, batch avg loss 0.3864, total avg loss: 0.3946, batch size: 34
2021-10-13 18:40:20,984 INFO [train.py:451] Epoch 0, batch 9260, batch avg loss 0.2964, total avg loss: 0.3908, batch size: 31
2021-10-13 18:40:25,513 INFO [train.py:451] Epoch 0, batch 9270, batch avg loss 0.4179, total avg loss: 0.3958, batch size: 57
2021-10-13 18:40:30,321 INFO [train.py:451] Epoch 0, batch 9280, batch avg loss 0.3372, total avg loss: 0.3961, batch size: 29
2021-10-13 18:40:35,263 INFO [train.py:451] Epoch 0, batch 9290, batch avg loss 0.3790, total avg loss: 0.3954, batch size: 28
2021-10-13 18:40:40,169 INFO [train.py:451] Epoch 0, batch 9300, batch avg loss 0.3554, total avg loss: 0.3942, batch size: 29
2021-10-13 18:40:44,816 INFO [train.py:451] Epoch 0, batch 9310, batch avg loss 0.4366, total avg loss: 0.3972, batch size: 38
2021-10-13 18:40:49,749 INFO [train.py:451] Epoch 0, batch 9320, batch avg loss 0.3783, total avg loss: 0.3966, batch size: 32
2021-10-13 18:40:54,514 INFO [train.py:451] Epoch 0, batch 9330, batch avg loss 0.4002, total avg loss: 0.3951, batch size: 38
2021-10-13 18:40:59,219 INFO [train.py:451] Epoch 0, batch 9340, batch avg loss 0.4201, total avg loss: 0.3957, batch size: 35
2021-10-13 18:41:03,894 INFO [train.py:451] Epoch 0, batch 9350, batch avg loss 0.4437, total avg loss: 0.3959, batch size: 56
2021-10-13 18:41:08,768 INFO [train.py:451] Epoch 0, batch 9360, batch avg loss 0.3634, total avg loss: 0.3959, batch size: 36
2021-10-13 18:41:13,590 INFO [train.py:451] Epoch 0, batch 9370, batch avg loss 0.3013, total avg loss: 0.3954, batch size: 29
2021-10-13 18:41:18,468 INFO [train.py:451] Epoch 0, batch 9380, batch avg loss 0.4018, total avg loss: 0.3955, batch size: 32
2021-10-13 18:41:23,250 INFO [train.py:451] Epoch 0, batch 9390, batch avg loss 0.3609, total avg loss: 0.3960, batch size: 32
2021-10-13 18:41:27,981 INFO [train.py:451] Epoch 0, batch 9400, batch avg loss 0.3911, total avg loss: 0.3955, batch size: 39
2021-10-13 18:41:32,742 INFO [train.py:451] Epoch 0, batch 9410, batch avg loss 0.3067, total avg loss: 0.3810, batch size: 34
2021-10-13 18:41:37,491 INFO [train.py:451] Epoch 0, batch 9420, batch avg loss 0.4298, total avg loss: 0.3942, batch size: 49
2021-10-13 18:41:42,142 INFO [train.py:451] Epoch 0, batch 9430, batch avg loss 0.3960, total avg loss: 0.4012, batch size: 45
2021-10-13 18:41:46,880 INFO [train.py:451] Epoch 0, batch 9440, batch avg loss 0.3672, total avg loss: 0.3977, batch size: 33
2021-10-13 18:41:51,754 INFO [train.py:451] Epoch 0, batch 9450, batch avg loss 0.4207, total avg loss: 0.4008, batch size: 38
2021-10-13 18:41:56,441 INFO [train.py:451] Epoch 0, batch 9460, batch avg loss 0.3933, total avg loss: 0.3978, batch size: 49
2021-10-13 18:42:01,102 INFO [train.py:451] Epoch 0, batch 9470, batch avg loss 0.4335, total avg loss: 0.3995, batch size: 33
2021-10-13 18:42:05,906 INFO [train.py:451] Epoch 0, batch 9480, batch avg loss 0.3463, total avg loss: 0.3985, batch size: 30
2021-10-13 18:42:10,656 INFO [train.py:451] Epoch 0, batch 9490, batch avg loss 0.4254, total avg loss: 0.3992, batch size: 42
2021-10-13 18:42:15,468 INFO [train.py:451] Epoch 0, batch 9500, batch avg loss 0.3888, total avg loss: 0.3969, batch size: 33
2021-10-13 18:42:20,149 INFO [train.py:451] Epoch 0, batch 9510, batch avg loss 0.4309, total avg loss: 0.3981, batch size: 45
2021-10-13 18:42:24,929 INFO [train.py:451] Epoch 0, batch 9520, batch avg loss 0.4466, total avg loss: 0.3974, batch size: 33
2021-10-13 18:42:29,676 INFO [train.py:451] Epoch 0, batch 9530, batch avg loss 0.4062, total avg loss: 0.3960, batch size: 39
2021-10-13 18:42:34,516 INFO [train.py:451] Epoch 0, batch 9540, batch avg loss 0.3679, total avg loss: 0.3961, batch size: 34
2021-10-13 18:42:39,773 INFO [train.py:451] Epoch 0, batch 9550, batch avg loss 0.3691, total avg loss: 0.3963, batch size: 27
2021-10-13 18:42:44,849 INFO [train.py:451] Epoch 0, batch 9560, batch avg loss 0.4536, total avg loss: 0.3965, batch size: 39
2021-10-13 18:42:49,790 INFO [train.py:451] Epoch 0, batch 9570, batch avg loss 0.4154, total avg loss: 0.3961, batch size: 31
2021-10-13 18:42:54,608 INFO [train.py:451] Epoch 0, batch 9580, batch avg loss 0.3747, total avg loss: 0.3954, batch size: 32
2021-10-13 18:42:59,666 INFO [train.py:451] Epoch 0, batch 9590, batch avg loss 0.3970, total avg loss: 0.3940, batch size: 34
2021-10-13 18:43:04,308 INFO [train.py:451] Epoch 0, batch 9600, batch avg loss 0.4230, total avg loss: 0.3947, batch size: 32
2021-10-13 18:43:08,927 INFO [train.py:451] Epoch 0, batch 9610, batch avg loss 0.4179, total avg loss: 0.4055, batch size: 39
2021-10-13 18:43:13,792 INFO [train.py:451] Epoch 0, batch 9620, batch avg loss 0.3862, total avg loss: 0.4021, batch size: 32
2021-10-13 18:43:18,862 INFO [train.py:451] Epoch 0, batch 9630, batch avg loss 0.3440, total avg loss: 0.3950, batch size: 37
2021-10-13 18:43:23,599 INFO [train.py:451] Epoch 0, batch 9640, batch avg loss 0.3117, total avg loss: 0.3949, batch size: 32
2021-10-13 18:43:28,434 INFO [train.py:451] Epoch 0, batch 9650, batch avg loss 0.4132, total avg loss: 0.3922, batch size: 37
2021-10-13 18:43:32,934 INFO [train.py:451] Epoch 0, batch 9660, batch avg loss 0.4326, total avg loss: 0.3933, batch size: 72
2021-10-13 18:43:37,914 INFO [train.py:451] Epoch 0, batch 9670, batch avg loss 0.3939, total avg loss: 0.3933, batch size: 33
2021-10-13 18:43:42,829 INFO [train.py:451] Epoch 0, batch 9680, batch avg loss 0.3956, total avg loss: 0.3898, batch size: 32
2021-10-13 18:43:47,530 INFO [train.py:451] Epoch 0, batch 9690, batch avg loss 0.4323, total avg loss: 0.3897, batch size: 58
2021-10-13 18:43:52,376 INFO [train.py:451] Epoch 0, batch 9700, batch avg loss 0.3181, total avg loss: 0.3890, batch size: 28
2021-10-13 18:43:57,332 INFO [train.py:451] Epoch 0, batch 9710, batch avg loss 0.3362, total avg loss: 0.3897, batch size: 32
2021-10-13 18:44:02,198 INFO [train.py:451] Epoch 0, batch 9720, batch avg loss 0.3931, total avg loss: 0.3894, batch size: 36
2021-10-13 18:44:07,167 INFO [train.py:451] Epoch 0, batch 9730, batch avg loss 0.3365, total avg loss: 0.3901, batch size: 28
2021-10-13 18:44:12,104 INFO [train.py:451] Epoch 0, batch 9740, batch avg loss 0.3731, total avg loss: 0.3908, batch size: 32
2021-10-13 18:44:17,178 INFO [train.py:451] Epoch 0, batch 9750, batch avg loss 0.4258, total avg loss: 0.3906, batch size: 34
2021-10-13 18:44:22,040 INFO [train.py:451] Epoch 0, batch 9760, batch avg loss 0.3977, total avg loss: 0.3918, batch size: 35
2021-10-13 18:44:26,948 INFO [train.py:451] Epoch 0, batch 9770, batch avg loss 0.3732, total avg loss: 0.3911, batch size: 35
2021-10-13 18:44:31,869 INFO [train.py:451] Epoch 0, batch 9780, batch avg loss 0.3802, total avg loss: 0.3905, batch size: 33
2021-10-13 18:44:36,789 INFO [train.py:451] Epoch 0, batch 9790, batch avg loss 0.3984, total avg loss: 0.3904, batch size: 45
2021-10-13 18:44:41,715 INFO [train.py:451] Epoch 0, batch 9800, batch avg loss 0.3150, total avg loss: 0.3907, batch size: 28
2021-10-13 18:44:46,677 INFO [train.py:451] Epoch 0, batch 9810, batch avg loss 0.3160, total avg loss: 0.3517, batch size: 33
2021-10-13 18:44:51,708 INFO [train.py:451] Epoch 0, batch 9820, batch avg loss 0.5031, total avg loss: 0.3717, batch size: 127
2021-10-13 18:44:56,572 INFO [train.py:451] Epoch 0, batch 9830, batch avg loss 0.3978, total avg loss: 0.3830, batch size: 35
2021-10-13 18:45:01,424 INFO [train.py:451] Epoch 0, batch 9840, batch avg loss 0.3248, total avg loss: 0.3800, batch size: 29
2021-10-13 18:45:06,306 INFO [train.py:451] Epoch 0, batch 9850, batch avg loss 0.4664, total avg loss: 0.3824, batch size: 42
2021-10-13 18:45:11,246 INFO [train.py:451] Epoch 0, batch 9860, batch avg loss 0.4353, total avg loss: 0.3822, batch size: 37
2021-10-13 18:45:16,305 INFO [train.py:451] Epoch 0, batch 9870, batch avg loss 0.3728, total avg loss: 0.3839, batch size: 34
2021-10-13 18:45:20,908 INFO [train.py:451] Epoch 0, batch 9880, batch avg loss 0.3986, total avg loss: 0.3861, batch size: 34
2021-10-13 18:45:25,903 INFO [train.py:451] Epoch 0, batch 9890, batch avg loss 0.4112, total avg loss: 0.3871, batch size: 36
2021-10-13 18:45:30,818 INFO [train.py:451] Epoch 0, batch 9900, batch avg loss 0.2746, total avg loss: 0.3847, batch size: 27
2021-10-13 18:45:35,727 INFO [train.py:451] Epoch 0, batch 9910, batch avg loss 0.4333, total avg loss: 0.3847, batch size: 57
2021-10-13 18:45:40,759 INFO [train.py:451] Epoch 0, batch 9920, batch avg loss 0.3224, total avg loss: 0.3833, batch size: 29
2021-10-13 18:45:45,670 INFO [train.py:451] Epoch 0, batch 9930, batch avg loss 0.3188, total avg loss: 0.3831, batch size: 29
2021-10-13 18:45:50,583 INFO [train.py:451] Epoch 0, batch 9940, batch avg loss 0.4302, total avg loss: 0.3846, batch size: 38
2021-10-13 18:45:55,729 INFO [train.py:451] Epoch 0, batch 9950, batch avg loss 0.3730, total avg loss: 0.3846, batch size: 31
2021-10-13 18:46:00,562 INFO [train.py:451] Epoch 0, batch 9960, batch avg loss 0.4088, total avg loss: 0.3857, batch size: 70
2021-10-13 18:46:05,664 INFO [train.py:451] Epoch 0, batch 9970, batch avg loss 0.3558, total avg loss: 0.3857, batch size: 31
2021-10-13 18:46:10,566 INFO [train.py:451] Epoch 0, batch 9980, batch avg loss 0.4852, total avg loss: 0.3873, batch size: 124
2021-10-13 18:46:15,558 INFO [train.py:451] Epoch 0, batch 9990, batch avg loss 0.4030, total avg loss: 0.3875, batch size: 34
2021-10-13 18:46:20,340 INFO [train.py:451] Epoch 0, batch 10000, batch avg loss 0.3861, total avg loss: 0.3875, batch size: 42
2021-10-13 18:47:00,463 INFO [train.py:483] Epoch 0, valid loss 0.2787, best valid loss: 0.2787 best valid epoch: 0
2021-10-13 18:47:05,256 INFO [train.py:451] Epoch 0, batch 10010, batch avg loss 0.3391, total avg loss: 0.3841, batch size: 30
2021-10-13 18:47:10,046 INFO [train.py:451] Epoch 0, batch 10020, batch avg loss 0.3959, total avg loss: 0.3923, batch size: 34
2021-10-13 18:47:14,918 INFO [train.py:451] Epoch 0, batch 10030, batch avg loss 0.4577, total avg loss: 0.3921, batch size: 34
2021-10-13 18:47:19,826 INFO [train.py:451] Epoch 0, batch 10040, batch avg loss 0.3340, total avg loss: 0.3899, batch size: 30
2021-10-13 18:47:25,047 INFO [train.py:451] Epoch 0, batch 10050, batch avg loss 0.2998, total avg loss: 0.3853, batch size: 28
2021-10-13 18:47:30,125 INFO [train.py:451] Epoch 0, batch 10060, batch avg loss 0.4013, total avg loss: 0.3830, batch size: 32
2021-10-13 18:47:35,076 INFO [train.py:451] Epoch 0, batch 10070, batch avg loss 0.3890, total avg loss: 0.3845, batch size: 34
2021-10-13 18:47:40,014 INFO [train.py:451] Epoch 0, batch 10080, batch avg loss 0.4631, total avg loss: 0.3851, batch size: 57
2021-10-13 18:47:45,104 INFO [train.py:451] Epoch 0, batch 10090, batch avg loss 0.3178, total avg loss: 0.3819, batch size: 27
2021-10-13 18:47:49,974 INFO [train.py:451] Epoch 0, batch 10100, batch avg loss 0.3632, total avg loss: 0.3822, batch size: 34
2021-10-13 18:47:54,880 INFO [train.py:451] Epoch 0, batch 10110, batch avg loss 0.3058, total avg loss: 0.3817, batch size: 28
2021-10-13 18:47:59,822 INFO [train.py:451] Epoch 0, batch 10120, batch avg loss 0.4111, total avg loss: 0.3818, batch size: 42
2021-10-13 18:48:04,780 INFO [train.py:451] Epoch 0, batch 10130, batch avg loss 0.4700, total avg loss: 0.3817, batch size: 33
2021-10-13 18:48:09,587 INFO [train.py:451] Epoch 0, batch 10140, batch avg loss 0.3986, total avg loss: 0.3822, batch size: 35
2021-10-13 18:48:14,629 INFO [train.py:451] Epoch 0, batch 10150, batch avg loss 0.4256, total avg loss: 0.3820, batch size: 39
2021-10-13 18:48:19,506 INFO [train.py:451] Epoch 0, batch 10160, batch avg loss 0.4076, total avg loss: 0.3828, batch size: 38
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2021-10-13 18:48:49,205 INFO [train.py:451] Epoch 0, batch 10220, batch avg loss 0.3687, total avg loss: 0.3826, batch size: 32
2021-10-13 18:48:54,157 INFO [train.py:451] Epoch 0, batch 10230, batch avg loss 0.4650, total avg loss: 0.3856, batch size: 40
2021-10-13 18:48:58,901 INFO [train.py:451] Epoch 0, batch 10240, batch avg loss 0.3689, total avg loss: 0.3881, batch size: 31
2021-10-13 18:49:03,710 INFO [train.py:451] Epoch 0, batch 10250, batch avg loss 0.4306, total avg loss: 0.3932, batch size: 36
2021-10-13 18:49:08,744 INFO [train.py:451] Epoch 0, batch 10260, batch avg loss 0.4367, total avg loss: 0.3889, batch size: 45
2021-10-13 18:49:13,583 INFO [train.py:451] Epoch 0, batch 10270, batch avg loss 0.3126, total avg loss: 0.3900, batch size: 31
2021-10-13 18:49:18,506 INFO [train.py:451] Epoch 0, batch 10280, batch avg loss 0.3872, total avg loss: 0.3921, batch size: 35
2021-10-13 18:49:23,493 INFO [train.py:451] Epoch 0, batch 10290, batch avg loss 0.3770, total avg loss: 0.3923, batch size: 32
2021-10-13 18:49:28,392 INFO [train.py:451] Epoch 0, batch 10300, batch avg loss 0.3310, total avg loss: 0.3927, batch size: 31
2021-10-13 18:49:33,191 INFO [train.py:451] Epoch 0, batch 10310, batch avg loss 0.4022, total avg loss: 0.3931, batch size: 29
2021-10-13 18:49:37,939 INFO [train.py:451] Epoch 0, batch 10320, batch avg loss 0.3101, total avg loss: 0.3938, batch size: 29
2021-10-13 18:49:42,948 INFO [train.py:451] Epoch 0, batch 10330, batch avg loss 0.3652, total avg loss: 0.3916, batch size: 33
2021-10-13 18:49:47,924 INFO [train.py:451] Epoch 0, batch 10340, batch avg loss 0.3237, total avg loss: 0.3908, batch size: 27
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2021-10-13 18:50:02,635 INFO [train.py:451] Epoch 0, batch 10370, batch avg loss 0.3423, total avg loss: 0.3898, batch size: 29
2021-10-13 18:50:07,811 INFO [train.py:451] Epoch 0, batch 10380, batch avg loss 0.3959, total avg loss: 0.3887, batch size: 34
2021-10-13 18:50:12,544 INFO [train.py:451] Epoch 0, batch 10390, batch avg loss 0.3958, total avg loss: 0.3893, batch size: 36
2021-10-13 18:50:17,409 INFO [train.py:451] Epoch 0, batch 10400, batch avg loss 0.4146, total avg loss: 0.3896, batch size: 45
2021-10-13 18:50:22,325 INFO [train.py:451] Epoch 0, batch 10410, batch avg loss 0.4330, total avg loss: 0.3934, batch size: 37
2021-10-13 18:50:27,294 INFO [train.py:451] Epoch 0, batch 10420, batch avg loss 0.4171, total avg loss: 0.3934, batch size: 41
2021-10-13 18:50:32,295 INFO [train.py:451] Epoch 0, batch 10430, batch avg loss 0.3568, total avg loss: 0.3868, batch size: 31
2021-10-13 18:50:37,210 INFO [train.py:451] Epoch 0, batch 10440, batch avg loss 0.3300, total avg loss: 0.3881, batch size: 32
2021-10-13 18:50:42,105 INFO [train.py:451] Epoch 0, batch 10450, batch avg loss 0.4523, total avg loss: 0.3925, batch size: 128
2021-10-13 18:50:46,919 INFO [train.py:451] Epoch 0, batch 10460, batch avg loss 0.3837, total avg loss: 0.3892, batch size: 32
2021-10-13 18:50:51,835 INFO [train.py:451] Epoch 0, batch 10470, batch avg loss 0.3279, total avg loss: 0.3865, batch size: 28
2021-10-13 18:50:56,672 INFO [train.py:451] Epoch 0, batch 10480, batch avg loss 0.3762, total avg loss: 0.3835, batch size: 35
2021-10-13 18:51:01,581 INFO [train.py:451] Epoch 0, batch 10490, batch avg loss 0.3789, total avg loss: 0.3825, batch size: 42
2021-10-13 18:51:06,397 INFO [train.py:451] Epoch 0, batch 10500, batch avg loss 0.3503, total avg loss: 0.3844, batch size: 34
2021-10-13 18:51:11,295 INFO [train.py:451] Epoch 0, batch 10510, batch avg loss 0.4679, total avg loss: 0.3850, batch size: 72
2021-10-13 18:51:16,306 INFO [train.py:451] Epoch 0, batch 10520, batch avg loss 0.3845, total avg loss: 0.3836, batch size: 38
2021-10-13 18:51:21,123 INFO [train.py:451] Epoch 0, batch 10530, batch avg loss 0.3129, total avg loss: 0.3823, batch size: 29
2021-10-13 18:51:25,977 INFO [train.py:451] Epoch 0, batch 10540, batch avg loss 0.3706, total avg loss: 0.3814, batch size: 37
2021-10-13 18:51:30,870 INFO [train.py:451] Epoch 0, batch 10550, batch avg loss 0.4736, total avg loss: 0.3828, batch size: 127
2021-10-13 18:51:35,755 INFO [train.py:451] Epoch 0, batch 10560, batch avg loss 0.3080, total avg loss: 0.3826, batch size: 28
2021-10-13 18:51:40,591 INFO [train.py:451] Epoch 0, batch 10570, batch avg loss 0.3539, total avg loss: 0.3842, batch size: 35
2021-10-13 18:51:45,366 INFO [train.py:451] Epoch 0, batch 10580, batch avg loss 0.3211, total avg loss: 0.3848, batch size: 30
2021-10-13 18:51:50,363 INFO [train.py:451] Epoch 0, batch 10590, batch avg loss 0.3423, total avg loss: 0.3841, batch size: 27
2021-10-13 18:51:55,503 INFO [train.py:451] Epoch 0, batch 10600, batch avg loss 0.4235, total avg loss: 0.3845, batch size: 26
2021-10-13 18:52:00,439 INFO [train.py:451] Epoch 0, batch 10610, batch avg loss 0.3110, total avg loss: 0.3932, batch size: 27
2021-10-13 18:52:05,872 INFO [train.py:451] Epoch 0, batch 10620, batch avg loss 0.3087, total avg loss: 0.3811, batch size: 28
2021-10-13 18:52:10,621 INFO [train.py:451] Epoch 0, batch 10630, batch avg loss 0.3661, total avg loss: 0.3904, batch size: 29
2021-10-13 18:52:13,889 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "0390c7c5-b2a8-4257-6378-9959cd7cb44c" will not be mixed in.
2021-10-13 18:52:15,792 INFO [train.py:451] Epoch 0, batch 10640, batch avg loss 0.3454, total avg loss: 0.3855, batch size: 45
2021-10-13 18:52:20,663 INFO [train.py:451] Epoch 0, batch 10650, batch avg loss 0.4066, total avg loss: 0.3860, batch size: 74
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2021-10-13 18:52:30,468 INFO [train.py:451] Epoch 0, batch 10670, batch avg loss 0.4299, total avg loss: 0.3887, batch size: 45
2021-10-13 18:52:35,364 INFO [train.py:451] Epoch 0, batch 10680, batch avg loss 0.3904, total avg loss: 0.3891, batch size: 32
2021-10-13 18:52:40,439 INFO [train.py:451] Epoch 0, batch 10690, batch avg loss 0.4308, total avg loss: 0.3881, batch size: 49
2021-10-13 18:52:45,475 INFO [train.py:451] Epoch 0, batch 10700, batch avg loss 0.4000, total avg loss: 0.3872, batch size: 33
2021-10-13 18:52:50,377 INFO [train.py:451] Epoch 0, batch 10710, batch avg loss 0.3308, total avg loss: 0.3846, batch size: 28
2021-10-13 18:52:55,218 INFO [train.py:451] Epoch 0, batch 10720, batch avg loss 0.3469, total avg loss: 0.3833, batch size: 49
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2021-10-13 18:53:05,306 INFO [train.py:451] Epoch 0, batch 10740, batch avg loss 0.3889, total avg loss: 0.3817, batch size: 32
2021-10-13 18:53:10,444 INFO [train.py:451] Epoch 0, batch 10750, batch avg loss 0.4062, total avg loss: 0.3804, batch size: 49
2021-10-13 18:53:15,138 INFO [train.py:451] Epoch 0, batch 10760, batch avg loss 0.3609, total avg loss: 0.3808, batch size: 36
2021-10-13 18:53:19,754 INFO [train.py:451] Epoch 0, batch 10770, batch avg loss 0.4993, total avg loss: 0.3822, batch size: 130
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2021-10-13 18:53:34,705 INFO [train.py:451] Epoch 0, batch 10800, batch avg loss 0.4262, total avg loss: 0.3807, batch size: 73
2021-10-13 18:53:39,637 INFO [train.py:451] Epoch 0, batch 10810, batch avg loss 0.4719, total avg loss: 0.3700, batch size: 56
2021-10-13 18:53:44,495 INFO [train.py:451] Epoch 0, batch 10820, batch avg loss 0.3545, total avg loss: 0.3763, batch size: 32
2021-10-13 18:53:49,556 INFO [train.py:451] Epoch 0, batch 10830, batch avg loss 0.3564, total avg loss: 0.3794, batch size: 36
2021-10-13 18:53:54,521 INFO [train.py:451] Epoch 0, batch 10840, batch avg loss 0.3369, total avg loss: 0.3827, batch size: 31
2021-10-13 18:53:59,400 INFO [train.py:451] Epoch 0, batch 10850, batch avg loss 0.4502, total avg loss: 0.3871, batch size: 39
2021-10-13 18:54:04,423 INFO [train.py:451] Epoch 0, batch 10860, batch avg loss 0.3804, total avg loss: 0.3838, batch size: 38
2021-10-13 18:54:09,477 INFO [train.py:451] Epoch 0, batch 10870, batch avg loss 0.3451, total avg loss: 0.3843, batch size: 32
2021-10-13 18:54:14,509 INFO [train.py:451] Epoch 0, batch 10880, batch avg loss 0.4196, total avg loss: 0.3854, batch size: 42
2021-10-13 18:54:19,605 INFO [train.py:451] Epoch 0, batch 10890, batch avg loss 0.3472, total avg loss: 0.3859, batch size: 33
2021-10-13 18:54:24,857 INFO [train.py:451] Epoch 0, batch 10900, batch avg loss 0.3677, total avg loss: 0.3854, batch size: 35
2021-10-13 18:54:29,809 INFO [train.py:451] Epoch 0, batch 10910, batch avg loss 0.3736, total avg loss: 0.3838, batch size: 42
2021-10-13 18:54:34,890 INFO [train.py:451] Epoch 0, batch 10920, batch avg loss 0.4154, total avg loss: 0.3827, batch size: 49
2021-10-13 18:54:39,585 INFO [train.py:451] Epoch 0, batch 10930, batch avg loss 0.4206, total avg loss: 0.3836, batch size: 36
2021-10-13 18:54:44,561 INFO [train.py:451] Epoch 0, batch 10940, batch avg loss 0.3871, total avg loss: 0.3835, batch size: 36
2021-10-13 18:54:49,351 INFO [train.py:451] Epoch 0, batch 10950, batch avg loss 0.4133, total avg loss: 0.3839, batch size: 49
2021-10-13 18:54:54,369 INFO [train.py:451] Epoch 0, batch 10960, batch avg loss 0.3778, total avg loss: 0.3829, batch size: 33
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2021-10-13 18:55:04,236 INFO [train.py:451] Epoch 0, batch 10980, batch avg loss 0.3684, total avg loss: 0.3857, batch size: 33
2021-10-13 18:55:09,217 INFO [train.py:451] Epoch 0, batch 10990, batch avg loss 0.3244, total avg loss: 0.3840, batch size: 29
2021-10-13 18:55:13,880 INFO [train.py:451] Epoch 0, batch 11000, batch avg loss 0.4169, total avg loss: 0.3850, batch size: 74
2021-10-13 18:55:53,709 INFO [train.py:483] Epoch 0, valid loss 0.2763, best valid loss: 0.2763 best valid epoch: 0
2021-10-13 18:55:58,738 INFO [train.py:451] Epoch 0, batch 11010, batch avg loss 0.3880, total avg loss: 0.3616, batch size: 41
2021-10-13 18:56:03,708 INFO [train.py:451] Epoch 0, batch 11020, batch avg loss 0.3630, total avg loss: 0.3809, batch size: 34
2021-10-13 18:56:08,500 INFO [train.py:451] Epoch 0, batch 11030, batch avg loss 0.4181, total avg loss: 0.3820, batch size: 38
2021-10-13 18:56:13,449 INFO [train.py:451] Epoch 0, batch 11040, batch avg loss 0.3011, total avg loss: 0.3788, batch size: 29
2021-10-13 18:56:18,277 INFO [train.py:451] Epoch 0, batch 11050, batch avg loss 0.4194, total avg loss: 0.3812, batch size: 42
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2021-10-13 18:56:28,220 INFO [train.py:451] Epoch 0, batch 11070, batch avg loss 0.4640, total avg loss: 0.3824, batch size: 39
2021-10-13 18:56:33,231 INFO [train.py:451] Epoch 0, batch 11080, batch avg loss 0.3641, total avg loss: 0.3823, batch size: 36
2021-10-13 18:56:38,108 INFO [train.py:451] Epoch 0, batch 11090, batch avg loss 0.3313, total avg loss: 0.3829, batch size: 31
2021-10-13 18:56:43,004 INFO [train.py:451] Epoch 0, batch 11100, batch avg loss 0.3438, total avg loss: 0.3801, batch size: 32
2021-10-13 18:56:47,974 INFO [train.py:451] Epoch 0, batch 11110, batch avg loss 0.4313, total avg loss: 0.3806, batch size: 37
2021-10-13 18:56:52,883 INFO [train.py:451] Epoch 0, batch 11120, batch avg loss 0.3195, total avg loss: 0.3795, batch size: 29
2021-10-13 18:56:57,776 INFO [train.py:451] Epoch 0, batch 11130, batch avg loss 0.3682, total avg loss: 0.3785, batch size: 35
2021-10-13 18:57:02,589 INFO [train.py:451] Epoch 0, batch 11140, batch avg loss 0.3652, total avg loss: 0.3792, batch size: 31
2021-10-13 18:57:07,524 INFO [train.py:451] Epoch 0, batch 11150, batch avg loss 0.4084, total avg loss: 0.3790, batch size: 34
2021-10-13 18:57:12,558 INFO [train.py:451] Epoch 0, batch 11160, batch avg loss 0.2970, total avg loss: 0.3770, batch size: 29
2021-10-13 18:57:17,322 INFO [train.py:451] Epoch 0, batch 11170, batch avg loss 0.4492, total avg loss: 0.3787, batch size: 35
2021-10-13 18:57:22,276 INFO [train.py:451] Epoch 0, batch 11180, batch avg loss 0.4228, total avg loss: 0.3780, batch size: 73
2021-10-13 18:57:27,081 INFO [train.py:451] Epoch 0, batch 11190, batch avg loss 0.3821, total avg loss: 0.3780, batch size: 30
2021-10-13 18:57:32,006 INFO [train.py:451] Epoch 0, batch 11200, batch avg loss 0.4394, total avg loss: 0.3786, batch size: 45
2021-10-13 18:57:36,899 INFO [train.py:451] Epoch 0, batch 11210, batch avg loss 0.2982, total avg loss: 0.3705, batch size: 29
2021-10-13 18:57:41,902 INFO [train.py:451] Epoch 0, batch 11220, batch avg loss 0.5178, total avg loss: 0.3726, batch size: 125
2021-10-13 18:57:46,886 INFO [train.py:451] Epoch 0, batch 11230, batch avg loss 0.4213, total avg loss: 0.3807, batch size: 41
2021-10-13 18:57:51,793 INFO [train.py:451] Epoch 0, batch 11240, batch avg loss 0.2940, total avg loss: 0.3819, batch size: 28
2021-10-13 18:57:56,579 INFO [train.py:451] Epoch 0, batch 11250, batch avg loss 0.3702, total avg loss: 0.3831, batch size: 29
2021-10-13 18:58:01,508 INFO [train.py:451] Epoch 0, batch 11260, batch avg loss 0.3832, total avg loss: 0.3852, batch size: 36
2021-10-13 18:58:06,382 INFO [train.py:451] Epoch 0, batch 11270, batch avg loss 0.4130, total avg loss: 0.3876, batch size: 39
2021-10-13 18:58:11,307 INFO [train.py:451] Epoch 0, batch 11280, batch avg loss 0.3398, total avg loss: 0.3857, batch size: 29
2021-10-13 18:58:16,117 INFO [train.py:451] Epoch 0, batch 11290, batch avg loss 0.3727, total avg loss: 0.3885, batch size: 38
2021-10-13 18:58:20,877 INFO [train.py:451] Epoch 0, batch 11300, batch avg loss 0.3561, total avg loss: 0.3893, batch size: 49
2021-10-13 18:58:25,917 INFO [train.py:451] Epoch 0, batch 11310, batch avg loss 0.3513, total avg loss: 0.3900, batch size: 27
2021-10-13 18:58:30,833 INFO [train.py:451] Epoch 0, batch 11320, batch avg loss 0.4590, total avg loss: 0.3891, batch size: 36
2021-10-13 18:58:35,843 INFO [train.py:451] Epoch 0, batch 11330, batch avg loss 0.3673, total avg loss: 0.3882, batch size: 34
2021-10-13 18:58:40,517 INFO [train.py:451] Epoch 0, batch 11340, batch avg loss 0.3804, total avg loss: 0.3891, batch size: 34
2021-10-13 18:58:45,571 INFO [train.py:451] Epoch 0, batch 11350, batch avg loss 0.4269, total avg loss: 0.3881, batch size: 36
2021-10-13 18:58:50,397 INFO [train.py:451] Epoch 0, batch 11360, batch avg loss 0.3557, total avg loss: 0.3875, batch size: 41
2021-10-13 18:58:55,308 INFO [train.py:451] Epoch 0, batch 11370, batch avg loss 0.4550, total avg loss: 0.3875, batch size: 130
2021-10-13 18:59:00,310 INFO [train.py:451] Epoch 0, batch 11380, batch avg loss 0.3247, total avg loss: 0.3865, batch size: 33
2021-10-13 18:59:05,395 INFO [train.py:451] Epoch 0, batch 11390, batch avg loss 0.3895, total avg loss: 0.3859, batch size: 31
2021-10-13 18:59:10,212 INFO [train.py:451] Epoch 0, batch 11400, batch avg loss 0.3983, total avg loss: 0.3869, batch size: 33
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2021-10-13 18:59:20,151 INFO [train.py:451] Epoch 0, batch 11420, batch avg loss 0.4033, total avg loss: 0.3745, batch size: 36
2021-10-13 18:59:25,185 INFO [train.py:451] Epoch 0, batch 11430, batch avg loss 0.4461, total avg loss: 0.3780, batch size: 34
2021-10-13 18:59:30,107 INFO [train.py:451] Epoch 0, batch 11440, batch avg loss 0.3687, total avg loss: 0.3758, batch size: 36
2021-10-13 18:59:35,164 INFO [train.py:451] Epoch 0, batch 11450, batch avg loss 0.3752, total avg loss: 0.3766, batch size: 34
2021-10-13 18:59:39,977 INFO [train.py:451] Epoch 0, batch 11460, batch avg loss 0.3911, total avg loss: 0.3792, batch size: 38
2021-10-13 18:59:44,832 INFO [train.py:451] Epoch 0, batch 11470, batch avg loss 0.3590, total avg loss: 0.3822, batch size: 31
2021-10-13 18:59:49,978 INFO [train.py:451] Epoch 0, batch 11480, batch avg loss 0.3097, total avg loss: 0.3786, batch size: 33
2021-10-13 18:59:54,904 INFO [train.py:451] Epoch 0, batch 11490, batch avg loss 0.3388, total avg loss: 0.3787, batch size: 33
2021-10-13 18:59:59,735 INFO [train.py:451] Epoch 0, batch 11500, batch avg loss 0.3945, total avg loss: 0.3783, batch size: 35
2021-10-13 19:00:04,740 INFO [train.py:451] Epoch 0, batch 11510, batch avg loss 0.3327, total avg loss: 0.3782, batch size: 27
2021-10-13 19:00:09,573 INFO [train.py:451] Epoch 0, batch 11520, batch avg loss 0.3296, total avg loss: 0.3763, batch size: 30
2021-10-13 19:00:14,570 INFO [train.py:451] Epoch 0, batch 11530, batch avg loss 0.5118, total avg loss: 0.3781, batch size: 127
2021-10-13 19:00:19,424 INFO [train.py:451] Epoch 0, batch 11540, batch avg loss 0.3546, total avg loss: 0.3781, batch size: 36
2021-10-13 19:00:24,462 INFO [train.py:451] Epoch 0, batch 11550, batch avg loss 0.4005, total avg loss: 0.3782, batch size: 35
2021-10-13 19:00:29,360 INFO [train.py:451] Epoch 0, batch 11560, batch avg loss 0.4293, total avg loss: 0.3795, batch size: 34
2021-10-13 19:00:34,203 INFO [train.py:451] Epoch 0, batch 11570, batch avg loss 0.4305, total avg loss: 0.3802, batch size: 38
2021-10-13 19:00:39,224 INFO [train.py:451] Epoch 0, batch 11580, batch avg loss 0.3297, total avg loss: 0.3797, batch size: 32
2021-10-13 19:00:44,203 INFO [train.py:451] Epoch 0, batch 11590, batch avg loss 0.3590, total avg loss: 0.3793, batch size: 27
2021-10-13 19:00:49,097 INFO [train.py:451] Epoch 0, batch 11600, batch avg loss 0.3821, total avg loss: 0.3790, batch size: 45
2021-10-13 19:00:53,904 INFO [train.py:451] Epoch 0, batch 11610, batch avg loss 0.3255, total avg loss: 0.3700, batch size: 28
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2021-10-13 19:04:44,610 INFO [train.py:483] Epoch 0, valid loss 0.2701, best valid loss: 0.2701 best valid epoch: 0
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2021-10-13 19:11:03,857 INFO [train.py:451] Epoch 0, batch 12770, batch avg loss 0.3624, total avg loss: 0.3744, batch size: 35
2021-10-13 19:11:08,680 INFO [train.py:451] Epoch 0, batch 12780, batch avg loss 0.4588, total avg loss: 0.3752, batch size: 128
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2021-10-13 19:11:18,398 INFO [train.py:451] Epoch 0, batch 12800, batch avg loss 0.3923, total avg loss: 0.3742, batch size: 41
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2021-10-13 19:11:33,081 INFO [train.py:451] Epoch 0, batch 12830, batch avg loss 0.3403, total avg loss: 0.3754, batch size: 33
2021-10-13 19:11:38,039 INFO [train.py:451] Epoch 0, batch 12840, batch avg loss 0.2888, total avg loss: 0.3745, batch size: 31
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2021-10-13 19:11:52,894 INFO [train.py:451] Epoch 0, batch 12870, batch avg loss 0.3490, total avg loss: 0.3762, batch size: 26
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2021-10-13 19:12:51,566 INFO [train.py:451] Epoch 0, batch 12990, batch avg loss 0.3863, total avg loss: 0.3725, batch size: 35
2021-10-13 19:12:56,307 INFO [train.py:451] Epoch 0, batch 13000, batch avg loss 0.3290, total avg loss: 0.3731, batch size: 33
2021-10-13 19:13:36,178 INFO [train.py:483] Epoch 0, valid loss 0.2649, best valid loss: 0.2649 best valid epoch: 0
2021-10-13 19:13:41,172 INFO [train.py:451] Epoch 0, batch 13010, batch avg loss 0.3965, total avg loss: 0.3654, batch size: 57
2021-10-13 19:13:46,135 INFO [train.py:451] Epoch 0, batch 13020, batch avg loss 0.3562, total avg loss: 0.3677, batch size: 35
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2021-10-13 19:14:01,152 INFO [train.py:451] Epoch 0, batch 13050, batch avg loss 0.4240, total avg loss: 0.3671, batch size: 35
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2021-10-13 19:14:15,898 INFO [train.py:451] Epoch 0, batch 13080, batch avg loss 0.4633, total avg loss: 0.3682, batch size: 39
2021-10-13 19:14:21,014 INFO [train.py:451] Epoch 0, batch 13090, batch avg loss 0.3509, total avg loss: 0.3684, batch size: 31
2021-10-13 19:14:26,151 INFO [train.py:451] Epoch 0, batch 13100, batch avg loss 0.4308, total avg loss: 0.3663, batch size: 34
2021-10-13 19:14:31,118 INFO [train.py:451] Epoch 0, batch 13110, batch avg loss 0.3564, total avg loss: 0.3662, batch size: 33
2021-10-13 19:14:36,199 INFO [train.py:451] Epoch 0, batch 13120, batch avg loss 0.3768, total avg loss: 0.3653, batch size: 34
2021-10-13 19:14:41,266 INFO [train.py:451] Epoch 0, batch 13130, batch avg loss 0.4528, total avg loss: 0.3658, batch size: 38
2021-10-13 19:14:46,213 INFO [train.py:451] Epoch 0, batch 13140, batch avg loss 0.3629, total avg loss: 0.3665, batch size: 29
2021-10-13 19:14:51,357 INFO [train.py:451] Epoch 0, batch 13150, batch avg loss 0.3518, total avg loss: 0.3656, batch size: 34
2021-10-13 19:14:56,202 INFO [train.py:451] Epoch 0, batch 13160, batch avg loss 0.5087, total avg loss: 0.3665, batch size: 130
2021-10-13 19:15:01,121 INFO [train.py:451] Epoch 0, batch 13170, batch avg loss 0.4025, total avg loss: 0.3679, batch size: 34
2021-10-13 19:15:06,253 INFO [train.py:451] Epoch 0, batch 13180, batch avg loss 0.3130, total avg loss: 0.3679, batch size: 28
2021-10-13 19:15:11,263 INFO [train.py:451] Epoch 0, batch 13190, batch avg loss 0.5252, total avg loss: 0.3687, batch size: 129
2021-10-13 19:15:16,160 INFO [train.py:451] Epoch 0, batch 13200, batch avg loss 0.3431, total avg loss: 0.3674, batch size: 49
2021-10-13 19:15:21,251 INFO [train.py:451] Epoch 0, batch 13210, batch avg loss 0.4003, total avg loss: 0.3633, batch size: 45
2021-10-13 19:15:26,247 INFO [train.py:451] Epoch 0, batch 13220, batch avg loss 0.3537, total avg loss: 0.3638, batch size: 41
2021-10-13 19:15:31,392 INFO [train.py:451] Epoch 0, batch 13230, batch avg loss 0.3272, total avg loss: 0.3602, batch size: 29
2021-10-13 19:15:36,450 INFO [train.py:451] Epoch 0, batch 13240, batch avg loss 0.2978, total avg loss: 0.3565, batch size: 32
2021-10-13 19:15:41,464 INFO [train.py:451] Epoch 0, batch 13250, batch avg loss 0.3741, total avg loss: 0.3605, batch size: 33
2021-10-13 19:15:46,533 INFO [train.py:451] Epoch 0, batch 13260, batch avg loss 0.3206, total avg loss: 0.3601, batch size: 38
2021-10-13 19:15:51,418 INFO [train.py:451] Epoch 0, batch 13270, batch avg loss 0.3080, total avg loss: 0.3576, batch size: 31
2021-10-13 19:15:56,441 INFO [train.py:451] Epoch 0, batch 13280, batch avg loss 0.4066, total avg loss: 0.3604, batch size: 34
2021-10-13 19:16:01,494 INFO [train.py:451] Epoch 0, batch 13290, batch avg loss 0.3494, total avg loss: 0.3608, batch size: 35
2021-10-13 19:16:06,660 INFO [train.py:451] Epoch 0, batch 13300, batch avg loss 0.4236, total avg loss: 0.3622, batch size: 35
2021-10-13 19:16:11,805 INFO [train.py:451] Epoch 0, batch 13310, batch avg loss 0.2985, total avg loss: 0.3633, batch size: 30
2021-10-13 19:16:16,662 INFO [train.py:451] Epoch 0, batch 13320, batch avg loss 0.5133, total avg loss: 0.3647, batch size: 133
2021-10-13 19:16:21,465 INFO [train.py:451] Epoch 0, batch 13330, batch avg loss 0.4072, total avg loss: 0.3658, batch size: 45
2021-10-13 19:16:26,364 INFO [train.py:451] Epoch 0, batch 13340, batch avg loss 0.3983, total avg loss: 0.3663, batch size: 72
2021-10-13 19:16:31,374 INFO [train.py:451] Epoch 0, batch 13350, batch avg loss 0.4039, total avg loss: 0.3661, batch size: 72
2021-10-13 19:16:36,423 INFO [train.py:451] Epoch 0, batch 13360, batch avg loss 0.3324, total avg loss: 0.3666, batch size: 37
2021-10-13 19:16:41,506 INFO [train.py:451] Epoch 0, batch 13370, batch avg loss 0.3728, total avg loss: 0.3654, batch size: 42
2021-10-13 19:16:46,489 INFO [train.py:451] Epoch 0, batch 13380, batch avg loss 0.2920, total avg loss: 0.3640, batch size: 29
2021-10-13 19:16:51,328 INFO [train.py:451] Epoch 0, batch 13390, batch avg loss 0.4228, total avg loss: 0.3659, batch size: 34
2021-10-13 19:16:56,549 INFO [train.py:451] Epoch 0, batch 13400, batch avg loss 0.3290, total avg loss: 0.3658, batch size: 34
2021-10-13 19:17:01,393 INFO [train.py:451] Epoch 0, batch 13410, batch avg loss 0.3359, total avg loss: 0.3808, batch size: 35
2021-10-13 19:17:06,132 INFO [train.py:451] Epoch 0, batch 13420, batch avg loss 0.3519, total avg loss: 0.3802, batch size: 41
2021-10-13 19:17:11,219 INFO [train.py:451] Epoch 0, batch 13430, batch avg loss 0.3226, total avg loss: 0.3766, batch size: 35
2021-10-13 19:17:16,248 INFO [train.py:451] Epoch 0, batch 13440, batch avg loss 0.3849, total avg loss: 0.3727, batch size: 57
2021-10-13 19:17:21,211 INFO [train.py:451] Epoch 0, batch 13450, batch avg loss 0.3966, total avg loss: 0.3729, batch size: 45
2021-10-13 19:17:26,234 INFO [train.py:451] Epoch 0, batch 13460, batch avg loss 0.3400, total avg loss: 0.3713, batch size: 34
2021-10-13 19:17:31,149 INFO [train.py:451] Epoch 0, batch 13470, batch avg loss 0.4448, total avg loss: 0.3716, batch size: 56
2021-10-13 19:17:36,250 INFO [train.py:451] Epoch 0, batch 13480, batch avg loss 0.2770, total avg loss: 0.3720, batch size: 28
2021-10-13 19:17:41,271 INFO [train.py:451] Epoch 0, batch 13490, batch avg loss 0.3671, total avg loss: 0.3695, batch size: 32
2021-10-13 19:17:46,184 INFO [train.py:451] Epoch 0, batch 13500, batch avg loss 0.3954, total avg loss: 0.3703, batch size: 38
2021-10-13 19:17:51,122 INFO [train.py:451] Epoch 0, batch 13510, batch avg loss 0.3801, total avg loss: 0.3708, batch size: 73
2021-10-13 19:17:55,991 INFO [train.py:451] Epoch 0, batch 13520, batch avg loss 0.4309, total avg loss: 0.3694, batch size: 57
2021-10-13 19:18:00,913 INFO [train.py:451] Epoch 0, batch 13530, batch avg loss 0.3048, total avg loss: 0.3683, batch size: 28
2021-10-13 19:18:05,907 INFO [train.py:451] Epoch 0, batch 13540, batch avg loss 0.3143, total avg loss: 0.3664, batch size: 29
2021-10-13 19:18:10,881 INFO [train.py:451] Epoch 0, batch 13550, batch avg loss 0.3653, total avg loss: 0.3661, batch size: 33
2021-10-13 19:18:15,862 INFO [train.py:451] Epoch 0, batch 13560, batch avg loss 0.3073, total avg loss: 0.3649, batch size: 27
2021-10-13 19:18:20,630 INFO [train.py:451] Epoch 0, batch 13570, batch avg loss 0.3765, total avg loss: 0.3656, batch size: 32
2021-10-13 19:18:23,199 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "fd8ec549-36dc-e0ea-0ac4-c28a70b5b2f0" will not be mixed in.
2021-10-13 19:18:25,434 INFO [train.py:451] Epoch 0, batch 13580, batch avg loss 0.4432, total avg loss: 0.3654, batch size: 124
2021-10-13 19:18:30,439 INFO [train.py:451] Epoch 0, batch 13590, batch avg loss 0.3063, total avg loss: 0.3658, batch size: 41
2021-10-13 19:18:35,375 INFO [train.py:451] Epoch 0, batch 13600, batch avg loss 0.3699, total avg loss: 0.3658, batch size: 34
2021-10-13 19:18:40,462 INFO [train.py:451] Epoch 0, batch 13610, batch avg loss 0.3689, total avg loss: 0.3448, batch size: 32
2021-10-13 19:18:45,397 INFO [train.py:451] Epoch 0, batch 13620, batch avg loss 0.4195, total avg loss: 0.3615, batch size: 42
2021-10-13 19:18:50,487 INFO [train.py:451] Epoch 0, batch 13630, batch avg loss 0.3182, total avg loss: 0.3622, batch size: 33
2021-10-13 19:18:55,234 INFO [train.py:451] Epoch 0, batch 13640, batch avg loss 0.3783, total avg loss: 0.3698, batch size: 32
2021-10-13 19:19:00,068 INFO [train.py:451] Epoch 0, batch 13650, batch avg loss 0.3644, total avg loss: 0.3722, batch size: 42
2021-10-13 19:19:05,003 INFO [train.py:451] Epoch 0, batch 13660, batch avg loss 0.3953, total avg loss: 0.3700, batch size: 42
2021-10-13 19:19:09,926 INFO [train.py:451] Epoch 0, batch 13670, batch avg loss 0.3497, total avg loss: 0.3679, batch size: 31
2021-10-13 19:19:14,933 INFO [train.py:451] Epoch 0, batch 13680, batch avg loss 0.3248, total avg loss: 0.3666, batch size: 32
2021-10-13 19:19:19,714 INFO [train.py:451] Epoch 0, batch 13690, batch avg loss 0.3927, total avg loss: 0.3702, batch size: 35
2021-10-13 19:19:24,688 INFO [train.py:451] Epoch 0, batch 13700, batch avg loss 0.3608, total avg loss: 0.3696, batch size: 45
2021-10-13 19:19:29,658 INFO [train.py:451] Epoch 0, batch 13710, batch avg loss 0.3150, total avg loss: 0.3705, batch size: 30
2021-10-13 19:19:34,735 INFO [train.py:451] Epoch 0, batch 13720, batch avg loss 0.3213, total avg loss: 0.3680, batch size: 31
2021-10-13 19:19:39,723 INFO [train.py:451] Epoch 0, batch 13730, batch avg loss 0.3669, total avg loss: 0.3668, batch size: 31
2021-10-13 19:19:44,706 INFO [train.py:451] Epoch 0, batch 13740, batch avg loss 0.3266, total avg loss: 0.3683, batch size: 34
2021-10-13 19:19:49,917 INFO [train.py:451] Epoch 0, batch 13750, batch avg loss 0.4203, total avg loss: 0.3671, batch size: 35
2021-10-13 19:19:54,675 INFO [train.py:451] Epoch 0, batch 13760, batch avg loss 0.3995, total avg loss: 0.3676, batch size: 56
2021-10-13 19:19:59,659 INFO [train.py:451] Epoch 0, batch 13770, batch avg loss 0.4176, total avg loss: 0.3675, batch size: 34
2021-10-13 19:20:04,508 INFO [train.py:451] Epoch 0, batch 13780, batch avg loss 0.3552, total avg loss: 0.3685, batch size: 29
2021-10-13 19:20:09,576 INFO [train.py:451] Epoch 0, batch 13790, batch avg loss 0.4666, total avg loss: 0.3695, batch size: 34
2021-10-13 19:20:14,551 INFO [train.py:451] Epoch 0, batch 13800, batch avg loss 0.3121, total avg loss: 0.3688, batch size: 34
2021-10-13 19:20:19,492 INFO [train.py:451] Epoch 0, batch 13810, batch avg loss 0.3262, total avg loss: 0.3424, batch size: 29
2021-10-13 19:20:24,347 INFO [train.py:451] Epoch 0, batch 13820, batch avg loss 0.3140, total avg loss: 0.3572, batch size: 31
2021-10-13 19:20:29,232 INFO [train.py:451] Epoch 0, batch 13830, batch avg loss 0.3253, total avg loss: 0.3719, batch size: 30
2021-10-13 19:20:34,166 INFO [train.py:451] Epoch 0, batch 13840, batch avg loss 0.3028, total avg loss: 0.3700, batch size: 28
2021-10-13 19:20:38,956 INFO [train.py:451] Epoch 0, batch 13850, batch avg loss 0.4061, total avg loss: 0.3702, batch size: 42
2021-10-13 19:20:43,975 INFO [train.py:451] Epoch 0, batch 13860, batch avg loss 0.3802, total avg loss: 0.3692, batch size: 34
2021-10-13 19:20:48,748 INFO [train.py:451] Epoch 0, batch 13870, batch avg loss 0.3258, total avg loss: 0.3691, batch size: 28
2021-10-13 19:20:53,592 INFO [train.py:451] Epoch 0, batch 13880, batch avg loss 0.4523, total avg loss: 0.3675, batch size: 126
2021-10-13 19:20:58,655 INFO [train.py:451] Epoch 0, batch 13890, batch avg loss 0.3868, total avg loss: 0.3641, batch size: 33
2021-10-13 19:21:03,670 INFO [train.py:451] Epoch 0, batch 13900, batch avg loss 0.3912, total avg loss: 0.3641, batch size: 57
2021-10-13 19:21:08,497 INFO [train.py:451] Epoch 0, batch 13910, batch avg loss 0.3622, total avg loss: 0.3649, batch size: 32
2021-10-13 19:21:13,290 INFO [train.py:451] Epoch 0, batch 13920, batch avg loss 0.3459, total avg loss: 0.3669, batch size: 30
2021-10-13 19:21:18,193 INFO [train.py:451] Epoch 0, batch 13930, batch avg loss 0.3214, total avg loss: 0.3668, batch size: 31
2021-10-13 19:21:23,281 INFO [train.py:451] Epoch 0, batch 13940, batch avg loss 0.3524, total avg loss: 0.3654, batch size: 32
2021-10-13 19:21:28,208 INFO [train.py:451] Epoch 0, batch 13950, batch avg loss 0.3626, total avg loss: 0.3656, batch size: 38
2021-10-13 19:21:33,030 INFO [train.py:451] Epoch 0, batch 13960, batch avg loss 0.3093, total avg loss: 0.3655, batch size: 32
2021-10-13 19:21:37,838 INFO [train.py:451] Epoch 0, batch 13970, batch avg loss 0.2842, total avg loss: 0.3662, batch size: 32
2021-10-13 19:21:42,844 INFO [train.py:451] Epoch 0, batch 13980, batch avg loss 0.3058, total avg loss: 0.3666, batch size: 31
2021-10-13 19:21:47,771 INFO [train.py:451] Epoch 0, batch 13990, batch avg loss 0.2914, total avg loss: 0.3652, batch size: 31
2021-10-13 19:21:52,759 INFO [train.py:451] Epoch 0, batch 14000, batch avg loss 0.3343, total avg loss: 0.3655, batch size: 30
2021-10-13 19:22:32,393 INFO [train.py:483] Epoch 0, valid loss 0.2630, best valid loss: 0.2630 best valid epoch: 0
2021-10-13 19:22:37,298 INFO [train.py:451] Epoch 0, batch 14010, batch avg loss 0.3462, total avg loss: 0.3533, batch size: 36
2021-10-13 19:22:42,137 INFO [train.py:451] Epoch 0, batch 14020, batch avg loss 0.3253, total avg loss: 0.3718, batch size: 34
2021-10-13 19:22:47,135 INFO [train.py:451] Epoch 0, batch 14030, batch avg loss 0.3837, total avg loss: 0.3665, batch size: 37
2021-10-13 19:22:51,904 INFO [train.py:451] Epoch 0, batch 14040, batch avg loss 0.3525, total avg loss: 0.3732, batch size: 35
2021-10-13 19:22:57,059 INFO [train.py:451] Epoch 0, batch 14050, batch avg loss 0.3263, total avg loss: 0.3710, batch size: 37
2021-10-13 19:23:02,065 INFO [train.py:451] Epoch 0, batch 14060, batch avg loss 0.3766, total avg loss: 0.3719, batch size: 38
2021-10-13 19:23:06,841 INFO [train.py:451] Epoch 0, batch 14070, batch avg loss 0.3283, total avg loss: 0.3709, batch size: 49
2021-10-13 19:23:11,754 INFO [train.py:451] Epoch 0, batch 14080, batch avg loss 0.4626, total avg loss: 0.3695, batch size: 126
2021-10-13 19:23:16,779 INFO [train.py:451] Epoch 0, batch 14090, batch avg loss 0.3921, total avg loss: 0.3677, batch size: 35
2021-10-13 19:23:21,679 INFO [train.py:451] Epoch 0, batch 14100, batch avg loss 0.2951, total avg loss: 0.3667, batch size: 33
2021-10-13 19:23:26,666 INFO [train.py:451] Epoch 0, batch 14110, batch avg loss 0.3553, total avg loss: 0.3671, batch size: 33
2021-10-13 19:23:31,713 INFO [train.py:451] Epoch 0, batch 14120, batch avg loss 0.3373, total avg loss: 0.3677, batch size: 31
2021-10-13 19:23:36,510 INFO [train.py:451] Epoch 0, batch 14130, batch avg loss 0.3642, total avg loss: 0.3682, batch size: 30
2021-10-13 19:23:41,371 INFO [train.py:451] Epoch 0, batch 14140, batch avg loss 0.3418, total avg loss: 0.3675, batch size: 35
2021-10-13 19:23:46,110 INFO [train.py:451] Epoch 0, batch 14150, batch avg loss 0.3277, total avg loss: 0.3670, batch size: 36
2021-10-13 19:23:51,129 INFO [train.py:451] Epoch 0, batch 14160, batch avg loss 0.3347, total avg loss: 0.3655, batch size: 42
2021-10-13 19:23:56,088 INFO [train.py:451] Epoch 0, batch 14170, batch avg loss 0.3407, total avg loss: 0.3649, batch size: 34
2021-10-13 19:24:01,077 INFO [train.py:451] Epoch 0, batch 14180, batch avg loss 0.3031, total avg loss: 0.3646, batch size: 28
2021-10-13 19:24:05,916 INFO [train.py:451] Epoch 0, batch 14190, batch avg loss 0.3257, total avg loss: 0.3639, batch size: 37
2021-10-13 19:24:10,888 INFO [train.py:451] Epoch 0, batch 14200, batch avg loss 0.3416, total avg loss: 0.3627, batch size: 49
2021-10-13 19:24:15,818 INFO [train.py:451] Epoch 0, batch 14210, batch avg loss 0.4106, total avg loss: 0.3682, batch size: 73
2021-10-13 19:24:20,746 INFO [train.py:451] Epoch 0, batch 14220, batch avg loss 0.3628, total avg loss: 0.3665, batch size: 36
2021-10-13 19:24:25,630 INFO [train.py:451] Epoch 0, batch 14230, batch avg loss 0.3493, total avg loss: 0.3670, batch size: 33
2021-10-13 19:24:30,567 INFO [train.py:451] Epoch 0, batch 14240, batch avg loss 0.3492, total avg loss: 0.3606, batch size: 31
2021-10-13 19:24:35,572 INFO [train.py:451] Epoch 0, batch 14250, batch avg loss 0.3490, total avg loss: 0.3565, batch size: 36
2021-10-13 19:24:40,352 INFO [train.py:451] Epoch 0, batch 14260, batch avg loss 0.3861, total avg loss: 0.3603, batch size: 45
2021-10-13 19:24:45,568 INFO [train.py:451] Epoch 0, batch 14270, batch avg loss 0.4446, total avg loss: 0.3609, batch size: 41
2021-10-13 19:24:50,722 INFO [train.py:451] Epoch 0, batch 14280, batch avg loss 0.3801, total avg loss: 0.3585, batch size: 42
2021-10-13 19:24:55,797 INFO [train.py:451] Epoch 0, batch 14290, batch avg loss 0.3357, total avg loss: 0.3576, batch size: 29
2021-10-13 19:25:00,726 INFO [train.py:451] Epoch 0, batch 14300, batch avg loss 0.3456, total avg loss: 0.3580, batch size: 36
2021-10-13 19:25:05,610 INFO [train.py:451] Epoch 0, batch 14310, batch avg loss 0.3709, total avg loss: 0.3609, batch size: 33
2021-10-13 19:25:10,494 INFO [train.py:451] Epoch 0, batch 14320, batch avg loss 0.4389, total avg loss: 0.3643, batch size: 127
2021-10-13 19:25:15,366 INFO [train.py:451] Epoch 0, batch 14330, batch avg loss 0.3385, total avg loss: 0.3633, batch size: 31
2021-10-13 19:25:20,294 INFO [train.py:451] Epoch 0, batch 14340, batch avg loss 0.3900, total avg loss: 0.3632, batch size: 57
2021-10-13 19:25:25,314 INFO [train.py:451] Epoch 0, batch 14350, batch avg loss 0.4075, total avg loss: 0.3635, batch size: 73
2021-10-13 19:25:30,371 INFO [train.py:451] Epoch 0, batch 14360, batch avg loss 0.4052, total avg loss: 0.3634, batch size: 33
2021-10-13 19:25:35,290 INFO [train.py:451] Epoch 0, batch 14370, batch avg loss 0.4197, total avg loss: 0.3633, batch size: 38
2021-10-13 19:25:40,251 INFO [train.py:451] Epoch 0, batch 14380, batch avg loss 0.3120, total avg loss: 0.3628, batch size: 36
2021-10-13 19:25:45,373 INFO [train.py:451] Epoch 0, batch 14390, batch avg loss 0.3884, total avg loss: 0.3622, batch size: 33
2021-10-13 19:25:50,273 INFO [train.py:451] Epoch 0, batch 14400, batch avg loss 0.3375, total avg loss: 0.3625, batch size: 34
2021-10-13 19:25:55,301 INFO [train.py:451] Epoch 0, batch 14410, batch avg loss 0.3575, total avg loss: 0.3703, batch size: 35
2021-10-13 19:26:00,473 INFO [train.py:451] Epoch 0, batch 14420, batch avg loss 0.3290, total avg loss: 0.3530, batch size: 30
2021-10-13 19:26:05,308 INFO [train.py:451] Epoch 0, batch 14430, batch avg loss 0.4084, total avg loss: 0.3669, batch size: 57
2021-10-13 19:26:10,315 INFO [train.py:451] Epoch 0, batch 14440, batch avg loss 0.3731, total avg loss: 0.3605, batch size: 45
2021-10-13 19:26:15,284 INFO [train.py:451] Epoch 0, batch 14450, batch avg loss 0.3370, total avg loss: 0.3627, batch size: 33
2021-10-13 19:26:20,280 INFO [train.py:451] Epoch 0, batch 14460, batch avg loss 0.4161, total avg loss: 0.3601, batch size: 32
2021-10-13 19:26:25,308 INFO [train.py:451] Epoch 0, batch 14470, batch avg loss 0.3347, total avg loss: 0.3623, batch size: 27
2021-10-13 19:26:30,077 INFO [train.py:451] Epoch 0, batch 14480, batch avg loss 0.4462, total avg loss: 0.3670, batch size: 129
2021-10-13 19:26:35,084 INFO [train.py:451] Epoch 0, batch 14490, batch avg loss 0.3372, total avg loss: 0.3651, batch size: 28
2021-10-13 19:26:39,847 INFO [train.py:451] Epoch 0, batch 14500, batch avg loss 0.3155, total avg loss: 0.3642, batch size: 30
2021-10-13 19:26:44,686 INFO [train.py:451] Epoch 0, batch 14510, batch avg loss 0.4007, total avg loss: 0.3644, batch size: 57
2021-10-13 19:26:49,938 INFO [train.py:451] Epoch 0, batch 14520, batch avg loss 0.3485, total avg loss: 0.3633, batch size: 36
2021-10-13 19:26:54,689 INFO [train.py:451] Epoch 0, batch 14530, batch avg loss 0.2892, total avg loss: 0.3629, batch size: 33
2021-10-13 19:26:59,719 INFO [train.py:451] Epoch 0, batch 14540, batch avg loss 0.3319, total avg loss: 0.3624, batch size: 28
2021-10-13 19:27:04,504 INFO [train.py:451] Epoch 0, batch 14550, batch avg loss 0.3127, total avg loss: 0.3637, batch size: 34
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2021-10-13 19:31:25,320 INFO [train.py:483] Epoch 0, valid loss 0.2552, best valid loss: 0.2552 best valid epoch: 0
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2021-10-13 19:39:35,331 INFO [train.py:451] Epoch 0, batch 15990, batch avg loss 0.3646, total avg loss: 0.3506, batch size: 41
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2021-10-13 19:40:18,382 INFO [train.py:483] Epoch 0, valid loss 0.2519, best valid loss: 0.2519 best valid epoch: 0
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2021-10-13 19:49:18,018 INFO [train.py:483] Epoch 0, valid loss 0.2514, best valid loss: 0.2514 best valid epoch: 0
2021-10-13 19:49:23,112 INFO [train.py:451] Epoch 0, batch 17010, batch avg loss 0.3458, total avg loss: 0.3288, batch size: 32
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2021-10-13 19:58:11,776 INFO [train.py:483] Epoch 0, valid loss 0.2460, best valid loss: 0.2460 best valid epoch: 0
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2021-10-13 20:07:04,798 INFO [train.py:483] Epoch 0, valid loss 0.2439, best valid loss: 0.2439 best valid epoch: 0
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2021-10-13 20:15:07,007 INFO [train.py:451] Epoch 0, batch 19970, batch avg loss 0.4019, total avg loss: 0.3387, batch size: 35
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2021-10-13 20:16:01,552 INFO [train.py:483] Epoch 0, valid loss 0.2431, best valid loss: 0.2431 best valid epoch: 0
2021-10-13 20:16:06,503 INFO [train.py:451] Epoch 0, batch 20010, batch avg loss 0.3837, total avg loss: 0.3470, batch size: 37
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2021-10-13 20:16:31,133 INFO [train.py:451] Epoch 0, batch 20060, batch avg loss 0.3791, total avg loss: 0.3404, batch size: 30
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2021-10-13 20:16:40,956 INFO [train.py:451] Epoch 0, batch 20080, batch avg loss 0.3508, total avg loss: 0.3424, batch size: 42
2021-10-13 20:16:46,134 INFO [train.py:451] Epoch 0, batch 20090, batch avg loss 0.2569, total avg loss: 0.3404, batch size: 29
2021-10-13 20:16:50,873 INFO [train.py:451] Epoch 0, batch 20100, batch avg loss 0.2425, total avg loss: 0.3393, batch size: 32
2021-10-13 20:16:55,683 INFO [train.py:451] Epoch 0, batch 20110, batch avg loss 0.4283, total avg loss: 0.3415, batch size: 39
2021-10-13 20:17:00,764 INFO [train.py:451] Epoch 0, batch 20120, batch avg loss 0.3851, total avg loss: 0.3436, batch size: 45
2021-10-13 20:17:05,668 INFO [train.py:451] Epoch 0, batch 20130, batch avg loss 0.2976, total avg loss: 0.3427, batch size: 31
2021-10-13 20:17:10,668 INFO [train.py:451] Epoch 0, batch 20140, batch avg loss 0.3565, total avg loss: 0.3448, batch size: 37
2021-10-13 20:17:15,801 INFO [train.py:451] Epoch 0, batch 20150, batch avg loss 0.3009, total avg loss: 0.3443, batch size: 30
2021-10-13 20:17:20,828 INFO [train.py:451] Epoch 0, batch 20160, batch avg loss 0.3480, total avg loss: 0.3422, batch size: 45
2021-10-13 20:17:25,896 INFO [train.py:451] Epoch 0, batch 20170, batch avg loss 0.3020, total avg loss: 0.3409, batch size: 34
2021-10-13 20:17:30,850 INFO [train.py:451] Epoch 0, batch 20180, batch avg loss 0.3104, total avg loss: 0.3401, batch size: 32
2021-10-13 20:17:35,636 INFO [train.py:451] Epoch 0, batch 20190, batch avg loss 0.3817, total avg loss: 0.3415, batch size: 35
2021-10-13 20:17:40,569 INFO [train.py:451] Epoch 0, batch 20200, batch avg loss 0.3381, total avg loss: 0.3405, batch size: 35
2021-10-13 20:17:45,369 INFO [train.py:451] Epoch 0, batch 20210, batch avg loss 0.2887, total avg loss: 0.3459, batch size: 30
2021-10-13 20:17:50,281 INFO [train.py:451] Epoch 0, batch 20220, batch avg loss 0.3006, total avg loss: 0.3364, batch size: 35
2021-10-13 20:17:55,123 INFO [train.py:451] Epoch 0, batch 20230, batch avg loss 0.3687, total avg loss: 0.3313, batch size: 45
2021-10-13 20:18:00,003 INFO [train.py:451] Epoch 0, batch 20240, batch avg loss 0.2981, total avg loss: 0.3293, batch size: 33
2021-10-13 20:18:04,891 INFO [train.py:451] Epoch 0, batch 20250, batch avg loss 0.3925, total avg loss: 0.3310, batch size: 72
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2021-10-13 20:18:20,108 INFO [train.py:451] Epoch 0, batch 20280, batch avg loss 0.2762, total avg loss: 0.3348, batch size: 30
2021-10-13 20:18:25,127 INFO [train.py:451] Epoch 0, batch 20290, batch avg loss 0.3072, total avg loss: 0.3340, batch size: 34
2021-10-13 20:18:30,012 INFO [train.py:451] Epoch 0, batch 20300, batch avg loss 0.3366, total avg loss: 0.3333, batch size: 36
2021-10-13 20:18:34,879 INFO [train.py:451] Epoch 0, batch 20310, batch avg loss 0.3127, total avg loss: 0.3339, batch size: 32
2021-10-13 20:18:39,694 INFO [train.py:451] Epoch 0, batch 20320, batch avg loss 0.2978, total avg loss: 0.3341, batch size: 34
2021-10-13 20:18:44,657 INFO [train.py:451] Epoch 0, batch 20330, batch avg loss 0.3441, total avg loss: 0.3357, batch size: 34
2021-10-13 20:18:49,916 INFO [train.py:451] Epoch 0, batch 20340, batch avg loss 0.3368, total avg loss: 0.3356, batch size: 33
2021-10-13 20:18:55,042 INFO [train.py:451] Epoch 0, batch 20350, batch avg loss 0.3078, total avg loss: 0.3349, batch size: 34
2021-10-13 20:18:59,813 INFO [train.py:451] Epoch 0, batch 20360, batch avg loss 0.3299, total avg loss: 0.3363, batch size: 29
2021-10-13 20:19:04,709 INFO [train.py:451] Epoch 0, batch 20370, batch avg loss 0.3588, total avg loss: 0.3377, batch size: 38
2021-10-13 20:19:09,561 INFO [train.py:451] Epoch 0, batch 20380, batch avg loss 0.3173, total avg loss: 0.3387, batch size: 38
2021-10-13 20:19:14,415 INFO [train.py:451] Epoch 0, batch 20390, batch avg loss 0.3905, total avg loss: 0.3388, batch size: 73
2021-10-13 20:19:19,460 INFO [train.py:451] Epoch 0, batch 20400, batch avg loss 0.3621, total avg loss: 0.3394, batch size: 33
2021-10-13 20:19:24,401 INFO [train.py:451] Epoch 0, batch 20410, batch avg loss 0.3822, total avg loss: 0.3544, batch size: 72
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2021-10-13 20:19:34,521 INFO [train.py:451] Epoch 0, batch 20430, batch avg loss 0.3750, total avg loss: 0.3410, batch size: 35
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2021-10-13 20:26:31,335 INFO [checkpoint.py:62] Saving checkpoint to tdnn_lstm_ctc-v2/exp/epoch-0.pt
2021-10-13 20:26:32,135 INFO [train.py:564] epoch 1, lr: 0.00025
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2021-10-13 20:32:46,417 INFO [train.py:451] Epoch 1, batch 750, batch avg loss 0.4191, total avg loss: 0.3372, batch size: 35
2021-10-13 20:32:51,355 INFO [train.py:451] Epoch 1, batch 760, batch avg loss 0.3792, total avg loss: 0.3370, batch size: 57
2021-10-13 20:32:56,470 INFO [train.py:451] Epoch 1, batch 770, batch avg loss 0.2882, total avg loss: 0.3351, batch size: 34
2021-10-13 20:33:01,257 INFO [train.py:451] Epoch 1, batch 780, batch avg loss 0.3854, total avg loss: 0.3347, batch size: 73
2021-10-13 20:33:06,138 INFO [train.py:451] Epoch 1, batch 790, batch avg loss 0.3232, total avg loss: 0.3344, batch size: 31
2021-10-13 20:33:11,031 INFO [train.py:451] Epoch 1, batch 800, batch avg loss 0.4447, total avg loss: 0.3353, batch size: 131
2021-10-13 20:33:16,065 INFO [train.py:451] Epoch 1, batch 810, batch avg loss 0.3016, total avg loss: 0.3211, batch size: 29
2021-10-13 20:33:20,892 INFO [train.py:451] Epoch 1, batch 820, batch avg loss 0.3238, total avg loss: 0.3248, batch size: 34
2021-10-13 20:33:25,844 INFO [train.py:451] Epoch 1, batch 830, batch avg loss 0.3373, total avg loss: 0.3370, batch size: 42
2021-10-13 20:33:30,651 INFO [train.py:451] Epoch 1, batch 840, batch avg loss 0.2949, total avg loss: 0.3386, batch size: 30
2021-10-13 20:33:35,526 INFO [train.py:451] Epoch 1, batch 850, batch avg loss 0.3352, total avg loss: 0.3368, batch size: 37
2021-10-13 20:33:40,354 INFO [train.py:451] Epoch 1, batch 860, batch avg loss 0.2578, total avg loss: 0.3380, batch size: 31
2021-10-13 20:33:45,080 INFO [train.py:451] Epoch 1, batch 870, batch avg loss 0.3677, total avg loss: 0.3361, batch size: 73
2021-10-13 20:33:50,080 INFO [train.py:451] Epoch 1, batch 880, batch avg loss 0.3220, total avg loss: 0.3371, batch size: 37
2021-10-13 20:33:55,036 INFO [train.py:451] Epoch 1, batch 890, batch avg loss 0.4279, total avg loss: 0.3368, batch size: 33
2021-10-13 20:33:59,742 INFO [train.py:451] Epoch 1, batch 900, batch avg loss 0.3665, total avg loss: 0.3407, batch size: 33
2021-10-13 20:34:04,612 INFO [train.py:451] Epoch 1, batch 910, batch avg loss 0.3454, total avg loss: 0.3409, batch size: 33
2021-10-13 20:34:09,574 INFO [train.py:451] Epoch 1, batch 920, batch avg loss 0.3335, total avg loss: 0.3407, batch size: 41
2021-10-13 20:34:14,442 INFO [train.py:451] Epoch 1, batch 930, batch avg loss 0.3947, total avg loss: 0.3402, batch size: 72
2021-10-13 20:34:19,560 INFO [train.py:451] Epoch 1, batch 940, batch avg loss 0.3315, total avg loss: 0.3406, batch size: 38
2021-10-13 20:34:24,437 INFO [train.py:451] Epoch 1, batch 950, batch avg loss 0.3494, total avg loss: 0.3394, batch size: 49
2021-10-13 20:34:29,440 INFO [train.py:451] Epoch 1, batch 960, batch avg loss 0.3199, total avg loss: 0.3385, batch size: 37
2021-10-13 20:34:34,429 INFO [train.py:451] Epoch 1, batch 970, batch avg loss 0.3187, total avg loss: 0.3374, batch size: 33
2021-10-13 20:34:39,161 INFO [train.py:451] Epoch 1, batch 980, batch avg loss 0.2941, total avg loss: 0.3382, batch size: 35
2021-10-13 20:34:44,021 INFO [train.py:451] Epoch 1, batch 990, batch avg loss 0.3481, total avg loss: 0.3386, batch size: 36
2021-10-13 20:34:49,077 INFO [train.py:451] Epoch 1, batch 1000, batch avg loss 0.2809, total avg loss: 0.3383, batch size: 27
2021-10-13 20:35:28,725 INFO [train.py:483] Epoch 1, valid loss 0.2383, best valid loss: 0.2383 best valid epoch: 1
2021-10-13 20:35:33,692 INFO [train.py:451] Epoch 1, batch 1010, batch avg loss 0.2703, total avg loss: 0.3386, batch size: 33
2021-10-13 20:35:38,715 INFO [train.py:451] Epoch 1, batch 1020, batch avg loss 0.3442, total avg loss: 0.3321, batch size: 31
2021-10-13 20:35:43,446 INFO [train.py:451] Epoch 1, batch 1030, batch avg loss 0.3946, total avg loss: 0.3355, batch size: 45
2021-10-13 20:35:48,477 INFO [train.py:451] Epoch 1, batch 1040, batch avg loss 0.3533, total avg loss: 0.3317, batch size: 39
2021-10-13 20:35:53,359 INFO [train.py:451] Epoch 1, batch 1050, batch avg loss 0.2835, total avg loss: 0.3299, batch size: 31
2021-10-13 20:35:58,322 INFO [train.py:451] Epoch 1, batch 1060, batch avg loss 0.3694, total avg loss: 0.3314, batch size: 49
2021-10-13 20:36:03,316 INFO [train.py:451] Epoch 1, batch 1070, batch avg loss 0.3520, total avg loss: 0.3296, batch size: 39
2021-10-13 20:36:08,302 INFO [train.py:451] Epoch 1, batch 1080, batch avg loss 0.2970, total avg loss: 0.3272, batch size: 34
2021-10-13 20:36:13,303 INFO [train.py:451] Epoch 1, batch 1090, batch avg loss 0.3840, total avg loss: 0.3281, batch size: 72
2021-10-13 20:36:18,373 INFO [train.py:451] Epoch 1, batch 1100, batch avg loss 0.4024, total avg loss: 0.3290, batch size: 34
2021-10-13 20:36:23,278 INFO [train.py:451] Epoch 1, batch 1110, batch avg loss 0.3570, total avg loss: 0.3295, batch size: 35
2021-10-13 20:36:28,174 INFO [train.py:451] Epoch 1, batch 1120, batch avg loss 0.3054, total avg loss: 0.3303, batch size: 33
2021-10-13 20:36:32,939 INFO [train.py:451] Epoch 1, batch 1130, batch avg loss 0.3026, total avg loss: 0.3306, batch size: 28
2021-10-13 20:36:38,032 INFO [train.py:451] Epoch 1, batch 1140, batch avg loss 0.2907, total avg loss: 0.3309, batch size: 29
2021-10-13 20:36:43,181 INFO [train.py:451] Epoch 1, batch 1150, batch avg loss 0.2480, total avg loss: 0.3298, batch size: 30
2021-10-13 20:36:48,099 INFO [train.py:451] Epoch 1, batch 1160, batch avg loss 0.3485, total avg loss: 0.3308, batch size: 34
2021-10-13 20:36:53,096 INFO [train.py:451] Epoch 1, batch 1170, batch avg loss 0.3447, total avg loss: 0.3302, batch size: 34
2021-10-13 20:36:58,107 INFO [train.py:451] Epoch 1, batch 1180, batch avg loss 0.3091, total avg loss: 0.3286, batch size: 36
2021-10-13 20:37:02,900 INFO [train.py:451] Epoch 1, batch 1190, batch avg loss 0.3581, total avg loss: 0.3302, batch size: 38
2021-10-13 20:37:07,814 INFO [train.py:451] Epoch 1, batch 1200, batch avg loss 0.3207, total avg loss: 0.3308, batch size: 32
2021-10-13 20:37:12,555 INFO [train.py:451] Epoch 1, batch 1210, batch avg loss 0.3063, total avg loss: 0.3423, batch size: 38
2021-10-13 20:37:17,513 INFO [train.py:451] Epoch 1, batch 1220, batch avg loss 0.3434, total avg loss: 0.3317, batch size: 36
2021-10-13 20:37:22,426 INFO [train.py:451] Epoch 1, batch 1230, batch avg loss 0.4509, total avg loss: 0.3418, batch size: 126
2021-10-13 20:37:27,406 INFO [train.py:451] Epoch 1, batch 1240, batch avg loss 0.3408, total avg loss: 0.3431, batch size: 42
2021-10-13 20:37:32,341 INFO [train.py:451] Epoch 1, batch 1250, batch avg loss 0.3845, total avg loss: 0.3424, batch size: 38
2021-10-13 20:37:37,321 INFO [train.py:451] Epoch 1, batch 1260, batch avg loss 0.3107, total avg loss: 0.3367, batch size: 31
2021-10-13 20:37:42,279 INFO [train.py:451] Epoch 1, batch 1270, batch avg loss 0.4024, total avg loss: 0.3355, batch size: 36
2021-10-13 20:37:47,152 INFO [train.py:451] Epoch 1, batch 1280, batch avg loss 0.3730, total avg loss: 0.3355, batch size: 38
2021-10-13 20:37:51,997 INFO [train.py:451] Epoch 1, batch 1290, batch avg loss 0.3343, total avg loss: 0.3362, batch size: 57
2021-10-13 20:37:56,764 INFO [train.py:451] Epoch 1, batch 1300, batch avg loss 0.2779, total avg loss: 0.3361, batch size: 29
2021-10-13 20:38:01,891 INFO [train.py:451] Epoch 1, batch 1310, batch avg loss 0.3654, total avg loss: 0.3356, batch size: 41
2021-10-13 20:38:06,915 INFO [train.py:451] Epoch 1, batch 1320, batch avg loss 0.2921, total avg loss: 0.3352, batch size: 31
2021-10-13 20:38:11,928 INFO [train.py:451] Epoch 1, batch 1330, batch avg loss 0.2975, total avg loss: 0.3324, batch size: 42
2021-10-13 20:38:16,795 INFO [train.py:451] Epoch 1, batch 1340, batch avg loss 0.3533, total avg loss: 0.3325, batch size: 36
2021-10-13 20:38:21,875 INFO [train.py:451] Epoch 1, batch 1350, batch avg loss 0.2823, total avg loss: 0.3308, batch size: 29
2021-10-13 20:38:26,843 INFO [train.py:451] Epoch 1, batch 1360, batch avg loss 0.3581, total avg loss: 0.3303, batch size: 34
2021-10-13 20:38:31,873 INFO [train.py:451] Epoch 1, batch 1370, batch avg loss 0.3115, total avg loss: 0.3316, batch size: 27
2021-10-13 20:38:36,753 INFO [train.py:451] Epoch 1, batch 1380, batch avg loss 0.3405, total avg loss: 0.3303, batch size: 37
2021-10-13 20:38:41,717 INFO [train.py:451] Epoch 1, batch 1390, batch avg loss 0.3020, total avg loss: 0.3302, batch size: 31
2021-10-13 20:38:46,666 INFO [train.py:451] Epoch 1, batch 1400, batch avg loss 0.3270, total avg loss: 0.3303, batch size: 34
2021-10-13 20:38:51,541 INFO [train.py:451] Epoch 1, batch 1410, batch avg loss 0.3071, total avg loss: 0.3377, batch size: 35
2021-10-13 20:38:56,411 INFO [train.py:451] Epoch 1, batch 1420, batch avg loss 0.3072, total avg loss: 0.3429, batch size: 29
2021-10-13 20:39:01,462 INFO [train.py:451] Epoch 1, batch 1430, batch avg loss 0.3149, total avg loss: 0.3336, batch size: 33
2021-10-13 20:39:06,500 INFO [train.py:451] Epoch 1, batch 1440, batch avg loss 0.3319, total avg loss: 0.3315, batch size: 27
2021-10-13 20:39:11,235 INFO [train.py:451] Epoch 1, batch 1450, batch avg loss 0.4399, total avg loss: 0.3355, batch size: 126
2021-10-13 20:39:16,214 INFO [train.py:451] Epoch 1, batch 1460, batch avg loss 0.3180, total avg loss: 0.3314, batch size: 33
2021-10-13 20:39:21,071 INFO [train.py:451] Epoch 1, batch 1470, batch avg loss 0.3051, total avg loss: 0.3346, batch size: 37
2021-10-13 20:39:26,063 INFO [train.py:451] Epoch 1, batch 1480, batch avg loss 0.3239, total avg loss: 0.3336, batch size: 39
2021-10-13 20:39:31,128 INFO [train.py:451] Epoch 1, batch 1490, batch avg loss 0.3224, total avg loss: 0.3324, batch size: 34
2021-10-13 20:39:36,187 INFO [train.py:451] Epoch 1, batch 1500, batch avg loss 0.2449, total avg loss: 0.3328, batch size: 28
2021-10-13 20:39:41,098 INFO [train.py:451] Epoch 1, batch 1510, batch avg loss 0.3578, total avg loss: 0.3326, batch size: 35
2021-10-13 20:39:45,981 INFO [train.py:451] Epoch 1, batch 1520, batch avg loss 0.3310, total avg loss: 0.3331, batch size: 35
2021-10-13 20:39:51,062 INFO [train.py:451] Epoch 1, batch 1530, batch avg loss 0.3610, total avg loss: 0.3323, batch size: 38
2021-10-13 20:39:55,823 INFO [train.py:451] Epoch 1, batch 1540, batch avg loss 0.2994, total avg loss: 0.3337, batch size: 31
2021-10-13 20:40:00,782 INFO [train.py:451] Epoch 1, batch 1550, batch avg loss 0.3603, total avg loss: 0.3330, batch size: 57
2021-10-13 20:40:05,694 INFO [train.py:451] Epoch 1, batch 1560, batch avg loss 0.3958, total avg loss: 0.3331, batch size: 129
2021-10-13 20:40:10,562 INFO [train.py:451] Epoch 1, batch 1570, batch avg loss 0.3591, total avg loss: 0.3341, batch size: 38
2021-10-13 20:40:15,605 INFO [train.py:451] Epoch 1, batch 1580, batch avg loss 0.3515, total avg loss: 0.3339, batch size: 36
2021-10-13 20:40:20,634 INFO [train.py:451] Epoch 1, batch 1590, batch avg loss 0.3364, total avg loss: 0.3329, batch size: 42
2021-10-13 20:40:25,527 INFO [train.py:451] Epoch 1, batch 1600, batch avg loss 0.3811, total avg loss: 0.3330, batch size: 73
2021-10-13 20:40:30,255 INFO [train.py:451] Epoch 1, batch 1610, batch avg loss 0.3531, total avg loss: 0.3512, batch size: 39
2021-10-13 20:40:35,270 INFO [train.py:451] Epoch 1, batch 1620, batch avg loss 0.2952, total avg loss: 0.3417, batch size: 30
2021-10-13 20:40:40,191 INFO [train.py:451] Epoch 1, batch 1630, batch avg loss 0.3632, total avg loss: 0.3422, batch size: 45
2021-10-13 20:40:44,946 INFO [train.py:451] Epoch 1, batch 1640, batch avg loss 0.3668, total avg loss: 0.3428, batch size: 42
2021-10-13 20:40:49,933 INFO [train.py:451] Epoch 1, batch 1650, batch avg loss 0.2643, total avg loss: 0.3431, batch size: 29
2021-10-13 20:40:54,858 INFO [train.py:451] Epoch 1, batch 1660, batch avg loss 0.3710, total avg loss: 0.3434, batch size: 38
2021-10-13 20:40:59,909 INFO [train.py:451] Epoch 1, batch 1670, batch avg loss 0.3458, total avg loss: 0.3403, batch size: 31
2021-10-13 20:41:05,015 INFO [train.py:451] Epoch 1, batch 1680, batch avg loss 0.3052, total avg loss: 0.3384, batch size: 31
2021-10-13 20:41:09,963 INFO [train.py:451] Epoch 1, batch 1690, batch avg loss 0.2751, total avg loss: 0.3356, batch size: 30
2021-10-13 20:41:14,985 INFO [train.py:451] Epoch 1, batch 1700, batch avg loss 0.3545, total avg loss: 0.3352, batch size: 27
2021-10-13 20:41:19,868 INFO [train.py:451] Epoch 1, batch 1710, batch avg loss 0.3121, total avg loss: 0.3364, batch size: 35
2021-10-13 20:41:24,894 INFO [train.py:451] Epoch 1, batch 1720, batch avg loss 0.3182, total avg loss: 0.3360, batch size: 45
2021-10-13 20:41:29,937 INFO [train.py:451] Epoch 1, batch 1730, batch avg loss 0.2574, total avg loss: 0.3328, batch size: 31
2021-10-13 20:41:34,977 INFO [train.py:451] Epoch 1, batch 1740, batch avg loss 0.3830, total avg loss: 0.3325, batch size: 41
2021-10-13 20:41:39,828 INFO [train.py:451] Epoch 1, batch 1750, batch avg loss 0.2898, total avg loss: 0.3321, batch size: 32
2021-10-13 20:41:44,712 INFO [train.py:451] Epoch 1, batch 1760, batch avg loss 0.3404, total avg loss: 0.3320, batch size: 71
2021-10-13 20:41:49,405 INFO [train.py:451] Epoch 1, batch 1770, batch avg loss 0.3200, total avg loss: 0.3334, batch size: 45
2021-10-13 20:41:54,262 INFO [train.py:451] Epoch 1, batch 1780, batch avg loss 0.3541, total avg loss: 0.3341, batch size: 35
2021-10-13 20:41:59,058 INFO [train.py:451] Epoch 1, batch 1790, batch avg loss 0.3123, total avg loss: 0.3341, batch size: 29
2021-10-13 20:42:03,939 INFO [train.py:451] Epoch 1, batch 1800, batch avg loss 0.3386, total avg loss: 0.3333, batch size: 73
2021-10-13 20:42:08,798 INFO [train.py:451] Epoch 1, batch 1810, batch avg loss 0.3429, total avg loss: 0.3404, batch size: 32
2021-10-13 20:42:14,080 INFO [train.py:451] Epoch 1, batch 1820, batch avg loss 0.2771, total avg loss: 0.3326, batch size: 28
2021-10-13 20:42:19,134 INFO [train.py:451] Epoch 1, batch 1830, batch avg loss 0.3556, total avg loss: 0.3298, batch size: 38
2021-10-13 20:42:23,843 INFO [train.py:451] Epoch 1, batch 1840, batch avg loss 0.2647, total avg loss: 0.3313, batch size: 32
2021-10-13 20:42:28,850 INFO [train.py:451] Epoch 1, batch 1850, batch avg loss 0.3081, total avg loss: 0.3300, batch size: 35
2021-10-13 20:42:33,884 INFO [train.py:451] Epoch 1, batch 1860, batch avg loss 0.3557, total avg loss: 0.3302, batch size: 34
2021-10-13 20:42:38,966 INFO [train.py:451] Epoch 1, batch 1870, batch avg loss 0.3186, total avg loss: 0.3317, batch size: 37
2021-10-13 20:42:44,187 INFO [train.py:451] Epoch 1, batch 1880, batch avg loss 0.3266, total avg loss: 0.3325, batch size: 29
2021-10-13 20:42:49,034 INFO [train.py:451] Epoch 1, batch 1890, batch avg loss 0.3340, total avg loss: 0.3320, batch size: 45
2021-10-13 20:42:54,059 INFO [train.py:451] Epoch 1, batch 1900, batch avg loss 0.3410, total avg loss: 0.3305, batch size: 57
2021-10-13 20:42:59,120 INFO [train.py:451] Epoch 1, batch 1910, batch avg loss 0.3504, total avg loss: 0.3308, batch size: 28
2021-10-13 20:43:03,942 INFO [train.py:451] Epoch 1, batch 1920, batch avg loss 0.4324, total avg loss: 0.3314, batch size: 126
2021-10-13 20:43:08,947 INFO [train.py:451] Epoch 1, batch 1930, batch avg loss 0.3059, total avg loss: 0.3300, batch size: 32
2021-10-13 20:43:13,920 INFO [train.py:451] Epoch 1, batch 1940, batch avg loss 0.4616, total avg loss: 0.3311, batch size: 124
2021-10-13 20:43:18,902 INFO [train.py:451] Epoch 1, batch 1950, batch avg loss 0.3622, total avg loss: 0.3323, batch size: 73
2021-10-13 20:43:24,171 INFO [train.py:451] Epoch 1, batch 1960, batch avg loss 0.3058, total avg loss: 0.3315, batch size: 29
2021-10-13 20:43:29,307 INFO [train.py:451] Epoch 1, batch 1970, batch avg loss 0.3412, total avg loss: 0.3311, batch size: 31
2021-10-13 20:43:34,279 INFO [train.py:451] Epoch 1, batch 1980, batch avg loss 0.3815, total avg loss: 0.3318, batch size: 38
2021-10-13 20:43:39,493 INFO [train.py:451] Epoch 1, batch 1990, batch avg loss 0.3131, total avg loss: 0.3302, batch size: 34
2021-10-13 20:43:44,562 INFO [train.py:451] Epoch 1, batch 2000, batch avg loss 0.2859, total avg loss: 0.3296, batch size: 27
2021-10-13 20:44:24,351 INFO [train.py:483] Epoch 1, valid loss 0.2355, best valid loss: 0.2355 best valid epoch: 1
2021-10-13 20:44:29,129 INFO [train.py:451] Epoch 1, batch 2010, batch avg loss 0.4073, total avg loss: 0.3576, batch size: 49
2021-10-13 20:44:34,052 INFO [train.py:451] Epoch 1, batch 2020, batch avg loss 0.2812, total avg loss: 0.3433, batch size: 33
2021-10-13 20:44:39,217 INFO [train.py:451] Epoch 1, batch 2030, batch avg loss 0.3155, total avg loss: 0.3328, batch size: 33
2021-10-13 20:44:44,367 INFO [train.py:451] Epoch 1, batch 2040, batch avg loss 0.3721, total avg loss: 0.3379, batch size: 34
2021-10-13 20:44:49,305 INFO [train.py:451] Epoch 1, batch 2050, batch avg loss 0.2610, total avg loss: 0.3349, batch size: 28
2021-10-13 20:44:54,141 INFO [train.py:451] Epoch 1, batch 2060, batch avg loss 0.3584, total avg loss: 0.3365, batch size: 38
2021-10-13 20:44:59,040 INFO [train.py:451] Epoch 1, batch 2070, batch avg loss 0.4029, total avg loss: 0.3391, batch size: 72
2021-10-13 20:45:03,942 INFO [train.py:451] Epoch 1, batch 2080, batch avg loss 0.3342, total avg loss: 0.3374, batch size: 35
2021-10-13 20:45:08,767 INFO [train.py:451] Epoch 1, batch 2090, batch avg loss 0.2938, total avg loss: 0.3375, batch size: 29
2021-10-13 20:45:13,681 INFO [train.py:451] Epoch 1, batch 2100, batch avg loss 0.2987, total avg loss: 0.3359, batch size: 32
2021-10-13 20:45:18,885 INFO [train.py:451] Epoch 1, batch 2110, batch avg loss 0.2901, total avg loss: 0.3345, batch size: 31
2021-10-13 20:45:23,764 INFO [train.py:451] Epoch 1, batch 2120, batch avg loss 0.3826, total avg loss: 0.3365, batch size: 45
2021-10-13 20:45:28,848 INFO [train.py:451] Epoch 1, batch 2130, batch avg loss 0.2976, total avg loss: 0.3356, batch size: 34
2021-10-13 20:45:33,941 INFO [train.py:451] Epoch 1, batch 2140, batch avg loss 0.3216, total avg loss: 0.3325, batch size: 35
2021-10-13 20:45:38,786 INFO [train.py:451] Epoch 1, batch 2150, batch avg loss 0.3269, total avg loss: 0.3348, batch size: 31
2021-10-13 20:45:43,810 INFO [train.py:451] Epoch 1, batch 2160, batch avg loss 0.3160, total avg loss: 0.3344, batch size: 28
2021-10-13 20:45:48,540 INFO [train.py:451] Epoch 1, batch 2170, batch avg loss 0.3841, total avg loss: 0.3359, batch size: 73
2021-10-13 20:45:53,309 INFO [train.py:451] Epoch 1, batch 2180, batch avg loss 0.3091, total avg loss: 0.3351, batch size: 28
2021-10-13 20:45:58,237 INFO [train.py:451] Epoch 1, batch 2190, batch avg loss 0.3479, total avg loss: 0.3353, batch size: 34
2021-10-13 20:46:03,163 INFO [train.py:451] Epoch 1, batch 2200, batch avg loss 0.3247, total avg loss: 0.3345, batch size: 49
2021-10-13 20:46:08,241 INFO [train.py:451] Epoch 1, batch 2210, batch avg loss 0.3489, total avg loss: 0.3444, batch size: 33
2021-10-13 20:46:13,317 INFO [train.py:451] Epoch 1, batch 2220, batch avg loss 0.4019, total avg loss: 0.3416, batch size: 34
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2021-10-13 20:46:28,260 INFO [train.py:451] Epoch 1, batch 2250, batch avg loss 0.2500, total avg loss: 0.3333, batch size: 30
2021-10-13 20:46:33,072 INFO [train.py:451] Epoch 1, batch 2260, batch avg loss 0.3192, total avg loss: 0.3319, batch size: 32
2021-10-13 20:46:38,064 INFO [train.py:451] Epoch 1, batch 2270, batch avg loss 0.3053, total avg loss: 0.3288, batch size: 34
2021-10-13 20:46:42,974 INFO [train.py:451] Epoch 1, batch 2280, batch avg loss 0.3424, total avg loss: 0.3275, batch size: 36
2021-10-13 20:46:47,993 INFO [train.py:451] Epoch 1, batch 2290, batch avg loss 0.3603, total avg loss: 0.3277, batch size: 38
2021-10-13 20:46:52,866 INFO [train.py:451] Epoch 1, batch 2300, batch avg loss 0.4107, total avg loss: 0.3282, batch size: 72
2021-10-13 20:46:57,774 INFO [train.py:451] Epoch 1, batch 2310, batch avg loss 0.3378, total avg loss: 0.3292, batch size: 37
2021-10-13 20:47:02,754 INFO [train.py:451] Epoch 1, batch 2320, batch avg loss 0.3344, total avg loss: 0.3291, batch size: 57
2021-10-13 20:47:07,671 INFO [train.py:451] Epoch 1, batch 2330, batch avg loss 0.3565, total avg loss: 0.3283, batch size: 36
2021-10-13 20:47:12,702 INFO [train.py:451] Epoch 1, batch 2340, batch avg loss 0.3675, total avg loss: 0.3276, batch size: 31
2021-10-13 20:47:17,619 INFO [train.py:451] Epoch 1, batch 2350, batch avg loss 0.2889, total avg loss: 0.3283, batch size: 29
2021-10-13 20:47:22,427 INFO [train.py:451] Epoch 1, batch 2360, batch avg loss 0.3263, total avg loss: 0.3284, batch size: 34
2021-10-13 20:47:27,290 INFO [train.py:451] Epoch 1, batch 2370, batch avg loss 0.3205, total avg loss: 0.3284, batch size: 31
2021-10-13 20:47:32,235 INFO [train.py:451] Epoch 1, batch 2380, batch avg loss 0.3067, total avg loss: 0.3284, batch size: 30
2021-10-13 20:47:37,282 INFO [train.py:451] Epoch 1, batch 2390, batch avg loss 0.2767, total avg loss: 0.3277, batch size: 30
2021-10-13 20:47:42,132 INFO [train.py:451] Epoch 1, batch 2400, batch avg loss 0.2492, total avg loss: 0.3286, batch size: 28
2021-10-13 20:47:46,995 INFO [train.py:451] Epoch 1, batch 2410, batch avg loss 0.2960, total avg loss: 0.3052, batch size: 30
2021-10-13 20:47:52,064 INFO [train.py:451] Epoch 1, batch 2420, batch avg loss 0.2983, total avg loss: 0.3208, batch size: 30
2021-10-13 20:47:57,185 INFO [train.py:451] Epoch 1, batch 2430, batch avg loss 0.3999, total avg loss: 0.3308, batch size: 35
2021-10-13 20:48:02,246 INFO [train.py:451] Epoch 1, batch 2440, batch avg loss 0.3495, total avg loss: 0.3351, batch size: 41
2021-10-13 20:48:07,195 INFO [train.py:451] Epoch 1, batch 2450, batch avg loss 0.3151, total avg loss: 0.3324, batch size: 57
2021-10-13 20:48:12,218 INFO [train.py:451] Epoch 1, batch 2460, batch avg loss 0.3042, total avg loss: 0.3326, batch size: 36
2021-10-13 20:48:17,232 INFO [train.py:451] Epoch 1, batch 2470, batch avg loss 0.4116, total avg loss: 0.3335, batch size: 38
2021-10-13 20:48:22,323 INFO [train.py:451] Epoch 1, batch 2480, batch avg loss 0.3445, total avg loss: 0.3323, batch size: 37
2021-10-13 20:48:27,272 INFO [train.py:451] Epoch 1, batch 2490, batch avg loss 0.2848, total avg loss: 0.3329, batch size: 28
2021-10-13 20:48:32,291 INFO [train.py:451] Epoch 1, batch 2500, batch avg loss 0.3027, total avg loss: 0.3314, batch size: 37
2021-10-13 20:48:37,272 INFO [train.py:451] Epoch 1, batch 2510, batch avg loss 0.2197, total avg loss: 0.3289, batch size: 27
2021-10-13 20:48:42,172 INFO [train.py:451] Epoch 1, batch 2520, batch avg loss 0.3661, total avg loss: 0.3287, batch size: 36
2021-10-13 20:48:46,950 INFO [train.py:451] Epoch 1, batch 2530, batch avg loss 0.3243, total avg loss: 0.3307, batch size: 29
2021-10-13 20:48:51,819 INFO [train.py:451] Epoch 1, batch 2540, batch avg loss 0.4110, total avg loss: 0.3311, batch size: 57
2021-10-13 20:48:56,517 INFO [train.py:451] Epoch 1, batch 2550, batch avg loss 0.2978, total avg loss: 0.3321, batch size: 31
2021-10-13 20:49:01,714 INFO [train.py:451] Epoch 1, batch 2560, batch avg loss 0.3396, total avg loss: 0.3326, batch size: 39
2021-10-13 20:49:06,745 INFO [train.py:451] Epoch 1, batch 2570, batch avg loss 0.3124, total avg loss: 0.3317, batch size: 33
2021-10-13 20:49:12,190 INFO [train.py:451] Epoch 1, batch 2580, batch avg loss 0.2933, total avg loss: 0.3313, batch size: 26
2021-10-13 20:49:17,048 INFO [train.py:451] Epoch 1, batch 2590, batch avg loss 0.4344, total avg loss: 0.3318, batch size: 129
2021-10-13 20:49:22,115 INFO [train.py:451] Epoch 1, batch 2600, batch avg loss 0.3642, total avg loss: 0.3315, batch size: 33
2021-10-13 20:49:27,396 INFO [train.py:451] Epoch 1, batch 2610, batch avg loss 0.3761, total avg loss: 0.3198, batch size: 37
2021-10-13 20:49:32,131 INFO [train.py:451] Epoch 1, batch 2620, batch avg loss 0.3973, total avg loss: 0.3249, batch size: 35
2021-10-13 20:49:37,161 INFO [train.py:451] Epoch 1, batch 2630, batch avg loss 0.2908, total avg loss: 0.3247, batch size: 41
2021-10-13 20:49:42,252 INFO [train.py:451] Epoch 1, batch 2640, batch avg loss 0.3413, total avg loss: 0.3249, batch size: 34
2021-10-13 20:49:47,340 INFO [train.py:451] Epoch 1, batch 2650, batch avg loss 0.2588, total avg loss: 0.3258, batch size: 29
2021-10-13 20:49:52,353 INFO [train.py:451] Epoch 1, batch 2660, batch avg loss 0.4221, total avg loss: 0.3253, batch size: 36
2021-10-13 20:49:57,210 INFO [train.py:451] Epoch 1, batch 2670, batch avg loss 0.3510, total avg loss: 0.3273, batch size: 36
2021-10-13 20:50:02,059 INFO [train.py:451] Epoch 1, batch 2680, batch avg loss 0.3217, total avg loss: 0.3288, batch size: 38
2021-10-13 20:50:06,867 INFO [train.py:451] Epoch 1, batch 2690, batch avg loss 0.3132, total avg loss: 0.3292, batch size: 30
2021-10-13 20:50:11,761 INFO [train.py:451] Epoch 1, batch 2700, batch avg loss 0.3462, total avg loss: 0.3291, batch size: 34
2021-10-13 20:50:16,593 INFO [train.py:451] Epoch 1, batch 2710, batch avg loss 0.3285, total avg loss: 0.3271, batch size: 38
2021-10-13 20:50:21,587 INFO [train.py:451] Epoch 1, batch 2720, batch avg loss 0.2934, total avg loss: 0.3260, batch size: 31
2021-10-13 20:50:26,498 INFO [train.py:451] Epoch 1, batch 2730, batch avg loss 0.3699, total avg loss: 0.3272, batch size: 36
2021-10-13 20:50:31,208 INFO [train.py:451] Epoch 1, batch 2740, batch avg loss 0.3905, total avg loss: 0.3299, batch size: 124
2021-10-13 20:50:36,237 INFO [train.py:451] Epoch 1, batch 2750, batch avg loss 0.3217, total avg loss: 0.3314, batch size: 33
2021-10-13 20:50:41,414 INFO [train.py:451] Epoch 1, batch 2760, batch avg loss 0.4090, total avg loss: 0.3305, batch size: 35
2021-10-13 20:50:46,446 INFO [train.py:451] Epoch 1, batch 2770, batch avg loss 0.3441, total avg loss: 0.3292, batch size: 37
2021-10-13 20:50:51,410 INFO [train.py:451] Epoch 1, batch 2780, batch avg loss 0.2899, total avg loss: 0.3287, batch size: 32
2021-10-13 20:50:56,756 INFO [train.py:451] Epoch 1, batch 2790, batch avg loss 0.2874, total avg loss: 0.3298, batch size: 27
2021-10-13 20:51:01,934 INFO [train.py:451] Epoch 1, batch 2800, batch avg loss 0.3622, total avg loss: 0.3291, batch size: 33
2021-10-13 20:51:06,765 INFO [train.py:451] Epoch 1, batch 2810, batch avg loss 0.3785, total avg loss: 0.3483, batch size: 49
2021-10-13 20:51:11,739 INFO [train.py:451] Epoch 1, batch 2820, batch avg loss 0.3191, total avg loss: 0.3360, batch size: 29
2021-10-13 20:51:16,767 INFO [train.py:451] Epoch 1, batch 2830, batch avg loss 0.3199, total avg loss: 0.3270, batch size: 38
2021-10-13 20:51:21,726 INFO [train.py:451] Epoch 1, batch 2840, batch avg loss 0.3646, total avg loss: 0.3268, batch size: 49
2021-10-13 20:51:27,005 INFO [train.py:451] Epoch 1, batch 2850, batch avg loss 0.2787, total avg loss: 0.3225, batch size: 29
2021-10-13 20:51:31,980 INFO [train.py:451] Epoch 1, batch 2860, batch avg loss 0.3229, total avg loss: 0.3221, batch size: 37
2021-10-13 20:51:37,223 INFO [train.py:451] Epoch 1, batch 2870, batch avg loss 0.2813, total avg loss: 0.3234, batch size: 33
2021-10-13 20:51:42,197 INFO [train.py:451] Epoch 1, batch 2880, batch avg loss 0.3202, total avg loss: 0.3245, batch size: 36
2021-10-13 20:51:47,046 INFO [train.py:451] Epoch 1, batch 2890, batch avg loss 0.3238, total avg loss: 0.3264, batch size: 35
2021-10-13 20:51:51,854 INFO [train.py:451] Epoch 1, batch 2900, batch avg loss 0.4373, total avg loss: 0.3283, batch size: 73
2021-10-13 20:51:56,706 INFO [train.py:451] Epoch 1, batch 2910, batch avg loss 0.3090, total avg loss: 0.3289, batch size: 35
2021-10-13 20:52:01,798 INFO [train.py:451] Epoch 1, batch 2920, batch avg loss 0.3171, total avg loss: 0.3276, batch size: 27
2021-10-13 20:52:06,677 INFO [train.py:451] Epoch 1, batch 2930, batch avg loss 0.3464, total avg loss: 0.3278, batch size: 39
2021-10-13 20:52:11,895 INFO [train.py:451] Epoch 1, batch 2940, batch avg loss 0.3255, total avg loss: 0.3260, batch size: 34
2021-10-13 20:52:16,850 INFO [train.py:451] Epoch 1, batch 2950, batch avg loss 0.3007, total avg loss: 0.3261, batch size: 36
2021-10-13 20:52:21,894 INFO [train.py:451] Epoch 1, batch 2960, batch avg loss 0.3379, total avg loss: 0.3259, batch size: 29
2021-10-13 20:52:26,687 INFO [train.py:451] Epoch 1, batch 2970, batch avg loss 0.2951, total avg loss: 0.3264, batch size: 31
2021-10-13 20:52:31,499 INFO [train.py:451] Epoch 1, batch 2980, batch avg loss 0.3053, total avg loss: 0.3266, batch size: 36
2021-10-13 20:52:36,399 INFO [train.py:451] Epoch 1, batch 2990, batch avg loss 0.2731, total avg loss: 0.3267, batch size: 37
2021-10-13 20:52:41,338 INFO [train.py:451] Epoch 1, batch 3000, batch avg loss 0.3863, total avg loss: 0.3267, batch size: 36
2021-10-13 20:53:22,240 INFO [train.py:483] Epoch 1, valid loss 0.2344, best valid loss: 0.2344 best valid epoch: 1
2021-10-13 20:53:27,198 INFO [train.py:451] Epoch 1, batch 3010, batch avg loss 0.3265, total avg loss: 0.3122, batch size: 34
2021-10-13 20:53:32,012 INFO [train.py:451] Epoch 1, batch 3020, batch avg loss 0.3736, total avg loss: 0.3260, batch size: 45
2021-10-13 20:53:36,800 INFO [train.py:451] Epoch 1, batch 3030, batch avg loss 0.3164, total avg loss: 0.3294, batch size: 38
2021-10-13 20:53:41,697 INFO [train.py:451] Epoch 1, batch 3040, batch avg loss 0.3871, total avg loss: 0.3316, batch size: 34
2021-10-13 20:53:46,713 INFO [train.py:451] Epoch 1, batch 3050, batch avg loss 0.3510, total avg loss: 0.3279, batch size: 49
2021-10-13 20:53:51,719 INFO [train.py:451] Epoch 1, batch 3060, batch avg loss 0.2713, total avg loss: 0.3276, batch size: 32
2021-10-13 20:53:56,803 INFO [train.py:451] Epoch 1, batch 3070, batch avg loss 0.3167, total avg loss: 0.3252, batch size: 33
2021-10-13 20:54:01,564 INFO [train.py:451] Epoch 1, batch 3080, batch avg loss 0.2668, total avg loss: 0.3272, batch size: 31
2021-10-13 20:54:06,583 INFO [train.py:451] Epoch 1, batch 3090, batch avg loss 0.2877, total avg loss: 0.3274, batch size: 28
2021-10-13 20:54:11,413 INFO [train.py:451] Epoch 1, batch 3100, batch avg loss 0.3276, total avg loss: 0.3289, batch size: 37
2021-10-13 20:54:16,427 INFO [train.py:451] Epoch 1, batch 3110, batch avg loss 0.3453, total avg loss: 0.3282, batch size: 73
2021-10-13 20:54:21,525 INFO [train.py:451] Epoch 1, batch 3120, batch avg loss 0.3144, total avg loss: 0.3267, batch size: 33
2021-10-13 20:54:26,632 INFO [train.py:451] Epoch 1, batch 3130, batch avg loss 0.2659, total avg loss: 0.3243, batch size: 32
2021-10-13 20:54:31,651 INFO [train.py:451] Epoch 1, batch 3140, batch avg loss 0.3309, total avg loss: 0.3244, batch size: 32
2021-10-13 20:54:36,606 INFO [train.py:451] Epoch 1, batch 3150, batch avg loss 0.2770, total avg loss: 0.3240, batch size: 31
2021-10-13 20:54:41,549 INFO [train.py:451] Epoch 1, batch 3160, batch avg loss 0.2904, total avg loss: 0.3242, batch size: 32
2021-10-13 20:54:46,486 INFO [train.py:451] Epoch 1, batch 3170, batch avg loss 0.3888, total avg loss: 0.3253, batch size: 43
2021-10-13 20:54:51,599 INFO [train.py:451] Epoch 1, batch 3180, batch avg loss 0.3677, total avg loss: 0.3253, batch size: 34
2021-10-13 20:54:56,767 INFO [train.py:451] Epoch 1, batch 3190, batch avg loss 0.3442, total avg loss: 0.3265, batch size: 29
2021-10-13 20:55:01,630 INFO [train.py:451] Epoch 1, batch 3200, batch avg loss 0.3300, total avg loss: 0.3265, batch size: 49
2021-10-13 20:55:06,474 INFO [train.py:451] Epoch 1, batch 3210, batch avg loss 0.3149, total avg loss: 0.3089, batch size: 37
2021-10-13 20:55:11,702 INFO [train.py:451] Epoch 1, batch 3220, batch avg loss 0.2637, total avg loss: 0.3079, batch size: 32
2021-10-13 20:55:16,873 INFO [train.py:451] Epoch 1, batch 3230, batch avg loss 0.2774, total avg loss: 0.3081, batch size: 29
2021-10-13 20:55:21,634 INFO [train.py:451] Epoch 1, batch 3240, batch avg loss 0.3704, total avg loss: 0.3161, batch size: 72
2021-10-13 20:55:26,398 INFO [train.py:451] Epoch 1, batch 3250, batch avg loss 0.3752, total avg loss: 0.3216, batch size: 41
2021-10-13 20:55:31,350 INFO [train.py:451] Epoch 1, batch 3260, batch avg loss 0.3385, total avg loss: 0.3222, batch size: 37
2021-10-13 20:55:36,195 INFO [train.py:451] Epoch 1, batch 3270, batch avg loss 0.3636, total avg loss: 0.3266, batch size: 34
2021-10-13 20:55:41,217 INFO [train.py:451] Epoch 1, batch 3280, batch avg loss 0.3110, total avg loss: 0.3263, batch size: 27
2021-10-13 20:55:53,941 INFO [train.py:451] Epoch 1, batch 3290, batch avg loss 0.3691, total avg loss: 0.3264, batch size: 38
2021-10-13 20:55:58,823 INFO [train.py:451] Epoch 1, batch 3300, batch avg loss 0.2901, total avg loss: 0.3265, batch size: 34
2021-10-13 20:56:03,658 INFO [train.py:451] Epoch 1, batch 3310, batch avg loss 0.2930, total avg loss: 0.3265, batch size: 32
2021-10-13 20:56:08,696 INFO [train.py:451] Epoch 1, batch 3320, batch avg loss 0.3269, total avg loss: 0.3267, batch size: 45
2021-10-13 20:56:13,659 INFO [train.py:451] Epoch 1, batch 3330, batch avg loss 0.3463, total avg loss: 0.3262, batch size: 41
2021-10-13 20:56:18,697 INFO [train.py:451] Epoch 1, batch 3340, batch avg loss 0.3149, total avg loss: 0.3259, batch size: 35
2021-10-13 20:56:23,606 INFO [train.py:451] Epoch 1, batch 3350, batch avg loss 0.2503, total avg loss: 0.3252, batch size: 32
2021-10-13 20:56:28,698 INFO [train.py:451] Epoch 1, batch 3360, batch avg loss 0.2975, total avg loss: 0.3242, batch size: 27
2021-10-13 20:56:33,518 INFO [train.py:451] Epoch 1, batch 3370, batch avg loss 0.2624, total avg loss: 0.3236, batch size: 30
2021-10-13 20:56:38,439 INFO [train.py:451] Epoch 1, batch 3380, batch avg loss 0.2431, total avg loss: 0.3230, batch size: 30
2021-10-13 20:56:43,459 INFO [train.py:451] Epoch 1, batch 3390, batch avg loss 0.3263, total avg loss: 0.3229, batch size: 33
2021-10-13 20:56:48,278 INFO [train.py:451] Epoch 1, batch 3400, batch avg loss 0.4563, total avg loss: 0.3224, batch size: 130
2021-10-13 20:56:53,290 INFO [train.py:451] Epoch 1, batch 3410, batch avg loss 0.2783, total avg loss: 0.3204, batch size: 29
2021-10-13 20:56:58,356 INFO [train.py:451] Epoch 1, batch 3420, batch avg loss 0.3252, total avg loss: 0.3185, batch size: 38
2021-10-13 20:57:03,269 INFO [train.py:451] Epoch 1, batch 3430, batch avg loss 0.3038, total avg loss: 0.3225, batch size: 32
2021-10-13 20:57:08,316 INFO [train.py:451] Epoch 1, batch 3440, batch avg loss 0.3220, total avg loss: 0.3226, batch size: 33
2021-10-13 20:57:13,446 INFO [train.py:451] Epoch 1, batch 3450, batch avg loss 0.2920, total avg loss: 0.3190, batch size: 31
2021-10-13 20:57:18,339 INFO [train.py:451] Epoch 1, batch 3460, batch avg loss 0.4732, total avg loss: 0.3226, batch size: 129
2021-10-13 20:57:23,063 INFO [train.py:451] Epoch 1, batch 3470, batch avg loss 0.3363, total avg loss: 0.3248, batch size: 72
2021-10-13 20:57:27,915 INFO [train.py:451] Epoch 1, batch 3480, batch avg loss 0.4429, total avg loss: 0.3282, batch size: 128
2021-10-13 20:57:32,832 INFO [train.py:451] Epoch 1, batch 3490, batch avg loss 0.2514, total avg loss: 0.3277, batch size: 30
2021-10-13 20:57:37,803 INFO [train.py:451] Epoch 1, batch 3500, batch avg loss 0.3279, total avg loss: 0.3276, batch size: 39
2021-10-13 20:57:42,649 INFO [train.py:451] Epoch 1, batch 3510, batch avg loss 0.2785, total avg loss: 0.3263, batch size: 31
2021-10-13 20:57:47,614 INFO [train.py:451] Epoch 1, batch 3520, batch avg loss 0.2747, total avg loss: 0.3250, batch size: 31
2021-10-13 20:57:52,560 INFO [train.py:451] Epoch 1, batch 3530, batch avg loss 0.4004, total avg loss: 0.3243, batch size: 35
2021-10-13 20:57:57,451 INFO [train.py:451] Epoch 1, batch 3540, batch avg loss 0.3529, total avg loss: 0.3259, batch size: 35
2021-10-13 20:58:02,455 INFO [train.py:451] Epoch 1, batch 3550, batch avg loss 0.2813, total avg loss: 0.3249, batch size: 31
2021-10-13 20:58:07,475 INFO [train.py:451] Epoch 1, batch 3560, batch avg loss 0.2839, total avg loss: 0.3245, batch size: 29
2021-10-13 20:58:12,441 INFO [train.py:451] Epoch 1, batch 3570, batch avg loss 0.2737, total avg loss: 0.3246, batch size: 32
2021-10-13 20:58:17,336 INFO [train.py:451] Epoch 1, batch 3580, batch avg loss 0.3594, total avg loss: 0.3242, batch size: 73
2021-10-13 20:58:22,277 INFO [train.py:451] Epoch 1, batch 3590, batch avg loss 0.2902, total avg loss: 0.3235, batch size: 31
2021-10-13 20:58:27,181 INFO [train.py:451] Epoch 1, batch 3600, batch avg loss 0.2941, total avg loss: 0.3237, batch size: 30
2021-10-13 20:58:32,305 INFO [train.py:451] Epoch 1, batch 3610, batch avg loss 0.3604, total avg loss: 0.3397, batch size: 33
2021-10-13 20:58:37,340 INFO [train.py:451] Epoch 1, batch 3620, batch avg loss 0.2724, total avg loss: 0.3350, batch size: 33
2021-10-13 20:58:42,275 INFO [train.py:451] Epoch 1, batch 3630, batch avg loss 0.2800, total avg loss: 0.3269, batch size: 33
2021-10-13 20:58:47,152 INFO [train.py:451] Epoch 1, batch 3640, batch avg loss 0.3691, total avg loss: 0.3269, batch size: 72
2021-10-13 20:58:52,134 INFO [train.py:451] Epoch 1, batch 3650, batch avg loss 0.2669, total avg loss: 0.3249, batch size: 32
2021-10-13 20:58:57,081 INFO [train.py:451] Epoch 1, batch 3660, batch avg loss 0.2926, total avg loss: 0.3232, batch size: 30
2021-10-13 20:59:02,109 INFO [train.py:451] Epoch 1, batch 3670, batch avg loss 0.3527, total avg loss: 0.3207, batch size: 35
2021-10-13 20:59:06,983 INFO [train.py:451] Epoch 1, batch 3680, batch avg loss 0.3075, total avg loss: 0.3205, batch size: 35
2021-10-13 20:59:12,186 INFO [train.py:451] Epoch 1, batch 3690, batch avg loss 0.3079, total avg loss: 0.3205, batch size: 27
2021-10-13 20:59:17,218 INFO [train.py:451] Epoch 1, batch 3700, batch avg loss 0.3027, total avg loss: 0.3195, batch size: 31
2021-10-13 20:59:22,297 INFO [train.py:451] Epoch 1, batch 3710, batch avg loss 0.2855, total avg loss: 0.3178, batch size: 31
2021-10-13 20:59:27,274 INFO [train.py:451] Epoch 1, batch 3720, batch avg loss 0.4153, total avg loss: 0.3197, batch size: 41
2021-10-13 20:59:32,087 INFO [train.py:451] Epoch 1, batch 3730, batch avg loss 0.2955, total avg loss: 0.3224, batch size: 33
2021-10-13 20:59:37,022 INFO [train.py:451] Epoch 1, batch 3740, batch avg loss 0.3575, total avg loss: 0.3236, batch size: 33
2021-10-13 20:59:42,113 INFO [train.py:451] Epoch 1, batch 3750, batch avg loss 0.3102, total avg loss: 0.3231, batch size: 31
2021-10-13 20:59:47,362 INFO [train.py:451] Epoch 1, batch 3760, batch avg loss 0.3722, total avg loss: 0.3224, batch size: 35
2021-10-13 20:59:52,153 INFO [train.py:451] Epoch 1, batch 3770, batch avg loss 0.2963, total avg loss: 0.3235, batch size: 31
2021-10-13 20:59:57,151 INFO [train.py:451] Epoch 1, batch 3780, batch avg loss 0.2864, total avg loss: 0.3229, batch size: 27
2021-10-13 21:00:01,929 INFO [train.py:451] Epoch 1, batch 3790, batch avg loss 0.3771, total avg loss: 0.3242, batch size: 36
2021-10-13 21:00:06,915 INFO [train.py:451] Epoch 1, batch 3800, batch avg loss 0.3074, total avg loss: 0.3241, batch size: 31
2021-10-13 21:00:11,975 INFO [train.py:451] Epoch 1, batch 3810, batch avg loss 0.3458, total avg loss: 0.3127, batch size: 39
2021-10-13 21:00:16,932 INFO [train.py:451] Epoch 1, batch 3820, batch avg loss 0.2731, total avg loss: 0.3143, batch size: 34
2021-10-13 21:00:21,706 INFO [train.py:451] Epoch 1, batch 3830, batch avg loss 0.4368, total avg loss: 0.3272, batch size: 49
2021-10-13 21:00:26,576 INFO [train.py:451] Epoch 1, batch 3840, batch avg loss 0.3535, total avg loss: 0.3287, batch size: 49
2021-10-13 21:00:31,446 INFO [train.py:451] Epoch 1, batch 3850, batch avg loss 0.3030, total avg loss: 0.3288, batch size: 33
2021-10-13 21:00:36,297 INFO [train.py:451] Epoch 1, batch 3860, batch avg loss 0.3117, total avg loss: 0.3319, batch size: 37
2021-10-13 21:00:41,219 INFO [train.py:451] Epoch 1, batch 3870, batch avg loss 0.3605, total avg loss: 0.3289, batch size: 42
2021-10-13 21:00:46,207 INFO [train.py:451] Epoch 1, batch 3880, batch avg loss 0.2904, total avg loss: 0.3291, batch size: 33
2021-10-13 21:00:51,332 INFO [train.py:451] Epoch 1, batch 3890, batch avg loss 0.3300, total avg loss: 0.3280, batch size: 37
2021-10-13 21:00:56,290 INFO [train.py:451] Epoch 1, batch 3900, batch avg loss 0.2860, total avg loss: 0.3280, batch size: 35
2021-10-13 21:01:01,116 INFO [train.py:451] Epoch 1, batch 3910, batch avg loss 0.3001, total avg loss: 0.3313, batch size: 31
2021-10-13 21:01:06,072 INFO [train.py:451] Epoch 1, batch 3920, batch avg loss 0.3209, total avg loss: 0.3308, batch size: 36
2021-10-13 21:01:11,047 INFO [train.py:451] Epoch 1, batch 3930, batch avg loss 0.3460, total avg loss: 0.3299, batch size: 49
2021-10-13 21:01:15,940 INFO [train.py:451] Epoch 1, batch 3940, batch avg loss 0.2635, total avg loss: 0.3291, batch size: 31
2021-10-13 21:01:20,832 INFO [train.py:451] Epoch 1, batch 3950, batch avg loss 0.3445, total avg loss: 0.3289, batch size: 73
2021-10-13 21:01:25,539 INFO [train.py:451] Epoch 1, batch 3960, batch avg loss 0.4081, total avg loss: 0.3300, batch size: 122
2021-10-13 21:01:30,561 INFO [train.py:451] Epoch 1, batch 3970, batch avg loss 0.3324, total avg loss: 0.3303, batch size: 36
2021-10-13 21:01:35,501 INFO [train.py:451] Epoch 1, batch 3980, batch avg loss 0.3606, total avg loss: 0.3296, batch size: 42
2021-10-13 21:01:40,269 INFO [train.py:451] Epoch 1, batch 3990, batch avg loss 0.4204, total avg loss: 0.3304, batch size: 125
2021-10-13 21:01:45,337 INFO [train.py:451] Epoch 1, batch 4000, batch avg loss 0.3425, total avg loss: 0.3301, batch size: 41
2021-10-13 21:02:23,546 INFO [train.py:483] Epoch 1, valid loss 0.2324, best valid loss: 0.2324 best valid epoch: 1
2021-10-13 21:02:28,621 INFO [train.py:451] Epoch 1, batch 4010, batch avg loss 0.2877, total avg loss: 0.3150, batch size: 29
2021-10-13 21:02:33,698 INFO [train.py:451] Epoch 1, batch 4020, batch avg loss 0.3435, total avg loss: 0.3101, batch size: 41
2021-10-13 21:02:38,431 INFO [train.py:451] Epoch 1, batch 4030, batch avg loss 0.3845, total avg loss: 0.3206, batch size: 57
2021-10-13 21:02:43,414 INFO [train.py:451] Epoch 1, batch 4040, batch avg loss 0.3101, total avg loss: 0.3174, batch size: 28
2021-10-13 21:02:48,307 INFO [train.py:451] Epoch 1, batch 4050, batch avg loss 0.3477, total avg loss: 0.3166, batch size: 34
2021-10-13 21:02:53,243 INFO [train.py:451] Epoch 1, batch 4060, batch avg loss 0.2792, total avg loss: 0.3215, batch size: 30
2021-10-13 21:02:58,329 INFO [train.py:451] Epoch 1, batch 4070, batch avg loss 0.2714, total avg loss: 0.3233, batch size: 31
2021-10-13 21:03:03,414 INFO [train.py:451] Epoch 1, batch 4080, batch avg loss 0.3426, total avg loss: 0.3228, batch size: 45
2021-10-13 21:03:08,320 INFO [train.py:451] Epoch 1, batch 4090, batch avg loss 0.3241, total avg loss: 0.3230, batch size: 37
2021-10-13 21:03:13,463 INFO [train.py:451] Epoch 1, batch 4100, batch avg loss 0.3201, total avg loss: 0.3235, batch size: 29
2021-10-13 21:03:18,468 INFO [train.py:451] Epoch 1, batch 4110, batch avg loss 0.3673, total avg loss: 0.3247, batch size: 36
2021-10-13 21:03:23,592 INFO [train.py:451] Epoch 1, batch 4120, batch avg loss 0.3590, total avg loss: 0.3246, batch size: 41
2021-10-13 21:03:28,427 INFO [train.py:451] Epoch 1, batch 4130, batch avg loss 0.2909, total avg loss: 0.3251, batch size: 35
2021-10-13 21:03:33,250 INFO [train.py:451] Epoch 1, batch 4140, batch avg loss 0.2617, total avg loss: 0.3255, batch size: 32
2021-10-13 21:03:38,258 INFO [train.py:451] Epoch 1, batch 4150, batch avg loss 0.2251, total avg loss: 0.3258, batch size: 33
2021-10-13 21:03:43,146 INFO [train.py:451] Epoch 1, batch 4160, batch avg loss 0.2798, total avg loss: 0.3267, batch size: 36
2021-10-13 21:03:47,947 INFO [train.py:451] Epoch 1, batch 4170, batch avg loss 0.2358, total avg loss: 0.3266, batch size: 29
2021-10-13 21:03:52,866 INFO [train.py:451] Epoch 1, batch 4180, batch avg loss 0.2600, total avg loss: 0.3272, batch size: 33
2021-10-13 21:03:57,902 INFO [train.py:451] Epoch 1, batch 4190, batch avg loss 0.3499, total avg loss: 0.3261, batch size: 41
2021-10-13 21:04:02,867 INFO [train.py:451] Epoch 1, batch 4200, batch avg loss 0.3104, total avg loss: 0.3254, batch size: 32
2021-10-13 21:04:07,692 INFO [train.py:451] Epoch 1, batch 4210, batch avg loss 0.3297, total avg loss: 0.3161, batch size: 38
2021-10-13 21:04:12,558 INFO [train.py:451] Epoch 1, batch 4220, batch avg loss 0.3949, total avg loss: 0.3291, batch size: 45
2021-10-13 21:04:17,733 INFO [train.py:451] Epoch 1, batch 4230, batch avg loss 0.3144, total avg loss: 0.3319, batch size: 26
2021-10-13 21:04:22,876 INFO [train.py:451] Epoch 1, batch 4240, batch avg loss 0.2938, total avg loss: 0.3288, batch size: 29
2021-10-13 21:04:27,763 INFO [train.py:451] Epoch 1, batch 4250, batch avg loss 0.2858, total avg loss: 0.3283, batch size: 32
2021-10-13 21:04:32,607 INFO [train.py:451] Epoch 1, batch 4260, batch avg loss 0.3245, total avg loss: 0.3254, batch size: 39
2021-10-13 21:04:37,713 INFO [train.py:451] Epoch 1, batch 4270, batch avg loss 0.3748, total avg loss: 0.3244, batch size: 73
2021-10-13 21:04:42,555 INFO [train.py:451] Epoch 1, batch 4280, batch avg loss 0.2868, total avg loss: 0.3246, batch size: 32
2021-10-13 21:04:47,352 INFO [train.py:451] Epoch 1, batch 4290, batch avg loss 0.3589, total avg loss: 0.3249, batch size: 71
2021-10-13 21:04:52,176 INFO [train.py:451] Epoch 1, batch 4300, batch avg loss 0.3106, total avg loss: 0.3258, batch size: 30
2021-10-13 21:04:57,163 INFO [train.py:451] Epoch 1, batch 4310, batch avg loss 0.3866, total avg loss: 0.3270, batch size: 35
2021-10-13 21:05:01,919 INFO [train.py:451] Epoch 1, batch 4320, batch avg loss 0.2932, total avg loss: 0.3300, batch size: 39
2021-10-13 21:05:06,795 INFO [train.py:451] Epoch 1, batch 4330, batch avg loss 0.2975, total avg loss: 0.3290, batch size: 32
2021-10-13 21:05:11,814 INFO [train.py:451] Epoch 1, batch 4340, batch avg loss 0.4072, total avg loss: 0.3294, batch size: 57
2021-10-13 21:05:16,623 INFO [train.py:451] Epoch 1, batch 4350, batch avg loss 0.3099, total avg loss: 0.3288, batch size: 39
2021-10-13 21:05:21,739 INFO [train.py:451] Epoch 1, batch 4360, batch avg loss 0.3198, total avg loss: 0.3285, batch size: 27
2021-10-13 21:05:26,594 INFO [train.py:451] Epoch 1, batch 4370, batch avg loss 0.3735, total avg loss: 0.3302, batch size: 35
2021-10-13 21:05:31,740 INFO [train.py:451] Epoch 1, batch 4380, batch avg loss 0.3999, total avg loss: 0.3299, batch size: 37
2021-10-13 21:05:36,846 INFO [train.py:451] Epoch 1, batch 4390, batch avg loss 0.3294, total avg loss: 0.3285, batch size: 41
2021-10-13 21:05:42,132 INFO [train.py:451] Epoch 1, batch 4400, batch avg loss 0.3470, total avg loss: 0.3289, batch size: 49
2021-10-13 21:05:47,078 INFO [train.py:451] Epoch 1, batch 4410, batch avg loss 0.2721, total avg loss: 0.3273, batch size: 31
2021-10-13 21:05:52,238 INFO [train.py:451] Epoch 1, batch 4420, batch avg loss 0.3018, total avg loss: 0.3249, batch size: 33
2021-10-13 21:05:57,013 INFO [train.py:451] Epoch 1, batch 4430, batch avg loss 0.3378, total avg loss: 0.3371, batch size: 42
2021-10-13 21:06:01,911 INFO [train.py:451] Epoch 1, batch 4440, batch avg loss 0.2681, total avg loss: 0.3380, batch size: 33
2021-10-13 21:06:06,782 INFO [train.py:451] Epoch 1, batch 4450, batch avg loss 0.2951, total avg loss: 0.3351, batch size: 32
2021-10-13 21:06:11,754 INFO [train.py:451] Epoch 1, batch 4460, batch avg loss 0.2976, total avg loss: 0.3348, batch size: 35
2021-10-13 21:06:16,526 INFO [train.py:451] Epoch 1, batch 4470, batch avg loss 0.3155, total avg loss: 0.3349, batch size: 32
2021-10-13 21:06:21,565 INFO [train.py:451] Epoch 1, batch 4480, batch avg loss 0.3232, total avg loss: 0.3313, batch size: 29
2021-10-13 21:06:26,618 INFO [train.py:451] Epoch 1, batch 4490, batch avg loss 0.2383, total avg loss: 0.3299, batch size: 28
2021-10-13 21:06:31,641 INFO [train.py:451] Epoch 1, batch 4500, batch avg loss 0.3252, total avg loss: 0.3278, batch size: 42
2021-10-13 21:06:36,588 INFO [train.py:451] Epoch 1, batch 4510, batch avg loss 0.3699, total avg loss: 0.3281, batch size: 36
2021-10-13 21:06:41,430 INFO [train.py:451] Epoch 1, batch 4520, batch avg loss 0.3048, total avg loss: 0.3286, batch size: 36
2021-10-13 21:06:46,339 INFO [train.py:451] Epoch 1, batch 4530, batch avg loss 0.3981, total avg loss: 0.3286, batch size: 33
2021-10-13 21:06:51,539 INFO [train.py:451] Epoch 1, batch 4540, batch avg loss 0.2956, total avg loss: 0.3286, batch size: 34
2021-10-13 21:06:56,410 INFO [train.py:451] Epoch 1, batch 4550, batch avg loss 0.3070, total avg loss: 0.3279, batch size: 38
2021-10-13 21:07:01,376 INFO [train.py:451] Epoch 1, batch 4560, batch avg loss 0.3033, total avg loss: 0.3284, batch size: 36
2021-10-13 21:07:06,341 INFO [train.py:451] Epoch 1, batch 4570, batch avg loss 0.2914, total avg loss: 0.3276, batch size: 33
2021-10-13 21:07:11,305 INFO [train.py:451] Epoch 1, batch 4580, batch avg loss 0.4296, total avg loss: 0.3280, batch size: 36
2021-10-13 21:07:16,310 INFO [train.py:451] Epoch 1, batch 4590, batch avg loss 0.2812, total avg loss: 0.3284, batch size: 37
2021-10-13 21:07:21,175 INFO [train.py:451] Epoch 1, batch 4600, batch avg loss 0.3872, total avg loss: 0.3289, batch size: 45
2021-10-13 21:07:26,194 INFO [train.py:451] Epoch 1, batch 4610, batch avg loss 0.2622, total avg loss: 0.3017, batch size: 30
2021-10-13 21:07:30,951 INFO [train.py:451] Epoch 1, batch 4620, batch avg loss 0.3753, total avg loss: 0.3147, batch size: 49
2021-10-13 21:07:35,961 INFO [train.py:451] Epoch 1, batch 4630, batch avg loss 0.2840, total avg loss: 0.3135, batch size: 31
2021-10-13 21:07:40,821 INFO [train.py:451] Epoch 1, batch 4640, batch avg loss 0.4378, total avg loss: 0.3188, batch size: 134
2021-10-13 21:07:45,647 INFO [train.py:451] Epoch 1, batch 4650, batch avg loss 0.3252, total avg loss: 0.3237, batch size: 32
2021-10-13 21:07:50,546 INFO [train.py:451] Epoch 1, batch 4660, batch avg loss 0.3184, total avg loss: 0.3211, batch size: 38
2021-10-13 21:07:55,580 INFO [train.py:451] Epoch 1, batch 4670, batch avg loss 0.3297, total avg loss: 0.3220, batch size: 31
2021-10-13 21:08:00,348 INFO [train.py:451] Epoch 1, batch 4680, batch avg loss 0.3335, total avg loss: 0.3227, batch size: 57
2021-10-13 21:08:05,301 INFO [train.py:451] Epoch 1, batch 4690, batch avg loss 0.4442, total avg loss: 0.3237, batch size: 132
2021-10-13 21:08:10,123 INFO [train.py:451] Epoch 1, batch 4700, batch avg loss 0.3383, total avg loss: 0.3224, batch size: 38
2021-10-13 21:08:14,959 INFO [train.py:451] Epoch 1, batch 4710, batch avg loss 0.3735, total avg loss: 0.3238, batch size: 73
2021-10-13 21:08:15,697 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "0062f055-bfc2-82b8-9b90-86a9b1c5dcf2" will not be mixed in.
2021-10-13 21:08:19,923 INFO [train.py:451] Epoch 1, batch 4720, batch avg loss 0.2897, total avg loss: 0.3237, batch size: 30
2021-10-13 21:08:24,989 INFO [train.py:451] Epoch 1, batch 4730, batch avg loss 0.3861, total avg loss: 0.3249, batch size: 31
2021-10-13 21:08:29,888 INFO [train.py:451] Epoch 1, batch 4740, batch avg loss 0.2479, total avg loss: 0.3231, batch size: 28
2021-10-13 21:08:34,785 INFO [train.py:451] Epoch 1, batch 4750, batch avg loss 0.3207, total avg loss: 0.3224, batch size: 49
2021-10-13 21:08:39,716 INFO [train.py:451] Epoch 1, batch 4760, batch avg loss 0.3169, total avg loss: 0.3216, batch size: 35
2021-10-13 21:08:44,494 INFO [train.py:451] Epoch 1, batch 4770, batch avg loss 0.3842, total avg loss: 0.3231, batch size: 41
2021-10-13 21:08:49,649 INFO [train.py:451] Epoch 1, batch 4780, batch avg loss 0.4159, total avg loss: 0.3235, batch size: 37
2021-10-13 21:08:54,618 INFO [train.py:451] Epoch 1, batch 4790, batch avg loss 0.3103, total avg loss: 0.3231, batch size: 34
2021-10-13 21:08:59,631 INFO [train.py:451] Epoch 1, batch 4800, batch avg loss 0.3394, total avg loss: 0.3240, batch size: 30
2021-10-13 21:09:04,485 INFO [train.py:451] Epoch 1, batch 4810, batch avg loss 0.3506, total avg loss: 0.3153, batch size: 49
2021-10-13 21:09:09,106 INFO [train.py:451] Epoch 1, batch 4820, batch avg loss 0.3548, total avg loss: 0.3273, batch size: 57
2021-10-13 21:09:13,781 INFO [train.py:451] Epoch 1, batch 4830, batch avg loss 0.3751, total avg loss: 0.3315, batch size: 35
2021-10-13 21:09:18,573 INFO [train.py:451] Epoch 1, batch 4840, batch avg loss 0.3137, total avg loss: 0.3365, batch size: 45
2021-10-13 21:09:23,463 INFO [train.py:451] Epoch 1, batch 4850, batch avg loss 0.2812, total avg loss: 0.3337, batch size: 30
2021-10-13 21:09:28,480 INFO [train.py:451] Epoch 1, batch 4860, batch avg loss 0.3555, total avg loss: 0.3312, batch size: 33
2021-10-13 21:09:33,377 INFO [train.py:451] Epoch 1, batch 4870, batch avg loss 0.4453, total avg loss: 0.3330, batch size: 130
2021-10-13 21:09:38,384 INFO [train.py:451] Epoch 1, batch 4880, batch avg loss 0.2840, total avg loss: 0.3309, batch size: 28
2021-10-13 21:09:43,275 INFO [train.py:451] Epoch 1, batch 4890, batch avg loss 0.3354, total avg loss: 0.3292, batch size: 37
2021-10-13 21:09:48,202 INFO [train.py:451] Epoch 1, batch 4900, batch avg loss 0.3909, total avg loss: 0.3285, batch size: 35
2021-10-13 21:09:53,029 INFO [train.py:451] Epoch 1, batch 4910, batch avg loss 0.2456, total avg loss: 0.3299, batch size: 29
2021-10-13 21:09:58,201 INFO [train.py:451] Epoch 1, batch 4920, batch avg loss 0.3733, total avg loss: 0.3309, batch size: 56
2021-10-13 21:10:03,201 INFO [train.py:451] Epoch 1, batch 4930, batch avg loss 0.3733, total avg loss: 0.3311, batch size: 56
2021-10-13 21:10:08,206 INFO [train.py:451] Epoch 1, batch 4940, batch avg loss 0.3342, total avg loss: 0.3289, batch size: 31
2021-10-13 21:10:13,077 INFO [train.py:451] Epoch 1, batch 4950, batch avg loss 0.2822, total avg loss: 0.3291, batch size: 31
2021-10-13 21:10:17,797 INFO [train.py:451] Epoch 1, batch 4960, batch avg loss 0.3125, total avg loss: 0.3309, batch size: 41
2021-10-13 21:10:22,708 INFO [train.py:451] Epoch 1, batch 4970, batch avg loss 0.3359, total avg loss: 0.3306, batch size: 45
2021-10-13 21:10:27,669 INFO [train.py:451] Epoch 1, batch 4980, batch avg loss 0.3052, total avg loss: 0.3302, batch size: 35
2021-10-13 21:10:32,607 INFO [train.py:451] Epoch 1, batch 4990, batch avg loss 0.2944, total avg loss: 0.3297, batch size: 29
2021-10-13 21:10:37,684 INFO [train.py:451] Epoch 1, batch 5000, batch avg loss 0.3076, total avg loss: 0.3296, batch size: 32
2021-10-13 21:11:17,851 INFO [train.py:483] Epoch 1, valid loss 0.2292, best valid loss: 0.2292 best valid epoch: 1
2021-10-13 21:11:22,815 INFO [train.py:451] Epoch 1, batch 5010, batch avg loss 0.3754, total avg loss: 0.3278, batch size: 36
2021-10-13 21:11:27,820 INFO [train.py:451] Epoch 1, batch 5020, batch avg loss 0.3361, total avg loss: 0.3265, batch size: 28
2021-10-13 21:11:32,761 INFO [train.py:451] Epoch 1, batch 5030, batch avg loss 0.2731, total avg loss: 0.3256, batch size: 37
2021-10-13 21:11:37,654 INFO [train.py:451] Epoch 1, batch 5040, batch avg loss 0.3169, total avg loss: 0.3242, batch size: 34
2021-10-13 21:11:42,694 INFO [train.py:451] Epoch 1, batch 5050, batch avg loss 0.3665, total avg loss: 0.3203, batch size: 38
2021-10-13 21:11:47,620 INFO [train.py:451] Epoch 1, batch 5060, batch avg loss 0.3249, total avg loss: 0.3211, batch size: 34
2021-10-13 21:11:52,655 INFO [train.py:451] Epoch 1, batch 5070, batch avg loss 0.2909, total avg loss: 0.3212, batch size: 32
2021-10-13 21:11:57,482 INFO [train.py:451] Epoch 1, batch 5080, batch avg loss 0.2419, total avg loss: 0.3192, batch size: 27
2021-10-13 21:12:02,360 INFO [train.py:451] Epoch 1, batch 5090, batch avg loss 0.2879, total avg loss: 0.3205, batch size: 34
2021-10-13 21:12:07,324 INFO [train.py:451] Epoch 1, batch 5100, batch avg loss 0.2648, total avg loss: 0.3200, batch size: 28
2021-10-13 21:12:12,184 INFO [train.py:451] Epoch 1, batch 5110, batch avg loss 0.3354, total avg loss: 0.3211, batch size: 41
2021-10-13 21:12:17,237 INFO [train.py:451] Epoch 1, batch 5120, batch avg loss 0.2575, total avg loss: 0.3200, batch size: 31
2021-10-13 21:12:22,295 INFO [train.py:451] Epoch 1, batch 5130, batch avg loss 0.3242, total avg loss: 0.3196, batch size: 36
2021-10-13 21:12:27,456 INFO [train.py:451] Epoch 1, batch 5140, batch avg loss 0.4200, total avg loss: 0.3193, batch size: 38
2021-10-13 21:12:32,290 INFO [train.py:451] Epoch 1, batch 5150, batch avg loss 0.2949, total avg loss: 0.3197, batch size: 33
2021-10-13 21:12:37,155 INFO [train.py:451] Epoch 1, batch 5160, batch avg loss 0.3785, total avg loss: 0.3201, batch size: 49
2021-10-13 21:12:42,196 INFO [train.py:451] Epoch 1, batch 5170, batch avg loss 0.2904, total avg loss: 0.3213, batch size: 30
2021-10-13 21:12:46,875 INFO [train.py:451] Epoch 1, batch 5180, batch avg loss 0.2607, total avg loss: 0.3221, batch size: 32
2021-10-13 21:12:51,828 INFO [train.py:451] Epoch 1, batch 5190, batch avg loss 0.2763, total avg loss: 0.3224, batch size: 32
2021-10-13 21:12:56,604 INFO [train.py:451] Epoch 1, batch 5200, batch avg loss 0.3209, total avg loss: 0.3234, batch size: 38
2021-10-13 21:13:01,414 INFO [train.py:451] Epoch 1, batch 5210, batch avg loss 0.2837, total avg loss: 0.3227, batch size: 31
2021-10-13 21:13:06,383 INFO [train.py:451] Epoch 1, batch 5220, batch avg loss 0.2651, total avg loss: 0.3224, batch size: 29
2021-10-13 21:13:11,333 INFO [train.py:451] Epoch 1, batch 5230, batch avg loss 0.3553, total avg loss: 0.3165, batch size: 45
2021-10-13 21:13:16,219 INFO [train.py:451] Epoch 1, batch 5240, batch avg loss 0.3586, total avg loss: 0.3178, batch size: 42
2021-10-13 21:13:21,221 INFO [train.py:451] Epoch 1, batch 5250, batch avg loss 0.2477, total avg loss: 0.3179, batch size: 29
2021-10-13 21:13:26,303 INFO [train.py:451] Epoch 1, batch 5260, batch avg loss 0.3053, total avg loss: 0.3148, batch size: 35
2021-10-13 21:13:31,192 INFO [train.py:451] Epoch 1, batch 5270, batch avg loss 0.3996, total avg loss: 0.3202, batch size: 45
2021-10-13 21:13:36,265 INFO [train.py:451] Epoch 1, batch 5280, batch avg loss 0.3290, total avg loss: 0.3194, batch size: 35
2021-10-13 21:13:41,175 INFO [train.py:451] Epoch 1, batch 5290, batch avg loss 0.4051, total avg loss: 0.3200, batch size: 38
2021-10-13 21:13:46,240 INFO [train.py:451] Epoch 1, batch 5300, batch avg loss 0.2979, total avg loss: 0.3186, batch size: 31
2021-10-13 21:13:51,020 INFO [train.py:451] Epoch 1, batch 5310, batch avg loss 0.3784, total avg loss: 0.3201, batch size: 72
2021-10-13 21:13:55,902 INFO [train.py:451] Epoch 1, batch 5320, batch avg loss 0.3154, total avg loss: 0.3224, batch size: 37
2021-10-13 21:14:01,060 INFO [train.py:451] Epoch 1, batch 5330, batch avg loss 0.3619, total avg loss: 0.3221, batch size: 34
2021-10-13 21:14:06,041 INFO [train.py:451] Epoch 1, batch 5340, batch avg loss 0.3356, total avg loss: 0.3236, batch size: 28
2021-10-13 21:14:11,069 INFO [train.py:451] Epoch 1, batch 5350, batch avg loss 0.4016, total avg loss: 0.3239, batch size: 125
2021-10-13 21:14:16,072 INFO [train.py:451] Epoch 1, batch 5360, batch avg loss 0.3748, total avg loss: 0.3248, batch size: 34
2021-10-13 21:14:20,877 INFO [train.py:451] Epoch 1, batch 5370, batch avg loss 0.3430, total avg loss: 0.3274, batch size: 49
2021-10-13 21:14:25,682 INFO [train.py:451] Epoch 1, batch 5380, batch avg loss 0.3173, total avg loss: 0.3262, batch size: 42
2021-10-13 21:14:30,532 INFO [train.py:451] Epoch 1, batch 5390, batch avg loss 0.2621, total avg loss: 0.3265, batch size: 33
2021-10-13 21:14:35,331 INFO [train.py:451] Epoch 1, batch 5400, batch avg loss 0.3022, total avg loss: 0.3262, batch size: 33
2021-10-13 21:14:40,174 INFO [train.py:451] Epoch 1, batch 5410, batch avg loss 0.3153, total avg loss: 0.3415, batch size: 36
2021-10-13 21:14:45,631 INFO [train.py:451] Epoch 1, batch 5420, batch avg loss 0.3077, total avg loss: 0.3373, batch size: 34
2021-10-13 21:14:50,745 INFO [train.py:451] Epoch 1, batch 5430, batch avg loss 0.3607, total avg loss: 0.3279, batch size: 35
2021-10-13 21:14:55,593 INFO [train.py:451] Epoch 1, batch 5440, batch avg loss 0.3571, total avg loss: 0.3336, batch size: 39
2021-10-13 21:15:00,553 INFO [train.py:451] Epoch 1, batch 5450, batch avg loss 0.3609, total avg loss: 0.3288, batch size: 41
2021-10-13 21:15:05,586 INFO [train.py:451] Epoch 1, batch 5460, batch avg loss 0.3864, total avg loss: 0.3305, batch size: 42
2021-10-13 21:15:10,590 INFO [train.py:451] Epoch 1, batch 5470, batch avg loss 0.3413, total avg loss: 0.3289, batch size: 41
2021-10-13 21:15:15,475 INFO [train.py:451] Epoch 1, batch 5480, batch avg loss 0.2932, total avg loss: 0.3298, batch size: 49
2021-10-13 21:15:20,449 INFO [train.py:451] Epoch 1, batch 5490, batch avg loss 0.3790, total avg loss: 0.3283, batch size: 42
2021-10-13 21:15:25,416 INFO [train.py:451] Epoch 1, batch 5500, batch avg loss 0.3232, total avg loss: 0.3287, batch size: 37
2021-10-13 21:15:30,141 INFO [train.py:451] Epoch 1, batch 5510, batch avg loss 0.3630, total avg loss: 0.3297, batch size: 35
2021-10-13 21:15:34,983 INFO [train.py:451] Epoch 1, batch 5520, batch avg loss 0.3274, total avg loss: 0.3290, batch size: 36
2021-10-13 21:15:39,932 INFO [train.py:451] Epoch 1, batch 5530, batch avg loss 0.3415, total avg loss: 0.3296, batch size: 38
2021-10-13 21:15:45,089 INFO [train.py:451] Epoch 1, batch 5540, batch avg loss 0.3734, total avg loss: 0.3287, batch size: 34
2021-10-13 21:15:49,966 INFO [train.py:451] Epoch 1, batch 5550, batch avg loss 0.3068, total avg loss: 0.3275, batch size: 38
2021-10-13 21:15:55,008 INFO [train.py:451] Epoch 1, batch 5560, batch avg loss 0.3395, total avg loss: 0.3268, batch size: 57
2021-10-13 21:16:00,028 INFO [train.py:451] Epoch 1, batch 5570, batch avg loss 0.3062, total avg loss: 0.3255, batch size: 30
2021-10-13 21:16:05,006 INFO [train.py:451] Epoch 1, batch 5580, batch avg loss 0.3536, total avg loss: 0.3251, batch size: 35
2021-10-13 21:16:09,841 INFO [train.py:451] Epoch 1, batch 5590, batch avg loss 0.2817, total avg loss: 0.3251, batch size: 36
2021-10-13 21:16:14,780 INFO [train.py:451] Epoch 1, batch 5600, batch avg loss 0.3482, total avg loss: 0.3255, batch size: 35
2021-10-13 21:16:19,673 INFO [train.py:451] Epoch 1, batch 5610, batch avg loss 0.3334, total avg loss: 0.3110, batch size: 38
2021-10-13 21:16:24,667 INFO [train.py:451] Epoch 1, batch 5620, batch avg loss 0.3561, total avg loss: 0.3167, batch size: 38
2021-10-13 21:16:29,620 INFO [train.py:451] Epoch 1, batch 5630, batch avg loss 0.3526, total avg loss: 0.3128, batch size: 42
2021-10-13 21:16:34,547 INFO [train.py:451] Epoch 1, batch 5640, batch avg loss 0.3303, total avg loss: 0.3143, batch size: 31
2021-10-13 21:16:39,510 INFO [train.py:451] Epoch 1, batch 5650, batch avg loss 0.2748, total avg loss: 0.3122, batch size: 29
2021-10-13 21:16:44,417 INFO [train.py:451] Epoch 1, batch 5660, batch avg loss 0.3534, total avg loss: 0.3134, batch size: 33
2021-10-13 21:16:49,350 INFO [train.py:451] Epoch 1, batch 5670, batch avg loss 0.3306, total avg loss: 0.3154, batch size: 30
2021-10-13 21:16:54,282 INFO [train.py:451] Epoch 1, batch 5680, batch avg loss 0.3321, total avg loss: 0.3154, batch size: 35
2021-10-13 21:16:59,236 INFO [train.py:451] Epoch 1, batch 5690, batch avg loss 0.3038, total avg loss: 0.3153, batch size: 36
2021-10-13 21:17:04,113 INFO [train.py:451] Epoch 1, batch 5700, batch avg loss 0.3411, total avg loss: 0.3163, batch size: 49
2021-10-13 21:17:09,041 INFO [train.py:451] Epoch 1, batch 5710, batch avg loss 0.3610, total avg loss: 0.3176, batch size: 42
2021-10-13 21:17:14,065 INFO [train.py:451] Epoch 1, batch 5720, batch avg loss 0.3293, total avg loss: 0.3186, batch size: 36
2021-10-13 21:17:19,231 INFO [train.py:451] Epoch 1, batch 5730, batch avg loss 0.3501, total avg loss: 0.3183, batch size: 36
2021-10-13 21:17:24,170 INFO [train.py:451] Epoch 1, batch 5740, batch avg loss 0.2866, total avg loss: 0.3192, batch size: 36
2021-10-13 21:17:29,257 INFO [train.py:451] Epoch 1, batch 5750, batch avg loss 0.4677, total avg loss: 0.3188, batch size: 129
2021-10-13 21:17:34,111 INFO [train.py:451] Epoch 1, batch 5760, batch avg loss 0.2536, total avg loss: 0.3191, batch size: 29
2021-10-13 21:17:39,050 INFO [train.py:451] Epoch 1, batch 5770, batch avg loss 0.2642, total avg loss: 0.3184, batch size: 31
2021-10-13 21:17:44,022 INFO [train.py:451] Epoch 1, batch 5780, batch avg loss 0.3295, total avg loss: 0.3180, batch size: 32
2021-10-13 21:17:49,108 INFO [train.py:451] Epoch 1, batch 5790, batch avg loss 0.3364, total avg loss: 0.3169, batch size: 34
2021-10-13 21:17:53,979 INFO [train.py:451] Epoch 1, batch 5800, batch avg loss 0.2841, total avg loss: 0.3158, batch size: 31
2021-10-13 21:17:58,715 INFO [train.py:451] Epoch 1, batch 5810, batch avg loss 0.3093, total avg loss: 0.3427, batch size: 33
2021-10-13 21:18:03,483 INFO [train.py:451] Epoch 1, batch 5820, batch avg loss 0.3491, total avg loss: 0.3352, batch size: 42
2021-10-13 21:18:08,558 INFO [train.py:451] Epoch 1, batch 5830, batch avg loss 0.3392, total avg loss: 0.3305, batch size: 31
2021-10-13 21:18:13,422 INFO [train.py:451] Epoch 1, batch 5840, batch avg loss 0.3295, total avg loss: 0.3308, batch size: 41
2021-10-13 21:18:18,345 INFO [train.py:451] Epoch 1, batch 5850, batch avg loss 0.3454, total avg loss: 0.3301, batch size: 45
2021-10-13 21:18:23,301 INFO [train.py:451] Epoch 1, batch 5860, batch avg loss 0.2265, total avg loss: 0.3256, batch size: 29
2021-10-13 21:18:28,197 INFO [train.py:451] Epoch 1, batch 5870, batch avg loss 0.2601, total avg loss: 0.3223, batch size: 30
2021-10-13 21:18:33,149 INFO [train.py:451] Epoch 1, batch 5880, batch avg loss 0.3258, total avg loss: 0.3235, batch size: 57
2021-10-13 21:18:38,000 INFO [train.py:451] Epoch 1, batch 5890, batch avg loss 0.2974, total avg loss: 0.3234, batch size: 32
2021-10-13 21:18:42,903 INFO [train.py:451] Epoch 1, batch 5900, batch avg loss 0.3185, total avg loss: 0.3249, batch size: 29
2021-10-13 21:18:47,923 INFO [train.py:451] Epoch 1, batch 5910, batch avg loss 0.3173, total avg loss: 0.3223, batch size: 35
2021-10-13 21:18:52,977 INFO [train.py:451] Epoch 1, batch 5920, batch avg loss 0.4245, total avg loss: 0.3215, batch size: 125
2021-10-13 21:18:57,911 INFO [train.py:451] Epoch 1, batch 5930, batch avg loss 0.3041, total avg loss: 0.3211, batch size: 38
2021-10-13 21:19:02,837 INFO [train.py:451] Epoch 1, batch 5940, batch avg loss 0.3488, total avg loss: 0.3205, batch size: 41
2021-10-13 21:19:07,883 INFO [train.py:451] Epoch 1, batch 5950, batch avg loss 0.2653, total avg loss: 0.3200, batch size: 31
2021-10-13 21:19:12,733 INFO [train.py:451] Epoch 1, batch 5960, batch avg loss 0.3448, total avg loss: 0.3201, batch size: 40
2021-10-13 21:19:17,804 INFO [train.py:451] Epoch 1, batch 5970, batch avg loss 0.3257, total avg loss: 0.3193, batch size: 31
2021-10-13 21:19:22,785 INFO [train.py:451] Epoch 1, batch 5980, batch avg loss 0.2611, total avg loss: 0.3193, batch size: 29
2021-10-13 21:19:27,724 INFO [train.py:451] Epoch 1, batch 5990, batch avg loss 0.3844, total avg loss: 0.3193, batch size: 34
2021-10-13 21:19:32,534 INFO [train.py:451] Epoch 1, batch 6000, batch avg loss 0.3293, total avg loss: 0.3197, batch size: 36
2021-10-13 21:20:12,208 INFO [train.py:483] Epoch 1, valid loss 0.2271, best valid loss: 0.2271 best valid epoch: 1
2021-10-13 21:20:17,125 INFO [train.py:451] Epoch 1, batch 6010, batch avg loss 0.3116, total avg loss: 0.3165, batch size: 37
2021-10-13 21:20:22,142 INFO [train.py:451] Epoch 1, batch 6020, batch avg loss 0.3010, total avg loss: 0.3123, batch size: 34
2021-10-13 21:20:26,979 INFO [train.py:451] Epoch 1, batch 6030, batch avg loss 0.2859, total avg loss: 0.3203, batch size: 31
2021-10-13 21:20:32,026 INFO [train.py:451] Epoch 1, batch 6040, batch avg loss 0.2868, total avg loss: 0.3158, batch size: 27
2021-10-13 21:20:36,734 INFO [train.py:451] Epoch 1, batch 6050, batch avg loss 0.3374, total avg loss: 0.3205, batch size: 73
2021-10-13 21:20:41,699 INFO [train.py:451] Epoch 1, batch 6060, batch avg loss 0.2603, total avg loss: 0.3174, batch size: 30
2021-10-13 21:20:46,635 INFO [train.py:451] Epoch 1, batch 6070, batch avg loss 0.2934, total avg loss: 0.3175, batch size: 27
2021-10-13 21:20:51,384 INFO [train.py:451] Epoch 1, batch 6080, batch avg loss 0.3393, total avg loss: 0.3177, batch size: 49
2021-10-13 21:20:56,101 INFO [train.py:451] Epoch 1, batch 6090, batch avg loss 0.2976, total avg loss: 0.3194, batch size: 32
2021-10-13 21:21:01,080 INFO [train.py:451] Epoch 1, batch 6100, batch avg loss 0.2488, total avg loss: 0.3208, batch size: 34
2021-10-13 21:21:06,070 INFO [train.py:451] Epoch 1, batch 6110, batch avg loss 0.3659, total avg loss: 0.3204, batch size: 38
2021-10-13 21:21:11,156 INFO [train.py:451] Epoch 1, batch 6120, batch avg loss 0.3484, total avg loss: 0.3196, batch size: 34
2021-10-13 21:21:16,206 INFO [train.py:451] Epoch 1, batch 6130, batch avg loss 0.2571, total avg loss: 0.3193, batch size: 31
2021-10-13 21:21:20,936 INFO [train.py:451] Epoch 1, batch 6140, batch avg loss 0.2853, total avg loss: 0.3198, batch size: 34
2021-10-13 21:21:25,770 INFO [train.py:451] Epoch 1, batch 6150, batch avg loss 0.3080, total avg loss: 0.3201, batch size: 49
2021-10-13 21:21:30,697 INFO [train.py:451] Epoch 1, batch 6160, batch avg loss 0.3029, total avg loss: 0.3196, batch size: 34
2021-10-13 21:21:35,843 INFO [train.py:451] Epoch 1, batch 6170, batch avg loss 0.2783, total avg loss: 0.3181, batch size: 35
2021-10-13 21:21:40,799 INFO [train.py:451] Epoch 1, batch 6180, batch avg loss 0.3332, total avg loss: 0.3192, batch size: 34
2021-10-13 21:21:45,686 INFO [train.py:451] Epoch 1, batch 6190, batch avg loss 0.2518, total avg loss: 0.3193, batch size: 27
2021-10-13 21:21:50,602 INFO [train.py:451] Epoch 1, batch 6200, batch avg loss 0.3145, total avg loss: 0.3194, batch size: 34
2021-10-13 21:21:55,561 INFO [train.py:451] Epoch 1, batch 6210, batch avg loss 0.3431, total avg loss: 0.3365, batch size: 33
2021-10-13 21:22:00,575 INFO [train.py:451] Epoch 1, batch 6220, batch avg loss 0.3186, total avg loss: 0.3307, batch size: 33
2021-10-13 21:22:05,629 INFO [train.py:451] Epoch 1, batch 6230, batch avg loss 0.2671, total avg loss: 0.3164, batch size: 32
2021-10-13 21:22:10,505 INFO [train.py:451] Epoch 1, batch 6240, batch avg loss 0.2416, total avg loss: 0.3171, batch size: 31
2021-10-13 21:22:15,330 INFO [train.py:451] Epoch 1, batch 6250, batch avg loss 0.3097, total avg loss: 0.3197, batch size: 31
2021-10-13 21:22:20,152 INFO [train.py:451] Epoch 1, batch 6260, batch avg loss 0.4012, total avg loss: 0.3218, batch size: 73
2021-10-13 21:22:25,049 INFO [train.py:451] Epoch 1, batch 6270, batch avg loss 0.2722, total avg loss: 0.3219, batch size: 34
2021-10-13 21:22:30,004 INFO [train.py:451] Epoch 1, batch 6280, batch avg loss 0.2972, total avg loss: 0.3219, batch size: 30
2021-10-13 21:22:34,933 INFO [train.py:451] Epoch 1, batch 6290, batch avg loss 0.3016, total avg loss: 0.3200, batch size: 37
2021-10-13 21:22:39,743 INFO [train.py:451] Epoch 1, batch 6300, batch avg loss 0.4001, total avg loss: 0.3228, batch size: 39
2021-10-13 21:22:44,640 INFO [train.py:451] Epoch 1, batch 6310, batch avg loss 0.2727, total avg loss: 0.3215, batch size: 31
2021-10-13 21:22:49,517 INFO [train.py:451] Epoch 1, batch 6320, batch avg loss 0.3195, total avg loss: 0.3216, batch size: 35
2021-10-13 21:22:54,324 INFO [train.py:451] Epoch 1, batch 6330, batch avg loss 0.3549, total avg loss: 0.3233, batch size: 36
2021-10-13 21:22:59,361 INFO [train.py:451] Epoch 1, batch 6340, batch avg loss 0.2611, total avg loss: 0.3230, batch size: 29
2021-10-13 21:23:04,428 INFO [train.py:451] Epoch 1, batch 6350, batch avg loss 0.3647, total avg loss: 0.3233, batch size: 27
2021-10-13 21:23:09,187 INFO [train.py:451] Epoch 1, batch 6360, batch avg loss 0.3367, total avg loss: 0.3250, batch size: 29
2021-10-13 21:23:13,935 INFO [train.py:451] Epoch 1, batch 6370, batch avg loss 0.3508, total avg loss: 0.3252, batch size: 74
2021-10-13 21:23:18,729 INFO [train.py:451] Epoch 1, batch 6380, batch avg loss 0.3770, total avg loss: 0.3248, batch size: 73
2021-10-13 21:23:23,930 INFO [train.py:451] Epoch 1, batch 6390, batch avg loss 0.3301, total avg loss: 0.3244, batch size: 33
2021-10-13 21:23:29,024 INFO [train.py:451] Epoch 1, batch 6400, batch avg loss 0.4377, total avg loss: 0.3244, batch size: 131
2021-10-13 21:23:33,923 INFO [train.py:451] Epoch 1, batch 6410, batch avg loss 0.3616, total avg loss: 0.3190, batch size: 42
2021-10-13 21:23:38,985 INFO [train.py:451] Epoch 1, batch 6420, batch avg loss 0.3111, total avg loss: 0.3239, batch size: 34
2021-10-13 21:23:44,116 INFO [train.py:451] Epoch 1, batch 6430, batch avg loss 0.3548, total avg loss: 0.3186, batch size: 49
2021-10-13 21:23:49,059 INFO [train.py:451] Epoch 1, batch 6440, batch avg loss 0.2948, total avg loss: 0.3227, batch size: 33
2021-10-13 21:23:53,986 INFO [train.py:451] Epoch 1, batch 6450, batch avg loss 0.3445, total avg loss: 0.3222, batch size: 57
2021-10-13 21:23:58,898 INFO [train.py:451] Epoch 1, batch 6460, batch avg loss 0.3313, total avg loss: 0.3228, batch size: 32
2021-10-13 21:24:03,554 INFO [train.py:451] Epoch 1, batch 6470, batch avg loss 0.2781, total avg loss: 0.3231, batch size: 38
2021-10-13 21:24:08,553 INFO [train.py:451] Epoch 1, batch 6480, batch avg loss 0.2717, total avg loss: 0.3216, batch size: 33
2021-10-13 21:24:13,589 INFO [train.py:451] Epoch 1, batch 6490, batch avg loss 0.2829, total avg loss: 0.3172, batch size: 34
2021-10-13 21:24:18,406 INFO [train.py:451] Epoch 1, batch 6500, batch avg loss 0.3224, total avg loss: 0.3188, batch size: 34
2021-10-13 21:24:23,108 INFO [train.py:451] Epoch 1, batch 6510, batch avg loss 0.2899, total avg loss: 0.3189, batch size: 32
2021-10-13 21:24:28,064 INFO [train.py:451] Epoch 1, batch 6520, batch avg loss 0.2943, total avg loss: 0.3180, batch size: 32
2021-10-13 21:24:32,921 INFO [train.py:451] Epoch 1, batch 6530, batch avg loss 0.3274, total avg loss: 0.3196, batch size: 39
2021-10-13 21:24:37,909 INFO [train.py:451] Epoch 1, batch 6540, batch avg loss 0.2672, total avg loss: 0.3190, batch size: 28
2021-10-13 21:24:42,786 INFO [train.py:451] Epoch 1, batch 6550, batch avg loss 0.3590, total avg loss: 0.3189, batch size: 41
2021-10-13 21:24:47,643 INFO [train.py:451] Epoch 1, batch 6560, batch avg loss 0.3166, total avg loss: 0.3193, batch size: 35
2021-10-13 21:24:52,665 INFO [train.py:451] Epoch 1, batch 6570, batch avg loss 0.2490, total avg loss: 0.3189, batch size: 29
2021-10-13 21:24:57,655 INFO [train.py:451] Epoch 1, batch 6580, batch avg loss 0.3337, total avg loss: 0.3183, batch size: 35
2021-10-13 21:25:02,433 INFO [train.py:451] Epoch 1, batch 6590, batch avg loss 0.3560, total avg loss: 0.3195, batch size: 56
2021-10-13 21:25:07,343 INFO [train.py:451] Epoch 1, batch 6600, batch avg loss 0.3231, total avg loss: 0.3194, batch size: 35
2021-10-13 21:25:12,359 INFO [train.py:451] Epoch 1, batch 6610, batch avg loss 0.2517, total avg loss: 0.3043, batch size: 31
2021-10-13 21:25:17,455 INFO [train.py:451] Epoch 1, batch 6620, batch avg loss 0.3460, total avg loss: 0.3009, batch size: 31
2021-10-13 21:25:22,310 INFO [train.py:451] Epoch 1, batch 6630, batch avg loss 0.4279, total avg loss: 0.3147, batch size: 127
2021-10-13 21:25:27,239 INFO [train.py:451] Epoch 1, batch 6640, batch avg loss 0.3258, total avg loss: 0.3153, batch size: 31
2021-10-13 21:25:32,228 INFO [train.py:451] Epoch 1, batch 6650, batch avg loss 0.2979, total avg loss: 0.3143, batch size: 31
2021-10-13 21:25:37,280 INFO [train.py:451] Epoch 1, batch 6660, batch avg loss 0.2682, total avg loss: 0.3166, batch size: 29
2021-10-13 21:25:42,326 INFO [train.py:451] Epoch 1, batch 6670, batch avg loss 0.2451, total avg loss: 0.3146, batch size: 33
2021-10-13 21:25:47,389 INFO [train.py:451] Epoch 1, batch 6680, batch avg loss 0.2489, total avg loss: 0.3122, batch size: 31
2021-10-13 21:25:52,276 INFO [train.py:451] Epoch 1, batch 6690, batch avg loss 0.2853, total avg loss: 0.3149, batch size: 34
2021-10-13 21:25:57,203 INFO [train.py:451] Epoch 1, batch 6700, batch avg loss 0.3048, total avg loss: 0.3154, batch size: 45
2021-10-13 21:26:02,296 INFO [train.py:451] Epoch 1, batch 6710, batch avg loss 0.2995, total avg loss: 0.3152, batch size: 32
2021-10-13 21:26:07,218 INFO [train.py:451] Epoch 1, batch 6720, batch avg loss 0.2772, total avg loss: 0.3153, batch size: 32
2021-10-13 21:26:12,314 INFO [train.py:451] Epoch 1, batch 6730, batch avg loss 0.3141, total avg loss: 0.3155, batch size: 36
2021-10-13 21:26:17,291 INFO [train.py:451] Epoch 1, batch 6740, batch avg loss 0.3220, total avg loss: 0.3152, batch size: 36
2021-10-13 21:26:22,243 INFO [train.py:451] Epoch 1, batch 6750, batch avg loss 0.3174, total avg loss: 0.3147, batch size: 35
2021-10-13 21:26:27,190 INFO [train.py:451] Epoch 1, batch 6760, batch avg loss 0.3321, total avg loss: 0.3172, batch size: 34
2021-10-13 21:26:32,347 INFO [train.py:451] Epoch 1, batch 6770, batch avg loss 0.3160, total avg loss: 0.3167, batch size: 33
2021-10-13 21:26:37,312 INFO [train.py:451] Epoch 1, batch 6780, batch avg loss 0.3479, total avg loss: 0.3167, batch size: 28
2021-10-13 21:26:42,163 INFO [train.py:451] Epoch 1, batch 6790, batch avg loss 0.4214, total avg loss: 0.3178, batch size: 128
2021-10-13 21:26:47,118 INFO [train.py:451] Epoch 1, batch 6800, batch avg loss 0.3479, total avg loss: 0.3177, batch size: 37
2021-10-13 21:26:52,318 INFO [train.py:451] Epoch 1, batch 6810, batch avg loss 0.3553, total avg loss: 0.2957, batch size: 45
2021-10-13 21:26:57,212 INFO [train.py:451] Epoch 1, batch 6820, batch avg loss 0.3951, total avg loss: 0.3170, batch size: 34
2021-10-13 21:27:02,120 INFO [train.py:451] Epoch 1, batch 6830, batch avg loss 0.3026, total avg loss: 0.3167, batch size: 33
2021-10-13 21:27:06,995 INFO [train.py:451] Epoch 1, batch 6840, batch avg loss 0.3448, total avg loss: 0.3210, batch size: 56
2021-10-13 21:27:11,969 INFO [train.py:451] Epoch 1, batch 6850, batch avg loss 0.3615, total avg loss: 0.3214, batch size: 38
2021-10-13 21:27:16,997 INFO [train.py:451] Epoch 1, batch 6860, batch avg loss 0.4384, total avg loss: 0.3206, batch size: 134
2021-10-13 21:27:22,084 INFO [train.py:451] Epoch 1, batch 6870, batch avg loss 0.2562, total avg loss: 0.3200, batch size: 27
2021-10-13 21:27:26,943 INFO [train.py:451] Epoch 1, batch 6880, batch avg loss 0.3283, total avg loss: 0.3201, batch size: 73
2021-10-13 21:27:31,868 INFO [train.py:451] Epoch 1, batch 6890, batch avg loss 0.3053, total avg loss: 0.3182, batch size: 29
2021-10-13 21:27:36,899 INFO [train.py:451] Epoch 1, batch 6900, batch avg loss 0.2924, total avg loss: 0.3180, batch size: 29
2021-10-13 21:27:41,801 INFO [train.py:451] Epoch 1, batch 6910, batch avg loss 0.2681, total avg loss: 0.3183, batch size: 29
2021-10-13 21:27:46,726 INFO [train.py:451] Epoch 1, batch 6920, batch avg loss 0.3106, total avg loss: 0.3183, batch size: 30
2021-10-13 21:27:51,584 INFO [train.py:451] Epoch 1, batch 6930, batch avg loss 0.3533, total avg loss: 0.3185, batch size: 45
2021-10-13 21:27:56,457 INFO [train.py:451] Epoch 1, batch 6940, batch avg loss 0.3134, total avg loss: 0.3186, batch size: 49
2021-10-13 21:28:01,524 INFO [train.py:451] Epoch 1, batch 6950, batch avg loss 0.2871, total avg loss: 0.3177, batch size: 29
2021-10-13 21:28:06,328 INFO [train.py:451] Epoch 1, batch 6960, batch avg loss 0.3350, total avg loss: 0.3187, batch size: 36
2021-10-13 21:28:11,162 INFO [train.py:451] Epoch 1, batch 6970, batch avg loss 0.3482, total avg loss: 0.3195, batch size: 37
2021-10-13 21:28:16,034 INFO [train.py:451] Epoch 1, batch 6980, batch avg loss 0.3059, total avg loss: 0.3193, batch size: 34
2021-10-13 21:28:21,039 INFO [train.py:451] Epoch 1, batch 6990, batch avg loss 0.2408, total avg loss: 0.3197, batch size: 29
2021-10-13 21:28:25,809 INFO [train.py:451] Epoch 1, batch 7000, batch avg loss 0.4304, total avg loss: 0.3200, batch size: 136
2021-10-13 21:29:05,023 INFO [train.py:483] Epoch 1, valid loss 0.2272, best valid loss: 0.2271 best valid epoch: 1
2021-10-13 21:29:10,047 INFO [train.py:451] Epoch 1, batch 7010, batch avg loss 0.3915, total avg loss: 0.3067, batch size: 73
2021-10-13 21:29:15,219 INFO [train.py:451] Epoch 1, batch 7020, batch avg loss 0.2484, total avg loss: 0.3060, batch size: 29
2021-10-13 21:29:20,170 INFO [train.py:451] Epoch 1, batch 7030, batch avg loss 0.3318, total avg loss: 0.3080, batch size: 39
2021-10-13 21:29:24,852 INFO [train.py:451] Epoch 1, batch 7040, batch avg loss 0.3304, total avg loss: 0.3169, batch size: 34
2021-10-13 21:29:29,784 INFO [train.py:451] Epoch 1, batch 7050, batch avg loss 0.2684, total avg loss: 0.3190, batch size: 32
2021-10-13 21:29:34,753 INFO [train.py:451] Epoch 1, batch 7060, batch avg loss 0.3144, total avg loss: 0.3212, batch size: 30
2021-10-13 21:29:39,596 INFO [train.py:451] Epoch 1, batch 7070, batch avg loss 0.2636, total avg loss: 0.3207, batch size: 27
2021-10-13 21:29:44,342 INFO [train.py:451] Epoch 1, batch 7080, batch avg loss 0.3407, total avg loss: 0.3218, batch size: 57
2021-10-13 21:29:49,255 INFO [train.py:451] Epoch 1, batch 7090, batch avg loss 0.3655, total avg loss: 0.3219, batch size: 34
2021-10-13 21:29:54,157 INFO [train.py:451] Epoch 1, batch 7100, batch avg loss 0.3011, total avg loss: 0.3211, batch size: 32
2021-10-13 21:29:59,079 INFO [train.py:451] Epoch 1, batch 7110, batch avg loss 0.2642, total avg loss: 0.3202, batch size: 31
2021-10-13 21:30:04,178 INFO [train.py:451] Epoch 1, batch 7120, batch avg loss 0.2498, total avg loss: 0.3184, batch size: 28
2021-10-13 21:30:09,347 INFO [train.py:451] Epoch 1, batch 7130, batch avg loss 0.2649, total avg loss: 0.3164, batch size: 32
2021-10-13 21:30:14,526 INFO [train.py:451] Epoch 1, batch 7140, batch avg loss 0.3653, total avg loss: 0.3165, batch size: 39
2021-10-13 21:30:19,724 INFO [train.py:451] Epoch 1, batch 7150, batch avg loss 0.3497, total avg loss: 0.3158, batch size: 42
2021-10-13 21:30:24,748 INFO [train.py:451] Epoch 1, batch 7160, batch avg loss 0.2931, total avg loss: 0.3158, batch size: 31
2021-10-13 21:30:29,694 INFO [train.py:451] Epoch 1, batch 7170, batch avg loss 0.4240, total avg loss: 0.3171, batch size: 128
2021-10-13 21:30:34,651 INFO [train.py:451] Epoch 1, batch 7180, batch avg loss 0.2782, total avg loss: 0.3178, batch size: 39
2021-10-13 21:30:39,662 INFO [train.py:451] Epoch 1, batch 7190, batch avg loss 0.2765, total avg loss: 0.3181, batch size: 31
2021-10-13 21:30:44,807 INFO [train.py:451] Epoch 1, batch 7200, batch avg loss 0.2843, total avg loss: 0.3171, batch size: 27
2021-10-13 21:30:49,648 INFO [train.py:451] Epoch 1, batch 7210, batch avg loss 0.4112, total avg loss: 0.3116, batch size: 57
2021-10-13 21:30:54,565 INFO [train.py:451] Epoch 1, batch 7220, batch avg loss 0.3582, total avg loss: 0.3120, batch size: 49
2021-10-13 21:30:59,696 INFO [train.py:451] Epoch 1, batch 7230, batch avg loss 0.2723, total avg loss: 0.3156, batch size: 29
2021-10-13 21:31:04,653 INFO [train.py:451] Epoch 1, batch 7240, batch avg loss 0.4085, total avg loss: 0.3166, batch size: 35
2021-10-13 21:31:09,626 INFO [train.py:451] Epoch 1, batch 7250, batch avg loss 0.2851, total avg loss: 0.3179, batch size: 31
2021-10-13 21:31:14,239 INFO [train.py:451] Epoch 1, batch 7260, batch avg loss 0.3187, total avg loss: 0.3210, batch size: 56
2021-10-13 21:31:19,110 INFO [train.py:451] Epoch 1, batch 7270, batch avg loss 0.4067, total avg loss: 0.3220, batch size: 135
2021-10-13 21:31:23,884 INFO [train.py:451] Epoch 1, batch 7280, batch avg loss 0.3601, total avg loss: 0.3235, batch size: 45
2021-10-13 21:31:28,839 INFO [train.py:451] Epoch 1, batch 7290, batch avg loss 0.2912, total avg loss: 0.3230, batch size: 32
2021-10-13 21:31:33,621 INFO [train.py:451] Epoch 1, batch 7300, batch avg loss 0.3331, total avg loss: 0.3268, batch size: 34
2021-10-13 21:31:38,527 INFO [train.py:451] Epoch 1, batch 7310, batch avg loss 0.2867, total avg loss: 0.3255, batch size: 30
2021-10-13 21:31:43,443 INFO [train.py:451] Epoch 1, batch 7320, batch avg loss 0.3335, total avg loss: 0.3252, batch size: 57
2021-10-13 21:31:48,392 INFO [train.py:451] Epoch 1, batch 7330, batch avg loss 0.2330, total avg loss: 0.3227, batch size: 29
2021-10-13 21:31:53,298 INFO [train.py:451] Epoch 1, batch 7340, batch avg loss 0.2768, total avg loss: 0.3210, batch size: 31
2021-10-13 21:31:58,157 INFO [train.py:451] Epoch 1, batch 7350, batch avg loss 0.3094, total avg loss: 0.3210, batch size: 41
2021-10-13 21:32:03,121 INFO [train.py:451] Epoch 1, batch 7360, batch avg loss 0.2763, total avg loss: 0.3203, batch size: 27
2021-10-13 21:32:07,991 INFO [train.py:451] Epoch 1, batch 7370, batch avg loss 0.3315, total avg loss: 0.3212, batch size: 30
2021-10-13 21:32:12,909 INFO [train.py:451] Epoch 1, batch 7380, batch avg loss 0.3416, total avg loss: 0.3208, batch size: 45
2021-10-13 21:32:17,877 INFO [train.py:451] Epoch 1, batch 7390, batch avg loss 0.2720, total avg loss: 0.3208, batch size: 34
2021-10-13 21:32:22,894 INFO [train.py:451] Epoch 1, batch 7400, batch avg loss 0.3103, total avg loss: 0.3203, batch size: 38
2021-10-13 21:32:27,995 INFO [train.py:451] Epoch 1, batch 7410, batch avg loss 0.2307, total avg loss: 0.3259, batch size: 27
2021-10-13 21:32:32,861 INFO [train.py:451] Epoch 1, batch 7420, batch avg loss 0.2880, total avg loss: 0.3252, batch size: 49
2021-10-13 21:32:37,877 INFO [train.py:451] Epoch 1, batch 7430, batch avg loss 0.3222, total avg loss: 0.3222, batch size: 29
2021-10-13 21:32:42,599 INFO [train.py:451] Epoch 1, batch 7440, batch avg loss 0.3459, total avg loss: 0.3244, batch size: 57
2021-10-13 21:32:47,544 INFO [train.py:451] Epoch 1, batch 7450, batch avg loss 0.2309, total avg loss: 0.3201, batch size: 29
2021-10-13 21:32:52,693 INFO [train.py:451] Epoch 1, batch 7460, batch avg loss 0.3248, total avg loss: 0.3164, batch size: 34
2021-10-13 21:32:57,615 INFO [train.py:451] Epoch 1, batch 7470, batch avg loss 0.3330, total avg loss: 0.3161, batch size: 32
2021-10-13 21:33:02,573 INFO [train.py:451] Epoch 1, batch 7480, batch avg loss 0.3173, total avg loss: 0.3191, batch size: 37
2021-10-13 21:33:07,727 INFO [train.py:451] Epoch 1, batch 7490, batch avg loss 0.2361, total avg loss: 0.3182, batch size: 29
2021-10-13 21:33:12,709 INFO [train.py:451] Epoch 1, batch 7500, batch avg loss 0.3614, total avg loss: 0.3199, batch size: 34
2021-10-13 21:33:17,622 INFO [train.py:451] Epoch 1, batch 7510, batch avg loss 0.2420, total avg loss: 0.3199, batch size: 29
2021-10-13 21:33:22,785 INFO [train.py:451] Epoch 1, batch 7520, batch avg loss 0.3558, total avg loss: 0.3208, batch size: 35
2021-10-13 21:33:27,731 INFO [train.py:451] Epoch 1, batch 7530, batch avg loss 0.2883, total avg loss: 0.3207, batch size: 38
2021-10-13 21:33:32,448 INFO [train.py:451] Epoch 1, batch 7540, batch avg loss 0.3252, total avg loss: 0.3213, batch size: 34
2021-10-13 21:33:37,436 INFO [train.py:451] Epoch 1, batch 7550, batch avg loss 0.2988, total avg loss: 0.3210, batch size: 34
2021-10-13 21:33:42,278 INFO [train.py:451] Epoch 1, batch 7560, batch avg loss 0.3762, total avg loss: 0.3208, batch size: 49
2021-10-13 21:33:47,582 INFO [train.py:451] Epoch 1, batch 7570, batch avg loss 0.2767, total avg loss: 0.3192, batch size: 30
2021-10-13 21:33:52,515 INFO [train.py:451] Epoch 1, batch 7580, batch avg loss 0.2952, total avg loss: 0.3190, batch size: 34
2021-10-13 21:33:57,359 INFO [train.py:451] Epoch 1, batch 7590, batch avg loss 0.2912, total avg loss: 0.3194, batch size: 28
2021-10-13 21:34:02,302 INFO [train.py:451] Epoch 1, batch 7600, batch avg loss 0.3233, total avg loss: 0.3182, batch size: 37
2021-10-13 21:34:07,317 INFO [train.py:451] Epoch 1, batch 7610, batch avg loss 0.3642, total avg loss: 0.3031, batch size: 57
2021-10-13 21:34:12,044 INFO [train.py:451] Epoch 1, batch 7620, batch avg loss 0.3780, total avg loss: 0.3180, batch size: 45
2021-10-13 21:34:16,978 INFO [train.py:451] Epoch 1, batch 7630, batch avg loss 0.2885, total avg loss: 0.3172, batch size: 29
2021-10-13 21:34:21,951 INFO [train.py:451] Epoch 1, batch 7640, batch avg loss 0.2879, total avg loss: 0.3153, batch size: 33
2021-10-13 21:34:26,948 INFO [train.py:451] Epoch 1, batch 7650, batch avg loss 0.4078, total avg loss: 0.3172, batch size: 42
2021-10-13 21:34:31,908 INFO [train.py:451] Epoch 1, batch 7660, batch avg loss 0.2920, total avg loss: 0.3215, batch size: 30
2021-10-13 21:34:36,778 INFO [train.py:451] Epoch 1, batch 7670, batch avg loss 0.2980, total avg loss: 0.3205, batch size: 36
2021-10-13 21:34:41,585 INFO [train.py:451] Epoch 1, batch 7680, batch avg loss 0.3530, total avg loss: 0.3226, batch size: 42
2021-10-13 21:34:46,598 INFO [train.py:451] Epoch 1, batch 7690, batch avg loss 0.3587, total avg loss: 0.3222, batch size: 35
2021-10-13 21:34:51,622 INFO [train.py:451] Epoch 1, batch 7700, batch avg loss 0.4107, total avg loss: 0.3202, batch size: 128
2021-10-13 21:34:56,707 INFO [train.py:451] Epoch 1, batch 7710, batch avg loss 0.3405, total avg loss: 0.3196, batch size: 36
2021-10-13 21:35:01,640 INFO [train.py:451] Epoch 1, batch 7720, batch avg loss 0.3752, total avg loss: 0.3198, batch size: 37
2021-10-13 21:35:06,428 INFO [train.py:451] Epoch 1, batch 7730, batch avg loss 0.2906, total avg loss: 0.3212, batch size: 34
2021-10-13 21:35:11,336 INFO [train.py:451] Epoch 1, batch 7740, batch avg loss 0.3295, total avg loss: 0.3212, batch size: 38
2021-10-13 21:35:16,371 INFO [train.py:451] Epoch 1, batch 7750, batch avg loss 0.3153, total avg loss: 0.3204, batch size: 45
2021-10-13 21:35:21,215 INFO [train.py:451] Epoch 1, batch 7760, batch avg loss 0.3675, total avg loss: 0.3208, batch size: 39
2021-10-13 21:35:26,263 INFO [train.py:451] Epoch 1, batch 7770, batch avg loss 0.2742, total avg loss: 0.3213, batch size: 34
2021-10-13 21:35:31,110 INFO [train.py:451] Epoch 1, batch 7780, batch avg loss 0.2467, total avg loss: 0.3211, batch size: 29
2021-10-13 21:35:36,016 INFO [train.py:451] Epoch 1, batch 7790, batch avg loss 0.2860, total avg loss: 0.3197, batch size: 42
2021-10-13 21:35:40,814 INFO [train.py:451] Epoch 1, batch 7800, batch avg loss 0.3442, total avg loss: 0.3201, batch size: 38
2021-10-13 21:35:45,732 INFO [train.py:451] Epoch 1, batch 7810, batch avg loss 0.3367, total avg loss: 0.3094, batch size: 57
2021-10-13 21:35:50,703 INFO [train.py:451] Epoch 1, batch 7820, batch avg loss 0.3060, total avg loss: 0.3099, batch size: 35
2021-10-13 21:35:55,578 INFO [train.py:451] Epoch 1, batch 7830, batch avg loss 0.2946, total avg loss: 0.3094, batch size: 32
2021-10-13 21:36:00,641 INFO [train.py:451] Epoch 1, batch 7840, batch avg loss 0.3262, total avg loss: 0.3092, batch size: 41
2021-10-13 21:36:05,811 INFO [train.py:451] Epoch 1, batch 7850, batch avg loss 0.3608, total avg loss: 0.3088, batch size: 30
2021-10-13 21:36:10,751 INFO [train.py:451] Epoch 1, batch 7860, batch avg loss 0.3460, total avg loss: 0.3121, batch size: 72
2021-10-13 21:36:15,869 INFO [train.py:451] Epoch 1, batch 7870, batch avg loss 0.2445, total avg loss: 0.3148, batch size: 31
2021-10-13 21:36:20,831 INFO [train.py:451] Epoch 1, batch 7880, batch avg loss 0.2774, total avg loss: 0.3162, batch size: 33
2021-10-13 21:36:25,864 INFO [train.py:451] Epoch 1, batch 7890, batch avg loss 0.4220, total avg loss: 0.3165, batch size: 127
2021-10-13 21:36:30,713 INFO [train.py:451] Epoch 1, batch 7900, batch avg loss 0.3823, total avg loss: 0.3202, batch size: 42
2021-10-13 21:36:35,696 INFO [train.py:451] Epoch 1, batch 7910, batch avg loss 0.3483, total avg loss: 0.3192, batch size: 49
2021-10-13 21:36:40,573 INFO [train.py:451] Epoch 1, batch 7920, batch avg loss 0.2335, total avg loss: 0.3193, batch size: 31
2021-10-13 21:36:45,433 INFO [train.py:451] Epoch 1, batch 7930, batch avg loss 0.2499, total avg loss: 0.3184, batch size: 33
2021-10-13 21:36:50,390 INFO [train.py:451] Epoch 1, batch 7940, batch avg loss 0.2508, total avg loss: 0.3170, batch size: 33
2021-10-13 21:36:55,224 INFO [train.py:451] Epoch 1, batch 7950, batch avg loss 0.2370, total avg loss: 0.3168, batch size: 28
2021-10-13 21:37:00,006 INFO [train.py:451] Epoch 1, batch 7960, batch avg loss 0.2877, total avg loss: 0.3176, batch size: 32
2021-10-13 21:37:05,070 INFO [train.py:451] Epoch 1, batch 7970, batch avg loss 0.2768, total avg loss: 0.3182, batch size: 29
2021-10-13 21:37:10,051 INFO [train.py:451] Epoch 1, batch 7980, batch avg loss 0.3710, total avg loss: 0.3185, batch size: 38
2021-10-13 21:37:15,108 INFO [train.py:451] Epoch 1, batch 7990, batch avg loss 0.3653, total avg loss: 0.3193, batch size: 42
2021-10-13 21:37:20,161 INFO [train.py:451] Epoch 1, batch 8000, batch avg loss 0.3299, total avg loss: 0.3188, batch size: 42
2021-10-13 21:37:59,025 INFO [train.py:483] Epoch 1, valid loss 0.2254, best valid loss: 0.2254 best valid epoch: 1
2021-10-13 21:38:03,848 INFO [train.py:451] Epoch 1, batch 8010, batch avg loss 0.3722, total avg loss: 0.3271, batch size: 56
2021-10-13 21:38:08,766 INFO [train.py:451] Epoch 1, batch 8020, batch avg loss 0.3289, total avg loss: 0.3187, batch size: 39
2021-10-13 21:38:13,797 INFO [train.py:451] Epoch 1, batch 8030, batch avg loss 0.3292, total avg loss: 0.3189, batch size: 33
2021-10-13 21:38:18,590 INFO [train.py:451] Epoch 1, batch 8040, batch avg loss 0.3043, total avg loss: 0.3184, batch size: 35
2021-10-13 21:38:23,757 INFO [train.py:451] Epoch 1, batch 8050, batch avg loss 0.3212, total avg loss: 0.3189, batch size: 35
2021-10-13 21:38:28,555 INFO [train.py:451] Epoch 1, batch 8060, batch avg loss 0.3620, total avg loss: 0.3203, batch size: 35
2021-10-13 21:38:33,414 INFO [train.py:451] Epoch 1, batch 8070, batch avg loss 0.4283, total avg loss: 0.3228, batch size: 130
2021-10-13 21:38:38,216 INFO [train.py:451] Epoch 1, batch 8080, batch avg loss 0.3460, total avg loss: 0.3243, batch size: 45
2021-10-13 21:38:43,073 INFO [train.py:451] Epoch 1, batch 8090, batch avg loss 0.3388, total avg loss: 0.3237, batch size: 34
2021-10-13 21:38:48,272 INFO [train.py:451] Epoch 1, batch 8100, batch avg loss 0.3280, total avg loss: 0.3236, batch size: 32
2021-10-13 21:38:53,336 INFO [train.py:451] Epoch 1, batch 8110, batch avg loss 0.2755, total avg loss: 0.3209, batch size: 31
2021-10-13 21:38:58,089 INFO [train.py:451] Epoch 1, batch 8120, batch avg loss 0.3245, total avg loss: 0.3223, batch size: 39
2021-10-13 21:39:03,145 INFO [train.py:451] Epoch 1, batch 8130, batch avg loss 0.2896, total avg loss: 0.3207, batch size: 33
2021-10-13 21:39:08,051 INFO [train.py:451] Epoch 1, batch 8140, batch avg loss 0.4789, total avg loss: 0.3205, batch size: 130
2021-10-13 21:39:13,011 INFO [train.py:451] Epoch 1, batch 8150, batch avg loss 0.3266, total avg loss: 0.3200, batch size: 30
2021-10-13 21:39:17,905 INFO [train.py:451] Epoch 1, batch 8160, batch avg loss 0.3260, total avg loss: 0.3203, batch size: 38
2021-10-13 21:39:22,813 INFO [train.py:451] Epoch 1, batch 8170, batch avg loss 0.2394, total avg loss: 0.3201, batch size: 31
2021-10-13 21:39:27,721 INFO [train.py:451] Epoch 1, batch 8180, batch avg loss 0.2698, total avg loss: 0.3194, batch size: 33
2021-10-13 21:39:32,777 INFO [train.py:451] Epoch 1, batch 8190, batch avg loss 0.2368, total avg loss: 0.3192, batch size: 30
2021-10-13 21:39:37,785 INFO [train.py:451] Epoch 1, batch 8200, batch avg loss 0.3381, total avg loss: 0.3196, batch size: 37
2021-10-13 21:39:42,766 INFO [train.py:451] Epoch 1, batch 8210, batch avg loss 0.2795, total avg loss: 0.3065, batch size: 29
2021-10-13 21:39:47,752 INFO [train.py:451] Epoch 1, batch 8220, batch avg loss 0.3426, total avg loss: 0.3073, batch size: 31
2021-10-13 21:39:52,813 INFO [train.py:451] Epoch 1, batch 8230, batch avg loss 0.3194, total avg loss: 0.3052, batch size: 36
2021-10-13 21:39:58,003 INFO [train.py:451] Epoch 1, batch 8240, batch avg loss 0.2517, total avg loss: 0.3027, batch size: 27
2021-10-13 21:40:02,778 INFO [train.py:451] Epoch 1, batch 8250, batch avg loss 0.3258, total avg loss: 0.3053, batch size: 49
2021-10-13 21:40:07,711 INFO [train.py:451] Epoch 1, batch 8260, batch avg loss 0.3292, total avg loss: 0.3051, batch size: 38
2021-10-13 21:40:12,668 INFO [train.py:451] Epoch 1, batch 8270, batch avg loss 0.2859, total avg loss: 0.3056, batch size: 35
2021-10-13 21:40:17,681 INFO [train.py:451] Epoch 1, batch 8280, batch avg loss 0.2522, total avg loss: 0.3077, batch size: 28
2021-10-13 21:40:22,527 INFO [train.py:451] Epoch 1, batch 8290, batch avg loss 0.3190, total avg loss: 0.3096, batch size: 36
2021-10-13 21:40:27,637 INFO [train.py:451] Epoch 1, batch 8300, batch avg loss 0.3417, total avg loss: 0.3077, batch size: 34
2021-10-13 21:40:32,600 INFO [train.py:451] Epoch 1, batch 8310, batch avg loss 0.3583, total avg loss: 0.3094, batch size: 42
2021-10-13 21:40:37,550 INFO [train.py:451] Epoch 1, batch 8320, batch avg loss 0.2847, total avg loss: 0.3108, batch size: 35
2021-10-13 21:40:42,673 INFO [train.py:451] Epoch 1, batch 8330, batch avg loss 0.3455, total avg loss: 0.3098, batch size: 34
2021-10-13 21:40:47,599 INFO [train.py:451] Epoch 1, batch 8340, batch avg loss 0.2273, total avg loss: 0.3100, batch size: 30
2021-10-13 21:40:52,589 INFO [train.py:451] Epoch 1, batch 8350, batch avg loss 0.3004, total avg loss: 0.3099, batch size: 36
2021-10-13 21:40:57,604 INFO [train.py:451] Epoch 1, batch 8360, batch avg loss 0.3497, total avg loss: 0.3099, batch size: 56
2021-10-13 21:41:02,572 INFO [train.py:451] Epoch 1, batch 8370, batch avg loss 0.2843, total avg loss: 0.3104, batch size: 27
2021-10-13 21:41:07,694 INFO [train.py:451] Epoch 1, batch 8380, batch avg loss 0.3010, total avg loss: 0.3098, batch size: 33
2021-10-13 21:41:12,404 INFO [train.py:451] Epoch 1, batch 8390, batch avg loss 0.3590, total avg loss: 0.3114, batch size: 33
2021-10-13 21:41:17,356 INFO [train.py:451] Epoch 1, batch 8400, batch avg loss 0.3328, total avg loss: 0.3122, batch size: 41
2021-10-13 21:41:22,321 INFO [train.py:451] Epoch 1, batch 8410, batch avg loss 0.2415, total avg loss: 0.2946, batch size: 29
2021-10-13 21:41:27,195 INFO [train.py:451] Epoch 1, batch 8420, batch avg loss 0.3955, total avg loss: 0.3160, batch size: 57
2021-10-13 21:41:32,010 INFO [train.py:451] Epoch 1, batch 8430, batch avg loss 0.3395, total avg loss: 0.3219, batch size: 57
2021-10-13 21:41:37,030 INFO [train.py:451] Epoch 1, batch 8440, batch avg loss 0.3004, total avg loss: 0.3175, batch size: 28
2021-10-13 21:41:41,944 INFO [train.py:451] Epoch 1, batch 8450, batch avg loss 0.3076, total avg loss: 0.3174, batch size: 33
2021-10-13 21:41:46,922 INFO [train.py:451] Epoch 1, batch 8460, batch avg loss 0.2824, total avg loss: 0.3169, batch size: 28
2021-10-13 21:41:51,979 INFO [train.py:451] Epoch 1, batch 8470, batch avg loss 0.2650, total avg loss: 0.3137, batch size: 32
2021-10-13 21:41:57,000 INFO [train.py:451] Epoch 1, batch 8480, batch avg loss 0.2484, total avg loss: 0.3138, batch size: 32
2021-10-13 21:42:01,885 INFO [train.py:451] Epoch 1, batch 8490, batch avg loss 0.2844, total avg loss: 0.3146, batch size: 30
2021-10-13 21:42:06,796 INFO [train.py:451] Epoch 1, batch 8500, batch avg loss 0.2809, total avg loss: 0.3137, batch size: 31
2021-10-13 21:42:11,740 INFO [train.py:451] Epoch 1, batch 8510, batch avg loss 0.2683, total avg loss: 0.3132, batch size: 30
2021-10-13 21:42:16,923 INFO [train.py:451] Epoch 1, batch 8520, batch avg loss 0.2957, total avg loss: 0.3135, batch size: 34
2021-10-13 21:42:21,881 INFO [train.py:451] Epoch 1, batch 8530, batch avg loss 0.2324, total avg loss: 0.3138, batch size: 32
2021-10-13 21:42:26,756 INFO [train.py:451] Epoch 1, batch 8540, batch avg loss 0.3271, total avg loss: 0.3133, batch size: 49
2021-10-13 21:42:31,567 INFO [train.py:451] Epoch 1, batch 8550, batch avg loss 0.3813, total avg loss: 0.3151, batch size: 56
2021-10-13 21:42:36,400 INFO [train.py:451] Epoch 1, batch 8560, batch avg loss 0.2715, total avg loss: 0.3139, batch size: 36
2021-10-13 21:42:41,272 INFO [train.py:451] Epoch 1, batch 8570, batch avg loss 0.2711, total avg loss: 0.3152, batch size: 33
2021-10-13 21:42:46,322 INFO [train.py:451] Epoch 1, batch 8580, batch avg loss 0.3761, total avg loss: 0.3159, batch size: 38
2021-10-13 21:42:51,342 INFO [train.py:451] Epoch 1, batch 8590, batch avg loss 0.2920, total avg loss: 0.3158, batch size: 28
2021-10-13 21:42:56,511 INFO [train.py:451] Epoch 1, batch 8600, batch avg loss 0.2839, total avg loss: 0.3147, batch size: 30
2021-10-13 21:43:01,472 INFO [train.py:451] Epoch 1, batch 8610, batch avg loss 0.3644, total avg loss: 0.3105, batch size: 73
2021-10-13 21:43:06,254 INFO [train.py:451] Epoch 1, batch 8620, batch avg loss 0.3135, total avg loss: 0.3241, batch size: 57
2021-10-13 21:43:11,339 INFO [train.py:451] Epoch 1, batch 8630, batch avg loss 0.2788, total avg loss: 0.3129, batch size: 36
2021-10-13 21:43:16,238 INFO [train.py:451] Epoch 1, batch 8640, batch avg loss 0.2500, total avg loss: 0.3122, batch size: 29
2021-10-13 21:43:21,127 INFO [train.py:451] Epoch 1, batch 8650, batch avg loss 0.3435, total avg loss: 0.3152, batch size: 37
2021-10-13 21:43:26,133 INFO [train.py:451] Epoch 1, batch 8660, batch avg loss 0.4666, total avg loss: 0.3168, batch size: 128
2021-10-13 21:43:31,051 INFO [train.py:451] Epoch 1, batch 8670, batch avg loss 0.2942, total avg loss: 0.3172, batch size: 34
2021-10-13 21:43:35,965 INFO [train.py:451] Epoch 1, batch 8680, batch avg loss 0.3304, total avg loss: 0.3179, batch size: 39
2021-10-13 21:43:40,858 INFO [train.py:451] Epoch 1, batch 8690, batch avg loss 0.2907, total avg loss: 0.3166, batch size: 38
2021-10-13 21:43:45,904 INFO [train.py:451] Epoch 1, batch 8700, batch avg loss 0.2697, total avg loss: 0.3162, batch size: 34
2021-10-13 21:43:50,969 INFO [train.py:451] Epoch 1, batch 8710, batch avg loss 0.2725, total avg loss: 0.3166, batch size: 27
2021-10-13 21:43:56,061 INFO [train.py:451] Epoch 1, batch 8720, batch avg loss 0.2613, total avg loss: 0.3150, batch size: 31
2021-10-13 21:44:00,980 INFO [train.py:451] Epoch 1, batch 8730, batch avg loss 0.3129, total avg loss: 0.3152, batch size: 35
2021-10-13 21:44:05,959 INFO [train.py:451] Epoch 1, batch 8740, batch avg loss 0.2752, total avg loss: 0.3174, batch size: 29
2021-10-13 21:44:11,190 INFO [train.py:451] Epoch 1, batch 8750, batch avg loss 0.3450, total avg loss: 0.3165, batch size: 49
2021-10-13 21:44:16,163 INFO [train.py:451] Epoch 1, batch 8760, batch avg loss 0.2538, total avg loss: 0.3157, batch size: 31
2021-10-13 21:44:20,937 INFO [train.py:451] Epoch 1, batch 8770, batch avg loss 0.3294, total avg loss: 0.3169, batch size: 49
2021-10-13 21:44:25,898 INFO [train.py:451] Epoch 1, batch 8780, batch avg loss 0.2843, total avg loss: 0.3160, batch size: 34
2021-10-13 21:44:30,870 INFO [train.py:451] Epoch 1, batch 8790, batch avg loss 0.3407, total avg loss: 0.3161, batch size: 57
2021-10-13 21:44:35,914 INFO [train.py:451] Epoch 1, batch 8800, batch avg loss 0.3396, total avg loss: 0.3158, batch size: 49
2021-10-13 21:44:40,747 INFO [train.py:451] Epoch 1, batch 8810, batch avg loss 0.3365, total avg loss: 0.3409, batch size: 33
2021-10-13 21:44:45,688 INFO [train.py:451] Epoch 1, batch 8820, batch avg loss 0.2900, total avg loss: 0.3286, batch size: 30
2021-10-13 21:44:50,611 INFO [train.py:451] Epoch 1, batch 8830, batch avg loss 0.3259, total avg loss: 0.3257, batch size: 30
2021-10-13 21:44:55,574 INFO [train.py:451] Epoch 1, batch 8840, batch avg loss 0.3022, total avg loss: 0.3218, batch size: 49
2021-10-13 21:45:00,439 INFO [train.py:451] Epoch 1, batch 8850, batch avg loss 0.4144, total avg loss: 0.3222, batch size: 73
2021-10-13 21:45:05,326 INFO [train.py:451] Epoch 1, batch 8860, batch avg loss 0.3231, total avg loss: 0.3254, batch size: 32
2021-10-13 21:45:10,356 INFO [train.py:451] Epoch 1, batch 8870, batch avg loss 0.3084, total avg loss: 0.3284, batch size: 33
2021-10-13 21:45:15,313 INFO [train.py:451] Epoch 1, batch 8880, batch avg loss 0.3688, total avg loss: 0.3269, batch size: 34
2021-10-13 21:45:20,294 INFO [train.py:451] Epoch 1, batch 8890, batch avg loss 0.3385, total avg loss: 0.3269, batch size: 35
2021-10-13 21:45:25,406 INFO [train.py:451] Epoch 1, batch 8900, batch avg loss 0.2946, total avg loss: 0.3235, batch size: 35
2021-10-13 21:45:30,416 INFO [train.py:451] Epoch 1, batch 8910, batch avg loss 0.3333, total avg loss: 0.3224, batch size: 45
2021-10-13 21:45:35,462 INFO [train.py:451] Epoch 1, batch 8920, batch avg loss 0.3161, total avg loss: 0.3216, batch size: 34
2021-10-13 21:45:40,581 INFO [train.py:451] Epoch 1, batch 8930, batch avg loss 0.2905, total avg loss: 0.3214, batch size: 34
2021-10-13 21:45:45,529 INFO [train.py:451] Epoch 1, batch 8940, batch avg loss 0.2901, total avg loss: 0.3203, batch size: 28
2021-10-13 21:45:50,693 INFO [train.py:451] Epoch 1, batch 8950, batch avg loss 0.2711, total avg loss: 0.3199, batch size: 27
2021-10-13 21:45:55,600 INFO [train.py:451] Epoch 1, batch 8960, batch avg loss 0.3127, total avg loss: 0.3193, batch size: 35
2021-10-13 21:46:00,410 INFO [train.py:451] Epoch 1, batch 8970, batch avg loss 0.2377, total avg loss: 0.3183, batch size: 28
2021-10-13 21:46:05,352 INFO [train.py:451] Epoch 1, batch 8980, batch avg loss 0.4008, total avg loss: 0.3181, batch size: 72
2021-10-13 21:46:10,163 INFO [train.py:451] Epoch 1, batch 8990, batch avg loss 0.3568, total avg loss: 0.3187, batch size: 42
2021-10-13 21:46:15,111 INFO [train.py:451] Epoch 1, batch 9000, batch avg loss 0.2470, total avg loss: 0.3174, batch size: 27
2021-10-13 21:46:55,360 INFO [train.py:483] Epoch 1, valid loss 0.2237, best valid loss: 0.2237 best valid epoch: 1
2021-10-13 21:47:00,206 INFO [train.py:451] Epoch 1, batch 9010, batch avg loss 0.3311, total avg loss: 0.3142, batch size: 34
2021-10-13 21:47:05,081 INFO [train.py:451] Epoch 1, batch 9020, batch avg loss 0.3656, total avg loss: 0.3110, batch size: 73
2021-10-13 21:47:10,100 INFO [train.py:451] Epoch 1, batch 9030, batch avg loss 0.2732, total avg loss: 0.3071, batch size: 33
2021-10-13 21:47:15,038 INFO [train.py:451] Epoch 1, batch 9040, batch avg loss 0.3761, total avg loss: 0.3108, batch size: 42
2021-10-13 21:47:19,960 INFO [train.py:451] Epoch 1, batch 9050, batch avg loss 0.3011, total avg loss: 0.3125, batch size: 36
2021-10-13 21:47:24,923 INFO [train.py:451] Epoch 1, batch 9060, batch avg loss 0.3050, total avg loss: 0.3191, batch size: 33
2021-10-13 21:47:29,837 INFO [train.py:451] Epoch 1, batch 9070, batch avg loss 0.2591, total avg loss: 0.3191, batch size: 28
2021-10-13 21:47:34,656 INFO [train.py:451] Epoch 1, batch 9080, batch avg loss 0.3923, total avg loss: 0.3206, batch size: 125
2021-10-13 21:47:39,635 INFO [train.py:451] Epoch 1, batch 9090, batch avg loss 0.3355, total avg loss: 0.3179, batch size: 72
2021-10-13 21:47:44,799 INFO [train.py:451] Epoch 1, batch 9100, batch avg loss 0.2712, total avg loss: 0.3168, batch size: 27
2021-10-13 21:47:49,556 INFO [train.py:451] Epoch 1, batch 9110, batch avg loss 0.3395, total avg loss: 0.3166, batch size: 29
2021-10-13 21:47:54,461 INFO [train.py:451] Epoch 1, batch 9120, batch avg loss 0.2552, total avg loss: 0.3179, batch size: 30
2021-10-13 21:47:59,476 INFO [train.py:451] Epoch 1, batch 9130, batch avg loss 0.3329, total avg loss: 0.3157, batch size: 35
2021-10-13 21:48:04,282 INFO [train.py:451] Epoch 1, batch 9140, batch avg loss 0.3079, total avg loss: 0.3159, batch size: 38
2021-10-13 21:48:09,326 INFO [train.py:451] Epoch 1, batch 9150, batch avg loss 0.3454, total avg loss: 0.3156, batch size: 49
2021-10-13 21:48:14,302 INFO [train.py:451] Epoch 1, batch 9160, batch avg loss 0.3474, total avg loss: 0.3154, batch size: 38
2021-10-13 21:48:19,216 INFO [train.py:451] Epoch 1, batch 9170, batch avg loss 0.3522, total avg loss: 0.3149, batch size: 31
2021-10-13 21:48:23,998 INFO [train.py:451] Epoch 1, batch 9180, batch avg loss 0.2985, total avg loss: 0.3161, batch size: 38
2021-10-13 21:48:29,041 INFO [train.py:451] Epoch 1, batch 9190, batch avg loss 0.3706, total avg loss: 0.3163, batch size: 72
2021-10-13 21:48:34,040 INFO [train.py:451] Epoch 1, batch 9200, batch avg loss 0.2454, total avg loss: 0.3162, batch size: 33
2021-10-13 21:48:38,876 INFO [train.py:451] Epoch 1, batch 9210, batch avg loss 0.3242, total avg loss: 0.3334, batch size: 35
2021-10-13 21:48:43,823 INFO [train.py:451] Epoch 1, batch 9220, batch avg loss 0.2493, total avg loss: 0.3103, batch size: 29
2021-10-13 21:48:48,683 INFO [train.py:451] Epoch 1, batch 9230, batch avg loss 0.3973, total avg loss: 0.3112, batch size: 132
2021-10-13 21:48:53,499 INFO [train.py:451] Epoch 1, batch 9240, batch avg loss 0.3665, total avg loss: 0.3121, batch size: 45
2021-10-13 21:48:58,755 INFO [train.py:451] Epoch 1, batch 9250, batch avg loss 0.3190, total avg loss: 0.3157, batch size: 35
2021-10-13 21:49:03,533 INFO [train.py:451] Epoch 1, batch 9260, batch avg loss 0.3767, total avg loss: 0.3178, batch size: 34
2021-10-13 21:49:08,334 INFO [train.py:451] Epoch 1, batch 9270, batch avg loss 0.3292, total avg loss: 0.3197, batch size: 42
2021-10-13 21:49:13,232 INFO [train.py:451] Epoch 1, batch 9280, batch avg loss 0.3909, total avg loss: 0.3239, batch size: 36
2021-10-13 21:49:18,108 INFO [train.py:451] Epoch 1, batch 9290, batch avg loss 0.3261, total avg loss: 0.3230, batch size: 42
2021-10-13 21:49:23,027 INFO [train.py:451] Epoch 1, batch 9300, batch avg loss 0.3051, total avg loss: 0.3215, batch size: 34
2021-10-13 21:49:28,204 INFO [train.py:451] Epoch 1, batch 9310, batch avg loss 0.2578, total avg loss: 0.3195, batch size: 31
2021-10-13 21:49:33,139 INFO [train.py:451] Epoch 1, batch 9320, batch avg loss 0.3322, total avg loss: 0.3182, batch size: 49
2021-10-13 21:49:37,954 INFO [train.py:451] Epoch 1, batch 9330, batch avg loss 0.3684, total avg loss: 0.3190, batch size: 35
2021-10-13 21:49:43,010 INFO [train.py:451] Epoch 1, batch 9340, batch avg loss 0.2896, total avg loss: 0.3180, batch size: 27
2021-10-13 21:49:47,989 INFO [train.py:451] Epoch 1, batch 9350, batch avg loss 0.3370, total avg loss: 0.3172, batch size: 49
2021-10-13 21:49:52,989 INFO [train.py:451] Epoch 1, batch 9360, batch avg loss 0.3389, total avg loss: 0.3161, batch size: 45
2021-10-13 21:49:58,087 INFO [train.py:451] Epoch 1, batch 9370, batch avg loss 0.3338, total avg loss: 0.3160, batch size: 38
2021-10-13 21:50:03,136 INFO [train.py:451] Epoch 1, batch 9380, batch avg loss 0.3042, total avg loss: 0.3166, batch size: 36
2021-10-13 21:50:08,088 INFO [train.py:451] Epoch 1, batch 9390, batch avg loss 0.3008, total avg loss: 0.3169, batch size: 35
2021-10-13 21:50:13,132 INFO [train.py:451] Epoch 1, batch 9400, batch avg loss 0.3138, total avg loss: 0.3171, batch size: 37
2021-10-13 21:50:17,898 INFO [train.py:451] Epoch 1, batch 9410, batch avg loss 0.3431, total avg loss: 0.3023, batch size: 38
2021-10-13 21:50:22,787 INFO [train.py:451] Epoch 1, batch 9420, batch avg loss 0.3068, total avg loss: 0.3026, batch size: 35
2021-10-13 21:50:27,683 INFO [train.py:451] Epoch 1, batch 9430, batch avg loss 0.2297, total avg loss: 0.2994, batch size: 31
2021-10-13 21:50:32,640 INFO [train.py:451] Epoch 1, batch 9440, batch avg loss 0.2671, total avg loss: 0.3042, batch size: 29
2021-10-13 21:50:37,432 INFO [train.py:451] Epoch 1, batch 9450, batch avg loss 0.3094, total avg loss: 0.3064, batch size: 45
2021-10-13 21:50:42,513 INFO [train.py:451] Epoch 1, batch 9460, batch avg loss 0.3210, total avg loss: 0.3081, batch size: 34
2021-10-13 21:50:47,281 INFO [train.py:451] Epoch 1, batch 9470, batch avg loss 0.3287, total avg loss: 0.3134, batch size: 30
2021-10-13 21:50:52,220 INFO [train.py:451] Epoch 1, batch 9480, batch avg loss 0.2954, total avg loss: 0.3116, batch size: 41
2021-10-13 21:50:57,207 INFO [train.py:451] Epoch 1, batch 9490, batch avg loss 0.3734, total avg loss: 0.3115, batch size: 71
2021-10-13 21:51:02,026 INFO [train.py:451] Epoch 1, batch 9500, batch avg loss 0.3263, total avg loss: 0.3130, batch size: 34
2021-10-13 21:51:06,986 INFO [train.py:451] Epoch 1, batch 9510, batch avg loss 0.2799, total avg loss: 0.3110, batch size: 29
2021-10-13 21:51:11,799 INFO [train.py:451] Epoch 1, batch 9520, batch avg loss 0.2943, total avg loss: 0.3105, batch size: 35
2021-10-13 21:51:16,633 INFO [train.py:451] Epoch 1, batch 9530, batch avg loss 0.3598, total avg loss: 0.3130, batch size: 73
2021-10-13 21:51:21,567 INFO [train.py:451] Epoch 1, batch 9540, batch avg loss 0.3186, total avg loss: 0.3118, batch size: 32
2021-10-13 21:51:26,303 INFO [train.py:451] Epoch 1, batch 9550, batch avg loss 0.2989, total avg loss: 0.3134, batch size: 38
2021-10-13 21:51:31,027 INFO [train.py:451] Epoch 1, batch 9560, batch avg loss 0.2819, total avg loss: 0.3143, batch size: 30
2021-10-13 21:51:35,738 INFO [train.py:451] Epoch 1, batch 9570, batch avg loss 0.3395, total avg loss: 0.3156, batch size: 72
2021-10-13 21:51:40,673 INFO [train.py:451] Epoch 1, batch 9580, batch avg loss 0.3265, total avg loss: 0.3158, batch size: 44
2021-10-13 21:51:45,487 INFO [train.py:451] Epoch 1, batch 9590, batch avg loss 0.3862, total avg loss: 0.3164, batch size: 73
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2021-10-13 21:52:10,097 INFO [train.py:451] Epoch 1, batch 9640, batch avg loss 0.2761, total avg loss: 0.3108, batch size: 35
2021-10-13 21:52:14,932 INFO [train.py:451] Epoch 1, batch 9650, batch avg loss 0.3328, total avg loss: 0.3153, batch size: 34
2021-10-13 21:52:20,114 INFO [train.py:451] Epoch 1, batch 9660, batch avg loss 0.3118, total avg loss: 0.3126, batch size: 33
2021-10-13 21:52:25,167 INFO [train.py:451] Epoch 1, batch 9670, batch avg loss 0.3149, total avg loss: 0.3137, batch size: 39
2021-10-13 21:52:30,115 INFO [train.py:451] Epoch 1, batch 9680, batch avg loss 0.3204, total avg loss: 0.3134, batch size: 49
2021-10-13 21:52:35,021 INFO [train.py:451] Epoch 1, batch 9690, batch avg loss 0.2601, total avg loss: 0.3127, batch size: 27
2021-10-13 21:52:40,040 INFO [train.py:451] Epoch 1, batch 9700, batch avg loss 0.2528, total avg loss: 0.3113, batch size: 32
2021-10-13 21:52:44,965 INFO [train.py:451] Epoch 1, batch 9710, batch avg loss 0.3137, total avg loss: 0.3107, batch size: 29
2021-10-13 21:52:49,958 INFO [train.py:451] Epoch 1, batch 9720, batch avg loss 0.3840, total avg loss: 0.3111, batch size: 73
2021-10-13 21:52:54,973 INFO [train.py:451] Epoch 1, batch 9730, batch avg loss 0.3002, total avg loss: 0.3090, batch size: 31
2021-10-13 21:52:59,564 INFO [train.py:451] Epoch 1, batch 9740, batch avg loss 0.2756, total avg loss: 0.3103, batch size: 41
2021-10-13 21:53:04,354 INFO [train.py:451] Epoch 1, batch 9750, batch avg loss 0.2324, total avg loss: 0.3117, batch size: 33
2021-10-13 21:53:08,977 INFO [train.py:451] Epoch 1, batch 9760, batch avg loss 0.3142, total avg loss: 0.3129, batch size: 34
2021-10-13 21:53:13,898 INFO [train.py:451] Epoch 1, batch 9770, batch avg loss 0.3312, total avg loss: 0.3141, batch size: 34
2021-10-13 21:53:18,775 INFO [train.py:451] Epoch 1, batch 9780, batch avg loss 0.3459, total avg loss: 0.3150, batch size: 34
2021-10-13 21:53:23,797 INFO [train.py:451] Epoch 1, batch 9790, batch avg loss 0.2533, total avg loss: 0.3154, batch size: 33
2021-10-13 21:53:28,607 INFO [train.py:451] Epoch 1, batch 9800, batch avg loss 0.2713, total avg loss: 0.3146, batch size: 32
2021-10-13 21:53:33,360 INFO [train.py:451] Epoch 1, batch 9810, batch avg loss 0.2964, total avg loss: 0.3275, batch size: 38
2021-10-13 21:53:38,269 INFO [train.py:451] Epoch 1, batch 9820, batch avg loss 0.3771, total avg loss: 0.3234, batch size: 39
2021-10-13 21:53:43,175 INFO [train.py:451] Epoch 1, batch 9830, batch avg loss 0.2868, total avg loss: 0.3199, batch size: 31
2021-10-13 21:53:48,316 INFO [train.py:451] Epoch 1, batch 9840, batch avg loss 0.2575, total avg loss: 0.3112, batch size: 31
2021-10-13 21:53:53,245 INFO [train.py:451] Epoch 1, batch 9850, batch avg loss 0.4528, total avg loss: 0.3180, batch size: 127
2021-10-13 21:53:58,217 INFO [train.py:451] Epoch 1, batch 9860, batch avg loss 0.3310, total avg loss: 0.3138, batch size: 49
2021-10-13 21:54:03,156 INFO [train.py:451] Epoch 1, batch 9870, batch avg loss 0.3115, total avg loss: 0.3139, batch size: 36
2021-10-13 21:54:07,827 INFO [train.py:451] Epoch 1, batch 9880, batch avg loss 0.3667, total avg loss: 0.3129, batch size: 42
2021-10-13 21:54:12,550 INFO [train.py:451] Epoch 1, batch 9890, batch avg loss 0.3517, total avg loss: 0.3163, batch size: 36
2021-10-13 21:54:17,464 INFO [train.py:451] Epoch 1, batch 9900, batch avg loss 0.3480, total avg loss: 0.3162, batch size: 72
2021-10-13 21:54:22,426 INFO [train.py:451] Epoch 1, batch 9910, batch avg loss 0.3102, total avg loss: 0.3158, batch size: 45
2021-10-13 21:54:27,391 INFO [train.py:451] Epoch 1, batch 9920, batch avg loss 0.2966, total avg loss: 0.3161, batch size: 36
2021-10-13 21:54:32,029 INFO [train.py:451] Epoch 1, batch 9930, batch avg loss 0.2678, total avg loss: 0.3179, batch size: 33
2021-10-13 21:54:36,992 INFO [train.py:451] Epoch 1, batch 9940, batch avg loss 0.3259, total avg loss: 0.3175, batch size: 49
2021-10-13 21:54:41,914 INFO [train.py:451] Epoch 1, batch 9950, batch avg loss 0.3119, total avg loss: 0.3164, batch size: 34
2021-10-13 21:54:46,782 INFO [train.py:451] Epoch 1, batch 9960, batch avg loss 0.3475, total avg loss: 0.3163, batch size: 36
2021-10-13 21:54:51,649 INFO [train.py:451] Epoch 1, batch 9970, batch avg loss 0.3564, total avg loss: 0.3154, batch size: 42
2021-10-13 21:54:56,684 INFO [train.py:451] Epoch 1, batch 9980, batch avg loss 0.3012, total avg loss: 0.3151, batch size: 34
2021-10-13 21:55:01,673 INFO [train.py:451] Epoch 1, batch 9990, batch avg loss 0.2572, total avg loss: 0.3148, batch size: 31
2021-10-13 21:55:06,796 INFO [train.py:451] Epoch 1, batch 10000, batch avg loss 0.3770, total avg loss: 0.3151, batch size: 38
2021-10-13 21:55:44,730 INFO [train.py:483] Epoch 1, valid loss 0.2253, best valid loss: 0.2237 best valid epoch: 1
2021-10-13 21:55:49,453 INFO [train.py:451] Epoch 1, batch 10010, batch avg loss 0.3258, total avg loss: 0.3227, batch size: 38
2021-10-13 21:55:54,322 INFO [train.py:451] Epoch 1, batch 10020, batch avg loss 0.3452, total avg loss: 0.3085, batch size: 42
2021-10-13 21:55:59,294 INFO [train.py:451] Epoch 1, batch 10030, batch avg loss 0.2915, total avg loss: 0.3067, batch size: 31
2021-10-13 21:56:04,213 INFO [train.py:451] Epoch 1, batch 10040, batch avg loss 0.3434, total avg loss: 0.3074, batch size: 39
2021-10-13 21:56:09,245 INFO [train.py:451] Epoch 1, batch 10050, batch avg loss 0.2359, total avg loss: 0.3040, batch size: 29
2021-10-13 21:56:14,104 INFO [train.py:451] Epoch 1, batch 10060, batch avg loss 0.2880, total avg loss: 0.3023, batch size: 45
2021-10-13 21:56:19,027 INFO [train.py:451] Epoch 1, batch 10070, batch avg loss 0.2288, total avg loss: 0.2999, batch size: 29
2021-10-13 21:56:23,880 INFO [train.py:451] Epoch 1, batch 10080, batch avg loss 0.2658, total avg loss: 0.3014, batch size: 29
2021-10-13 21:56:28,816 INFO [train.py:451] Epoch 1, batch 10090, batch avg loss 0.2660, total avg loss: 0.3023, batch size: 29
2021-10-13 21:56:33,918 INFO [train.py:451] Epoch 1, batch 10100, batch avg loss 0.2774, total avg loss: 0.3022, batch size: 31
2021-10-13 21:56:38,811 INFO [train.py:451] Epoch 1, batch 10110, batch avg loss 0.2609, total avg loss: 0.3019, batch size: 38
2021-10-13 21:56:43,854 INFO [train.py:451] Epoch 1, batch 10120, batch avg loss 0.3561, total avg loss: 0.3032, batch size: 49
2021-10-13 21:56:48,829 INFO [train.py:451] Epoch 1, batch 10130, batch avg loss 0.3707, total avg loss: 0.3039, batch size: 73
2021-10-13 21:56:53,707 INFO [train.py:451] Epoch 1, batch 10140, batch avg loss 0.3136, total avg loss: 0.3039, batch size: 49
2021-10-13 21:56:58,449 INFO [train.py:451] Epoch 1, batch 10150, batch avg loss 0.2606, total avg loss: 0.3049, batch size: 34
2021-10-13 21:57:03,430 INFO [train.py:451] Epoch 1, batch 10160, batch avg loss 0.3282, total avg loss: 0.3048, batch size: 37
2021-10-13 21:57:08,325 INFO [train.py:451] Epoch 1, batch 10170, batch avg loss 0.2522, total avg loss: 0.3053, batch size: 31
2021-10-13 21:57:13,419 INFO [train.py:451] Epoch 1, batch 10180, batch avg loss 0.3596, total avg loss: 0.3056, batch size: 38
2021-10-13 21:57:18,352 INFO [train.py:451] Epoch 1, batch 10190, batch avg loss 0.3956, total avg loss: 0.3057, batch size: 32
2021-10-13 21:57:23,213 INFO [train.py:451] Epoch 1, batch 10200, batch avg loss 0.2913, total avg loss: 0.3056, batch size: 41
2021-10-13 21:57:28,201 INFO [train.py:451] Epoch 1, batch 10210, batch avg loss 0.3753, total avg loss: 0.3148, batch size: 72
2021-10-13 21:57:33,104 INFO [train.py:451] Epoch 1, batch 10220, batch avg loss 0.3134, total avg loss: 0.3069, batch size: 49
2021-10-13 21:57:38,191 INFO [train.py:451] Epoch 1, batch 10230, batch avg loss 0.3587, total avg loss: 0.3070, batch size: 30
2021-10-13 21:57:43,057 INFO [train.py:451] Epoch 1, batch 10240, batch avg loss 0.2974, total avg loss: 0.3084, batch size: 30
2021-10-13 21:57:48,043 INFO [train.py:451] Epoch 1, batch 10250, batch avg loss 0.3359, total avg loss: 0.3066, batch size: 45
2021-10-13 21:57:52,881 INFO [train.py:451] Epoch 1, batch 10260, batch avg loss 0.2748, total avg loss: 0.3064, batch size: 31
2021-10-13 21:57:57,681 INFO [train.py:451] Epoch 1, batch 10270, batch avg loss 0.2723, total avg loss: 0.3074, batch size: 33
2021-10-13 21:58:02,654 INFO [train.py:451] Epoch 1, batch 10280, batch avg loss 0.3347, total avg loss: 0.3058, batch size: 41
2021-10-13 21:58:07,358 INFO [train.py:451] Epoch 1, batch 10290, batch avg loss 0.2676, total avg loss: 0.3110, batch size: 32
2021-10-13 21:58:12,179 INFO [train.py:451] Epoch 1, batch 10300, batch avg loss 0.3034, total avg loss: 0.3121, batch size: 34
2021-10-13 21:58:17,278 INFO [train.py:451] Epoch 1, batch 10310, batch avg loss 0.2733, total avg loss: 0.3093, batch size: 32
2021-10-13 21:58:22,118 INFO [train.py:451] Epoch 1, batch 10320, batch avg loss 0.3097, total avg loss: 0.3112, batch size: 42
2021-10-13 21:58:26,837 INFO [train.py:451] Epoch 1, batch 10330, batch avg loss 0.2664, total avg loss: 0.3123, batch size: 31
2021-10-13 21:58:31,786 INFO [train.py:451] Epoch 1, batch 10340, batch avg loss 0.2841, total avg loss: 0.3111, batch size: 35
2021-10-13 21:58:36,745 INFO [train.py:451] Epoch 1, batch 10350, batch avg loss 0.3240, total avg loss: 0.3093, batch size: 34
2021-10-13 21:58:41,680 INFO [train.py:451] Epoch 1, batch 10360, batch avg loss 0.3716, total avg loss: 0.3094, batch size: 34
2021-10-13 21:58:46,548 INFO [train.py:451] Epoch 1, batch 10370, batch avg loss 0.2916, total avg loss: 0.3080, batch size: 30
2021-10-13 21:58:51,438 INFO [train.py:451] Epoch 1, batch 10380, batch avg loss 0.2486, total avg loss: 0.3072, batch size: 31
2021-10-13 21:58:56,275 INFO [train.py:451] Epoch 1, batch 10390, batch avg loss 0.3075, total avg loss: 0.3071, batch size: 36
2021-10-13 21:59:00,907 INFO [train.py:451] Epoch 1, batch 10400, batch avg loss 0.3338, total avg loss: 0.3089, batch size: 35
2021-10-13 21:59:05,764 INFO [train.py:451] Epoch 1, batch 10410, batch avg loss 0.3469, total avg loss: 0.3256, batch size: 49
2021-10-13 21:59:10,645 INFO [train.py:451] Epoch 1, batch 10420, batch avg loss 0.2759, total avg loss: 0.3227, batch size: 36
2021-10-13 21:59:15,431 INFO [train.py:451] Epoch 1, batch 10430, batch avg loss 0.3332, total avg loss: 0.3208, batch size: 31
2021-10-13 21:59:20,288 INFO [train.py:451] Epoch 1, batch 10440, batch avg loss 0.3639, total avg loss: 0.3213, batch size: 73
2021-10-13 21:59:25,455 INFO [train.py:451] Epoch 1, batch 10450, batch avg loss 0.2401, total avg loss: 0.3163, batch size: 29
2021-10-13 21:59:30,246 INFO [train.py:451] Epoch 1, batch 10460, batch avg loss 0.3256, total avg loss: 0.3164, batch size: 43
2021-10-13 21:59:35,327 INFO [train.py:451] Epoch 1, batch 10470, batch avg loss 0.3061, total avg loss: 0.3144, batch size: 34
2021-10-13 21:59:40,457 INFO [train.py:451] Epoch 1, batch 10480, batch avg loss 0.2859, total avg loss: 0.3143, batch size: 35
2021-10-13 21:59:45,353 INFO [train.py:451] Epoch 1, batch 10490, batch avg loss 0.2456, total avg loss: 0.3130, batch size: 30
2021-10-13 21:59:50,216 INFO [train.py:451] Epoch 1, batch 10500, batch avg loss 0.2622, total avg loss: 0.3124, batch size: 29
2021-10-13 21:59:54,852 INFO [train.py:451] Epoch 1, batch 10510, batch avg loss 0.3868, total avg loss: 0.3134, batch size: 74
2021-10-13 21:59:59,897 INFO [train.py:451] Epoch 1, batch 10520, batch avg loss 0.3495, total avg loss: 0.3136, batch size: 38
2021-10-13 22:00:04,759 INFO [train.py:451] Epoch 1, batch 10530, batch avg loss 0.3459, total avg loss: 0.3139, batch size: 42
2021-10-13 22:00:09,620 INFO [train.py:451] Epoch 1, batch 10540, batch avg loss 0.4004, total avg loss: 0.3140, batch size: 125
2021-10-13 22:00:14,563 INFO [train.py:451] Epoch 1, batch 10550, batch avg loss 0.3375, total avg loss: 0.3144, batch size: 38
2021-10-13 22:00:19,267 INFO [train.py:451] Epoch 1, batch 10560, batch avg loss 0.3585, total avg loss: 0.3145, batch size: 74
2021-10-13 22:00:24,293 INFO [train.py:451] Epoch 1, batch 10570, batch avg loss 0.3116, total avg loss: 0.3140, batch size: 57
2021-10-13 22:00:29,010 INFO [train.py:451] Epoch 1, batch 10580, batch avg loss 0.3182, total avg loss: 0.3157, batch size: 34
2021-10-13 22:00:33,732 INFO [train.py:451] Epoch 1, batch 10590, batch avg loss 0.2635, total avg loss: 0.3158, batch size: 32
2021-10-13 22:00:38,659 INFO [train.py:451] Epoch 1, batch 10600, batch avg loss 0.4431, total avg loss: 0.3156, batch size: 129
2021-10-13 22:00:43,593 INFO [train.py:451] Epoch 1, batch 10610, batch avg loss 0.2845, total avg loss: 0.3103, batch size: 33
2021-10-13 22:00:48,575 INFO [train.py:451] Epoch 1, batch 10620, batch avg loss 0.3714, total avg loss: 0.3071, batch size: 45
2021-10-13 22:00:53,442 INFO [train.py:451] Epoch 1, batch 10630, batch avg loss 0.2771, total avg loss: 0.3090, batch size: 30
2021-10-13 22:00:58,459 INFO [train.py:451] Epoch 1, batch 10640, batch avg loss 0.2988, total avg loss: 0.3052, batch size: 29
2021-10-13 22:01:03,285 INFO [train.py:451] Epoch 1, batch 10650, batch avg loss 0.3422, total avg loss: 0.3142, batch size: 34
2021-10-13 22:01:08,224 INFO [train.py:451] Epoch 1, batch 10660, batch avg loss 0.3474, total avg loss: 0.3172, batch size: 37
2021-10-13 22:01:13,173 INFO [train.py:451] Epoch 1, batch 10670, batch avg loss 0.2819, total avg loss: 0.3164, batch size: 32
2021-10-13 22:01:18,299 INFO [train.py:451] Epoch 1, batch 10680, batch avg loss 0.2686, total avg loss: 0.3138, batch size: 29
2021-10-13 22:01:23,416 INFO [train.py:451] Epoch 1, batch 10690, batch avg loss 0.2605, total avg loss: 0.3125, batch size: 31
2021-10-13 22:01:28,409 INFO [train.py:451] Epoch 1, batch 10700, batch avg loss 0.2614, total avg loss: 0.3102, batch size: 28
2021-10-13 22:01:33,183 INFO [train.py:451] Epoch 1, batch 10710, batch avg loss 0.2700, total avg loss: 0.3106, batch size: 34
2021-10-13 22:01:38,217 INFO [train.py:451] Epoch 1, batch 10720, batch avg loss 0.3050, total avg loss: 0.3102, batch size: 34
2021-10-13 22:01:43,087 INFO [train.py:451] Epoch 1, batch 10730, batch avg loss 0.3844, total avg loss: 0.3116, batch size: 34
2021-10-13 22:01:47,915 INFO [train.py:451] Epoch 1, batch 10740, batch avg loss 0.3572, total avg loss: 0.3131, batch size: 34
2021-10-13 22:01:52,606 INFO [train.py:451] Epoch 1, batch 10750, batch avg loss 0.3366, total avg loss: 0.3143, batch size: 73
2021-10-13 22:01:57,571 INFO [train.py:451] Epoch 1, batch 10760, batch avg loss 0.3155, total avg loss: 0.3145, batch size: 29
2021-10-13 22:02:02,435 INFO [train.py:451] Epoch 1, batch 10770, batch avg loss 0.2061, total avg loss: 0.3137, batch size: 32
2021-10-13 22:02:07,347 INFO [train.py:451] Epoch 1, batch 10780, batch avg loss 0.3803, total avg loss: 0.3153, batch size: 35
2021-10-13 22:02:12,364 INFO [train.py:451] Epoch 1, batch 10790, batch avg loss 0.4099, total avg loss: 0.3147, batch size: 38
2021-10-13 22:02:17,235 INFO [train.py:451] Epoch 1, batch 10800, batch avg loss 0.2784, total avg loss: 0.3148, batch size: 33
2021-10-13 22:02:21,998 INFO [train.py:451] Epoch 1, batch 10810, batch avg loss 0.3555, total avg loss: 0.3354, batch size: 45
2021-10-13 22:02:26,918 INFO [train.py:451] Epoch 1, batch 10820, batch avg loss 0.3747, total avg loss: 0.3230, batch size: 57
2021-10-13 22:02:31,864 INFO [train.py:451] Epoch 1, batch 10830, batch avg loss 0.2745, total avg loss: 0.3223, batch size: 35
2021-10-13 22:02:36,917 INFO [train.py:451] Epoch 1, batch 10840, batch avg loss 0.2494, total avg loss: 0.3150, batch size: 32
2021-10-13 22:02:41,840 INFO [train.py:451] Epoch 1, batch 10850, batch avg loss 0.3178, total avg loss: 0.3129, batch size: 45
2021-10-13 22:02:46,784 INFO [train.py:451] Epoch 1, batch 10860, batch avg loss 0.2613, total avg loss: 0.3141, batch size: 28
2021-10-13 22:02:51,656 INFO [train.py:451] Epoch 1, batch 10870, batch avg loss 0.2799, total avg loss: 0.3127, batch size: 35
2021-10-13 22:02:56,659 INFO [train.py:451] Epoch 1, batch 10880, batch avg loss 0.2455, total avg loss: 0.3114, batch size: 29
2021-10-13 22:03:01,804 INFO [train.py:451] Epoch 1, batch 10890, batch avg loss 0.2961, total avg loss: 0.3118, batch size: 36
2021-10-13 22:03:06,595 INFO [train.py:451] Epoch 1, batch 10900, batch avg loss 0.4063, total avg loss: 0.3128, batch size: 122
2021-10-13 22:03:11,508 INFO [train.py:451] Epoch 1, batch 10910, batch avg loss 0.3339, total avg loss: 0.3111, batch size: 31
2021-10-13 22:03:16,379 INFO [train.py:451] Epoch 1, batch 10920, batch avg loss 0.3193, total avg loss: 0.3133, batch size: 38
2021-10-13 22:03:21,248 INFO [train.py:451] Epoch 1, batch 10930, batch avg loss 0.3704, total avg loss: 0.3152, batch size: 36
2021-10-13 22:03:26,325 INFO [train.py:451] Epoch 1, batch 10940, batch avg loss 0.3017, total avg loss: 0.3138, batch size: 36
2021-10-13 22:03:31,255 INFO [train.py:451] Epoch 1, batch 10950, batch avg loss 0.4540, total avg loss: 0.3140, batch size: 130
2021-10-13 22:03:36,284 INFO [train.py:451] Epoch 1, batch 10960, batch avg loss 0.2585, total avg loss: 0.3137, batch size: 30
2021-10-13 22:03:41,285 INFO [train.py:451] Epoch 1, batch 10970, batch avg loss 0.2693, total avg loss: 0.3126, batch size: 35
2021-10-13 22:03:46,031 INFO [train.py:451] Epoch 1, batch 10980, batch avg loss 0.3507, total avg loss: 0.3138, batch size: 49
2021-10-13 22:03:50,850 INFO [train.py:451] Epoch 1, batch 10990, batch avg loss 0.2523, total avg loss: 0.3139, batch size: 31
2021-10-13 22:03:56,050 INFO [train.py:451] Epoch 1, batch 11000, batch avg loss 0.2432, total avg loss: 0.3130, batch size: 30
2021-10-13 22:04:36,242 INFO [train.py:483] Epoch 1, valid loss 0.2201, best valid loss: 0.2201 best valid epoch: 1
2021-10-13 22:04:41,385 INFO [train.py:451] Epoch 1, batch 11010, batch avg loss 0.3070, total avg loss: 0.3003, batch size: 29
2021-10-13 22:04:46,332 INFO [train.py:451] Epoch 1, batch 11020, batch avg loss 0.3287, total avg loss: 0.3166, batch size: 30
2021-10-13 22:04:51,289 INFO [train.py:451] Epoch 1, batch 11030, batch avg loss 0.3269, total avg loss: 0.3111, batch size: 41
2021-10-13 22:04:56,309 INFO [train.py:451] Epoch 1, batch 11040, batch avg loss 0.2344, total avg loss: 0.3085, batch size: 28
2021-10-13 22:05:01,540 INFO [train.py:451] Epoch 1, batch 11050, batch avg loss 0.2799, total avg loss: 0.3060, batch size: 31
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2021-10-13 22:11:40,875 INFO [train.py:451] Epoch 1, batch 11840, batch avg loss 0.3023, total avg loss: 0.3117, batch size: 33
2021-10-13 22:11:45,828 INFO [train.py:451] Epoch 1, batch 11850, batch avg loss 0.3104, total avg loss: 0.3134, batch size: 38
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2021-10-13 22:13:00,465 INFO [train.py:451] Epoch 1, batch 12000, batch avg loss 0.3770, total avg loss: 0.3114, batch size: 36
2021-10-13 22:13:39,741 INFO [train.py:483] Epoch 1, valid loss 0.2195, best valid loss: 0.2195 best valid epoch: 1
2021-10-13 22:13:44,812 INFO [train.py:451] Epoch 1, batch 12010, batch avg loss 0.3077, total avg loss: 0.2908, batch size: 28
2021-10-13 22:13:49,631 INFO [train.py:451] Epoch 1, batch 12020, batch avg loss 0.3005, total avg loss: 0.3160, batch size: 33
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2021-10-13 22:15:43,417 INFO [train.py:451] Epoch 1, batch 12250, batch avg loss 0.3523, total avg loss: 0.3120, batch size: 28
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2021-10-13 22:15:53,699 INFO [train.py:451] Epoch 1, batch 12270, batch avg loss 0.3102, total avg loss: 0.3085, batch size: 31
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2021-10-13 22:16:03,809 INFO [train.py:451] Epoch 1, batch 12290, batch avg loss 0.3220, total avg loss: 0.3091, batch size: 34
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2021-10-13 22:18:36,640 INFO [train.py:451] Epoch 1, batch 12600, batch avg loss 0.3177, total avg loss: 0.3141, batch size: 57
2021-10-13 22:18:41,581 INFO [train.py:451] Epoch 1, batch 12610, batch avg loss 0.2681, total avg loss: 0.2810, batch size: 30
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2021-10-13 22:18:56,334 INFO [train.py:451] Epoch 1, batch 12640, batch avg loss 0.2774, total avg loss: 0.3014, batch size: 32
2021-10-13 22:19:01,039 INFO [train.py:451] Epoch 1, batch 12650, batch avg loss 0.3209, total avg loss: 0.3044, batch size: 73
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2021-10-13 22:19:21,178 INFO [train.py:451] Epoch 1, batch 12690, batch avg loss 0.2623, total avg loss: 0.3047, batch size: 30
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2021-10-13 22:19:31,159 INFO [train.py:451] Epoch 1, batch 12710, batch avg loss 0.3032, total avg loss: 0.3052, batch size: 38
2021-10-13 22:19:35,888 INFO [train.py:451] Epoch 1, batch 12720, batch avg loss 0.4094, total avg loss: 0.3055, batch size: 130
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2021-10-13 22:19:45,803 INFO [train.py:451] Epoch 1, batch 12740, batch avg loss 0.2895, total avg loss: 0.3040, batch size: 33
2021-10-13 22:19:50,778 INFO [train.py:451] Epoch 1, batch 12750, batch avg loss 0.2484, total avg loss: 0.3022, batch size: 27
2021-10-13 22:19:55,699 INFO [train.py:451] Epoch 1, batch 12760, batch avg loss 0.3387, total avg loss: 0.3023, batch size: 39
2021-10-13 22:20:00,696 INFO [train.py:451] Epoch 1, batch 12770, batch avg loss 0.2541, total avg loss: 0.3009, batch size: 29
2021-10-13 22:20:05,561 INFO [train.py:451] Epoch 1, batch 12780, batch avg loss 0.2317, total avg loss: 0.3008, batch size: 33
2021-10-13 22:20:10,640 INFO [train.py:451] Epoch 1, batch 12790, batch avg loss 0.2260, total avg loss: 0.3002, batch size: 34
2021-10-13 22:20:15,578 INFO [train.py:451] Epoch 1, batch 12800, batch avg loss 0.2941, total avg loss: 0.3002, batch size: 38
2021-10-13 22:20:20,526 INFO [train.py:451] Epoch 1, batch 12810, batch avg loss 0.2615, total avg loss: 0.3010, batch size: 31
2021-10-13 22:20:25,361 INFO [train.py:451] Epoch 1, batch 12820, batch avg loss 0.4369, total avg loss: 0.3196, batch size: 132
2021-10-13 22:20:30,483 INFO [train.py:451] Epoch 1, batch 12830, batch avg loss 0.2622, total avg loss: 0.3179, batch size: 32
2021-10-13 22:20:35,474 INFO [train.py:451] Epoch 1, batch 12840, batch avg loss 0.3159, total avg loss: 0.3185, batch size: 31
2021-10-13 22:20:40,421 INFO [train.py:451] Epoch 1, batch 12850, batch avg loss 0.3126, total avg loss: 0.3141, batch size: 31
2021-10-13 22:20:45,277 INFO [train.py:451] Epoch 1, batch 12860, batch avg loss 0.3125, total avg loss: 0.3135, batch size: 41
2021-10-13 22:20:50,172 INFO [train.py:451] Epoch 1, batch 12870, batch avg loss 0.2536, total avg loss: 0.3134, batch size: 31
2021-10-13 22:20:55,130 INFO [train.py:451] Epoch 1, batch 12880, batch avg loss 0.2464, total avg loss: 0.3125, batch size: 31
2021-10-13 22:20:59,874 INFO [train.py:451] Epoch 1, batch 12890, batch avg loss 0.3708, total avg loss: 0.3119, batch size: 57
2021-10-13 22:21:04,768 INFO [train.py:451] Epoch 1, batch 12900, batch avg loss 0.2791, total avg loss: 0.3116, batch size: 33
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2021-10-13 22:21:14,498 INFO [train.py:451] Epoch 1, batch 12920, batch avg loss 0.2644, total avg loss: 0.3120, batch size: 34
2021-10-13 22:21:19,283 INFO [train.py:451] Epoch 1, batch 12930, batch avg loss 0.2989, total avg loss: 0.3113, batch size: 30
2021-10-13 22:21:24,322 INFO [train.py:451] Epoch 1, batch 12940, batch avg loss 0.2928, total avg loss: 0.3095, batch size: 35
2021-10-13 22:21:29,252 INFO [train.py:451] Epoch 1, batch 12950, batch avg loss 0.2795, total avg loss: 0.3090, batch size: 30
2021-10-13 22:21:34,359 INFO [train.py:451] Epoch 1, batch 12960, batch avg loss 0.3579, total avg loss: 0.3087, batch size: 36
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2021-10-13 22:21:44,344 INFO [train.py:451] Epoch 1, batch 12980, batch avg loss 0.2228, total avg loss: 0.3069, batch size: 28
2021-10-13 22:21:49,191 INFO [train.py:451] Epoch 1, batch 12990, batch avg loss 0.2783, total avg loss: 0.3070, batch size: 35
2021-10-13 22:21:54,186 INFO [train.py:451] Epoch 1, batch 13000, batch avg loss 0.3052, total avg loss: 0.3085, batch size: 32
2021-10-13 22:22:34,583 INFO [train.py:483] Epoch 1, valid loss 0.2186, best valid loss: 0.2186 best valid epoch: 1
2021-10-13 22:22:39,835 INFO [train.py:451] Epoch 1, batch 13010, batch avg loss 0.2659, total avg loss: 0.3035, batch size: 34
2021-10-13 22:22:44,880 INFO [train.py:451] Epoch 1, batch 13020, batch avg loss 0.3001, total avg loss: 0.2937, batch size: 39
2021-10-13 22:22:49,706 INFO [train.py:451] Epoch 1, batch 13030, batch avg loss 0.2412, total avg loss: 0.2964, batch size: 29
2021-10-13 22:22:54,644 INFO [train.py:451] Epoch 1, batch 13040, batch avg loss 0.3160, total avg loss: 0.2990, batch size: 56
2021-10-13 22:22:59,658 INFO [train.py:451] Epoch 1, batch 13050, batch avg loss 0.3173, total avg loss: 0.2994, batch size: 31
2021-10-13 22:23:04,911 INFO [train.py:451] Epoch 1, batch 13060, batch avg loss 0.2355, total avg loss: 0.2948, batch size: 29
2021-10-13 22:23:09,925 INFO [train.py:451] Epoch 1, batch 13070, batch avg loss 0.4062, total avg loss: 0.2975, batch size: 42
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2021-10-13 22:23:45,310 INFO [train.py:451] Epoch 1, batch 13140, batch avg loss 0.4261, total avg loss: 0.2982, batch size: 130
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2021-10-13 22:24:00,223 INFO [train.py:451] Epoch 1, batch 13170, batch avg loss 0.3812, total avg loss: 0.3002, batch size: 36
2021-10-13 22:24:05,087 INFO [train.py:451] Epoch 1, batch 13180, batch avg loss 0.3696, total avg loss: 0.3017, batch size: 41
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2021-10-13 22:24:14,931 INFO [train.py:451] Epoch 1, batch 13200, batch avg loss 0.3247, total avg loss: 0.3029, batch size: 31
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2021-10-13 22:24:34,887 INFO [train.py:451] Epoch 1, batch 13240, batch avg loss 0.2938, total avg loss: 0.3166, batch size: 32
2021-10-13 22:24:39,835 INFO [train.py:451] Epoch 1, batch 13250, batch avg loss 0.3068, total avg loss: 0.3156, batch size: 31
2021-10-13 22:24:45,001 INFO [train.py:451] Epoch 1, batch 13260, batch avg loss 0.3153, total avg loss: 0.3150, batch size: 33
2021-10-13 22:24:49,810 INFO [train.py:451] Epoch 1, batch 13270, batch avg loss 0.3517, total avg loss: 0.3153, batch size: 49
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2021-10-13 22:24:59,644 INFO [train.py:451] Epoch 1, batch 13290, batch avg loss 0.3365, total avg loss: 0.3120, batch size: 45
2021-10-13 22:25:04,746 INFO [train.py:451] Epoch 1, batch 13300, batch avg loss 0.2903, total avg loss: 0.3124, batch size: 29
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2021-10-13 22:25:14,545 INFO [train.py:451] Epoch 1, batch 13320, batch avg loss 0.3056, total avg loss: 0.3137, batch size: 39
2021-10-13 22:25:19,404 INFO [train.py:451] Epoch 1, batch 13330, batch avg loss 0.3126, total avg loss: 0.3137, batch size: 34
2021-10-13 22:25:24,403 INFO [train.py:451] Epoch 1, batch 13340, batch avg loss 0.3113, total avg loss: 0.3117, batch size: 41
2021-10-13 22:25:29,469 INFO [train.py:451] Epoch 1, batch 13350, batch avg loss 0.3173, total avg loss: 0.3115, batch size: 37
2021-10-13 22:25:34,576 INFO [train.py:451] Epoch 1, batch 13360, batch avg loss 0.3040, total avg loss: 0.3108, batch size: 34
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2021-10-13 22:26:04,318 INFO [train.py:451] Epoch 1, batch 13420, batch avg loss 0.2605, total avg loss: 0.3138, batch size: 28
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2021-10-13 22:26:34,026 INFO [train.py:451] Epoch 1, batch 13480, batch avg loss 0.3278, total avg loss: 0.3161, batch size: 35
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2021-10-13 22:26:44,134 INFO [train.py:451] Epoch 1, batch 13500, batch avg loss 0.2793, total avg loss: 0.3139, batch size: 32
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2021-10-13 22:26:58,768 INFO [train.py:451] Epoch 1, batch 13530, batch avg loss 0.3256, total avg loss: 0.3118, batch size: 31
2021-10-13 22:27:03,758 INFO [train.py:451] Epoch 1, batch 13540, batch avg loss 0.2685, total avg loss: 0.3115, batch size: 28
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2021-10-13 22:27:34,043 INFO [train.py:451] Epoch 1, batch 13600, batch avg loss 0.2849, total avg loss: 0.3077, batch size: 28
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2021-10-13 22:27:43,909 INFO [train.py:451] Epoch 1, batch 13620, batch avg loss 0.2453, total avg loss: 0.3102, batch size: 31
2021-10-13 22:27:48,926 INFO [train.py:451] Epoch 1, batch 13630, batch avg loss 0.3046, total avg loss: 0.3080, batch size: 28
2021-10-13 22:27:53,973 INFO [train.py:451] Epoch 1, batch 13640, batch avg loss 0.2727, total avg loss: 0.3077, batch size: 28
2021-10-13 22:27:59,028 INFO [train.py:451] Epoch 1, batch 13650, batch avg loss 0.2534, total avg loss: 0.3058, batch size: 30
2021-10-13 22:28:04,043 INFO [train.py:451] Epoch 1, batch 13660, batch avg loss 0.3083, total avg loss: 0.3044, batch size: 45
2021-10-13 22:28:08,962 INFO [train.py:451] Epoch 1, batch 13670, batch avg loss 0.3499, total avg loss: 0.3068, batch size: 72
2021-10-13 22:28:13,661 INFO [train.py:451] Epoch 1, batch 13680, batch avg loss 0.3727, total avg loss: 0.3093, batch size: 49
2021-10-13 22:28:18,618 INFO [train.py:451] Epoch 1, batch 13690, batch avg loss 0.2931, total avg loss: 0.3086, batch size: 29
2021-10-13 22:28:23,531 INFO [train.py:451] Epoch 1, batch 13700, batch avg loss 0.3788, total avg loss: 0.3081, batch size: 45
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2021-10-13 22:28:33,173 INFO [train.py:451] Epoch 1, batch 13720, batch avg loss 0.3158, total avg loss: 0.3082, batch size: 36
2021-10-13 22:28:38,151 INFO [train.py:451] Epoch 1, batch 13730, batch avg loss 0.3046, total avg loss: 0.3095, batch size: 39
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2021-10-13 22:28:48,235 INFO [train.py:451] Epoch 1, batch 13750, batch avg loss 0.3283, total avg loss: 0.3076, batch size: 37
2021-10-13 22:28:53,146 INFO [train.py:451] Epoch 1, batch 13760, batch avg loss 0.3133, total avg loss: 0.3070, batch size: 41
2021-10-13 22:28:58,142 INFO [train.py:451] Epoch 1, batch 13770, batch avg loss 0.3662, total avg loss: 0.3059, batch size: 49
2021-10-13 22:29:02,982 INFO [train.py:451] Epoch 1, batch 13780, batch avg loss 0.2790, total avg loss: 0.3061, batch size: 34
2021-10-13 22:29:07,808 INFO [train.py:451] Epoch 1, batch 13790, batch avg loss 0.3625, total avg loss: 0.3058, batch size: 37
2021-10-13 22:29:12,699 INFO [train.py:451] Epoch 1, batch 13800, batch avg loss 0.2405, total avg loss: 0.3065, batch size: 29
2021-10-13 22:29:17,635 INFO [train.py:451] Epoch 1, batch 13810, batch avg loss 0.2634, total avg loss: 0.3099, batch size: 28
2021-10-13 22:29:22,889 INFO [train.py:451] Epoch 1, batch 13820, batch avg loss 0.2963, total avg loss: 0.3053, batch size: 45
2021-10-13 22:29:27,836 INFO [train.py:451] Epoch 1, batch 13830, batch avg loss 0.2320, total avg loss: 0.3026, batch size: 30
2021-10-13 22:29:32,626 INFO [train.py:451] Epoch 1, batch 13840, batch avg loss 0.3851, total avg loss: 0.3087, batch size: 34
2021-10-13 22:29:37,629 INFO [train.py:451] Epoch 1, batch 13850, batch avg loss 0.3651, total avg loss: 0.3100, batch size: 39
2021-10-13 22:29:42,425 INFO [train.py:451] Epoch 1, batch 13860, batch avg loss 0.3114, total avg loss: 0.3098, batch size: 42
2021-10-13 22:29:47,496 INFO [train.py:451] Epoch 1, batch 13870, batch avg loss 0.3109, total avg loss: 0.3066, batch size: 29
2021-10-13 22:29:52,327 INFO [train.py:451] Epoch 1, batch 13880, batch avg loss 0.3045, total avg loss: 0.3068, batch size: 31
2021-10-13 22:29:57,341 INFO [train.py:451] Epoch 1, batch 13890, batch avg loss 0.2442, total avg loss: 0.3062, batch size: 29
2021-10-13 22:30:02,083 INFO [train.py:451] Epoch 1, batch 13900, batch avg loss 0.3356, total avg loss: 0.3081, batch size: 34
2021-10-13 22:30:07,243 INFO [train.py:451] Epoch 1, batch 13910, batch avg loss 0.2198, total avg loss: 0.3080, batch size: 27
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2021-10-13 22:30:17,191 INFO [train.py:451] Epoch 1, batch 13930, batch avg loss 0.2994, total avg loss: 0.3063, batch size: 33
2021-10-13 22:30:21,957 INFO [train.py:451] Epoch 1, batch 13940, batch avg loss 0.3204, total avg loss: 0.3075, batch size: 34
2021-10-13 22:30:26,867 INFO [train.py:451] Epoch 1, batch 13950, batch avg loss 0.4180, total avg loss: 0.3072, batch size: 42
2021-10-13 22:30:31,839 INFO [train.py:451] Epoch 1, batch 13960, batch avg loss 0.3149, total avg loss: 0.3076, batch size: 49
2021-10-13 22:30:36,945 INFO [train.py:451] Epoch 1, batch 13970, batch avg loss 0.2191, total avg loss: 0.3066, batch size: 29
2021-10-13 22:30:41,997 INFO [train.py:451] Epoch 1, batch 13980, batch avg loss 0.2710, total avg loss: 0.3062, batch size: 35
2021-10-13 22:30:47,100 INFO [train.py:451] Epoch 1, batch 13990, batch avg loss 0.2763, total avg loss: 0.3066, batch size: 34
2021-10-13 22:30:52,208 INFO [train.py:451] Epoch 1, batch 14000, batch avg loss 0.3732, total avg loss: 0.3068, batch size: 39
2021-10-13 22:31:32,276 INFO [train.py:483] Epoch 1, valid loss 0.2169, best valid loss: 0.2169 best valid epoch: 1
2021-10-13 22:31:37,324 INFO [train.py:451] Epoch 1, batch 14010, batch avg loss 0.2737, total avg loss: 0.2934, batch size: 27
2021-10-13 22:31:42,267 INFO [train.py:451] Epoch 1, batch 14020, batch avg loss 0.2712, total avg loss: 0.3034, batch size: 30
2021-10-13 22:31:47,058 INFO [train.py:451] Epoch 1, batch 14030, batch avg loss 0.2993, total avg loss: 0.3092, batch size: 35
2021-10-13 22:31:51,992 INFO [train.py:451] Epoch 1, batch 14040, batch avg loss 0.3392, total avg loss: 0.3084, batch size: 42
2021-10-13 22:31:56,895 INFO [train.py:451] Epoch 1, batch 14050, batch avg loss 0.3706, total avg loss: 0.3085, batch size: 57
2021-10-13 22:32:01,850 INFO [train.py:451] Epoch 1, batch 14060, batch avg loss 0.2985, total avg loss: 0.3059, batch size: 33
2021-10-13 22:32:06,847 INFO [train.py:451] Epoch 1, batch 14070, batch avg loss 0.3505, total avg loss: 0.3052, batch size: 72
2021-10-13 22:32:11,679 INFO [train.py:451] Epoch 1, batch 14080, batch avg loss 0.3536, total avg loss: 0.3055, batch size: 39
2021-10-13 22:32:16,474 INFO [train.py:451] Epoch 1, batch 14090, batch avg loss 0.3809, total avg loss: 0.3050, batch size: 74
2021-10-13 22:32:21,317 INFO [train.py:451] Epoch 1, batch 14100, batch avg loss 0.2936, total avg loss: 0.3051, batch size: 32
2021-10-13 22:32:26,127 INFO [train.py:451] Epoch 1, batch 14110, batch avg loss 0.3355, total avg loss: 0.3047, batch size: 42
2021-10-13 22:32:31,136 INFO [train.py:451] Epoch 1, batch 14120, batch avg loss 0.3397, total avg loss: 0.3025, batch size: 45
2021-10-13 22:32:36,103 INFO [train.py:451] Epoch 1, batch 14130, batch avg loss 0.3287, total avg loss: 0.3032, batch size: 34
2021-10-13 22:32:41,138 INFO [train.py:451] Epoch 1, batch 14140, batch avg loss 0.3202, total avg loss: 0.3026, batch size: 32
2021-10-13 22:32:46,079 INFO [train.py:451] Epoch 1, batch 14150, batch avg loss 0.2819, total avg loss: 0.3016, batch size: 30
2021-10-13 22:32:51,142 INFO [train.py:451] Epoch 1, batch 14160, batch avg loss 0.3442, total avg loss: 0.3011, batch size: 36
2021-10-13 22:32:56,062 INFO [train.py:451] Epoch 1, batch 14170, batch avg loss 0.3153, total avg loss: 0.3016, batch size: 56
2021-10-13 22:33:00,908 INFO [train.py:451] Epoch 1, batch 14180, batch avg loss 0.3591, total avg loss: 0.3028, batch size: 41
2021-10-13 22:33:05,826 INFO [train.py:451] Epoch 1, batch 14190, batch avg loss 0.3035, total avg loss: 0.3025, batch size: 42
2021-10-13 22:33:10,956 INFO [train.py:451] Epoch 1, batch 14200, batch avg loss 0.2450, total avg loss: 0.3024, batch size: 29
2021-10-13 22:33:16,026 INFO [train.py:451] Epoch 1, batch 14210, batch avg loss 0.3401, total avg loss: 0.3161, batch size: 36
2021-10-13 22:33:21,030 INFO [train.py:451] Epoch 1, batch 14220, batch avg loss 0.3043, total avg loss: 0.3028, batch size: 39
2021-10-13 22:33:25,873 INFO [train.py:451] Epoch 1, batch 14230, batch avg loss 0.2663, total avg loss: 0.3020, batch size: 34
2021-10-13 22:33:30,601 INFO [train.py:451] Epoch 1, batch 14240, batch avg loss 0.2939, total avg loss: 0.3074, batch size: 38
2021-10-13 22:33:35,674 INFO [train.py:451] Epoch 1, batch 14250, batch avg loss 0.2433, total avg loss: 0.3094, batch size: 27
2021-10-13 22:33:40,675 INFO [train.py:451] Epoch 1, batch 14260, batch avg loss 0.4266, total avg loss: 0.3095, batch size: 39
2021-10-13 22:33:45,479 INFO [train.py:451] Epoch 1, batch 14270, batch avg loss 0.2435, total avg loss: 0.3094, batch size: 27
2021-10-13 22:33:50,273 INFO [train.py:451] Epoch 1, batch 14280, batch avg loss 0.2416, total avg loss: 0.3087, batch size: 32
2021-10-13 22:33:55,286 INFO [train.py:451] Epoch 1, batch 14290, batch avg loss 0.2892, total avg loss: 0.3068, batch size: 29
2021-10-13 22:34:00,180 INFO [train.py:451] Epoch 1, batch 14300, batch avg loss 0.3265, total avg loss: 0.3068, batch size: 56
2021-10-13 22:34:05,110 INFO [train.py:451] Epoch 1, batch 14310, batch avg loss 0.2629, total avg loss: 0.3052, batch size: 30
2021-10-13 22:34:10,001 INFO [train.py:451] Epoch 1, batch 14320, batch avg loss 0.3119, total avg loss: 0.3045, batch size: 57
2021-10-13 22:34:15,032 INFO [train.py:451] Epoch 1, batch 14330, batch avg loss 0.3411, total avg loss: 0.3051, batch size: 44
2021-10-13 22:34:19,783 INFO [train.py:451] Epoch 1, batch 14340, batch avg loss 0.3328, total avg loss: 0.3071, batch size: 49
2021-10-13 22:34:24,602 INFO [train.py:451] Epoch 1, batch 14350, batch avg loss 0.3429, total avg loss: 0.3068, batch size: 36
2021-10-13 22:34:29,383 INFO [train.py:451] Epoch 1, batch 14360, batch avg loss 0.2804, total avg loss: 0.3068, batch size: 38
2021-10-13 22:34:34,312 INFO [train.py:451] Epoch 1, batch 14370, batch avg loss 0.3745, total avg loss: 0.3064, batch size: 30
2021-10-13 22:34:39,326 INFO [train.py:451] Epoch 1, batch 14380, batch avg loss 0.3069, total avg loss: 0.3054, batch size: 27
2021-10-13 22:34:44,295 INFO [train.py:451] Epoch 1, batch 14390, batch avg loss 0.4013, total avg loss: 0.3060, batch size: 130
2021-10-13 22:34:49,797 INFO [train.py:451] Epoch 1, batch 14400, batch avg loss 0.3331, total avg loss: 0.3053, batch size: 45
2021-10-13 22:34:54,731 INFO [train.py:451] Epoch 1, batch 14410, batch avg loss 0.3056, total avg loss: 0.3295, batch size: 42
2021-10-13 22:34:59,639 INFO [train.py:451] Epoch 1, batch 14420, batch avg loss 0.3060, total avg loss: 0.3274, batch size: 34
2021-10-13 22:35:04,509 INFO [train.py:451] Epoch 1, batch 14430, batch avg loss 0.2875, total avg loss: 0.3166, batch size: 30
2021-10-13 22:35:09,494 INFO [train.py:451] Epoch 1, batch 14440, batch avg loss 0.2474, total avg loss: 0.3043, batch size: 29
2021-10-13 22:35:14,482 INFO [train.py:451] Epoch 1, batch 14450, batch avg loss 0.3155, total avg loss: 0.3069, batch size: 35
2021-10-13 22:35:19,432 INFO [train.py:451] Epoch 1, batch 14460, batch avg loss 0.2704, total avg loss: 0.3065, batch size: 34
2021-10-13 22:35:24,480 INFO [train.py:451] Epoch 1, batch 14470, batch avg loss 0.3482, total avg loss: 0.3083, batch size: 37
2021-10-13 22:35:29,294 INFO [train.py:451] Epoch 1, batch 14480, batch avg loss 0.4570, total avg loss: 0.3082, batch size: 130
2021-10-13 22:35:34,269 INFO [train.py:451] Epoch 1, batch 14490, batch avg loss 0.3639, total avg loss: 0.3088, batch size: 49
2021-10-13 22:35:39,217 INFO [train.py:451] Epoch 1, batch 14500, batch avg loss 0.2299, total avg loss: 0.3054, batch size: 29
2021-10-13 22:35:44,081 INFO [train.py:451] Epoch 1, batch 14510, batch avg loss 0.3368, total avg loss: 0.3053, batch size: 74
2021-10-13 22:35:49,022 INFO [train.py:451] Epoch 1, batch 14520, batch avg loss 0.2926, total avg loss: 0.3052, batch size: 30
2021-10-13 22:35:54,341 INFO [train.py:451] Epoch 1, batch 14530, batch avg loss 0.2568, total avg loss: 0.3041, batch size: 30
2021-10-13 22:35:59,334 INFO [train.py:451] Epoch 1, batch 14540, batch avg loss 0.2845, total avg loss: 0.3038, batch size: 30
2021-10-13 22:36:04,379 INFO [train.py:451] Epoch 1, batch 14550, batch avg loss 0.3059, total avg loss: 0.3019, batch size: 45
2021-10-13 22:36:09,424 INFO [train.py:451] Epoch 1, batch 14560, batch avg loss 0.2652, total avg loss: 0.3030, batch size: 32
2021-10-13 22:36:14,234 INFO [train.py:451] Epoch 1, batch 14570, batch avg loss 0.3120, total avg loss: 0.3030, batch size: 57
2021-10-13 22:36:18,962 INFO [train.py:451] Epoch 1, batch 14580, batch avg loss 0.2803, total avg loss: 0.3032, batch size: 36
2021-10-13 22:36:23,933 INFO [train.py:451] Epoch 1, batch 14590, batch avg loss 0.3480, total avg loss: 0.3027, batch size: 57
2021-10-13 22:36:28,876 INFO [train.py:451] Epoch 1, batch 14600, batch avg loss 0.3255, total avg loss: 0.3027, batch size: 33
2021-10-13 22:36:33,850 INFO [train.py:451] Epoch 1, batch 14610, batch avg loss 0.3341, total avg loss: 0.3141, batch size: 57
2021-10-13 22:36:38,613 INFO [train.py:451] Epoch 1, batch 14620, batch avg loss 0.2952, total avg loss: 0.3176, batch size: 30
2021-10-13 22:36:43,458 INFO [train.py:451] Epoch 1, batch 14630, batch avg loss 0.2883, total avg loss: 0.3145, batch size: 29
2021-10-13 22:36:48,381 INFO [train.py:451] Epoch 1, batch 14640, batch avg loss 0.2632, total avg loss: 0.3155, batch size: 30
2021-10-13 22:36:53,330 INFO [train.py:451] Epoch 1, batch 14650, batch avg loss 0.3619, total avg loss: 0.3121, batch size: 34
2021-10-13 22:36:58,376 INFO [train.py:451] Epoch 1, batch 14660, batch avg loss 0.2442, total avg loss: 0.3119, batch size: 29
2021-10-13 22:37:03,129 INFO [train.py:451] Epoch 1, batch 14670, batch avg loss 0.3381, total avg loss: 0.3135, batch size: 29
2021-10-13 22:37:08,088 INFO [train.py:451] Epoch 1, batch 14680, batch avg loss 0.2382, total avg loss: 0.3109, batch size: 29
2021-10-13 22:37:13,003 INFO [train.py:451] Epoch 1, batch 14690, batch avg loss 0.2786, total avg loss: 0.3095, batch size: 32
2021-10-13 22:37:18,178 INFO [train.py:451] Epoch 1, batch 14700, batch avg loss 0.2569, total avg loss: 0.3083, batch size: 27
2021-10-13 22:37:23,038 INFO [train.py:451] Epoch 1, batch 14710, batch avg loss 0.3148, total avg loss: 0.3100, batch size: 42
2021-10-13 22:37:27,973 INFO [train.py:451] Epoch 1, batch 14720, batch avg loss 0.3113, total avg loss: 0.3112, batch size: 38
2021-10-13 22:37:33,091 INFO [train.py:451] Epoch 1, batch 14730, batch avg loss 0.3158, total avg loss: 0.3107, batch size: 42
2021-10-13 22:37:38,023 INFO [train.py:451] Epoch 1, batch 14740, batch avg loss 0.3008, total avg loss: 0.3097, batch size: 33
2021-10-13 22:37:43,142 INFO [train.py:451] Epoch 1, batch 14750, batch avg loss 0.2687, total avg loss: 0.3092, batch size: 27
2021-10-13 22:37:48,365 INFO [train.py:451] Epoch 1, batch 14760, batch avg loss 0.2504, total avg loss: 0.3079, batch size: 29
2021-10-13 22:37:53,355 INFO [train.py:451] Epoch 1, batch 14770, batch avg loss 0.2943, total avg loss: 0.3064, batch size: 33
2021-10-13 22:37:58,455 INFO [train.py:451] Epoch 1, batch 14780, batch avg loss 0.2992, total avg loss: 0.3068, batch size: 34
2021-10-13 22:38:03,395 INFO [train.py:451] Epoch 1, batch 14790, batch avg loss 0.2761, total avg loss: 0.3079, batch size: 27
2021-10-13 22:38:08,242 INFO [train.py:451] Epoch 1, batch 14800, batch avg loss 0.4013, total avg loss: 0.3087, batch size: 128
2021-10-13 22:38:13,276 INFO [train.py:451] Epoch 1, batch 14810, batch avg loss 0.3068, total avg loss: 0.2982, batch size: 39
2021-10-13 22:38:18,117 INFO [train.py:451] Epoch 1, batch 14820, batch avg loss 0.2265, total avg loss: 0.3037, batch size: 30
2021-10-13 22:38:23,012 INFO [train.py:451] Epoch 1, batch 14830, batch avg loss 0.2588, total avg loss: 0.2913, batch size: 31
2021-10-13 22:38:27,971 INFO [train.py:451] Epoch 1, batch 14840, batch avg loss 0.2774, total avg loss: 0.2896, batch size: 31
2021-10-13 22:38:32,740 INFO [train.py:451] Epoch 1, batch 14850, batch avg loss 0.3251, total avg loss: 0.2954, batch size: 41
2021-10-13 22:38:37,664 INFO [train.py:451] Epoch 1, batch 14860, batch avg loss 0.3123, total avg loss: 0.2965, batch size: 36
2021-10-13 22:38:42,555 INFO [train.py:451] Epoch 1, batch 14870, batch avg loss 0.3169, total avg loss: 0.2967, batch size: 38
2021-10-13 22:38:47,566 INFO [train.py:451] Epoch 1, batch 14880, batch avg loss 0.2999, total avg loss: 0.2990, batch size: 34
2021-10-13 22:38:52,482 INFO [train.py:451] Epoch 1, batch 14890, batch avg loss 0.2732, total avg loss: 0.3025, batch size: 33
2021-10-13 22:38:57,324 INFO [train.py:451] Epoch 1, batch 14900, batch avg loss 0.3283, total avg loss: 0.3041, batch size: 49
2021-10-13 22:39:02,497 INFO [train.py:451] Epoch 1, batch 14910, batch avg loss 0.3134, total avg loss: 0.3044, batch size: 45
2021-10-13 22:39:07,418 INFO [train.py:451] Epoch 1, batch 14920, batch avg loss 0.3800, total avg loss: 0.3078, batch size: 42
2021-10-13 22:39:12,274 INFO [train.py:451] Epoch 1, batch 14930, batch avg loss 0.2892, total avg loss: 0.3092, batch size: 35
2021-10-13 22:39:17,236 INFO [train.py:451] Epoch 1, batch 14940, batch avg loss 0.2639, total avg loss: 0.3074, batch size: 32
2021-10-13 22:39:22,179 INFO [train.py:451] Epoch 1, batch 14950, batch avg loss 0.3182, total avg loss: 0.3067, batch size: 35
2021-10-13 22:39:27,166 INFO [train.py:451] Epoch 1, batch 14960, batch avg loss 0.2986, total avg loss: 0.3068, batch size: 49
2021-10-13 22:39:32,141 INFO [train.py:451] Epoch 1, batch 14970, batch avg loss 0.2761, total avg loss: 0.3070, batch size: 27
2021-10-13 22:39:37,020 INFO [train.py:451] Epoch 1, batch 14980, batch avg loss 0.3324, total avg loss: 0.3089, batch size: 45
2021-10-13 22:39:42,262 INFO [train.py:451] Epoch 1, batch 14990, batch avg loss 0.3470, total avg loss: 0.3084, batch size: 39
2021-10-13 22:39:47,229 INFO [train.py:451] Epoch 1, batch 15000, batch avg loss 0.3269, total avg loss: 0.3091, batch size: 39
2021-10-13 22:40:29,217 INFO [train.py:483] Epoch 1, valid loss 0.2171, best valid loss: 0.2169 best valid epoch: 1
2021-10-13 22:40:34,402 INFO [train.py:451] Epoch 1, batch 15010, batch avg loss 0.3831, total avg loss: 0.3217, batch size: 130
2021-10-13 22:40:39,297 INFO [train.py:451] Epoch 1, batch 15020, batch avg loss 0.2905, total avg loss: 0.3206, batch size: 34
2021-10-13 22:40:44,339 INFO [train.py:451] Epoch 1, batch 15030, batch avg loss 0.2912, total avg loss: 0.3150, batch size: 33
2021-10-13 22:40:49,349 INFO [train.py:451] Epoch 1, batch 15040, batch avg loss 0.2953, total avg loss: 0.3110, batch size: 34
2021-10-13 22:40:54,383 INFO [train.py:451] Epoch 1, batch 15050, batch avg loss 0.3176, total avg loss: 0.3044, batch size: 35
2021-10-13 22:40:59,025 INFO [train.py:451] Epoch 1, batch 15060, batch avg loss 0.3651, total avg loss: 0.3083, batch size: 72
2021-10-13 22:41:03,843 INFO [train.py:451] Epoch 1, batch 15070, batch avg loss 0.3389, total avg loss: 0.3096, batch size: 38
2021-10-13 22:41:08,803 INFO [train.py:451] Epoch 1, batch 15080, batch avg loss 0.2822, total avg loss: 0.3087, batch size: 41
2021-10-13 22:41:13,884 INFO [train.py:451] Epoch 1, batch 15090, batch avg loss 0.2991, total avg loss: 0.3062, batch size: 34
2021-10-13 22:41:18,741 INFO [train.py:451] Epoch 1, batch 15100, batch avg loss 0.2703, total avg loss: 0.3088, batch size: 31
2021-10-13 22:41:23,756 INFO [train.py:451] Epoch 1, batch 15110, batch avg loss 0.2484, total avg loss: 0.3082, batch size: 29
2021-10-13 22:41:28,771 INFO [train.py:451] Epoch 1, batch 15120, batch avg loss 0.2651, total avg loss: 0.3062, batch size: 27
2021-10-13 22:41:33,843 INFO [train.py:451] Epoch 1, batch 15130, batch avg loss 0.2825, total avg loss: 0.3054, batch size: 27
2021-10-13 22:41:38,913 INFO [train.py:451] Epoch 1, batch 15140, batch avg loss 0.3000, total avg loss: 0.3058, batch size: 42
2021-10-13 22:41:43,761 INFO [train.py:451] Epoch 1, batch 15150, batch avg loss 0.3090, total avg loss: 0.3066, batch size: 39
2021-10-13 22:41:48,734 INFO [train.py:451] Epoch 1, batch 15160, batch avg loss 0.2902, total avg loss: 0.3064, batch size: 45
2021-10-13 22:41:53,782 INFO [train.py:451] Epoch 1, batch 15170, batch avg loss 0.3250, total avg loss: 0.3072, batch size: 31
2021-10-13 22:41:58,629 INFO [train.py:451] Epoch 1, batch 15180, batch avg loss 0.2846, total avg loss: 0.3075, batch size: 33
2021-10-13 22:42:03,536 INFO [train.py:451] Epoch 1, batch 15190, batch avg loss 0.3018, total avg loss: 0.3065, batch size: 35
2021-10-13 22:42:08,648 INFO [train.py:451] Epoch 1, batch 15200, batch avg loss 0.3056, total avg loss: 0.3050, batch size: 34
2021-10-13 22:42:13,569 INFO [train.py:451] Epoch 1, batch 15210, batch avg loss 0.3207, total avg loss: 0.3267, batch size: 38
2021-10-13 22:42:18,454 INFO [train.py:451] Epoch 1, batch 15220, batch avg loss 0.3440, total avg loss: 0.3129, batch size: 73
2021-10-13 22:42:23,380 INFO [train.py:451] Epoch 1, batch 15230, batch avg loss 0.2770, total avg loss: 0.3173, batch size: 35
2021-10-13 22:42:28,323 INFO [train.py:451] Epoch 1, batch 15240, batch avg loss 0.4187, total avg loss: 0.3156, batch size: 121
2021-10-13 22:42:33,412 INFO [train.py:451] Epoch 1, batch 15250, batch avg loss 0.2903, total avg loss: 0.3115, batch size: 36
2021-10-13 22:42:38,478 INFO [train.py:451] Epoch 1, batch 15260, batch avg loss 0.2963, total avg loss: 0.3144, batch size: 35
2021-10-13 22:42:43,340 INFO [train.py:451] Epoch 1, batch 15270, batch avg loss 0.2907, total avg loss: 0.3129, batch size: 33
2021-10-13 22:42:48,193 INFO [train.py:451] Epoch 1, batch 15280, batch avg loss 0.2639, total avg loss: 0.3128, batch size: 31
2021-10-13 22:42:53,017 INFO [train.py:451] Epoch 1, batch 15290, batch avg loss 0.2523, total avg loss: 0.3118, batch size: 31
2021-10-13 22:42:57,985 INFO [train.py:451] Epoch 1, batch 15300, batch avg loss 0.3233, total avg loss: 0.3123, batch size: 34
2021-10-13 22:43:03,034 INFO [train.py:451] Epoch 1, batch 15310, batch avg loss 0.2783, total avg loss: 0.3092, batch size: 32
2021-10-13 22:43:08,237 INFO [train.py:451] Epoch 1, batch 15320, batch avg loss 0.3040, total avg loss: 0.3082, batch size: 32
2021-10-13 22:43:13,156 INFO [train.py:451] Epoch 1, batch 15330, batch avg loss 0.2246, total avg loss: 0.3062, batch size: 27
2021-10-13 22:43:18,283 INFO [train.py:451] Epoch 1, batch 15340, batch avg loss 0.4495, total avg loss: 0.3069, batch size: 131
2021-10-13 22:43:23,371 INFO [train.py:451] Epoch 1, batch 15350, batch avg loss 0.2979, total avg loss: 0.3064, batch size: 33
2021-10-13 22:43:28,366 INFO [train.py:451] Epoch 1, batch 15360, batch avg loss 0.4125, total avg loss: 0.3057, batch size: 121
2021-10-13 22:43:33,323 INFO [train.py:451] Epoch 1, batch 15370, batch avg loss 0.3158, total avg loss: 0.3048, batch size: 33
2021-10-13 22:43:38,197 INFO [train.py:451] Epoch 1, batch 15380, batch avg loss 0.3254, total avg loss: 0.3046, batch size: 57
2021-10-13 22:43:43,006 INFO [train.py:451] Epoch 1, batch 15390, batch avg loss 0.4222, total avg loss: 0.3046, batch size: 125
2021-10-13 22:43:47,979 INFO [train.py:451] Epoch 1, batch 15400, batch avg loss 0.3317, total avg loss: 0.3043, batch size: 38
2021-10-13 22:43:52,780 INFO [train.py:451] Epoch 1, batch 15410, batch avg loss 0.3158, total avg loss: 0.3182, batch size: 57
2021-10-13 22:43:57,596 INFO [train.py:451] Epoch 1, batch 15420, batch avg loss 0.3219, total avg loss: 0.3168, batch size: 49
2021-10-13 22:44:02,438 INFO [train.py:451] Epoch 1, batch 15430, batch avg loss 0.2679, total avg loss: 0.3124, batch size: 32
2021-10-13 22:44:07,282 INFO [train.py:451] Epoch 1, batch 15440, batch avg loss 0.2309, total avg loss: 0.3073, batch size: 33
2021-10-13 22:44:12,168 INFO [train.py:451] Epoch 1, batch 15450, batch avg loss 0.2198, total avg loss: 0.3052, batch size: 34
2021-10-13 22:44:16,901 INFO [train.py:451] Epoch 1, batch 15460, batch avg loss 0.3032, total avg loss: 0.3040, batch size: 42
2021-10-13 22:44:21,858 INFO [train.py:451] Epoch 1, batch 15470, batch avg loss 0.3276, total avg loss: 0.3051, batch size: 72
2021-10-13 22:44:26,841 INFO [train.py:451] Epoch 1, batch 15480, batch avg loss 0.2543, total avg loss: 0.3014, batch size: 32
2021-10-13 22:44:31,745 INFO [train.py:451] Epoch 1, batch 15490, batch avg loss 0.3419, total avg loss: 0.3033, batch size: 32
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2021-10-13 22:45:30,922 INFO [train.py:451] Epoch 1, batch 15610, batch avg loss 0.3052, total avg loss: 0.3083, batch size: 29
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2021-10-13 22:45:45,673 INFO [train.py:451] Epoch 1, batch 15640, batch avg loss 0.3543, total avg loss: 0.3119, batch size: 41
2021-10-13 22:45:50,606 INFO [train.py:451] Epoch 1, batch 15650, batch avg loss 0.2810, total avg loss: 0.3090, batch size: 29
2021-10-13 22:45:55,938 INFO [train.py:451] Epoch 1, batch 15660, batch avg loss 0.3696, total avg loss: 0.3067, batch size: 38
2021-10-13 22:46:00,961 INFO [train.py:451] Epoch 1, batch 15670, batch avg loss 0.3449, total avg loss: 0.3073, batch size: 37
2021-10-13 22:46:05,854 INFO [train.py:451] Epoch 1, batch 15680, batch avg loss 0.2728, total avg loss: 0.3053, batch size: 31
2021-10-13 22:46:10,743 INFO [train.py:451] Epoch 1, batch 15690, batch avg loss 0.2648, total avg loss: 0.3062, batch size: 31
2021-10-13 22:46:15,486 INFO [train.py:451] Epoch 1, batch 15700, batch avg loss 0.4026, total avg loss: 0.3081, batch size: 122
2021-10-13 22:46:20,403 INFO [train.py:451] Epoch 1, batch 15710, batch avg loss 0.2393, total avg loss: 0.3069, batch size: 30
2021-10-13 22:46:25,342 INFO [train.py:451] Epoch 1, batch 15720, batch avg loss 0.2207, total avg loss: 0.3054, batch size: 29
2021-10-13 22:46:30,058 INFO [train.py:451] Epoch 1, batch 15730, batch avg loss 0.3120, total avg loss: 0.3056, batch size: 33
2021-10-13 22:46:35,096 INFO [train.py:451] Epoch 1, batch 15740, batch avg loss 0.3009, total avg loss: 0.3058, batch size: 31
2021-10-13 22:46:40,126 INFO [train.py:451] Epoch 1, batch 15750, batch avg loss 0.2303, total avg loss: 0.3056, batch size: 31
2021-10-13 22:46:45,002 INFO [train.py:451] Epoch 1, batch 15760, batch avg loss 0.3090, total avg loss: 0.3054, batch size: 38
2021-10-13 22:46:49,886 INFO [train.py:451] Epoch 1, batch 15770, batch avg loss 0.3410, total avg loss: 0.3058, batch size: 45
2021-10-13 22:46:54,846 INFO [train.py:451] Epoch 1, batch 15780, batch avg loss 0.2769, total avg loss: 0.3047, batch size: 32
2021-10-13 22:46:59,545 INFO [train.py:451] Epoch 1, batch 15790, batch avg loss 0.3169, total avg loss: 0.3060, batch size: 34
2021-10-13 22:47:04,601 INFO [train.py:451] Epoch 1, batch 15800, batch avg loss 0.2833, total avg loss: 0.3052, batch size: 34
2021-10-13 22:47:09,624 INFO [train.py:451] Epoch 1, batch 15810, batch avg loss 0.2909, total avg loss: 0.2991, batch size: 42
2021-10-13 22:47:14,479 INFO [train.py:451] Epoch 1, batch 15820, batch avg loss 0.3045, total avg loss: 0.2984, batch size: 34
2021-10-13 22:47:19,293 INFO [train.py:451] Epoch 1, batch 15830, batch avg loss 0.3116, total avg loss: 0.3055, batch size: 38
2021-10-13 22:47:24,230 INFO [train.py:451] Epoch 1, batch 15840, batch avg loss 0.2918, total avg loss: 0.3037, batch size: 49
2021-10-13 22:47:29,057 INFO [train.py:451] Epoch 1, batch 15850, batch avg loss 0.2972, total avg loss: 0.3039, batch size: 57
2021-10-13 22:47:33,928 INFO [train.py:451] Epoch 1, batch 15860, batch avg loss 0.2645, total avg loss: 0.3034, batch size: 29
2021-10-13 22:47:38,804 INFO [train.py:451] Epoch 1, batch 15870, batch avg loss 0.2754, total avg loss: 0.3047, batch size: 49
2021-10-13 22:47:43,860 INFO [train.py:451] Epoch 1, batch 15880, batch avg loss 0.3221, total avg loss: 0.3052, batch size: 33
2021-10-13 22:47:48,791 INFO [train.py:451] Epoch 1, batch 15890, batch avg loss 0.3560, total avg loss: 0.3054, batch size: 36
2021-10-13 22:47:53,488 INFO [train.py:451] Epoch 1, batch 15900, batch avg loss 0.2644, total avg loss: 0.3064, batch size: 33
2021-10-13 22:47:58,334 INFO [train.py:451] Epoch 1, batch 15910, batch avg loss 0.2933, total avg loss: 0.3063, batch size: 41
2021-10-13 22:48:03,165 INFO [train.py:451] Epoch 1, batch 15920, batch avg loss 0.2675, total avg loss: 0.3066, batch size: 28
2021-10-13 22:48:08,023 INFO [train.py:451] Epoch 1, batch 15930, batch avg loss 0.2934, total avg loss: 0.3076, batch size: 45
2021-10-13 22:48:13,127 INFO [train.py:451] Epoch 1, batch 15940, batch avg loss 0.3570, total avg loss: 0.3083, batch size: 36
2021-10-13 22:48:18,019 INFO [train.py:451] Epoch 1, batch 15950, batch avg loss 0.2819, total avg loss: 0.3076, batch size: 35
2021-10-13 22:48:23,122 INFO [train.py:451] Epoch 1, batch 15960, batch avg loss 0.2913, total avg loss: 0.3074, batch size: 28
2021-10-13 22:48:28,174 INFO [train.py:451] Epoch 1, batch 15970, batch avg loss 0.3160, total avg loss: 0.3070, batch size: 30
2021-10-13 22:48:32,968 INFO [train.py:451] Epoch 1, batch 15980, batch avg loss 0.2584, total avg loss: 0.3066, batch size: 36
2021-10-13 22:48:37,992 INFO [train.py:451] Epoch 1, batch 15990, batch avg loss 0.2524, total avg loss: 0.3058, batch size: 27
2021-10-13 22:48:42,935 INFO [train.py:451] Epoch 1, batch 16000, batch avg loss 0.2811, total avg loss: 0.3057, batch size: 35
2021-10-13 22:49:22,387 INFO [train.py:483] Epoch 1, valid loss 0.2163, best valid loss: 0.2163 best valid epoch: 1
2021-10-13 22:49:27,462 INFO [train.py:451] Epoch 1, batch 16010, batch avg loss 0.2866, total avg loss: 0.2986, batch size: 34
2021-10-13 22:49:32,469 INFO [train.py:451] Epoch 1, batch 16020, batch avg loss 0.2523, total avg loss: 0.2949, batch size: 29
2021-10-13 22:49:37,389 INFO [train.py:451] Epoch 1, batch 16030, batch avg loss 0.3177, total avg loss: 0.2967, batch size: 41
2021-10-13 22:49:42,238 INFO [train.py:451] Epoch 1, batch 16040, batch avg loss 0.3312, total avg loss: 0.3009, batch size: 35
2021-10-13 22:49:47,045 INFO [train.py:451] Epoch 1, batch 16050, batch avg loss 0.2979, total avg loss: 0.3013, batch size: 36
2021-10-13 22:49:51,926 INFO [train.py:451] Epoch 1, batch 16060, batch avg loss 0.2581, total avg loss: 0.2994, batch size: 31
2021-10-13 22:49:56,813 INFO [train.py:451] Epoch 1, batch 16070, batch avg loss 0.3536, total avg loss: 0.3023, batch size: 49
2021-10-13 22:50:01,720 INFO [train.py:451] Epoch 1, batch 16080, batch avg loss 0.2894, total avg loss: 0.3002, batch size: 39
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2021-10-13 22:50:11,562 INFO [train.py:451] Epoch 1, batch 16100, batch avg loss 0.3657, total avg loss: 0.3003, batch size: 49
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2021-10-13 22:50:21,264 INFO [train.py:451] Epoch 1, batch 16120, batch avg loss 0.2791, total avg loss: 0.3008, batch size: 32
2021-10-13 22:50:26,395 INFO [train.py:451] Epoch 1, batch 16130, batch avg loss 0.3647, total avg loss: 0.3014, batch size: 34
2021-10-13 22:50:31,273 INFO [train.py:451] Epoch 1, batch 16140, batch avg loss 0.3917, total avg loss: 0.3024, batch size: 34
2021-10-13 22:50:36,165 INFO [train.py:451] Epoch 1, batch 16150, batch avg loss 0.3160, total avg loss: 0.3040, batch size: 35
2021-10-13 22:50:41,038 INFO [train.py:451] Epoch 1, batch 16160, batch avg loss 0.2767, total avg loss: 0.3052, batch size: 29
2021-10-13 22:50:45,995 INFO [train.py:451] Epoch 1, batch 16170, batch avg loss 0.2566, total avg loss: 0.3061, batch size: 34
2021-10-13 22:50:50,895 INFO [train.py:451] Epoch 1, batch 16180, batch avg loss 0.2539, total avg loss: 0.3046, batch size: 31
2021-10-13 22:50:55,727 INFO [train.py:451] Epoch 1, batch 16190, batch avg loss 0.3467, total avg loss: 0.3042, batch size: 33
2021-10-13 22:51:00,622 INFO [train.py:451] Epoch 1, batch 16200, batch avg loss 0.2788, total avg loss: 0.3042, batch size: 41
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2021-10-13 22:51:15,464 INFO [train.py:451] Epoch 1, batch 16230, batch avg loss 0.3027, total avg loss: 0.3040, batch size: 33
2021-10-13 22:51:20,648 INFO [train.py:451] Epoch 1, batch 16240, batch avg loss 0.2816, total avg loss: 0.3052, batch size: 29
2021-10-13 22:51:25,730 INFO [train.py:451] Epoch 1, batch 16250, batch avg loss 0.2605, total avg loss: 0.3050, batch size: 27
2021-10-13 22:51:30,845 INFO [train.py:451] Epoch 1, batch 16260, batch avg loss 0.2843, total avg loss: 0.3089, batch size: 33
2021-10-13 22:51:35,725 INFO [train.py:451] Epoch 1, batch 16270, batch avg loss 0.3322, total avg loss: 0.3093, batch size: 35
2021-10-13 22:51:40,576 INFO [train.py:451] Epoch 1, batch 16280, batch avg loss 0.2675, total avg loss: 0.3080, batch size: 30
2021-10-13 22:51:45,292 INFO [train.py:451] Epoch 1, batch 16290, batch avg loss 0.3073, total avg loss: 0.3107, batch size: 37
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2021-10-13 22:51:55,040 INFO [train.py:451] Epoch 1, batch 16310, batch avg loss 0.3016, total avg loss: 0.3111, batch size: 38
2021-10-13 22:52:00,039 INFO [train.py:451] Epoch 1, batch 16320, batch avg loss 0.2772, total avg loss: 0.3096, batch size: 34
2021-10-13 22:52:04,975 INFO [train.py:451] Epoch 1, batch 16330, batch avg loss 0.2473, total avg loss: 0.3101, batch size: 32
2021-10-13 22:52:09,732 INFO [train.py:451] Epoch 1, batch 16340, batch avg loss 0.2368, total avg loss: 0.3094, batch size: 30
2021-10-13 22:52:14,668 INFO [train.py:451] Epoch 1, batch 16350, batch avg loss 0.3389, total avg loss: 0.3085, batch size: 41
2021-10-13 22:52:19,604 INFO [train.py:451] Epoch 1, batch 16360, batch avg loss 0.2677, total avg loss: 0.3084, batch size: 31
2021-10-13 22:52:24,305 INFO [train.py:451] Epoch 1, batch 16370, batch avg loss 0.3051, total avg loss: 0.3090, batch size: 32
2021-10-13 22:52:29,446 INFO [train.py:451] Epoch 1, batch 16380, batch avg loss 0.2241, total avg loss: 0.3076, batch size: 27
2021-10-13 22:52:34,069 INFO [train.py:451] Epoch 1, batch 16390, batch avg loss 0.3456, total avg loss: 0.3087, batch size: 57
2021-10-13 22:52:38,979 INFO [train.py:451] Epoch 1, batch 16400, batch avg loss 0.2938, total avg loss: 0.3081, batch size: 30
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2021-10-13 22:52:53,779 INFO [train.py:451] Epoch 1, batch 16430, batch avg loss 0.3189, total avg loss: 0.2916, batch size: 45
2021-10-13 22:52:58,535 INFO [train.py:451] Epoch 1, batch 16440, batch avg loss 0.2919, total avg loss: 0.2953, batch size: 49
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2021-10-13 22:53:18,323 INFO [train.py:451] Epoch 1, batch 16480, batch avg loss 0.2933, total avg loss: 0.3002, batch size: 39
2021-10-13 22:53:23,218 INFO [train.py:451] Epoch 1, batch 16490, batch avg loss 0.3215, total avg loss: 0.3010, batch size: 31
2021-10-13 22:53:28,048 INFO [train.py:451] Epoch 1, batch 16500, batch avg loss 0.2908, total avg loss: 0.3013, batch size: 36
2021-10-13 22:53:32,945 INFO [train.py:451] Epoch 1, batch 16510, batch avg loss 0.2966, total avg loss: 0.3009, batch size: 41
2021-10-13 22:53:38,050 INFO [train.py:451] Epoch 1, batch 16520, batch avg loss 0.3450, total avg loss: 0.3027, batch size: 38
2021-10-13 22:53:43,037 INFO [train.py:451] Epoch 1, batch 16530, batch avg loss 0.3100, total avg loss: 0.3021, batch size: 35
2021-10-13 22:53:47,864 INFO [train.py:451] Epoch 1, batch 16540, batch avg loss 0.2993, total avg loss: 0.3026, batch size: 41
2021-10-13 22:53:52,753 INFO [train.py:451] Epoch 1, batch 16550, batch avg loss 0.3671, total avg loss: 0.3027, batch size: 38
2021-10-13 22:53:57,661 INFO [train.py:451] Epoch 1, batch 16560, batch avg loss 0.2775, total avg loss: 0.3035, batch size: 32
2021-10-13 22:54:02,520 INFO [train.py:451] Epoch 1, batch 16570, batch avg loss 0.3117, total avg loss: 0.3035, batch size: 34
2021-10-13 22:54:07,372 INFO [train.py:451] Epoch 1, batch 16580, batch avg loss 0.2428, total avg loss: 0.3033, batch size: 31
2021-10-13 22:54:12,301 INFO [train.py:451] Epoch 1, batch 16590, batch avg loss 0.2305, total avg loss: 0.3033, batch size: 28
2021-10-13 22:54:17,110 INFO [train.py:451] Epoch 1, batch 16600, batch avg loss 0.3031, total avg loss: 0.3039, batch size: 34
2021-10-13 22:54:21,930 INFO [train.py:451] Epoch 1, batch 16610, batch avg loss 0.2939, total avg loss: 0.3137, batch size: 36
2021-10-13 22:54:26,859 INFO [train.py:451] Epoch 1, batch 16620, batch avg loss 0.3430, total avg loss: 0.3102, batch size: 73
2021-10-13 22:54:31,903 INFO [train.py:451] Epoch 1, batch 16630, batch avg loss 0.3675, total avg loss: 0.3018, batch size: 49
2021-10-13 22:54:36,740 INFO [train.py:451] Epoch 1, batch 16640, batch avg loss 0.3257, total avg loss: 0.3024, batch size: 56
2021-10-13 22:54:41,801 INFO [train.py:451] Epoch 1, batch 16650, batch avg loss 0.2824, total avg loss: 0.2997, batch size: 34
2021-10-13 22:54:46,631 INFO [train.py:451] Epoch 1, batch 16660, batch avg loss 0.2791, total avg loss: 0.3036, batch size: 36
2021-10-13 22:54:51,560 INFO [train.py:451] Epoch 1, batch 16670, batch avg loss 0.3413, total avg loss: 0.3013, batch size: 39
2021-10-13 22:54:56,457 INFO [train.py:451] Epoch 1, batch 16680, batch avg loss 0.3117, total avg loss: 0.3021, batch size: 34
2021-10-13 22:55:01,332 INFO [train.py:451] Epoch 1, batch 16690, batch avg loss 0.3280, total avg loss: 0.3023, batch size: 72
2021-10-13 22:55:06,263 INFO [train.py:451] Epoch 1, batch 16700, batch avg loss 0.4302, total avg loss: 0.3037, batch size: 129
2021-10-13 22:55:11,142 INFO [train.py:451] Epoch 1, batch 16710, batch avg loss 0.2822, total avg loss: 0.3033, batch size: 42
2021-10-13 22:55:16,060 INFO [train.py:451] Epoch 1, batch 16720, batch avg loss 0.2811, total avg loss: 0.3026, batch size: 35
2021-10-13 22:55:20,904 INFO [train.py:451] Epoch 1, batch 16730, batch avg loss 0.2790, total avg loss: 0.3026, batch size: 31
2021-10-13 22:55:25,906 INFO [train.py:451] Epoch 1, batch 16740, batch avg loss 0.2610, total avg loss: 0.3015, batch size: 31
2021-10-13 22:55:30,823 INFO [train.py:451] Epoch 1, batch 16750, batch avg loss 0.3558, total avg loss: 0.3022, batch size: 57
2021-10-13 22:55:35,796 INFO [train.py:451] Epoch 1, batch 16760, batch avg loss 0.3485, total avg loss: 0.3011, batch size: 34
2021-10-13 22:55:40,755 INFO [train.py:451] Epoch 1, batch 16770, batch avg loss 0.2187, total avg loss: 0.2999, batch size: 31
2021-10-13 22:55:45,572 INFO [train.py:451] Epoch 1, batch 16780, batch avg loss 0.3255, total avg loss: 0.3019, batch size: 45
2021-10-13 22:55:50,727 INFO [train.py:451] Epoch 1, batch 16790, batch avg loss 0.2978, total avg loss: 0.3025, batch size: 29
2021-10-13 22:55:55,792 INFO [train.py:451] Epoch 1, batch 16800, batch avg loss 0.3351, total avg loss: 0.3017, batch size: 37
2021-10-13 22:56:00,640 INFO [train.py:451] Epoch 1, batch 16810, batch avg loss 0.3341, total avg loss: 0.2980, batch size: 38
2021-10-13 22:56:05,574 INFO [train.py:451] Epoch 1, batch 16820, batch avg loss 0.2399, total avg loss: 0.3051, batch size: 32
2021-10-13 22:56:10,307 INFO [train.py:451] Epoch 1, batch 16830, batch avg loss 0.3186, total avg loss: 0.3029, batch size: 42
2021-10-13 22:56:15,148 INFO [train.py:451] Epoch 1, batch 16840, batch avg loss 0.3650, total avg loss: 0.2987, batch size: 57
2021-10-13 22:56:20,234 INFO [train.py:451] Epoch 1, batch 16850, batch avg loss 0.3351, total avg loss: 0.2987, batch size: 34
2021-10-13 22:56:25,203 INFO [train.py:451] Epoch 1, batch 16860, batch avg loss 0.2464, total avg loss: 0.2973, batch size: 38
2021-10-13 22:56:30,008 INFO [train.py:451] Epoch 1, batch 16870, batch avg loss 0.2724, total avg loss: 0.2976, batch size: 28
2021-10-13 22:56:35,087 INFO [train.py:451] Epoch 1, batch 16880, batch avg loss 0.2409, total avg loss: 0.2975, batch size: 27
2021-10-13 22:56:39,935 INFO [train.py:451] Epoch 1, batch 16890, batch avg loss 0.2652, total avg loss: 0.2979, batch size: 36
2021-10-13 22:56:45,106 INFO [train.py:451] Epoch 1, batch 16900, batch avg loss 0.2313, total avg loss: 0.2960, batch size: 28
2021-10-13 22:56:50,126 INFO [train.py:451] Epoch 1, batch 16910, batch avg loss 0.3210, total avg loss: 0.2969, batch size: 33
2021-10-13 22:56:55,190 INFO [train.py:451] Epoch 1, batch 16920, batch avg loss 0.2074, total avg loss: 0.2968, batch size: 29
2021-10-13 22:57:00,127 INFO [train.py:451] Epoch 1, batch 16930, batch avg loss 0.2308, total avg loss: 0.2969, batch size: 29
2021-10-13 22:57:05,164 INFO [train.py:451] Epoch 1, batch 16940, batch avg loss 0.3122, total avg loss: 0.2974, batch size: 31
2021-10-13 22:57:09,966 INFO [train.py:451] Epoch 1, batch 16950, batch avg loss 0.2810, total avg loss: 0.2971, batch size: 42
2021-10-13 22:57:15,219 INFO [train.py:451] Epoch 1, batch 16960, batch avg loss 0.2947, total avg loss: 0.2974, batch size: 34
2021-10-13 22:57:20,094 INFO [train.py:451] Epoch 1, batch 16970, batch avg loss 0.3357, total avg loss: 0.2978, batch size: 71
2021-10-13 22:57:24,964 INFO [train.py:451] Epoch 1, batch 16980, batch avg loss 0.3090, total avg loss: 0.2981, batch size: 38
2021-10-13 22:57:29,837 INFO [train.py:451] Epoch 1, batch 16990, batch avg loss 0.2305, total avg loss: 0.2988, batch size: 28
2021-10-13 22:57:34,909 INFO [train.py:451] Epoch 1, batch 17000, batch avg loss 0.3072, total avg loss: 0.2992, batch size: 32
2021-10-13 22:58:14,795 INFO [train.py:483] Epoch 1, valid loss 0.2152, best valid loss: 0.2152 best valid epoch: 1
2021-10-13 22:58:19,694 INFO [train.py:451] Epoch 1, batch 17010, batch avg loss 0.3007, total avg loss: 0.2927, batch size: 45
2021-10-13 22:58:24,847 INFO [train.py:451] Epoch 1, batch 17020, batch avg loss 0.2811, total avg loss: 0.2893, batch size: 29
2021-10-13 22:58:29,608 INFO [train.py:451] Epoch 1, batch 17030, batch avg loss 0.3459, total avg loss: 0.3010, batch size: 39
2021-10-13 22:58:34,662 INFO [train.py:451] Epoch 1, batch 17040, batch avg loss 0.3237, total avg loss: 0.2977, batch size: 49
2021-10-13 22:58:39,582 INFO [train.py:451] Epoch 1, batch 17050, batch avg loss 0.3058, total avg loss: 0.2976, batch size: 34
2021-10-13 22:58:44,597 INFO [train.py:451] Epoch 1, batch 17060, batch avg loss 0.2883, total avg loss: 0.2980, batch size: 31
2021-10-13 22:58:49,489 INFO [train.py:451] Epoch 1, batch 17070, batch avg loss 0.3462, total avg loss: 0.3007, batch size: 45
2021-10-13 22:58:54,414 INFO [train.py:451] Epoch 1, batch 17080, batch avg loss 0.2998, total avg loss: 0.3003, batch size: 49
2021-10-13 22:58:59,282 INFO [train.py:451] Epoch 1, batch 17090, batch avg loss 0.3656, total avg loss: 0.3013, batch size: 42
2021-10-13 22:59:04,228 INFO [train.py:451] Epoch 1, batch 17100, batch avg loss 0.3390, total avg loss: 0.2997, batch size: 57
2021-10-13 22:59:09,265 INFO [train.py:451] Epoch 1, batch 17110, batch avg loss 0.2854, total avg loss: 0.3000, batch size: 31
2021-10-13 22:59:14,089 INFO [train.py:451] Epoch 1, batch 17120, batch avg loss 0.3227, total avg loss: 0.3013, batch size: 42
2021-10-13 22:59:19,068 INFO [train.py:451] Epoch 1, batch 17130, batch avg loss 0.3603, total avg loss: 0.3014, batch size: 38
2021-10-13 22:59:24,080 INFO [train.py:451] Epoch 1, batch 17140, batch avg loss 0.2986, total avg loss: 0.3009, batch size: 31
2021-10-13 22:59:29,047 INFO [train.py:451] Epoch 1, batch 17150, batch avg loss 0.2469, total avg loss: 0.3011, batch size: 30
2021-10-13 22:59:33,889 INFO [train.py:451] Epoch 1, batch 17160, batch avg loss 0.3292, total avg loss: 0.3025, batch size: 49
2021-10-13 22:59:38,990 INFO [train.py:451] Epoch 1, batch 17170, batch avg loss 0.3055, total avg loss: 0.3026, batch size: 36
2021-10-13 22:59:43,853 INFO [train.py:451] Epoch 1, batch 17180, batch avg loss 0.3211, total avg loss: 0.3036, batch size: 41
2021-10-13 22:59:48,630 INFO [train.py:451] Epoch 1, batch 17190, batch avg loss 0.2721, total avg loss: 0.3041, batch size: 36
2021-10-13 22:59:53,514 INFO [train.py:451] Epoch 1, batch 17200, batch avg loss 0.3145, total avg loss: 0.3041, batch size: 34
2021-10-13 22:59:58,390 INFO [train.py:451] Epoch 1, batch 17210, batch avg loss 0.3393, total avg loss: 0.3080, batch size: 34
2021-10-13 23:00:03,480 INFO [train.py:451] Epoch 1, batch 17220, batch avg loss 0.4319, total avg loss: 0.3000, batch size: 129
2021-10-13 23:00:08,441 INFO [train.py:451] Epoch 1, batch 17230, batch avg loss 0.3095, total avg loss: 0.2924, batch size: 42
2021-10-13 23:00:13,393 INFO [train.py:451] Epoch 1, batch 17240, batch avg loss 0.2841, total avg loss: 0.2966, batch size: 31
2021-10-13 23:00:18,292 INFO [train.py:451] Epoch 1, batch 17250, batch avg loss 0.3386, total avg loss: 0.3024, batch size: 49
2021-10-13 23:00:23,251 INFO [train.py:451] Epoch 1, batch 17260, batch avg loss 0.2648, total avg loss: 0.3021, batch size: 33
2021-10-13 23:00:28,123 INFO [train.py:451] Epoch 1, batch 17270, batch avg loss 0.2623, total avg loss: 0.3043, batch size: 33
2021-10-13 23:00:33,050 INFO [train.py:451] Epoch 1, batch 17280, batch avg loss 0.3111, total avg loss: 0.3043, batch size: 36
2021-10-13 23:00:37,846 INFO [train.py:451] Epoch 1, batch 17290, batch avg loss 0.3338, total avg loss: 0.3047, batch size: 38
2021-10-13 23:00:42,709 INFO [train.py:451] Epoch 1, batch 17300, batch avg loss 0.2851, total avg loss: 0.3048, batch size: 35
2021-10-13 23:00:47,692 INFO [train.py:451] Epoch 1, batch 17310, batch avg loss 0.2765, total avg loss: 0.3024, batch size: 39
2021-10-13 23:00:52,648 INFO [train.py:451] Epoch 1, batch 17320, batch avg loss 0.2930, total avg loss: 0.3014, batch size: 35
2021-10-13 23:00:57,818 INFO [train.py:451] Epoch 1, batch 17330, batch avg loss 0.3346, total avg loss: 0.3016, batch size: 38
2021-10-13 23:01:02,726 INFO [train.py:451] Epoch 1, batch 17340, batch avg loss 0.2634, total avg loss: 0.3009, batch size: 30
2021-10-13 23:01:07,833 INFO [train.py:451] Epoch 1, batch 17350, batch avg loss 0.3232, total avg loss: 0.3002, batch size: 36
2021-10-13 23:01:12,751 INFO [train.py:451] Epoch 1, batch 17360, batch avg loss 0.2780, total avg loss: 0.3003, batch size: 32
2021-10-13 23:01:17,725 INFO [train.py:451] Epoch 1, batch 17370, batch avg loss 0.2837, total avg loss: 0.2995, batch size: 31
2021-10-13 23:01:22,850 INFO [train.py:451] Epoch 1, batch 17380, batch avg loss 0.3140, total avg loss: 0.3001, batch size: 49
2021-10-13 23:01:27,809 INFO [train.py:451] Epoch 1, batch 17390, batch avg loss 0.2626, total avg loss: 0.2993, batch size: 49
2021-10-13 23:01:32,838 INFO [train.py:451] Epoch 1, batch 17400, batch avg loss 0.2768, total avg loss: 0.2995, batch size: 28
2021-10-13 23:01:37,779 INFO [train.py:451] Epoch 1, batch 17410, batch avg loss 0.2292, total avg loss: 0.3064, batch size: 31
2021-10-13 23:01:42,662 INFO [train.py:451] Epoch 1, batch 17420, batch avg loss 0.3567, total avg loss: 0.2994, batch size: 70
2021-10-13 23:01:47,575 INFO [train.py:451] Epoch 1, batch 17430, batch avg loss 0.3025, total avg loss: 0.2957, batch size: 39
2021-10-13 23:01:52,537 INFO [train.py:451] Epoch 1, batch 17440, batch avg loss 0.2852, total avg loss: 0.2922, batch size: 28
2021-10-13 23:01:57,635 INFO [train.py:451] Epoch 1, batch 17450, batch avg loss 0.3299, total avg loss: 0.2910, batch size: 41
2021-10-13 23:02:02,433 INFO [train.py:451] Epoch 1, batch 17460, batch avg loss 0.3469, total avg loss: 0.2949, batch size: 33
2021-10-13 23:02:07,255 INFO [train.py:451] Epoch 1, batch 17470, batch avg loss 0.3482, total avg loss: 0.2992, batch size: 72
2021-10-13 23:02:12,231 INFO [train.py:451] Epoch 1, batch 17480, batch avg loss 0.2730, total avg loss: 0.3005, batch size: 49
2021-10-13 23:02:16,998 INFO [train.py:451] Epoch 1, batch 17490, batch avg loss 0.3036, total avg loss: 0.3017, batch size: 33
2021-10-13 23:02:22,192 INFO [train.py:451] Epoch 1, batch 17500, batch avg loss 0.2395, total avg loss: 0.2982, batch size: 27
2021-10-13 23:02:27,274 INFO [train.py:451] Epoch 1, batch 17510, batch avg loss 0.2551, total avg loss: 0.2971, batch size: 31
2021-10-13 23:02:32,232 INFO [train.py:451] Epoch 1, batch 17520, batch avg loss 0.3967, total avg loss: 0.2976, batch size: 129
2021-10-13 23:02:37,190 INFO [train.py:451] Epoch 1, batch 17530, batch avg loss 0.3220, total avg loss: 0.2981, batch size: 36
2021-10-13 23:02:42,022 INFO [train.py:451] Epoch 1, batch 17540, batch avg loss 0.3926, total avg loss: 0.3012, batch size: 135
2021-10-13 23:02:47,243 INFO [train.py:451] Epoch 1, batch 17550, batch avg loss 0.2953, total avg loss: 0.3004, batch size: 34
2021-10-13 23:02:48,357 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "ac6a7e72-02a3-8c14-ade4-24d69c28fe04" will not be mixed in.
2021-10-13 23:02:52,162 INFO [train.py:451] Epoch 1, batch 17560, batch avg loss 0.2669, total avg loss: 0.3010, batch size: 34
2021-10-13 23:02:57,020 INFO [train.py:451] Epoch 1, batch 17570, batch avg loss 0.3271, total avg loss: 0.3021, batch size: 41
2021-10-13 23:03:02,005 INFO [train.py:451] Epoch 1, batch 17580, batch avg loss 0.3116, total avg loss: 0.3020, batch size: 32
2021-10-13 23:03:07,033 INFO [train.py:451] Epoch 1, batch 17590, batch avg loss 0.3168, total avg loss: 0.3020, batch size: 57
2021-10-13 23:03:11,952 INFO [train.py:451] Epoch 1, batch 17600, batch avg loss 0.3121, total avg loss: 0.3016, batch size: 35
2021-10-13 23:03:16,897 INFO [train.py:451] Epoch 1, batch 17610, batch avg loss 0.2703, total avg loss: 0.3055, batch size: 38
2021-10-13 23:03:21,699 INFO [train.py:451] Epoch 1, batch 17620, batch avg loss 0.4321, total avg loss: 0.3044, batch size: 127
2021-10-13 23:03:26,755 INFO [train.py:451] Epoch 1, batch 17630, batch avg loss 0.2891, total avg loss: 0.2973, batch size: 35
2021-10-13 23:03:31,844 INFO [train.py:451] Epoch 1, batch 17640, batch avg loss 0.3006, total avg loss: 0.2979, batch size: 33
2021-10-13 23:03:37,007 INFO [train.py:451] Epoch 1, batch 17650, batch avg loss 0.2606, total avg loss: 0.2955, batch size: 33
2021-10-13 23:03:41,987 INFO [train.py:451] Epoch 1, batch 17660, batch avg loss 0.3202, total avg loss: 0.2961, batch size: 35
2021-10-13 23:03:47,336 INFO [train.py:451] Epoch 1, batch 17670, batch avg loss 0.3137, total avg loss: 0.2965, batch size: 29
2021-10-13 23:03:52,416 INFO [train.py:451] Epoch 1, batch 17680, batch avg loss 0.2401, total avg loss: 0.2964, batch size: 27
2021-10-13 23:03:57,250 INFO [train.py:451] Epoch 1, batch 17690, batch avg loss 0.3689, total avg loss: 0.2990, batch size: 41
2021-10-13 23:04:02,186 INFO [train.py:451] Epoch 1, batch 17700, batch avg loss 0.2268, total avg loss: 0.2975, batch size: 29
2021-10-13 23:04:07,112 INFO [train.py:451] Epoch 1, batch 17710, batch avg loss 0.2864, total avg loss: 0.2997, batch size: 30
2021-10-13 23:04:11,949 INFO [train.py:451] Epoch 1, batch 17720, batch avg loss 0.2773, total avg loss: 0.3021, batch size: 31
2021-10-13 23:04:16,933 INFO [train.py:451] Epoch 1, batch 17730, batch avg loss 0.3072, total avg loss: 0.3025, batch size: 38
2021-10-13 23:04:21,713 INFO [train.py:451] Epoch 1, batch 17740, batch avg loss 0.3011, total avg loss: 0.3022, batch size: 33
2021-10-13 23:04:26,732 INFO [train.py:451] Epoch 1, batch 17750, batch avg loss 0.3314, total avg loss: 0.3026, batch size: 36
2021-10-13 23:04:31,887 INFO [train.py:451] Epoch 1, batch 17760, batch avg loss 0.2972, total avg loss: 0.3023, batch size: 34
2021-10-13 23:04:36,941 INFO [train.py:451] Epoch 1, batch 17770, batch avg loss 0.2524, total avg loss: 0.3013, batch size: 34
2021-10-13 23:04:41,811 INFO [train.py:451] Epoch 1, batch 17780, batch avg loss 0.3235, total avg loss: 0.3014, batch size: 57
2021-10-13 23:04:46,744 INFO [train.py:451] Epoch 1, batch 17790, batch avg loss 0.2372, total avg loss: 0.3017, batch size: 29
2021-10-13 23:04:51,686 INFO [train.py:451] Epoch 1, batch 17800, batch avg loss 0.2875, total avg loss: 0.3006, batch size: 41
2021-10-13 23:04:56,600 INFO [train.py:451] Epoch 1, batch 17810, batch avg loss 0.2995, total avg loss: 0.3097, batch size: 35
2021-10-13 23:05:01,449 INFO [train.py:451] Epoch 1, batch 17820, batch avg loss 0.2469, total avg loss: 0.3099, batch size: 32
2021-10-13 23:05:06,347 INFO [train.py:451] Epoch 1, batch 17830, batch avg loss 0.3186, total avg loss: 0.3102, batch size: 49
2021-10-13 23:05:10,976 INFO [train.py:451] Epoch 1, batch 17840, batch avg loss 0.3441, total avg loss: 0.3157, batch size: 72
2021-10-13 23:05:15,829 INFO [train.py:451] Epoch 1, batch 17850, batch avg loss 0.3158, total avg loss: 0.3124, batch size: 39
2021-10-13 23:05:20,772 INFO [train.py:451] Epoch 1, batch 17860, batch avg loss 0.3252, total avg loss: 0.3104, batch size: 36
2021-10-13 23:05:25,671 INFO [train.py:451] Epoch 1, batch 17870, batch avg loss 0.2679, total avg loss: 0.3079, batch size: 28
2021-10-13 23:05:30,550 INFO [train.py:451] Epoch 1, batch 17880, batch avg loss 0.2435, total avg loss: 0.3059, batch size: 30
2021-10-13 23:05:35,422 INFO [train.py:451] Epoch 1, batch 17890, batch avg loss 0.3550, total avg loss: 0.3039, batch size: 57
2021-10-13 23:05:40,260 INFO [train.py:451] Epoch 1, batch 17900, batch avg loss 0.3816, total avg loss: 0.3032, batch size: 132
2021-10-13 23:05:45,452 INFO [train.py:451] Epoch 1, batch 17910, batch avg loss 0.2852, total avg loss: 0.3016, batch size: 35
2021-10-13 23:05:50,456 INFO [train.py:451] Epoch 1, batch 17920, batch avg loss 0.2903, total avg loss: 0.2999, batch size: 30
2021-10-13 23:05:55,421 INFO [train.py:451] Epoch 1, batch 17930, batch avg loss 0.2844, total avg loss: 0.3006, batch size: 34
2021-10-13 23:06:00,280 INFO [train.py:451] Epoch 1, batch 17940, batch avg loss 0.2620, total avg loss: 0.2999, batch size: 31
2021-10-13 23:06:05,193 INFO [train.py:451] Epoch 1, batch 17950, batch avg loss 0.3374, total avg loss: 0.2998, batch size: 45
2021-10-13 23:06:10,084 INFO [train.py:451] Epoch 1, batch 17960, batch avg loss 0.2647, total avg loss: 0.2992, batch size: 34
2021-10-13 23:06:15,049 INFO [train.py:451] Epoch 1, batch 17970, batch avg loss 0.3157, total avg loss: 0.2981, batch size: 28
2021-10-13 23:06:20,028 INFO [train.py:451] Epoch 1, batch 17980, batch avg loss 0.2800, total avg loss: 0.2966, batch size: 29
2021-10-13 23:06:24,918 INFO [train.py:451] Epoch 1, batch 17990, batch avg loss 0.3183, total avg loss: 0.2960, batch size: 41
2021-10-13 23:06:29,877 INFO [train.py:451] Epoch 1, batch 18000, batch avg loss 0.3359, total avg loss: 0.2960, batch size: 34
2021-10-13 23:07:09,943 INFO [train.py:483] Epoch 1, valid loss 0.2148, best valid loss: 0.2148 best valid epoch: 1
2021-10-13 23:07:14,811 INFO [train.py:451] Epoch 1, batch 18010, batch avg loss 0.2342, total avg loss: 0.2946, batch size: 31
2021-10-13 23:07:19,625 INFO [train.py:451] Epoch 1, batch 18020, batch avg loss 0.3071, total avg loss: 0.3058, batch size: 33
2021-10-13 23:07:24,493 INFO [train.py:451] Epoch 1, batch 18030, batch avg loss 0.3036, total avg loss: 0.3070, batch size: 35
2021-10-13 23:07:29,437 INFO [train.py:451] Epoch 1, batch 18040, batch avg loss 0.2210, total avg loss: 0.3009, batch size: 29
2021-10-13 23:07:34,409 INFO [train.py:451] Epoch 1, batch 18050, batch avg loss 0.3020, total avg loss: 0.3034, batch size: 33
2021-10-13 23:07:39,242 INFO [train.py:451] Epoch 1, batch 18060, batch avg loss 0.2471, total avg loss: 0.3055, batch size: 31
2021-10-13 23:07:44,158 INFO [train.py:451] Epoch 1, batch 18070, batch avg loss 0.2663, total avg loss: 0.3046, batch size: 32
2021-10-13 23:07:49,039 INFO [train.py:451] Epoch 1, batch 18080, batch avg loss 0.2578, total avg loss: 0.3031, batch size: 30
2021-10-13 23:07:53,900 INFO [train.py:451] Epoch 1, batch 18090, batch avg loss 0.2287, total avg loss: 0.3008, batch size: 32
2021-10-13 23:07:58,766 INFO [train.py:451] Epoch 1, batch 18100, batch avg loss 0.3000, total avg loss: 0.3023, batch size: 36
2021-10-13 23:08:03,551 INFO [train.py:451] Epoch 1, batch 18110, batch avg loss 0.2621, total avg loss: 0.3032, batch size: 39
2021-10-13 23:08:08,337 INFO [train.py:451] Epoch 1, batch 18120, batch avg loss 0.3598, total avg loss: 0.3041, batch size: 49
2021-10-13 23:08:13,338 INFO [train.py:451] Epoch 1, batch 18130, batch avg loss 0.2950, total avg loss: 0.3049, batch size: 27
2021-10-13 23:08:18,494 INFO [train.py:451] Epoch 1, batch 18140, batch avg loss 0.2934, total avg loss: 0.3038, batch size: 34
2021-10-13 23:08:23,453 INFO [train.py:451] Epoch 1, batch 18150, batch avg loss 0.2970, total avg loss: 0.3034, batch size: 32
2021-10-13 23:08:28,456 INFO [train.py:451] Epoch 1, batch 18160, batch avg loss 0.2506, total avg loss: 0.3024, batch size: 29
2021-10-13 23:08:33,440 INFO [train.py:451] Epoch 1, batch 18170, batch avg loss 0.3288, total avg loss: 0.3034, batch size: 36
2021-10-13 23:08:38,285 INFO [train.py:451] Epoch 1, batch 18180, batch avg loss 0.2582, total avg loss: 0.3029, batch size: 41
2021-10-13 23:08:43,255 INFO [train.py:451] Epoch 1, batch 18190, batch avg loss 0.2820, total avg loss: 0.3015, batch size: 35
2021-10-13 23:08:48,191 INFO [train.py:451] Epoch 1, batch 18200, batch avg loss 0.2586, total avg loss: 0.3010, batch size: 31
2021-10-13 23:08:52,928 INFO [train.py:451] Epoch 1, batch 18210, batch avg loss 0.3447, total avg loss: 0.3078, batch size: 72
2021-10-13 23:08:58,033 INFO [train.py:451] Epoch 1, batch 18220, batch avg loss 0.3091, total avg loss: 0.2943, batch size: 38
2021-10-13 23:09:02,913 INFO [train.py:451] Epoch 1, batch 18230, batch avg loss 0.2930, total avg loss: 0.2984, batch size: 34
2021-10-13 23:09:07,902 INFO [train.py:451] Epoch 1, batch 18240, batch avg loss 0.3319, total avg loss: 0.3019, batch size: 41
2021-10-13 23:09:12,924 INFO [train.py:451] Epoch 1, batch 18250, batch avg loss 0.2712, total avg loss: 0.3014, batch size: 34
2021-10-13 23:09:17,924 INFO [train.py:451] Epoch 1, batch 18260, batch avg loss 0.3676, total avg loss: 0.2990, batch size: 57
2021-10-13 23:09:22,887 INFO [train.py:451] Epoch 1, batch 18270, batch avg loss 0.3023, total avg loss: 0.3003, batch size: 38
2021-10-13 23:09:27,791 INFO [train.py:451] Epoch 1, batch 18280, batch avg loss 0.2533, total avg loss: 0.3001, batch size: 36
2021-10-13 23:09:32,640 INFO [train.py:451] Epoch 1, batch 18290, batch avg loss 0.2683, total avg loss: 0.2999, batch size: 35
2021-10-13 23:09:37,885 INFO [train.py:451] Epoch 1, batch 18300, batch avg loss 0.2649, total avg loss: 0.2982, batch size: 27
2021-10-13 23:09:42,757 INFO [train.py:451] Epoch 1, batch 18310, batch avg loss 0.3007, total avg loss: 0.2990, batch size: 33
2021-10-13 23:09:47,693 INFO [train.py:451] Epoch 1, batch 18320, batch avg loss 0.2762, total avg loss: 0.2998, batch size: 35
2021-10-13 23:09:52,681 INFO [train.py:451] Epoch 1, batch 18330, batch avg loss 0.2215, total avg loss: 0.2984, batch size: 29
2021-10-13 23:09:57,627 INFO [train.py:451] Epoch 1, batch 18340, batch avg loss 0.2878, total avg loss: 0.2987, batch size: 36
2021-10-13 23:10:02,433 INFO [train.py:451] Epoch 1, batch 18350, batch avg loss 0.2930, total avg loss: 0.2984, batch size: 45
2021-10-13 23:10:07,431 INFO [train.py:451] Epoch 1, batch 18360, batch avg loss 0.3095, total avg loss: 0.2983, batch size: 35
2021-10-13 23:10:12,372 INFO [train.py:451] Epoch 1, batch 18370, batch avg loss 0.3166, total avg loss: 0.2981, batch size: 35
2021-10-13 23:10:17,476 INFO [train.py:451] Epoch 1, batch 18380, batch avg loss 0.2817, total avg loss: 0.2974, batch size: 36
2021-10-13 23:10:22,329 INFO [train.py:451] Epoch 1, batch 18390, batch avg loss 0.2967, total avg loss: 0.2983, batch size: 35
2021-10-13 23:10:27,293 INFO [train.py:451] Epoch 1, batch 18400, batch avg loss 0.2805, total avg loss: 0.2985, batch size: 34
2021-10-13 23:10:32,181 INFO [train.py:451] Epoch 1, batch 18410, batch avg loss 0.2997, total avg loss: 0.3087, batch size: 49
2021-10-13 23:10:37,265 INFO [train.py:451] Epoch 1, batch 18420, batch avg loss 0.2824, total avg loss: 0.2916, batch size: 32
2021-10-13 23:10:42,282 INFO [train.py:451] Epoch 1, batch 18430, batch avg loss 0.3237, total avg loss: 0.2969, batch size: 36
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2021-10-13 23:11:21,940 INFO [train.py:451] Epoch 1, batch 18510, batch avg loss 0.2107, total avg loss: 0.2971, batch size: 30
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2021-10-13 23:11:31,660 INFO [train.py:451] Epoch 1, batch 18530, batch avg loss 0.2884, total avg loss: 0.2993, batch size: 32
2021-10-13 23:11:36,402 INFO [train.py:451] Epoch 1, batch 18540, batch avg loss 0.2768, total avg loss: 0.3006, batch size: 38
2021-10-13 23:11:41,388 INFO [train.py:451] Epoch 1, batch 18550, batch avg loss 0.2710, total avg loss: 0.3015, batch size: 32
2021-10-13 23:11:46,149 INFO [train.py:451] Epoch 1, batch 18560, batch avg loss 0.3936, total avg loss: 0.3026, batch size: 127
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2021-10-13 23:12:01,093 INFO [train.py:451] Epoch 1, batch 18590, batch avg loss 0.2892, total avg loss: 0.3031, batch size: 30
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2021-10-13 23:12:10,972 INFO [train.py:451] Epoch 1, batch 18610, batch avg loss 0.3074, total avg loss: 0.3127, batch size: 39
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2021-10-13 23:12:20,774 INFO [train.py:451] Epoch 1, batch 18630, batch avg loss 0.3504, total avg loss: 0.3200, batch size: 36
2021-10-13 23:12:25,873 INFO [train.py:451] Epoch 1, batch 18640, batch avg loss 0.2623, total avg loss: 0.3112, batch size: 32
2021-10-13 23:12:30,915 INFO [train.py:451] Epoch 1, batch 18650, batch avg loss 0.3242, total avg loss: 0.3082, batch size: 33
2021-10-13 23:12:35,966 INFO [train.py:451] Epoch 1, batch 18660, batch avg loss 0.3599, total avg loss: 0.3037, batch size: 38
2021-10-13 23:12:40,832 INFO [train.py:451] Epoch 1, batch 18670, batch avg loss 0.2746, total avg loss: 0.3068, batch size: 33
2021-10-13 23:12:45,764 INFO [train.py:451] Epoch 1, batch 18680, batch avg loss 0.2607, total avg loss: 0.3058, batch size: 34
2021-10-13 23:12:50,673 INFO [train.py:451] Epoch 1, batch 18690, batch avg loss 0.2735, total avg loss: 0.3046, batch size: 29
2021-10-13 23:12:55,601 INFO [train.py:451] Epoch 1, batch 18700, batch avg loss 0.2734, total avg loss: 0.3042, batch size: 34
2021-10-13 23:13:00,556 INFO [train.py:451] Epoch 1, batch 18710, batch avg loss 0.3006, total avg loss: 0.3046, batch size: 31
2021-10-13 23:13:05,577 INFO [train.py:451] Epoch 1, batch 18720, batch avg loss 0.2822, total avg loss: 0.3029, batch size: 39
2021-10-13 23:13:10,787 INFO [train.py:451] Epoch 1, batch 18730, batch avg loss 0.2568, total avg loss: 0.3019, batch size: 31
2021-10-13 23:13:15,823 INFO [train.py:451] Epoch 1, batch 18740, batch avg loss 0.2433, total avg loss: 0.3018, batch size: 28
2021-10-13 23:13:20,921 INFO [train.py:451] Epoch 1, batch 18750, batch avg loss 0.3543, total avg loss: 0.3020, batch size: 49
2021-10-13 23:13:25,914 INFO [train.py:451] Epoch 1, batch 18760, batch avg loss 0.3470, total avg loss: 0.3017, batch size: 39
2021-10-13 23:13:30,822 INFO [train.py:451] Epoch 1, batch 18770, batch avg loss 0.3168, total avg loss: 0.3016, batch size: 41
2021-10-13 23:13:35,812 INFO [train.py:451] Epoch 1, batch 18780, batch avg loss 0.3075, total avg loss: 0.3018, batch size: 38
2021-10-13 23:13:40,612 INFO [train.py:451] Epoch 1, batch 18790, batch avg loss 0.3020, total avg loss: 0.3015, batch size: 33
2021-10-13 23:13:45,536 INFO [train.py:451] Epoch 1, batch 18800, batch avg loss 0.3480, total avg loss: 0.3011, batch size: 45
2021-10-13 23:13:50,466 INFO [train.py:451] Epoch 1, batch 18810, batch avg loss 0.2628, total avg loss: 0.2909, batch size: 38
2021-10-13 23:13:55,591 INFO [train.py:451] Epoch 1, batch 18820, batch avg loss 0.2797, total avg loss: 0.2845, batch size: 36
2021-10-13 23:14:00,299 INFO [train.py:451] Epoch 1, batch 18830, batch avg loss 0.4006, total avg loss: 0.2945, batch size: 130
2021-10-13 23:14:05,341 INFO [train.py:451] Epoch 1, batch 18840, batch avg loss 0.3496, total avg loss: 0.2950, batch size: 38
2021-10-13 23:14:10,090 INFO [train.py:451] Epoch 1, batch 18850, batch avg loss 0.2701, total avg loss: 0.2969, batch size: 29
2021-10-13 23:14:15,043 INFO [train.py:451] Epoch 1, batch 18860, batch avg loss 0.2966, total avg loss: 0.3006, batch size: 31
2021-10-13 23:14:20,033 INFO [train.py:451] Epoch 1, batch 18870, batch avg loss 0.2590, total avg loss: 0.2997, batch size: 33
2021-10-13 23:14:25,060 INFO [train.py:451] Epoch 1, batch 18880, batch avg loss 0.3367, total avg loss: 0.3005, batch size: 39
2021-10-13 23:14:29,924 INFO [train.py:451] Epoch 1, batch 18890, batch avg loss 0.4407, total avg loss: 0.3010, batch size: 127
2021-10-13 23:14:34,954 INFO [train.py:451] Epoch 1, batch 18900, batch avg loss 0.2647, total avg loss: 0.3010, batch size: 28
2021-10-13 23:14:39,877 INFO [train.py:451] Epoch 1, batch 18910, batch avg loss 0.2727, total avg loss: 0.3008, batch size: 36
2021-10-13 23:14:44,580 INFO [train.py:451] Epoch 1, batch 18920, batch avg loss 0.2978, total avg loss: 0.3022, batch size: 56
2021-10-13 23:14:49,531 INFO [train.py:451] Epoch 1, batch 18930, batch avg loss 0.3178, total avg loss: 0.3027, batch size: 40
2021-10-13 23:14:54,560 INFO [train.py:451] Epoch 1, batch 18940, batch avg loss 0.2785, total avg loss: 0.3021, batch size: 28
2021-10-13 23:14:59,393 INFO [train.py:451] Epoch 1, batch 18950, batch avg loss 0.3578, total avg loss: 0.3036, batch size: 42
2021-10-13 23:15:04,090 INFO [train.py:451] Epoch 1, batch 18960, batch avg loss 0.2659, total avg loss: 0.3043, batch size: 31
2021-10-13 23:15:09,281 INFO [train.py:451] Epoch 1, batch 18970, batch avg loss 0.3187, total avg loss: 0.3033, batch size: 33
2021-10-13 23:15:14,257 INFO [train.py:451] Epoch 1, batch 18980, batch avg loss 0.2985, total avg loss: 0.3032, batch size: 36
2021-10-13 23:15:19,439 INFO [train.py:451] Epoch 1, batch 18990, batch avg loss 0.2715, total avg loss: 0.3024, batch size: 27
2021-10-13 23:15:24,369 INFO [train.py:451] Epoch 1, batch 19000, batch avg loss 0.2967, total avg loss: 0.3022, batch size: 33
2021-10-13 23:16:03,767 INFO [train.py:483] Epoch 1, valid loss 0.2135, best valid loss: 0.2135 best valid epoch: 1
2021-10-13 23:16:08,840 INFO [train.py:451] Epoch 1, batch 19010, batch avg loss 0.3007, total avg loss: 0.2901, batch size: 35
2021-10-13 23:16:13,662 INFO [train.py:451] Epoch 1, batch 19020, batch avg loss 0.2801, total avg loss: 0.2958, batch size: 32
2021-10-13 23:16:18,508 INFO [train.py:451] Epoch 1, batch 19030, batch avg loss 0.4359, total avg loss: 0.3043, batch size: 131
2021-10-13 23:16:23,542 INFO [train.py:451] Epoch 1, batch 19040, batch avg loss 0.2693, total avg loss: 0.2971, batch size: 32
2021-10-13 23:16:28,426 INFO [train.py:451] Epoch 1, batch 19050, batch avg loss 0.3159, total avg loss: 0.2963, batch size: 42
2021-10-13 23:16:33,337 INFO [train.py:451] Epoch 1, batch 19060, batch avg loss 0.3157, total avg loss: 0.3004, batch size: 38
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2021-10-13 23:16:53,074 INFO [train.py:451] Epoch 1, batch 19100, batch avg loss 0.3307, total avg loss: 0.2985, batch size: 36
2021-10-13 23:16:57,968 INFO [train.py:451] Epoch 1, batch 19110, batch avg loss 0.3081, total avg loss: 0.2972, batch size: 37
2021-10-13 23:17:02,776 INFO [train.py:451] Epoch 1, batch 19120, batch avg loss 0.3335, total avg loss: 0.2997, batch size: 41
2021-10-13 23:17:07,770 INFO [train.py:451] Epoch 1, batch 19130, batch avg loss 0.3093, total avg loss: 0.3004, batch size: 36
2021-10-13 23:17:12,882 INFO [train.py:451] Epoch 1, batch 19140, batch avg loss 0.2684, total avg loss: 0.2998, batch size: 38
2021-10-13 23:17:17,960 INFO [train.py:451] Epoch 1, batch 19150, batch avg loss 0.2907, total avg loss: 0.2999, batch size: 42
2021-10-13 23:17:23,163 INFO [train.py:451] Epoch 1, batch 19160, batch avg loss 0.3016, total avg loss: 0.2994, batch size: 29
2021-10-13 23:17:28,235 INFO [train.py:451] Epoch 1, batch 19170, batch avg loss 0.3663, total avg loss: 0.2999, batch size: 39
2021-10-13 23:17:33,107 INFO [train.py:451] Epoch 1, batch 19180, batch avg loss 0.2704, total avg loss: 0.2992, batch size: 33
2021-10-13 23:17:37,921 INFO [train.py:451] Epoch 1, batch 19190, batch avg loss 0.2352, total avg loss: 0.3003, batch size: 28
2021-10-13 23:17:42,995 INFO [train.py:451] Epoch 1, batch 19200, batch avg loss 0.2606, total avg loss: 0.3004, batch size: 27
2021-10-13 23:17:47,888 INFO [train.py:451] Epoch 1, batch 19210, batch avg loss 0.3485, total avg loss: 0.3054, batch size: 38
2021-10-13 23:17:52,796 INFO [train.py:451] Epoch 1, batch 19220, batch avg loss 0.2410, total avg loss: 0.3024, batch size: 33
2021-10-13 23:17:57,862 INFO [train.py:451] Epoch 1, batch 19230, batch avg loss 0.2435, total avg loss: 0.2976, batch size: 28
2021-10-13 23:18:02,874 INFO [train.py:451] Epoch 1, batch 19240, batch avg loss 0.3484, total avg loss: 0.2940, batch size: 57
2021-10-13 23:18:07,888 INFO [train.py:451] Epoch 1, batch 19250, batch avg loss 0.2632, total avg loss: 0.2941, batch size: 30
2021-10-13 23:18:12,871 INFO [train.py:451] Epoch 1, batch 19260, batch avg loss 0.3093, total avg loss: 0.2945, batch size: 37
2021-10-13 23:18:17,895 INFO [train.py:451] Epoch 1, batch 19270, batch avg loss 0.3575, total avg loss: 0.2954, batch size: 45
2021-10-13 23:18:22,999 INFO [train.py:451] Epoch 1, batch 19280, batch avg loss 0.2828, total avg loss: 0.2952, batch size: 35
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2021-10-13 23:18:33,019 INFO [train.py:451] Epoch 1, batch 19300, batch avg loss 0.3455, total avg loss: 0.2964, batch size: 38
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2021-10-13 23:18:42,802 INFO [train.py:451] Epoch 1, batch 19320, batch avg loss 0.3025, total avg loss: 0.2976, batch size: 38
2021-10-13 23:18:47,797 INFO [train.py:451] Epoch 1, batch 19330, batch avg loss 0.3273, total avg loss: 0.2983, batch size: 31
2021-10-13 23:18:52,792 INFO [train.py:451] Epoch 1, batch 19340, batch avg loss 0.3371, total avg loss: 0.2983, batch size: 39
2021-10-13 23:18:57,796 INFO [train.py:451] Epoch 1, batch 19350, batch avg loss 0.3151, total avg loss: 0.2975, batch size: 35
2021-10-13 23:19:02,715 INFO [train.py:451] Epoch 1, batch 19360, batch avg loss 0.3133, total avg loss: 0.2970, batch size: 29
2021-10-13 23:19:07,682 INFO [train.py:451] Epoch 1, batch 19370, batch avg loss 0.2327, total avg loss: 0.2972, batch size: 30
2021-10-13 23:19:20,531 INFO [train.py:451] Epoch 1, batch 19380, batch avg loss 0.2726, total avg loss: 0.2969, batch size: 31
2021-10-13 23:19:25,386 INFO [train.py:451] Epoch 1, batch 19390, batch avg loss 0.3129, total avg loss: 0.2972, batch size: 35
2021-10-13 23:19:30,358 INFO [train.py:451] Epoch 1, batch 19400, batch avg loss 0.3092, total avg loss: 0.2963, batch size: 30
2021-10-13 23:19:35,319 INFO [train.py:451] Epoch 1, batch 19410, batch avg loss 0.2580, total avg loss: 0.2847, batch size: 36
2021-10-13 23:19:40,290 INFO [train.py:451] Epoch 1, batch 19420, batch avg loss 0.3895, total avg loss: 0.2944, batch size: 129
2021-10-13 23:19:45,230 INFO [train.py:451] Epoch 1, batch 19430, batch avg loss 0.2936, total avg loss: 0.2869, batch size: 45
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2021-10-13 23:19:55,361 INFO [train.py:451] Epoch 1, batch 19450, batch avg loss 0.3121, total avg loss: 0.2965, batch size: 39
2021-10-13 23:20:00,147 INFO [train.py:451] Epoch 1, batch 19460, batch avg loss 0.3092, total avg loss: 0.3000, batch size: 37
2021-10-13 23:20:05,028 INFO [train.py:451] Epoch 1, batch 19470, batch avg loss 0.2858, total avg loss: 0.2999, batch size: 35
2021-10-13 23:20:09,841 INFO [train.py:451] Epoch 1, batch 19480, batch avg loss 0.3052, total avg loss: 0.2990, batch size: 38
2021-10-13 23:20:14,877 INFO [train.py:451] Epoch 1, batch 19490, batch avg loss 0.2392, total avg loss: 0.2960, batch size: 30
2021-10-13 23:20:19,878 INFO [train.py:451] Epoch 1, batch 19500, batch avg loss 0.2796, total avg loss: 0.2943, batch size: 33
2021-10-13 23:20:24,901 INFO [train.py:451] Epoch 1, batch 19510, batch avg loss 0.2744, total avg loss: 0.2944, batch size: 29
2021-10-13 23:20:29,891 INFO [train.py:451] Epoch 1, batch 19520, batch avg loss 0.2804, total avg loss: 0.2959, batch size: 34
2021-10-13 23:20:34,831 INFO [train.py:451] Epoch 1, batch 19530, batch avg loss 0.2937, total avg loss: 0.2976, batch size: 33
2021-10-13 23:20:40,037 INFO [train.py:451] Epoch 1, batch 19540, batch avg loss 0.2876, total avg loss: 0.2977, batch size: 37
2021-10-13 23:20:44,717 INFO [train.py:451] Epoch 1, batch 19550, batch avg loss 0.2181, total avg loss: 0.2978, batch size: 29
2021-10-13 23:20:49,794 INFO [train.py:451] Epoch 1, batch 19560, batch avg loss 0.3204, total avg loss: 0.2972, batch size: 33
2021-10-13 23:20:54,621 INFO [train.py:451] Epoch 1, batch 19570, batch avg loss 0.3387, total avg loss: 0.2967, batch size: 72
2021-10-13 23:20:59,565 INFO [train.py:451] Epoch 1, batch 19580, batch avg loss 0.3246, total avg loss: 0.2968, batch size: 49
2021-10-13 23:21:04,648 INFO [train.py:451] Epoch 1, batch 19590, batch avg loss 0.3347, total avg loss: 0.2960, batch size: 49
2021-10-13 23:21:09,839 INFO [train.py:451] Epoch 1, batch 19600, batch avg loss 0.2504, total avg loss: 0.2945, batch size: 34
2021-10-13 23:21:14,989 INFO [train.py:451] Epoch 1, batch 19610, batch avg loss 0.3440, total avg loss: 0.3011, batch size: 33
2021-10-13 23:21:20,006 INFO [train.py:451] Epoch 1, batch 19620, batch avg loss 0.2505, total avg loss: 0.3016, batch size: 30
2021-10-13 23:21:25,041 INFO [train.py:451] Epoch 1, batch 19630, batch avg loss 0.3191, total avg loss: 0.2941, batch size: 32
2021-10-13 23:21:29,757 INFO [train.py:451] Epoch 1, batch 19640, batch avg loss 0.3043, total avg loss: 0.2948, batch size: 49
2021-10-13 23:21:34,664 INFO [train.py:451] Epoch 1, batch 19650, batch avg loss 0.4494, total avg loss: 0.2994, batch size: 123
2021-10-13 23:21:39,510 INFO [train.py:451] Epoch 1, batch 19660, batch avg loss 0.3050, total avg loss: 0.2987, batch size: 57
2021-10-13 23:21:44,637 INFO [train.py:451] Epoch 1, batch 19670, batch avg loss 0.3011, total avg loss: 0.2955, batch size: 36
2021-10-13 23:21:49,746 INFO [train.py:451] Epoch 1, batch 19680, batch avg loss 0.2608, total avg loss: 0.2946, batch size: 29
2021-10-13 23:21:54,975 INFO [train.py:451] Epoch 1, batch 19690, batch avg loss 0.2844, total avg loss: 0.2924, batch size: 34
2021-10-13 23:21:59,930 INFO [train.py:451] Epoch 1, batch 19700, batch avg loss 0.2670, total avg loss: 0.2928, batch size: 35
2021-10-13 23:22:04,922 INFO [train.py:451] Epoch 1, batch 19710, batch avg loss 0.3060, total avg loss: 0.2940, batch size: 35
2021-10-13 23:22:09,931 INFO [train.py:451] Epoch 1, batch 19720, batch avg loss 0.3161, total avg loss: 0.2935, batch size: 34
2021-10-13 23:22:14,930 INFO [train.py:451] Epoch 1, batch 19730, batch avg loss 0.3065, total avg loss: 0.2919, batch size: 34
2021-10-13 23:22:19,838 INFO [train.py:451] Epoch 1, batch 19740, batch avg loss 0.2668, total avg loss: 0.2921, batch size: 31
2021-10-13 23:22:24,612 INFO [train.py:451] Epoch 1, batch 19750, batch avg loss 0.2516, total avg loss: 0.2923, batch size: 29
2021-10-13 23:22:29,291 INFO [train.py:451] Epoch 1, batch 19760, batch avg loss 0.3992, total avg loss: 0.2932, batch size: 125
2021-10-13 23:22:34,231 INFO [train.py:451] Epoch 1, batch 19770, batch avg loss 0.2888, total avg loss: 0.2943, batch size: 30
2021-10-13 23:22:39,146 INFO [train.py:451] Epoch 1, batch 19780, batch avg loss 0.3329, total avg loss: 0.2955, batch size: 38
2021-10-13 23:22:44,203 INFO [train.py:451] Epoch 1, batch 19790, batch avg loss 0.2653, total avg loss: 0.2948, batch size: 27
2021-10-13 23:22:49,278 INFO [train.py:451] Epoch 1, batch 19800, batch avg loss 0.2551, total avg loss: 0.2946, batch size: 32
2021-10-13 23:22:54,273 INFO [train.py:451] Epoch 1, batch 19810, batch avg loss 0.2116, total avg loss: 0.2762, batch size: 28
2021-10-13 23:22:59,104 INFO [train.py:451] Epoch 1, batch 19820, batch avg loss 0.3587, total avg loss: 0.2774, batch size: 41
2021-10-13 23:23:04,019 INFO [train.py:451] Epoch 1, batch 19830, batch avg loss 0.2879, total avg loss: 0.2788, batch size: 32
2021-10-13 23:23:08,871 INFO [train.py:451] Epoch 1, batch 19840, batch avg loss 0.2955, total avg loss: 0.2854, batch size: 33
2021-10-13 23:23:13,823 INFO [train.py:451] Epoch 1, batch 19850, batch avg loss 0.3036, total avg loss: 0.2909, batch size: 33
2021-10-13 23:23:18,577 INFO [train.py:451] Epoch 1, batch 19860, batch avg loss 0.2923, total avg loss: 0.2940, batch size: 29
2021-10-13 23:23:23,496 INFO [train.py:451] Epoch 1, batch 19870, batch avg loss 0.3411, total avg loss: 0.2939, batch size: 35
2021-10-13 23:23:28,392 INFO [train.py:451] Epoch 1, batch 19880, batch avg loss 0.3702, total avg loss: 0.2960, batch size: 39
2021-10-13 23:23:33,280 INFO [train.py:451] Epoch 1, batch 19890, batch avg loss 0.3025, total avg loss: 0.2958, batch size: 32
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2021-10-13 23:34:09,695 INFO [train.py:483] Epoch 1, valid loss 0.2121, best valid loss: 0.2121 best valid epoch: 1
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2021-10-13 23:35:49,690 INFO [checkpoint.py:62] Saving checkpoint to tdnn_lstm_ctc-v2/exp/epoch-1.pt
2021-10-13 23:35:50,531 INFO [train.py:564] epoch 2, lr: 0.00025
2021-10-13 23:35:54,904 INFO [train.py:451] Epoch 2, batch 0, batch avg loss 0.2494, total avg loss: 0.2494, batch size: 29
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2021-10-13 23:37:19,470 INFO [train.py:451] Epoch 2, batch 170, batch avg loss 0.2454, total avg loss: 0.2967, batch size: 27
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2021-10-13 23:37:29,485 INFO [train.py:451] Epoch 2, batch 190, batch avg loss 0.2883, total avg loss: 0.2959, batch size: 33
2021-10-13 23:37:34,429 INFO [train.py:451] Epoch 2, batch 200, batch avg loss 0.2920, total avg loss: 0.2966, batch size: 35
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2021-10-13 23:37:44,258 INFO [train.py:451] Epoch 2, batch 220, batch avg loss 0.2936, total avg loss: 0.2814, batch size: 37
2021-10-13 23:37:49,110 INFO [train.py:451] Epoch 2, batch 230, batch avg loss 0.2029, total avg loss: 0.2827, batch size: 30
2021-10-13 23:37:53,906 INFO [train.py:451] Epoch 2, batch 240, batch avg loss 0.2800, total avg loss: 0.2863, batch size: 30
2021-10-13 23:37:58,912 INFO [train.py:451] Epoch 2, batch 250, batch avg loss 0.3199, total avg loss: 0.2894, batch size: 49
2021-10-13 23:38:03,681 INFO [train.py:451] Epoch 2, batch 260, batch avg loss 0.3556, total avg loss: 0.2919, batch size: 45
2021-10-13 23:38:08,709 INFO [train.py:451] Epoch 2, batch 270, batch avg loss 0.2995, total avg loss: 0.2894, batch size: 28
2021-10-13 23:38:13,641 INFO [train.py:451] Epoch 2, batch 280, batch avg loss 0.3663, total avg loss: 0.2888, batch size: 38
2021-10-13 23:38:18,411 INFO [train.py:451] Epoch 2, batch 290, batch avg loss 0.2917, total avg loss: 0.2903, batch size: 41
2021-10-13 23:38:23,352 INFO [train.py:451] Epoch 2, batch 300, batch avg loss 0.3631, total avg loss: 0.2907, batch size: 32
2021-10-13 23:38:28,473 INFO [train.py:451] Epoch 2, batch 310, batch avg loss 0.2920, total avg loss: 0.2906, batch size: 33
2021-10-13 23:38:33,536 INFO [train.py:451] Epoch 2, batch 320, batch avg loss 0.2865, total avg loss: 0.2920, batch size: 37
2021-10-13 23:38:38,581 INFO [train.py:451] Epoch 2, batch 330, batch avg loss 0.2159, total avg loss: 0.2911, batch size: 27
2021-10-13 23:38:43,692 INFO [train.py:451] Epoch 2, batch 340, batch avg loss 0.2768, total avg loss: 0.2896, batch size: 36
2021-10-13 23:38:48,560 INFO [train.py:451] Epoch 2, batch 350, batch avg loss 0.2916, total avg loss: 0.2900, batch size: 39
2021-10-13 23:38:53,388 INFO [train.py:451] Epoch 2, batch 360, batch avg loss 0.3412, total avg loss: 0.2911, batch size: 36
2021-10-13 23:38:58,268 INFO [train.py:451] Epoch 2, batch 370, batch avg loss 0.3393, total avg loss: 0.2909, batch size: 57
2021-10-13 23:39:03,137 INFO [train.py:451] Epoch 2, batch 380, batch avg loss 0.3600, total avg loss: 0.2917, batch size: 49
2021-10-13 23:39:08,080 INFO [train.py:451] Epoch 2, batch 390, batch avg loss 0.3130, total avg loss: 0.2916, batch size: 42
2021-10-13 23:39:12,983 INFO [train.py:451] Epoch 2, batch 400, batch avg loss 0.2874, total avg loss: 0.2921, batch size: 30
2021-10-13 23:39:18,240 INFO [train.py:451] Epoch 2, batch 410, batch avg loss 0.2342, total avg loss: 0.2805, batch size: 27
2021-10-13 23:39:23,245 INFO [train.py:451] Epoch 2, batch 420, batch avg loss 0.2760, total avg loss: 0.2797, batch size: 32
2021-10-13 23:39:28,018 INFO [train.py:451] Epoch 2, batch 430, batch avg loss 0.2981, total avg loss: 0.2924, batch size: 37
2021-10-13 23:39:32,831 INFO [train.py:451] Epoch 2, batch 440, batch avg loss 0.4142, total avg loss: 0.2956, batch size: 129
2021-10-13 23:39:37,899 INFO [train.py:451] Epoch 2, batch 450, batch avg loss 0.2824, total avg loss: 0.2931, batch size: 31
2021-10-13 23:39:42,857 INFO [train.py:451] Epoch 2, batch 460, batch avg loss 0.2602, total avg loss: 0.2943, batch size: 31
2021-10-13 23:39:47,941 INFO [train.py:451] Epoch 2, batch 470, batch avg loss 0.3060, total avg loss: 0.2935, batch size: 45
2021-10-13 23:39:52,971 INFO [train.py:451] Epoch 2, batch 480, batch avg loss 0.3375, total avg loss: 0.2937, batch size: 36
2021-10-13 23:39:57,823 INFO [train.py:451] Epoch 2, batch 490, batch avg loss 0.3159, total avg loss: 0.2925, batch size: 37
2021-10-13 23:40:02,709 INFO [train.py:451] Epoch 2, batch 500, batch avg loss 0.2661, total avg loss: 0.2928, batch size: 30
2021-10-13 23:40:07,651 INFO [train.py:451] Epoch 2, batch 510, batch avg loss 0.3226, total avg loss: 0.2927, batch size: 49
2021-10-13 23:40:12,688 INFO [train.py:451] Epoch 2, batch 520, batch avg loss 0.2928, total avg loss: 0.2923, batch size: 39
2021-10-13 23:40:17,582 INFO [train.py:451] Epoch 2, batch 530, batch avg loss 0.2673, total avg loss: 0.2926, batch size: 35
2021-10-13 23:40:22,483 INFO [train.py:451] Epoch 2, batch 540, batch avg loss 0.2860, total avg loss: 0.2938, batch size: 36
2021-10-13 23:40:27,483 INFO [train.py:451] Epoch 2, batch 550, batch avg loss 0.2784, total avg loss: 0.2936, batch size: 36
2021-10-13 23:40:32,469 INFO [train.py:451] Epoch 2, batch 560, batch avg loss 0.2899, total avg loss: 0.2932, batch size: 36
2021-10-13 23:40:37,497 INFO [train.py:451] Epoch 2, batch 570, batch avg loss 0.3194, total avg loss: 0.2928, batch size: 37
2021-10-13 23:40:42,432 INFO [train.py:451] Epoch 2, batch 580, batch avg loss 0.3617, total avg loss: 0.2924, batch size: 32
2021-10-13 23:40:47,504 INFO [train.py:451] Epoch 2, batch 590, batch avg loss 0.2689, total avg loss: 0.2924, batch size: 31
2021-10-13 23:40:52,424 INFO [train.py:451] Epoch 2, batch 600, batch avg loss 0.2890, total avg loss: 0.2932, batch size: 34
2021-10-13 23:40:57,370 INFO [train.py:451] Epoch 2, batch 610, batch avg loss 0.2852, total avg loss: 0.3031, batch size: 39
2021-10-13 23:41:02,373 INFO [train.py:451] Epoch 2, batch 620, batch avg loss 0.2499, total avg loss: 0.2909, batch size: 27
2021-10-13 23:41:07,217 INFO [train.py:451] Epoch 2, batch 630, batch avg loss 0.3458, total avg loss: 0.3028, batch size: 38
2021-10-13 23:41:12,348 INFO [train.py:451] Epoch 2, batch 640, batch avg loss 0.2676, total avg loss: 0.3006, batch size: 34
2021-10-13 23:41:16,524 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "143ab410-a00d-b387-653f-265cbff31c0c" will not be mixed in.
2021-10-13 23:41:17,208 INFO [train.py:451] Epoch 2, batch 650, batch avg loss 0.3961, total avg loss: 0.3033, batch size: 126
2021-10-13 23:41:22,407 INFO [train.py:451] Epoch 2, batch 660, batch avg loss 0.3282, total avg loss: 0.2998, batch size: 38
2021-10-13 23:41:27,240 INFO [train.py:451] Epoch 2, batch 670, batch avg loss 0.2650, total avg loss: 0.3003, batch size: 30
2021-10-13 23:41:32,277 INFO [train.py:451] Epoch 2, batch 680, batch avg loss 0.3074, total avg loss: 0.3007, batch size: 33
2021-10-13 23:41:37,200 INFO [train.py:451] Epoch 2, batch 690, batch avg loss 0.3357, total avg loss: 0.2986, batch size: 41
2021-10-13 23:41:41,995 INFO [train.py:451] Epoch 2, batch 700, batch avg loss 0.3035, total avg loss: 0.2991, batch size: 38
2021-10-13 23:41:46,911 INFO [train.py:451] Epoch 2, batch 710, batch avg loss 0.2559, total avg loss: 0.2977, batch size: 30
2021-10-13 23:41:51,854 INFO [train.py:451] Epoch 2, batch 720, batch avg loss 0.2456, total avg loss: 0.2959, batch size: 31
2021-10-13 23:41:56,913 INFO [train.py:451] Epoch 2, batch 730, batch avg loss 0.2886, total avg loss: 0.2954, batch size: 28
2021-10-13 23:42:01,791 INFO [train.py:451] Epoch 2, batch 740, batch avg loss 0.3571, total avg loss: 0.2950, batch size: 56
2021-10-13 23:42:06,854 INFO [train.py:451] Epoch 2, batch 750, batch avg loss 0.3302, total avg loss: 0.2934, batch size: 33
2021-10-13 23:42:11,683 INFO [train.py:451] Epoch 2, batch 760, batch avg loss 0.3684, total avg loss: 0.2935, batch size: 37
2021-10-13 23:42:16,504 INFO [train.py:451] Epoch 2, batch 770, batch avg loss 0.2810, total avg loss: 0.2941, batch size: 32
2021-10-13 23:42:21,553 INFO [train.py:451] Epoch 2, batch 780, batch avg loss 0.3561, total avg loss: 0.2943, batch size: 49
2021-10-13 23:42:26,465 INFO [train.py:451] Epoch 2, batch 790, batch avg loss 0.3038, total avg loss: 0.2936, batch size: 38
2021-10-13 23:42:31,523 INFO [train.py:451] Epoch 2, batch 800, batch avg loss 0.2437, total avg loss: 0.2936, batch size: 28
2021-10-13 23:42:36,373 INFO [train.py:451] Epoch 2, batch 810, batch avg loss 0.3227, total avg loss: 0.2853, batch size: 32
2021-10-13 23:42:41,306 INFO [train.py:451] Epoch 2, batch 820, batch avg loss 0.2994, total avg loss: 0.2873, batch size: 38
2021-10-13 23:42:46,230 INFO [train.py:451] Epoch 2, batch 830, batch avg loss 0.2722, total avg loss: 0.2882, batch size: 30
2021-10-13 23:42:51,005 INFO [train.py:451] Epoch 2, batch 840, batch avg loss 0.3447, total avg loss: 0.2968, batch size: 49
2021-10-13 23:42:56,019 INFO [train.py:451] Epoch 2, batch 850, batch avg loss 0.2530, total avg loss: 0.2925, batch size: 32
2021-10-13 23:43:00,873 INFO [train.py:451] Epoch 2, batch 860, batch avg loss 0.4256, total avg loss: 0.2963, batch size: 129
2021-10-13 23:43:05,592 INFO [train.py:451] Epoch 2, batch 870, batch avg loss 0.2668, total avg loss: 0.2978, batch size: 39
2021-10-13 23:43:10,745 INFO [train.py:451] Epoch 2, batch 880, batch avg loss 0.2530, total avg loss: 0.2945, batch size: 33
2021-10-13 23:43:15,855 INFO [train.py:451] Epoch 2, batch 890, batch avg loss 0.3095, total avg loss: 0.2927, batch size: 38
2021-10-13 23:43:20,851 INFO [train.py:451] Epoch 2, batch 900, batch avg loss 0.2898, total avg loss: 0.2915, batch size: 34
2021-10-13 23:43:25,710 INFO [train.py:451] Epoch 2, batch 910, batch avg loss 0.4022, total avg loss: 0.2920, batch size: 129
2021-10-13 23:43:30,834 INFO [train.py:451] Epoch 2, batch 920, batch avg loss 0.2900, total avg loss: 0.2907, batch size: 37
2021-10-13 23:43:35,784 INFO [train.py:451] Epoch 2, batch 930, batch avg loss 0.2708, total avg loss: 0.2907, batch size: 31
2021-10-13 23:43:40,949 INFO [train.py:451] Epoch 2, batch 940, batch avg loss 0.2262, total avg loss: 0.2902, batch size: 29
2021-10-13 23:43:45,733 INFO [train.py:451] Epoch 2, batch 950, batch avg loss 0.3541, total avg loss: 0.2921, batch size: 72
2021-10-13 23:43:50,771 INFO [train.py:451] Epoch 2, batch 960, batch avg loss 0.2594, total avg loss: 0.2912, batch size: 34
2021-10-13 23:43:55,825 INFO [train.py:451] Epoch 2, batch 970, batch avg loss 0.2546, total avg loss: 0.2910, batch size: 28
2021-10-13 23:44:00,846 INFO [train.py:451] Epoch 2, batch 980, batch avg loss 0.2686, total avg loss: 0.2899, batch size: 37
2021-10-13 23:44:06,028 INFO [train.py:451] Epoch 2, batch 990, batch avg loss 0.3648, total avg loss: 0.2893, batch size: 37
2021-10-13 23:44:10,849 INFO [train.py:451] Epoch 2, batch 1000, batch avg loss 0.2477, total avg loss: 0.2892, batch size: 30
2021-10-13 23:44:48,683 INFO [train.py:483] Epoch 2, valid loss 0.2107, best valid loss: 0.2107 best valid epoch: 2
2021-10-13 23:44:53,549 INFO [train.py:451] Epoch 2, batch 1010, batch avg loss 0.2463, total avg loss: 0.2925, batch size: 30
2021-10-13 23:44:58,499 INFO [train.py:451] Epoch 2, batch 1020, batch avg loss 0.3085, total avg loss: 0.2915, batch size: 34
2021-10-13 23:45:03,424 INFO [train.py:451] Epoch 2, batch 1030, batch avg loss 0.2441, total avg loss: 0.2902, batch size: 28
2021-10-13 23:45:08,275 INFO [train.py:451] Epoch 2, batch 1040, batch avg loss 0.2796, total avg loss: 0.2957, batch size: 31
2021-10-13 23:45:13,228 INFO [train.py:451] Epoch 2, batch 1050, batch avg loss 0.2926, total avg loss: 0.2900, batch size: 35
2021-10-13 23:45:17,999 INFO [train.py:451] Epoch 2, batch 1060, batch avg loss 0.3025, total avg loss: 0.2917, batch size: 31
2021-10-13 23:45:22,929 INFO [train.py:451] Epoch 2, batch 1070, batch avg loss 0.2736, total avg loss: 0.2920, batch size: 30
2021-10-13 23:45:27,836 INFO [train.py:451] Epoch 2, batch 1080, batch avg loss 0.2498, total avg loss: 0.2941, batch size: 28
2021-10-13 23:45:33,036 INFO [train.py:451] Epoch 2, batch 1090, batch avg loss 0.2896, total avg loss: 0.2902, batch size: 42
2021-10-13 23:45:38,192 INFO [train.py:451] Epoch 2, batch 1100, batch avg loss 0.3056, total avg loss: 0.2902, batch size: 32
2021-10-13 23:45:43,083 INFO [train.py:451] Epoch 2, batch 1110, batch avg loss 0.3191, total avg loss: 0.2913, batch size: 38
2021-10-13 23:45:48,349 INFO [train.py:451] Epoch 2, batch 1120, batch avg loss 0.3334, total avg loss: 0.2911, batch size: 42
2021-10-13 23:45:53,449 INFO [train.py:451] Epoch 2, batch 1130, batch avg loss 0.3174, total avg loss: 0.2918, batch size: 34
2021-10-13 23:45:58,419 INFO [train.py:451] Epoch 2, batch 1140, batch avg loss 0.2659, total avg loss: 0.2918, batch size: 37
2021-10-13 23:46:03,460 INFO [train.py:451] Epoch 2, batch 1150, batch avg loss 0.2602, total avg loss: 0.2905, batch size: 32
2021-10-13 23:46:08,262 INFO [train.py:451] Epoch 2, batch 1160, batch avg loss 0.3035, total avg loss: 0.2918, batch size: 33
2021-10-13 23:46:13,163 INFO [train.py:451] Epoch 2, batch 1170, batch avg loss 0.2698, total avg loss: 0.2929, batch size: 33
2021-10-13 23:46:18,246 INFO [train.py:451] Epoch 2, batch 1180, batch avg loss 0.3655, total avg loss: 0.2930, batch size: 38
2021-10-13 23:46:23,228 INFO [train.py:451] Epoch 2, batch 1190, batch avg loss 0.3069, total avg loss: 0.2930, batch size: 39
2021-10-13 23:46:28,133 INFO [train.py:451] Epoch 2, batch 1200, batch avg loss 0.2660, total avg loss: 0.2926, batch size: 29
2021-10-13 23:46:33,060 INFO [train.py:451] Epoch 2, batch 1210, batch avg loss 0.3148, total avg loss: 0.2840, batch size: 36
2021-10-13 23:46:38,122 INFO [train.py:451] Epoch 2, batch 1220, batch avg loss 0.2645, total avg loss: 0.2798, batch size: 34
2021-10-13 23:46:42,994 INFO [train.py:451] Epoch 2, batch 1230, batch avg loss 0.2479, total avg loss: 0.2870, batch size: 29
2021-10-13 23:46:47,912 INFO [train.py:451] Epoch 2, batch 1240, batch avg loss 0.2843, total avg loss: 0.2870, batch size: 34
2021-10-13 23:46:52,763 INFO [train.py:451] Epoch 2, batch 1250, batch avg loss 0.2240, total avg loss: 0.2902, batch size: 29
2021-10-13 23:46:57,802 INFO [train.py:451] Epoch 2, batch 1260, batch avg loss 0.3274, total avg loss: 0.2918, batch size: 34
2021-10-13 23:47:02,543 INFO [train.py:451] Epoch 2, batch 1270, batch avg loss 0.2688, total avg loss: 0.2924, batch size: 32
2021-10-13 23:47:07,253 INFO [train.py:451] Epoch 2, batch 1280, batch avg loss 0.3156, total avg loss: 0.2937, batch size: 49
2021-10-13 23:47:12,219 INFO [train.py:451] Epoch 2, batch 1290, batch avg loss 0.2878, total avg loss: 0.2951, batch size: 38
2021-10-13 23:47:17,358 INFO [train.py:451] Epoch 2, batch 1300, batch avg loss 0.2715, total avg loss: 0.2933, batch size: 33
2021-10-13 23:47:22,189 INFO [train.py:451] Epoch 2, batch 1310, batch avg loss 0.2873, total avg loss: 0.2941, batch size: 42
2021-10-13 23:47:27,247 INFO [train.py:451] Epoch 2, batch 1320, batch avg loss 0.2294, total avg loss: 0.2928, batch size: 27
2021-10-13 23:47:32,191 INFO [train.py:451] Epoch 2, batch 1330, batch avg loss 0.3108, total avg loss: 0.2925, batch size: 41
2021-10-13 23:47:37,208 INFO [train.py:451] Epoch 2, batch 1340, batch avg loss 0.2952, total avg loss: 0.2924, batch size: 33
2021-10-13 23:47:41,917 INFO [train.py:451] Epoch 2, batch 1350, batch avg loss 0.3403, total avg loss: 0.2937, batch size: 45
2021-10-13 23:47:46,919 INFO [train.py:451] Epoch 2, batch 1360, batch avg loss 0.3850, total avg loss: 0.2935, batch size: 71
2021-10-13 23:47:51,643 INFO [train.py:451] Epoch 2, batch 1370, batch avg loss 0.2719, total avg loss: 0.2946, batch size: 34
2021-10-13 23:47:56,518 INFO [train.py:451] Epoch 2, batch 1380, batch avg loss 0.3541, total avg loss: 0.2952, batch size: 39
2021-10-13 23:48:01,426 INFO [train.py:451] Epoch 2, batch 1390, batch avg loss 0.2580, total avg loss: 0.2943, batch size: 33
2021-10-13 23:48:06,363 INFO [train.py:451] Epoch 2, batch 1400, batch avg loss 0.2561, total avg loss: 0.2933, batch size: 32
2021-10-13 23:48:11,423 INFO [train.py:451] Epoch 2, batch 1410, batch avg loss 0.3235, total avg loss: 0.2944, batch size: 32
2021-10-13 23:48:16,596 INFO [train.py:451] Epoch 2, batch 1420, batch avg loss 0.2829, total avg loss: 0.3053, batch size: 29
2021-10-13 23:48:21,340 INFO [train.py:451] Epoch 2, batch 1430, batch avg loss 0.3323, total avg loss: 0.3095, batch size: 39
2021-10-13 23:48:26,443 INFO [train.py:451] Epoch 2, batch 1440, batch avg loss 0.2816, total avg loss: 0.3015, batch size: 42
2021-10-13 23:48:31,478 INFO [train.py:451] Epoch 2, batch 1450, batch avg loss 0.2573, total avg loss: 0.2974, batch size: 30
2021-10-13 23:48:36,391 INFO [train.py:451] Epoch 2, batch 1460, batch avg loss 0.2557, total avg loss: 0.2997, batch size: 32
2021-10-13 23:48:41,377 INFO [train.py:451] Epoch 2, batch 1470, batch avg loss 0.2738, total avg loss: 0.2963, batch size: 33
2021-10-13 23:48:46,276 INFO [train.py:451] Epoch 2, batch 1480, batch avg loss 0.2645, total avg loss: 0.2951, batch size: 27
2021-10-13 23:48:51,006 INFO [train.py:451] Epoch 2, batch 1490, batch avg loss 0.2486, total avg loss: 0.2937, batch size: 30
2021-10-13 23:48:55,855 INFO [train.py:451] Epoch 2, batch 1500, batch avg loss 0.2314, total avg loss: 0.2938, batch size: 35
2021-10-13 23:49:01,047 INFO [train.py:451] Epoch 2, batch 1510, batch avg loss 0.2885, total avg loss: 0.2927, batch size: 34
2021-10-13 23:49:06,083 INFO [train.py:451] Epoch 2, batch 1520, batch avg loss 0.3286, total avg loss: 0.2943, batch size: 72
2021-10-13 23:49:11,360 INFO [train.py:451] Epoch 2, batch 1530, batch avg loss 0.2210, total avg loss: 0.2932, batch size: 29
2021-10-13 23:49:16,444 INFO [train.py:451] Epoch 2, batch 1540, batch avg loss 0.3046, total avg loss: 0.2928, batch size: 39
2021-10-13 23:49:21,331 INFO [train.py:451] Epoch 2, batch 1550, batch avg loss 0.3072, total avg loss: 0.2930, batch size: 41
2021-10-13 23:49:26,345 INFO [train.py:451] Epoch 2, batch 1560, batch avg loss 0.3237, total avg loss: 0.2926, batch size: 38
2021-10-13 23:49:31,223 INFO [train.py:451] Epoch 2, batch 1570, batch avg loss 0.2993, total avg loss: 0.2941, batch size: 36
2021-10-13 23:49:36,178 INFO [train.py:451] Epoch 2, batch 1580, batch avg loss 0.2856, total avg loss: 0.2934, batch size: 49
2021-10-13 23:49:41,231 INFO [train.py:451] Epoch 2, batch 1590, batch avg loss 0.3053, total avg loss: 0.2929, batch size: 30
2021-10-13 23:49:46,017 INFO [train.py:451] Epoch 2, batch 1600, batch avg loss 0.2077, total avg loss: 0.2933, batch size: 27
2021-10-13 23:49:50,850 INFO [train.py:451] Epoch 2, batch 1610, batch avg loss 0.2487, total avg loss: 0.2945, batch size: 31
2021-10-13 23:49:55,735 INFO [train.py:451] Epoch 2, batch 1620, batch avg loss 0.4544, total avg loss: 0.2987, batch size: 131
2021-10-13 23:50:00,534 INFO [train.py:451] Epoch 2, batch 1630, batch avg loss 0.3780, total avg loss: 0.2937, batch size: 73
2021-10-13 23:50:05,489 INFO [train.py:451] Epoch 2, batch 1640, batch avg loss 0.2713, total avg loss: 0.2923, batch size: 35
2021-10-13 23:50:10,414 INFO [train.py:451] Epoch 2, batch 1650, batch avg loss 0.3700, total avg loss: 0.2903, batch size: 73
2021-10-13 23:50:15,234 INFO [train.py:451] Epoch 2, batch 1660, batch avg loss 0.3263, total avg loss: 0.2900, batch size: 38
2021-10-13 23:50:19,915 INFO [train.py:451] Epoch 2, batch 1670, batch avg loss 0.3624, total avg loss: 0.2937, batch size: 73
2021-10-13 23:50:24,900 INFO [train.py:451] Epoch 2, batch 1680, batch avg loss 0.2717, total avg loss: 0.2928, batch size: 35
2021-10-13 23:50:29,739 INFO [train.py:451] Epoch 2, batch 1690, batch avg loss 0.3379, total avg loss: 0.2931, batch size: 38
2021-10-13 23:50:34,608 INFO [train.py:451] Epoch 2, batch 1700, batch avg loss 0.3438, total avg loss: 0.2948, batch size: 45
2021-10-13 23:50:39,534 INFO [train.py:451] Epoch 2, batch 1710, batch avg loss 0.2585, total avg loss: 0.2923, batch size: 28
2021-10-13 23:50:44,582 INFO [train.py:451] Epoch 2, batch 1720, batch avg loss 0.2299, total avg loss: 0.2914, batch size: 31
2021-10-13 23:50:49,527 INFO [train.py:451] Epoch 2, batch 1730, batch avg loss 0.2756, total avg loss: 0.2911, batch size: 35
2021-10-13 23:50:54,286 INFO [train.py:451] Epoch 2, batch 1740, batch avg loss 0.2831, total avg loss: 0.2935, batch size: 36
2021-10-13 23:50:59,168 INFO [train.py:451] Epoch 2, batch 1750, batch avg loss 0.2655, total avg loss: 0.2942, batch size: 29
2021-10-13 23:51:04,101 INFO [train.py:451] Epoch 2, batch 1760, batch avg loss 0.2516, total avg loss: 0.2947, batch size: 34
2021-10-13 23:51:09,070 INFO [train.py:451] Epoch 2, batch 1770, batch avg loss 0.2918, total avg loss: 0.2946, batch size: 38
2021-10-13 23:51:14,062 INFO [train.py:451] Epoch 2, batch 1780, batch avg loss 0.2585, total avg loss: 0.2939, batch size: 30
2021-10-13 23:51:19,023 INFO [train.py:451] Epoch 2, batch 1790, batch avg loss 0.3397, total avg loss: 0.2939, batch size: 35
2021-10-13 23:51:23,703 INFO [train.py:451] Epoch 2, batch 1800, batch avg loss 0.3132, total avg loss: 0.2948, batch size: 39
2021-10-13 23:51:28,557 INFO [train.py:451] Epoch 2, batch 1810, batch avg loss 0.3867, total avg loss: 0.3164, batch size: 126
2021-10-13 23:51:33,405 INFO [train.py:451] Epoch 2, batch 1820, batch avg loss 0.3231, total avg loss: 0.3041, batch size: 72
2021-10-13 23:51:38,360 INFO [train.py:451] Epoch 2, batch 1830, batch avg loss 0.2664, total avg loss: 0.2986, batch size: 32
2021-10-13 23:51:43,274 INFO [train.py:451] Epoch 2, batch 1840, batch avg loss 0.3424, total avg loss: 0.2954, batch size: 41
2021-10-13 23:51:48,014 INFO [train.py:451] Epoch 2, batch 1850, batch avg loss 0.3127, total avg loss: 0.2998, batch size: 36
2021-10-13 23:51:52,733 INFO [train.py:451] Epoch 2, batch 1860, batch avg loss 0.3880, total avg loss: 0.3036, batch size: 126
2021-10-13 23:51:57,613 INFO [train.py:451] Epoch 2, batch 1870, batch avg loss 0.3386, total avg loss: 0.3029, batch size: 41
2021-10-13 23:52:02,658 INFO [train.py:451] Epoch 2, batch 1880, batch avg loss 0.3781, total avg loss: 0.3054, batch size: 39
2021-10-13 23:52:07,612 INFO [train.py:451] Epoch 2, batch 1890, batch avg loss 0.3127, total avg loss: 0.3053, batch size: 38
2021-10-13 23:52:12,520 INFO [train.py:451] Epoch 2, batch 1900, batch avg loss 0.3111, total avg loss: 0.3036, batch size: 39
2021-10-13 23:52:17,344 INFO [train.py:451] Epoch 2, batch 1910, batch avg loss 0.2183, total avg loss: 0.3016, batch size: 30
2021-10-13 23:52:22,213 INFO [train.py:451] Epoch 2, batch 1920, batch avg loss 0.3566, total avg loss: 0.3013, batch size: 73
2021-10-13 23:52:27,097 INFO [train.py:451] Epoch 2, batch 1930, batch avg loss 0.2764, total avg loss: 0.3007, batch size: 31
2021-10-13 23:52:32,042 INFO [train.py:451] Epoch 2, batch 1940, batch avg loss 0.2595, total avg loss: 0.2997, batch size: 36
2021-10-13 23:52:36,808 INFO [train.py:451] Epoch 2, batch 1950, batch avg loss 0.2571, total avg loss: 0.2998, batch size: 31
2021-10-13 23:52:41,779 INFO [train.py:451] Epoch 2, batch 1960, batch avg loss 0.2378, total avg loss: 0.2986, batch size: 28
2021-10-13 23:52:46,766 INFO [train.py:451] Epoch 2, batch 1970, batch avg loss 0.3638, total avg loss: 0.2971, batch size: 35
2021-10-13 23:52:51,510 INFO [train.py:451] Epoch 2, batch 1980, batch avg loss 0.3338, total avg loss: 0.2986, batch size: 72
2021-10-13 23:52:56,560 INFO [train.py:451] Epoch 2, batch 1990, batch avg loss 0.2626, total avg loss: 0.2989, batch size: 35
2021-10-13 23:53:01,474 INFO [train.py:451] Epoch 2, batch 2000, batch avg loss 0.2635, total avg loss: 0.2979, batch size: 37
2021-10-13 23:53:42,313 INFO [train.py:483] Epoch 2, valid loss 0.2104, best valid loss: 0.2104 best valid epoch: 2
2021-10-13 23:53:47,462 INFO [train.py:451] Epoch 2, batch 2010, batch avg loss 0.3739, total avg loss: 0.2909, batch size: 42
2021-10-13 23:53:52,382 INFO [train.py:451] Epoch 2, batch 2020, batch avg loss 0.2791, total avg loss: 0.2826, batch size: 36
2021-10-13 23:53:57,389 INFO [train.py:451] Epoch 2, batch 2030, batch avg loss 0.2623, total avg loss: 0.2839, batch size: 35
2021-10-13 23:54:02,331 INFO [train.py:451] Epoch 2, batch 2040, batch avg loss 0.3025, total avg loss: 0.2859, batch size: 31
2021-10-13 23:54:07,158 INFO [train.py:451] Epoch 2, batch 2050, batch avg loss 0.3455, total avg loss: 0.2887, batch size: 73
2021-10-13 23:54:12,219 INFO [train.py:451] Epoch 2, batch 2060, batch avg loss 0.2144, total avg loss: 0.2886, batch size: 30
2021-10-13 23:54:17,217 INFO [train.py:451] Epoch 2, batch 2070, batch avg loss 0.3398, total avg loss: 0.2880, batch size: 33
2021-10-13 23:54:22,197 INFO [train.py:451] Epoch 2, batch 2080, batch avg loss 0.3140, total avg loss: 0.2865, batch size: 73
2021-10-13 23:54:27,327 INFO [train.py:451] Epoch 2, batch 2090, batch avg loss 0.2839, total avg loss: 0.2875, batch size: 31
2021-10-13 23:54:31,983 INFO [train.py:451] Epoch 2, batch 2100, batch avg loss 0.4118, total avg loss: 0.2923, batch size: 133
2021-10-13 23:54:36,944 INFO [train.py:451] Epoch 2, batch 2110, batch avg loss 0.2531, total avg loss: 0.2923, batch size: 30
2021-10-13 23:54:41,838 INFO [train.py:451] Epoch 2, batch 2120, batch avg loss 0.2707, total avg loss: 0.2941, batch size: 27
2021-10-13 23:54:46,863 INFO [train.py:451] Epoch 2, batch 2130, batch avg loss 0.2679, total avg loss: 0.2941, batch size: 38
2021-10-13 23:54:51,966 INFO [train.py:451] Epoch 2, batch 2140, batch avg loss 0.2306, total avg loss: 0.2930, batch size: 30
2021-10-13 23:54:56,689 INFO [train.py:451] Epoch 2, batch 2150, batch avg loss 0.2802, total avg loss: 0.2931, batch size: 36
2021-10-13 23:55:01,667 INFO [train.py:451] Epoch 2, batch 2160, batch avg loss 0.2910, total avg loss: 0.2926, batch size: 30
2021-10-13 23:55:05,667 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "e5eefa22-bec4-19a5-a2ee-a6ec093b6b7e" will not be mixed in.
2021-10-13 23:55:06,601 INFO [train.py:451] Epoch 2, batch 2170, batch avg loss 0.3340, total avg loss: 0.2941, batch size: 38
2021-10-13 23:55:11,369 INFO [train.py:451] Epoch 2, batch 2180, batch avg loss 0.3074, total avg loss: 0.2940, batch size: 35
2021-10-13 23:55:16,223 INFO [train.py:451] Epoch 2, batch 2190, batch avg loss 0.3348, total avg loss: 0.2953, batch size: 37
2021-10-13 23:55:21,480 INFO [train.py:451] Epoch 2, batch 2200, batch avg loss 0.3321, total avg loss: 0.2952, batch size: 36
2021-10-13 23:55:26,555 INFO [train.py:451] Epoch 2, batch 2210, batch avg loss 0.2800, total avg loss: 0.2958, batch size: 34
2021-10-13 23:55:31,613 INFO [train.py:451] Epoch 2, batch 2220, batch avg loss 0.3063, total avg loss: 0.2951, batch size: 34
2021-10-13 23:55:36,447 INFO [train.py:451] Epoch 2, batch 2230, batch avg loss 0.3105, total avg loss: 0.2984, batch size: 34
2021-10-13 23:55:41,451 INFO [train.py:451] Epoch 2, batch 2240, batch avg loss 0.3260, total avg loss: 0.2965, batch size: 45
2021-10-13 23:55:46,300 INFO [train.py:451] Epoch 2, batch 2250, batch avg loss 0.3113, total avg loss: 0.2987, batch size: 35
2021-10-13 23:55:51,257 INFO [train.py:451] Epoch 2, batch 2260, batch avg loss 0.2865, total avg loss: 0.2964, batch size: 35
2021-10-13 23:55:56,155 INFO [train.py:451] Epoch 2, batch 2270, batch avg loss 0.2854, total avg loss: 0.2935, batch size: 35
2021-10-13 23:56:01,350 INFO [train.py:451] Epoch 2, batch 2280, batch avg loss 0.2482, total avg loss: 0.2906, batch size: 34
2021-10-13 23:56:06,455 INFO [train.py:451] Epoch 2, batch 2290, batch avg loss 0.2289, total avg loss: 0.2877, batch size: 27
2021-10-13 23:56:11,390 INFO [train.py:451] Epoch 2, batch 2300, batch avg loss 0.2519, total avg loss: 0.2858, batch size: 32
2021-10-13 23:56:16,309 INFO [train.py:451] Epoch 2, batch 2310, batch avg loss 0.3004, total avg loss: 0.2872, batch size: 37
2021-10-13 23:56:21,226 INFO [train.py:451] Epoch 2, batch 2320, batch avg loss 0.2522, total avg loss: 0.2890, batch size: 34
2021-10-13 23:56:26,323 INFO [train.py:451] Epoch 2, batch 2330, batch avg loss 0.2275, total avg loss: 0.2890, batch size: 30
2021-10-13 23:56:31,384 INFO [train.py:451] Epoch 2, batch 2340, batch avg loss 0.3209, total avg loss: 0.2883, batch size: 38
2021-10-13 23:56:36,516 INFO [train.py:451] Epoch 2, batch 2350, batch avg loss 0.3078, total avg loss: 0.2872, batch size: 73
2021-10-13 23:56:41,608 INFO [train.py:451] Epoch 2, batch 2360, batch avg loss 0.3411, total avg loss: 0.2887, batch size: 71
2021-10-13 23:56:46,866 INFO [train.py:451] Epoch 2, batch 2370, batch avg loss 0.3149, total avg loss: 0.2884, batch size: 30
2021-10-13 23:56:51,831 INFO [train.py:451] Epoch 2, batch 2380, batch avg loss 0.2841, total avg loss: 0.2878, batch size: 32
2021-10-13 23:56:56,574 INFO [train.py:451] Epoch 2, batch 2390, batch avg loss 0.2910, total avg loss: 0.2885, batch size: 34
2021-10-13 23:57:01,533 INFO [train.py:451] Epoch 2, batch 2400, batch avg loss 0.3135, total avg loss: 0.2897, batch size: 28
2021-10-13 23:57:06,398 INFO [train.py:451] Epoch 2, batch 2410, batch avg loss 0.3186, total avg loss: 0.2895, batch size: 56
2021-10-13 23:57:11,292 INFO [train.py:451] Epoch 2, batch 2420, batch avg loss 0.2766, total avg loss: 0.2799, batch size: 31
2021-10-13 23:57:15,923 INFO [train.py:451] Epoch 2, batch 2430, batch avg loss 0.2648, total avg loss: 0.2945, batch size: 31
2021-10-13 23:57:20,876 INFO [train.py:451] Epoch 2, batch 2440, batch avg loss 0.2840, total avg loss: 0.2890, batch size: 30
2021-10-13 23:57:25,739 INFO [train.py:451] Epoch 2, batch 2450, batch avg loss 0.3083, total avg loss: 0.2933, batch size: 35
2021-10-13 23:57:30,694 INFO [train.py:451] Epoch 2, batch 2460, batch avg loss 0.3192, total avg loss: 0.2923, batch size: 36
2021-10-13 23:57:35,716 INFO [train.py:451] Epoch 2, batch 2470, batch avg loss 0.3076, total avg loss: 0.2929, batch size: 39
2021-10-13 23:57:40,584 INFO [train.py:451] Epoch 2, batch 2480, batch avg loss 0.4127, total avg loss: 0.2959, batch size: 126
2021-10-13 23:57:45,264 INFO [train.py:451] Epoch 2, batch 2490, batch avg loss 0.3057, total avg loss: 0.2990, batch size: 42
2021-10-13 23:57:50,382 INFO [train.py:451] Epoch 2, batch 2500, batch avg loss 0.2317, total avg loss: 0.2970, batch size: 30
2021-10-13 23:57:55,220 INFO [train.py:451] Epoch 2, batch 2510, batch avg loss 0.2852, total avg loss: 0.2978, batch size: 35
2021-10-13 23:57:59,914 INFO [train.py:451] Epoch 2, batch 2520, batch avg loss 0.4056, total avg loss: 0.3012, batch size: 128
2021-10-13 23:58:04,910 INFO [train.py:451] Epoch 2, batch 2530, batch avg loss 0.3319, total avg loss: 0.2996, batch size: 56
2021-10-13 23:58:09,672 INFO [train.py:451] Epoch 2, batch 2540, batch avg loss 0.3136, total avg loss: 0.3002, batch size: 45
2021-10-13 23:58:14,672 INFO [train.py:451] Epoch 2, batch 2550, batch avg loss 0.2271, total avg loss: 0.2988, batch size: 33
2021-10-13 23:58:19,669 INFO [train.py:451] Epoch 2, batch 2560, batch avg loss 0.3035, total avg loss: 0.2979, batch size: 32
2021-10-13 23:58:24,668 INFO [train.py:451] Epoch 2, batch 2570, batch avg loss 0.2424, total avg loss: 0.2978, batch size: 32
2021-10-13 23:58:29,630 INFO [train.py:451] Epoch 2, batch 2580, batch avg loss 0.2577, total avg loss: 0.2972, batch size: 35
2021-10-13 23:58:34,602 INFO [train.py:451] Epoch 2, batch 2590, batch avg loss 0.3100, total avg loss: 0.2973, batch size: 34
2021-10-13 23:58:39,449 INFO [train.py:451] Epoch 2, batch 2600, batch avg loss 0.2537, total avg loss: 0.2975, batch size: 29
2021-10-13 23:58:44,512 INFO [train.py:451] Epoch 2, batch 2610, batch avg loss 0.3086, total avg loss: 0.2863, batch size: 56
2021-10-13 23:58:49,575 INFO [train.py:451] Epoch 2, batch 2620, batch avg loss 0.2117, total avg loss: 0.2834, batch size: 31
2021-10-13 23:58:54,449 INFO [train.py:451] Epoch 2, batch 2630, batch avg loss 0.3444, total avg loss: 0.2914, batch size: 72
2021-10-13 23:58:59,319 INFO [train.py:451] Epoch 2, batch 2640, batch avg loss 0.2697, total avg loss: 0.2913, batch size: 38
2021-10-13 23:59:04,416 INFO [train.py:451] Epoch 2, batch 2650, batch avg loss 0.4008, total avg loss: 0.2920, batch size: 34
2021-10-13 23:59:09,617 INFO [train.py:451] Epoch 2, batch 2660, batch avg loss 0.2893, total avg loss: 0.2918, batch size: 34
2021-10-13 23:59:14,563 INFO [train.py:451] Epoch 2, batch 2670, batch avg loss 0.3312, total avg loss: 0.2928, batch size: 35
2021-10-13 23:59:19,450 INFO [train.py:451] Epoch 2, batch 2680, batch avg loss 0.2631, total avg loss: 0.2931, batch size: 29
2021-10-13 23:59:24,372 INFO [train.py:451] Epoch 2, batch 2690, batch avg loss 0.2766, total avg loss: 0.2918, batch size: 31
2021-10-13 23:59:29,367 INFO [train.py:451] Epoch 2, batch 2700, batch avg loss 0.2900, total avg loss: 0.2895, batch size: 36
2021-10-13 23:59:34,423 INFO [train.py:451] Epoch 2, batch 2710, batch avg loss 0.3125, total avg loss: 0.2902, batch size: 34
2021-10-13 23:59:39,339 INFO [train.py:451] Epoch 2, batch 2720, batch avg loss 0.2999, total avg loss: 0.2916, batch size: 42
2021-10-13 23:59:44,146 INFO [train.py:451] Epoch 2, batch 2730, batch avg loss 0.3551, total avg loss: 0.2926, batch size: 45
2021-10-13 23:59:49,372 INFO [train.py:451] Epoch 2, batch 2740, batch avg loss 0.2602, total avg loss: 0.2922, batch size: 30
2021-10-13 23:59:54,324 INFO [train.py:451] Epoch 2, batch 2750, batch avg loss 0.2645, total avg loss: 0.2942, batch size: 33
2021-10-13 23:59:59,465 INFO [train.py:451] Epoch 2, batch 2760, batch avg loss 0.2916, total avg loss: 0.2930, batch size: 30
2021-10-14 00:00:04,433 INFO [train.py:451] Epoch 2, batch 2770, batch avg loss 0.4212, total avg loss: 0.2943, batch size: 129
2021-10-14 00:00:07,076 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "e39e01b0-3d0f-1dbd-f989-24bf77ce4dbf" will not be mixed in.
2021-10-14 00:00:09,507 INFO [train.py:451] Epoch 2, batch 2780, batch avg loss 0.2829, total avg loss: 0.2953, batch size: 32
2021-10-14 00:00:14,500 INFO [train.py:451] Epoch 2, batch 2790, batch avg loss 0.3250, total avg loss: 0.2962, batch size: 41
2021-10-14 00:00:19,605 INFO [train.py:451] Epoch 2, batch 2800, batch avg loss 0.3549, total avg loss: 0.2952, batch size: 29
2021-10-14 00:00:24,798 INFO [train.py:451] Epoch 2, batch 2810, batch avg loss 0.2661, total avg loss: 0.2886, batch size: 29
2021-10-14 00:00:29,947 INFO [train.py:451] Epoch 2, batch 2820, batch avg loss 0.3066, total avg loss: 0.2932, batch size: 35
2021-10-14 00:00:34,913 INFO [train.py:451] Epoch 2, batch 2830, batch avg loss 0.2184, total avg loss: 0.2888, batch size: 30
2021-10-14 00:00:39,779 INFO [train.py:451] Epoch 2, batch 2840, batch avg loss 0.2307, total avg loss: 0.2905, batch size: 28
2021-10-14 00:00:44,843 INFO [train.py:451] Epoch 2, batch 2850, batch avg loss 0.3055, total avg loss: 0.2866, batch size: 56
2021-10-14 00:00:49,835 INFO [train.py:451] Epoch 2, batch 2860, batch avg loss 0.3244, total avg loss: 0.2894, batch size: 30
2021-10-14 00:00:54,769 INFO [train.py:451] Epoch 2, batch 2870, batch avg loss 0.2555, total avg loss: 0.2918, batch size: 33
2021-10-14 00:00:59,613 INFO [train.py:451] Epoch 2, batch 2880, batch avg loss 0.3394, total avg loss: 0.2929, batch size: 73
2021-10-14 00:01:04,723 INFO [train.py:451] Epoch 2, batch 2890, batch avg loss 0.2651, total avg loss: 0.2908, batch size: 33
2021-10-14 00:01:09,796 INFO [train.py:451] Epoch 2, batch 2900, batch avg loss 0.2957, total avg loss: 0.2911, batch size: 74
2021-10-14 00:01:14,839 INFO [train.py:451] Epoch 2, batch 2910, batch avg loss 0.2612, total avg loss: 0.2906, batch size: 31
2021-10-14 00:01:19,878 INFO [train.py:451] Epoch 2, batch 2920, batch avg loss 0.2833, total avg loss: 0.2897, batch size: 45
2021-10-14 00:01:24,750 INFO [train.py:451] Epoch 2, batch 2930, batch avg loss 0.3467, total avg loss: 0.2892, batch size: 49
2021-10-14 00:01:29,586 INFO [train.py:451] Epoch 2, batch 2940, batch avg loss 0.2643, total avg loss: 0.2892, batch size: 36
2021-10-14 00:01:34,408 INFO [train.py:451] Epoch 2, batch 2950, batch avg loss 0.3340, total avg loss: 0.2897, batch size: 73
2021-10-14 00:01:39,357 INFO [train.py:451] Epoch 2, batch 2960, batch avg loss 0.2897, total avg loss: 0.2894, batch size: 33
2021-10-14 00:01:44,323 INFO [train.py:451] Epoch 2, batch 2970, batch avg loss 0.3066, total avg loss: 0.2915, batch size: 36
2021-10-14 00:01:49,320 INFO [train.py:451] Epoch 2, batch 2980, batch avg loss 0.3252, total avg loss: 0.2924, batch size: 49
2021-10-14 00:01:54,298 INFO [train.py:451] Epoch 2, batch 2990, batch avg loss 0.2635, total avg loss: 0.2916, batch size: 33
2021-10-14 00:01:59,211 INFO [train.py:451] Epoch 2, batch 3000, batch avg loss 0.4092, total avg loss: 0.2914, batch size: 124
2021-10-14 00:02:38,825 INFO [train.py:483] Epoch 2, valid loss 0.2091, best valid loss: 0.2091 best valid epoch: 2
2021-10-14 00:02:43,723 INFO [train.py:451] Epoch 2, batch 3010, batch avg loss 0.2516, total avg loss: 0.3023, batch size: 35
2021-10-14 00:02:48,629 INFO [train.py:451] Epoch 2, batch 3020, batch avg loss 0.3282, total avg loss: 0.2869, batch size: 36
2021-10-14 00:02:53,508 INFO [train.py:451] Epoch 2, batch 3030, batch avg loss 0.3144, total avg loss: 0.2898, batch size: 36
2021-10-14 00:02:58,418 INFO [train.py:451] Epoch 2, batch 3040, batch avg loss 0.2871, total avg loss: 0.2898, batch size: 38
2021-10-14 00:03:03,269 INFO [train.py:451] Epoch 2, batch 3050, batch avg loss 0.3017, total avg loss: 0.2892, batch size: 36
2021-10-14 00:03:07,860 INFO [train.py:451] Epoch 2, batch 3060, batch avg loss 0.3156, total avg loss: 0.2970, batch size: 49
2021-10-14 00:03:12,672 INFO [train.py:451] Epoch 2, batch 3070, batch avg loss 0.2670, total avg loss: 0.2977, batch size: 36
2021-10-14 00:03:17,820 INFO [train.py:451] Epoch 2, batch 3080, batch avg loss 0.2754, total avg loss: 0.2941, batch size: 34
2021-10-14 00:03:22,871 INFO [train.py:451] Epoch 2, batch 3090, batch avg loss 0.2845, total avg loss: 0.2943, batch size: 32
2021-10-14 00:03:27,803 INFO [train.py:451] Epoch 2, batch 3100, batch avg loss 0.2618, total avg loss: 0.2945, batch size: 29
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2021-10-14 00:11:32,850 INFO [train.py:483] Epoch 2, valid loss 0.2075, best valid loss: 0.2075 best valid epoch: 2
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2021-10-14 00:16:13,336 INFO [train.py:451] Epoch 2, batch 4570, batch avg loss 0.2950, total avg loss: 0.2906, batch size: 33
2021-10-14 00:16:18,095 INFO [train.py:451] Epoch 2, batch 4580, batch avg loss 0.2835, total avg loss: 0.2916, batch size: 41
2021-10-14 00:16:23,027 INFO [train.py:451] Epoch 2, batch 4590, batch avg loss 0.2188, total avg loss: 0.2911, batch size: 32
2021-10-14 00:16:28,254 INFO [train.py:451] Epoch 2, batch 4600, batch avg loss 0.3355, total avg loss: 0.2912, batch size: 36
2021-10-14 00:16:33,070 INFO [train.py:451] Epoch 2, batch 4610, batch avg loss 0.2310, total avg loss: 0.2970, batch size: 30
2021-10-14 00:16:37,834 INFO [train.py:451] Epoch 2, batch 4620, batch avg loss 0.3674, total avg loss: 0.3023, batch size: 42
2021-10-14 00:16:42,873 INFO [train.py:451] Epoch 2, batch 4630, batch avg loss 0.3002, total avg loss: 0.2942, batch size: 36
2021-10-14 00:16:47,808 INFO [train.py:451] Epoch 2, batch 4640, batch avg loss 0.2334, total avg loss: 0.2942, batch size: 27
2021-10-14 00:16:52,765 INFO [train.py:451] Epoch 2, batch 4650, batch avg loss 0.4036, total avg loss: 0.2961, batch size: 133
2021-10-14 00:16:57,751 INFO [train.py:451] Epoch 2, batch 4660, batch avg loss 0.2584, total avg loss: 0.2957, batch size: 33
2021-10-14 00:17:02,740 INFO [train.py:451] Epoch 2, batch 4670, batch avg loss 0.4009, total avg loss: 0.2978, batch size: 38
2021-10-14 00:17:07,705 INFO [train.py:451] Epoch 2, batch 4680, batch avg loss 0.2693, total avg loss: 0.2951, batch size: 34
2021-10-14 00:17:12,380 INFO [train.py:451] Epoch 2, batch 4690, batch avg loss 0.3243, total avg loss: 0.2968, batch size: 57
2021-10-14 00:17:17,245 INFO [train.py:451] Epoch 2, batch 4700, batch avg loss 0.2700, total avg loss: 0.2967, batch size: 41
2021-10-14 00:17:22,147 INFO [train.py:451] Epoch 2, batch 4710, batch avg loss 0.3279, total avg loss: 0.2964, batch size: 32
2021-10-14 00:17:27,276 INFO [train.py:451] Epoch 2, batch 4720, batch avg loss 0.3447, total avg loss: 0.2957, batch size: 42
2021-10-14 00:17:32,317 INFO [train.py:451] Epoch 2, batch 4730, batch avg loss 0.2833, total avg loss: 0.2960, batch size: 29
2021-10-14 00:17:37,586 INFO [train.py:451] Epoch 2, batch 4740, batch avg loss 0.3346, total avg loss: 0.2953, batch size: 36
2021-10-14 00:17:42,492 INFO [train.py:451] Epoch 2, batch 4750, batch avg loss 0.2567, total avg loss: 0.2940, batch size: 28
2021-10-14 00:17:47,465 INFO [train.py:451] Epoch 2, batch 4760, batch avg loss 0.2747, total avg loss: 0.2940, batch size: 29
2021-10-14 00:17:52,551 INFO [train.py:451] Epoch 2, batch 4770, batch avg loss 0.2495, total avg loss: 0.2932, batch size: 31
2021-10-14 00:17:57,692 INFO [train.py:451] Epoch 2, batch 4780, batch avg loss 0.3629, total avg loss: 0.2927, batch size: 49
2021-10-14 00:18:02,522 INFO [train.py:451] Epoch 2, batch 4790, batch avg loss 0.3208, total avg loss: 0.2930, batch size: 37
2021-10-14 00:18:07,434 INFO [train.py:451] Epoch 2, batch 4800, batch avg loss 0.2953, total avg loss: 0.2928, batch size: 34
2021-10-14 00:18:12,140 INFO [train.py:451] Epoch 2, batch 4810, batch avg loss 0.2855, total avg loss: 0.3118, batch size: 32
2021-10-14 00:18:16,966 INFO [train.py:451] Epoch 2, batch 4820, batch avg loss 0.3388, total avg loss: 0.2991, batch size: 49
2021-10-14 00:18:21,764 INFO [train.py:451] Epoch 2, batch 4830, batch avg loss 0.3222, total avg loss: 0.3021, batch size: 57
2021-10-14 00:18:26,685 INFO [train.py:451] Epoch 2, batch 4840, batch avg loss 0.2332, total avg loss: 0.2958, batch size: 28
2021-10-14 00:18:31,608 INFO [train.py:451] Epoch 2, batch 4850, batch avg loss 0.2271, total avg loss: 0.2987, batch size: 32
2021-10-14 00:18:36,571 INFO [train.py:451] Epoch 2, batch 4860, batch avg loss 0.3089, total avg loss: 0.2960, batch size: 35
2021-10-14 00:18:41,539 INFO [train.py:451] Epoch 2, batch 4870, batch avg loss 0.3425, total avg loss: 0.2947, batch size: 34
2021-10-14 00:18:46,662 INFO [train.py:451] Epoch 2, batch 4880, batch avg loss 0.2981, total avg loss: 0.2952, batch size: 36
2021-10-14 00:18:51,739 INFO [train.py:451] Epoch 2, batch 4890, batch avg loss 0.3147, total avg loss: 0.2939, batch size: 38
2021-10-14 00:18:56,540 INFO [train.py:451] Epoch 2, batch 4900, batch avg loss 0.4026, total avg loss: 0.2944, batch size: 124
2021-10-14 00:19:01,377 INFO [train.py:451] Epoch 2, batch 4910, batch avg loss 0.2696, total avg loss: 0.2941, batch size: 35
2021-10-14 00:19:06,411 INFO [train.py:451] Epoch 2, batch 4920, batch avg loss 0.3227, total avg loss: 0.2937, batch size: 73
2021-10-14 00:19:11,593 INFO [train.py:451] Epoch 2, batch 4930, batch avg loss 0.3312, total avg loss: 0.2938, batch size: 37
2021-10-14 00:19:16,489 INFO [train.py:451] Epoch 2, batch 4940, batch avg loss 0.2833, total avg loss: 0.2933, batch size: 49
2021-10-14 00:19:21,776 INFO [train.py:451] Epoch 2, batch 4950, batch avg loss 0.2554, total avg loss: 0.2921, batch size: 27
2021-10-14 00:19:26,614 INFO [train.py:451] Epoch 2, batch 4960, batch avg loss 0.3396, total avg loss: 0.2926, batch size: 73
2021-10-14 00:19:31,628 INFO [train.py:451] Epoch 2, batch 4970, batch avg loss 0.2761, total avg loss: 0.2941, batch size: 30
2021-10-14 00:19:36,452 INFO [train.py:451] Epoch 2, batch 4980, batch avg loss 0.2405, total avg loss: 0.2943, batch size: 32
2021-10-14 00:19:41,395 INFO [train.py:451] Epoch 2, batch 4990, batch avg loss 0.2858, total avg loss: 0.2949, batch size: 29
2021-10-14 00:19:46,117 INFO [train.py:451] Epoch 2, batch 5000, batch avg loss 0.2853, total avg loss: 0.2968, batch size: 31
2021-10-14 00:20:24,054 INFO [train.py:483] Epoch 2, valid loss 0.2063, best valid loss: 0.2063 best valid epoch: 2
2021-10-14 00:20:28,994 INFO [train.py:451] Epoch 2, batch 5010, batch avg loss 0.2543, total avg loss: 0.2805, batch size: 34
2021-10-14 00:20:34,014 INFO [train.py:451] Epoch 2, batch 5020, batch avg loss 0.4120, total avg loss: 0.2849, batch size: 41
2021-10-14 00:20:38,849 INFO [train.py:451] Epoch 2, batch 5030, batch avg loss 0.2658, total avg loss: 0.2825, batch size: 31
2021-10-14 00:20:43,716 INFO [train.py:451] Epoch 2, batch 5040, batch avg loss 0.3132, total avg loss: 0.2851, batch size: 45
2021-10-14 00:20:48,710 INFO [train.py:451] Epoch 2, batch 5050, batch avg loss 0.3253, total avg loss: 0.2860, batch size: 42
2021-10-14 00:20:53,732 INFO [train.py:451] Epoch 2, batch 5060, batch avg loss 0.2977, total avg loss: 0.2844, batch size: 30
2021-10-14 00:20:58,783 INFO [train.py:451] Epoch 2, batch 5070, batch avg loss 0.3033, total avg loss: 0.2840, batch size: 34
2021-10-14 00:21:03,866 INFO [train.py:451] Epoch 2, batch 5080, batch avg loss 0.2457, total avg loss: 0.2826, batch size: 34
2021-10-14 00:21:08,891 INFO [train.py:451] Epoch 2, batch 5090, batch avg loss 0.2537, total avg loss: 0.2823, batch size: 29
2021-10-14 00:21:13,823 INFO [train.py:451] Epoch 2, batch 5100, batch avg loss 0.2699, total avg loss: 0.2830, batch size: 34
2021-10-14 00:21:18,883 INFO [train.py:451] Epoch 2, batch 5110, batch avg loss 0.2827, total avg loss: 0.2826, batch size: 34
2021-10-14 00:21:23,773 INFO [train.py:451] Epoch 2, batch 5120, batch avg loss 0.2604, total avg loss: 0.2829, batch size: 32
2021-10-14 00:21:28,731 INFO [train.py:451] Epoch 2, batch 5130, batch avg loss 0.2342, total avg loss: 0.2838, batch size: 30
2021-10-14 00:21:33,804 INFO [train.py:451] Epoch 2, batch 5140, batch avg loss 0.3151, total avg loss: 0.2860, batch size: 30
2021-10-14 00:21:38,599 INFO [train.py:451] Epoch 2, batch 5150, batch avg loss 0.2712, total avg loss: 0.2869, batch size: 36
2021-10-14 00:21:41,397 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "ae64b5ed-565e-d345-a0fd-b4606c7d640b" will not be mixed in.
2021-10-14 00:21:43,596 INFO [train.py:451] Epoch 2, batch 5160, batch avg loss 0.4285, total avg loss: 0.2883, batch size: 133
2021-10-14 00:21:48,363 INFO [train.py:451] Epoch 2, batch 5170, batch avg loss 0.3319, total avg loss: 0.2892, batch size: 35
2021-10-14 00:21:53,190 INFO [train.py:451] Epoch 2, batch 5180, batch avg loss 0.3613, total avg loss: 0.2909, batch size: 35
2021-10-14 00:21:58,100 INFO [train.py:451] Epoch 2, batch 5190, batch avg loss 0.2100, total avg loss: 0.2901, batch size: 30
2021-10-14 00:22:02,997 INFO [train.py:451] Epoch 2, batch 5200, batch avg loss 0.2458, total avg loss: 0.2892, batch size: 32
2021-10-14 00:22:08,035 INFO [train.py:451] Epoch 2, batch 5210, batch avg loss 0.2498, total avg loss: 0.2692, batch size: 34
2021-10-14 00:22:13,179 INFO [train.py:451] Epoch 2, batch 5220, batch avg loss 0.2677, total avg loss: 0.2839, batch size: 31
2021-10-14 00:22:18,401 INFO [train.py:451] Epoch 2, batch 5230, batch avg loss 0.2763, total avg loss: 0.2783, batch size: 33
2021-10-14 00:22:23,249 INFO [train.py:451] Epoch 2, batch 5240, batch avg loss 0.2729, total avg loss: 0.2851, batch size: 49
2021-10-14 00:22:28,347 INFO [train.py:451] Epoch 2, batch 5250, batch avg loss 0.2907, total avg loss: 0.2856, batch size: 32
2021-10-14 00:22:33,230 INFO [train.py:451] Epoch 2, batch 5260, batch avg loss 0.3346, total avg loss: 0.2900, batch size: 34
2021-10-14 00:22:38,224 INFO [train.py:451] Epoch 2, batch 5270, batch avg loss 0.3029, total avg loss: 0.2898, batch size: 37
2021-10-14 00:22:43,115 INFO [train.py:451] Epoch 2, batch 5280, batch avg loss 0.2962, total avg loss: 0.2897, batch size: 32
2021-10-14 00:22:48,021 INFO [train.py:451] Epoch 2, batch 5290, batch avg loss 0.3360, total avg loss: 0.2890, batch size: 42
2021-10-14 00:22:52,946 INFO [train.py:451] Epoch 2, batch 5300, batch avg loss 0.2495, total avg loss: 0.2883, batch size: 27
2021-10-14 00:22:57,938 INFO [train.py:451] Epoch 2, batch 5310, batch avg loss 0.2342, total avg loss: 0.2869, batch size: 32
2021-10-14 00:23:02,926 INFO [train.py:451] Epoch 2, batch 5320, batch avg loss 0.2886, total avg loss: 0.2883, batch size: 38
2021-10-14 00:23:07,057 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "b40bc811-ae12-7bf5-0a05-5bc348961862" will not be mixed in.
2021-10-14 00:23:07,787 INFO [train.py:451] Epoch 2, batch 5330, batch avg loss 0.2876, total avg loss: 0.2881, batch size: 29
2021-10-14 00:23:12,600 INFO [train.py:451] Epoch 2, batch 5340, batch avg loss 0.2659, total avg loss: 0.2892, batch size: 31
2021-10-14 00:23:17,809 INFO [train.py:451] Epoch 2, batch 5350, batch avg loss 0.2535, total avg loss: 0.2886, batch size: 27
2021-10-14 00:23:22,969 INFO [train.py:451] Epoch 2, batch 5360, batch avg loss 0.2785, total avg loss: 0.2885, batch size: 49
2021-10-14 00:23:28,175 INFO [train.py:451] Epoch 2, batch 5370, batch avg loss 0.3349, total avg loss: 0.2881, batch size: 49
2021-10-14 00:23:33,023 INFO [train.py:451] Epoch 2, batch 5380, batch avg loss 0.3211, total avg loss: 0.2867, batch size: 38
2021-10-14 00:23:38,106 INFO [train.py:451] Epoch 2, batch 5390, batch avg loss 0.2244, total avg loss: 0.2858, batch size: 33
2021-10-14 00:23:43,022 INFO [train.py:451] Epoch 2, batch 5400, batch avg loss 0.2906, total avg loss: 0.2869, batch size: 30
2021-10-14 00:23:47,887 INFO [train.py:451] Epoch 2, batch 5410, batch avg loss 0.3888, total avg loss: 0.2788, batch size: 42
2021-10-14 00:23:52,858 INFO [train.py:451] Epoch 2, batch 5420, batch avg loss 0.3241, total avg loss: 0.2926, batch size: 34
2021-10-14 00:23:57,873 INFO [train.py:451] Epoch 2, batch 5430, batch avg loss 0.2778, total avg loss: 0.2842, batch size: 31
2021-10-14 00:24:02,815 INFO [train.py:451] Epoch 2, batch 5440, batch avg loss 0.3119, total avg loss: 0.2817, batch size: 45
2021-10-14 00:24:07,609 INFO [train.py:451] Epoch 2, batch 5450, batch avg loss 0.2459, total avg loss: 0.2844, batch size: 31
2021-10-14 00:24:12,570 INFO [train.py:451] Epoch 2, batch 5460, batch avg loss 0.3485, total avg loss: 0.2892, batch size: 45
2021-10-14 00:24:17,536 INFO [train.py:451] Epoch 2, batch 5470, batch avg loss 0.3433, total avg loss: 0.2883, batch size: 38
2021-10-14 00:24:22,502 INFO [train.py:451] Epoch 2, batch 5480, batch avg loss 0.3178, total avg loss: 0.2859, batch size: 34
2021-10-14 00:24:27,531 INFO [train.py:451] Epoch 2, batch 5490, batch avg loss 0.2422, total avg loss: 0.2841, batch size: 32
2021-10-14 00:24:32,290 INFO [train.py:451] Epoch 2, batch 5500, batch avg loss 0.3563, total avg loss: 0.2852, batch size: 45
2021-10-14 00:24:37,404 INFO [train.py:451] Epoch 2, batch 5510, batch avg loss 0.3467, total avg loss: 0.2835, batch size: 42
2021-10-14 00:24:42,555 INFO [train.py:451] Epoch 2, batch 5520, batch avg loss 0.2315, total avg loss: 0.2833, batch size: 27
2021-10-14 00:24:47,682 INFO [train.py:451] Epoch 2, batch 5530, batch avg loss 0.2770, total avg loss: 0.2829, batch size: 34
2021-10-14 00:24:52,666 INFO [train.py:451] Epoch 2, batch 5540, batch avg loss 0.3154, total avg loss: 0.2829, batch size: 34
2021-10-14 00:24:57,656 INFO [train.py:451] Epoch 2, batch 5550, batch avg loss 0.2839, total avg loss: 0.2824, batch size: 36
2021-10-14 00:25:02,368 INFO [train.py:451] Epoch 2, batch 5560, batch avg loss 0.4609, total avg loss: 0.2837, batch size: 125
2021-10-14 00:25:07,411 INFO [train.py:451] Epoch 2, batch 5570, batch avg loss 0.2210, total avg loss: 0.2835, batch size: 27
2021-10-14 00:25:12,469 INFO [train.py:451] Epoch 2, batch 5580, batch avg loss 0.2967, total avg loss: 0.2842, batch size: 45
2021-10-14 00:25:17,489 INFO [train.py:451] Epoch 2, batch 5590, batch avg loss 0.2385, total avg loss: 0.2847, batch size: 30
2021-10-14 00:25:22,360 INFO [train.py:451] Epoch 2, batch 5600, batch avg loss 0.2497, total avg loss: 0.2850, batch size: 38
2021-10-14 00:25:27,388 INFO [train.py:451] Epoch 2, batch 5610, batch avg loss 0.2785, total avg loss: 0.2759, batch size: 49
2021-10-14 00:25:32,573 INFO [train.py:451] Epoch 2, batch 5620, batch avg loss 0.3004, total avg loss: 0.2809, batch size: 37
2021-10-14 00:25:37,453 INFO [train.py:451] Epoch 2, batch 5630, batch avg loss 0.2897, total avg loss: 0.2809, batch size: 57
2021-10-14 00:25:42,262 INFO [train.py:451] Epoch 2, batch 5640, batch avg loss 0.2510, total avg loss: 0.2832, batch size: 35
2021-10-14 00:25:47,396 INFO [train.py:451] Epoch 2, batch 5650, batch avg loss 0.3063, total avg loss: 0.2859, batch size: 34
2021-10-14 00:25:52,510 INFO [train.py:451] Epoch 2, batch 5660, batch avg loss 0.1983, total avg loss: 0.2846, batch size: 28
2021-10-14 00:25:57,585 INFO [train.py:451] Epoch 2, batch 5670, batch avg loss 0.2701, total avg loss: 0.2875, batch size: 40
2021-10-14 00:26:02,583 INFO [train.py:451] Epoch 2, batch 5680, batch avg loss 0.3279, total avg loss: 0.2903, batch size: 36
2021-10-14 00:26:07,606 INFO [train.py:451] Epoch 2, batch 5690, batch avg loss 0.3214, total avg loss: 0.2905, batch size: 56
2021-10-14 00:26:12,775 INFO [train.py:451] Epoch 2, batch 5700, batch avg loss 0.3301, total avg loss: 0.2935, batch size: 37
2021-10-14 00:26:17,681 INFO [train.py:451] Epoch 2, batch 5710, batch avg loss 0.2947, total avg loss: 0.2939, batch size: 38
2021-10-14 00:26:22,977 INFO [train.py:451] Epoch 2, batch 5720, batch avg loss 0.2655, total avg loss: 0.2919, batch size: 36
2021-10-14 00:26:28,002 INFO [train.py:451] Epoch 2, batch 5730, batch avg loss 0.2637, total avg loss: 0.2920, batch size: 28
2021-10-14 00:26:33,050 INFO [train.py:451] Epoch 2, batch 5740, batch avg loss 0.3994, total avg loss: 0.2906, batch size: 73
2021-10-14 00:26:38,042 INFO [train.py:451] Epoch 2, batch 5750, batch avg loss 0.3576, total avg loss: 0.2896, batch size: 57
2021-10-14 00:26:42,919 INFO [train.py:451] Epoch 2, batch 5760, batch avg loss 0.2924, total avg loss: 0.2891, batch size: 32
2021-10-14 00:26:47,934 INFO [train.py:451] Epoch 2, batch 5770, batch avg loss 0.2576, total avg loss: 0.2890, batch size: 33
2021-10-14 00:26:52,929 INFO [train.py:451] Epoch 2, batch 5780, batch avg loss 0.2928, total avg loss: 0.2884, batch size: 73
2021-10-14 00:26:57,901 INFO [train.py:451] Epoch 2, batch 5790, batch avg loss 0.2815, total avg loss: 0.2885, batch size: 30
2021-10-14 00:27:02,927 INFO [train.py:451] Epoch 2, batch 5800, batch avg loss 0.3231, total avg loss: 0.2886, batch size: 36
2021-10-14 00:27:08,105 INFO [train.py:451] Epoch 2, batch 5810, batch avg loss 0.2824, total avg loss: 0.3022, batch size: 30
2021-10-14 00:27:12,265 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "8f344f46-3499-7a63-4f6b-85d7ec0b30aa" will not be mixed in.
2021-10-14 00:27:13,010 INFO [train.py:451] Epoch 2, batch 5820, batch avg loss 0.3088, total avg loss: 0.3026, batch size: 38
2021-10-14 00:27:18,180 INFO [train.py:451] Epoch 2, batch 5830, batch avg loss 0.2880, total avg loss: 0.3010, batch size: 34
2021-10-14 00:27:23,252 INFO [train.py:451] Epoch 2, batch 5840, batch avg loss 0.3065, total avg loss: 0.2981, batch size: 39
2021-10-14 00:27:28,070 INFO [train.py:451] Epoch 2, batch 5850, batch avg loss 0.2228, total avg loss: 0.2965, batch size: 31
2021-10-14 00:27:33,003 INFO [train.py:451] Epoch 2, batch 5860, batch avg loss 0.3286, total avg loss: 0.2961, batch size: 57
2021-10-14 00:27:37,770 INFO [train.py:451] Epoch 2, batch 5870, batch avg loss 0.2936, total avg loss: 0.2979, batch size: 37
2021-10-14 00:27:42,890 INFO [train.py:451] Epoch 2, batch 5880, batch avg loss 0.3648, total avg loss: 0.2994, batch size: 33
2021-10-14 00:27:48,051 INFO [train.py:451] Epoch 2, batch 5890, batch avg loss 0.3504, total avg loss: 0.2975, batch size: 34
2021-10-14 00:27:53,152 INFO [train.py:451] Epoch 2, batch 5900, batch avg loss 0.2977, total avg loss: 0.2948, batch size: 38
2021-10-14 00:27:58,272 INFO [train.py:451] Epoch 2, batch 5910, batch avg loss 0.3150, total avg loss: 0.2943, batch size: 42
2021-10-14 00:28:03,200 INFO [train.py:451] Epoch 2, batch 5920, batch avg loss 0.3737, total avg loss: 0.2947, batch size: 73
2021-10-14 00:28:08,365 INFO [train.py:451] Epoch 2, batch 5930, batch avg loss 0.3065, total avg loss: 0.2928, batch size: 35
2021-10-14 00:28:13,207 INFO [train.py:451] Epoch 2, batch 5940, batch avg loss 0.3323, total avg loss: 0.2922, batch size: 57
2021-10-14 00:28:18,065 INFO [train.py:451] Epoch 2, batch 5950, batch avg loss 0.3251, total avg loss: 0.2924, batch size: 57
2021-10-14 00:28:22,871 INFO [train.py:451] Epoch 2, batch 5960, batch avg loss 0.2494, total avg loss: 0.2922, batch size: 30
2021-10-14 00:28:27,972 INFO [train.py:451] Epoch 2, batch 5970, batch avg loss 0.2879, total avg loss: 0.2905, batch size: 34
2021-10-14 00:28:33,015 INFO [train.py:451] Epoch 2, batch 5980, batch avg loss 0.2899, total avg loss: 0.2902, batch size: 49
2021-10-14 00:28:37,971 INFO [train.py:451] Epoch 2, batch 5990, batch avg loss 0.3199, total avg loss: 0.2905, batch size: 36
2021-10-14 00:28:42,993 INFO [train.py:451] Epoch 2, batch 6000, batch avg loss 0.3018, total avg loss: 0.2899, batch size: 41
2021-10-14 00:29:22,485 INFO [train.py:483] Epoch 2, valid loss 0.2069, best valid loss: 0.2063 best valid epoch: 2
2021-10-14 00:29:27,530 INFO [train.py:451] Epoch 2, batch 6010, batch avg loss 0.3604, total avg loss: 0.2680, batch size: 42
2021-10-14 00:29:32,493 INFO [train.py:451] Epoch 2, batch 6020, batch avg loss 0.2454, total avg loss: 0.2743, batch size: 31
2021-10-14 00:29:37,506 INFO [train.py:451] Epoch 2, batch 6030, batch avg loss 0.2902, total avg loss: 0.2884, batch size: 34
2021-10-14 00:29:42,346 INFO [train.py:451] Epoch 2, batch 6040, batch avg loss 0.2886, total avg loss: 0.2873, batch size: 57
2021-10-14 00:29:47,311 INFO [train.py:451] Epoch 2, batch 6050, batch avg loss 0.3201, total avg loss: 0.2866, batch size: 37
2021-10-14 00:29:52,170 INFO [train.py:451] Epoch 2, batch 6060, batch avg loss 0.2644, total avg loss: 0.2869, batch size: 36
2021-10-14 00:29:57,244 INFO [train.py:451] Epoch 2, batch 6070, batch avg loss 0.3342, total avg loss: 0.2840, batch size: 34
2021-10-14 00:30:02,223 INFO [train.py:451] Epoch 2, batch 6080, batch avg loss 0.3814, total avg loss: 0.2855, batch size: 123
2021-10-14 00:30:07,401 INFO [train.py:451] Epoch 2, batch 6090, batch avg loss 0.2786, total avg loss: 0.2853, batch size: 41
2021-10-14 00:30:12,569 INFO [train.py:451] Epoch 2, batch 6100, batch avg loss 0.2682, total avg loss: 0.2847, batch size: 38
2021-10-14 00:30:17,468 INFO [train.py:451] Epoch 2, batch 6110, batch avg loss 0.3389, total avg loss: 0.2835, batch size: 72
2021-10-14 00:30:22,354 INFO [train.py:451] Epoch 2, batch 6120, batch avg loss 0.2914, total avg loss: 0.2844, batch size: 38
2021-10-14 00:30:27,411 INFO [train.py:451] Epoch 2, batch 6130, batch avg loss 0.2881, total avg loss: 0.2843, batch size: 34
2021-10-14 00:30:32,169 INFO [train.py:451] Epoch 2, batch 6140, batch avg loss 0.3449, total avg loss: 0.2855, batch size: 41
2021-10-14 00:30:37,028 INFO [train.py:451] Epoch 2, batch 6150, batch avg loss 0.3103, total avg loss: 0.2848, batch size: 38
2021-10-14 00:30:41,892 INFO [train.py:451] Epoch 2, batch 6160, batch avg loss 0.2828, total avg loss: 0.2854, batch size: 45
2021-10-14 00:30:46,878 INFO [train.py:451] Epoch 2, batch 6170, batch avg loss 0.2865, total avg loss: 0.2863, batch size: 31
2021-10-14 00:30:51,824 INFO [train.py:451] Epoch 2, batch 6180, batch avg loss 0.3350, total avg loss: 0.2869, batch size: 34
2021-10-14 00:30:56,808 INFO [train.py:451] Epoch 2, batch 6190, batch avg loss 0.2768, total avg loss: 0.2872, batch size: 27
2021-10-14 00:31:01,660 INFO [train.py:451] Epoch 2, batch 6200, batch avg loss 0.3054, total avg loss: 0.2879, batch size: 42
2021-10-14 00:31:06,686 INFO [train.py:451] Epoch 2, batch 6210, batch avg loss 0.2128, total avg loss: 0.2984, batch size: 30
2021-10-14 00:31:11,674 INFO [train.py:451] Epoch 2, batch 6220, batch avg loss 0.2508, total avg loss: 0.2914, batch size: 30
2021-10-14 00:31:16,613 INFO [train.py:451] Epoch 2, batch 6230, batch avg loss 0.2687, total avg loss: 0.2878, batch size: 49
2021-10-14 00:31:21,611 INFO [train.py:451] Epoch 2, batch 6240, batch avg loss 0.2658, total avg loss: 0.2875, batch size: 34
2021-10-14 00:31:26,492 INFO [train.py:451] Epoch 2, batch 6250, batch avg loss 0.3136, total avg loss: 0.2887, batch size: 38
2021-10-14 00:31:31,393 INFO [train.py:451] Epoch 2, batch 6260, batch avg loss 0.3118, total avg loss: 0.2877, batch size: 34
2021-10-14 00:31:36,400 INFO [train.py:451] Epoch 2, batch 6270, batch avg loss 0.3105, total avg loss: 0.2901, batch size: 39
2021-10-14 00:31:41,427 INFO [train.py:451] Epoch 2, batch 6280, batch avg loss 0.2820, total avg loss: 0.2886, batch size: 41
2021-10-14 00:31:46,215 INFO [train.py:451] Epoch 2, batch 6290, batch avg loss 0.3336, total avg loss: 0.2909, batch size: 36
2021-10-14 00:31:51,367 INFO [train.py:451] Epoch 2, batch 6300, batch avg loss 0.2240, total avg loss: 0.2894, batch size: 27
2021-10-14 00:31:56,195 INFO [train.py:451] Epoch 2, batch 6310, batch avg loss 0.3863, total avg loss: 0.2927, batch size: 39
2021-10-14 00:32:01,304 INFO [train.py:451] Epoch 2, batch 6320, batch avg loss 0.2915, total avg loss: 0.2915, batch size: 35
2021-10-14 00:32:06,200 INFO [train.py:451] Epoch 2, batch 6330, batch avg loss 0.2977, total avg loss: 0.2920, batch size: 39
2021-10-14 00:32:11,229 INFO [train.py:451] Epoch 2, batch 6340, batch avg loss 0.2751, total avg loss: 0.2914, batch size: 34
2021-10-14 00:32:16,262 INFO [train.py:451] Epoch 2, batch 6350, batch avg loss 0.2362, total avg loss: 0.2925, batch size: 29
2021-10-14 00:32:21,075 INFO [train.py:451] Epoch 2, batch 6360, batch avg loss 0.2948, total avg loss: 0.2937, batch size: 33
2021-10-14 00:32:25,894 INFO [train.py:451] Epoch 2, batch 6370, batch avg loss 0.2463, total avg loss: 0.2945, batch size: 32
2021-10-14 00:32:30,770 INFO [train.py:451] Epoch 2, batch 6380, batch avg loss 0.2292, total avg loss: 0.2941, batch size: 32
2021-10-14 00:32:35,687 INFO [train.py:451] Epoch 2, batch 6390, batch avg loss 0.3136, total avg loss: 0.2937, batch size: 39
2021-10-14 00:32:40,642 INFO [train.py:451] Epoch 2, batch 6400, batch avg loss 0.3965, total avg loss: 0.2942, batch size: 129
2021-10-14 00:32:45,526 INFO [train.py:451] Epoch 2, batch 6410, batch avg loss 0.2984, total avg loss: 0.3140, batch size: 49
2021-10-14 00:32:50,496 INFO [train.py:451] Epoch 2, batch 6420, batch avg loss 0.2550, total avg loss: 0.3061, batch size: 31
2021-10-14 00:32:55,574 INFO [train.py:451] Epoch 2, batch 6430, batch avg loss 0.2224, total avg loss: 0.2983, batch size: 30
2021-10-14 00:33:00,440 INFO [train.py:451] Epoch 2, batch 6440, batch avg loss 0.2818, total avg loss: 0.2949, batch size: 34
2021-10-14 00:33:05,299 INFO [train.py:451] Epoch 2, batch 6450, batch avg loss 0.3193, total avg loss: 0.2990, batch size: 35
2021-10-14 00:33:09,940 INFO [train.py:451] Epoch 2, batch 6460, batch avg loss 0.2209, total avg loss: 0.3015, batch size: 28
2021-10-14 00:33:14,789 INFO [train.py:451] Epoch 2, batch 6470, batch avg loss 0.2463, total avg loss: 0.3026, batch size: 33
2021-10-14 00:33:19,699 INFO [train.py:451] Epoch 2, batch 6480, batch avg loss 0.3072, total avg loss: 0.3015, batch size: 49
2021-10-14 00:33:24,680 INFO [train.py:451] Epoch 2, batch 6490, batch avg loss 0.3455, total avg loss: 0.3016, batch size: 38
2021-10-14 00:33:37,287 INFO [train.py:451] Epoch 2, batch 6500, batch avg loss 0.2786, total avg loss: 0.2999, batch size: 32
2021-10-14 00:33:42,147 INFO [train.py:451] Epoch 2, batch 6510, batch avg loss 0.2518, total avg loss: 0.3010, batch size: 28
2021-10-14 00:33:46,868 INFO [train.py:451] Epoch 2, batch 6520, batch avg loss 0.3648, total avg loss: 0.3010, batch size: 39
2021-10-14 00:33:51,738 INFO [train.py:451] Epoch 2, batch 6530, batch avg loss 0.2553, total avg loss: 0.2994, batch size: 31
2021-10-14 00:33:56,662 INFO [train.py:451] Epoch 2, batch 6540, batch avg loss 0.2671, total avg loss: 0.2990, batch size: 28
2021-10-14 00:34:01,625 INFO [train.py:451] Epoch 2, batch 6550, batch avg loss 0.2933, total avg loss: 0.2976, batch size: 39
2021-10-14 00:34:06,485 INFO [train.py:451] Epoch 2, batch 6560, batch avg loss 0.2104, total avg loss: 0.2969, batch size: 32
2021-10-14 00:34:11,537 INFO [train.py:451] Epoch 2, batch 6570, batch avg loss 0.2832, total avg loss: 0.2945, batch size: 35
2021-10-14 00:34:16,519 INFO [train.py:451] Epoch 2, batch 6580, batch avg loss 0.2594, total avg loss: 0.2950, batch size: 35
2021-10-14 00:34:21,243 INFO [train.py:451] Epoch 2, batch 6590, batch avg loss 0.2679, total avg loss: 0.2951, batch size: 37
2021-10-14 00:34:26,385 INFO [train.py:451] Epoch 2, batch 6600, batch avg loss 0.3240, total avg loss: 0.2950, batch size: 37
2021-10-14 00:34:31,382 INFO [train.py:451] Epoch 2, batch 6610, batch avg loss 0.3107, total avg loss: 0.2936, batch size: 56
2021-10-14 00:34:36,270 INFO [train.py:451] Epoch 2, batch 6620, batch avg loss 0.2628, total avg loss: 0.2798, batch size: 28
2021-10-14 00:34:41,237 INFO [train.py:451] Epoch 2, batch 6630, batch avg loss 0.2684, total avg loss: 0.2778, batch size: 33
2021-10-14 00:34:45,955 INFO [train.py:451] Epoch 2, batch 6640, batch avg loss 0.2303, total avg loss: 0.2853, batch size: 33
2021-10-14 00:34:50,813 INFO [train.py:451] Epoch 2, batch 6650, batch avg loss 0.2881, total avg loss: 0.2859, batch size: 35
2021-10-14 00:34:55,735 INFO [train.py:451] Epoch 2, batch 6660, batch avg loss 0.2675, total avg loss: 0.2851, batch size: 30
2021-10-14 00:35:00,626 INFO [train.py:451] Epoch 2, batch 6670, batch avg loss 0.2820, total avg loss: 0.2817, batch size: 34
2021-10-14 00:35:05,450 INFO [train.py:451] Epoch 2, batch 6680, batch avg loss 0.3679, total avg loss: 0.2849, batch size: 37
2021-10-14 00:35:10,327 INFO [train.py:451] Epoch 2, batch 6690, batch avg loss 0.3477, total avg loss: 0.2860, batch size: 57
2021-10-14 00:35:15,201 INFO [train.py:451] Epoch 2, batch 6700, batch avg loss 0.2719, total avg loss: 0.2857, batch size: 34
2021-10-14 00:35:20,141 INFO [train.py:451] Epoch 2, batch 6710, batch avg loss 0.2920, total avg loss: 0.2854, batch size: 36
2021-10-14 00:35:24,993 INFO [train.py:451] Epoch 2, batch 6720, batch avg loss 0.3108, total avg loss: 0.2849, batch size: 36
2021-10-14 00:35:30,074 INFO [train.py:451] Epoch 2, batch 6730, batch avg loss 0.2271, total avg loss: 0.2847, batch size: 32
2021-10-14 00:35:34,856 INFO [train.py:451] Epoch 2, batch 6740, batch avg loss 0.3269, total avg loss: 0.2846, batch size: 37
2021-10-14 00:35:39,700 INFO [train.py:451] Epoch 2, batch 6750, batch avg loss 0.2857, total avg loss: 0.2864, batch size: 37
2021-10-14 00:35:44,562 INFO [train.py:451] Epoch 2, batch 6760, batch avg loss 0.2499, total avg loss: 0.2865, batch size: 32
2021-10-14 00:35:49,478 INFO [train.py:451] Epoch 2, batch 6770, batch avg loss 0.2923, total avg loss: 0.2864, batch size: 35
2021-10-14 00:35:54,509 INFO [train.py:451] Epoch 2, batch 6780, batch avg loss 0.2853, total avg loss: 0.2866, batch size: 32
2021-10-14 00:35:59,479 INFO [train.py:451] Epoch 2, batch 6790, batch avg loss 0.3553, total avg loss: 0.2856, batch size: 38
2021-10-14 00:36:04,428 INFO [train.py:451] Epoch 2, batch 6800, batch avg loss 0.2275, total avg loss: 0.2856, batch size: 35
2021-10-14 00:36:09,445 INFO [train.py:451] Epoch 2, batch 6810, batch avg loss 0.3012, total avg loss: 0.2700, batch size: 28
2021-10-14 00:36:14,464 INFO [train.py:451] Epoch 2, batch 6820, batch avg loss 0.3630, total avg loss: 0.2783, batch size: 127
2021-10-14 00:36:19,301 INFO [train.py:451] Epoch 2, batch 6830, batch avg loss 0.2753, total avg loss: 0.2919, batch size: 35
2021-10-14 00:36:24,311 INFO [train.py:451] Epoch 2, batch 6840, batch avg loss 0.3079, total avg loss: 0.2856, batch size: 39
2021-10-14 00:36:29,290 INFO [train.py:451] Epoch 2, batch 6850, batch avg loss 0.2973, total avg loss: 0.2867, batch size: 29
2021-10-14 00:36:34,154 INFO [train.py:451] Epoch 2, batch 6860, batch avg loss 0.2415, total avg loss: 0.2838, batch size: 28
2021-10-14 00:36:39,201 INFO [train.py:451] Epoch 2, batch 6870, batch avg loss 0.2906, total avg loss: 0.2823, batch size: 31
2021-10-14 00:36:44,040 INFO [train.py:451] Epoch 2, batch 6880, batch avg loss 0.3236, total avg loss: 0.2833, batch size: 38
2021-10-14 00:36:48,962 INFO [train.py:451] Epoch 2, batch 6890, batch avg loss 0.2611, total avg loss: 0.2826, batch size: 29
2021-10-14 00:36:54,017 INFO [train.py:451] Epoch 2, batch 6900, batch avg loss 0.2753, total avg loss: 0.2818, batch size: 34
2021-10-14 00:36:58,896 INFO [train.py:451] Epoch 2, batch 6910, batch avg loss 0.2502, total avg loss: 0.2822, batch size: 34
2021-10-14 00:37:03,779 INFO [train.py:451] Epoch 2, batch 6920, batch avg loss 0.2418, total avg loss: 0.2832, batch size: 31
2021-10-14 00:37:08,636 INFO [train.py:451] Epoch 2, batch 6930, batch avg loss 0.2411, total avg loss: 0.2840, batch size: 34
2021-10-14 00:37:13,723 INFO [train.py:451] Epoch 2, batch 6940, batch avg loss 0.2546, total avg loss: 0.2838, batch size: 32
2021-10-14 00:37:18,679 INFO [train.py:451] Epoch 2, batch 6950, batch avg loss 0.3298, total avg loss: 0.2851, batch size: 34
2021-10-14 00:37:23,478 INFO [train.py:451] Epoch 2, batch 6960, batch avg loss 0.3337, total avg loss: 0.2851, batch size: 45
2021-10-14 00:37:28,390 INFO [train.py:451] Epoch 2, batch 6970, batch avg loss 0.2145, total avg loss: 0.2854, batch size: 30
2021-10-14 00:37:33,359 INFO [train.py:451] Epoch 2, batch 6980, batch avg loss 0.3147, total avg loss: 0.2855, batch size: 38
2021-10-14 00:37:38,308 INFO [train.py:451] Epoch 2, batch 6990, batch avg loss 0.3108, total avg loss: 0.2851, batch size: 42
2021-10-14 00:37:43,299 INFO [train.py:451] Epoch 2, batch 7000, batch avg loss 0.2675, total avg loss: 0.2856, batch size: 29
2021-10-14 00:38:22,802 INFO [train.py:483] Epoch 2, valid loss 0.2071, best valid loss: 0.2063 best valid epoch: 2
2021-10-14 00:38:27,511 INFO [train.py:451] Epoch 2, batch 7010, batch avg loss 0.3427, total avg loss: 0.3164, batch size: 45
2021-10-14 00:38:32,421 INFO [train.py:451] Epoch 2, batch 7020, batch avg loss 0.2842, total avg loss: 0.3133, batch size: 34
2021-10-14 00:38:37,218 INFO [train.py:451] Epoch 2, batch 7030, batch avg loss 0.2279, total avg loss: 0.3095, batch size: 32
2021-10-14 00:38:42,352 INFO [train.py:451] Epoch 2, batch 7040, batch avg loss 0.2917, total avg loss: 0.3016, batch size: 37
2021-10-14 00:38:47,421 INFO [train.py:451] Epoch 2, batch 7050, batch avg loss 0.2476, total avg loss: 0.2985, batch size: 30
2021-10-14 00:38:52,235 INFO [train.py:451] Epoch 2, batch 7060, batch avg loss 0.2966, total avg loss: 0.3000, batch size: 32
2021-10-14 00:38:56,951 INFO [train.py:451] Epoch 2, batch 7070, batch avg loss 0.3833, total avg loss: 0.3010, batch size: 128
2021-10-14 00:39:02,017 INFO [train.py:451] Epoch 2, batch 7080, batch avg loss 0.3254, total avg loss: 0.2976, batch size: 41
2021-10-14 00:39:07,012 INFO [train.py:451] Epoch 2, batch 7090, batch avg loss 0.2718, total avg loss: 0.2994, batch size: 31
2021-10-14 00:39:11,921 INFO [train.py:451] Epoch 2, batch 7100, batch avg loss 0.2363, total avg loss: 0.2966, batch size: 27
2021-10-14 00:39:16,758 INFO [train.py:451] Epoch 2, batch 7110, batch avg loss 0.2349, total avg loss: 0.2949, batch size: 31
2021-10-14 00:39:21,784 INFO [train.py:451] Epoch 2, batch 7120, batch avg loss 0.2658, total avg loss: 0.2924, batch size: 30
2021-10-14 00:39:26,718 INFO [train.py:451] Epoch 2, batch 7130, batch avg loss 0.2779, total avg loss: 0.2920, batch size: 36
2021-10-14 00:39:31,644 INFO [train.py:451] Epoch 2, batch 7140, batch avg loss 0.2403, total avg loss: 0.2909, batch size: 34
2021-10-14 00:39:36,547 INFO [train.py:451] Epoch 2, batch 7150, batch avg loss 0.2550, total avg loss: 0.2894, batch size: 29
2021-10-14 00:39:41,266 INFO [train.py:451] Epoch 2, batch 7160, batch avg loss 0.4151, total avg loss: 0.2903, batch size: 131
2021-10-14 00:39:46,409 INFO [train.py:451] Epoch 2, batch 7170, batch avg loss 0.2644, total avg loss: 0.2898, batch size: 34
2021-10-14 00:39:51,288 INFO [train.py:451] Epoch 2, batch 7180, batch avg loss 0.2641, total avg loss: 0.2890, batch size: 36
2021-10-14 00:39:56,275 INFO [train.py:451] Epoch 2, batch 7190, batch avg loss 0.2742, total avg loss: 0.2880, batch size: 36
2021-10-14 00:40:01,170 INFO [train.py:451] Epoch 2, batch 7200, batch avg loss 0.2099, total avg loss: 0.2876, batch size: 28
2021-10-14 00:40:06,116 INFO [train.py:451] Epoch 2, batch 7210, batch avg loss 0.3109, total avg loss: 0.3012, batch size: 42
2021-10-14 00:40:11,121 INFO [train.py:451] Epoch 2, batch 7220, batch avg loss 0.2751, total avg loss: 0.2865, batch size: 38
2021-10-14 00:40:16,222 INFO [train.py:451] Epoch 2, batch 7230, batch avg loss 0.2729, total avg loss: 0.2838, batch size: 31
2021-10-14 00:40:21,166 INFO [train.py:451] Epoch 2, batch 7240, batch avg loss 0.3466, total avg loss: 0.2842, batch size: 37
2021-10-14 00:40:26,232 INFO [train.py:451] Epoch 2, batch 7250, batch avg loss 0.2272, total avg loss: 0.2813, batch size: 33
2021-10-14 00:40:26,995 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "da48d881-75e1-7ead-b30b-95e6bb3d6543" will not be mixed in.
2021-10-14 00:40:31,270 INFO [train.py:451] Epoch 2, batch 7260, batch avg loss 0.2740, total avg loss: 0.2813, batch size: 42
2021-10-14 00:40:36,263 INFO [train.py:451] Epoch 2, batch 7270, batch avg loss 0.2351, total avg loss: 0.2838, batch size: 30
2021-10-14 00:40:41,193 INFO [train.py:451] Epoch 2, batch 7280, batch avg loss 0.2311, total avg loss: 0.2828, batch size: 29
2021-10-14 00:40:46,176 INFO [train.py:451] Epoch 2, batch 7290, batch avg loss 0.2411, total avg loss: 0.2829, batch size: 33
2021-10-14 00:40:51,206 INFO [train.py:451] Epoch 2, batch 7300, batch avg loss 0.2906, total avg loss: 0.2837, batch size: 34
2021-10-14 00:40:56,373 INFO [train.py:451] Epoch 2, batch 7310, batch avg loss 0.2850, total avg loss: 0.2835, batch size: 34
2021-10-14 00:41:01,199 INFO [train.py:451] Epoch 2, batch 7320, batch avg loss 0.2976, total avg loss: 0.2836, batch size: 31
2021-10-14 00:41:06,326 INFO [train.py:451] Epoch 2, batch 7330, batch avg loss 0.3321, total avg loss: 0.2827, batch size: 45
2021-10-14 00:41:11,283 INFO [train.py:451] Epoch 2, batch 7340, batch avg loss 0.4173, total avg loss: 0.2836, batch size: 128
2021-10-14 00:41:16,541 INFO [train.py:451] Epoch 2, batch 7350, batch avg loss 0.2523, total avg loss: 0.2836, batch size: 35
2021-10-14 00:41:21,385 INFO [train.py:451] Epoch 2, batch 7360, batch avg loss 0.2073, total avg loss: 0.2862, batch size: 29
2021-10-14 00:41:26,428 INFO [train.py:451] Epoch 2, batch 7370, batch avg loss 0.2742, total avg loss: 0.2860, batch size: 34
2021-10-14 00:41:31,431 INFO [train.py:451] Epoch 2, batch 7380, batch avg loss 0.2905, total avg loss: 0.2860, batch size: 39
2021-10-14 00:41:36,242 INFO [train.py:451] Epoch 2, batch 7390, batch avg loss 0.2940, total avg loss: 0.2861, batch size: 42
2021-10-14 00:41:41,255 INFO [train.py:451] Epoch 2, batch 7400, batch avg loss 0.2342, total avg loss: 0.2860, batch size: 31
2021-10-14 00:41:46,267 INFO [train.py:451] Epoch 2, batch 7410, batch avg loss 0.2558, total avg loss: 0.2988, batch size: 35
2021-10-14 00:41:51,248 INFO [train.py:451] Epoch 2, batch 7420, batch avg loss 0.2446, total avg loss: 0.2997, batch size: 27
2021-10-14 00:41:56,023 INFO [train.py:451] Epoch 2, batch 7430, batch avg loss 0.2127, total avg loss: 0.3016, batch size: 32
2021-10-14 00:42:00,917 INFO [train.py:451] Epoch 2, batch 7440, batch avg loss 0.3267, total avg loss: 0.3069, batch size: 32
2021-10-14 00:42:05,856 INFO [train.py:451] Epoch 2, batch 7450, batch avg loss 0.2622, total avg loss: 0.3004, batch size: 29
2021-10-14 00:42:10,982 INFO [train.py:451] Epoch 2, batch 7460, batch avg loss 0.2764, total avg loss: 0.2953, batch size: 37
2021-10-14 00:42:15,965 INFO [train.py:451] Epoch 2, batch 7470, batch avg loss 0.2635, total avg loss: 0.2947, batch size: 38
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2021-10-14 00:55:35,826 INFO [train.py:451] Epoch 2, batch 9000, batch avg loss 0.2600, total avg loss: 0.2838, batch size: 29
2021-10-14 00:56:15,159 INFO [train.py:483] Epoch 2, valid loss 0.2078, best valid loss: 0.2050 best valid epoch: 2
2021-10-14 00:56:20,076 INFO [train.py:451] Epoch 2, batch 9010, batch avg loss 0.2147, total avg loss: 0.2740, batch size: 30
2021-10-14 00:56:25,142 INFO [train.py:451] Epoch 2, batch 9020, batch avg loss 0.2911, total avg loss: 0.2711, batch size: 33
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2021-10-14 00:56:35,066 INFO [train.py:451] Epoch 2, batch 9040, batch avg loss 0.3225, total avg loss: 0.2795, batch size: 34
2021-10-14 00:56:39,669 INFO [train.py:451] Epoch 2, batch 9050, batch avg loss 0.2629, total avg loss: 0.2868, batch size: 31
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2021-10-14 00:56:49,538 INFO [train.py:451] Epoch 2, batch 9070, batch avg loss 0.3202, total avg loss: 0.2871, batch size: 32
2021-10-14 00:56:54,398 INFO [train.py:451] Epoch 2, batch 9080, batch avg loss 0.3141, total avg loss: 0.2896, batch size: 38
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2021-10-14 00:57:04,287 INFO [train.py:451] Epoch 2, batch 9100, batch avg loss 0.3230, total avg loss: 0.2912, batch size: 45
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2021-10-14 00:57:13,791 INFO [train.py:451] Epoch 2, batch 9120, batch avg loss 0.3112, total avg loss: 0.2939, batch size: 49
2021-10-14 00:57:18,749 INFO [train.py:451] Epoch 2, batch 9130, batch avg loss 0.3044, total avg loss: 0.2935, batch size: 35
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2021-10-14 00:57:28,430 INFO [train.py:451] Epoch 2, batch 9150, batch avg loss 0.2649, total avg loss: 0.2957, batch size: 30
2021-10-14 00:57:33,271 INFO [train.py:451] Epoch 2, batch 9160, batch avg loss 0.2384, total avg loss: 0.2958, batch size: 30
2021-10-14 00:57:38,295 INFO [train.py:451] Epoch 2, batch 9170, batch avg loss 0.3286, total avg loss: 0.2954, batch size: 56
2021-10-14 00:57:43,247 INFO [train.py:451] Epoch 2, batch 9180, batch avg loss 0.3152, total avg loss: 0.2944, batch size: 36
2021-10-14 00:57:47,951 INFO [train.py:451] Epoch 2, batch 9190, batch avg loss 0.3206, total avg loss: 0.2945, batch size: 72
2021-10-14 00:57:52,865 INFO [train.py:451] Epoch 2, batch 9200, batch avg loss 0.3498, total avg loss: 0.2941, batch size: 38
2021-10-14 00:57:57,887 INFO [train.py:451] Epoch 2, batch 9210, batch avg loss 0.2571, total avg loss: 0.2791, batch size: 34
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2021-10-14 00:58:07,674 INFO [train.py:451] Epoch 2, batch 9230, batch avg loss 0.2766, total avg loss: 0.2829, batch size: 33
2021-10-14 00:58:12,508 INFO [train.py:451] Epoch 2, batch 9240, batch avg loss 0.2915, total avg loss: 0.2910, batch size: 38
2021-10-14 00:58:17,434 INFO [train.py:451] Epoch 2, batch 9250, batch avg loss 0.2537, total avg loss: 0.2902, batch size: 36
2021-10-14 00:58:22,270 INFO [train.py:451] Epoch 2, batch 9260, batch avg loss 0.2040, total avg loss: 0.2890, batch size: 31
2021-10-14 00:58:27,189 INFO [train.py:451] Epoch 2, batch 9270, batch avg loss 0.2581, total avg loss: 0.2879, batch size: 34
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2021-10-14 00:58:37,035 INFO [train.py:451] Epoch 2, batch 9290, batch avg loss 0.2991, total avg loss: 0.2884, batch size: 38
2021-10-14 00:58:42,048 INFO [train.py:451] Epoch 2, batch 9300, batch avg loss 0.2631, total avg loss: 0.2874, batch size: 31
2021-10-14 00:58:46,847 INFO [train.py:451] Epoch 2, batch 9310, batch avg loss 0.3196, total avg loss: 0.2900, batch size: 39
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2021-10-14 00:58:56,587 INFO [train.py:451] Epoch 2, batch 9330, batch avg loss 0.3346, total avg loss: 0.2908, batch size: 126
2021-10-14 00:59:01,634 INFO [train.py:451] Epoch 2, batch 9340, batch avg loss 0.2979, total avg loss: 0.2894, batch size: 33
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2021-10-14 00:59:16,467 INFO [train.py:451] Epoch 2, batch 9370, batch avg loss 0.2438, total avg loss: 0.2885, batch size: 28
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2021-10-14 00:59:26,355 INFO [train.py:451] Epoch 2, batch 9390, batch avg loss 0.2511, total avg loss: 0.2878, batch size: 33
2021-10-14 00:59:31,196 INFO [train.py:451] Epoch 2, batch 9400, batch avg loss 0.3074, total avg loss: 0.2874, batch size: 38
2021-10-14 00:59:35,931 INFO [train.py:451] Epoch 2, batch 9410, batch avg loss 0.3255, total avg loss: 0.2891, batch size: 57
2021-10-14 00:59:40,806 INFO [train.py:451] Epoch 2, batch 9420, batch avg loss 0.2738, total avg loss: 0.2810, batch size: 36
2021-10-14 00:59:45,770 INFO [train.py:451] Epoch 2, batch 9430, batch avg loss 0.2763, total avg loss: 0.2839, batch size: 35
2021-10-14 00:59:50,761 INFO [train.py:451] Epoch 2, batch 9440, batch avg loss 0.3577, total avg loss: 0.2827, batch size: 36
2021-10-14 00:59:55,722 INFO [train.py:451] Epoch 2, batch 9450, batch avg loss 0.2817, total avg loss: 0.2801, batch size: 33
2021-10-14 01:00:00,612 INFO [train.py:451] Epoch 2, batch 9460, batch avg loss 0.2197, total avg loss: 0.2783, batch size: 30
2021-10-14 01:00:05,461 INFO [train.py:451] Epoch 2, batch 9470, batch avg loss 0.2287, total avg loss: 0.2787, batch size: 35
2021-10-14 01:00:10,654 INFO [train.py:451] Epoch 2, batch 9480, batch avg loss 0.2823, total avg loss: 0.2799, batch size: 35
2021-10-14 01:00:15,496 INFO [train.py:451] Epoch 2, batch 9490, batch avg loss 0.3428, total avg loss: 0.2812, batch size: 32
2021-10-14 01:00:20,386 INFO [train.py:451] Epoch 2, batch 9500, batch avg loss 0.2854, total avg loss: 0.2823, batch size: 34
2021-10-14 01:00:25,378 INFO [train.py:451] Epoch 2, batch 9510, batch avg loss 0.2765, total avg loss: 0.2827, batch size: 37
2021-10-14 01:00:30,412 INFO [train.py:451] Epoch 2, batch 9520, batch avg loss 0.3242, total avg loss: 0.2820, batch size: 73
2021-10-14 01:00:35,512 INFO [train.py:451] Epoch 2, batch 9530, batch avg loss 0.2916, total avg loss: 0.2810, batch size: 35
2021-10-14 01:00:40,872 INFO [train.py:451] Epoch 2, batch 9540, batch avg loss 0.2459, total avg loss: 0.2802, batch size: 27
2021-10-14 01:00:45,814 INFO [train.py:451] Epoch 2, batch 9550, batch avg loss 0.2561, total avg loss: 0.2790, batch size: 30
2021-10-14 01:00:50,634 INFO [train.py:451] Epoch 2, batch 9560, batch avg loss 0.2595, total avg loss: 0.2797, batch size: 30
2021-10-14 01:00:55,522 INFO [train.py:451] Epoch 2, batch 9570, batch avg loss 0.2722, total avg loss: 0.2791, batch size: 30
2021-10-14 01:01:00,472 INFO [train.py:451] Epoch 2, batch 9580, batch avg loss 0.2928, total avg loss: 0.2790, batch size: 35
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2021-10-14 01:01:10,407 INFO [train.py:451] Epoch 2, batch 9600, batch avg loss 0.3204, total avg loss: 0.2806, batch size: 38
2021-10-14 01:01:15,528 INFO [train.py:451] Epoch 2, batch 9610, batch avg loss 0.2810, total avg loss: 0.2593, batch size: 41
2021-10-14 01:01:20,190 INFO [train.py:451] Epoch 2, batch 9620, batch avg loss 0.4007, total avg loss: 0.2926, batch size: 129
2021-10-14 01:01:25,054 INFO [train.py:451] Epoch 2, batch 9630, batch avg loss 0.2702, total avg loss: 0.3002, batch size: 36
2021-10-14 01:01:30,060 INFO [train.py:451] Epoch 2, batch 9640, batch avg loss 0.2840, total avg loss: 0.2999, batch size: 38
2021-10-14 01:01:34,872 INFO [train.py:451] Epoch 2, batch 9650, batch avg loss 0.3213, total avg loss: 0.2991, batch size: 35
2021-10-14 01:01:39,615 INFO [train.py:451] Epoch 2, batch 9660, batch avg loss 0.3320, total avg loss: 0.2974, batch size: 57
2021-10-14 01:01:44,586 INFO [train.py:451] Epoch 2, batch 9670, batch avg loss 0.2903, total avg loss: 0.2952, batch size: 32
2021-10-14 01:01:49,383 INFO [train.py:451] Epoch 2, batch 9680, batch avg loss 0.2573, total avg loss: 0.2954, batch size: 30
2021-10-14 01:01:54,461 INFO [train.py:451] Epoch 2, batch 9690, batch avg loss 0.3320, total avg loss: 0.2942, batch size: 39
2021-10-14 01:01:59,228 INFO [train.py:451] Epoch 2, batch 9700, batch avg loss 0.3193, total avg loss: 0.2935, batch size: 57
2021-10-14 01:02:04,293 INFO [train.py:451] Epoch 2, batch 9710, batch avg loss 0.2951, total avg loss: 0.2915, batch size: 29
2021-10-14 01:02:09,232 INFO [train.py:451] Epoch 2, batch 9720, batch avg loss 0.2802, total avg loss: 0.2902, batch size: 40
2021-10-14 01:02:14,115 INFO [train.py:451] Epoch 2, batch 9730, batch avg loss 0.2602, total avg loss: 0.2895, batch size: 31
2021-10-14 01:02:18,960 INFO [train.py:451] Epoch 2, batch 9740, batch avg loss 0.2536, total avg loss: 0.2897, batch size: 34
2021-10-14 01:02:23,717 INFO [train.py:451] Epoch 2, batch 9750, batch avg loss 0.3194, total avg loss: 0.2904, batch size: 38
2021-10-14 01:02:28,895 INFO [train.py:451] Epoch 2, batch 9760, batch avg loss 0.2611, total avg loss: 0.2888, batch size: 31
2021-10-14 01:02:33,674 INFO [train.py:451] Epoch 2, batch 9770, batch avg loss 0.3067, total avg loss: 0.2908, batch size: 38
2021-10-14 01:02:38,684 INFO [train.py:451] Epoch 2, batch 9780, batch avg loss 0.2571, total avg loss: 0.2903, batch size: 41
2021-10-14 01:02:43,567 INFO [train.py:451] Epoch 2, batch 9790, batch avg loss 0.2389, total avg loss: 0.2895, batch size: 35
2021-10-14 01:02:48,458 INFO [train.py:451] Epoch 2, batch 9800, batch avg loss 0.2351, total avg loss: 0.2902, batch size: 28
2021-10-14 01:02:53,470 INFO [train.py:451] Epoch 2, batch 9810, batch avg loss 0.2249, total avg loss: 0.2711, batch size: 31
2021-10-14 01:02:58,288 INFO [train.py:451] Epoch 2, batch 9820, batch avg loss 0.2723, total avg loss: 0.2725, batch size: 30
2021-10-14 01:03:03,107 INFO [train.py:451] Epoch 2, batch 9830, batch avg loss 0.2603, total avg loss: 0.2867, batch size: 30
2021-10-14 01:03:08,074 INFO [train.py:451] Epoch 2, batch 9840, batch avg loss 0.2426, total avg loss: 0.2834, batch size: 32
2021-10-14 01:03:13,244 INFO [train.py:451] Epoch 2, batch 9850, batch avg loss 0.3233, total avg loss: 0.2815, batch size: 36
2021-10-14 01:03:17,957 INFO [train.py:451] Epoch 2, batch 9860, batch avg loss 0.2665, total avg loss: 0.2864, batch size: 40
2021-10-14 01:03:23,099 INFO [train.py:451] Epoch 2, batch 9870, batch avg loss 0.3297, total avg loss: 0.2855, batch size: 39
2021-10-14 01:03:27,915 INFO [train.py:451] Epoch 2, batch 9880, batch avg loss 0.2915, total avg loss: 0.2850, batch size: 56
2021-10-14 01:03:32,702 INFO [train.py:451] Epoch 2, batch 9890, batch avg loss 0.2931, total avg loss: 0.2875, batch size: 40
2021-10-14 01:03:37,819 INFO [train.py:451] Epoch 2, batch 9900, batch avg loss 0.3322, total avg loss: 0.2851, batch size: 35
2021-10-14 01:03:42,846 INFO [train.py:451] Epoch 2, batch 9910, batch avg loss 0.2552, total avg loss: 0.2837, batch size: 36
2021-10-14 01:03:47,630 INFO [train.py:451] Epoch 2, batch 9920, batch avg loss 0.2488, total avg loss: 0.2834, batch size: 31
2021-10-14 01:03:52,649 INFO [train.py:451] Epoch 2, batch 9930, batch avg loss 0.2349, total avg loss: 0.2843, batch size: 27
2021-10-14 01:03:57,528 INFO [train.py:451] Epoch 2, batch 9940, batch avg loss 0.3329, total avg loss: 0.2847, batch size: 57
2021-10-14 01:04:02,405 INFO [train.py:451] Epoch 2, batch 9950, batch avg loss 0.3341, total avg loss: 0.2842, batch size: 39
2021-10-14 01:04:07,253 INFO [train.py:451] Epoch 2, batch 9960, batch avg loss 0.2513, total avg loss: 0.2839, batch size: 33
2021-10-14 01:04:12,017 INFO [train.py:451] Epoch 2, batch 9970, batch avg loss 0.2913, total avg loss: 0.2839, batch size: 36
2021-10-14 01:04:16,885 INFO [train.py:451] Epoch 2, batch 9980, batch avg loss 0.2753, total avg loss: 0.2835, batch size: 38
2021-10-14 01:04:21,892 INFO [train.py:451] Epoch 2, batch 9990, batch avg loss 0.2992, total avg loss: 0.2843, batch size: 33
2021-10-14 01:04:26,829 INFO [train.py:451] Epoch 2, batch 10000, batch avg loss 0.2268, total avg loss: 0.2848, batch size: 30
2021-10-14 01:05:06,248 INFO [train.py:483] Epoch 2, valid loss 0.2035, best valid loss: 0.2035 best valid epoch: 2
2021-10-14 01:05:11,174 INFO [train.py:451] Epoch 2, batch 10010, batch avg loss 0.2778, total avg loss: 0.3035, batch size: 35
2021-10-14 01:05:15,953 INFO [train.py:451] Epoch 2, batch 10020, batch avg loss 0.2051, total avg loss: 0.2988, batch size: 30
2021-10-14 01:05:21,008 INFO [train.py:451] Epoch 2, batch 10030, batch avg loss 0.2899, total avg loss: 0.2931, batch size: 35
2021-10-14 01:05:25,841 INFO [train.py:451] Epoch 2, batch 10040, batch avg loss 0.2674, total avg loss: 0.2940, batch size: 30
2021-10-14 01:05:30,903 INFO [train.py:451] Epoch 2, batch 10050, batch avg loss 0.2777, total avg loss: 0.2909, batch size: 42
2021-10-14 01:05:35,755 INFO [train.py:451] Epoch 2, batch 10060, batch avg loss 0.3692, total avg loss: 0.2901, batch size: 34
2021-10-14 01:05:40,620 INFO [train.py:451] Epoch 2, batch 10070, batch avg loss 0.3250, total avg loss: 0.2910, batch size: 34
2021-10-14 01:05:45,759 INFO [train.py:451] Epoch 2, batch 10080, batch avg loss 0.2165, total avg loss: 0.2868, batch size: 29
2021-10-14 01:05:50,637 INFO [train.py:451] Epoch 2, batch 10090, batch avg loss 0.2626, total avg loss: 0.2854, batch size: 42
2021-10-14 01:05:55,643 INFO [train.py:451] Epoch 2, batch 10100, batch avg loss 0.3313, total avg loss: 0.2843, batch size: 39
2021-10-14 01:06:00,693 INFO [train.py:451] Epoch 2, batch 10110, batch avg loss 0.2843, total avg loss: 0.2870, batch size: 33
2021-10-14 01:06:05,524 INFO [train.py:451] Epoch 2, batch 10120, batch avg loss 0.2732, total avg loss: 0.2871, batch size: 37
2021-10-14 01:06:10,285 INFO [train.py:451] Epoch 2, batch 10130, batch avg loss 0.3067, total avg loss: 0.2873, batch size: 42
2021-10-14 01:06:15,344 INFO [train.py:451] Epoch 2, batch 10140, batch avg loss 0.2610, total avg loss: 0.2853, batch size: 38
2021-10-14 01:06:20,323 INFO [train.py:451] Epoch 2, batch 10150, batch avg loss 0.3016, total avg loss: 0.2842, batch size: 36
2021-10-14 01:06:25,283 INFO [train.py:451] Epoch 2, batch 10160, batch avg loss 0.2924, total avg loss: 0.2853, batch size: 34
2021-10-14 01:06:29,996 INFO [train.py:451] Epoch 2, batch 10170, batch avg loss 0.3153, total avg loss: 0.2860, batch size: 71
2021-10-14 01:06:34,861 INFO [train.py:451] Epoch 2, batch 10180, batch avg loss 0.2991, total avg loss: 0.2864, batch size: 49
2021-10-14 01:06:39,754 INFO [train.py:451] Epoch 2, batch 10190, batch avg loss 0.3024, total avg loss: 0.2871, batch size: 31
2021-10-14 01:06:44,624 INFO [train.py:451] Epoch 2, batch 10200, batch avg loss 0.2932, total avg loss: 0.2868, batch size: 56
2021-10-14 01:06:49,358 INFO [train.py:451] Epoch 2, batch 10210, batch avg loss 0.2657, total avg loss: 0.2771, batch size: 42
2021-10-14 01:06:54,400 INFO [train.py:451] Epoch 2, batch 10220, batch avg loss 0.2679, total avg loss: 0.2742, batch size: 29
2021-10-14 01:06:59,521 INFO [train.py:451] Epoch 2, batch 10230, batch avg loss 0.2998, total avg loss: 0.2791, batch size: 38
2021-10-14 01:07:04,454 INFO [train.py:451] Epoch 2, batch 10240, batch avg loss 0.2934, total avg loss: 0.2784, batch size: 31
2021-10-14 01:07:09,293 INFO [train.py:451] Epoch 2, batch 10250, batch avg loss 0.3023, total avg loss: 0.2800, batch size: 35
2021-10-14 01:07:14,381 INFO [train.py:451] Epoch 2, batch 10260, batch avg loss 0.2510, total avg loss: 0.2743, batch size: 28
2021-10-14 01:07:19,339 INFO [train.py:451] Epoch 2, batch 10270, batch avg loss 0.3743, total avg loss: 0.2771, batch size: 38
2021-10-14 01:07:24,108 INFO [train.py:451] Epoch 2, batch 10280, batch avg loss 0.3231, total avg loss: 0.2816, batch size: 36
2021-10-14 01:07:29,124 INFO [train.py:451] Epoch 2, batch 10290, batch avg loss 0.3011, total avg loss: 0.2828, batch size: 28
2021-10-14 01:07:34,119 INFO [train.py:451] Epoch 2, batch 10300, batch avg loss 0.2975, total avg loss: 0.2838, batch size: 31
2021-10-14 01:07:39,207 INFO [train.py:451] Epoch 2, batch 10310, batch avg loss 0.2860, total avg loss: 0.2829, batch size: 27
2021-10-14 01:07:44,109 INFO [train.py:451] Epoch 2, batch 10320, batch avg loss 0.3709, total avg loss: 0.2820, batch size: 128
2021-10-14 01:07:49,130 INFO [train.py:451] Epoch 2, batch 10330, batch avg loss 0.2100, total avg loss: 0.2803, batch size: 32
2021-10-14 01:07:53,858 INFO [train.py:451] Epoch 2, batch 10340, batch avg loss 0.2400, total avg loss: 0.2817, batch size: 28
2021-10-14 01:07:58,871 INFO [train.py:451] Epoch 2, batch 10350, batch avg loss 0.3077, total avg loss: 0.2823, batch size: 34
2021-10-14 01:08:03,628 INFO [train.py:451] Epoch 2, batch 10360, batch avg loss 0.2647, total avg loss: 0.2826, batch size: 32
2021-10-14 01:08:08,693 INFO [train.py:451] Epoch 2, batch 10370, batch avg loss 0.2748, total avg loss: 0.2812, batch size: 28
2021-10-14 01:08:13,715 INFO [train.py:451] Epoch 2, batch 10380, batch avg loss 0.3267, total avg loss: 0.2817, batch size: 33
2021-10-14 01:08:18,786 INFO [train.py:451] Epoch 2, batch 10390, batch avg loss 0.2397, total avg loss: 0.2816, batch size: 30
2021-10-14 01:08:23,927 INFO [train.py:451] Epoch 2, batch 10400, batch avg loss 0.3408, total avg loss: 0.2825, batch size: 57
2021-10-14 01:08:28,963 INFO [train.py:451] Epoch 2, batch 10410, batch avg loss 0.3007, total avg loss: 0.2671, batch size: 42
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2021-10-14 01:14:00,623 INFO [train.py:483] Epoch 2, valid loss 0.2041, best valid loss: 0.2035 best valid epoch: 2
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2021-10-14 01:22:16,525 INFO [train.py:451] Epoch 2, batch 12000, batch avg loss 0.2468, total avg loss: 0.2825, batch size: 32
2021-10-14 01:22:56,906 INFO [train.py:483] Epoch 2, valid loss 0.2030, best valid loss: 0.2030 best valid epoch: 2
2021-10-14 01:23:02,020 INFO [train.py:451] Epoch 2, batch 12010, batch avg loss 0.2809, total avg loss: 0.2558, batch size: 39
2021-10-14 01:23:06,994 INFO [train.py:451] Epoch 2, batch 12020, batch avg loss 0.2914, total avg loss: 0.2704, batch size: 34
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2021-10-14 01:23:56,365 INFO [train.py:451] Epoch 2, batch 12120, batch avg loss 0.2221, total avg loss: 0.2787, batch size: 27
2021-10-14 01:24:01,362 INFO [train.py:451] Epoch 2, batch 12130, batch avg loss 0.2976, total avg loss: 0.2777, batch size: 32
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2021-10-14 01:24:11,029 INFO [train.py:451] Epoch 2, batch 12150, batch avg loss 0.3045, total avg loss: 0.2813, batch size: 40
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2021-10-14 01:24:55,244 INFO [train.py:451] Epoch 2, batch 12240, batch avg loss 0.1811, total avg loss: 0.2848, batch size: 28
2021-10-14 01:25:00,331 INFO [train.py:451] Epoch 2, batch 12250, batch avg loss 0.3110, total avg loss: 0.2799, batch size: 32
2021-10-14 01:25:05,203 INFO [train.py:451] Epoch 2, batch 12260, batch avg loss 0.3100, total avg loss: 0.2792, batch size: 72
2021-10-14 01:25:10,038 INFO [train.py:451] Epoch 2, batch 12270, batch avg loss 0.3301, total avg loss: 0.2817, batch size: 42
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2021-10-14 01:25:59,275 INFO [train.py:451] Epoch 2, batch 12370, batch avg loss 0.3195, total avg loss: 0.2870, batch size: 41
2021-10-14 01:26:04,178 INFO [train.py:451] Epoch 2, batch 12380, batch avg loss 0.2277, total avg loss: 0.2865, batch size: 33
2021-10-14 01:26:09,253 INFO [train.py:451] Epoch 2, batch 12390, batch avg loss 0.4130, total avg loss: 0.2866, batch size: 126
2021-10-14 01:26:14,193 INFO [train.py:451] Epoch 2, batch 12400, batch avg loss 0.2327, total avg loss: 0.2862, batch size: 34
2021-10-14 01:26:19,155 INFO [train.py:451] Epoch 2, batch 12410, batch avg loss 0.2436, total avg loss: 0.2934, batch size: 34
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2021-10-14 01:26:38,867 INFO [train.py:451] Epoch 2, batch 12450, batch avg loss 0.2287, total avg loss: 0.2926, batch size: 32
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2021-10-14 01:27:03,650 INFO [train.py:451] Epoch 2, batch 12500, batch avg loss 0.3067, total avg loss: 0.2889, batch size: 35
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2021-10-14 01:27:18,520 INFO [train.py:451] Epoch 2, batch 12530, batch avg loss 0.1867, total avg loss: 0.2888, batch size: 29
2021-10-14 01:27:23,343 INFO [train.py:451] Epoch 2, batch 12540, batch avg loss 0.2700, total avg loss: 0.2888, batch size: 33
2021-10-14 01:27:28,264 INFO [train.py:451] Epoch 2, batch 12550, batch avg loss 0.2807, total avg loss: 0.2885, batch size: 36
2021-10-14 01:27:33,187 INFO [train.py:451] Epoch 2, batch 12560, batch avg loss 0.3022, total avg loss: 0.2881, batch size: 37
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2021-10-14 01:27:42,873 INFO [train.py:451] Epoch 2, batch 12580, batch avg loss 0.2497, total avg loss: 0.2869, batch size: 34
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2021-10-14 01:27:52,746 INFO [train.py:451] Epoch 2, batch 12600, batch avg loss 0.2451, total avg loss: 0.2849, batch size: 32
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2021-10-14 01:28:27,553 INFO [train.py:451] Epoch 2, batch 12670, batch avg loss 0.2974, total avg loss: 0.2870, batch size: 41
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2021-10-14 01:28:37,217 INFO [train.py:451] Epoch 2, batch 12690, batch avg loss 0.3294, total avg loss: 0.2888, batch size: 45
2021-10-14 01:28:42,170 INFO [train.py:451] Epoch 2, batch 12700, batch avg loss 0.2341, total avg loss: 0.2891, batch size: 29
2021-10-14 01:28:47,091 INFO [train.py:451] Epoch 2, batch 12710, batch avg loss 0.3982, total avg loss: 0.2895, batch size: 129
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2021-10-14 01:29:16,484 INFO [train.py:451] Epoch 2, batch 12770, batch avg loss 0.2125, total avg loss: 0.2899, batch size: 30
2021-10-14 01:29:21,433 INFO [train.py:451] Epoch 2, batch 12780, batch avg loss 0.2398, total avg loss: 0.2889, batch size: 29
2021-10-14 01:29:26,338 INFO [train.py:451] Epoch 2, batch 12790, batch avg loss 0.3935, total avg loss: 0.2888, batch size: 130
2021-10-14 01:29:31,275 INFO [train.py:451] Epoch 2, batch 12800, batch avg loss 0.3025, total avg loss: 0.2895, batch size: 34
2021-10-14 01:29:36,334 INFO [train.py:451] Epoch 2, batch 12810, batch avg loss 0.3528, total avg loss: 0.2598, batch size: 41
2021-10-14 01:29:41,419 INFO [train.py:451] Epoch 2, batch 12820, batch avg loss 0.2656, total avg loss: 0.2673, batch size: 29
2021-10-14 01:29:46,343 INFO [train.py:451] Epoch 2, batch 12830, batch avg loss 0.4059, total avg loss: 0.2748, batch size: 128
2021-10-14 01:29:51,140 INFO [train.py:451] Epoch 2, batch 12840, batch avg loss 0.2442, total avg loss: 0.2777, batch size: 30
2021-10-14 01:29:56,110 INFO [train.py:451] Epoch 2, batch 12850, batch avg loss 0.2829, total avg loss: 0.2768, batch size: 31
2021-10-14 01:30:01,160 INFO [train.py:451] Epoch 2, batch 12860, batch avg loss 0.2192, total avg loss: 0.2794, batch size: 30
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2021-10-14 01:30:11,155 INFO [train.py:451] Epoch 2, batch 12880, batch avg loss 0.2794, total avg loss: 0.2812, batch size: 41
2021-10-14 01:30:16,151 INFO [train.py:451] Epoch 2, batch 12890, batch avg loss 0.2233, total avg loss: 0.2855, batch size: 29
2021-10-14 01:30:21,194 INFO [train.py:451] Epoch 2, batch 12900, batch avg loss 0.2457, total avg loss: 0.2856, batch size: 32
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2021-10-14 01:30:35,857 INFO [train.py:451] Epoch 2, batch 12930, batch avg loss 0.3649, total avg loss: 0.2903, batch size: 130
2021-10-14 01:30:40,779 INFO [train.py:451] Epoch 2, batch 12940, batch avg loss 0.2373, total avg loss: 0.2888, batch size: 33
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2021-10-14 01:31:00,675 INFO [train.py:451] Epoch 2, batch 12980, batch avg loss 0.2689, total avg loss: 0.2857, batch size: 34
2021-10-14 01:31:05,493 INFO [train.py:451] Epoch 2, batch 12990, batch avg loss 0.2262, total avg loss: 0.2857, batch size: 33
2021-10-14 01:31:10,356 INFO [train.py:451] Epoch 2, batch 13000, batch avg loss 0.2702, total avg loss: 0.2858, batch size: 42
2021-10-14 01:31:50,073 INFO [train.py:483] Epoch 2, valid loss 0.2022, best valid loss: 0.2022 best valid epoch: 2
2021-10-14 01:31:54,963 INFO [train.py:451] Epoch 2, batch 13010, batch avg loss 0.3004, total avg loss: 0.2817, batch size: 34
2021-10-14 01:31:59,859 INFO [train.py:451] Epoch 2, batch 13020, batch avg loss 0.2270, total avg loss: 0.2794, batch size: 34
2021-10-14 01:32:04,832 INFO [train.py:451] Epoch 2, batch 13030, batch avg loss 0.2743, total avg loss: 0.2811, batch size: 35
2021-10-14 01:32:09,743 INFO [train.py:451] Epoch 2, batch 13040, batch avg loss 0.2224, total avg loss: 0.2750, batch size: 30
2021-10-14 01:32:14,459 INFO [train.py:451] Epoch 2, batch 13050, batch avg loss 0.2613, total avg loss: 0.2786, batch size: 36
2021-10-14 01:32:19,407 INFO [train.py:451] Epoch 2, batch 13060, batch avg loss 0.2385, total avg loss: 0.2804, batch size: 29
2021-10-14 01:32:24,659 INFO [train.py:451] Epoch 2, batch 13070, batch avg loss 0.3020, total avg loss: 0.2812, batch size: 36
2021-10-14 01:32:29,636 INFO [train.py:451] Epoch 2, batch 13080, batch avg loss 0.2533, total avg loss: 0.2799, batch size: 31
2021-10-14 01:32:34,641 INFO [train.py:451] Epoch 2, batch 13090, batch avg loss 0.2161, total avg loss: 0.2805, batch size: 29
2021-10-14 01:32:39,562 INFO [train.py:451] Epoch 2, batch 13100, batch avg loss 0.2749, total avg loss: 0.2809, batch size: 31
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2021-10-14 01:33:03,977 INFO [train.py:451] Epoch 2, batch 13150, batch avg loss 0.2941, total avg loss: 0.2825, batch size: 42
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2021-10-14 01:33:28,427 INFO [train.py:451] Epoch 2, batch 13200, batch avg loss 0.3129, total avg loss: 0.2845, batch size: 39
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2021-10-14 01:33:53,010 INFO [train.py:451] Epoch 2, batch 13250, batch avg loss 0.2565, total avg loss: 0.2818, batch size: 33
2021-10-14 01:33:57,996 INFO [train.py:451] Epoch 2, batch 13260, batch avg loss 0.3125, total avg loss: 0.2827, batch size: 39
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2021-10-14 01:34:17,991 INFO [train.py:451] Epoch 2, batch 13300, batch avg loss 0.2518, total avg loss: 0.2820, batch size: 32
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2021-10-14 01:37:37,978 INFO [train.py:451] Epoch 2, batch 13700, batch avg loss 0.2416, total avg loss: 0.2906, batch size: 29
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2021-10-14 01:39:18,186 INFO [train.py:451] Epoch 2, batch 13900, batch avg loss 0.2687, total avg loss: 0.2726, batch size: 35
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2021-10-14 01:40:48,253 INFO [train.py:483] Epoch 2, valid loss 0.2035, best valid loss: 0.2022 best valid epoch: 2
2021-10-14 01:40:53,168 INFO [train.py:451] Epoch 2, batch 14010, batch avg loss 0.2814, total avg loss: 0.2952, batch size: 41
2021-10-14 01:40:58,014 INFO [train.py:451] Epoch 2, batch 14020, batch avg loss 0.2665, total avg loss: 0.2913, batch size: 34
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2021-10-14 01:47:33,608 INFO [train.py:451] Epoch 2, batch 14800, batch avg loss 0.2999, total avg loss: 0.2864, batch size: 49
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2021-10-14 01:47:53,405 INFO [train.py:451] Epoch 2, batch 14840, batch avg loss 0.2711, total avg loss: 0.2911, batch size: 33
2021-10-14 01:47:58,121 INFO [train.py:451] Epoch 2, batch 14850, batch avg loss 0.3615, total avg loss: 0.2945, batch size: 39
2021-10-14 01:48:03,053 INFO [train.py:451] Epoch 2, batch 14860, batch avg loss 0.2436, total avg loss: 0.2931, batch size: 35
2021-10-14 01:48:07,995 INFO [train.py:451] Epoch 2, batch 14870, batch avg loss 0.3978, total avg loss: 0.2924, batch size: 121
2021-10-14 01:48:13,047 INFO [train.py:451] Epoch 2, batch 14880, batch avg loss 0.2611, total avg loss: 0.2908, batch size: 39
2021-10-14 01:48:18,170 INFO [train.py:451] Epoch 2, batch 14890, batch avg loss 0.2645, total avg loss: 0.2891, batch size: 36
2021-10-14 01:48:23,135 INFO [train.py:451] Epoch 2, batch 14900, batch avg loss 0.2133, total avg loss: 0.2900, batch size: 27
2021-10-14 01:48:28,116 INFO [train.py:451] Epoch 2, batch 14910, batch avg loss 0.2364, total avg loss: 0.2894, batch size: 33
2021-10-14 01:48:32,956 INFO [train.py:451] Epoch 2, batch 14920, batch avg loss 0.2512, total avg loss: 0.2887, batch size: 33
2021-10-14 01:48:37,956 INFO [train.py:451] Epoch 2, batch 14930, batch avg loss 0.3169, total avg loss: 0.2876, batch size: 49
2021-10-14 01:48:43,111 INFO [train.py:451] Epoch 2, batch 14940, batch avg loss 0.2480, total avg loss: 0.2864, batch size: 33
2021-10-14 01:48:48,130 INFO [train.py:451] Epoch 2, batch 14950, batch avg loss 0.3157, total avg loss: 0.2861, batch size: 36
2021-10-14 01:48:53,131 INFO [train.py:451] Epoch 2, batch 14960, batch avg loss 0.2183, total avg loss: 0.2850, batch size: 29
2021-10-14 01:48:57,836 INFO [train.py:451] Epoch 2, batch 14970, batch avg loss 0.2536, total avg loss: 0.2869, batch size: 28
2021-10-14 01:49:02,561 INFO [train.py:451] Epoch 2, batch 14980, batch avg loss 0.3025, total avg loss: 0.2881, batch size: 33
2021-10-14 01:49:07,368 INFO [train.py:451] Epoch 2, batch 14990, batch avg loss 0.2419, total avg loss: 0.2884, batch size: 28
2021-10-14 01:49:12,369 INFO [train.py:451] Epoch 2, batch 15000, batch avg loss 0.2344, total avg loss: 0.2885, batch size: 28
2021-10-14 01:49:51,880 INFO [train.py:483] Epoch 2, valid loss 0.2021, best valid loss: 0.2021 best valid epoch: 2
2021-10-14 01:49:56,703 INFO [train.py:451] Epoch 2, batch 15010, batch avg loss 0.2380, total avg loss: 0.2742, batch size: 32
2021-10-14 01:50:01,503 INFO [train.py:451] Epoch 2, batch 15020, batch avg loss 0.3116, total avg loss: 0.2782, batch size: 45
2021-10-14 01:50:06,307 INFO [train.py:451] Epoch 2, batch 15030, batch avg loss 0.2552, total avg loss: 0.2817, batch size: 38
2021-10-14 01:50:11,154 INFO [train.py:451] Epoch 2, batch 15040, batch avg loss 0.2868, total avg loss: 0.2821, batch size: 37
2021-10-14 01:50:16,049 INFO [train.py:451] Epoch 2, batch 15050, batch avg loss 0.2741, total avg loss: 0.2812, batch size: 38
2021-10-14 01:50:21,119 INFO [train.py:451] Epoch 2, batch 15060, batch avg loss 0.2779, total avg loss: 0.2787, batch size: 57
2021-10-14 01:50:26,041 INFO [train.py:451] Epoch 2, batch 15070, batch avg loss 0.2840, total avg loss: 0.2780, batch size: 38
2021-10-14 01:50:31,187 INFO [train.py:451] Epoch 2, batch 15080, batch avg loss 0.2662, total avg loss: 0.2790, batch size: 31
2021-10-14 01:50:36,048 INFO [train.py:451] Epoch 2, batch 15090, batch avg loss 0.3484, total avg loss: 0.2787, batch size: 49
2021-10-14 01:50:40,977 INFO [train.py:451] Epoch 2, batch 15100, batch avg loss 0.2924, total avg loss: 0.2800, batch size: 34
2021-10-14 01:50:45,676 INFO [train.py:451] Epoch 2, batch 15110, batch avg loss 0.2247, total avg loss: 0.2807, batch size: 30
2021-10-14 01:50:50,828 INFO [train.py:451] Epoch 2, batch 15120, batch avg loss 0.2093, total avg loss: 0.2787, batch size: 32
2021-10-14 01:50:55,737 INFO [train.py:451] Epoch 2, batch 15130, batch avg loss 0.4011, total avg loss: 0.2793, batch size: 132
2021-10-14 01:51:00,603 INFO [train.py:451] Epoch 2, batch 15140, batch avg loss 0.3077, total avg loss: 0.2793, batch size: 37
2021-10-14 01:51:05,574 INFO [train.py:451] Epoch 2, batch 15150, batch avg loss 0.2611, total avg loss: 0.2787, batch size: 57
2021-10-14 01:51:10,443 INFO [train.py:451] Epoch 2, batch 15160, batch avg loss 0.3213, total avg loss: 0.2790, batch size: 39
2021-10-14 01:51:15,325 INFO [train.py:451] Epoch 2, batch 15170, batch avg loss 0.2800, total avg loss: 0.2791, batch size: 34
2021-10-14 01:51:20,068 INFO [train.py:451] Epoch 2, batch 15180, batch avg loss 0.2172, total avg loss: 0.2799, batch size: 27
2021-10-14 01:51:25,018 INFO [train.py:451] Epoch 2, batch 15190, batch avg loss 0.4220, total avg loss: 0.2812, batch size: 128
2021-10-14 01:51:29,785 INFO [train.py:451] Epoch 2, batch 15200, batch avg loss 0.3883, total avg loss: 0.2828, batch size: 126
2021-10-14 01:51:35,019 INFO [train.py:451] Epoch 2, batch 15210, batch avg loss 0.3005, total avg loss: 0.2806, batch size: 33
2021-10-14 01:51:39,960 INFO [train.py:451] Epoch 2, batch 15220, batch avg loss 0.2997, total avg loss: 0.2786, batch size: 34
2021-10-14 01:51:44,967 INFO [train.py:451] Epoch 2, batch 15230, batch avg loss 0.2513, total avg loss: 0.2801, batch size: 29
2021-10-14 01:51:49,758 INFO [train.py:451] Epoch 2, batch 15240, batch avg loss 0.2653, total avg loss: 0.2831, batch size: 31
2021-10-14 01:51:54,772 INFO [train.py:451] Epoch 2, batch 15250, batch avg loss 0.1991, total avg loss: 0.2806, batch size: 30
2021-10-14 01:51:59,769 INFO [train.py:451] Epoch 2, batch 15260, batch avg loss 0.3145, total avg loss: 0.2840, batch size: 35
2021-10-14 01:52:04,466 INFO [train.py:451] Epoch 2, batch 15270, batch avg loss 0.3827, total avg loss: 0.2865, batch size: 128
2021-10-14 01:52:09,482 INFO [train.py:451] Epoch 2, batch 15280, batch avg loss 0.2357, total avg loss: 0.2834, batch size: 29
2021-10-14 01:52:14,280 INFO [train.py:451] Epoch 2, batch 15290, batch avg loss 0.3916, total avg loss: 0.2840, batch size: 73
2021-10-14 01:52:19,298 INFO [train.py:451] Epoch 2, batch 15300, batch avg loss 0.2664, total avg loss: 0.2843, batch size: 38
2021-10-14 01:52:24,133 INFO [train.py:451] Epoch 2, batch 15310, batch avg loss 0.2200, total avg loss: 0.2854, batch size: 32
2021-10-14 01:52:29,047 INFO [train.py:451] Epoch 2, batch 15320, batch avg loss 0.2287, total avg loss: 0.2834, batch size: 32
2021-10-14 01:52:33,926 INFO [train.py:451] Epoch 2, batch 15330, batch avg loss 0.2592, total avg loss: 0.2838, batch size: 34
2021-10-14 01:52:38,844 INFO [train.py:451] Epoch 2, batch 15340, batch avg loss 0.3206, total avg loss: 0.2832, batch size: 49
2021-10-14 01:52:43,844 INFO [train.py:451] Epoch 2, batch 15350, batch avg loss 0.3108, total avg loss: 0.2822, batch size: 32
2021-10-14 01:52:48,640 INFO [train.py:451] Epoch 2, batch 15360, batch avg loss 0.2724, total avg loss: 0.2819, batch size: 34
2021-10-14 01:52:53,596 INFO [train.py:451] Epoch 2, batch 15370, batch avg loss 0.3199, total avg loss: 0.2806, batch size: 49
2021-10-14 01:52:58,470 INFO [train.py:451] Epoch 2, batch 15380, batch avg loss 0.2908, total avg loss: 0.2806, batch size: 49
2021-10-14 01:53:03,336 INFO [train.py:451] Epoch 2, batch 15390, batch avg loss 0.2894, total avg loss: 0.2813, batch size: 34
2021-10-14 01:53:08,112 INFO [train.py:451] Epoch 2, batch 15400, batch avg loss 0.3093, total avg loss: 0.2827, batch size: 45
2021-10-14 01:53:13,070 INFO [train.py:451] Epoch 2, batch 15410, batch avg loss 0.2985, total avg loss: 0.2981, batch size: 39
2021-10-14 01:53:17,910 INFO [train.py:451] Epoch 2, batch 15420, batch avg loss 0.3033, total avg loss: 0.2948, batch size: 38
2021-10-14 01:53:22,918 INFO [train.py:451] Epoch 2, batch 15430, batch avg loss 0.2211, total avg loss: 0.2950, batch size: 30
2021-10-14 01:53:27,859 INFO [train.py:451] Epoch 2, batch 15440, batch avg loss 0.3212, total avg loss: 0.2953, batch size: 71
2021-10-14 01:53:32,815 INFO [train.py:451] Epoch 2, batch 15450, batch avg loss 0.2864, total avg loss: 0.2907, batch size: 45
2021-10-14 01:53:37,663 INFO [train.py:451] Epoch 2, batch 15460, batch avg loss 0.3703, total avg loss: 0.2915, batch size: 42
2021-10-14 01:53:42,679 INFO [train.py:451] Epoch 2, batch 15470, batch avg loss 0.2034, total avg loss: 0.2869, batch size: 27
2021-10-14 01:53:47,735 INFO [train.py:451] Epoch 2, batch 15480, batch avg loss 0.2733, total avg loss: 0.2853, batch size: 28
2021-10-14 01:53:52,400 INFO [train.py:451] Epoch 2, batch 15490, batch avg loss 0.2962, total avg loss: 0.2876, batch size: 74
2021-10-14 01:53:57,371 INFO [train.py:451] Epoch 2, batch 15500, batch avg loss 0.3140, total avg loss: 0.2859, batch size: 56
2021-10-14 01:54:02,294 INFO [train.py:451] Epoch 2, batch 15510, batch avg loss 0.3573, total avg loss: 0.2867, batch size: 35
2021-10-14 01:54:07,092 INFO [train.py:451] Epoch 2, batch 15520, batch avg loss 0.2680, total avg loss: 0.2879, batch size: 36
2021-10-14 01:54:12,348 INFO [train.py:451] Epoch 2, batch 15530, batch avg loss 0.3117, total avg loss: 0.2873, batch size: 29
2021-10-14 01:54:17,242 INFO [train.py:451] Epoch 2, batch 15540, batch avg loss 0.2298, total avg loss: 0.2861, batch size: 28
2021-10-14 01:54:22,233 INFO [train.py:451] Epoch 2, batch 15550, batch avg loss 0.2204, total avg loss: 0.2853, batch size: 34
2021-10-14 01:54:27,077 INFO [train.py:451] Epoch 2, batch 15560, batch avg loss 0.2396, total avg loss: 0.2850, batch size: 31
2021-10-14 01:54:32,018 INFO [train.py:451] Epoch 2, batch 15570, batch avg loss 0.2407, total avg loss: 0.2838, batch size: 29
2021-10-14 01:54:36,881 INFO [train.py:451] Epoch 2, batch 15580, batch avg loss 0.2931, total avg loss: 0.2836, batch size: 36
2021-10-14 01:54:41,691 INFO [train.py:451] Epoch 2, batch 15590, batch avg loss 0.2215, total avg loss: 0.2834, batch size: 29
2021-10-14 01:54:46,495 INFO [train.py:451] Epoch 2, batch 15600, batch avg loss 0.2395, total avg loss: 0.2835, batch size: 30
2021-10-14 01:54:51,481 INFO [train.py:451] Epoch 2, batch 15610, batch avg loss 0.2599, total avg loss: 0.2981, batch size: 37
2021-10-14 01:54:56,737 INFO [train.py:451] Epoch 2, batch 15620, batch avg loss 0.2434, total avg loss: 0.2938, batch size: 34
2021-10-14 01:55:01,792 INFO [train.py:451] Epoch 2, batch 15630, batch avg loss 0.2597, total avg loss: 0.2862, batch size: 33
2021-10-14 01:55:06,596 INFO [train.py:451] Epoch 2, batch 15640, batch avg loss 0.2967, total avg loss: 0.2864, batch size: 42
2021-10-14 01:55:11,434 INFO [train.py:451] Epoch 2, batch 15650, batch avg loss 0.2825, total avg loss: 0.2856, batch size: 35
2021-10-14 01:55:16,569 INFO [train.py:451] Epoch 2, batch 15660, batch avg loss 0.3813, total avg loss: 0.2839, batch size: 126
2021-10-14 01:55:21,513 INFO [train.py:451] Epoch 2, batch 15670, batch avg loss 0.2746, total avg loss: 0.2851, batch size: 34
2021-10-14 01:55:26,533 INFO [train.py:451] Epoch 2, batch 15680, batch avg loss 0.2519, total avg loss: 0.2849, batch size: 34
2021-10-14 01:55:31,345 INFO [train.py:451] Epoch 2, batch 15690, batch avg loss 0.3073, total avg loss: 0.2860, batch size: 38
2021-10-14 01:55:36,267 INFO [train.py:451] Epoch 2, batch 15700, batch avg loss 0.3696, total avg loss: 0.2871, batch size: 73
2021-10-14 01:55:41,346 INFO [train.py:451] Epoch 2, batch 15710, batch avg loss 0.3191, total avg loss: 0.2853, batch size: 33
2021-10-14 01:55:46,294 INFO [train.py:451] Epoch 2, batch 15720, batch avg loss 0.2902, total avg loss: 0.2842, batch size: 35
2021-10-14 01:55:51,412 INFO [train.py:451] Epoch 2, batch 15730, batch avg loss 0.2572, total avg loss: 0.2829, batch size: 34
2021-10-14 01:55:56,275 INFO [train.py:451] Epoch 2, batch 15740, batch avg loss 0.2902, total avg loss: 0.2826, batch size: 36
2021-10-14 01:56:01,525 INFO [train.py:451] Epoch 2, batch 15750, batch avg loss 0.3892, total avg loss: 0.2830, batch size: 134
2021-10-14 01:56:06,506 INFO [train.py:451] Epoch 2, batch 15760, batch avg loss 0.2348, total avg loss: 0.2831, batch size: 34
2021-10-14 01:56:11,594 INFO [train.py:451] Epoch 2, batch 15770, batch avg loss 0.3507, total avg loss: 0.2818, batch size: 72
2021-10-14 01:56:16,806 INFO [train.py:451] Epoch 2, batch 15780, batch avg loss 0.3095, total avg loss: 0.2799, batch size: 35
2021-10-14 01:56:21,912 INFO [train.py:451] Epoch 2, batch 15790, batch avg loss 0.2226, total avg loss: 0.2792, batch size: 29
2021-10-14 01:56:26,805 INFO [train.py:451] Epoch 2, batch 15800, batch avg loss 0.2788, total avg loss: 0.2791, batch size: 39
2021-10-14 01:56:31,811 INFO [train.py:451] Epoch 2, batch 15810, batch avg loss 0.2363, total avg loss: 0.2633, batch size: 34
2021-10-14 01:56:36,487 INFO [train.py:451] Epoch 2, batch 15820, batch avg loss 0.3498, total avg loss: 0.2921, batch size: 73
2021-10-14 01:56:41,413 INFO [train.py:451] Epoch 2, batch 15830, batch avg loss 0.2925, total avg loss: 0.2905, batch size: 39
2021-10-14 01:56:46,353 INFO [train.py:451] Epoch 2, batch 15840, batch avg loss 0.2556, total avg loss: 0.2831, batch size: 31
2021-10-14 01:56:51,056 INFO [train.py:451] Epoch 2, batch 15850, batch avg loss 0.2746, total avg loss: 0.2850, batch size: 36
2021-10-14 01:56:55,953 INFO [train.py:451] Epoch 2, batch 15860, batch avg loss 0.3206, total avg loss: 0.2819, batch size: 49
2021-10-14 01:57:00,745 INFO [train.py:451] Epoch 2, batch 15870, batch avg loss 0.2628, total avg loss: 0.2830, batch size: 41
2021-10-14 01:57:05,610 INFO [train.py:451] Epoch 2, batch 15880, batch avg loss 0.2644, total avg loss: 0.2819, batch size: 36
2021-10-14 01:57:10,468 INFO [train.py:451] Epoch 2, batch 15890, batch avg loss 0.2772, total avg loss: 0.2818, batch size: 34
2021-10-14 01:57:15,544 INFO [train.py:451] Epoch 2, batch 15900, batch avg loss 0.2591, total avg loss: 0.2807, batch size: 30
2021-10-14 01:57:20,609 INFO [train.py:451] Epoch 2, batch 15910, batch avg loss 0.2938, total avg loss: 0.2813, batch size: 38
2021-10-14 01:57:25,628 INFO [train.py:451] Epoch 2, batch 15920, batch avg loss 0.2831, total avg loss: 0.2808, batch size: 33
2021-10-14 01:57:30,593 INFO [train.py:451] Epoch 2, batch 15930, batch avg loss 0.2357, total avg loss: 0.2800, batch size: 32
2021-10-14 01:57:35,364 INFO [train.py:451] Epoch 2, batch 15940, batch avg loss 0.2291, total avg loss: 0.2805, batch size: 32
2021-10-14 01:57:40,412 INFO [train.py:451] Epoch 2, batch 15950, batch avg loss 0.2724, total avg loss: 0.2807, batch size: 27
2021-10-14 01:57:44,416 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "f32fb47e-b8fe-4f6b-2eab-95456ed2dba1" will not be mixed in.
2021-10-14 01:57:45,248 INFO [train.py:451] Epoch 2, batch 15960, batch avg loss 0.3094, total avg loss: 0.2798, batch size: 35
2021-10-14 01:57:50,220 INFO [train.py:451] Epoch 2, batch 15970, batch avg loss 0.2424, total avg loss: 0.2796, batch size: 33
2021-10-14 01:57:55,056 INFO [train.py:451] Epoch 2, batch 15980, batch avg loss 0.2328, total avg loss: 0.2800, batch size: 29
2021-10-14 01:57:59,741 INFO [train.py:451] Epoch 2, batch 15990, batch avg loss 0.2819, total avg loss: 0.2814, batch size: 29
2021-10-14 01:58:04,440 INFO [train.py:451] Epoch 2, batch 16000, batch avg loss 0.2750, total avg loss: 0.2813, batch size: 35
2021-10-14 01:58:44,866 INFO [train.py:483] Epoch 2, valid loss 0.1996, best valid loss: 0.1996 best valid epoch: 2
2021-10-14 01:58:49,630 INFO [train.py:451] Epoch 2, batch 16010, batch avg loss 0.3331, total avg loss: 0.2974, batch size: 30
2021-10-14 01:58:54,535 INFO [train.py:451] Epoch 2, batch 16020, batch avg loss 0.3696, total avg loss: 0.2899, batch size: 36
2021-10-14 01:58:59,604 INFO [train.py:451] Epoch 2, batch 16030, batch avg loss 0.3119, total avg loss: 0.2855, batch size: 38
2021-10-14 01:59:04,455 INFO [train.py:451] Epoch 2, batch 16040, batch avg loss 0.2690, total avg loss: 0.2842, batch size: 33
2021-10-14 01:59:09,265 INFO [train.py:451] Epoch 2, batch 16050, batch avg loss 0.3782, total avg loss: 0.2878, batch size: 130
2021-10-14 01:59:14,154 INFO [train.py:451] Epoch 2, batch 16060, batch avg loss 0.2688, total avg loss: 0.2887, batch size: 38
2021-10-14 01:59:19,108 INFO [train.py:451] Epoch 2, batch 16070, batch avg loss 0.2307, total avg loss: 0.2849, batch size: 29
2021-10-14 01:59:23,831 INFO [train.py:451] Epoch 2, batch 16080, batch avg loss 0.3096, total avg loss: 0.2849, batch size: 49
2021-10-14 01:59:28,717 INFO [train.py:451] Epoch 2, batch 16090, batch avg loss 0.2422, total avg loss: 0.2866, batch size: 27
2021-10-14 01:59:33,466 INFO [train.py:451] Epoch 2, batch 16100, batch avg loss 0.3352, total avg loss: 0.2868, batch size: 73
2021-10-14 01:59:38,341 INFO [train.py:451] Epoch 2, batch 16110, batch avg loss 0.2982, total avg loss: 0.2851, batch size: 41
2021-10-14 01:59:43,179 INFO [train.py:451] Epoch 2, batch 16120, batch avg loss 0.2934, total avg loss: 0.2854, batch size: 28
2021-10-14 01:59:48,004 INFO [train.py:451] Epoch 2, batch 16130, batch avg loss 0.2789, total avg loss: 0.2849, batch size: 31
2021-10-14 01:59:52,856 INFO [train.py:451] Epoch 2, batch 16140, batch avg loss 0.3131, total avg loss: 0.2860, batch size: 32
2021-10-14 01:59:57,795 INFO [train.py:451] Epoch 2, batch 16150, batch avg loss 0.2288, total avg loss: 0.2869, batch size: 29
2021-10-14 02:00:02,852 INFO [train.py:451] Epoch 2, batch 16160, batch avg loss 0.2757, total avg loss: 0.2857, batch size: 56
2021-10-14 02:00:07,905 INFO [train.py:451] Epoch 2, batch 16170, batch avg loss 0.2311, total avg loss: 0.2847, batch size: 29
2021-10-14 02:00:12,784 INFO [train.py:451] Epoch 2, batch 16180, batch avg loss 0.2427, total avg loss: 0.2851, batch size: 31
2021-10-14 02:00:17,492 INFO [train.py:451] Epoch 2, batch 16190, batch avg loss 0.3132, total avg loss: 0.2862, batch size: 42
2021-10-14 02:00:22,329 INFO [train.py:451] Epoch 2, batch 16200, batch avg loss 0.3152, total avg loss: 0.2867, batch size: 38
2021-10-14 02:00:27,254 INFO [train.py:451] Epoch 2, batch 16210, batch avg loss 0.2693, total avg loss: 0.2937, batch size: 35
2021-10-14 02:00:31,913 INFO [train.py:451] Epoch 2, batch 16220, batch avg loss 0.3080, total avg loss: 0.2925, batch size: 32
2021-10-14 02:00:36,771 INFO [train.py:451] Epoch 2, batch 16230, batch avg loss 0.2654, total avg loss: 0.2868, batch size: 38
2021-10-14 02:00:41,671 INFO [train.py:451] Epoch 2, batch 16240, batch avg loss 0.3358, total avg loss: 0.2851, batch size: 49
2021-10-14 02:00:46,869 INFO [train.py:451] Epoch 2, batch 16250, batch avg loss 0.2461, total avg loss: 0.2773, batch size: 29
2021-10-14 02:00:51,604 INFO [train.py:451] Epoch 2, batch 16260, batch avg loss 0.2491, total avg loss: 0.2784, batch size: 35
2021-10-14 02:00:56,390 INFO [train.py:451] Epoch 2, batch 16270, batch avg loss 0.3353, total avg loss: 0.2812, batch size: 36
2021-10-14 02:01:01,327 INFO [train.py:451] Epoch 2, batch 16280, batch avg loss 0.2151, total avg loss: 0.2799, batch size: 32
2021-10-14 02:01:06,333 INFO [train.py:451] Epoch 2, batch 16290, batch avg loss 0.2827, total avg loss: 0.2795, batch size: 39
2021-10-14 02:01:11,241 INFO [train.py:451] Epoch 2, batch 16300, batch avg loss 0.2975, total avg loss: 0.2803, batch size: 33
2021-10-14 02:01:16,164 INFO [train.py:451] Epoch 2, batch 16310, batch avg loss 0.2658, total avg loss: 0.2792, batch size: 45
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2021-10-14 02:07:37,383 INFO [train.py:483] Epoch 2, valid loss 0.1983, best valid loss: 0.1983 best valid epoch: 2
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2021-10-14 02:16:35,541 INFO [train.py:483] Epoch 2, valid loss 0.1988, best valid loss: 0.1983 best valid epoch: 2
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2021-10-14 02:22:57,340 INFO [train.py:451] Epoch 2, batch 18770, batch avg loss 0.2459, total avg loss: 0.2791, batch size: 32
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2021-10-14 02:23:12,376 INFO [train.py:451] Epoch 2, batch 18800, batch avg loss 0.2823, total avg loss: 0.2796, batch size: 37
2021-10-14 02:23:17,332 INFO [train.py:451] Epoch 2, batch 18810, batch avg loss 0.3162, total avg loss: 0.2691, batch size: 74
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2021-10-14 02:23:46,885 INFO [train.py:451] Epoch 2, batch 18870, batch avg loss 0.2610, total avg loss: 0.2777, batch size: 36
2021-10-14 02:23:51,805 INFO [train.py:451] Epoch 2, batch 18880, batch avg loss 0.2728, total avg loss: 0.2770, batch size: 29
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2021-10-14 02:24:01,811 INFO [train.py:451] Epoch 2, batch 18900, batch avg loss 0.2694, total avg loss: 0.2790, batch size: 35
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2021-10-14 02:24:45,891 INFO [train.py:451] Epoch 2, batch 18990, batch avg loss 0.1914, total avg loss: 0.2818, batch size: 32
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2021-10-14 02:25:30,699 INFO [train.py:483] Epoch 2, valid loss 0.1976, best valid loss: 0.1976 best valid epoch: 2
2021-10-14 02:25:35,782 INFO [train.py:451] Epoch 2, batch 19010, batch avg loss 0.2370, total avg loss: 0.2750, batch size: 29
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2021-10-14 02:26:20,384 INFO [train.py:451] Epoch 2, batch 19100, batch avg loss 0.2522, total avg loss: 0.2739, batch size: 28
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2021-10-14 02:26:35,041 INFO [train.py:451] Epoch 2, batch 19130, batch avg loss 0.3810, total avg loss: 0.2744, batch size: 121
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2021-10-14 02:26:44,909 INFO [train.py:451] Epoch 2, batch 19150, batch avg loss 0.2454, total avg loss: 0.2751, batch size: 36
2021-10-14 02:26:49,794 INFO [train.py:451] Epoch 2, batch 19160, batch avg loss 0.3681, total avg loss: 0.2762, batch size: 36
2021-10-14 02:26:54,831 INFO [train.py:451] Epoch 2, batch 19170, batch avg loss 0.2353, total avg loss: 0.2755, batch size: 28
2021-10-14 02:26:59,635 INFO [train.py:451] Epoch 2, batch 19180, batch avg loss 0.2659, total avg loss: 0.2756, batch size: 33
2021-10-14 02:27:04,475 INFO [train.py:451] Epoch 2, batch 19190, batch avg loss 0.2718, total avg loss: 0.2757, batch size: 32
2021-10-14 02:27:09,580 INFO [train.py:451] Epoch 2, batch 19200, batch avg loss 0.2955, total avg loss: 0.2758, batch size: 57
2021-10-14 02:27:14,535 INFO [train.py:451] Epoch 2, batch 19210, batch avg loss 0.3092, total avg loss: 0.2757, batch size: 38
2021-10-14 02:27:19,473 INFO [train.py:451] Epoch 2, batch 19220, batch avg loss 0.2445, total avg loss: 0.2721, batch size: 41
2021-10-14 02:27:24,407 INFO [train.py:451] Epoch 2, batch 19230, batch avg loss 0.2579, total avg loss: 0.2752, batch size: 36
2021-10-14 02:27:29,295 INFO [train.py:451] Epoch 2, batch 19240, batch avg loss 0.2597, total avg loss: 0.2755, batch size: 34
2021-10-14 02:27:33,871 INFO [train.py:451] Epoch 2, batch 19250, batch avg loss 0.2833, total avg loss: 0.2838, batch size: 33
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2021-10-14 02:30:55,326 INFO [train.py:451] Epoch 2, batch 19660, batch avg loss 0.2791, total avg loss: 0.2781, batch size: 33
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2021-10-14 02:31:10,091 INFO [train.py:451] Epoch 2, batch 19690, batch avg loss 0.3385, total avg loss: 0.2776, batch size: 45
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2021-10-14 02:31:34,619 INFO [train.py:451] Epoch 2, batch 19740, batch avg loss 0.2920, total avg loss: 0.2791, batch size: 31
2021-10-14 02:31:39,467 INFO [train.py:451] Epoch 2, batch 19750, batch avg loss 0.2870, total avg loss: 0.2796, batch size: 35
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2021-10-14 02:32:04,117 INFO [train.py:451] Epoch 2, batch 19800, batch avg loss 0.2418, total avg loss: 0.2776, batch size: 31
2021-10-14 02:32:09,019 INFO [train.py:451] Epoch 2, batch 19810, batch avg loss 0.3067, total avg loss: 0.2812, batch size: 39
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2021-10-14 02:32:23,675 INFO [train.py:451] Epoch 2, batch 19840, batch avg loss 0.3618, total avg loss: 0.2885, batch size: 37
2021-10-14 02:32:28,499 INFO [train.py:451] Epoch 2, batch 19850, batch avg loss 0.2970, total avg loss: 0.2885, batch size: 39
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2021-10-14 02:32:52,894 INFO [train.py:451] Epoch 2, batch 19900, batch avg loss 0.2838, total avg loss: 0.2801, batch size: 35
2021-10-14 02:32:57,694 INFO [train.py:451] Epoch 2, batch 19910, batch avg loss 0.2424, total avg loss: 0.2793, batch size: 28
2021-10-14 02:33:02,466 INFO [train.py:451] Epoch 2, batch 19920, batch avg loss 0.2230, total avg loss: 0.2806, batch size: 31
2021-10-14 02:33:07,511 INFO [train.py:451] Epoch 2, batch 19930, batch avg loss 0.3212, total avg loss: 0.2787, batch size: 35
2021-10-14 02:33:12,470 INFO [train.py:451] Epoch 2, batch 19940, batch avg loss 0.2160, total avg loss: 0.2790, batch size: 28
2021-10-14 02:33:17,421 INFO [train.py:451] Epoch 2, batch 19950, batch avg loss 0.2527, total avg loss: 0.2778, batch size: 33
2021-10-14 02:33:22,263 INFO [train.py:451] Epoch 2, batch 19960, batch avg loss 0.2346, total avg loss: 0.2767, batch size: 29
2021-10-14 02:33:27,323 INFO [train.py:451] Epoch 2, batch 19970, batch avg loss 0.2860, total avg loss: 0.2762, batch size: 33
2021-10-14 02:33:32,181 INFO [train.py:451] Epoch 2, batch 19980, batch avg loss 0.3441, total avg loss: 0.2767, batch size: 38
2021-10-14 02:33:37,077 INFO [train.py:451] Epoch 2, batch 19990, batch avg loss 0.3700, total avg loss: 0.2765, batch size: 72
2021-10-14 02:33:42,055 INFO [train.py:451] Epoch 2, batch 20000, batch avg loss 0.3001, total avg loss: 0.2762, batch size: 35
2021-10-14 02:34:19,573 INFO [train.py:483] Epoch 2, valid loss 0.1993, best valid loss: 0.1976 best valid epoch: 2
2021-10-14 02:34:24,641 INFO [train.py:451] Epoch 2, batch 20010, batch avg loss 0.2257, total avg loss: 0.2731, batch size: 30
2021-10-14 02:34:29,579 INFO [train.py:451] Epoch 2, batch 20020, batch avg loss 0.3258, total avg loss: 0.2796, batch size: 38
2021-10-14 02:34:34,546 INFO [train.py:451] Epoch 2, batch 20030, batch avg loss 0.2715, total avg loss: 0.2747, batch size: 29
2021-10-14 02:34:39,588 INFO [train.py:451] Epoch 2, batch 20040, batch avg loss 0.2980, total avg loss: 0.2775, batch size: 36
2021-10-14 02:34:44,542 INFO [train.py:451] Epoch 2, batch 20050, batch avg loss 0.3401, total avg loss: 0.2775, batch size: 38
2021-10-14 02:34:49,658 INFO [train.py:451] Epoch 2, batch 20060, batch avg loss 0.2211, total avg loss: 0.2738, batch size: 31
2021-10-14 02:34:54,407 INFO [train.py:451] Epoch 2, batch 20070, batch avg loss 0.2823, total avg loss: 0.2778, batch size: 49
2021-10-14 02:34:59,595 INFO [train.py:451] Epoch 2, batch 20080, batch avg loss 0.2739, total avg loss: 0.2752, batch size: 34
2021-10-14 02:35:04,602 INFO [train.py:451] Epoch 2, batch 20090, batch avg loss 0.2879, total avg loss: 0.2741, batch size: 39
2021-10-14 02:35:09,439 INFO [train.py:451] Epoch 2, batch 20100, batch avg loss 0.2792, total avg loss: 0.2750, batch size: 34
2021-10-14 02:35:14,368 INFO [train.py:451] Epoch 2, batch 20110, batch avg loss 0.2347, total avg loss: 0.2752, batch size: 30
2021-10-14 02:35:19,398 INFO [train.py:451] Epoch 2, batch 20120, batch avg loss 0.2405, total avg loss: 0.2734, batch size: 29
2021-10-14 02:35:24,437 INFO [train.py:451] Epoch 2, batch 20130, batch avg loss 0.2527, total avg loss: 0.2727, batch size: 34
2021-10-14 02:35:29,356 INFO [train.py:451] Epoch 2, batch 20140, batch avg loss 0.2384, total avg loss: 0.2725, batch size: 29
2021-10-14 02:35:34,155 INFO [train.py:451] Epoch 2, batch 20150, batch avg loss 0.3055, total avg loss: 0.2734, batch size: 57
2021-10-14 02:35:38,935 INFO [train.py:451] Epoch 2, batch 20160, batch avg loss 0.3221, total avg loss: 0.2738, batch size: 73
2021-10-14 02:35:43,761 INFO [train.py:451] Epoch 2, batch 20170, batch avg loss 0.2685, total avg loss: 0.2745, batch size: 38
2021-10-14 02:35:48,695 INFO [train.py:451] Epoch 2, batch 20180, batch avg loss 0.2965, total avg loss: 0.2753, batch size: 56
2021-10-14 02:35:53,551 INFO [train.py:451] Epoch 2, batch 20190, batch avg loss 0.3001, total avg loss: 0.2765, batch size: 56
2021-10-14 02:35:58,381 INFO [train.py:451] Epoch 2, batch 20200, batch avg loss 0.2786, total avg loss: 0.2760, batch size: 34
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2021-10-14 02:36:22,682 INFO [train.py:451] Epoch 2, batch 20250, batch avg loss 0.3282, total avg loss: 0.2824, batch size: 34
2021-10-14 02:36:27,517 INFO [train.py:451] Epoch 2, batch 20260, batch avg loss 0.2827, total avg loss: 0.2821, batch size: 35
2021-10-14 02:36:32,510 INFO [train.py:451] Epoch 2, batch 20270, batch avg loss 0.2802, total avg loss: 0.2796, batch size: 37
2021-10-14 02:36:37,425 INFO [train.py:451] Epoch 2, batch 20280, batch avg loss 0.2455, total avg loss: 0.2802, batch size: 30
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2021-10-14 02:36:46,953 INFO [train.py:451] Epoch 2, batch 20300, batch avg loss 0.2825, total avg loss: 0.2828, batch size: 36
2021-10-14 02:36:51,750 INFO [train.py:451] Epoch 2, batch 20310, batch avg loss 0.2661, total avg loss: 0.2845, batch size: 34
2021-10-14 02:36:56,573 INFO [train.py:451] Epoch 2, batch 20320, batch avg loss 0.2913, total avg loss: 0.2846, batch size: 32
2021-10-14 02:37:01,450 INFO [train.py:451] Epoch 2, batch 20330, batch avg loss 0.3122, total avg loss: 0.2853, batch size: 45
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2021-10-14 02:37:16,167 INFO [train.py:451] Epoch 2, batch 20360, batch avg loss 0.2289, total avg loss: 0.2839, batch size: 32
2021-10-14 02:37:21,051 INFO [train.py:451] Epoch 2, batch 20370, batch avg loss 0.2770, total avg loss: 0.2834, batch size: 32
2021-10-14 02:37:25,914 INFO [train.py:451] Epoch 2, batch 20380, batch avg loss 0.2518, total avg loss: 0.2827, batch size: 28
2021-10-14 02:37:30,948 INFO [train.py:451] Epoch 2, batch 20390, batch avg loss 0.2416, total avg loss: 0.2820, batch size: 34
2021-10-14 02:37:35,812 INFO [train.py:451] Epoch 2, batch 20400, batch avg loss 0.2554, total avg loss: 0.2818, batch size: 31
2021-10-14 02:37:40,594 INFO [train.py:451] Epoch 2, batch 20410, batch avg loss 0.2466, total avg loss: 0.2809, batch size: 34
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2021-10-14 02:37:59,958 INFO [train.py:451] Epoch 2, batch 20450, batch avg loss 0.2560, total avg loss: 0.2836, batch size: 36
2021-10-14 02:38:04,924 INFO [train.py:451] Epoch 2, batch 20460, batch avg loss 0.2579, total avg loss: 0.2852, batch size: 33
2021-10-14 02:38:09,992 INFO [train.py:451] Epoch 2, batch 20470, batch avg loss 0.2577, total avg loss: 0.2877, batch size: 30
2021-10-14 02:38:15,178 INFO [train.py:451] Epoch 2, batch 20480, batch avg loss 0.2738, total avg loss: 0.2853, batch size: 30
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2021-10-14 02:38:44,775 INFO [train.py:451] Epoch 2, batch 20540, batch avg loss 0.3310, total avg loss: 0.2822, batch size: 74
2021-10-14 02:38:49,664 INFO [train.py:451] Epoch 2, batch 20550, batch avg loss 0.3196, total avg loss: 0.2826, batch size: 49
2021-10-14 02:38:54,641 INFO [train.py:451] Epoch 2, batch 20560, batch avg loss 0.2517, total avg loss: 0.2821, batch size: 31
2021-10-14 02:38:59,824 INFO [train.py:451] Epoch 2, batch 20570, batch avg loss 0.2704, total avg loss: 0.2808, batch size: 33
2021-10-14 02:39:04,849 INFO [train.py:451] Epoch 2, batch 20580, batch avg loss 0.2880, total avg loss: 0.2807, batch size: 57
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2021-10-14 02:39:14,540 INFO [train.py:451] Epoch 2, batch 20600, batch avg loss 0.2654, total avg loss: 0.2804, batch size: 33
2021-10-14 02:39:19,370 INFO [train.py:451] Epoch 2, batch 20610, batch avg loss 0.2640, total avg loss: 0.2863, batch size: 36
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2021-10-14 02:39:29,159 INFO [train.py:451] Epoch 2, batch 20630, batch avg loss 0.3187, total avg loss: 0.2880, batch size: 36
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2021-10-14 02:40:03,656 INFO [train.py:451] Epoch 2, batch 20700, batch avg loss 0.2157, total avg loss: 0.2819, batch size: 32
2021-10-14 02:40:08,567 INFO [train.py:451] Epoch 2, batch 20710, batch avg loss 0.2922, total avg loss: 0.2814, batch size: 38
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2021-10-14 02:44:43,052 INFO [checkpoint.py:62] Saving checkpoint to tdnn_lstm_ctc-v2/exp/epoch-2.pt
2021-10-14 02:44:43,863 INFO [train.py:564] epoch 3, lr: 0.00025
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2021-10-14 03:02:35,633 INFO [train.py:483] Epoch 3, valid loss 0.1992, best valid loss: 0.1976 best valid epoch: 2
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2021-10-14 03:06:51,299 INFO [train.py:451] Epoch 3, batch 2520, batch avg loss 0.2610, total avg loss: 0.2759, batch size: 33
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2021-10-14 03:07:01,571 INFO [train.py:451] Epoch 3, batch 2540, batch avg loss 0.2370, total avg loss: 0.2737, batch size: 30
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2021-10-14 03:07:11,560 INFO [train.py:451] Epoch 3, batch 2560, batch avg loss 0.2944, total avg loss: 0.2734, batch size: 39
2021-10-14 03:07:16,405 INFO [train.py:451] Epoch 3, batch 2570, batch avg loss 0.2678, total avg loss: 0.2740, batch size: 35
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2021-10-14 03:07:31,456 INFO [train.py:451] Epoch 3, batch 2600, batch avg loss 0.2673, total avg loss: 0.2735, batch size: 36
2021-10-14 03:07:36,268 INFO [train.py:451] Epoch 3, batch 2610, batch avg loss 0.2690, total avg loss: 0.2820, batch size: 35
2021-10-14 03:07:41,337 INFO [train.py:451] Epoch 3, batch 2620, batch avg loss 0.2954, total avg loss: 0.2846, batch size: 35
2021-10-14 03:07:46,373 INFO [train.py:451] Epoch 3, batch 2630, batch avg loss 0.3413, total avg loss: 0.2845, batch size: 41
2021-10-14 03:07:51,423 INFO [train.py:451] Epoch 3, batch 2640, batch avg loss 0.2945, total avg loss: 0.2803, batch size: 36
2021-10-14 03:07:56,348 INFO [train.py:451] Epoch 3, batch 2650, batch avg loss 0.2073, total avg loss: 0.2781, batch size: 31
2021-10-14 03:08:01,161 INFO [train.py:451] Epoch 3, batch 2660, batch avg loss 0.2643, total avg loss: 0.2789, batch size: 27
2021-10-14 03:08:06,099 INFO [train.py:451] Epoch 3, batch 2670, batch avg loss 0.3369, total avg loss: 0.2795, batch size: 74
2021-10-14 03:08:08,037 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "dff29783-1940-e0c2-b8e5-557519f43734" will not be mixed in.
2021-10-14 03:08:10,735 INFO [train.py:451] Epoch 3, batch 2680, batch avg loss 0.3200, total avg loss: 0.2808, batch size: 36
2021-10-14 03:08:15,648 INFO [train.py:451] Epoch 3, batch 2690, batch avg loss 0.2464, total avg loss: 0.2798, batch size: 30
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2021-10-14 03:09:09,163 INFO [train.py:451] Epoch 3, batch 2800, batch avg loss 0.3108, total avg loss: 0.2816, batch size: 34
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2021-10-14 03:09:19,125 INFO [train.py:451] Epoch 3, batch 2820, batch avg loss 0.2296, total avg loss: 0.2624, batch size: 27
2021-10-14 03:09:23,878 INFO [train.py:451] Epoch 3, batch 2830, batch avg loss 0.2511, total avg loss: 0.2651, batch size: 32
2021-10-14 03:09:28,800 INFO [train.py:451] Epoch 3, batch 2840, batch avg loss 0.2763, total avg loss: 0.2682, batch size: 35
2021-10-14 03:09:33,713 INFO [train.py:451] Epoch 3, batch 2850, batch avg loss 0.3015, total avg loss: 0.2699, batch size: 45
2021-10-14 03:09:38,651 INFO [train.py:451] Epoch 3, batch 2860, batch avg loss 0.2595, total avg loss: 0.2695, batch size: 34
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2021-10-14 03:09:48,862 INFO [train.py:451] Epoch 3, batch 2880, batch avg loss 0.3913, total avg loss: 0.2727, batch size: 130
2021-10-14 03:09:53,679 INFO [train.py:451] Epoch 3, batch 2890, batch avg loss 0.2752, total avg loss: 0.2760, batch size: 30
2021-10-14 03:09:58,754 INFO [train.py:451] Epoch 3, batch 2900, batch avg loss 0.2162, total avg loss: 0.2751, batch size: 29
2021-10-14 03:10:03,656 INFO [train.py:451] Epoch 3, batch 2910, batch avg loss 0.2780, total avg loss: 0.2748, batch size: 39
2021-10-14 03:10:08,570 INFO [train.py:451] Epoch 3, batch 2920, batch avg loss 0.2932, total avg loss: 0.2755, batch size: 34
2021-10-14 03:10:13,480 INFO [train.py:451] Epoch 3, batch 2930, batch avg loss 0.2912, total avg loss: 0.2750, batch size: 28
2021-10-14 03:10:18,320 INFO [train.py:451] Epoch 3, batch 2940, batch avg loss 0.2776, total avg loss: 0.2762, batch size: 39
2021-10-14 03:10:23,275 INFO [train.py:451] Epoch 3, batch 2950, batch avg loss 0.2938, total avg loss: 0.2762, batch size: 49
2021-10-14 03:10:28,009 INFO [train.py:451] Epoch 3, batch 2960, batch avg loss 0.2688, total avg loss: 0.2767, batch size: 49
2021-10-14 03:10:32,747 INFO [train.py:451] Epoch 3, batch 2970, batch avg loss 0.2820, total avg loss: 0.2777, batch size: 33
2021-10-14 03:10:37,676 INFO [train.py:451] Epoch 3, batch 2980, batch avg loss 0.2339, total avg loss: 0.2778, batch size: 30
2021-10-14 03:10:42,542 INFO [train.py:451] Epoch 3, batch 2990, batch avg loss 0.2582, total avg loss: 0.2779, batch size: 30
2021-10-14 03:10:47,274 INFO [train.py:451] Epoch 3, batch 3000, batch avg loss 0.2161, total avg loss: 0.2789, batch size: 32
2021-10-14 03:11:27,162 INFO [train.py:483] Epoch 3, valid loss 0.1971, best valid loss: 0.1971 best valid epoch: 3
2021-10-14 03:11:31,970 INFO [train.py:451] Epoch 3, batch 3010, batch avg loss 0.2684, total avg loss: 0.2998, batch size: 34
2021-10-14 03:11:36,986 INFO [train.py:451] Epoch 3, batch 3020, batch avg loss 0.2961, total avg loss: 0.2916, batch size: 38
2021-10-14 03:11:41,629 INFO [train.py:451] Epoch 3, batch 3030, batch avg loss 0.4054, total avg loss: 0.2959, batch size: 126
2021-10-14 03:11:46,596 INFO [train.py:451] Epoch 3, batch 3040, batch avg loss 0.2709, total avg loss: 0.2913, batch size: 28
2021-10-14 03:11:51,439 INFO [train.py:451] Epoch 3, batch 3050, batch avg loss 0.3524, total avg loss: 0.2899, batch size: 72
2021-10-14 03:11:56,332 INFO [train.py:451] Epoch 3, batch 3060, batch avg loss 0.1975, total avg loss: 0.2867, batch size: 30
2021-10-14 03:12:01,282 INFO [train.py:451] Epoch 3, batch 3070, batch avg loss 0.2409, total avg loss: 0.2859, batch size: 31
2021-10-14 03:12:06,171 INFO [train.py:451] Epoch 3, batch 3080, batch avg loss 0.3213, total avg loss: 0.2853, batch size: 49
2021-10-14 03:12:10,817 INFO [train.py:451] Epoch 3, batch 3090, batch avg loss 0.3323, total avg loss: 0.2866, batch size: 72
2021-10-14 03:12:15,900 INFO [train.py:451] Epoch 3, batch 3100, batch avg loss 0.2602, total avg loss: 0.2847, batch size: 32
2021-10-14 03:12:20,872 INFO [train.py:451] Epoch 3, batch 3110, batch avg loss 0.3347, total avg loss: 0.2833, batch size: 39
2021-10-14 03:12:25,678 INFO [train.py:451] Epoch 3, batch 3120, batch avg loss 0.2308, total avg loss: 0.2818, batch size: 32
2021-10-14 03:12:30,612 INFO [train.py:451] Epoch 3, batch 3130, batch avg loss 0.2590, total avg loss: 0.2805, batch size: 32
2021-10-14 03:12:35,398 INFO [train.py:451] Epoch 3, batch 3140, batch avg loss 0.2708, total avg loss: 0.2819, batch size: 36
2021-10-14 03:12:40,329 INFO [train.py:451] Epoch 3, batch 3150, batch avg loss 0.2461, total avg loss: 0.2816, batch size: 33
2021-10-14 03:12:45,269 INFO [train.py:451] Epoch 3, batch 3160, batch avg loss 0.2896, total avg loss: 0.2819, batch size: 42
2021-10-14 03:12:50,239 INFO [train.py:451] Epoch 3, batch 3170, batch avg loss 0.3108, total avg loss: 0.2812, batch size: 45
2021-10-14 03:12:55,145 INFO [train.py:451] Epoch 3, batch 3180, batch avg loss 0.2754, total avg loss: 0.2799, batch size: 42
2021-10-14 03:13:00,040 INFO [train.py:451] Epoch 3, batch 3190, batch avg loss 0.3004, total avg loss: 0.2797, batch size: 31
2021-10-14 03:13:04,949 INFO [train.py:451] Epoch 3, batch 3200, batch avg loss 0.3051, total avg loss: 0.2794, batch size: 41
2021-10-14 03:13:09,821 INFO [train.py:451] Epoch 3, batch 3210, batch avg loss 0.2184, total avg loss: 0.2707, batch size: 32
2021-10-14 03:13:14,523 INFO [train.py:451] Epoch 3, batch 3220, batch avg loss 0.3791, total avg loss: 0.2830, batch size: 119
2021-10-14 03:13:19,312 INFO [train.py:451] Epoch 3, batch 3230, batch avg loss 0.2695, total avg loss: 0.2889, batch size: 38
2021-10-14 03:13:24,241 INFO [train.py:451] Epoch 3, batch 3240, batch avg loss 0.2972, total avg loss: 0.2862, batch size: 39
2021-10-14 03:13:28,949 INFO [train.py:451] Epoch 3, batch 3250, batch avg loss 0.3210, total avg loss: 0.2863, batch size: 34
2021-10-14 03:13:33,815 INFO [train.py:451] Epoch 3, batch 3260, batch avg loss 0.3385, total avg loss: 0.2846, batch size: 134
2021-10-14 03:13:39,040 INFO [train.py:451] Epoch 3, batch 3270, batch avg loss 0.2426, total avg loss: 0.2776, batch size: 38
2021-10-14 03:13:43,980 INFO [train.py:451] Epoch 3, batch 3280, batch avg loss 0.2588, total avg loss: 0.2765, batch size: 33
2021-10-14 03:13:48,919 INFO [train.py:451] Epoch 3, batch 3290, batch avg loss 0.2615, total avg loss: 0.2745, batch size: 36
2021-10-14 03:13:53,753 INFO [train.py:451] Epoch 3, batch 3300, batch avg loss 0.3301, total avg loss: 0.2737, batch size: 37
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2021-10-14 03:14:03,681 INFO [train.py:451] Epoch 3, batch 3320, batch avg loss 0.3461, total avg loss: 0.2743, batch size: 42
2021-10-14 03:14:08,661 INFO [train.py:451] Epoch 3, batch 3330, batch avg loss 0.2706, total avg loss: 0.2749, batch size: 33
2021-10-14 03:14:13,486 INFO [train.py:451] Epoch 3, batch 3340, batch avg loss 0.2582, total avg loss: 0.2749, batch size: 28
2021-10-14 03:14:18,439 INFO [train.py:451] Epoch 3, batch 3350, batch avg loss 0.2547, total avg loss: 0.2747, batch size: 32
2021-10-14 03:14:23,333 INFO [train.py:451] Epoch 3, batch 3360, batch avg loss 0.2819, total avg loss: 0.2750, batch size: 34
2021-10-14 03:14:28,262 INFO [train.py:451] Epoch 3, batch 3370, batch avg loss 0.2692, total avg loss: 0.2749, batch size: 30
2021-10-14 03:14:33,334 INFO [train.py:451] Epoch 3, batch 3380, batch avg loss 0.2609, total avg loss: 0.2737, batch size: 33
2021-10-14 03:14:38,280 INFO [train.py:451] Epoch 3, batch 3390, batch avg loss 0.2771, total avg loss: 0.2735, batch size: 34
2021-10-14 03:14:43,140 INFO [train.py:451] Epoch 3, batch 3400, batch avg loss 0.2342, total avg loss: 0.2726, batch size: 33
2021-10-14 03:14:47,969 INFO [train.py:451] Epoch 3, batch 3410, batch avg loss 0.2697, total avg loss: 0.2885, batch size: 33
2021-10-14 03:14:52,929 INFO [train.py:451] Epoch 3, batch 3420, batch avg loss 0.2974, total avg loss: 0.2905, batch size: 38
2021-10-14 03:14:57,774 INFO [train.py:451] Epoch 3, batch 3430, batch avg loss 0.2848, total avg loss: 0.2829, batch size: 45
2021-10-14 03:15:02,566 INFO [train.py:451] Epoch 3, batch 3440, batch avg loss 0.2353, total avg loss: 0.2765, batch size: 36
2021-10-14 03:15:07,495 INFO [train.py:451] Epoch 3, batch 3450, batch avg loss 0.2206, total avg loss: 0.2733, batch size: 29
2021-10-14 03:15:12,403 INFO [train.py:451] Epoch 3, batch 3460, batch avg loss 0.2318, total avg loss: 0.2719, batch size: 29
2021-10-14 03:15:17,397 INFO [train.py:451] Epoch 3, batch 3470, batch avg loss 0.2739, total avg loss: 0.2735, batch size: 38
2021-10-14 03:15:22,474 INFO [train.py:451] Epoch 3, batch 3480, batch avg loss 0.2371, total avg loss: 0.2750, batch size: 30
2021-10-14 03:15:27,325 INFO [train.py:451] Epoch 3, batch 3490, batch avg loss 0.2980, total avg loss: 0.2754, batch size: 35
2021-10-14 03:15:32,325 INFO [train.py:451] Epoch 3, batch 3500, batch avg loss 0.2544, total avg loss: 0.2753, batch size: 30
2021-10-14 03:15:37,185 INFO [train.py:451] Epoch 3, batch 3510, batch avg loss 0.3173, total avg loss: 0.2770, batch size: 30
2021-10-14 03:15:42,044 INFO [train.py:451] Epoch 3, batch 3520, batch avg loss 0.2719, total avg loss: 0.2773, batch size: 42
2021-10-14 03:15:47,020 INFO [train.py:451] Epoch 3, batch 3530, batch avg loss 0.2729, total avg loss: 0.2777, batch size: 39
2021-10-14 03:15:52,004 INFO [train.py:451] Epoch 3, batch 3540, batch avg loss 0.2574, total avg loss: 0.2769, batch size: 34
2021-10-14 03:15:56,709 INFO [train.py:451] Epoch 3, batch 3550, batch avg loss 0.2935, total avg loss: 0.2770, batch size: 45
2021-10-14 03:16:01,514 INFO [train.py:451] Epoch 3, batch 3560, batch avg loss 0.3422, total avg loss: 0.2764, batch size: 72
2021-10-14 03:16:06,427 INFO [train.py:451] Epoch 3, batch 3570, batch avg loss 0.3224, total avg loss: 0.2754, batch size: 42
2021-10-14 03:16:11,297 INFO [train.py:451] Epoch 3, batch 3580, batch avg loss 0.2201, total avg loss: 0.2748, batch size: 30
2021-10-14 03:16:16,220 INFO [train.py:451] Epoch 3, batch 3590, batch avg loss 0.2633, total avg loss: 0.2741, batch size: 31
2021-10-14 03:16:21,056 INFO [train.py:451] Epoch 3, batch 3600, batch avg loss 0.3043, total avg loss: 0.2748, batch size: 57
2021-10-14 03:16:25,914 INFO [train.py:451] Epoch 3, batch 3610, batch avg loss 0.2579, total avg loss: 0.2927, batch size: 29
2021-10-14 03:16:30,899 INFO [train.py:451] Epoch 3, batch 3620, batch avg loss 0.2218, total avg loss: 0.2836, batch size: 27
2021-10-14 03:16:35,863 INFO [train.py:451] Epoch 3, batch 3630, batch avg loss 0.2943, total avg loss: 0.2827, batch size: 38
2021-10-14 03:16:40,773 INFO [train.py:451] Epoch 3, batch 3640, batch avg loss 0.3228, total avg loss: 0.2781, batch size: 42
2021-10-14 03:16:45,872 INFO [train.py:451] Epoch 3, batch 3650, batch avg loss 0.2517, total avg loss: 0.2751, batch size: 36
2021-10-14 03:16:50,948 INFO [train.py:451] Epoch 3, batch 3660, batch avg loss 0.2716, total avg loss: 0.2733, batch size: 36
2021-10-14 03:16:55,904 INFO [train.py:451] Epoch 3, batch 3670, batch avg loss 0.3612, total avg loss: 0.2725, batch size: 127
2021-10-14 03:17:00,874 INFO [train.py:451] Epoch 3, batch 3680, batch avg loss 0.2661, total avg loss: 0.2707, batch size: 33
2021-10-14 03:17:05,625 INFO [train.py:451] Epoch 3, batch 3690, batch avg loss 0.2820, total avg loss: 0.2720, batch size: 38
2021-10-14 03:17:10,477 INFO [train.py:451] Epoch 3, batch 3700, batch avg loss 0.3127, total avg loss: 0.2735, batch size: 42
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2021-10-14 03:17:25,230 INFO [train.py:451] Epoch 3, batch 3730, batch avg loss 0.3220, total avg loss: 0.2746, batch size: 38
2021-10-14 03:17:30,180 INFO [train.py:451] Epoch 3, batch 3740, batch avg loss 0.2984, total avg loss: 0.2738, batch size: 33
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2021-10-14 03:17:39,832 INFO [train.py:451] Epoch 3, batch 3760, batch avg loss 0.3083, total avg loss: 0.2745, batch size: 71
2021-10-14 03:17:44,700 INFO [train.py:451] Epoch 3, batch 3770, batch avg loss 0.2730, total avg loss: 0.2739, batch size: 42
2021-10-14 03:17:49,704 INFO [train.py:451] Epoch 3, batch 3780, batch avg loss 0.2563, total avg loss: 0.2740, batch size: 30
2021-10-14 03:17:54,579 INFO [train.py:451] Epoch 3, batch 3790, batch avg loss 0.2248, total avg loss: 0.2734, batch size: 28
2021-10-14 03:17:59,395 INFO [train.py:451] Epoch 3, batch 3800, batch avg loss 0.2328, total avg loss: 0.2744, batch size: 31
2021-10-14 03:18:04,388 INFO [train.py:451] Epoch 3, batch 3810, batch avg loss 0.3052, total avg loss: 0.2843, batch size: 57
2021-10-14 03:18:09,251 INFO [train.py:451] Epoch 3, batch 3820, batch avg loss 0.2032, total avg loss: 0.2785, batch size: 27
2021-10-14 03:18:14,005 INFO [train.py:451] Epoch 3, batch 3830, batch avg loss 0.3785, total avg loss: 0.2878, batch size: 133
2021-10-14 03:18:18,839 INFO [train.py:451] Epoch 3, batch 3840, batch avg loss 0.2455, total avg loss: 0.2858, batch size: 29
2021-10-14 03:18:23,719 INFO [train.py:451] Epoch 3, batch 3850, batch avg loss 0.2456, total avg loss: 0.2834, batch size: 35
2021-10-14 03:18:28,469 INFO [train.py:451] Epoch 3, batch 3860, batch avg loss 0.3426, total avg loss: 0.2838, batch size: 49
2021-10-14 03:18:33,356 INFO [train.py:451] Epoch 3, batch 3870, batch avg loss 0.2561, total avg loss: 0.2812, batch size: 36
2021-10-14 03:18:38,102 INFO [train.py:451] Epoch 3, batch 3880, batch avg loss 0.2378, total avg loss: 0.2819, batch size: 32
2021-10-14 03:18:42,953 INFO [train.py:451] Epoch 3, batch 3890, batch avg loss 0.2808, total avg loss: 0.2798, batch size: 34
2021-10-14 03:18:47,837 INFO [train.py:451] Epoch 3, batch 3900, batch avg loss 0.2468, total avg loss: 0.2780, batch size: 30
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2021-10-14 03:20:17,049 INFO [train.py:483] Epoch 3, valid loss 0.1977, best valid loss: 0.1971 best valid epoch: 3
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2021-10-14 03:29:08,387 INFO [train.py:483] Epoch 3, valid loss 0.1961, best valid loss: 0.1961 best valid epoch: 3
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2021-10-14 03:32:45,395 INFO [train.py:451] Epoch 3, batch 5440, batch avg loss 0.2749, total avg loss: 0.2816, batch size: 34
2021-10-14 03:32:50,246 INFO [train.py:451] Epoch 3, batch 5450, batch avg loss 0.2537, total avg loss: 0.2802, batch size: 41
2021-10-14 03:32:55,214 INFO [train.py:451] Epoch 3, batch 5460, batch avg loss 0.2727, total avg loss: 0.2760, batch size: 49
2021-10-14 03:33:00,200 INFO [train.py:451] Epoch 3, batch 5470, batch avg loss 0.2569, total avg loss: 0.2755, batch size: 42
2021-10-14 03:33:05,043 INFO [train.py:451] Epoch 3, batch 5480, batch avg loss 0.2337, total avg loss: 0.2738, batch size: 31
2021-10-14 03:33:10,084 INFO [train.py:451] Epoch 3, batch 5490, batch avg loss 0.2404, total avg loss: 0.2726, batch size: 29
2021-10-14 03:33:14,998 INFO [train.py:451] Epoch 3, batch 5500, batch avg loss 0.3387, total avg loss: 0.2735, batch size: 36
2021-10-14 03:33:19,764 INFO [train.py:451] Epoch 3, batch 5510, batch avg loss 0.2429, total avg loss: 0.2732, batch size: 32
2021-10-14 03:33:24,880 INFO [train.py:451] Epoch 3, batch 5520, batch avg loss 0.3326, total avg loss: 0.2726, batch size: 33
2021-10-14 03:33:30,021 INFO [train.py:451] Epoch 3, batch 5530, batch avg loss 0.3421, total avg loss: 0.2732, batch size: 38
2021-10-14 03:33:35,185 INFO [train.py:451] Epoch 3, batch 5540, batch avg loss 0.2432, total avg loss: 0.2719, batch size: 30
2021-10-14 03:33:40,031 INFO [train.py:451] Epoch 3, batch 5550, batch avg loss 0.3414, total avg loss: 0.2722, batch size: 72
2021-10-14 03:33:44,957 INFO [train.py:451] Epoch 3, batch 5560, batch avg loss 0.2459, total avg loss: 0.2723, batch size: 30
2021-10-14 03:33:49,914 INFO [train.py:451] Epoch 3, batch 5570, batch avg loss 0.2631, total avg loss: 0.2730, batch size: 33
2021-10-14 03:33:54,691 INFO [train.py:451] Epoch 3, batch 5580, batch avg loss 0.2513, total avg loss: 0.2740, batch size: 34
2021-10-14 03:33:59,337 INFO [train.py:451] Epoch 3, batch 5590, batch avg loss 0.4184, total avg loss: 0.2764, batch size: 126
2021-10-14 03:34:04,109 INFO [train.py:451] Epoch 3, batch 5600, batch avg loss 0.2883, total avg loss: 0.2765, batch size: 38
2021-10-14 03:34:09,072 INFO [train.py:451] Epoch 3, batch 5610, batch avg loss 0.2623, total avg loss: 0.2631, batch size: 33
2021-10-14 03:34:13,861 INFO [train.py:451] Epoch 3, batch 5620, batch avg loss 0.2646, total avg loss: 0.2765, batch size: 29
2021-10-14 03:34:18,990 INFO [train.py:451] Epoch 3, batch 5630, batch avg loss 0.2534, total avg loss: 0.2732, batch size: 34
2021-10-14 03:34:24,142 INFO [train.py:451] Epoch 3, batch 5640, batch avg loss 0.2775, total avg loss: 0.2704, batch size: 41
2021-10-14 03:34:29,174 INFO [train.py:451] Epoch 3, batch 5650, batch avg loss 0.2703, total avg loss: 0.2713, batch size: 33
2021-10-14 03:34:34,157 INFO [train.py:451] Epoch 3, batch 5660, batch avg loss 0.2518, total avg loss: 0.2723, batch size: 32
2021-10-14 03:34:39,041 INFO [train.py:451] Epoch 3, batch 5670, batch avg loss 0.2318, total avg loss: 0.2711, batch size: 32
2021-10-14 03:34:43,901 INFO [train.py:451] Epoch 3, batch 5680, batch avg loss 0.3041, total avg loss: 0.2710, batch size: 39
2021-10-14 03:34:48,803 INFO [train.py:451] Epoch 3, batch 5690, batch avg loss 0.2716, total avg loss: 0.2689, batch size: 34
2021-10-14 03:34:53,685 INFO [train.py:451] Epoch 3, batch 5700, batch avg loss 0.2332, total avg loss: 0.2706, batch size: 32
2021-10-14 03:34:58,629 INFO [train.py:451] Epoch 3, batch 5710, batch avg loss 0.2914, total avg loss: 0.2711, batch size: 45
2021-10-14 03:35:03,296 INFO [train.py:451] Epoch 3, batch 5720, batch avg loss 0.2568, total avg loss: 0.2717, batch size: 36
2021-10-14 03:35:08,269 INFO [train.py:451] Epoch 3, batch 5730, batch avg loss 0.2578, total avg loss: 0.2708, batch size: 36
2021-10-14 03:35:13,072 INFO [train.py:451] Epoch 3, batch 5740, batch avg loss 0.2596, total avg loss: 0.2725, batch size: 33
2021-10-14 03:35:17,858 INFO [train.py:451] Epoch 3, batch 5750, batch avg loss 0.2185, total avg loss: 0.2731, batch size: 28
2021-10-14 03:35:22,660 INFO [train.py:451] Epoch 3, batch 5760, batch avg loss 0.2241, total avg loss: 0.2723, batch size: 34
2021-10-14 03:35:27,756 INFO [train.py:451] Epoch 3, batch 5770, batch avg loss 0.3020, total avg loss: 0.2717, batch size: 34
2021-10-14 03:35:32,819 INFO [train.py:451] Epoch 3, batch 5780, batch avg loss 0.2658, total avg loss: 0.2723, batch size: 32
2021-10-14 03:35:37,876 INFO [train.py:451] Epoch 3, batch 5790, batch avg loss 0.2311, total avg loss: 0.2724, batch size: 29
2021-10-14 03:35:42,921 INFO [train.py:451] Epoch 3, batch 5800, batch avg loss 0.3469, total avg loss: 0.2728, batch size: 42
2021-10-14 03:35:48,026 INFO [train.py:451] Epoch 3, batch 5810, batch avg loss 0.2944, total avg loss: 0.2677, batch size: 38
2021-10-14 03:35:52,998 INFO [train.py:451] Epoch 3, batch 5820, batch avg loss 0.2795, total avg loss: 0.2711, batch size: 41
2021-10-14 03:35:57,813 INFO [train.py:451] Epoch 3, batch 5830, batch avg loss 0.3050, total avg loss: 0.2731, batch size: 35
2021-10-14 03:36:02,602 INFO [train.py:451] Epoch 3, batch 5840, batch avg loss 0.3743, total avg loss: 0.2760, batch size: 128
2021-10-14 03:36:07,489 INFO [train.py:451] Epoch 3, batch 5850, batch avg loss 0.2856, total avg loss: 0.2768, batch size: 33
2021-10-14 03:36:12,278 INFO [train.py:451] Epoch 3, batch 5860, batch avg loss 0.2462, total avg loss: 0.2808, batch size: 35
2021-10-14 03:36:17,283 INFO [train.py:451] Epoch 3, batch 5870, batch avg loss 0.2258, total avg loss: 0.2783, batch size: 30
2021-10-14 03:36:22,363 INFO [train.py:451] Epoch 3, batch 5880, batch avg loss 0.2942, total avg loss: 0.2780, batch size: 35
2021-10-14 03:36:27,071 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "c4426a1c-2f1b-4c7b-b715-16f6bd7ac8cf" will not be mixed in.
2021-10-14 03:36:27,340 INFO [train.py:451] Epoch 3, batch 5890, batch avg loss 0.3020, total avg loss: 0.2789, batch size: 38
2021-10-14 03:36:32,395 INFO [train.py:451] Epoch 3, batch 5900, batch avg loss 0.2895, total avg loss: 0.2781, batch size: 49
2021-10-14 03:36:37,356 INFO [train.py:451] Epoch 3, batch 5910, batch avg loss 0.2622, total avg loss: 0.2774, batch size: 38
2021-10-14 03:36:42,378 INFO [train.py:451] Epoch 3, batch 5920, batch avg loss 0.2650, total avg loss: 0.2755, batch size: 38
2021-10-14 03:36:47,244 INFO [train.py:451] Epoch 3, batch 5930, batch avg loss 0.2325, total avg loss: 0.2744, batch size: 38
2021-10-14 03:36:52,229 INFO [train.py:451] Epoch 3, batch 5940, batch avg loss 0.2727, total avg loss: 0.2741, batch size: 39
2021-10-14 03:36:57,332 INFO [train.py:451] Epoch 3, batch 5950, batch avg loss 0.2807, total avg loss: 0.2738, batch size: 41
2021-10-14 03:37:02,449 INFO [train.py:451] Epoch 3, batch 5960, batch avg loss 0.2560, total avg loss: 0.2728, batch size: 32
2021-10-14 03:37:07,627 INFO [train.py:451] Epoch 3, batch 5970, batch avg loss 0.2700, total avg loss: 0.2715, batch size: 34
2021-10-14 03:37:12,543 INFO [train.py:451] Epoch 3, batch 5980, batch avg loss 0.3187, total avg loss: 0.2717, batch size: 56
2021-10-14 03:37:17,631 INFO [train.py:451] Epoch 3, batch 5990, batch avg loss 0.2583, total avg loss: 0.2707, batch size: 36
2021-10-14 03:37:22,985 INFO [train.py:451] Epoch 3, batch 6000, batch avg loss 0.2121, total avg loss: 0.2705, batch size: 31
2021-10-14 03:38:02,632 INFO [train.py:483] Epoch 3, valid loss 0.1953, best valid loss: 0.1953 best valid epoch: 3
2021-10-14 03:38:07,555 INFO [train.py:451] Epoch 3, batch 6010, batch avg loss 0.3150, total avg loss: 0.2913, batch size: 34
2021-10-14 03:38:12,623 INFO [train.py:451] Epoch 3, batch 6020, batch avg loss 0.3259, total avg loss: 0.2803, batch size: 38
2021-10-14 03:38:17,417 INFO [train.py:451] Epoch 3, batch 6030, batch avg loss 0.2162, total avg loss: 0.2830, batch size: 30
2021-10-14 03:38:22,452 INFO [train.py:451] Epoch 3, batch 6040, batch avg loss 0.1935, total avg loss: 0.2783, batch size: 28
2021-10-14 03:38:27,426 INFO [train.py:451] Epoch 3, batch 6050, batch avg loss 0.2727, total avg loss: 0.2791, batch size: 33
2021-10-14 03:38:32,189 INFO [train.py:451] Epoch 3, batch 6060, batch avg loss 0.4040, total avg loss: 0.2815, batch size: 131
2021-10-14 03:38:37,182 INFO [train.py:451] Epoch 3, batch 6070, batch avg loss 0.2697, total avg loss: 0.2776, batch size: 38
2021-10-14 03:38:42,098 INFO [train.py:451] Epoch 3, batch 6080, batch avg loss 0.2433, total avg loss: 0.2761, batch size: 33
2021-10-14 03:38:47,200 INFO [train.py:451] Epoch 3, batch 6090, batch avg loss 0.2648, total avg loss: 0.2758, batch size: 45
2021-10-14 03:38:52,153 INFO [train.py:451] Epoch 3, batch 6100, batch avg loss 0.2223, total avg loss: 0.2755, batch size: 32
2021-10-14 03:38:57,212 INFO [train.py:451] Epoch 3, batch 6110, batch avg loss 0.2441, total avg loss: 0.2755, batch size: 32
2021-10-14 03:39:02,135 INFO [train.py:451] Epoch 3, batch 6120, batch avg loss 0.2926, total avg loss: 0.2765, batch size: 34
2021-10-14 03:39:07,172 INFO [train.py:451] Epoch 3, batch 6130, batch avg loss 0.2990, total avg loss: 0.2765, batch size: 35
2021-10-14 03:39:12,258 INFO [train.py:451] Epoch 3, batch 6140, batch avg loss 0.2273, total avg loss: 0.2741, batch size: 29
2021-10-14 03:39:17,145 INFO [train.py:451] Epoch 3, batch 6150, batch avg loss 0.2673, total avg loss: 0.2749, batch size: 35
2021-10-14 03:39:22,167 INFO [train.py:451] Epoch 3, batch 6160, batch avg loss 0.2460, total avg loss: 0.2740, batch size: 36
2021-10-14 03:39:27,134 INFO [train.py:451] Epoch 3, batch 6170, batch avg loss 0.2778, total avg loss: 0.2741, batch size: 41
2021-10-14 03:39:32,073 INFO [train.py:451] Epoch 3, batch 6180, batch avg loss 0.2488, total avg loss: 0.2735, batch size: 45
2021-10-14 03:39:36,931 INFO [train.py:451] Epoch 3, batch 6190, batch avg loss 0.2606, total avg loss: 0.2740, batch size: 31
2021-10-14 03:39:41,844 INFO [train.py:451] Epoch 3, batch 6200, batch avg loss 0.3424, total avg loss: 0.2736, batch size: 41
2021-10-14 03:39:46,697 INFO [train.py:451] Epoch 3, batch 6210, batch avg loss 0.2259, total avg loss: 0.2671, batch size: 29
2021-10-14 03:39:51,474 INFO [train.py:451] Epoch 3, batch 6220, batch avg loss 0.2031, total avg loss: 0.2754, batch size: 34
2021-10-14 03:39:56,273 INFO [train.py:451] Epoch 3, batch 6230, batch avg loss 0.2295, total avg loss: 0.2741, batch size: 29
2021-10-14 03:40:01,198 INFO [train.py:451] Epoch 3, batch 6240, batch avg loss 0.2814, total avg loss: 0.2718, batch size: 29
2021-10-14 03:40:05,974 INFO [train.py:451] Epoch 3, batch 6250, batch avg loss 0.2363, total avg loss: 0.2703, batch size: 31
2021-10-14 03:40:10,918 INFO [train.py:451] Epoch 3, batch 6260, batch avg loss 0.2553, total avg loss: 0.2703, batch size: 31
2021-10-14 03:40:15,891 INFO [train.py:451] Epoch 3, batch 6270, batch avg loss 0.3416, total avg loss: 0.2684, batch size: 74
2021-10-14 03:40:20,972 INFO [train.py:451] Epoch 3, batch 6280, batch avg loss 0.2352, total avg loss: 0.2666, batch size: 31
2021-10-14 03:40:25,833 INFO [train.py:451] Epoch 3, batch 6290, batch avg loss 0.3154, total avg loss: 0.2666, batch size: 57
2021-10-14 03:40:30,916 INFO [train.py:451] Epoch 3, batch 6300, batch avg loss 0.3108, total avg loss: 0.2680, batch size: 39
2021-10-14 03:40:35,803 INFO [train.py:451] Epoch 3, batch 6310, batch avg loss 0.2401, total avg loss: 0.2715, batch size: 29
2021-10-14 03:40:40,761 INFO [train.py:451] Epoch 3, batch 6320, batch avg loss 0.2061, total avg loss: 0.2707, batch size: 29
2021-10-14 03:40:45,755 INFO [train.py:451] Epoch 3, batch 6330, batch avg loss 0.2259, total avg loss: 0.2702, batch size: 29
2021-10-14 03:40:50,786 INFO [train.py:451] Epoch 3, batch 6340, batch avg loss 0.2676, total avg loss: 0.2687, batch size: 37
2021-10-14 03:40:55,594 INFO [train.py:451] Epoch 3, batch 6350, batch avg loss 0.2521, total avg loss: 0.2690, batch size: 33
2021-10-14 03:41:00,515 INFO [train.py:451] Epoch 3, batch 6360, batch avg loss 0.2207, total avg loss: 0.2686, batch size: 32
2021-10-14 03:41:05,463 INFO [train.py:451] Epoch 3, batch 6370, batch avg loss 0.2845, total avg loss: 0.2696, batch size: 36
2021-10-14 03:41:10,476 INFO [train.py:451] Epoch 3, batch 6380, batch avg loss 0.3204, total avg loss: 0.2700, batch size: 35
2021-10-14 03:41:15,399 INFO [train.py:451] Epoch 3, batch 6390, batch avg loss 0.2453, total avg loss: 0.2698, batch size: 31
2021-10-14 03:41:20,198 INFO [train.py:451] Epoch 3, batch 6400, batch avg loss 0.2671, total avg loss: 0.2703, batch size: 28
2021-10-14 03:41:25,219 INFO [train.py:451] Epoch 3, batch 6410, batch avg loss 0.3021, total avg loss: 0.2621, batch size: 36
2021-10-14 03:41:30,194 INFO [train.py:451] Epoch 3, batch 6420, batch avg loss 0.2542, total avg loss: 0.2732, batch size: 29
2021-10-14 03:41:35,057 INFO [train.py:451] Epoch 3, batch 6430, batch avg loss 0.2551, total avg loss: 0.2737, batch size: 34
2021-10-14 03:41:39,986 INFO [train.py:451] Epoch 3, batch 6440, batch avg loss 0.2755, total avg loss: 0.2747, batch size: 35
2021-10-14 03:41:44,804 INFO [train.py:451] Epoch 3, batch 6450, batch avg loss 0.2931, total avg loss: 0.2779, batch size: 49
2021-10-14 03:41:49,666 INFO [train.py:451] Epoch 3, batch 6460, batch avg loss 0.2400, total avg loss: 0.2782, batch size: 37
2021-10-14 03:41:54,768 INFO [train.py:451] Epoch 3, batch 6470, batch avg loss 0.2424, total avg loss: 0.2766, batch size: 30
2021-10-14 03:41:59,777 INFO [train.py:451] Epoch 3, batch 6480, batch avg loss 0.3557, total avg loss: 0.2776, batch size: 35
2021-10-14 03:42:04,865 INFO [train.py:451] Epoch 3, batch 6490, batch avg loss 0.2776, total avg loss: 0.2742, batch size: 37
2021-10-14 03:42:10,065 INFO [train.py:451] Epoch 3, batch 6500, batch avg loss 0.2211, total avg loss: 0.2717, batch size: 28
2021-10-14 03:42:10,777 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "539ad732-8571-5496-c471-5ca72ac6ebcf" will not be mixed in.
2021-10-14 03:42:14,826 INFO [train.py:451] Epoch 3, batch 6510, batch avg loss 0.2264, total avg loss: 0.2722, batch size: 32
2021-10-14 03:42:19,847 INFO [train.py:451] Epoch 3, batch 6520, batch avg loss 0.2720, total avg loss: 0.2725, batch size: 30
2021-10-14 03:42:24,903 INFO [train.py:451] Epoch 3, batch 6530, batch avg loss 0.2522, total avg loss: 0.2708, batch size: 30
2021-10-14 03:42:29,870 INFO [train.py:451] Epoch 3, batch 6540, batch avg loss 0.2497, total avg loss: 0.2706, batch size: 31
2021-10-14 03:42:34,728 INFO [train.py:451] Epoch 3, batch 6550, batch avg loss 0.2268, total avg loss: 0.2714, batch size: 32
2021-10-14 03:42:39,596 INFO [train.py:451] Epoch 3, batch 6560, batch avg loss 0.2591, total avg loss: 0.2707, batch size: 35
2021-10-14 03:42:44,730 INFO [train.py:451] Epoch 3, batch 6570, batch avg loss 0.3066, total avg loss: 0.2713, batch size: 34
2021-10-14 03:42:49,673 INFO [train.py:451] Epoch 3, batch 6580, batch avg loss 0.3288, total avg loss: 0.2725, batch size: 34
2021-10-14 03:42:54,543 INFO [train.py:451] Epoch 3, batch 6590, batch avg loss 0.2331, total avg loss: 0.2728, batch size: 34
2021-10-14 03:42:59,621 INFO [train.py:451] Epoch 3, batch 6600, batch avg loss 0.2961, total avg loss: 0.2727, batch size: 35
2021-10-14 03:43:04,532 INFO [train.py:451] Epoch 3, batch 6610, batch avg loss 0.2909, total avg loss: 0.2911, batch size: 38
2021-10-14 03:43:09,512 INFO [train.py:451] Epoch 3, batch 6620, batch avg loss 0.3101, total avg loss: 0.2834, batch size: 36
2021-10-14 03:43:14,367 INFO [train.py:451] Epoch 3, batch 6630, batch avg loss 0.2540, total avg loss: 0.2783, batch size: 39
2021-10-14 03:43:19,131 INFO [train.py:451] Epoch 3, batch 6640, batch avg loss 0.2511, total avg loss: 0.2820, batch size: 34
2021-10-14 03:43:24,199 INFO [train.py:451] Epoch 3, batch 6650, batch avg loss 0.2736, total avg loss: 0.2773, batch size: 34
2021-10-14 03:43:29,015 INFO [train.py:451] Epoch 3, batch 6660, batch avg loss 0.2362, total avg loss: 0.2786, batch size: 32
2021-10-14 03:43:33,980 INFO [train.py:451] Epoch 3, batch 6670, batch avg loss 0.2837, total avg loss: 0.2778, batch size: 33
2021-10-14 03:43:38,901 INFO [train.py:451] Epoch 3, batch 6680, batch avg loss 0.2826, total avg loss: 0.2753, batch size: 49
2021-10-14 03:43:43,793 INFO [train.py:451] Epoch 3, batch 6690, batch avg loss 0.2965, total avg loss: 0.2739, batch size: 37
2021-10-14 03:43:48,701 INFO [train.py:451] Epoch 3, batch 6700, batch avg loss 0.2822, total avg loss: 0.2750, batch size: 39
2021-10-14 03:43:53,474 INFO [train.py:451] Epoch 3, batch 6710, batch avg loss 0.2084, total avg loss: 0.2764, batch size: 39
2021-10-14 03:43:58,493 INFO [train.py:451] Epoch 3, batch 6720, batch avg loss 0.3041, total avg loss: 0.2759, batch size: 38
2021-10-14 03:44:03,401 INFO [train.py:451] Epoch 3, batch 6730, batch avg loss 0.2884, total avg loss: 0.2764, batch size: 35
2021-10-14 03:44:08,420 INFO [train.py:451] Epoch 3, batch 6740, batch avg loss 0.2827, total avg loss: 0.2747, batch size: 32
2021-10-14 03:44:13,251 INFO [train.py:451] Epoch 3, batch 6750, batch avg loss 0.2295, total avg loss: 0.2763, batch size: 33
2021-10-14 03:44:17,973 INFO [train.py:451] Epoch 3, batch 6760, batch avg loss 0.3907, total avg loss: 0.2776, batch size: 127
2021-10-14 03:44:22,895 INFO [train.py:451] Epoch 3, batch 6770, batch avg loss 0.3738, total avg loss: 0.2771, batch size: 132
2021-10-14 03:44:27,836 INFO [train.py:451] Epoch 3, batch 6780, batch avg loss 0.2367, total avg loss: 0.2765, batch size: 34
2021-10-14 03:44:32,786 INFO [train.py:451] Epoch 3, batch 6790, batch avg loss 0.2787, total avg loss: 0.2759, batch size: 50
2021-10-14 03:44:37,778 INFO [train.py:451] Epoch 3, batch 6800, batch avg loss 0.2708, total avg loss: 0.2762, batch size: 30
2021-10-14 03:44:42,826 INFO [train.py:451] Epoch 3, batch 6810, batch avg loss 0.2967, total avg loss: 0.2642, batch size: 56
2021-10-14 03:44:48,111 INFO [train.py:451] Epoch 3, batch 6820, batch avg loss 0.2553, total avg loss: 0.2619, batch size: 32
2021-10-14 03:44:53,150 INFO [train.py:451] Epoch 3, batch 6830, batch avg loss 0.2646, total avg loss: 0.2655, batch size: 34
2021-10-14 03:44:58,066 INFO [train.py:451] Epoch 3, batch 6840, batch avg loss 0.2519, total avg loss: 0.2623, batch size: 32
2021-10-14 03:45:02,983 INFO [train.py:451] Epoch 3, batch 6850, batch avg loss 0.2330, total avg loss: 0.2642, batch size: 36
2021-10-14 03:45:07,924 INFO [train.py:451] Epoch 3, batch 6860, batch avg loss 0.2398, total avg loss: 0.2649, batch size: 29
2021-10-14 03:45:12,979 INFO [train.py:451] Epoch 3, batch 6870, batch avg loss 0.3311, total avg loss: 0.2659, batch size: 49
2021-10-14 03:45:14,143 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "e5717c36-0746-be7b-bab4-f99190b0ea49" will not be mixed in.
2021-10-14 03:45:18,059 INFO [train.py:451] Epoch 3, batch 6880, batch avg loss 0.2643, total avg loss: 0.2671, batch size: 37
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2021-10-14 03:46:56,691 INFO [train.py:483] Epoch 3, valid loss 0.1956, best valid loss: 0.1953 best valid epoch: 3
2021-10-14 03:47:01,426 INFO [train.py:451] Epoch 3, batch 7010, batch avg loss 0.2668, total avg loss: 0.2689, batch size: 35
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2021-10-14 03:50:08,672 INFO [train.py:451] Epoch 3, batch 7390, batch avg loss 0.3078, total avg loss: 0.2738, batch size: 32
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2021-10-14 03:54:50,217 INFO [train.py:451] Epoch 3, batch 7960, batch avg loss 0.2748, total avg loss: 0.2796, batch size: 38
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2021-10-14 03:55:00,169 INFO [train.py:451] Epoch 3, batch 7980, batch avg loss 0.1965, total avg loss: 0.2789, batch size: 27
2021-10-14 03:55:05,096 INFO [train.py:451] Epoch 3, batch 7990, batch avg loss 0.3938, total avg loss: 0.2788, batch size: 128
2021-10-14 03:55:10,045 INFO [train.py:451] Epoch 3, batch 8000, batch avg loss 0.2888, total avg loss: 0.2792, batch size: 35
2021-10-14 03:55:49,585 INFO [train.py:483] Epoch 3, valid loss 0.1951, best valid loss: 0.1951 best valid epoch: 3
2021-10-14 03:55:54,560 INFO [train.py:451] Epoch 3, batch 8010, batch avg loss 0.2877, total avg loss: 0.2954, batch size: 39
2021-10-14 03:55:59,548 INFO [train.py:451] Epoch 3, batch 8020, batch avg loss 0.2766, total avg loss: 0.2892, batch size: 39
2021-10-14 03:56:04,464 INFO [train.py:451] Epoch 3, batch 8030, batch avg loss 0.2538, total avg loss: 0.2813, batch size: 41
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2021-10-14 03:57:48,551 INFO [train.py:451] Epoch 3, batch 8240, batch avg loss 0.2502, total avg loss: 0.2806, batch size: 32
2021-10-14 03:57:53,305 INFO [train.py:451] Epoch 3, batch 8250, batch avg loss 0.2322, total avg loss: 0.2838, batch size: 32
2021-10-14 03:57:58,361 INFO [train.py:451] Epoch 3, batch 8260, batch avg loss 0.2680, total avg loss: 0.2806, batch size: 49
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2021-10-14 03:58:37,783 INFO [train.py:451] Epoch 3, batch 8340, batch avg loss 0.2389, total avg loss: 0.2753, batch size: 27
2021-10-14 03:58:42,865 INFO [train.py:451] Epoch 3, batch 8350, batch avg loss 0.3884, total avg loss: 0.2747, batch size: 129
2021-10-14 03:58:47,788 INFO [train.py:451] Epoch 3, batch 8360, batch avg loss 0.2657, total avg loss: 0.2749, batch size: 38
2021-10-14 03:58:59,928 INFO [train.py:451] Epoch 3, batch 8370, batch avg loss 0.2968, total avg loss: 0.2746, batch size: 33
2021-10-14 03:59:04,863 INFO [train.py:451] Epoch 3, batch 8380, batch avg loss 0.2194, total avg loss: 0.2728, batch size: 32
2021-10-14 03:59:09,730 INFO [train.py:451] Epoch 3, batch 8390, batch avg loss 0.2817, total avg loss: 0.2724, batch size: 49
2021-10-14 03:59:14,601 INFO [train.py:451] Epoch 3, batch 8400, batch avg loss 0.3132, total avg loss: 0.2730, batch size: 45
2021-10-14 03:59:19,340 INFO [train.py:451] Epoch 3, batch 8410, batch avg loss 0.2391, total avg loss: 0.2758, batch size: 40
2021-10-14 03:59:24,169 INFO [train.py:451] Epoch 3, batch 8420, batch avg loss 0.3111, total avg loss: 0.2825, batch size: 42
2021-10-14 03:59:29,055 INFO [train.py:451] Epoch 3, batch 8430, batch avg loss 0.2452, total avg loss: 0.2804, batch size: 34
2021-10-14 03:59:33,899 INFO [train.py:451] Epoch 3, batch 8440, batch avg loss 0.2406, total avg loss: 0.2756, batch size: 31
2021-10-14 03:59:38,933 INFO [train.py:451] Epoch 3, batch 8450, batch avg loss 0.2560, total avg loss: 0.2754, batch size: 29
2021-10-14 03:59:43,743 INFO [train.py:451] Epoch 3, batch 8460, batch avg loss 0.2749, total avg loss: 0.2795, batch size: 34
2021-10-14 03:59:48,581 INFO [train.py:451] Epoch 3, batch 8470, batch avg loss 0.2904, total avg loss: 0.2763, batch size: 39
2021-10-14 03:59:53,474 INFO [train.py:451] Epoch 3, batch 8480, batch avg loss 0.2486, total avg loss: 0.2745, batch size: 30
2021-10-14 03:59:58,508 INFO [train.py:451] Epoch 3, batch 8490, batch avg loss 0.3539, total avg loss: 0.2747, batch size: 37
2021-10-14 04:00:03,622 INFO [train.py:451] Epoch 3, batch 8500, batch avg loss 0.2033, total avg loss: 0.2737, batch size: 29
2021-10-14 04:00:08,648 INFO [train.py:451] Epoch 3, batch 8510, batch avg loss 0.2168, total avg loss: 0.2717, batch size: 30
2021-10-14 04:00:13,579 INFO [train.py:451] Epoch 3, batch 8520, batch avg loss 0.2086, total avg loss: 0.2711, batch size: 32
2021-10-14 04:00:18,450 INFO [train.py:451] Epoch 3, batch 8530, batch avg loss 0.2472, total avg loss: 0.2717, batch size: 40
2021-10-14 04:00:29,980 INFO [train.py:451] Epoch 3, batch 8540, batch avg loss 0.2569, total avg loss: 0.2722, batch size: 31
2021-10-14 04:00:34,799 INFO [train.py:451] Epoch 3, batch 8550, batch avg loss 0.2789, total avg loss: 0.2732, batch size: 30
2021-10-14 04:00:39,778 INFO [train.py:451] Epoch 3, batch 8560, batch avg loss 0.2902, total avg loss: 0.2724, batch size: 45
2021-10-14 04:00:44,768 INFO [train.py:451] Epoch 3, batch 8570, batch avg loss 0.2341, total avg loss: 0.2720, batch size: 31
2021-10-14 04:00:49,645 INFO [train.py:451] Epoch 3, batch 8580, batch avg loss 0.2517, total avg loss: 0.2726, batch size: 35
2021-10-14 04:00:54,621 INFO [train.py:451] Epoch 3, batch 8590, batch avg loss 0.2407, total avg loss: 0.2717, batch size: 30
2021-10-14 04:00:59,229 INFO [train.py:451] Epoch 3, batch 8600, batch avg loss 0.2688, total avg loss: 0.2728, batch size: 38
2021-10-14 04:01:04,119 INFO [train.py:451] Epoch 3, batch 8610, batch avg loss 0.2601, total avg loss: 0.2591, batch size: 42
2021-10-14 04:01:09,087 INFO [train.py:451] Epoch 3, batch 8620, batch avg loss 0.3194, total avg loss: 0.2638, batch size: 36
2021-10-14 04:01:13,982 INFO [train.py:451] Epoch 3, batch 8630, batch avg loss 0.2370, total avg loss: 0.2599, batch size: 42
2021-10-14 04:01:18,875 INFO [train.py:451] Epoch 3, batch 8640, batch avg loss 0.2506, total avg loss: 0.2629, batch size: 31
2021-10-14 04:01:23,824 INFO [train.py:451] Epoch 3, batch 8650, batch avg loss 0.2541, total avg loss: 0.2662, batch size: 34
2021-10-14 04:01:28,788 INFO [train.py:451] Epoch 3, batch 8660, batch avg loss 0.1767, total avg loss: 0.2636, batch size: 28
2021-10-14 04:01:33,882 INFO [train.py:451] Epoch 3, batch 8670, batch avg loss 0.2640, total avg loss: 0.2649, batch size: 34
2021-10-14 04:01:38,643 INFO [train.py:451] Epoch 3, batch 8680, batch avg loss 0.2624, total avg loss: 0.2647, batch size: 49
2021-10-14 04:01:43,475 INFO [train.py:451] Epoch 3, batch 8690, batch avg loss 0.2131, total avg loss: 0.2648, batch size: 28
2021-10-14 04:01:48,453 INFO [train.py:451] Epoch 3, batch 8700, batch avg loss 0.3203, total avg loss: 0.2664, batch size: 33
2021-10-14 04:01:53,382 INFO [train.py:451] Epoch 3, batch 8710, batch avg loss 0.2661, total avg loss: 0.2673, batch size: 41
2021-10-14 04:01:58,149 INFO [train.py:451] Epoch 3, batch 8720, batch avg loss 0.2000, total avg loss: 0.2684, batch size: 29
2021-10-14 04:02:03,123 INFO [train.py:451] Epoch 3, batch 8730, batch avg loss 0.3031, total avg loss: 0.2706, batch size: 34
2021-10-14 04:02:08,000 INFO [train.py:451] Epoch 3, batch 8740, batch avg loss 0.3385, total avg loss: 0.2705, batch size: 37
2021-10-14 04:02:12,847 INFO [train.py:451] Epoch 3, batch 8750, batch avg loss 0.2658, total avg loss: 0.2709, batch size: 57
2021-10-14 04:02:17,741 INFO [train.py:451] Epoch 3, batch 8760, batch avg loss 0.3091, total avg loss: 0.2712, batch size: 49
2021-10-14 04:02:22,704 INFO [train.py:451] Epoch 3, batch 8770, batch avg loss 0.2666, total avg loss: 0.2711, batch size: 35
2021-10-14 04:02:27,670 INFO [train.py:451] Epoch 3, batch 8780, batch avg loss 0.2796, total avg loss: 0.2711, batch size: 35
2021-10-14 04:02:32,592 INFO [train.py:451] Epoch 3, batch 8790, batch avg loss 0.1878, total avg loss: 0.2705, batch size: 30
2021-10-14 04:02:37,509 INFO [train.py:451] Epoch 3, batch 8800, batch avg loss 0.2058, total avg loss: 0.2702, batch size: 29
2021-10-14 04:02:42,333 INFO [train.py:451] Epoch 3, batch 8810, batch avg loss 0.2181, total avg loss: 0.2839, batch size: 29
2021-10-14 04:02:47,217 INFO [train.py:451] Epoch 3, batch 8820, batch avg loss 0.2516, total avg loss: 0.2706, batch size: 31
2021-10-14 04:02:52,141 INFO [train.py:451] Epoch 3, batch 8830, batch avg loss 0.3152, total avg loss: 0.2719, batch size: 31
2021-10-14 04:02:56,851 INFO [train.py:451] Epoch 3, batch 8840, batch avg loss 0.2146, total avg loss: 0.2698, batch size: 31
2021-10-14 04:03:01,486 INFO [train.py:451] Epoch 3, batch 8850, batch avg loss 0.2619, total avg loss: 0.2732, batch size: 34
2021-10-14 04:03:02,601 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "417e9819-81fa-26be-cd7e-5e9dbfe9a593" will not be mixed in.
2021-10-14 04:03:06,310 INFO [train.py:451] Epoch 3, batch 8860, batch avg loss 0.2761, total avg loss: 0.2730, batch size: 35
2021-10-14 04:03:11,268 INFO [train.py:451] Epoch 3, batch 8870, batch avg loss 0.2262, total avg loss: 0.2717, batch size: 31
2021-10-14 04:03:16,271 INFO [train.py:451] Epoch 3, batch 8880, batch avg loss 0.2390, total avg loss: 0.2715, batch size: 27
2021-10-14 04:03:21,079 INFO [train.py:451] Epoch 3, batch 8890, batch avg loss 0.3070, total avg loss: 0.2723, batch size: 36
2021-10-14 04:03:25,984 INFO [train.py:451] Epoch 3, batch 8900, batch avg loss 0.2480, total avg loss: 0.2733, batch size: 37
2021-10-14 04:03:30,753 INFO [train.py:451] Epoch 3, batch 8910, batch avg loss 0.2123, total avg loss: 0.2723, batch size: 30
2021-10-14 04:03:35,691 INFO [train.py:451] Epoch 3, batch 8920, batch avg loss 0.2174, total avg loss: 0.2715, batch size: 36
2021-10-14 04:03:40,479 INFO [train.py:451] Epoch 3, batch 8930, batch avg loss 0.2239, total avg loss: 0.2715, batch size: 45
2021-10-14 04:03:45,404 INFO [train.py:451] Epoch 3, batch 8940, batch avg loss 0.3634, total avg loss: 0.2727, batch size: 131
2021-10-14 04:03:50,166 INFO [train.py:451] Epoch 3, batch 8950, batch avg loss 0.2918, total avg loss: 0.2731, batch size: 36
2021-10-14 04:03:54,887 INFO [train.py:451] Epoch 3, batch 8960, batch avg loss 0.2356, total avg loss: 0.2737, batch size: 30
2021-10-14 04:03:59,823 INFO [train.py:451] Epoch 3, batch 8970, batch avg loss 0.4573, total avg loss: 0.2743, batch size: 133
2021-10-14 04:04:04,648 INFO [train.py:451] Epoch 3, batch 8980, batch avg loss 0.2609, total avg loss: 0.2742, batch size: 42
2021-10-14 04:04:09,479 INFO [train.py:451] Epoch 3, batch 8990, batch avg loss 0.3040, total avg loss: 0.2736, batch size: 42
2021-10-14 04:04:14,355 INFO [train.py:451] Epoch 3, batch 9000, batch avg loss 0.2249, total avg loss: 0.2730, batch size: 29
2021-10-14 04:04:53,340 INFO [train.py:483] Epoch 3, valid loss 0.1948, best valid loss: 0.1948 best valid epoch: 3
2021-10-14 04:04:58,217 INFO [train.py:451] Epoch 3, batch 9010, batch avg loss 0.2550, total avg loss: 0.2862, batch size: 33
2021-10-14 04:05:03,126 INFO [train.py:451] Epoch 3, batch 9020, batch avg loss 0.2925, total avg loss: 0.2876, batch size: 32
2021-10-14 04:05:08,039 INFO [train.py:451] Epoch 3, batch 9030, batch avg loss 0.2808, total avg loss: 0.2768, batch size: 36
2021-10-14 04:05:13,034 INFO [train.py:451] Epoch 3, batch 9040, batch avg loss 0.2654, total avg loss: 0.2708, batch size: 38
2021-10-14 04:05:18,003 INFO [train.py:451] Epoch 3, batch 9050, batch avg loss 0.2785, total avg loss: 0.2714, batch size: 37
2021-10-14 04:05:22,833 INFO [train.py:451] Epoch 3, batch 9060, batch avg loss 0.3072, total avg loss: 0.2720, batch size: 45
2021-10-14 04:05:27,649 INFO [train.py:451] Epoch 3, batch 9070, batch avg loss 0.2674, total avg loss: 0.2707, batch size: 39
2021-10-14 04:05:32,524 INFO [train.py:451] Epoch 3, batch 9080, batch avg loss 0.3034, total avg loss: 0.2707, batch size: 34
2021-10-14 04:05:37,496 INFO [train.py:451] Epoch 3, batch 9090, batch avg loss 0.2458, total avg loss: 0.2696, batch size: 38
2021-10-14 04:05:42,207 INFO [train.py:451] Epoch 3, batch 9100, batch avg loss 0.2594, total avg loss: 0.2684, batch size: 32
2021-10-14 04:05:46,976 INFO [train.py:451] Epoch 3, batch 9110, batch avg loss 0.2508, total avg loss: 0.2687, batch size: 38
2021-10-14 04:05:51,947 INFO [train.py:451] Epoch 3, batch 9120, batch avg loss 0.2136, total avg loss: 0.2668, batch size: 29
2021-10-14 04:05:56,924 INFO [train.py:451] Epoch 3, batch 9130, batch avg loss 0.2242, total avg loss: 0.2665, batch size: 29
2021-10-14 04:06:01,570 INFO [train.py:451] Epoch 3, batch 9140, batch avg loss 0.2137, total avg loss: 0.2680, batch size: 30
2021-10-14 04:06:06,409 INFO [train.py:451] Epoch 3, batch 9150, batch avg loss 0.3829, total avg loss: 0.2690, batch size: 130
2021-10-14 04:06:11,303 INFO [train.py:451] Epoch 3, batch 9160, batch avg loss 0.2596, total avg loss: 0.2681, batch size: 31
2021-10-14 04:06:16,259 INFO [train.py:451] Epoch 3, batch 9170, batch avg loss 0.2784, total avg loss: 0.2693, batch size: 29
2021-10-14 04:06:21,216 INFO [train.py:451] Epoch 3, batch 9180, batch avg loss 0.2951, total avg loss: 0.2692, batch size: 37
2021-10-14 04:06:26,294 INFO [train.py:451] Epoch 3, batch 9190, batch avg loss 0.2630, total avg loss: 0.2688, batch size: 30
2021-10-14 04:06:31,345 INFO [train.py:451] Epoch 3, batch 9200, batch avg loss 0.2265, total avg loss: 0.2686, batch size: 27
2021-10-14 04:06:36,072 INFO [train.py:451] Epoch 3, batch 9210, batch avg loss 0.2996, total avg loss: 0.2896, batch size: 34
2021-10-14 04:06:40,919 INFO [train.py:451] Epoch 3, batch 9220, batch avg loss 0.2482, total avg loss: 0.2840, batch size: 33
2021-10-14 04:06:45,811 INFO [train.py:451] Epoch 3, batch 9230, batch avg loss 0.3004, total avg loss: 0.2863, batch size: 42
2021-10-14 04:06:50,783 INFO [train.py:451] Epoch 3, batch 9240, batch avg loss 0.2559, total avg loss: 0.2763, batch size: 39
2021-10-14 04:06:55,551 INFO [train.py:451] Epoch 3, batch 9250, batch avg loss 0.2311, total avg loss: 0.2767, batch size: 30
2021-10-14 04:07:00,400 INFO [train.py:451] Epoch 3, batch 9260, batch avg loss 0.2541, total avg loss: 0.2768, batch size: 42
2021-10-14 04:07:05,374 INFO [train.py:451] Epoch 3, batch 9270, batch avg loss 0.3340, total avg loss: 0.2775, batch size: 36
2021-10-14 04:07:10,284 INFO [train.py:451] Epoch 3, batch 9280, batch avg loss 0.2252, total avg loss: 0.2777, batch size: 32
2021-10-14 04:07:15,113 INFO [train.py:451] Epoch 3, batch 9290, batch avg loss 0.2859, total avg loss: 0.2789, batch size: 49
2021-10-14 04:07:19,960 INFO [train.py:451] Epoch 3, batch 9300, batch avg loss 0.3063, total avg loss: 0.2795, batch size: 34
2021-10-14 04:07:24,791 INFO [train.py:451] Epoch 3, batch 9310, batch avg loss 0.2907, total avg loss: 0.2801, batch size: 38
2021-10-14 04:07:29,542 INFO [train.py:451] Epoch 3, batch 9320, batch avg loss 0.2660, total avg loss: 0.2814, batch size: 38
2021-10-14 04:07:34,567 INFO [train.py:451] Epoch 3, batch 9330, batch avg loss 0.3391, total avg loss: 0.2794, batch size: 49
2021-10-14 04:07:46,710 INFO [train.py:451] Epoch 3, batch 9340, batch avg loss 0.2454, total avg loss: 0.2779, batch size: 38
2021-10-14 04:07:51,860 INFO [train.py:451] Epoch 3, batch 9350, batch avg loss 0.2179, total avg loss: 0.2763, batch size: 34
2021-10-14 04:07:56,828 INFO [train.py:451] Epoch 3, batch 9360, batch avg loss 0.2197, total avg loss: 0.2750, batch size: 31
2021-10-14 04:08:01,694 INFO [train.py:451] Epoch 3, batch 9370, batch avg loss 0.2998, total avg loss: 0.2747, batch size: 35
2021-10-14 04:08:06,476 INFO [train.py:451] Epoch 3, batch 9380, batch avg loss 0.2447, total avg loss: 0.2733, batch size: 32
2021-10-14 04:08:11,404 INFO [train.py:451] Epoch 3, batch 9390, batch avg loss 0.2153, total avg loss: 0.2729, batch size: 29
2021-10-14 04:08:16,222 INFO [train.py:451] Epoch 3, batch 9400, batch avg loss 0.2331, total avg loss: 0.2729, batch size: 30
2021-10-14 04:08:20,995 INFO [train.py:451] Epoch 3, batch 9410, batch avg loss 0.2969, total avg loss: 0.2802, batch size: 37
2021-10-14 04:08:25,851 INFO [train.py:451] Epoch 3, batch 9420, batch avg loss 0.2754, total avg loss: 0.2790, batch size: 34
2021-10-14 04:08:30,603 INFO [train.py:451] Epoch 3, batch 9430, batch avg loss 0.2756, total avg loss: 0.2810, batch size: 42
2021-10-14 04:08:35,505 INFO [train.py:451] Epoch 3, batch 9440, batch avg loss 0.3155, total avg loss: 0.2776, batch size: 34
2021-10-14 04:08:40,401 INFO [train.py:451] Epoch 3, batch 9450, batch avg loss 0.2592, total avg loss: 0.2753, batch size: 30
2021-10-14 04:08:45,267 INFO [train.py:451] Epoch 3, batch 9460, batch avg loss 0.3078, total avg loss: 0.2758, batch size: 73
2021-10-14 04:08:50,174 INFO [train.py:451] Epoch 3, batch 9470, batch avg loss 0.2928, total avg loss: 0.2754, batch size: 35
2021-10-14 04:08:54,946 INFO [train.py:451] Epoch 3, batch 9480, batch avg loss 0.3026, total avg loss: 0.2758, batch size: 34
2021-10-14 04:08:59,716 INFO [train.py:451] Epoch 3, batch 9490, batch avg loss 0.2737, total avg loss: 0.2772, batch size: 35
2021-10-14 04:09:04,710 INFO [train.py:451] Epoch 3, batch 9500, batch avg loss 0.2451, total avg loss: 0.2750, batch size: 33
2021-10-14 04:09:09,595 INFO [train.py:451] Epoch 3, batch 9510, batch avg loss 0.2312, total avg loss: 0.2738, batch size: 30
2021-10-14 04:09:14,315 INFO [train.py:451] Epoch 3, batch 9520, batch avg loss 0.2269, total avg loss: 0.2762, batch size: 30
2021-10-14 04:09:19,149 INFO [train.py:451] Epoch 3, batch 9530, batch avg loss 0.2685, total avg loss: 0.2753, batch size: 36
2021-10-14 04:09:23,927 INFO [train.py:451] Epoch 3, batch 9540, batch avg loss 0.2920, total avg loss: 0.2757, batch size: 38
2021-10-14 04:09:28,806 INFO [train.py:451] Epoch 3, batch 9550, batch avg loss 0.2745, total avg loss: 0.2766, batch size: 34
2021-10-14 04:09:33,921 INFO [train.py:451] Epoch 3, batch 9560, batch avg loss 0.2844, total avg loss: 0.2759, batch size: 31
2021-10-14 04:09:38,950 INFO [train.py:451] Epoch 3, batch 9570, batch avg loss 0.2418, total avg loss: 0.2744, batch size: 31
2021-10-14 04:09:44,028 INFO [train.py:451] Epoch 3, batch 9580, batch avg loss 0.2019, total avg loss: 0.2732, batch size: 35
2021-10-14 04:09:48,903 INFO [train.py:451] Epoch 3, batch 9590, batch avg loss 0.2426, total avg loss: 0.2735, batch size: 35
2021-10-14 04:09:53,954 INFO [train.py:451] Epoch 3, batch 9600, batch avg loss 0.2870, total avg loss: 0.2734, batch size: 34
2021-10-14 04:09:58,836 INFO [train.py:451] Epoch 3, batch 9610, batch avg loss 0.2643, total avg loss: 0.2826, batch size: 34
2021-10-14 04:10:03,729 INFO [train.py:451] Epoch 3, batch 9620, batch avg loss 0.2694, total avg loss: 0.2848, batch size: 27
2021-10-14 04:10:08,605 INFO [train.py:451] Epoch 3, batch 9630, batch avg loss 0.2724, total avg loss: 0.2852, batch size: 41
2021-10-14 04:10:13,597 INFO [train.py:451] Epoch 3, batch 9640, batch avg loss 0.2889, total avg loss: 0.2801, batch size: 39
2021-10-14 04:10:18,371 INFO [train.py:451] Epoch 3, batch 9650, batch avg loss 0.2982, total avg loss: 0.2845, batch size: 72
2021-10-14 04:10:23,276 INFO [train.py:451] Epoch 3, batch 9660, batch avg loss 0.3046, total avg loss: 0.2828, batch size: 49
2021-10-14 04:10:28,168 INFO [train.py:451] Epoch 3, batch 9670, batch avg loss 0.2784, total avg loss: 0.2819, batch size: 34
2021-10-14 04:10:33,298 INFO [train.py:451] Epoch 3, batch 9680, batch avg loss 0.2723, total avg loss: 0.2768, batch size: 33
2021-10-14 04:10:38,104 INFO [train.py:451] Epoch 3, batch 9690, batch avg loss 0.2446, total avg loss: 0.2764, batch size: 42
2021-10-14 04:10:42,991 INFO [train.py:451] Epoch 3, batch 9700, batch avg loss 0.2574, total avg loss: 0.2769, batch size: 34
2021-10-14 04:10:47,960 INFO [train.py:451] Epoch 3, batch 9710, batch avg loss 0.2301, total avg loss: 0.2749, batch size: 31
2021-10-14 04:10:52,986 INFO [train.py:451] Epoch 3, batch 9720, batch avg loss 0.2522, total avg loss: 0.2727, batch size: 31
2021-10-14 04:10:57,854 INFO [train.py:451] Epoch 3, batch 9730, batch avg loss 0.3224, total avg loss: 0.2713, batch size: 49
2021-10-14 04:11:02,766 INFO [train.py:451] Epoch 3, batch 9740, batch avg loss 0.3346, total avg loss: 0.2711, batch size: 56
2021-10-14 04:11:07,580 INFO [train.py:451] Epoch 3, batch 9750, batch avg loss 0.2401, total avg loss: 0.2708, batch size: 33
2021-10-14 04:11:12,672 INFO [train.py:451] Epoch 3, batch 9760, batch avg loss 0.2770, total avg loss: 0.2688, batch size: 42
2021-10-14 04:11:17,687 INFO [train.py:451] Epoch 3, batch 9770, batch avg loss 0.2329, total avg loss: 0.2693, batch size: 27
2021-10-14 04:11:22,770 INFO [train.py:451] Epoch 3, batch 9780, batch avg loss 0.2634, total avg loss: 0.2686, batch size: 34
2021-10-14 04:11:27,439 INFO [train.py:451] Epoch 3, batch 9790, batch avg loss 0.4135, total avg loss: 0.2694, batch size: 127
2021-10-14 04:11:32,155 INFO [train.py:451] Epoch 3, batch 9800, batch avg loss 0.3198, total avg loss: 0.2709, batch size: 49
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2021-10-14 04:13:47,043 INFO [train.py:483] Epoch 3, valid loss 0.1954, best valid loss: 0.1948 best valid epoch: 3
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2021-10-14 04:22:37,746 INFO [train.py:483] Epoch 3, valid loss 0.1938, best valid loss: 0.1938 best valid epoch: 3
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2021-10-14 04:31:29,049 INFO [train.py:483] Epoch 3, valid loss 0.1934, best valid loss: 0.1934 best valid epoch: 3
2021-10-14 04:31:33,819 INFO [train.py:451] Epoch 3, batch 12010, batch avg loss 0.2592, total avg loss: 0.2807, batch size: 33
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2021-10-14 04:33:27,517 INFO [train.py:451] Epoch 3, batch 12240, batch avg loss 0.2450, total avg loss: 0.2612, batch size: 33
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2021-10-14 04:34:30,890 INFO [train.py:451] Epoch 3, batch 12370, batch avg loss 0.2698, total avg loss: 0.2739, batch size: 38
2021-10-14 04:34:35,818 INFO [train.py:451] Epoch 3, batch 12380, batch avg loss 0.2641, total avg loss: 0.2745, batch size: 34
2021-10-14 04:34:40,969 INFO [train.py:451] Epoch 3, batch 12390, batch avg loss 0.2143, total avg loss: 0.2732, batch size: 32
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2021-10-14 04:35:05,842 INFO [train.py:451] Epoch 3, batch 12440, batch avg loss 0.2302, total avg loss: 0.2650, batch size: 30
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2021-10-14 04:35:35,646 INFO [train.py:451] Epoch 3, batch 12500, batch avg loss 0.2935, total avg loss: 0.2684, batch size: 35
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2021-10-14 04:35:45,298 INFO [train.py:451] Epoch 3, batch 12520, batch avg loss 0.4092, total avg loss: 0.2697, batch size: 137
2021-10-14 04:35:50,168 INFO [train.py:451] Epoch 3, batch 12530, batch avg loss 0.2492, total avg loss: 0.2721, batch size: 29
2021-10-14 04:35:54,985 INFO [train.py:451] Epoch 3, batch 12540, batch avg loss 0.3382, total avg loss: 0.2743, batch size: 37
2021-10-14 04:36:00,252 INFO [train.py:451] Epoch 3, batch 12550, batch avg loss 0.2225, total avg loss: 0.2717, batch size: 27
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2021-10-14 04:36:15,363 INFO [train.py:451] Epoch 3, batch 12580, batch avg loss 0.2447, total avg loss: 0.2717, batch size: 29
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2021-10-14 04:36:23,418 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "66af0c2b-509b-8b82-007e-1034f4f27c38" will not be mixed in.
2021-10-14 04:36:25,205 INFO [train.py:451] Epoch 3, batch 12600, batch avg loss 0.2311, total avg loss: 0.2720, batch size: 38
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2021-10-14 04:37:04,792 INFO [train.py:451] Epoch 3, batch 12680, batch avg loss 0.2565, total avg loss: 0.2730, batch size: 34
2021-10-14 04:37:09,687 INFO [train.py:451] Epoch 3, batch 12690, batch avg loss 0.2064, total avg loss: 0.2734, batch size: 31
2021-10-14 04:37:14,663 INFO [train.py:451] Epoch 3, batch 12700, batch avg loss 0.2778, total avg loss: 0.2734, batch size: 34
2021-10-14 04:37:19,581 INFO [train.py:451] Epoch 3, batch 12710, batch avg loss 0.2319, total avg loss: 0.2719, batch size: 32
2021-10-14 04:37:24,625 INFO [train.py:451] Epoch 3, batch 12720, batch avg loss 0.2732, total avg loss: 0.2712, batch size: 33
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2021-10-14 04:49:12,384 INFO [train.py:483] Epoch 3, valid loss 0.1927, best valid loss: 0.1927 best valid epoch: 3
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2021-10-14 04:58:00,631 INFO [train.py:483] Epoch 3, valid loss 0.1918, best valid loss: 0.1918 best valid epoch: 3
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2021-10-14 05:15:48,065 INFO [train.py:483] Epoch 3, valid loss 0.1918, best valid loss: 0.1918 best valid epoch: 3
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2021-10-14 05:17:22,437 INFO [train.py:451] Epoch 3, batch 17190, batch avg loss 0.3000, total avg loss: 0.2705, batch size: 36
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2021-10-14 05:17:30,471 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "75778823-8aa1-15e6-1a1f-73e57b39f158" will not be mixed in.
2021-10-14 05:17:32,249 INFO [train.py:451] Epoch 3, batch 17210, batch avg loss 0.2202, total avg loss: 0.2507, batch size: 29
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2021-10-14 05:18:11,812 INFO [train.py:451] Epoch 3, batch 17290, batch avg loss 0.2666, total avg loss: 0.2698, batch size: 32
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2021-10-14 05:19:08,143 INFO [train.py:451] Epoch 3, batch 17390, batch avg loss 0.3211, total avg loss: 0.2715, batch size: 33
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2021-10-14 05:19:17,832 INFO [train.py:451] Epoch 3, batch 17410, batch avg loss 0.2658, total avg loss: 0.2889, batch size: 38
2021-10-14 05:19:22,925 INFO [train.py:451] Epoch 3, batch 17420, batch avg loss 0.3241, total avg loss: 0.2770, batch size: 72
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2021-10-14 05:19:33,008 INFO [train.py:451] Epoch 3, batch 17440, batch avg loss 0.3818, total avg loss: 0.2778, batch size: 123
2021-10-14 05:19:37,976 INFO [train.py:451] Epoch 3, batch 17450, batch avg loss 0.2870, total avg loss: 0.2765, batch size: 36
2021-10-14 05:19:42,843 INFO [train.py:451] Epoch 3, batch 17460, batch avg loss 0.3710, total avg loss: 0.2781, batch size: 131
2021-10-14 05:19:47,798 INFO [train.py:451] Epoch 3, batch 17470, batch avg loss 0.2265, total avg loss: 0.2765, batch size: 32
2021-10-14 05:19:52,645 INFO [train.py:451] Epoch 3, batch 17480, batch avg loss 0.2235, total avg loss: 0.2762, batch size: 30
2021-10-14 05:19:57,612 INFO [train.py:451] Epoch 3, batch 17490, batch avg loss 0.2236, total avg loss: 0.2743, batch size: 32
2021-10-14 05:20:02,386 INFO [train.py:451] Epoch 3, batch 17500, batch avg loss 0.2551, total avg loss: 0.2732, batch size: 34
2021-10-14 05:20:07,392 INFO [train.py:451] Epoch 3, batch 17510, batch avg loss 0.2168, total avg loss: 0.2709, batch size: 30
2021-10-14 05:20:12,411 INFO [train.py:451] Epoch 3, batch 17520, batch avg loss 0.2467, total avg loss: 0.2699, batch size: 27
2021-10-14 05:20:17,617 INFO [train.py:451] Epoch 3, batch 17530, batch avg loss 0.2655, total avg loss: 0.2701, batch size: 34
2021-10-14 05:20:22,537 INFO [train.py:451] Epoch 3, batch 17540, batch avg loss 0.2614, total avg loss: 0.2693, batch size: 34
2021-10-14 05:20:27,534 INFO [train.py:451] Epoch 3, batch 17550, batch avg loss 0.2731, total avg loss: 0.2681, batch size: 38
2021-10-14 05:20:32,482 INFO [train.py:451] Epoch 3, batch 17560, batch avg loss 0.2788, total avg loss: 0.2690, batch size: 45
2021-10-14 05:20:37,353 INFO [train.py:451] Epoch 3, batch 17570, batch avg loss 0.2414, total avg loss: 0.2681, batch size: 28
2021-10-14 05:20:42,250 INFO [train.py:451] Epoch 3, batch 17580, batch avg loss 0.3158, total avg loss: 0.2685, batch size: 36
2021-10-14 05:20:47,071 INFO [train.py:451] Epoch 3, batch 17590, batch avg loss 0.2651, total avg loss: 0.2689, batch size: 38
2021-10-14 05:20:51,993 INFO [train.py:451] Epoch 3, batch 17600, batch avg loss 0.2830, total avg loss: 0.2688, batch size: 31
2021-10-14 05:20:56,770 INFO [train.py:451] Epoch 3, batch 17610, batch avg loss 0.2242, total avg loss: 0.2782, batch size: 32
2021-10-14 05:21:01,783 INFO [train.py:451] Epoch 3, batch 17620, batch avg loss 0.2323, total avg loss: 0.2758, batch size: 30
2021-10-14 05:21:06,763 INFO [train.py:451] Epoch 3, batch 17630, batch avg loss 0.4505, total avg loss: 0.2774, batch size: 131
2021-10-14 05:21:11,567 INFO [train.py:451] Epoch 3, batch 17640, batch avg loss 0.3181, total avg loss: 0.2774, batch size: 38
2021-10-14 05:21:16,567 INFO [train.py:451] Epoch 3, batch 17650, batch avg loss 0.2715, total avg loss: 0.2766, batch size: 33
2021-10-14 05:21:21,397 INFO [train.py:451] Epoch 3, batch 17660, batch avg loss 0.2577, total avg loss: 0.2761, batch size: 45
2021-10-14 05:21:26,415 INFO [train.py:451] Epoch 3, batch 17670, batch avg loss 0.3312, total avg loss: 0.2729, batch size: 74
2021-10-14 05:21:31,191 INFO [train.py:451] Epoch 3, batch 17680, batch avg loss 0.2978, total avg loss: 0.2732, batch size: 39
2021-10-14 05:21:35,959 INFO [train.py:451] Epoch 3, batch 17690, batch avg loss 0.2720, total avg loss: 0.2731, batch size: 38
2021-10-14 05:21:41,023 INFO [train.py:451] Epoch 3, batch 17700, batch avg loss 0.2755, total avg loss: 0.2716, batch size: 36
2021-10-14 05:21:45,880 INFO [train.py:451] Epoch 3, batch 17710, batch avg loss 0.2546, total avg loss: 0.2711, batch size: 34
2021-10-14 05:21:50,855 INFO [train.py:451] Epoch 3, batch 17720, batch avg loss 0.2785, total avg loss: 0.2699, batch size: 42
2021-10-14 05:21:55,693 INFO [train.py:451] Epoch 3, batch 17730, batch avg loss 0.2178, total avg loss: 0.2705, batch size: 30
2021-10-14 05:22:00,720 INFO [train.py:451] Epoch 3, batch 17740, batch avg loss 0.2683, total avg loss: 0.2705, batch size: 30
2021-10-14 05:22:05,441 INFO [train.py:451] Epoch 3, batch 17750, batch avg loss 0.2339, total avg loss: 0.2713, batch size: 31
2021-10-14 05:22:10,401 INFO [train.py:451] Epoch 3, batch 17760, batch avg loss 0.2555, total avg loss: 0.2697, batch size: 31
2021-10-14 05:22:15,182 INFO [train.py:451] Epoch 3, batch 17770, batch avg loss 0.2700, total avg loss: 0.2690, batch size: 49
2021-10-14 05:22:20,373 INFO [train.py:451] Epoch 3, batch 17780, batch avg loss 0.2148, total avg loss: 0.2668, batch size: 29
2021-10-14 05:22:25,289 INFO [train.py:451] Epoch 3, batch 17790, batch avg loss 0.2154, total avg loss: 0.2668, batch size: 31
2021-10-14 05:22:30,191 INFO [train.py:451] Epoch 3, batch 17800, batch avg loss 0.2689, total avg loss: 0.2672, batch size: 33
2021-10-14 05:22:35,176 INFO [train.py:451] Epoch 3, batch 17810, batch avg loss 0.2537, total avg loss: 0.2580, batch size: 33
2021-10-14 05:22:40,073 INFO [train.py:451] Epoch 3, batch 17820, batch avg loss 0.2303, total avg loss: 0.2565, batch size: 31
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2021-10-14 05:23:00,077 INFO [train.py:451] Epoch 3, batch 17860, batch avg loss 0.3113, total avg loss: 0.2636, batch size: 73
2021-10-14 05:23:05,049 INFO [train.py:451] Epoch 3, batch 17870, batch avg loss 0.2248, total avg loss: 0.2638, batch size: 31
2021-10-14 05:23:09,907 INFO [train.py:451] Epoch 3, batch 17880, batch avg loss 0.2785, total avg loss: 0.2626, batch size: 45
2021-10-14 05:23:14,800 INFO [train.py:451] Epoch 3, batch 17890, batch avg loss 0.2153, total avg loss: 0.2627, batch size: 27
2021-10-14 05:23:19,670 INFO [train.py:451] Epoch 3, batch 17900, batch avg loss 0.2317, total avg loss: 0.2632, batch size: 30
2021-10-14 05:23:24,536 INFO [train.py:451] Epoch 3, batch 17910, batch avg loss 0.2528, total avg loss: 0.2648, batch size: 36
2021-10-14 05:23:29,725 INFO [train.py:451] Epoch 3, batch 17920, batch avg loss 0.2436, total avg loss: 0.2649, batch size: 33
2021-10-14 05:23:34,455 INFO [train.py:451] Epoch 3, batch 17930, batch avg loss 0.2386, total avg loss: 0.2664, batch size: 31
2021-10-14 05:23:39,345 INFO [train.py:451] Epoch 3, batch 17940, batch avg loss 0.2717, total avg loss: 0.2669, batch size: 34
2021-10-14 05:23:44,419 INFO [train.py:451] Epoch 3, batch 17950, batch avg loss 0.2393, total avg loss: 0.2665, batch size: 27
2021-10-14 05:23:49,374 INFO [train.py:451] Epoch 3, batch 17960, batch avg loss 0.3049, total avg loss: 0.2686, batch size: 36
2021-10-14 05:23:54,342 INFO [train.py:451] Epoch 3, batch 17970, batch avg loss 0.2256, total avg loss: 0.2679, batch size: 31
2021-10-14 05:23:59,487 INFO [train.py:451] Epoch 3, batch 17980, batch avg loss 0.2852, total avg loss: 0.2672, batch size: 38
2021-10-14 05:24:04,429 INFO [train.py:451] Epoch 3, batch 17990, batch avg loss 0.2628, total avg loss: 0.2672, batch size: 41
2021-10-14 05:24:09,381 INFO [train.py:451] Epoch 3, batch 18000, batch avg loss 0.3184, total avg loss: 0.2677, batch size: 35
2021-10-14 05:24:47,106 INFO [train.py:483] Epoch 3, valid loss 0.1919, best valid loss: 0.1918 best valid epoch: 3
2021-10-14 05:24:51,960 INFO [train.py:451] Epoch 3, batch 18010, batch avg loss 0.3262, total avg loss: 0.2707, batch size: 42
2021-10-14 05:24:56,859 INFO [train.py:451] Epoch 3, batch 18020, batch avg loss 0.2328, total avg loss: 0.2651, batch size: 33
2021-10-14 05:25:01,960 INFO [train.py:451] Epoch 3, batch 18030, batch avg loss 0.2343, total avg loss: 0.2588, batch size: 33
2021-10-14 05:25:06,719 INFO [train.py:451] Epoch 3, batch 18040, batch avg loss 0.2861, total avg loss: 0.2673, batch size: 33
2021-10-14 05:25:11,610 INFO [train.py:451] Epoch 3, batch 18050, batch avg loss 0.2386, total avg loss: 0.2707, batch size: 38
2021-10-14 05:25:16,667 INFO [train.py:451] Epoch 3, batch 18060, batch avg loss 0.2411, total avg loss: 0.2723, batch size: 34
2021-10-14 05:25:21,482 INFO [train.py:451] Epoch 3, batch 18070, batch avg loss 0.2156, total avg loss: 0.2741, batch size: 29
2021-10-14 05:25:26,296 INFO [train.py:451] Epoch 3, batch 18080, batch avg loss 0.1984, total avg loss: 0.2715, batch size: 29
2021-10-14 05:25:31,251 INFO [train.py:451] Epoch 3, batch 18090, batch avg loss 0.3071, total avg loss: 0.2727, batch size: 35
2021-10-14 05:25:36,240 INFO [train.py:451] Epoch 3, batch 18100, batch avg loss 0.2801, total avg loss: 0.2705, batch size: 42
2021-10-14 05:25:41,181 INFO [train.py:451] Epoch 3, batch 18110, batch avg loss 0.2570, total avg loss: 0.2714, batch size: 33
2021-10-14 05:25:46,167 INFO [train.py:451] Epoch 3, batch 18120, batch avg loss 0.2742, total avg loss: 0.2708, batch size: 36
2021-10-14 05:25:51,070 INFO [train.py:451] Epoch 3, batch 18130, batch avg loss 0.3117, total avg loss: 0.2707, batch size: 57
2021-10-14 05:25:56,036 INFO [train.py:451] Epoch 3, batch 18140, batch avg loss 0.2483, total avg loss: 0.2708, batch size: 32
2021-10-14 05:26:00,980 INFO [train.py:451] Epoch 3, batch 18150, batch avg loss 0.2184, total avg loss: 0.2706, batch size: 29
2021-10-14 05:26:05,860 INFO [train.py:451] Epoch 3, batch 18160, batch avg loss 0.3198, total avg loss: 0.2712, batch size: 45
2021-10-14 05:26:10,992 INFO [train.py:451] Epoch 3, batch 18170, batch avg loss 0.2456, total avg loss: 0.2717, batch size: 32
2021-10-14 05:26:15,907 INFO [train.py:451] Epoch 3, batch 18180, batch avg loss 0.2570, total avg loss: 0.2711, batch size: 39
2021-10-14 05:26:20,775 INFO [train.py:451] Epoch 3, batch 18190, batch avg loss 0.2876, total avg loss: 0.2704, batch size: 38
2021-10-14 05:26:25,729 INFO [train.py:451] Epoch 3, batch 18200, batch avg loss 0.2848, total avg loss: 0.2691, batch size: 35
2021-10-14 05:26:30,608 INFO [train.py:451] Epoch 3, batch 18210, batch avg loss 0.2532, total avg loss: 0.2683, batch size: 39
2021-10-14 05:26:35,775 INFO [train.py:451] Epoch 3, batch 18220, batch avg loss 0.2063, total avg loss: 0.2535, batch size: 29
2021-10-14 05:26:40,702 INFO [train.py:451] Epoch 3, batch 18230, batch avg loss 0.2876, total avg loss: 0.2589, batch size: 39
2021-10-14 05:26:45,600 INFO [train.py:451] Epoch 3, batch 18240, batch avg loss 0.2183, total avg loss: 0.2643, batch size: 32
2021-10-14 05:26:50,359 INFO [train.py:451] Epoch 3, batch 18250, batch avg loss 0.2458, total avg loss: 0.2694, batch size: 45
2021-10-14 05:26:55,563 INFO [train.py:451] Epoch 3, batch 18260, batch avg loss 0.2797, total avg loss: 0.2694, batch size: 33
2021-10-14 05:27:00,421 INFO [train.py:451] Epoch 3, batch 18270, batch avg loss 0.2792, total avg loss: 0.2677, batch size: 39
2021-10-14 05:27:05,413 INFO [train.py:451] Epoch 3, batch 18280, batch avg loss 0.3117, total avg loss: 0.2682, batch size: 45
2021-10-14 05:27:10,534 INFO [train.py:451] Epoch 3, batch 18290, batch avg loss 0.2544, total avg loss: 0.2670, batch size: 31
2021-10-14 05:27:15,607 INFO [train.py:451] Epoch 3, batch 18300, batch avg loss 0.1949, total avg loss: 0.2657, batch size: 29
2021-10-14 05:27:20,795 INFO [train.py:451] Epoch 3, batch 18310, batch avg loss 0.2467, total avg loss: 0.2649, batch size: 28
2021-10-14 05:27:25,809 INFO [train.py:451] Epoch 3, batch 18320, batch avg loss 0.3235, total avg loss: 0.2651, batch size: 48
2021-10-14 05:27:30,684 INFO [train.py:451] Epoch 3, batch 18330, batch avg loss 0.2285, total avg loss: 0.2667, batch size: 32
2021-10-14 05:27:35,579 INFO [train.py:451] Epoch 3, batch 18340, batch avg loss 0.2475, total avg loss: 0.2662, batch size: 32
2021-10-14 05:27:40,860 INFO [train.py:451] Epoch 3, batch 18350, batch avg loss 0.2780, total avg loss: 0.2660, batch size: 33
2021-10-14 05:27:45,841 INFO [train.py:451] Epoch 3, batch 18360, batch avg loss 0.2552, total avg loss: 0.2668, batch size: 32
2021-10-14 05:27:50,792 INFO [train.py:451] Epoch 3, batch 18370, batch avg loss 0.2651, total avg loss: 0.2663, batch size: 38
2021-10-14 05:27:55,822 INFO [train.py:451] Epoch 3, batch 18380, batch avg loss 0.3152, total avg loss: 0.2669, batch size: 73
2021-10-14 05:28:00,711 INFO [train.py:451] Epoch 3, batch 18390, batch avg loss 0.3785, total avg loss: 0.2672, batch size: 129
2021-10-14 05:28:05,703 INFO [train.py:451] Epoch 3, batch 18400, batch avg loss 0.2661, total avg loss: 0.2680, batch size: 31
2021-10-14 05:28:10,732 INFO [train.py:451] Epoch 3, batch 18410, batch avg loss 0.2445, total avg loss: 0.2691, batch size: 27
2021-10-14 05:28:15,685 INFO [train.py:451] Epoch 3, batch 18420, batch avg loss 0.3243, total avg loss: 0.2660, batch size: 56
2021-10-14 05:28:20,491 INFO [train.py:451] Epoch 3, batch 18430, batch avg loss 0.3072, total avg loss: 0.2666, batch size: 73
2021-10-14 05:28:25,270 INFO [train.py:451] Epoch 3, batch 18440, batch avg loss 0.2406, total avg loss: 0.2694, batch size: 57
2021-10-14 05:28:30,031 INFO [train.py:451] Epoch 3, batch 18450, batch avg loss 0.3131, total avg loss: 0.2684, batch size: 57
2021-10-14 05:28:34,887 INFO [train.py:451] Epoch 3, batch 18460, batch avg loss 0.3524, total avg loss: 0.2692, batch size: 126
2021-10-14 05:28:39,884 INFO [train.py:451] Epoch 3, batch 18470, batch avg loss 0.2878, total avg loss: 0.2684, batch size: 45
2021-10-14 05:28:44,763 INFO [train.py:451] Epoch 3, batch 18480, batch avg loss 0.2314, total avg loss: 0.2700, batch size: 27
2021-10-14 05:28:49,670 INFO [train.py:451] Epoch 3, batch 18490, batch avg loss 0.2985, total avg loss: 0.2687, batch size: 37
2021-10-14 05:28:54,546 INFO [train.py:451] Epoch 3, batch 18500, batch avg loss 0.2880, total avg loss: 0.2701, batch size: 49
2021-10-14 05:28:59,663 INFO [train.py:451] Epoch 3, batch 18510, batch avg loss 0.2841, total avg loss: 0.2689, batch size: 35
2021-10-14 05:29:04,555 INFO [train.py:451] Epoch 3, batch 18520, batch avg loss 0.1934, total avg loss: 0.2687, batch size: 30
2021-10-14 05:29:09,439 INFO [train.py:451] Epoch 3, batch 18530, batch avg loss 0.2377, total avg loss: 0.2693, batch size: 31
2021-10-14 05:29:14,423 INFO [train.py:451] Epoch 3, batch 18540, batch avg loss 0.2355, total avg loss: 0.2689, batch size: 30
2021-10-14 05:29:19,401 INFO [train.py:451] Epoch 3, batch 18550, batch avg loss 0.3463, total avg loss: 0.2686, batch size: 73
2021-10-14 05:29:24,416 INFO [train.py:451] Epoch 3, batch 18560, batch avg loss 0.3978, total avg loss: 0.2699, batch size: 130
2021-10-14 05:29:29,371 INFO [train.py:451] Epoch 3, batch 18570, batch avg loss 0.2529, total avg loss: 0.2686, batch size: 34
2021-10-14 05:29:34,201 INFO [train.py:451] Epoch 3, batch 18580, batch avg loss 0.2807, total avg loss: 0.2685, batch size: 49
2021-10-14 05:29:39,095 INFO [train.py:451] Epoch 3, batch 18590, batch avg loss 0.2890, total avg loss: 0.2685, batch size: 38
2021-10-14 05:29:44,054 INFO [train.py:451] Epoch 3, batch 18600, batch avg loss 0.2584, total avg loss: 0.2688, batch size: 35
2021-10-14 05:29:48,936 INFO [train.py:451] Epoch 3, batch 18610, batch avg loss 0.2619, total avg loss: 0.2758, batch size: 38
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2021-10-14 05:33:39,851 INFO [train.py:483] Epoch 3, valid loss 0.1906, best valid loss: 0.1906 best valid epoch: 3
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2021-10-14 05:42:31,033 INFO [train.py:483] Epoch 3, valid loss 0.1909, best valid loss: 0.1906 best valid epoch: 3
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2021-10-14 05:52:59,621 INFO [checkpoint.py:62] Saving checkpoint to tdnn_lstm_ctc-v2/exp/epoch-3.pt
2021-10-14 05:53:00,453 INFO [train.py:564] epoch 4, lr: 0.00025
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2021-10-14 06:10:45,666 INFO [train.py:483] Epoch 4, valid loss 0.1895, best valid loss: 0.1893 best valid epoch: 3
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2021-10-14 06:19:35,672 INFO [train.py:483] Epoch 4, valid loss 0.1899, best valid loss: 0.1893 best valid epoch: 3
2021-10-14 06:19:40,765 INFO [train.py:451] Epoch 4, batch 3010, batch avg loss 0.2545, total avg loss: 0.2567, batch size: 35
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2021-10-14 06:22:18,910 INFO [train.py:451] Epoch 4, batch 3330, batch avg loss 0.3231, total avg loss: 0.2607, batch size: 36
2021-10-14 06:22:24,326 INFO [train.py:451] Epoch 4, batch 3340, batch avg loss 0.2556, total avg loss: 0.2601, batch size: 33
2021-10-14 06:22:29,400 INFO [train.py:451] Epoch 4, batch 3350, batch avg loss 0.2437, total avg loss: 0.2600, batch size: 29
2021-10-14 06:22:34,223 INFO [train.py:451] Epoch 4, batch 3360, batch avg loss 0.2608, total avg loss: 0.2602, batch size: 38
2021-10-14 06:22:39,381 INFO [train.py:451] Epoch 4, batch 3370, batch avg loss 0.2323, total avg loss: 0.2594, batch size: 31
2021-10-14 06:22:44,451 INFO [train.py:451] Epoch 4, batch 3380, batch avg loss 0.2241, total avg loss: 0.2584, batch size: 31
2021-10-14 06:22:49,364 INFO [train.py:451] Epoch 4, batch 3390, batch avg loss 0.2235, total avg loss: 0.2590, batch size: 29
2021-10-14 06:22:54,152 INFO [train.py:451] Epoch 4, batch 3400, batch avg loss 0.2921, total avg loss: 0.2600, batch size: 72
2021-10-14 06:22:58,836 INFO [train.py:451] Epoch 4, batch 3410, batch avg loss 0.2709, total avg loss: 0.2620, batch size: 45
2021-10-14 06:23:03,511 INFO [train.py:451] Epoch 4, batch 3420, batch avg loss 0.2271, total avg loss: 0.2746, batch size: 30
2021-10-14 06:23:08,527 INFO [train.py:451] Epoch 4, batch 3430, batch avg loss 0.2796, total avg loss: 0.2693, batch size: 45
2021-10-14 06:23:13,503 INFO [train.py:451] Epoch 4, batch 3440, batch avg loss 0.2134, total avg loss: 0.2641, batch size: 30
2021-10-14 06:23:18,368 INFO [train.py:451] Epoch 4, batch 3450, batch avg loss 0.2412, total avg loss: 0.2639, batch size: 31
2021-10-14 06:23:23,285 INFO [train.py:451] Epoch 4, batch 3460, batch avg loss 0.2230, total avg loss: 0.2612, batch size: 32
2021-10-14 06:23:28,247 INFO [train.py:451] Epoch 4, batch 3470, batch avg loss 0.3024, total avg loss: 0.2581, batch size: 39
2021-10-14 06:23:33,188 INFO [train.py:451] Epoch 4, batch 3480, batch avg loss 0.2723, total avg loss: 0.2613, batch size: 39
2021-10-14 06:23:38,092 INFO [train.py:451] Epoch 4, batch 3490, batch avg loss 0.3386, total avg loss: 0.2646, batch size: 38
2021-10-14 06:23:42,848 INFO [train.py:451] Epoch 4, batch 3500, batch avg loss 0.2538, total avg loss: 0.2664, batch size: 34
2021-10-14 06:23:47,581 INFO [train.py:451] Epoch 4, batch 3510, batch avg loss 0.3038, total avg loss: 0.2685, batch size: 38
2021-10-14 06:23:52,379 INFO [train.py:451] Epoch 4, batch 3520, batch avg loss 0.2475, total avg loss: 0.2678, batch size: 38
2021-10-14 06:23:57,458 INFO [train.py:451] Epoch 4, batch 3530, batch avg loss 0.2547, total avg loss: 0.2661, batch size: 39
2021-10-14 06:24:02,368 INFO [train.py:451] Epoch 4, batch 3540, batch avg loss 0.2563, total avg loss: 0.2657, batch size: 29
2021-10-14 06:24:07,200 INFO [train.py:451] Epoch 4, batch 3550, batch avg loss 0.3020, total avg loss: 0.2662, batch size: 36
2021-10-14 06:24:12,098 INFO [train.py:451] Epoch 4, batch 3560, batch avg loss 0.2405, total avg loss: 0.2657, batch size: 29
2021-10-14 06:24:13,306 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "72f21e3d-5b46-81fd-bc46-24f00a704500" will not be mixed in.
2021-10-14 06:24:17,027 INFO [train.py:451] Epoch 4, batch 3570, batch avg loss 0.2946, total avg loss: 0.2650, batch size: 39
2021-10-14 06:24:21,908 INFO [train.py:451] Epoch 4, batch 3580, batch avg loss 0.2949, total avg loss: 0.2645, batch size: 34
2021-10-14 06:24:26,931 INFO [train.py:451] Epoch 4, batch 3590, batch avg loss 0.2980, total avg loss: 0.2639, batch size: 35
2021-10-14 06:24:31,754 INFO [train.py:451] Epoch 4, batch 3600, batch avg loss 0.2288, total avg loss: 0.2631, batch size: 34
2021-10-14 06:24:36,511 INFO [train.py:451] Epoch 4, batch 3610, batch avg loss 0.3731, total avg loss: 0.2628, batch size: 132
2021-10-14 06:24:41,384 INFO [train.py:451] Epoch 4, batch 3620, batch avg loss 0.2767, total avg loss: 0.2645, batch size: 42
2021-10-14 06:24:46,319 INFO [train.py:451] Epoch 4, batch 3630, batch avg loss 0.2507, total avg loss: 0.2659, batch size: 37
2021-10-14 06:24:51,209 INFO [train.py:451] Epoch 4, batch 3640, batch avg loss 0.2867, total avg loss: 0.2713, batch size: 36
2021-10-14 06:24:56,130 INFO [train.py:451] Epoch 4, batch 3650, batch avg loss 0.2707, total avg loss: 0.2714, batch size: 29
2021-10-14 06:25:01,030 INFO [train.py:451] Epoch 4, batch 3660, batch avg loss 0.2516, total avg loss: 0.2688, batch size: 39
2021-10-14 06:25:05,884 INFO [train.py:451] Epoch 4, batch 3670, batch avg loss 0.2498, total avg loss: 0.2688, batch size: 34
2021-10-14 06:25:10,806 INFO [train.py:451] Epoch 4, batch 3680, batch avg loss 0.3128, total avg loss: 0.2678, batch size: 35
2021-10-14 06:25:15,659 INFO [train.py:451] Epoch 4, batch 3690, batch avg loss 0.2527, total avg loss: 0.2668, batch size: 33
2021-10-14 06:25:20,494 INFO [train.py:451] Epoch 4, batch 3700, batch avg loss 0.3020, total avg loss: 0.2664, batch size: 42
2021-10-14 06:25:25,421 INFO [train.py:451] Epoch 4, batch 3710, batch avg loss 0.2342, total avg loss: 0.2666, batch size: 31
2021-10-14 06:25:30,408 INFO [train.py:451] Epoch 4, batch 3720, batch avg loss 0.2207, total avg loss: 0.2650, batch size: 31
2021-10-14 06:25:35,175 INFO [train.py:451] Epoch 4, batch 3730, batch avg loss 0.2538, total avg loss: 0.2659, batch size: 40
2021-10-14 06:25:40,120 INFO [train.py:451] Epoch 4, batch 3740, batch avg loss 0.2508, total avg loss: 0.2685, batch size: 29
2021-10-14 06:25:45,004 INFO [train.py:451] Epoch 4, batch 3750, batch avg loss 0.2144, total avg loss: 0.2678, batch size: 28
2021-10-14 06:25:49,785 INFO [train.py:451] Epoch 4, batch 3760, batch avg loss 0.2723, total avg loss: 0.2675, batch size: 45
2021-10-14 06:25:54,536 INFO [train.py:451] Epoch 4, batch 3770, batch avg loss 0.2746, total avg loss: 0.2679, batch size: 36
2021-10-14 06:25:59,462 INFO [train.py:451] Epoch 4, batch 3780, batch avg loss 0.2648, total avg loss: 0.2684, batch size: 30
2021-10-14 06:26:04,337 INFO [train.py:451] Epoch 4, batch 3790, batch avg loss 0.1945, total avg loss: 0.2681, batch size: 30
2021-10-14 06:26:09,046 INFO [train.py:451] Epoch 4, batch 3800, batch avg loss 0.2838, total avg loss: 0.2695, batch size: 49
2021-10-14 06:26:14,006 INFO [train.py:451] Epoch 4, batch 3810, batch avg loss 0.2749, total avg loss: 0.2709, batch size: 33
2021-10-14 06:26:18,897 INFO [train.py:451] Epoch 4, batch 3820, batch avg loss 0.1950, total avg loss: 0.2761, batch size: 29
2021-10-14 06:26:23,772 INFO [train.py:451] Epoch 4, batch 3830, batch avg loss 0.3252, total avg loss: 0.2789, batch size: 39
2021-10-14 06:26:28,703 INFO [train.py:451] Epoch 4, batch 3840, batch avg loss 0.2905, total avg loss: 0.2754, batch size: 42
2021-10-14 06:26:33,641 INFO [train.py:451] Epoch 4, batch 3850, batch avg loss 0.2100, total avg loss: 0.2731, batch size: 29
2021-10-14 06:26:38,587 INFO [train.py:451] Epoch 4, batch 3860, batch avg loss 0.2617, total avg loss: 0.2741, batch size: 49
2021-10-14 06:26:43,483 INFO [train.py:451] Epoch 4, batch 3870, batch avg loss 0.2386, total avg loss: 0.2721, batch size: 38
2021-10-14 06:26:48,231 INFO [train.py:451] Epoch 4, batch 3880, batch avg loss 0.2896, total avg loss: 0.2713, batch size: 41
2021-10-14 06:26:53,064 INFO [train.py:451] Epoch 4, batch 3890, batch avg loss 0.2521, total avg loss: 0.2709, batch size: 41
2021-10-14 06:26:57,975 INFO [train.py:451] Epoch 4, batch 3900, batch avg loss 0.2154, total avg loss: 0.2713, batch size: 27
2021-10-14 06:27:02,984 INFO [train.py:451] Epoch 4, batch 3910, batch avg loss 0.2260, total avg loss: 0.2710, batch size: 33
2021-10-14 06:27:07,794 INFO [train.py:451] Epoch 4, batch 3920, batch avg loss 0.2399, total avg loss: 0.2713, batch size: 39
2021-10-14 06:27:12,639 INFO [train.py:451] Epoch 4, batch 3930, batch avg loss 0.2186, total avg loss: 0.2725, batch size: 32
2021-10-14 06:27:17,327 INFO [train.py:451] Epoch 4, batch 3940, batch avg loss 0.3717, total avg loss: 0.2749, batch size: 132
2021-10-14 06:27:22,200 INFO [train.py:451] Epoch 4, batch 3950, batch avg loss 0.2691, total avg loss: 0.2740, batch size: 41
2021-10-14 06:27:27,290 INFO [train.py:451] Epoch 4, batch 3960, batch avg loss 0.2324, total avg loss: 0.2729, batch size: 29
2021-10-14 06:27:32,273 INFO [train.py:451] Epoch 4, batch 3970, batch avg loss 0.2039, total avg loss: 0.2715, batch size: 31
2021-10-14 06:27:37,159 INFO [train.py:451] Epoch 4, batch 3980, batch avg loss 0.1947, total avg loss: 0.2703, batch size: 30
2021-10-14 06:27:42,117 INFO [train.py:451] Epoch 4, batch 3990, batch avg loss 0.3899, total avg loss: 0.2707, batch size: 130
2021-10-14 06:27:47,109 INFO [train.py:451] Epoch 4, batch 4000, batch avg loss 0.3197, total avg loss: 0.2704, batch size: 71
2021-10-14 06:28:26,749 INFO [train.py:483] Epoch 4, valid loss 0.1888, best valid loss: 0.1888 best valid epoch: 4
2021-10-14 06:28:31,522 INFO [train.py:451] Epoch 4, batch 4010, batch avg loss 0.2884, total avg loss: 0.2808, batch size: 34
2021-10-14 06:28:36,501 INFO [train.py:451] Epoch 4, batch 4020, batch avg loss 0.3586, total avg loss: 0.2753, batch size: 130
2021-10-14 06:28:41,350 INFO [train.py:451] Epoch 4, batch 4030, batch avg loss 0.2571, total avg loss: 0.2687, batch size: 36
2021-10-14 06:28:46,247 INFO [train.py:451] Epoch 4, batch 4040, batch avg loss 0.2636, total avg loss: 0.2716, batch size: 28
2021-10-14 06:28:51,292 INFO [train.py:451] Epoch 4, batch 4050, batch avg loss 0.2383, total avg loss: 0.2723, batch size: 35
2021-10-14 06:28:56,166 INFO [train.py:451] Epoch 4, batch 4060, batch avg loss 0.2762, total avg loss: 0.2701, batch size: 49
2021-10-14 06:29:00,909 INFO [train.py:451] Epoch 4, batch 4070, batch avg loss 0.2438, total avg loss: 0.2706, batch size: 29
2021-10-14 06:29:05,777 INFO [train.py:451] Epoch 4, batch 4080, batch avg loss 0.3365, total avg loss: 0.2718, batch size: 35
2021-10-14 06:29:10,838 INFO [train.py:451] Epoch 4, batch 4090, batch avg loss 0.2130, total avg loss: 0.2694, batch size: 27
2021-10-14 06:29:15,712 INFO [train.py:451] Epoch 4, batch 4100, batch avg loss 0.2451, total avg loss: 0.2706, batch size: 38
2021-10-14 06:29:20,694 INFO [train.py:451] Epoch 4, batch 4110, batch avg loss 0.2159, total avg loss: 0.2687, batch size: 31
2021-10-14 06:29:25,679 INFO [train.py:451] Epoch 4, batch 4120, batch avg loss 0.2491, total avg loss: 0.2687, batch size: 45
2021-10-14 06:29:30,357 INFO [train.py:451] Epoch 4, batch 4130, batch avg loss 0.2637, total avg loss: 0.2693, batch size: 42
2021-10-14 06:29:35,401 INFO [train.py:451] Epoch 4, batch 4140, batch avg loss 0.2385, total avg loss: 0.2688, batch size: 32
2021-10-14 06:29:40,430 INFO [train.py:451] Epoch 4, batch 4150, batch avg loss 0.1977, total avg loss: 0.2684, batch size: 32
2021-10-14 06:29:45,455 INFO [train.py:451] Epoch 4, batch 4160, batch avg loss 0.2879, total avg loss: 0.2686, batch size: 38
2021-10-14 06:29:50,421 INFO [train.py:451] Epoch 4, batch 4170, batch avg loss 0.3964, total avg loss: 0.2689, batch size: 127
2021-10-14 06:29:55,371 INFO [train.py:451] Epoch 4, batch 4180, batch avg loss 0.2451, total avg loss: 0.2694, batch size: 34
2021-10-14 06:30:00,279 INFO [train.py:451] Epoch 4, batch 4190, batch avg loss 0.3386, total avg loss: 0.2696, batch size: 38
2021-10-14 06:30:05,176 INFO [train.py:451] Epoch 4, batch 4200, batch avg loss 0.2723, total avg loss: 0.2703, batch size: 45
2021-10-14 06:30:10,122 INFO [train.py:451] Epoch 4, batch 4210, batch avg loss 0.2781, total avg loss: 0.2541, batch size: 29
2021-10-14 06:30:14,952 INFO [train.py:451] Epoch 4, batch 4220, batch avg loss 0.2416, total avg loss: 0.2546, batch size: 30
2021-10-14 06:30:19,926 INFO [train.py:451] Epoch 4, batch 4230, batch avg loss 0.3162, total avg loss: 0.2552, batch size: 42
2021-10-14 06:30:24,794 INFO [train.py:451] Epoch 4, batch 4240, batch avg loss 0.2824, total avg loss: 0.2558, batch size: 38
2021-10-14 06:30:29,679 INFO [train.py:451] Epoch 4, batch 4250, batch avg loss 0.2172, total avg loss: 0.2567, batch size: 31
2021-10-14 06:30:34,651 INFO [train.py:451] Epoch 4, batch 4260, batch avg loss 0.2537, total avg loss: 0.2570, batch size: 31
2021-10-14 06:30:39,478 INFO [train.py:451] Epoch 4, batch 4270, batch avg loss 0.3062, total avg loss: 0.2576, batch size: 38
2021-10-14 06:30:44,441 INFO [train.py:451] Epoch 4, batch 4280, batch avg loss 0.2229, total avg loss: 0.2566, batch size: 35
2021-10-14 06:30:49,303 INFO [train.py:451] Epoch 4, batch 4290, batch avg loss 0.3076, total avg loss: 0.2583, batch size: 39
2021-10-14 06:30:54,266 INFO [train.py:451] Epoch 4, batch 4300, batch avg loss 0.2681, total avg loss: 0.2603, batch size: 32
2021-10-14 06:30:59,242 INFO [train.py:451] Epoch 4, batch 4310, batch avg loss 0.2246, total avg loss: 0.2593, batch size: 32
2021-10-14 06:31:04,159 INFO [train.py:451] Epoch 4, batch 4320, batch avg loss 0.2524, total avg loss: 0.2600, batch size: 30
2021-10-14 06:31:09,186 INFO [train.py:451] Epoch 4, batch 4330, batch avg loss 0.2729, total avg loss: 0.2591, batch size: 34
2021-10-14 06:31:14,168 INFO [train.py:451] Epoch 4, batch 4340, batch avg loss 0.2534, total avg loss: 0.2598, batch size: 31
2021-10-14 06:31:19,258 INFO [train.py:451] Epoch 4, batch 4350, batch avg loss 0.3175, total avg loss: 0.2595, batch size: 42
2021-10-14 06:31:24,160 INFO [train.py:451] Epoch 4, batch 4360, batch avg loss 0.2765, total avg loss: 0.2611, batch size: 28
2021-10-14 06:31:29,014 INFO [train.py:451] Epoch 4, batch 4370, batch avg loss 0.2629, total avg loss: 0.2615, batch size: 35
2021-10-14 06:31:33,857 INFO [train.py:451] Epoch 4, batch 4380, batch avg loss 0.2510, total avg loss: 0.2614, batch size: 72
2021-10-14 06:31:38,747 INFO [train.py:451] Epoch 4, batch 4390, batch avg loss 0.2044, total avg loss: 0.2615, batch size: 29
2021-10-14 06:31:43,698 INFO [train.py:451] Epoch 4, batch 4400, batch avg loss 0.2360, total avg loss: 0.2615, batch size: 28
2021-10-14 06:31:48,607 INFO [train.py:451] Epoch 4, batch 4410, batch avg loss 0.3817, total avg loss: 0.2513, batch size: 131
2021-10-14 06:31:53,489 INFO [train.py:451] Epoch 4, batch 4420, batch avg loss 0.2845, total avg loss: 0.2634, batch size: 35
2021-10-14 06:31:58,348 INFO [train.py:451] Epoch 4, batch 4430, batch avg loss 0.2246, total avg loss: 0.2662, batch size: 29
2021-10-14 06:32:03,198 INFO [train.py:451] Epoch 4, batch 4440, batch avg loss 0.2592, total avg loss: 0.2654, batch size: 30
2021-10-14 06:32:08,248 INFO [train.py:451] Epoch 4, batch 4450, batch avg loss 0.2493, total avg loss: 0.2656, batch size: 33
2021-10-14 06:32:13,171 INFO [train.py:451] Epoch 4, batch 4460, batch avg loss 0.2303, total avg loss: 0.2663, batch size: 33
2021-10-14 06:32:18,264 INFO [train.py:451] Epoch 4, batch 4470, batch avg loss 0.2731, total avg loss: 0.2656, batch size: 41
2021-10-14 06:32:23,054 INFO [train.py:451] Epoch 4, batch 4480, batch avg loss 0.2919, total avg loss: 0.2676, batch size: 45
2021-10-14 06:32:27,881 INFO [train.py:451] Epoch 4, batch 4490, batch avg loss 0.3213, total avg loss: 0.2670, batch size: 57
2021-10-14 06:32:33,033 INFO [train.py:451] Epoch 4, batch 4500, batch avg loss 0.2745, total avg loss: 0.2670, batch size: 34
2021-10-14 06:32:38,043 INFO [train.py:451] Epoch 4, batch 4510, batch avg loss 0.2135, total avg loss: 0.2654, batch size: 27
2021-10-14 06:32:42,968 INFO [train.py:451] Epoch 4, batch 4520, batch avg loss 0.3073, total avg loss: 0.2634, batch size: 73
2021-10-14 06:32:47,846 INFO [train.py:451] Epoch 4, batch 4530, batch avg loss 0.2261, total avg loss: 0.2620, batch size: 31
2021-10-14 06:32:52,899 INFO [train.py:451] Epoch 4, batch 4540, batch avg loss 0.2380, total avg loss: 0.2601, batch size: 27
2021-10-14 06:32:57,781 INFO [train.py:451] Epoch 4, batch 4550, batch avg loss 0.2065, total avg loss: 0.2610, batch size: 30
2021-10-14 06:33:02,761 INFO [train.py:451] Epoch 4, batch 4560, batch avg loss 0.2686, total avg loss: 0.2617, batch size: 33
2021-10-14 06:33:07,680 INFO [train.py:451] Epoch 4, batch 4570, batch avg loss 0.2335, total avg loss: 0.2613, batch size: 34
2021-10-14 06:33:12,593 INFO [train.py:451] Epoch 4, batch 4580, batch avg loss 0.2404, total avg loss: 0.2611, batch size: 36
2021-10-14 06:33:17,317 INFO [train.py:451] Epoch 4, batch 4590, batch avg loss 0.2760, total avg loss: 0.2619, batch size: 34
2021-10-14 06:33:29,755 INFO [train.py:451] Epoch 4, batch 4600, batch avg loss 0.2590, total avg loss: 0.2622, batch size: 39
2021-10-14 06:33:34,865 INFO [train.py:451] Epoch 4, batch 4610, batch avg loss 0.2280, total avg loss: 0.2665, batch size: 29
2021-10-14 06:33:39,806 INFO [train.py:451] Epoch 4, batch 4620, batch avg loss 0.2607, total avg loss: 0.2671, batch size: 38
2021-10-14 06:33:44,900 INFO [train.py:451] Epoch 4, batch 4630, batch avg loss 0.2809, total avg loss: 0.2680, batch size: 41
2021-10-14 06:33:49,779 INFO [train.py:451] Epoch 4, batch 4640, batch avg loss 0.3000, total avg loss: 0.2679, batch size: 34
2021-10-14 06:33:54,726 INFO [train.py:451] Epoch 4, batch 4650, batch avg loss 0.2427, total avg loss: 0.2637, batch size: 32
2021-10-14 06:33:59,654 INFO [train.py:451] Epoch 4, batch 4660, batch avg loss 0.2514, total avg loss: 0.2644, batch size: 39
2021-10-14 06:34:04,751 INFO [train.py:451] Epoch 4, batch 4670, batch avg loss 0.2173, total avg loss: 0.2624, batch size: 31
2021-10-14 06:34:09,550 INFO [train.py:451] Epoch 4, batch 4680, batch avg loss 0.2955, total avg loss: 0.2625, batch size: 35
2021-10-14 06:34:14,834 INFO [train.py:451] Epoch 4, batch 4690, batch avg loss 0.2928, total avg loss: 0.2621, batch size: 36
2021-10-14 06:34:19,558 INFO [train.py:451] Epoch 4, batch 4700, batch avg loss 0.3132, total avg loss: 0.2662, batch size: 56
2021-10-14 06:34:24,615 INFO [train.py:451] Epoch 4, batch 4710, batch avg loss 0.2222, total avg loss: 0.2646, batch size: 30
2021-10-14 06:34:29,540 INFO [train.py:451] Epoch 4, batch 4720, batch avg loss 0.2504, total avg loss: 0.2630, batch size: 32
2021-10-14 06:34:34,378 INFO [train.py:451] Epoch 4, batch 4730, batch avg loss 0.3080, total avg loss: 0.2639, batch size: 39
2021-10-14 06:34:39,335 INFO [train.py:451] Epoch 4, batch 4740, batch avg loss 0.2461, total avg loss: 0.2646, batch size: 36
2021-10-14 06:34:44,446 INFO [train.py:451] Epoch 4, batch 4750, batch avg loss 0.2768, total avg loss: 0.2644, batch size: 31
2021-10-14 06:34:49,470 INFO [train.py:451] Epoch 4, batch 4760, batch avg loss 0.2656, total avg loss: 0.2647, batch size: 32
2021-10-14 06:34:54,493 INFO [train.py:451] Epoch 4, batch 4770, batch avg loss 0.2314, total avg loss: 0.2643, batch size: 35
2021-10-14 06:34:59,208 INFO [train.py:451] Epoch 4, batch 4780, batch avg loss 0.2775, total avg loss: 0.2645, batch size: 49
2021-10-14 06:35:04,145 INFO [train.py:451] Epoch 4, batch 4790, batch avg loss 0.2683, total avg loss: 0.2650, batch size: 45
2021-10-14 06:35:09,092 INFO [train.py:451] Epoch 4, batch 4800, batch avg loss 0.2556, total avg loss: 0.2646, batch size: 42
2021-10-14 06:35:13,942 INFO [train.py:451] Epoch 4, batch 4810, batch avg loss 0.3007, total avg loss: 0.2526, batch size: 57
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2021-10-14 06:37:27,421 INFO [train.py:483] Epoch 4, valid loss 0.1880, best valid loss: 0.1880 best valid epoch: 4
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2021-10-14 06:46:18,415 INFO [train.py:483] Epoch 4, valid loss 0.1901, best valid loss: 0.1880 best valid epoch: 4
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2021-10-14 07:04:05,857 INFO [train.py:483] Epoch 4, valid loss 0.1900, best valid loss: 0.1877 best valid epoch: 4
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2021-10-14 07:12:58,790 INFO [train.py:483] Epoch 4, valid loss 0.1870, best valid loss: 0.1870 best valid epoch: 4
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2021-10-14 07:21:44,421 INFO [train.py:483] Epoch 4, valid loss 0.1869, best valid loss: 0.1869 best valid epoch: 4
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2021-10-14 07:39:25,746 INFO [train.py:483] Epoch 4, valid loss 0.1862, best valid loss: 0.1862 best valid epoch: 4
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2021-10-14 07:40:49,129 INFO [train.py:451] Epoch 4, batch 12170, batch avg loss 0.3375, total avg loss: 0.2601, batch size: 38
2021-10-14 07:40:53,981 INFO [train.py:451] Epoch 4, batch 12180, batch avg loss 0.2479, total avg loss: 0.2594, batch size: 41
2021-10-14 07:40:58,648 INFO [train.py:451] Epoch 4, batch 12190, batch avg loss 0.2826, total avg loss: 0.2592, batch size: 41
2021-10-14 07:41:03,631 INFO [train.py:451] Epoch 4, batch 12200, batch avg loss 0.2316, total avg loss: 0.2591, batch size: 33
2021-10-14 07:41:08,570 INFO [train.py:451] Epoch 4, batch 12210, batch avg loss 0.2888, total avg loss: 0.2669, batch size: 38
2021-10-14 07:41:13,536 INFO [train.py:451] Epoch 4, batch 12220, batch avg loss 0.2125, total avg loss: 0.2630, batch size: 30
2021-10-14 07:41:18,398 INFO [train.py:451] Epoch 4, batch 12230, batch avg loss 0.2495, total avg loss: 0.2637, batch size: 35
2021-10-14 07:41:23,287 INFO [train.py:451] Epoch 4, batch 12240, batch avg loss 0.2941, total avg loss: 0.2661, batch size: 39
2021-10-14 07:41:28,066 INFO [train.py:451] Epoch 4, batch 12250, batch avg loss 0.2558, total avg loss: 0.2673, batch size: 41
2021-10-14 07:41:33,004 INFO [train.py:451] Epoch 4, batch 12260, batch avg loss 0.2117, total avg loss: 0.2646, batch size: 30
2021-10-14 07:41:38,028 INFO [train.py:451] Epoch 4, batch 12270, batch avg loss 0.2482, total avg loss: 0.2631, batch size: 29
2021-10-14 07:41:42,825 INFO [train.py:451] Epoch 4, batch 12280, batch avg loss 0.2694, total avg loss: 0.2631, batch size: 37
2021-10-14 07:41:47,855 INFO [train.py:451] Epoch 4, batch 12290, batch avg loss 0.3166, total avg loss: 0.2612, batch size: 38
2021-10-14 07:41:52,625 INFO [train.py:451] Epoch 4, batch 12300, batch avg loss 0.2687, total avg loss: 0.2610, batch size: 34
2021-10-14 07:41:57,514 INFO [train.py:451] Epoch 4, batch 12310, batch avg loss 0.2750, total avg loss: 0.2640, batch size: 39
2021-10-14 07:42:02,405 INFO [train.py:451] Epoch 4, batch 12320, batch avg loss 0.2325, total avg loss: 0.2618, batch size: 49
2021-10-14 07:42:07,406 INFO [train.py:451] Epoch 4, batch 12330, batch avg loss 0.2584, total avg loss: 0.2608, batch size: 33
2021-10-14 07:42:12,426 INFO [train.py:451] Epoch 4, batch 12340, batch avg loss 0.2339, total avg loss: 0.2583, batch size: 35
2021-10-14 07:42:17,498 INFO [train.py:451] Epoch 4, batch 12350, batch avg loss 0.1840, total avg loss: 0.2573, batch size: 30
2021-10-14 07:42:22,374 INFO [train.py:451] Epoch 4, batch 12360, batch avg loss 0.2948, total avg loss: 0.2581, batch size: 45
2021-10-14 07:42:27,255 INFO [train.py:451] Epoch 4, batch 12370, batch avg loss 0.2349, total avg loss: 0.2592, batch size: 32
2021-10-14 07:42:31,935 INFO [train.py:451] Epoch 4, batch 12380, batch avg loss 0.2643, total avg loss: 0.2603, batch size: 57
2021-10-14 07:42:32,046 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "9735bade-84b8-0c5d-9dbc-a86be600761a" will not be mixed in.
2021-10-14 07:42:37,057 INFO [train.py:451] Epoch 4, batch 12390, batch avg loss 0.3070, total avg loss: 0.2613, batch size: 57
2021-10-14 07:42:42,343 INFO [train.py:451] Epoch 4, batch 12400, batch avg loss 0.2292, total avg loss: 0.2606, batch size: 36
2021-10-14 07:42:47,263 INFO [train.py:451] Epoch 4, batch 12410, batch avg loss 0.2174, total avg loss: 0.2807, batch size: 29
2021-10-14 07:42:52,167 INFO [train.py:451] Epoch 4, batch 12420, batch avg loss 0.2718, total avg loss: 0.2762, batch size: 36
2021-10-14 07:42:56,976 INFO [train.py:451] Epoch 4, batch 12430, batch avg loss 0.2859, total avg loss: 0.2749, batch size: 38
2021-10-14 07:43:01,837 INFO [train.py:451] Epoch 4, batch 12440, batch avg loss 0.2051, total avg loss: 0.2682, batch size: 36
2021-10-14 07:43:06,830 INFO [train.py:451] Epoch 4, batch 12450, batch avg loss 0.2419, total avg loss: 0.2663, batch size: 34
2021-10-14 07:43:11,667 INFO [train.py:451] Epoch 4, batch 12460, batch avg loss 0.2563, total avg loss: 0.2659, batch size: 34
2021-10-14 07:43:16,563 INFO [train.py:451] Epoch 4, batch 12470, batch avg loss 0.2641, total avg loss: 0.2686, batch size: 42
2021-10-14 07:43:21,458 INFO [train.py:451] Epoch 4, batch 12480, batch avg loss 0.2696, total avg loss: 0.2675, batch size: 30
2021-10-14 07:43:26,257 INFO [train.py:451] Epoch 4, batch 12490, batch avg loss 0.2850, total avg loss: 0.2673, batch size: 45
2021-10-14 07:43:31,186 INFO [train.py:451] Epoch 4, batch 12500, batch avg loss 0.2731, total avg loss: 0.2645, batch size: 38
2021-10-14 07:43:36,057 INFO [train.py:451] Epoch 4, batch 12510, batch avg loss 0.2657, total avg loss: 0.2660, batch size: 42
2021-10-14 07:43:41,124 INFO [train.py:451] Epoch 4, batch 12520, batch avg loss 0.2302, total avg loss: 0.2652, batch size: 37
2021-10-14 07:43:46,143 INFO [train.py:451] Epoch 4, batch 12530, batch avg loss 0.2493, total avg loss: 0.2642, batch size: 30
2021-10-14 07:43:51,251 INFO [train.py:451] Epoch 4, batch 12540, batch avg loss 0.3049, total avg loss: 0.2638, batch size: 35
2021-10-14 07:43:56,252 INFO [train.py:451] Epoch 4, batch 12550, batch avg loss 0.2411, total avg loss: 0.2636, batch size: 37
2021-10-14 07:44:01,103 INFO [train.py:451] Epoch 4, batch 12560, batch avg loss 0.2883, total avg loss: 0.2636, batch size: 31
2021-10-14 07:44:05,952 INFO [train.py:451] Epoch 4, batch 12570, batch avg loss 0.2686, total avg loss: 0.2636, batch size: 36
2021-10-14 07:44:10,985 INFO [train.py:451] Epoch 4, batch 12580, batch avg loss 0.2494, total avg loss: 0.2629, batch size: 36
2021-10-14 07:44:15,731 INFO [train.py:451] Epoch 4, batch 12590, batch avg loss 0.2754, total avg loss: 0.2639, batch size: 34
2021-10-14 07:44:20,641 INFO [train.py:451] Epoch 4, batch 12600, batch avg loss 0.2313, total avg loss: 0.2636, batch size: 32
2021-10-14 07:44:25,594 INFO [train.py:451] Epoch 4, batch 12610, batch avg loss 0.1996, total avg loss: 0.2546, batch size: 27
2021-10-14 07:44:30,450 INFO [train.py:451] Epoch 4, batch 12620, batch avg loss 0.2396, total avg loss: 0.2583, batch size: 38
2021-10-14 07:44:35,323 INFO [train.py:451] Epoch 4, batch 12630, batch avg loss 0.3430, total avg loss: 0.2609, batch size: 38
2021-10-14 07:44:40,298 INFO [train.py:451] Epoch 4, batch 12640, batch avg loss 0.2630, total avg loss: 0.2613, batch size: 35
2021-10-14 07:44:45,051 INFO [train.py:451] Epoch 4, batch 12650, batch avg loss 0.2354, total avg loss: 0.2628, batch size: 34
2021-10-14 07:44:49,832 INFO [train.py:451] Epoch 4, batch 12660, batch avg loss 0.2693, total avg loss: 0.2655, batch size: 30
2021-10-14 07:45:01,786 INFO [train.py:451] Epoch 4, batch 12670, batch avg loss 0.2530, total avg loss: 0.2673, batch size: 41
2021-10-14 07:45:06,642 INFO [train.py:451] Epoch 4, batch 12680, batch avg loss 0.2755, total avg loss: 0.2654, batch size: 35
2021-10-14 07:45:11,658 INFO [train.py:451] Epoch 4, batch 12690, batch avg loss 0.2759, total avg loss: 0.2639, batch size: 39
2021-10-14 07:45:16,509 INFO [train.py:451] Epoch 4, batch 12700, batch avg loss 0.2692, total avg loss: 0.2644, batch size: 33
2021-10-14 07:45:21,476 INFO [train.py:451] Epoch 4, batch 12710, batch avg loss 0.2277, total avg loss: 0.2633, batch size: 31
2021-10-14 07:45:26,409 INFO [train.py:451] Epoch 4, batch 12720, batch avg loss 0.2548, total avg loss: 0.2622, batch size: 36
2021-10-14 07:45:31,138 INFO [train.py:451] Epoch 4, batch 12730, batch avg loss 0.3555, total avg loss: 0.2639, batch size: 34
2021-10-14 07:45:36,116 INFO [train.py:451] Epoch 4, batch 12740, batch avg loss 0.1787, total avg loss: 0.2619, batch size: 29
2021-10-14 07:45:41,009 INFO [train.py:451] Epoch 4, batch 12750, batch avg loss 0.2357, total avg loss: 0.2608, batch size: 37
2021-10-14 07:45:45,863 INFO [train.py:451] Epoch 4, batch 12760, batch avg loss 0.3020, total avg loss: 0.2623, batch size: 42
2021-10-14 07:45:50,842 INFO [train.py:451] Epoch 4, batch 12770, batch avg loss 0.4039, total avg loss: 0.2626, batch size: 132
2021-10-14 07:45:55,644 INFO [train.py:451] Epoch 4, batch 12780, batch avg loss 0.2200, total avg loss: 0.2633, batch size: 32
2021-10-14 07:46:00,686 INFO [train.py:451] Epoch 4, batch 12790, batch avg loss 0.2320, total avg loss: 0.2633, batch size: 30
2021-10-14 07:46:05,665 INFO [train.py:451] Epoch 4, batch 12800, batch avg loss 0.3473, total avg loss: 0.2632, batch size: 128
2021-10-14 07:46:10,616 INFO [train.py:451] Epoch 4, batch 12810, batch avg loss 0.2641, total avg loss: 0.2596, batch size: 28
2021-10-14 07:46:15,464 INFO [train.py:451] Epoch 4, batch 12820, batch avg loss 0.1998, total avg loss: 0.2546, batch size: 33
2021-10-14 07:46:20,061 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "d7a1d92f-49ba-8a84-ede5-af9a2b80da23" will not be mixed in.
2021-10-14 07:46:20,319 INFO [train.py:451] Epoch 4, batch 12830, batch avg loss 0.2813, total avg loss: 0.2604, batch size: 56
2021-10-14 07:46:25,050 INFO [train.py:451] Epoch 4, batch 12840, batch avg loss 0.2608, total avg loss: 0.2650, batch size: 34
2021-10-14 07:46:29,981 INFO [train.py:451] Epoch 4, batch 12850, batch avg loss 0.2715, total avg loss: 0.2639, batch size: 36
2021-10-14 07:46:35,076 INFO [train.py:451] Epoch 4, batch 12860, batch avg loss 0.1990, total avg loss: 0.2623, batch size: 30
2021-10-14 07:46:40,097 INFO [train.py:451] Epoch 4, batch 12870, batch avg loss 0.2255, total avg loss: 0.2599, batch size: 34
2021-10-14 07:46:45,286 INFO [train.py:451] Epoch 4, batch 12880, batch avg loss 0.2360, total avg loss: 0.2597, batch size: 35
2021-10-14 07:46:50,284 INFO [train.py:451] Epoch 4, batch 12890, batch avg loss 0.2555, total avg loss: 0.2581, batch size: 30
2021-10-14 07:46:55,321 INFO [train.py:451] Epoch 4, batch 12900, batch avg loss 0.2192, total avg loss: 0.2574, batch size: 29
2021-10-14 07:47:00,168 INFO [train.py:451] Epoch 4, batch 12910, batch avg loss 0.2975, total avg loss: 0.2596, batch size: 37
2021-10-14 07:47:05,026 INFO [train.py:451] Epoch 4, batch 12920, batch avg loss 0.2640, total avg loss: 0.2600, batch size: 45
2021-10-14 07:47:09,930 INFO [train.py:451] Epoch 4, batch 12930, batch avg loss 0.2698, total avg loss: 0.2609, batch size: 38
2021-10-14 07:47:14,867 INFO [train.py:451] Epoch 4, batch 12940, batch avg loss 0.2526, total avg loss: 0.2615, batch size: 57
2021-10-14 07:47:19,742 INFO [train.py:451] Epoch 4, batch 12950, batch avg loss 0.2636, total avg loss: 0.2608, batch size: 31
2021-10-14 07:47:24,665 INFO [train.py:451] Epoch 4, batch 12960, batch avg loss 0.2746, total avg loss: 0.2610, batch size: 37
2021-10-14 07:47:29,868 INFO [train.py:451] Epoch 4, batch 12970, batch avg loss 0.2620, total avg loss: 0.2607, batch size: 34
2021-10-14 07:47:34,867 INFO [train.py:451] Epoch 4, batch 12980, batch avg loss 0.2739, total avg loss: 0.2608, batch size: 27
2021-10-14 07:47:39,874 INFO [train.py:451] Epoch 4, batch 12990, batch avg loss 0.2589, total avg loss: 0.2605, batch size: 38
2021-10-14 07:47:44,900 INFO [train.py:451] Epoch 4, batch 13000, batch avg loss 0.2360, total avg loss: 0.2595, batch size: 38
2021-10-14 07:48:24,323 INFO [train.py:483] Epoch 4, valid loss 0.1871, best valid loss: 0.1862 best valid epoch: 4
2021-10-14 07:48:29,344 INFO [train.py:451] Epoch 4, batch 13010, batch avg loss 0.2186, total avg loss: 0.2248, batch size: 28
2021-10-14 07:48:34,291 INFO [train.py:451] Epoch 4, batch 13020, batch avg loss 0.2901, total avg loss: 0.2411, batch size: 45
2021-10-14 07:48:39,256 INFO [train.py:451] Epoch 4, batch 13030, batch avg loss 0.2353, total avg loss: 0.2440, batch size: 32
2021-10-14 07:48:44,021 INFO [train.py:451] Epoch 4, batch 13040, batch avg loss 0.2915, total avg loss: 0.2539, batch size: 34
2021-10-14 07:48:49,055 INFO [train.py:451] Epoch 4, batch 13050, batch avg loss 0.2710, total avg loss: 0.2529, batch size: 38
2021-10-14 07:48:54,154 INFO [train.py:451] Epoch 4, batch 13060, batch avg loss 0.2938, total avg loss: 0.2505, batch size: 30
2021-10-14 07:48:59,099 INFO [train.py:451] Epoch 4, batch 13070, batch avg loss 0.2370, total avg loss: 0.2484, batch size: 39
2021-10-14 07:49:04,046 INFO [train.py:451] Epoch 4, batch 13080, batch avg loss 0.3363, total avg loss: 0.2485, batch size: 42
2021-10-14 07:49:09,044 INFO [train.py:451] Epoch 4, batch 13090, batch avg loss 0.2247, total avg loss: 0.2494, batch size: 34
2021-10-14 07:49:13,844 INFO [train.py:451] Epoch 4, batch 13100, batch avg loss 0.3047, total avg loss: 0.2521, batch size: 37
2021-10-14 07:49:18,733 INFO [train.py:451] Epoch 4, batch 13110, batch avg loss 0.2180, total avg loss: 0.2511, batch size: 30
2021-10-14 07:49:23,968 INFO [train.py:451] Epoch 4, batch 13120, batch avg loss 0.2686, total avg loss: 0.2498, batch size: 36
2021-10-14 07:49:28,889 INFO [train.py:451] Epoch 4, batch 13130, batch avg loss 0.2648, total avg loss: 0.2502, batch size: 36
2021-10-14 07:49:34,070 INFO [train.py:451] Epoch 4, batch 13140, batch avg loss 0.2356, total avg loss: 0.2485, batch size: 29
2021-10-14 07:49:39,022 INFO [train.py:451] Epoch 4, batch 13150, batch avg loss 0.2464, total avg loss: 0.2498, batch size: 41
2021-10-14 07:49:43,979 INFO [train.py:451] Epoch 4, batch 13160, batch avg loss 0.2745, total avg loss: 0.2505, batch size: 34
2021-10-14 07:49:48,827 INFO [train.py:451] Epoch 4, batch 13170, batch avg loss 0.3900, total avg loss: 0.2518, batch size: 127
2021-10-14 07:49:53,830 INFO [train.py:451] Epoch 4, batch 13180, batch avg loss 0.2697, total avg loss: 0.2518, batch size: 71
2021-10-14 07:49:58,895 INFO [train.py:451] Epoch 4, batch 13190, batch avg loss 0.1993, total avg loss: 0.2519, batch size: 29
2021-10-14 07:50:03,889 INFO [train.py:451] Epoch 4, batch 13200, batch avg loss 0.2351, total avg loss: 0.2518, batch size: 33
2021-10-14 07:50:08,918 INFO [train.py:451] Epoch 4, batch 13210, batch avg loss 0.2138, total avg loss: 0.2541, batch size: 29
2021-10-14 07:50:13,929 INFO [train.py:451] Epoch 4, batch 13220, batch avg loss 0.2390, total avg loss: 0.2536, batch size: 28
2021-10-14 07:50:18,765 INFO [train.py:451] Epoch 4, batch 13230, batch avg loss 0.2256, total avg loss: 0.2553, batch size: 33
2021-10-14 07:50:23,613 INFO [train.py:451] Epoch 4, batch 13240, batch avg loss 0.2099, total avg loss: 0.2525, batch size: 34
2021-10-14 07:50:28,522 INFO [train.py:451] Epoch 4, batch 13250, batch avg loss 0.2290, total avg loss: 0.2518, batch size: 31
2021-10-14 07:50:33,412 INFO [train.py:451] Epoch 4, batch 13260, batch avg loss 0.2219, total avg loss: 0.2557, batch size: 29
2021-10-14 07:50:38,278 INFO [train.py:451] Epoch 4, batch 13270, batch avg loss 0.2712, total avg loss: 0.2576, batch size: 34
2021-10-14 07:50:43,118 INFO [train.py:451] Epoch 4, batch 13280, batch avg loss 0.2075, total avg loss: 0.2594, batch size: 27
2021-10-14 07:50:47,962 INFO [train.py:451] Epoch 4, batch 13290, batch avg loss 0.2543, total avg loss: 0.2593, batch size: 34
2021-10-14 07:50:53,094 INFO [train.py:451] Epoch 4, batch 13300, batch avg loss 0.2014, total avg loss: 0.2590, batch size: 27
2021-10-14 07:50:58,060 INFO [train.py:451] Epoch 4, batch 13310, batch avg loss 0.2690, total avg loss: 0.2601, batch size: 41
2021-10-14 07:51:02,977 INFO [train.py:451] Epoch 4, batch 13320, batch avg loss 0.2002, total avg loss: 0.2596, batch size: 27
2021-10-14 07:51:07,865 INFO [train.py:451] Epoch 4, batch 13330, batch avg loss 0.1881, total avg loss: 0.2597, batch size: 31
2021-10-14 07:51:12,847 INFO [train.py:451] Epoch 4, batch 13340, batch avg loss 0.2293, total avg loss: 0.2587, batch size: 34
2021-10-14 07:51:18,037 INFO [train.py:451] Epoch 4, batch 13350, batch avg loss 0.2224, total avg loss: 0.2568, batch size: 28
2021-10-14 07:51:23,082 INFO [train.py:451] Epoch 4, batch 13360, batch avg loss 0.2319, total avg loss: 0.2558, batch size: 37
2021-10-14 07:51:27,992 INFO [train.py:451] Epoch 4, batch 13370, batch avg loss 0.2834, total avg loss: 0.2557, batch size: 45
2021-10-14 07:51:32,866 INFO [train.py:451] Epoch 4, batch 13380, batch avg loss 0.1994, total avg loss: 0.2578, batch size: 30
2021-10-14 07:51:37,807 INFO [train.py:451] Epoch 4, batch 13390, batch avg loss 0.2894, total avg loss: 0.2586, batch size: 45
2021-10-14 07:51:42,858 INFO [train.py:451] Epoch 4, batch 13400, batch avg loss 0.2754, total avg loss: 0.2584, batch size: 33
2021-10-14 07:51:47,842 INFO [train.py:451] Epoch 4, batch 13410, batch avg loss 0.2307, total avg loss: 0.2575, batch size: 28
2021-10-14 07:51:52,828 INFO [train.py:451] Epoch 4, batch 13420, batch avg loss 0.3377, total avg loss: 0.2659, batch size: 72
2021-10-14 07:51:57,623 INFO [train.py:451] Epoch 4, batch 13430, batch avg loss 0.2962, total avg loss: 0.2693, batch size: 57
2021-10-14 07:52:02,633 INFO [train.py:451] Epoch 4, batch 13440, batch avg loss 0.2852, total avg loss: 0.2662, batch size: 38
2021-10-14 07:52:07,509 INFO [train.py:451] Epoch 4, batch 13450, batch avg loss 0.2492, total avg loss: 0.2675, batch size: 30
2021-10-14 07:52:12,424 INFO [train.py:451] Epoch 4, batch 13460, batch avg loss 0.2825, total avg loss: 0.2675, batch size: 35
2021-10-14 07:52:17,355 INFO [train.py:451] Epoch 4, batch 13470, batch avg loss 0.2647, total avg loss: 0.2677, batch size: 37
2021-10-14 07:52:22,372 INFO [train.py:451] Epoch 4, batch 13480, batch avg loss 0.3011, total avg loss: 0.2678, batch size: 35
2021-10-14 07:52:27,379 INFO [train.py:451] Epoch 4, batch 13490, batch avg loss 0.2496, total avg loss: 0.2666, batch size: 32
2021-10-14 07:52:32,201 INFO [train.py:451] Epoch 4, batch 13500, batch avg loss 0.3395, total avg loss: 0.2675, batch size: 45
2021-10-14 07:52:37,234 INFO [train.py:451] Epoch 4, batch 13510, batch avg loss 0.2416, total avg loss: 0.2662, batch size: 35
2021-10-14 07:52:41,997 INFO [train.py:451] Epoch 4, batch 13520, batch avg loss 0.3505, total avg loss: 0.2669, batch size: 136
2021-10-14 07:52:46,832 INFO [train.py:451] Epoch 4, batch 13530, batch avg loss 0.3344, total avg loss: 0.2681, batch size: 42
2021-10-14 07:52:51,796 INFO [train.py:451] Epoch 4, batch 13540, batch avg loss 0.2477, total avg loss: 0.2681, batch size: 34
2021-10-14 07:52:56,668 INFO [train.py:451] Epoch 4, batch 13550, batch avg loss 0.2891, total avg loss: 0.2674, batch size: 36
2021-10-14 07:53:01,608 INFO [train.py:451] Epoch 4, batch 13560, batch avg loss 0.2546, total avg loss: 0.2680, batch size: 49
2021-10-14 07:53:06,678 INFO [train.py:451] Epoch 4, batch 13570, batch avg loss 0.2381, total avg loss: 0.2669, batch size: 34
2021-10-14 07:53:11,508 INFO [train.py:451] Epoch 4, batch 13580, batch avg loss 0.3321, total avg loss: 0.2679, batch size: 37
2021-10-14 07:53:16,253 INFO [train.py:451] Epoch 4, batch 13590, batch avg loss 0.3497, total avg loss: 0.2690, batch size: 127
2021-10-14 07:53:21,141 INFO [train.py:451] Epoch 4, batch 13600, batch avg loss 0.2816, total avg loss: 0.2688, batch size: 38
2021-10-14 07:53:25,972 INFO [train.py:451] Epoch 4, batch 13610, batch avg loss 0.2064, total avg loss: 0.2812, batch size: 34
2021-10-14 07:53:30,767 INFO [train.py:451] Epoch 4, batch 13620, batch avg loss 0.2312, total avg loss: 0.2773, batch size: 34
2021-10-14 07:53:35,706 INFO [train.py:451] Epoch 4, batch 13630, batch avg loss 0.3842, total avg loss: 0.2759, batch size: 126
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2021-10-14 08:06:08,155 INFO [train.py:483] Epoch 4, valid loss 0.1874, best valid loss: 0.1862 best valid epoch: 4
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2021-10-14 08:32:47,597 INFO [train.py:483] Epoch 4, valid loss 0.1851, best valid loss: 0.1846 best valid epoch: 4
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2021-10-14 08:41:41,564 INFO [train.py:483] Epoch 4, valid loss 0.1853, best valid loss: 0.1846 best valid epoch: 4
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2021-10-14 08:50:34,977 INFO [train.py:483] Epoch 4, valid loss 0.1839, best valid loss: 0.1839 best valid epoch: 4
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2021-10-14 08:59:36,647 INFO [train.py:483] Epoch 4, valid loss 0.1855, best valid loss: 0.1839 best valid epoch: 4
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2021-10-14 09:01:09,677 INFO [checkpoint.py:62] Saving checkpoint to tdnn_lstm_ctc-v2/exp/epoch-4.pt
2021-10-14 09:01:10,491 INFO [train.py:564] epoch 5, lr: 0.00025
2021-10-14 09:01:14,821 INFO [train.py:451] Epoch 5, batch 0, batch avg loss 0.2727, total avg loss: 0.2727, batch size: 38
2021-10-14 09:01:19,627 INFO [train.py:451] Epoch 5, batch 10, batch avg loss 0.2594, total avg loss: 0.2608, batch size: 42
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2021-10-14 09:01:39,085 INFO [train.py:451] Epoch 5, batch 50, batch avg loss 0.2525, total avg loss: 0.2664, batch size: 36
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2021-10-14 09:08:25,457 INFO [train.py:451] Epoch 5, batch 880, batch avg loss 0.2545, total avg loss: 0.2563, batch size: 35
2021-10-14 09:08:30,278 INFO [train.py:451] Epoch 5, batch 890, batch avg loss 0.2840, total avg loss: 0.2570, batch size: 45
2021-10-14 09:08:35,188 INFO [train.py:451] Epoch 5, batch 900, batch avg loss 0.3677, total avg loss: 0.2587, batch size: 133
2021-10-14 09:08:40,096 INFO [train.py:451] Epoch 5, batch 910, batch avg loss 0.3061, total avg loss: 0.2581, batch size: 36
2021-10-14 09:08:44,886 INFO [train.py:451] Epoch 5, batch 920, batch avg loss 0.2776, total avg loss: 0.2590, batch size: 34
2021-10-14 09:08:49,849 INFO [train.py:451] Epoch 5, batch 930, batch avg loss 0.2346, total avg loss: 0.2574, batch size: 35
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2021-10-14 09:08:59,658 INFO [train.py:451] Epoch 5, batch 950, batch avg loss 0.2605, total avg loss: 0.2573, batch size: 34
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2021-10-14 09:09:19,128 INFO [train.py:451] Epoch 5, batch 990, batch avg loss 0.2782, total avg loss: 0.2578, batch size: 32
2021-10-14 09:09:24,222 INFO [train.py:451] Epoch 5, batch 1000, batch avg loss 0.2462, total avg loss: 0.2571, batch size: 42
2021-10-14 09:10:01,887 INFO [train.py:483] Epoch 5, valid loss 0.1846, best valid loss: 0.1839 best valid epoch: 4
2021-10-14 09:10:06,923 INFO [train.py:451] Epoch 5, batch 1010, batch avg loss 0.2250, total avg loss: 0.2553, batch size: 27
2021-10-14 09:10:11,707 INFO [train.py:451] Epoch 5, batch 1020, batch avg loss 0.2305, total avg loss: 0.2535, batch size: 31
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2021-10-14 09:10:21,672 INFO [train.py:451] Epoch 5, batch 1040, batch avg loss 0.1919, total avg loss: 0.2565, batch size: 27
2021-10-14 09:10:26,539 INFO [train.py:451] Epoch 5, batch 1050, batch avg loss 0.2298, total avg loss: 0.2549, batch size: 32
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2021-10-14 09:10:41,223 INFO [train.py:451] Epoch 5, batch 1080, batch avg loss 0.2242, total avg loss: 0.2583, batch size: 41
2021-10-14 09:10:46,126 INFO [train.py:451] Epoch 5, batch 1090, batch avg loss 0.2587, total avg loss: 0.2557, batch size: 31
2021-10-14 09:10:51,248 INFO [train.py:451] Epoch 5, batch 1100, batch avg loss 0.1850, total avg loss: 0.2544, batch size: 32
2021-10-14 09:10:56,172 INFO [train.py:451] Epoch 5, batch 1110, batch avg loss 0.2264, total avg loss: 0.2543, batch size: 32
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2021-10-14 09:11:05,735 INFO [train.py:451] Epoch 5, batch 1130, batch avg loss 0.2222, total avg loss: 0.2565, batch size: 34
2021-10-14 09:11:10,736 INFO [train.py:451] Epoch 5, batch 1140, batch avg loss 0.2775, total avg loss: 0.2558, batch size: 42
2021-10-14 09:11:15,614 INFO [train.py:451] Epoch 5, batch 1150, batch avg loss 0.2546, total avg loss: 0.2563, batch size: 39
2021-10-14 09:11:20,699 INFO [train.py:451] Epoch 5, batch 1160, batch avg loss 0.2904, total avg loss: 0.2550, batch size: 39
2021-10-14 09:11:25,417 INFO [train.py:451] Epoch 5, batch 1170, batch avg loss 0.2526, total avg loss: 0.2561, batch size: 38
2021-10-14 09:11:30,282 INFO [train.py:451] Epoch 5, batch 1180, batch avg loss 0.2563, total avg loss: 0.2563, batch size: 35
2021-10-14 09:11:35,176 INFO [train.py:451] Epoch 5, batch 1190, batch avg loss 0.2768, total avg loss: 0.2565, batch size: 33
2021-10-14 09:11:39,951 INFO [train.py:451] Epoch 5, batch 1200, batch avg loss 0.2384, total avg loss: 0.2566, batch size: 28
2021-10-14 09:11:44,867 INFO [train.py:451] Epoch 5, batch 1210, batch avg loss 0.2443, total avg loss: 0.2604, batch size: 33
2021-10-14 09:11:49,875 INFO [train.py:451] Epoch 5, batch 1220, batch avg loss 0.2448, total avg loss: 0.2605, batch size: 34
2021-10-14 09:11:54,724 INFO [train.py:451] Epoch 5, batch 1230, batch avg loss 0.2533, total avg loss: 0.2614, batch size: 36
2021-10-14 09:11:59,651 INFO [train.py:451] Epoch 5, batch 1240, batch avg loss 0.2154, total avg loss: 0.2588, batch size: 31
2021-10-14 09:11:59,854 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "bd2ac774-4473-d123-4c7b-e01c000f2776" will not be mixed in.
2021-10-14 09:12:04,478 INFO [train.py:451] Epoch 5, batch 1250, batch avg loss 0.2906, total avg loss: 0.2612, batch size: 34
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2021-10-14 09:18:54,629 INFO [train.py:483] Epoch 5, valid loss 0.1842, best valid loss: 0.1839 best valid epoch: 4
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2021-10-14 09:27:47,416 INFO [train.py:483] Epoch 5, valid loss 0.1841, best valid loss: 0.1839 best valid epoch: 4
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2021-10-14 09:36:38,957 INFO [train.py:483] Epoch 5, valid loss 0.1842, best valid loss: 0.1839 best valid epoch: 4
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2021-10-14 09:45:32,467 INFO [train.py:483] Epoch 5, valid loss 0.1842, best valid loss: 0.1839 best valid epoch: 4
2021-10-14 09:45:37,342 INFO [train.py:451] Epoch 5, batch 5010, batch avg loss 0.2302, total avg loss: 0.2614, batch size: 28
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2021-10-14 09:54:23,245 INFO [train.py:483] Epoch 5, valid loss 0.1828, best valid loss: 0.1828 best valid epoch: 5
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2021-10-14 10:03:15,078 INFO [train.py:483] Epoch 5, valid loss 0.1833, best valid loss: 0.1828 best valid epoch: 5
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2021-10-14 10:04:53,578 INFO [train.py:451] Epoch 5, batch 7200, batch avg loss 0.2872, total avg loss: 0.2537, batch size: 39
2021-10-14 10:04:58,498 INFO [train.py:451] Epoch 5, batch 7210, batch avg loss 0.2239, total avg loss: 0.2669, batch size: 29
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2021-10-14 10:05:13,292 INFO [train.py:451] Epoch 5, batch 7240, batch avg loss 0.2155, total avg loss: 0.2556, batch size: 41
2021-10-14 10:05:18,093 INFO [train.py:451] Epoch 5, batch 7250, batch avg loss 0.2750, total avg loss: 0.2623, batch size: 36
2021-10-14 10:05:22,898 INFO [train.py:451] Epoch 5, batch 7260, batch avg loss 0.2873, total avg loss: 0.2626, batch size: 45
2021-10-14 10:05:27,832 INFO [train.py:451] Epoch 5, batch 7270, batch avg loss 0.2568, total avg loss: 0.2607, batch size: 39
2021-10-14 10:05:32,749 INFO [train.py:451] Epoch 5, batch 7280, batch avg loss 0.3608, total avg loss: 0.2597, batch size: 128
2021-10-14 10:05:37,664 INFO [train.py:451] Epoch 5, batch 7290, batch avg loss 0.2790, total avg loss: 0.2594, batch size: 34
2021-10-14 10:05:42,652 INFO [train.py:451] Epoch 5, batch 7300, batch avg loss 0.2508, total avg loss: 0.2577, batch size: 31
2021-10-14 10:05:47,606 INFO [train.py:451] Epoch 5, batch 7310, batch avg loss 0.2422, total avg loss: 0.2566, batch size: 32
2021-10-14 10:05:52,466 INFO [train.py:451] Epoch 5, batch 7320, batch avg loss 0.2711, total avg loss: 0.2561, batch size: 33
2021-10-14 10:05:57,237 INFO [train.py:451] Epoch 5, batch 7330, batch avg loss 0.3789, total avg loss: 0.2560, batch size: 130
2021-10-14 10:06:02,190 INFO [train.py:451] Epoch 5, batch 7340, batch avg loss 0.2566, total avg loss: 0.2555, batch size: 31
2021-10-14 10:06:07,113 INFO [train.py:451] Epoch 5, batch 7350, batch avg loss 0.2483, total avg loss: 0.2558, batch size: 34
2021-10-14 10:06:12,109 INFO [train.py:451] Epoch 5, batch 7360, batch avg loss 0.2329, total avg loss: 0.2541, batch size: 38
2021-10-14 10:06:17,055 INFO [train.py:451] Epoch 5, batch 7370, batch avg loss 0.2154, total avg loss: 0.2541, batch size: 31
2021-10-14 10:06:21,983 INFO [train.py:451] Epoch 5, batch 7380, batch avg loss 0.2812, total avg loss: 0.2545, batch size: 42
2021-10-14 10:06:26,979 INFO [train.py:451] Epoch 5, batch 7390, batch avg loss 0.3636, total avg loss: 0.2547, batch size: 129
2021-10-14 10:06:31,859 INFO [train.py:451] Epoch 5, batch 7400, batch avg loss 0.2721, total avg loss: 0.2551, batch size: 31
2021-10-14 10:06:36,835 INFO [train.py:451] Epoch 5, batch 7410, batch avg loss 0.2806, total avg loss: 0.2525, batch size: 36
2021-10-14 10:06:41,731 INFO [train.py:451] Epoch 5, batch 7420, batch avg loss 0.2917, total avg loss: 0.2509, batch size: 27
2021-10-14 10:06:46,692 INFO [train.py:451] Epoch 5, batch 7430, batch avg loss 0.2329, total avg loss: 0.2506, batch size: 29
2021-10-14 10:06:51,626 INFO [train.py:451] Epoch 5, batch 7440, batch avg loss 0.3482, total avg loss: 0.2590, batch size: 132
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2021-10-14 10:07:01,674 INFO [train.py:451] Epoch 5, batch 7460, batch avg loss 0.2251, total avg loss: 0.2549, batch size: 30
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2021-10-14 10:07:21,213 INFO [train.py:451] Epoch 5, batch 7500, batch avg loss 0.2304, total avg loss: 0.2533, batch size: 31
2021-10-14 10:07:26,386 INFO [train.py:451] Epoch 5, batch 7510, batch avg loss 0.2951, total avg loss: 0.2528, batch size: 33
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2021-10-14 10:07:36,287 INFO [train.py:451] Epoch 5, batch 7530, batch avg loss 0.2794, total avg loss: 0.2524, batch size: 41
2021-10-14 10:07:41,098 INFO [train.py:451] Epoch 5, batch 7540, batch avg loss 0.2735, total avg loss: 0.2542, batch size: 35
2021-10-14 10:07:45,992 INFO [train.py:451] Epoch 5, batch 7550, batch avg loss 0.3005, total avg loss: 0.2540, batch size: 49
2021-10-14 10:07:50,959 INFO [train.py:451] Epoch 5, batch 7560, batch avg loss 0.2654, total avg loss: 0.2550, batch size: 36
2021-10-14 10:07:55,945 INFO [train.py:451] Epoch 5, batch 7570, batch avg loss 0.2070, total avg loss: 0.2543, batch size: 32
2021-10-14 10:08:01,019 INFO [train.py:451] Epoch 5, batch 7580, batch avg loss 0.2587, total avg loss: 0.2535, batch size: 36
2021-10-14 10:08:05,882 INFO [train.py:451] Epoch 5, batch 7590, batch avg loss 0.2184, total avg loss: 0.2524, batch size: 28
2021-10-14 10:08:10,936 INFO [train.py:451] Epoch 5, batch 7600, batch avg loss 0.2193, total avg loss: 0.2517, batch size: 32
2021-10-14 10:08:15,739 INFO [train.py:451] Epoch 5, batch 7610, batch avg loss 0.2738, total avg loss: 0.2779, batch size: 72
2021-10-14 10:08:21,094 INFO [train.py:451] Epoch 5, batch 7620, batch avg loss 0.2009, total avg loss: 0.2511, batch size: 27
2021-10-14 10:08:26,149 INFO [train.py:451] Epoch 5, batch 7630, batch avg loss 0.2239, total avg loss: 0.2543, batch size: 28
2021-10-14 10:08:31,224 INFO [train.py:451] Epoch 5, batch 7640, batch avg loss 0.2041, total avg loss: 0.2522, batch size: 33
2021-10-14 10:08:36,416 INFO [train.py:451] Epoch 5, batch 7650, batch avg loss 0.2050, total avg loss: 0.2508, batch size: 29
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2021-10-14 10:08:56,269 INFO [train.py:451] Epoch 5, batch 7690, batch avg loss 0.1787, total avg loss: 0.2515, batch size: 28
2021-10-14 10:09:01,250 INFO [train.py:451] Epoch 5, batch 7700, batch avg loss 0.2309, total avg loss: 0.2519, batch size: 33
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2021-10-14 10:09:11,077 INFO [train.py:451] Epoch 5, batch 7720, batch avg loss 0.2593, total avg loss: 0.2551, batch size: 35
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2021-10-14 10:09:21,174 INFO [train.py:451] Epoch 5, batch 7740, batch avg loss 0.1848, total avg loss: 0.2542, batch size: 33
2021-10-14 10:09:26,161 INFO [train.py:451] Epoch 5, batch 7750, batch avg loss 0.1861, total avg loss: 0.2530, batch size: 29
2021-10-14 10:09:31,218 INFO [train.py:451] Epoch 5, batch 7760, batch avg loss 0.2248, total avg loss: 0.2524, batch size: 35
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2021-10-14 10:09:41,171 INFO [train.py:451] Epoch 5, batch 7780, batch avg loss 0.2570, total avg loss: 0.2532, batch size: 39
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2021-10-14 10:09:51,027 INFO [train.py:451] Epoch 5, batch 7800, batch avg loss 0.2605, total avg loss: 0.2534, batch size: 36
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2021-10-14 10:10:12,789 INFO [train.py:451] Epoch 5, batch 7830, batch avg loss 0.2242, total avg loss: 0.2642, batch size: 32
2021-10-14 10:10:17,569 INFO [train.py:451] Epoch 5, batch 7840, batch avg loss 0.2010, total avg loss: 0.2637, batch size: 31
2021-10-14 10:10:22,599 INFO [train.py:451] Epoch 5, batch 7850, batch avg loss 0.2313, total avg loss: 0.2602, batch size: 28
2021-10-14 10:10:27,511 INFO [train.py:451] Epoch 5, batch 7860, batch avg loss 0.2558, total avg loss: 0.2579, batch size: 36
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2021-10-14 10:10:37,089 INFO [train.py:451] Epoch 5, batch 7880, batch avg loss 0.4099, total avg loss: 0.2599, batch size: 127
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2021-10-14 10:10:46,784 INFO [train.py:451] Epoch 5, batch 7900, batch avg loss 0.3084, total avg loss: 0.2592, batch size: 72
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2021-10-14 10:10:56,837 INFO [train.py:451] Epoch 5, batch 7920, batch avg loss 0.2421, total avg loss: 0.2567, batch size: 28
2021-10-14 10:11:01,840 INFO [train.py:451] Epoch 5, batch 7930, batch avg loss 0.2069, total avg loss: 0.2557, batch size: 32
2021-10-14 10:11:06,699 INFO [train.py:451] Epoch 5, batch 7940, batch avg loss 0.2780, total avg loss: 0.2553, batch size: 38
2021-10-14 10:11:11,692 INFO [train.py:451] Epoch 5, batch 7950, batch avg loss 0.2809, total avg loss: 0.2545, batch size: 35
2021-10-14 10:11:16,747 INFO [train.py:451] Epoch 5, batch 7960, batch avg loss 0.2770, total avg loss: 0.2537, batch size: 41
2021-10-14 10:11:21,688 INFO [train.py:451] Epoch 5, batch 7970, batch avg loss 0.2240, total avg loss: 0.2528, batch size: 29
2021-10-14 10:11:26,552 INFO [train.py:451] Epoch 5, batch 7980, batch avg loss 0.3625, total avg loss: 0.2545, batch size: 128
2021-10-14 10:11:31,501 INFO [train.py:451] Epoch 5, batch 7990, batch avg loss 0.2795, total avg loss: 0.2545, batch size: 36
2021-10-14 10:11:36,471 INFO [train.py:451] Epoch 5, batch 8000, batch avg loss 0.2447, total avg loss: 0.2533, batch size: 33
2021-10-14 10:12:16,068 INFO [train.py:483] Epoch 5, valid loss 0.1825, best valid loss: 0.1825 best valid epoch: 5
2021-10-14 10:12:20,803 INFO [train.py:451] Epoch 5, batch 8010, batch avg loss 0.2054, total avg loss: 0.2522, batch size: 29
2021-10-14 10:12:25,705 INFO [train.py:451] Epoch 5, batch 8020, batch avg loss 0.2462, total avg loss: 0.2516, batch size: 38
2021-10-14 10:12:30,550 INFO [train.py:451] Epoch 5, batch 8030, batch avg loss 0.2705, total avg loss: 0.2546, batch size: 38
2021-10-14 10:12:35,383 INFO [train.py:451] Epoch 5, batch 8040, batch avg loss 0.2898, total avg loss: 0.2523, batch size: 30
2021-10-14 10:12:40,338 INFO [train.py:451] Epoch 5, batch 8050, batch avg loss 0.2056, total avg loss: 0.2500, batch size: 30
2021-10-14 10:12:45,204 INFO [train.py:451] Epoch 5, batch 8060, batch avg loss 0.2178, total avg loss: 0.2491, batch size: 32
2021-10-14 10:12:50,015 INFO [train.py:451] Epoch 5, batch 8070, batch avg loss 0.2385, total avg loss: 0.2514, batch size: 30
2021-10-14 10:12:54,796 INFO [train.py:451] Epoch 5, batch 8080, batch avg loss 0.2891, total avg loss: 0.2521, batch size: 57
2021-10-14 10:12:59,709 INFO [train.py:451] Epoch 5, batch 8090, batch avg loss 0.2519, total avg loss: 0.2519, batch size: 42
2021-10-14 10:13:04,687 INFO [train.py:451] Epoch 5, batch 8100, batch avg loss 0.1690, total avg loss: 0.2517, batch size: 29
2021-10-14 10:13:09,735 INFO [train.py:451] Epoch 5, batch 8110, batch avg loss 0.2630, total avg loss: 0.2500, batch size: 36
2021-10-14 10:13:14,700 INFO [train.py:451] Epoch 5, batch 8120, batch avg loss 0.2842, total avg loss: 0.2489, batch size: 35
2021-10-14 10:13:19,708 INFO [train.py:451] Epoch 5, batch 8130, batch avg loss 0.2620, total avg loss: 0.2482, batch size: 49
2021-10-14 10:13:24,670 INFO [train.py:451] Epoch 5, batch 8140, batch avg loss 0.2587, total avg loss: 0.2486, batch size: 34
2021-10-14 10:13:29,497 INFO [train.py:451] Epoch 5, batch 8150, batch avg loss 0.2338, total avg loss: 0.2498, batch size: 31
2021-10-14 10:13:34,408 INFO [train.py:451] Epoch 5, batch 8160, batch avg loss 0.2490, total avg loss: 0.2499, batch size: 41
2021-10-14 10:13:39,160 INFO [train.py:451] Epoch 5, batch 8170, batch avg loss 0.3017, total avg loss: 0.2512, batch size: 45
2021-10-14 10:13:43,897 INFO [train.py:451] Epoch 5, batch 8180, batch avg loss 0.2893, total avg loss: 0.2531, batch size: 29
2021-10-14 10:13:48,903 INFO [train.py:451] Epoch 5, batch 8190, batch avg loss 0.2700, total avg loss: 0.2526, batch size: 45
2021-10-14 10:13:53,910 INFO [train.py:451] Epoch 5, batch 8200, batch avg loss 0.2238, total avg loss: 0.2524, batch size: 33
2021-10-14 10:13:58,826 INFO [train.py:451] Epoch 5, batch 8210, batch avg loss 0.2722, total avg loss: 0.2410, batch size: 39
2021-10-14 10:14:03,759 INFO [train.py:451] Epoch 5, batch 8220, batch avg loss 0.2215, total avg loss: 0.2475, batch size: 31
2021-10-14 10:14:08,761 INFO [train.py:451] Epoch 5, batch 8230, batch avg loss 0.2354, total avg loss: 0.2438, batch size: 45
2021-10-14 10:14:13,722 INFO [train.py:451] Epoch 5, batch 8240, batch avg loss 0.2584, total avg loss: 0.2443, batch size: 34
2021-10-14 10:14:18,518 INFO [train.py:451] Epoch 5, batch 8250, batch avg loss 0.2059, total avg loss: 0.2480, batch size: 34
2021-10-14 10:14:23,388 INFO [train.py:451] Epoch 5, batch 8260, batch avg loss 0.2449, total avg loss: 0.2500, batch size: 36
2021-10-14 10:14:28,168 INFO [train.py:451] Epoch 5, batch 8270, batch avg loss 0.2559, total avg loss: 0.2544, batch size: 40
2021-10-14 10:14:32,994 INFO [train.py:451] Epoch 5, batch 8280, batch avg loss 0.2291, total avg loss: 0.2556, batch size: 35
2021-10-14 10:14:38,087 INFO [train.py:451] Epoch 5, batch 8290, batch avg loss 0.2313, total avg loss: 0.2547, batch size: 31
2021-10-14 10:14:43,092 INFO [train.py:451] Epoch 5, batch 8300, batch avg loss 0.3437, total avg loss: 0.2536, batch size: 126
2021-10-14 10:14:47,971 INFO [train.py:451] Epoch 5, batch 8310, batch avg loss 0.2251, total avg loss: 0.2524, batch size: 30
2021-10-14 10:14:52,874 INFO [train.py:451] Epoch 5, batch 8320, batch avg loss 0.2553, total avg loss: 0.2545, batch size: 34
2021-10-14 10:14:57,855 INFO [train.py:451] Epoch 5, batch 8330, batch avg loss 0.2464, total avg loss: 0.2535, batch size: 34
2021-10-14 10:15:02,864 INFO [train.py:451] Epoch 5, batch 8340, batch avg loss 0.2213, total avg loss: 0.2532, batch size: 28
2021-10-14 10:15:07,692 INFO [train.py:451] Epoch 5, batch 8350, batch avg loss 0.2532, total avg loss: 0.2525, batch size: 42
2021-10-14 10:15:12,734 INFO [train.py:451] Epoch 5, batch 8360, batch avg loss 0.2971, total avg loss: 0.2527, batch size: 35
2021-10-14 10:15:17,658 INFO [train.py:451] Epoch 5, batch 8370, batch avg loss 0.2480, total avg loss: 0.2520, batch size: 33
2021-10-14 10:15:22,555 INFO [train.py:451] Epoch 5, batch 8380, batch avg loss 0.2769, total avg loss: 0.2531, batch size: 38
2021-10-14 10:15:27,242 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "16c060c8-cb05-562d-6955-3d54b32d1cfe" will not be mixed in.
2021-10-14 10:15:27,533 INFO [train.py:451] Epoch 5, batch 8390, batch avg loss 0.2014, total avg loss: 0.2523, batch size: 33
2021-10-14 10:15:32,321 INFO [train.py:451] Epoch 5, batch 8400, batch avg loss 0.3975, total avg loss: 0.2543, batch size: 129
2021-10-14 10:15:37,113 INFO [train.py:451] Epoch 5, batch 8410, batch avg loss 0.2368, total avg loss: 0.2376, batch size: 37
2021-10-14 10:15:42,069 INFO [train.py:451] Epoch 5, batch 8420, batch avg loss 0.2670, total avg loss: 0.2483, batch size: 33
2021-10-14 10:15:46,961 INFO [train.py:451] Epoch 5, batch 8430, batch avg loss 0.2583, total avg loss: 0.2542, batch size: 36
2021-10-14 10:15:51,948 INFO [train.py:451] Epoch 5, batch 8440, batch avg loss 0.2631, total avg loss: 0.2530, batch size: 34
2021-10-14 10:15:56,877 INFO [train.py:451] Epoch 5, batch 8450, batch avg loss 0.2812, total avg loss: 0.2492, batch size: 37
2021-10-14 10:16:01,651 INFO [train.py:451] Epoch 5, batch 8460, batch avg loss 0.2499, total avg loss: 0.2487, batch size: 37
2021-10-14 10:16:06,555 INFO [train.py:451] Epoch 5, batch 8470, batch avg loss 0.3112, total avg loss: 0.2519, batch size: 38
2021-10-14 10:16:11,506 INFO [train.py:451] Epoch 5, batch 8480, batch avg loss 0.2596, total avg loss: 0.2522, batch size: 34
2021-10-14 10:16:16,453 INFO [train.py:451] Epoch 5, batch 8490, batch avg loss 0.2781, total avg loss: 0.2510, batch size: 41
2021-10-14 10:16:21,427 INFO [train.py:451] Epoch 5, batch 8500, batch avg loss 0.2079, total avg loss: 0.2506, batch size: 30
2021-10-14 10:16:26,470 INFO [train.py:451] Epoch 5, batch 8510, batch avg loss 0.2220, total avg loss: 0.2491, batch size: 34
2021-10-14 10:16:31,356 INFO [train.py:451] Epoch 5, batch 8520, batch avg loss 0.2677, total avg loss: 0.2514, batch size: 39
2021-10-14 10:16:36,214 INFO [train.py:451] Epoch 5, batch 8530, batch avg loss 0.3392, total avg loss: 0.2523, batch size: 57
2021-10-14 10:16:41,000 INFO [train.py:451] Epoch 5, batch 8540, batch avg loss 0.2501, total avg loss: 0.2527, batch size: 31
2021-10-14 10:16:45,921 INFO [train.py:451] Epoch 5, batch 8550, batch avg loss 0.2303, total avg loss: 0.2527, batch size: 36
2021-10-14 10:16:51,100 INFO [train.py:451] Epoch 5, batch 8560, batch avg loss 0.2735, total avg loss: 0.2525, batch size: 33
2021-10-14 10:16:55,814 INFO [train.py:451] Epoch 5, batch 8570, batch avg loss 0.2377, total avg loss: 0.2525, batch size: 35
2021-10-14 10:17:00,751 INFO [train.py:451] Epoch 5, batch 8580, batch avg loss 0.2792, total avg loss: 0.2532, batch size: 32
2021-10-14 10:17:05,694 INFO [train.py:451] Epoch 5, batch 8590, batch avg loss 0.2045, total avg loss: 0.2530, batch size: 29
2021-10-14 10:17:10,611 INFO [train.py:451] Epoch 5, batch 8600, batch avg loss 0.2970, total avg loss: 0.2531, batch size: 57
2021-10-14 10:17:15,494 INFO [train.py:451] Epoch 5, batch 8610, batch avg loss 0.2829, total avg loss: 0.2481, batch size: 56
2021-10-14 10:17:20,556 INFO [train.py:451] Epoch 5, batch 8620, batch avg loss 0.3132, total avg loss: 0.2387, batch size: 31
2021-10-14 10:17:25,473 INFO [train.py:451] Epoch 5, batch 8630, batch avg loss 0.1877, total avg loss: 0.2504, batch size: 27
2021-10-14 10:17:30,280 INFO [train.py:451] Epoch 5, batch 8640, batch avg loss 0.2315, total avg loss: 0.2514, batch size: 34
2021-10-14 10:17:35,175 INFO [train.py:451] Epoch 5, batch 8650, batch avg loss 0.3596, total avg loss: 0.2537, batch size: 127
2021-10-14 10:17:40,158 INFO [train.py:451] Epoch 5, batch 8660, batch avg loss 0.2541, total avg loss: 0.2550, batch size: 32
2021-10-14 10:17:45,001 INFO [train.py:451] Epoch 5, batch 8670, batch avg loss 0.2892, total avg loss: 0.2571, batch size: 33
2021-10-14 10:17:49,836 INFO [train.py:451] Epoch 5, batch 8680, batch avg loss 0.2606, total avg loss: 0.2561, batch size: 56
2021-10-14 10:17:54,582 INFO [train.py:451] Epoch 5, batch 8690, batch avg loss 0.2190, total avg loss: 0.2568, batch size: 31
2021-10-14 10:17:59,701 INFO [train.py:451] Epoch 5, batch 8700, batch avg loss 0.1867, total avg loss: 0.2546, batch size: 28
2021-10-14 10:18:04,586 INFO [train.py:451] Epoch 5, batch 8710, batch avg loss 0.2055, total avg loss: 0.2556, batch size: 30
2021-10-14 10:18:09,298 INFO [train.py:451] Epoch 5, batch 8720, batch avg loss 0.2568, total avg loss: 0.2573, batch size: 34
2021-10-14 10:18:14,114 INFO [train.py:451] Epoch 5, batch 8730, batch avg loss 0.2226, total avg loss: 0.2574, batch size: 35
2021-10-14 10:18:19,144 INFO [train.py:451] Epoch 5, batch 8740, batch avg loss 0.2650, total avg loss: 0.2579, batch size: 38
2021-10-14 10:18:24,178 INFO [train.py:451] Epoch 5, batch 8750, batch avg loss 0.3003, total avg loss: 0.2575, batch size: 35
2021-10-14 10:18:29,071 INFO [train.py:451] Epoch 5, batch 8760, batch avg loss 0.3119, total avg loss: 0.2585, batch size: 49
2021-10-14 10:18:34,001 INFO [train.py:451] Epoch 5, batch 8770, batch avg loss 0.2589, total avg loss: 0.2581, batch size: 35
2021-10-14 10:18:38,756 INFO [train.py:451] Epoch 5, batch 8780, batch avg loss 0.2757, total avg loss: 0.2581, batch size: 36
2021-10-14 10:18:43,769 INFO [train.py:451] Epoch 5, batch 8790, batch avg loss 0.2102, total avg loss: 0.2569, batch size: 33
2021-10-14 10:18:48,619 INFO [train.py:451] Epoch 5, batch 8800, batch avg loss 0.2604, total avg loss: 0.2578, batch size: 49
2021-10-14 10:18:53,467 INFO [train.py:451] Epoch 5, batch 8810, batch avg loss 0.2481, total avg loss: 0.2529, batch size: 32
2021-10-14 10:18:58,448 INFO [train.py:451] Epoch 5, batch 8820, batch avg loss 0.2731, total avg loss: 0.2540, batch size: 35
2021-10-14 10:19:03,377 INFO [train.py:451] Epoch 5, batch 8830, batch avg loss 0.2218, total avg loss: 0.2551, batch size: 32
2021-10-14 10:19:08,244 INFO [train.py:451] Epoch 5, batch 8840, batch avg loss 0.2850, total avg loss: 0.2605, batch size: 49
2021-10-14 10:19:13,140 INFO [train.py:451] Epoch 5, batch 8850, batch avg loss 0.2177, total avg loss: 0.2599, batch size: 37
2021-10-14 10:19:18,248 INFO [train.py:451] Epoch 5, batch 8860, batch avg loss 0.1953, total avg loss: 0.2561, batch size: 30
2021-10-14 10:19:23,130 INFO [train.py:451] Epoch 5, batch 8870, batch avg loss 0.2317, total avg loss: 0.2541, batch size: 36
2021-10-14 10:19:28,239 INFO [train.py:451] Epoch 5, batch 8880, batch avg loss 0.2298, total avg loss: 0.2550, batch size: 29
2021-10-14 10:19:33,065 INFO [train.py:451] Epoch 5, batch 8890, batch avg loss 0.2600, total avg loss: 0.2572, batch size: 34
2021-10-14 10:19:38,081 INFO [train.py:451] Epoch 5, batch 8900, batch avg loss 0.2413, total avg loss: 0.2558, batch size: 38
2021-10-14 10:19:42,988 INFO [train.py:451] Epoch 5, batch 8910, batch avg loss 0.2458, total avg loss: 0.2566, batch size: 31
2021-10-14 10:19:48,041 INFO [train.py:451] Epoch 5, batch 8920, batch avg loss 0.2823, total avg loss: 0.2553, batch size: 38
2021-10-14 10:19:53,116 INFO [train.py:451] Epoch 5, batch 8930, batch avg loss 0.2288, total avg loss: 0.2543, batch size: 35
2021-10-14 10:19:57,754 INFO [train.py:451] Epoch 5, batch 8940, batch avg loss 0.3743, total avg loss: 0.2564, batch size: 136
2021-10-14 10:20:02,521 INFO [train.py:451] Epoch 5, batch 8950, batch avg loss 0.2262, total avg loss: 0.2569, batch size: 29
2021-10-14 10:20:07,421 INFO [train.py:451] Epoch 5, batch 8960, batch avg loss 0.2453, total avg loss: 0.2568, batch size: 31
2021-10-14 10:20:12,353 INFO [train.py:451] Epoch 5, batch 8970, batch avg loss 0.2318, total avg loss: 0.2563, batch size: 34
2021-10-14 10:20:17,214 INFO [train.py:451] Epoch 5, batch 8980, batch avg loss 0.3628, total avg loss: 0.2570, batch size: 49
2021-10-14 10:20:22,181 INFO [train.py:451] Epoch 5, batch 8990, batch avg loss 0.2462, total avg loss: 0.2557, batch size: 34
2021-10-14 10:20:27,208 INFO [train.py:451] Epoch 5, batch 9000, batch avg loss 0.2375, total avg loss: 0.2559, batch size: 34
2021-10-14 10:21:04,576 INFO [train.py:483] Epoch 5, valid loss 0.1831, best valid loss: 0.1825 best valid epoch: 5
2021-10-14 10:21:09,432 INFO [train.py:451] Epoch 5, batch 9010, batch avg loss 0.1921, total avg loss: 0.2523, batch size: 28
2021-10-14 10:21:14,350 INFO [train.py:451] Epoch 5, batch 9020, batch avg loss 0.2816, total avg loss: 0.2509, batch size: 57
2021-10-14 10:21:19,047 INFO [train.py:451] Epoch 5, batch 9030, batch avg loss 0.2016, total avg loss: 0.2506, batch size: 30
2021-10-14 10:21:24,059 INFO [train.py:451] Epoch 5, batch 9040, batch avg loss 0.2553, total avg loss: 0.2496, batch size: 34
2021-10-14 10:21:28,722 INFO [train.py:451] Epoch 5, batch 9050, batch avg loss 0.2645, total avg loss: 0.2529, batch size: 45
2021-10-14 10:21:33,665 INFO [train.py:451] Epoch 5, batch 9060, batch avg loss 0.2283, total avg loss: 0.2516, batch size: 32
2021-10-14 10:21:38,630 INFO [train.py:451] Epoch 5, batch 9070, batch avg loss 0.2493, total avg loss: 0.2476, batch size: 33
2021-10-14 10:21:43,672 INFO [train.py:451] Epoch 5, batch 9080, batch avg loss 0.1946, total avg loss: 0.2454, batch size: 27
2021-10-14 10:21:48,671 INFO [train.py:451] Epoch 5, batch 9090, batch avg loss 0.1951, total avg loss: 0.2452, batch size: 29
2021-10-14 10:21:53,553 INFO [train.py:451] Epoch 5, batch 9100, batch avg loss 0.2248, total avg loss: 0.2471, batch size: 30
2021-10-14 10:21:58,436 INFO [train.py:451] Epoch 5, batch 9110, batch avg loss 0.2715, total avg loss: 0.2480, batch size: 36
2021-10-14 10:22:03,453 INFO [train.py:451] Epoch 5, batch 9120, batch avg loss 0.1745, total avg loss: 0.2478, batch size: 28
2021-10-14 10:22:08,599 INFO [train.py:451] Epoch 5, batch 9130, batch avg loss 0.2390, total avg loss: 0.2469, batch size: 37
2021-10-14 10:22:13,345 INFO [train.py:451] Epoch 5, batch 9140, batch avg loss 0.2766, total avg loss: 0.2485, batch size: 37
2021-10-14 10:22:18,181 INFO [train.py:451] Epoch 5, batch 9150, batch avg loss 0.2754, total avg loss: 0.2498, batch size: 34
2021-10-14 10:22:22,968 INFO [train.py:451] Epoch 5, batch 9160, batch avg loss 0.2043, total avg loss: 0.2498, batch size: 30
2021-10-14 10:22:27,915 INFO [train.py:451] Epoch 5, batch 9170, batch avg loss 0.2273, total avg loss: 0.2488, batch size: 38
2021-10-14 10:22:32,853 INFO [train.py:451] Epoch 5, batch 9180, batch avg loss 0.3025, total avg loss: 0.2500, batch size: 37
2021-10-14 10:22:37,816 INFO [train.py:451] Epoch 5, batch 9190, batch avg loss 0.2902, total avg loss: 0.2499, batch size: 38
2021-10-14 10:22:42,770 INFO [train.py:451] Epoch 5, batch 9200, batch avg loss 0.2865, total avg loss: 0.2500, batch size: 36
2021-10-14 10:22:47,649 INFO [train.py:451] Epoch 5, batch 9210, batch avg loss 0.2876, total avg loss: 0.2725, batch size: 37
2021-10-14 10:22:52,571 INFO [train.py:451] Epoch 5, batch 9220, batch avg loss 0.2096, total avg loss: 0.2589, batch size: 30
2021-10-14 10:22:57,555 INFO [train.py:451] Epoch 5, batch 9230, batch avg loss 0.2215, total avg loss: 0.2540, batch size: 33
2021-10-14 10:23:02,475 INFO [train.py:451] Epoch 5, batch 9240, batch avg loss 0.3103, total avg loss: 0.2545, batch size: 41
2021-10-14 10:23:07,363 INFO [train.py:451] Epoch 5, batch 9250, batch avg loss 0.1887, total avg loss: 0.2532, batch size: 32
2021-10-14 10:23:12,188 INFO [train.py:451] Epoch 5, batch 9260, batch avg loss 0.3676, total avg loss: 0.2516, batch size: 123
2021-10-14 10:23:17,230 INFO [train.py:451] Epoch 5, batch 9270, batch avg loss 0.2024, total avg loss: 0.2490, batch size: 30
2021-10-14 10:23:21,877 INFO [train.py:451] Epoch 5, batch 9280, batch avg loss 0.3056, total avg loss: 0.2521, batch size: 73
2021-10-14 10:23:26,888 INFO [train.py:451] Epoch 5, batch 9290, batch avg loss 0.2152, total avg loss: 0.2521, batch size: 33
2021-10-14 10:23:31,794 INFO [train.py:451] Epoch 5, batch 9300, batch avg loss 0.2224, total avg loss: 0.2514, batch size: 41
2021-10-14 10:23:36,779 INFO [train.py:451] Epoch 5, batch 9310, batch avg loss 0.2538, total avg loss: 0.2502, batch size: 32
2021-10-14 10:23:41,654 INFO [train.py:451] Epoch 5, batch 9320, batch avg loss 0.2133, total avg loss: 0.2502, batch size: 27
2021-10-14 10:23:46,586 INFO [train.py:451] Epoch 5, batch 9330, batch avg loss 0.1968, total avg loss: 0.2497, batch size: 27
2021-10-14 10:23:51,516 INFO [train.py:451] Epoch 5, batch 9340, batch avg loss 0.2573, total avg loss: 0.2485, batch size: 31
2021-10-14 10:23:56,311 INFO [train.py:451] Epoch 5, batch 9350, batch avg loss 0.2868, total avg loss: 0.2508, batch size: 42
2021-10-14 10:24:01,200 INFO [train.py:451] Epoch 5, batch 9360, batch avg loss 0.2298, total avg loss: 0.2502, batch size: 31
2021-10-14 10:24:06,246 INFO [train.py:451] Epoch 5, batch 9370, batch avg loss 0.3547, total avg loss: 0.2515, batch size: 35
2021-10-14 10:24:11,215 INFO [train.py:451] Epoch 5, batch 9380, batch avg loss 0.2545, total avg loss: 0.2512, batch size: 37
2021-10-14 10:24:16,136 INFO [train.py:451] Epoch 5, batch 9390, batch avg loss 0.2218, total avg loss: 0.2510, batch size: 33
2021-10-14 10:24:21,077 INFO [train.py:451] Epoch 5, batch 9400, batch avg loss 0.2262, total avg loss: 0.2506, batch size: 33
2021-10-14 10:24:25,861 INFO [train.py:451] Epoch 5, batch 9410, batch avg loss 0.2976, total avg loss: 0.2649, batch size: 73
2021-10-14 10:24:30,668 INFO [train.py:451] Epoch 5, batch 9420, batch avg loss 0.2035, total avg loss: 0.2618, batch size: 29
2021-10-14 10:24:35,542 INFO [train.py:451] Epoch 5, batch 9430, batch avg loss 0.2340, total avg loss: 0.2558, batch size: 35
2021-10-14 10:24:40,573 INFO [train.py:451] Epoch 5, batch 9440, batch avg loss 0.2519, total avg loss: 0.2523, batch size: 41
2021-10-14 10:24:45,553 INFO [train.py:451] Epoch 5, batch 9450, batch avg loss 0.2862, total avg loss: 0.2497, batch size: 38
2021-10-14 10:24:50,354 INFO [train.py:451] Epoch 5, batch 9460, batch avg loss 0.2642, total avg loss: 0.2512, batch size: 56
2021-10-14 10:24:55,090 INFO [train.py:451] Epoch 5, batch 9470, batch avg loss 0.2278, total avg loss: 0.2516, batch size: 30
2021-10-14 10:24:59,939 INFO [train.py:451] Epoch 5, batch 9480, batch avg loss 0.2812, total avg loss: 0.2522, batch size: 41
2021-10-14 10:25:04,731 INFO [train.py:451] Epoch 5, batch 9490, batch avg loss 0.2220, total avg loss: 0.2517, batch size: 32
2021-10-14 10:25:09,631 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "d556fbe4-9474-7e2f-e042-2678a7f44aef" will not be mixed in.
2021-10-14 10:25:09,709 INFO [train.py:451] Epoch 5, batch 9500, batch avg loss 0.3110, total avg loss: 0.2526, batch size: 49
2021-10-14 10:25:14,622 INFO [train.py:451] Epoch 5, batch 9510, batch avg loss 0.2400, total avg loss: 0.2524, batch size: 34
2021-10-14 10:25:19,587 INFO [train.py:451] Epoch 5, batch 9520, batch avg loss 0.2252, total avg loss: 0.2529, batch size: 37
2021-10-14 10:25:24,669 INFO [train.py:451] Epoch 5, batch 9530, batch avg loss 0.1826, total avg loss: 0.2522, batch size: 31
2021-10-14 10:25:29,638 INFO [train.py:451] Epoch 5, batch 9540, batch avg loss 0.2333, total avg loss: 0.2535, batch size: 38
2021-10-14 10:25:34,492 INFO [train.py:451] Epoch 5, batch 9550, batch avg loss 0.2782, total avg loss: 0.2535, batch size: 37
2021-10-14 10:25:39,130 INFO [train.py:451] Epoch 5, batch 9560, batch avg loss 0.2475, total avg loss: 0.2551, batch size: 36
2021-10-14 10:25:44,169 INFO [train.py:451] Epoch 5, batch 9570, batch avg loss 0.2640, total avg loss: 0.2554, batch size: 39
2021-10-14 10:25:49,018 INFO [train.py:451] Epoch 5, batch 9580, batch avg loss 0.2454, total avg loss: 0.2562, batch size: 29
2021-10-14 10:25:53,905 INFO [train.py:451] Epoch 5, batch 9590, batch avg loss 0.2532, total avg loss: 0.2563, batch size: 29
2021-10-14 10:25:58,650 INFO [train.py:451] Epoch 5, batch 9600, batch avg loss 0.3350, total avg loss: 0.2573, batch size: 45
2021-10-14 10:26:03,935 INFO [train.py:451] Epoch 5, batch 9610, batch avg loss 0.2612, total avg loss: 0.2575, batch size: 36
2021-10-14 10:26:08,651 INFO [train.py:451] Epoch 5, batch 9620, batch avg loss 0.3401, total avg loss: 0.2576, batch size: 130
2021-10-14 10:26:13,575 INFO [train.py:451] Epoch 5, batch 9630, batch avg loss 0.2181, total avg loss: 0.2555, batch size: 38
2021-10-14 10:26:18,396 INFO [train.py:451] Epoch 5, batch 9640, batch avg loss 0.2575, total avg loss: 0.2529, batch size: 72
2021-10-14 10:26:23,257 INFO [train.py:451] Epoch 5, batch 9650, batch avg loss 0.2819, total avg loss: 0.2528, batch size: 49
2021-10-14 10:26:28,125 INFO [train.py:451] Epoch 5, batch 9660, batch avg loss 0.2367, total avg loss: 0.2525, batch size: 39
2021-10-14 10:26:32,851 INFO [train.py:451] Epoch 5, batch 9670, batch avg loss 0.3403, total avg loss: 0.2534, batch size: 126
2021-10-14 10:26:37,803 INFO [train.py:451] Epoch 5, batch 9680, batch avg loss 0.1789, total avg loss: 0.2517, batch size: 32
2021-10-14 10:26:42,648 INFO [train.py:451] Epoch 5, batch 9690, batch avg loss 0.2749, total avg loss: 0.2537, batch size: 49
2021-10-14 10:26:47,452 INFO [train.py:451] Epoch 5, batch 9700, batch avg loss 0.2448, total avg loss: 0.2549, batch size: 29
2021-10-14 10:26:52,371 INFO [train.py:451] Epoch 5, batch 9710, batch avg loss 0.3145, total avg loss: 0.2538, batch size: 38
2021-10-14 10:26:57,323 INFO [train.py:451] Epoch 5, batch 9720, batch avg loss 0.2566, total avg loss: 0.2537, batch size: 35
2021-10-14 10:27:02,309 INFO [train.py:451] Epoch 5, batch 9730, batch avg loss 0.1903, total avg loss: 0.2534, batch size: 29
2021-10-14 10:27:07,318 INFO [train.py:451] Epoch 5, batch 9740, batch avg loss 0.2313, total avg loss: 0.2526, batch size: 34
2021-10-14 10:27:12,192 INFO [train.py:451] Epoch 5, batch 9750, batch avg loss 0.2671, total avg loss: 0.2526, batch size: 38
2021-10-14 10:27:17,088 INFO [train.py:451] Epoch 5, batch 9760, batch avg loss 0.2604, total avg loss: 0.2524, batch size: 34
2021-10-14 10:27:21,970 INFO [train.py:451] Epoch 5, batch 9770, batch avg loss 0.2119, total avg loss: 0.2520, batch size: 36
2021-10-14 10:27:26,957 INFO [train.py:451] Epoch 5, batch 9780, batch avg loss 0.2547, total avg loss: 0.2523, batch size: 36
2021-10-14 10:27:31,869 INFO [train.py:451] Epoch 5, batch 9790, batch avg loss 0.2501, total avg loss: 0.2523, batch size: 32
2021-10-14 10:27:36,716 INFO [train.py:451] Epoch 5, batch 9800, batch avg loss 0.2491, total avg loss: 0.2520, batch size: 34
2021-10-14 10:27:41,674 INFO [train.py:451] Epoch 5, batch 9810, batch avg loss 0.2690, total avg loss: 0.2492, batch size: 38
2021-10-14 10:27:46,760 INFO [train.py:451] Epoch 5, batch 9820, batch avg loss 0.2767, total avg loss: 0.2488, batch size: 34
2021-10-14 10:27:51,648 INFO [train.py:451] Epoch 5, batch 9830, batch avg loss 0.2580, total avg loss: 0.2491, batch size: 42
2021-10-14 10:27:56,558 INFO [train.py:451] Epoch 5, batch 9840, batch avg loss 0.2833, total avg loss: 0.2486, batch size: 36
2021-10-14 10:28:01,414 INFO [train.py:451] Epoch 5, batch 9850, batch avg loss 0.2525, total avg loss: 0.2513, batch size: 34
2021-10-14 10:28:06,155 INFO [train.py:451] Epoch 5, batch 9860, batch avg loss 0.2889, total avg loss: 0.2543, batch size: 38
2021-10-14 10:28:11,023 INFO [train.py:451] Epoch 5, batch 9870, batch avg loss 0.2136, total avg loss: 0.2525, batch size: 29
2021-10-14 10:28:16,074 INFO [train.py:451] Epoch 5, batch 9880, batch avg loss 0.2196, total avg loss: 0.2497, batch size: 28
2021-10-14 10:28:21,073 INFO [train.py:451] Epoch 5, batch 9890, batch avg loss 0.2486, total avg loss: 0.2506, batch size: 34
2021-10-14 10:28:25,889 INFO [train.py:451] Epoch 5, batch 9900, batch avg loss 0.3809, total avg loss: 0.2534, batch size: 132
2021-10-14 10:28:30,619 INFO [train.py:451] Epoch 5, batch 9910, batch avg loss 0.3756, total avg loss: 0.2559, batch size: 131
2021-10-14 10:28:35,707 INFO [train.py:451] Epoch 5, batch 9920, batch avg loss 0.1951, total avg loss: 0.2533, batch size: 29
2021-10-14 10:28:40,676 INFO [train.py:451] Epoch 5, batch 9930, batch avg loss 0.2079, total avg loss: 0.2524, batch size: 34
2021-10-14 10:28:45,612 INFO [train.py:451] Epoch 5, batch 9940, batch avg loss 0.2467, total avg loss: 0.2535, batch size: 31
2021-10-14 10:28:50,490 INFO [train.py:451] Epoch 5, batch 9950, batch avg loss 0.2230, total avg loss: 0.2537, batch size: 30
2021-10-14 10:28:55,259 INFO [train.py:451] Epoch 5, batch 9960, batch avg loss 0.3704, total avg loss: 0.2554, batch size: 134
2021-10-14 10:29:00,087 INFO [train.py:451] Epoch 5, batch 9970, batch avg loss 0.2808, total avg loss: 0.2565, batch size: 32
2021-10-14 10:29:04,944 INFO [train.py:451] Epoch 5, batch 9980, batch avg loss 0.2906, total avg loss: 0.2567, batch size: 72
2021-10-14 10:29:09,794 INFO [train.py:451] Epoch 5, batch 9990, batch avg loss 0.2768, total avg loss: 0.2576, batch size: 36
2021-10-14 10:29:14,886 INFO [train.py:451] Epoch 5, batch 10000, batch avg loss 0.2537, total avg loss: 0.2568, batch size: 30
2021-10-14 10:29:53,515 INFO [train.py:483] Epoch 5, valid loss 0.1824, best valid loss: 0.1824 best valid epoch: 5
2021-10-14 10:29:58,429 INFO [train.py:451] Epoch 5, batch 10010, batch avg loss 0.2030, total avg loss: 0.2486, batch size: 32
2021-10-14 10:30:03,299 INFO [train.py:451] Epoch 5, batch 10020, batch avg loss 0.2333, total avg loss: 0.2599, batch size: 36
2021-10-14 10:30:07,973 INFO [train.py:451] Epoch 5, batch 10030, batch avg loss 0.2285, total avg loss: 0.2664, batch size: 30
2021-10-14 10:30:12,904 INFO [train.py:451] Epoch 5, batch 10040, batch avg loss 0.2585, total avg loss: 0.2599, batch size: 35
2021-10-14 10:30:17,733 INFO [train.py:451] Epoch 5, batch 10050, batch avg loss 0.2351, total avg loss: 0.2578, batch size: 30
2021-10-14 10:30:22,603 INFO [train.py:451] Epoch 5, batch 10060, batch avg loss 0.2650, total avg loss: 0.2582, batch size: 38
2021-10-14 10:30:27,431 INFO [train.py:451] Epoch 5, batch 10070, batch avg loss 0.3459, total avg loss: 0.2599, batch size: 130
2021-10-14 10:30:32,427 INFO [train.py:451] Epoch 5, batch 10080, batch avg loss 0.2232, total avg loss: 0.2562, batch size: 30
2021-10-14 10:30:37,239 INFO [train.py:451] Epoch 5, batch 10090, batch avg loss 0.2635, total avg loss: 0.2587, batch size: 35
2021-10-14 10:30:42,241 INFO [train.py:451] Epoch 5, batch 10100, batch avg loss 0.2568, total avg loss: 0.2569, batch size: 34
2021-10-14 10:30:47,072 INFO [train.py:451] Epoch 5, batch 10110, batch avg loss 0.2328, total avg loss: 0.2582, batch size: 34
2021-10-14 10:30:51,855 INFO [train.py:451] Epoch 5, batch 10120, batch avg loss 0.3097, total avg loss: 0.2580, batch size: 49
2021-10-14 10:30:56,737 INFO [train.py:451] Epoch 5, batch 10130, batch avg loss 0.2676, total avg loss: 0.2574, batch size: 41
2021-10-14 10:31:01,640 INFO [train.py:451] Epoch 5, batch 10140, batch avg loss 0.2473, total avg loss: 0.2559, batch size: 34
2021-10-14 10:31:06,300 INFO [train.py:451] Epoch 5, batch 10150, batch avg loss 0.2218, total avg loss: 0.2566, batch size: 39
2021-10-14 10:31:11,148 INFO [train.py:451] Epoch 5, batch 10160, batch avg loss 0.2059, total avg loss: 0.2563, batch size: 31
2021-10-14 10:31:16,144 INFO [train.py:451] Epoch 5, batch 10170, batch avg loss 0.2700, total avg loss: 0.2555, batch size: 33
2021-10-14 10:31:21,139 INFO [train.py:451] Epoch 5, batch 10180, batch avg loss 0.2478, total avg loss: 0.2545, batch size: 42
2021-10-14 10:31:26,005 INFO [train.py:451] Epoch 5, batch 10190, batch avg loss 0.3035, total avg loss: 0.2552, batch size: 38
2021-10-14 10:31:31,006 INFO [train.py:451] Epoch 5, batch 10200, batch avg loss 0.2033, total avg loss: 0.2548, batch size: 29
2021-10-14 10:31:35,857 INFO [train.py:451] Epoch 5, batch 10210, batch avg loss 0.3234, total avg loss: 0.2558, batch size: 126
2021-10-14 10:31:40,845 INFO [train.py:451] Epoch 5, batch 10220, batch avg loss 0.2470, total avg loss: 0.2526, batch size: 35
2021-10-14 10:31:45,603 INFO [train.py:451] Epoch 5, batch 10230, batch avg loss 0.3161, total avg loss: 0.2575, batch size: 58
2021-10-14 10:31:50,504 INFO [train.py:451] Epoch 5, batch 10240, batch avg loss 0.2743, total avg loss: 0.2573, batch size: 56
2021-10-14 10:31:55,287 INFO [train.py:451] Epoch 5, batch 10250, batch avg loss 0.3810, total avg loss: 0.2629, batch size: 128
2021-10-14 10:32:00,228 INFO [train.py:451] Epoch 5, batch 10260, batch avg loss 0.2855, total avg loss: 0.2618, batch size: 32
2021-10-14 10:32:05,227 INFO [train.py:451] Epoch 5, batch 10270, batch avg loss 0.2655, total avg loss: 0.2590, batch size: 45
2021-10-14 10:32:10,065 INFO [train.py:451] Epoch 5, batch 10280, batch avg loss 0.2424, total avg loss: 0.2588, batch size: 34
2021-10-14 10:32:14,953 INFO [train.py:451] Epoch 5, batch 10290, batch avg loss 0.2395, total avg loss: 0.2605, batch size: 28
2021-10-14 10:32:19,896 INFO [train.py:451] Epoch 5, batch 10300, batch avg loss 0.3118, total avg loss: 0.2605, batch size: 41
2021-10-14 10:32:24,547 INFO [train.py:451] Epoch 5, batch 10310, batch avg loss 0.2655, total avg loss: 0.2617, batch size: 35
2021-10-14 10:32:29,452 INFO [train.py:451] Epoch 5, batch 10320, batch avg loss 0.2009, total avg loss: 0.2609, batch size: 30
2021-10-14 10:32:34,437 INFO [train.py:451] Epoch 5, batch 10330, batch avg loss 0.2460, total avg loss: 0.2599, batch size: 33
2021-10-14 10:32:39,205 INFO [train.py:451] Epoch 5, batch 10340, batch avg loss 0.2798, total avg loss: 0.2602, batch size: 39
2021-10-14 10:32:43,940 INFO [train.py:451] Epoch 5, batch 10350, batch avg loss 0.2573, total avg loss: 0.2624, batch size: 37
2021-10-14 10:32:48,809 INFO [train.py:451] Epoch 5, batch 10360, batch avg loss 0.3161, total avg loss: 0.2621, batch size: 40
2021-10-14 10:32:53,562 INFO [train.py:451] Epoch 5, batch 10370, batch avg loss 0.2929, total avg loss: 0.2626, batch size: 32
2021-10-14 10:32:58,552 INFO [train.py:451] Epoch 5, batch 10380, batch avg loss 0.2194, total avg loss: 0.2617, batch size: 32
2021-10-14 10:33:03,468 INFO [train.py:451] Epoch 5, batch 10390, batch avg loss 0.2421, total avg loss: 0.2615, batch size: 30
2021-10-14 10:33:08,389 INFO [train.py:451] Epoch 5, batch 10400, batch avg loss 0.2348, total avg loss: 0.2609, batch size: 32
2021-10-14 10:33:13,377 INFO [train.py:451] Epoch 5, batch 10410, batch avg loss 0.2781, total avg loss: 0.2426, batch size: 35
2021-10-14 10:33:18,389 INFO [train.py:451] Epoch 5, batch 10420, batch avg loss 0.2295, total avg loss: 0.2406, batch size: 38
2021-10-14 10:33:23,231 INFO [train.py:451] Epoch 5, batch 10430, batch avg loss 0.2413, total avg loss: 0.2432, batch size: 39
2021-10-14 10:33:28,071 INFO [train.py:451] Epoch 5, batch 10440, batch avg loss 0.2549, total avg loss: 0.2513, batch size: 31
2021-10-14 10:33:32,937 INFO [train.py:451] Epoch 5, batch 10450, batch avg loss 0.2752, total avg loss: 0.2527, batch size: 38
2021-10-14 10:33:37,755 INFO [train.py:451] Epoch 5, batch 10460, batch avg loss 0.2286, total avg loss: 0.2517, batch size: 29
2021-10-14 10:33:42,620 INFO [train.py:451] Epoch 5, batch 10470, batch avg loss 0.2457, total avg loss: 0.2506, batch size: 37
2021-10-14 10:33:47,574 INFO [train.py:451] Epoch 5, batch 10480, batch avg loss 0.2291, total avg loss: 0.2490, batch size: 34
2021-10-14 10:33:52,323 INFO [train.py:451] Epoch 5, batch 10490, batch avg loss 0.3370, total avg loss: 0.2517, batch size: 38
2021-10-14 10:33:57,167 INFO [train.py:451] Epoch 5, batch 10500, batch avg loss 0.2928, total avg loss: 0.2521, batch size: 49
2021-10-14 10:34:02,121 INFO [train.py:451] Epoch 5, batch 10510, batch avg loss 0.2044, total avg loss: 0.2509, batch size: 29
2021-10-14 10:34:06,910 INFO [train.py:451] Epoch 5, batch 10520, batch avg loss 0.2322, total avg loss: 0.2501, batch size: 36
2021-10-14 10:34:11,696 INFO [train.py:451] Epoch 5, batch 10530, batch avg loss 0.2700, total avg loss: 0.2515, batch size: 38
2021-10-14 10:34:16,583 INFO [train.py:451] Epoch 5, batch 10540, batch avg loss 0.1849, total avg loss: 0.2522, batch size: 28
2021-10-14 10:34:21,375 INFO [train.py:451] Epoch 5, batch 10550, batch avg loss 0.3806, total avg loss: 0.2540, batch size: 126
2021-10-14 10:34:26,308 INFO [train.py:451] Epoch 5, batch 10560, batch avg loss 0.2528, total avg loss: 0.2539, batch size: 41
2021-10-14 10:34:31,227 INFO [train.py:451] Epoch 5, batch 10570, batch avg loss 0.3851, total avg loss: 0.2546, batch size: 126
2021-10-14 10:34:35,889 INFO [train.py:451] Epoch 5, batch 10580, batch avg loss 0.2554, total avg loss: 0.2563, batch size: 34
2021-10-14 10:34:40,571 INFO [train.py:451] Epoch 5, batch 10590, batch avg loss 0.3245, total avg loss: 0.2561, batch size: 73
2021-10-14 10:34:45,309 INFO [train.py:451] Epoch 5, batch 10600, batch avg loss 0.2739, total avg loss: 0.2572, batch size: 36
2021-10-14 10:34:50,001 INFO [train.py:451] Epoch 5, batch 10610, batch avg loss 0.2149, total avg loss: 0.2615, batch size: 32
2021-10-14 10:34:54,847 INFO [train.py:451] Epoch 5, batch 10620, batch avg loss 0.2534, total avg loss: 0.2634, batch size: 38
2021-10-14 10:34:59,653 INFO [train.py:451] Epoch 5, batch 10630, batch avg loss 0.2067, total avg loss: 0.2606, batch size: 27
2021-10-14 10:35:04,443 INFO [train.py:451] Epoch 5, batch 10640, batch avg loss 0.3448, total avg loss: 0.2692, batch size: 128
2021-10-14 10:35:09,211 INFO [train.py:451] Epoch 5, batch 10650, batch avg loss 0.2358, total avg loss: 0.2687, batch size: 32
2021-10-14 10:35:14,095 INFO [train.py:451] Epoch 5, batch 10660, batch avg loss 0.2731, total avg loss: 0.2688, batch size: 49
2021-10-14 10:35:19,119 INFO [train.py:451] Epoch 5, batch 10670, batch avg loss 0.2639, total avg loss: 0.2646, batch size: 42
2021-10-14 10:35:23,862 INFO [train.py:451] Epoch 5, batch 10680, batch avg loss 0.3502, total avg loss: 0.2680, batch size: 126
2021-10-14 10:35:28,686 INFO [train.py:451] Epoch 5, batch 10690, batch avg loss 0.3039, total avg loss: 0.2670, batch size: 73
2021-10-14 10:35:33,478 INFO [train.py:451] Epoch 5, batch 10700, batch avg loss 0.2396, total avg loss: 0.2684, batch size: 35
2021-10-14 10:35:38,355 INFO [train.py:451] Epoch 5, batch 10710, batch avg loss 0.2264, total avg loss: 0.2664, batch size: 30
2021-10-14 10:35:43,001 INFO [train.py:451] Epoch 5, batch 10720, batch avg loss 0.2321, total avg loss: 0.2666, batch size: 38
2021-10-14 10:35:47,912 INFO [train.py:451] Epoch 5, batch 10730, batch avg loss 0.3030, total avg loss: 0.2659, batch size: 35
2021-10-14 10:35:53,102 INFO [train.py:451] Epoch 5, batch 10740, batch avg loss 0.2751, total avg loss: 0.2646, batch size: 36
2021-10-14 10:35:58,082 INFO [train.py:451] Epoch 5, batch 10750, batch avg loss 0.2271, total avg loss: 0.2637, batch size: 34
2021-10-14 10:36:03,029 INFO [train.py:451] Epoch 5, batch 10760, batch avg loss 0.2477, total avg loss: 0.2635, batch size: 34
2021-10-14 10:36:07,921 INFO [train.py:451] Epoch 5, batch 10770, batch avg loss 0.3039, total avg loss: 0.2628, batch size: 49
2021-10-14 10:36:12,878 INFO [train.py:451] Epoch 5, batch 10780, batch avg loss 0.2889, total avg loss: 0.2626, batch size: 49
2021-10-14 10:36:17,638 INFO [train.py:451] Epoch 5, batch 10790, batch avg loss 0.2301, total avg loss: 0.2632, batch size: 30
2021-10-14 10:36:22,777 INFO [train.py:451] Epoch 5, batch 10800, batch avg loss 0.4043, total avg loss: 0.2628, batch size: 129
2021-10-14 10:36:27,722 INFO [train.py:451] Epoch 5, batch 10810, batch avg loss 0.2809, total avg loss: 0.2512, batch size: 39
2021-10-14 10:36:32,655 INFO [train.py:451] Epoch 5, batch 10820, batch avg loss 0.3128, total avg loss: 0.2474, batch size: 49
2021-10-14 10:36:37,458 INFO [train.py:451] Epoch 5, batch 10830, batch avg loss 0.2689, total avg loss: 0.2502, batch size: 57
2021-10-14 10:36:42,443 INFO [train.py:451] Epoch 5, batch 10840, batch avg loss 0.2325, total avg loss: 0.2514, batch size: 27
2021-10-14 10:36:47,162 INFO [train.py:451] Epoch 5, batch 10850, batch avg loss 0.2771, total avg loss: 0.2546, batch size: 42
2021-10-14 10:36:51,758 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "88ae167e-d4a3-3293-25cf-3dac7e17b7f6" will not be mixed in.
2021-10-14 10:36:52,011 INFO [train.py:451] Epoch 5, batch 10860, batch avg loss 0.2451, total avg loss: 0.2542, batch size: 29
2021-10-14 10:36:56,849 INFO [train.py:451] Epoch 5, batch 10870, batch avg loss 0.2138, total avg loss: 0.2546, batch size: 33
2021-10-14 10:37:01,715 INFO [train.py:451] Epoch 5, batch 10880, batch avg loss 0.1995, total avg loss: 0.2530, batch size: 32
2021-10-14 10:37:06,662 INFO [train.py:451] Epoch 5, batch 10890, batch avg loss 0.3517, total avg loss: 0.2552, batch size: 36
2021-10-14 10:37:11,512 INFO [train.py:451] Epoch 5, batch 10900, batch avg loss 0.2101, total avg loss: 0.2553, batch size: 29
2021-10-14 10:37:16,445 INFO [train.py:451] Epoch 5, batch 10910, batch avg loss 0.2176, total avg loss: 0.2543, batch size: 32
2021-10-14 10:37:21,187 INFO [train.py:451] Epoch 5, batch 10920, batch avg loss 0.2293, total avg loss: 0.2548, batch size: 36
2021-10-14 10:37:26,107 INFO [train.py:451] Epoch 5, batch 10930, batch avg loss 0.2835, total avg loss: 0.2561, batch size: 34
2021-10-14 10:37:31,132 INFO [train.py:451] Epoch 5, batch 10940, batch avg loss 0.2274, total avg loss: 0.2555, batch size: 32
2021-10-14 10:37:35,812 INFO [train.py:451] Epoch 5, batch 10950, batch avg loss 0.2238, total avg loss: 0.2558, batch size: 39
2021-10-14 10:37:40,676 INFO [train.py:451] Epoch 5, batch 10960, batch avg loss 0.2565, total avg loss: 0.2559, batch size: 45
2021-10-14 10:37:45,428 INFO [train.py:451] Epoch 5, batch 10970, batch avg loss 0.1850, total avg loss: 0.2565, batch size: 31
2021-10-14 10:37:50,442 INFO [train.py:451] Epoch 5, batch 10980, batch avg loss 0.2685, total avg loss: 0.2558, batch size: 49
2021-10-14 10:37:55,251 INFO [train.py:451] Epoch 5, batch 10990, batch avg loss 0.2200, total avg loss: 0.2560, batch size: 33
2021-10-14 10:38:00,189 INFO [train.py:451] Epoch 5, batch 11000, batch avg loss 0.3127, total avg loss: 0.2570, batch size: 39
2021-10-14 10:38:39,413 INFO [train.py:483] Epoch 5, valid loss 0.1829, best valid loss: 0.1824 best valid epoch: 5
2021-10-14 10:38:44,293 INFO [train.py:451] Epoch 5, batch 11010, batch avg loss 0.2145, total avg loss: 0.2613, batch size: 30
2021-10-14 10:38:49,194 INFO [train.py:451] Epoch 5, batch 11020, batch avg loss 0.1909, total avg loss: 0.2534, batch size: 29
2021-10-14 10:38:54,165 INFO [train.py:451] Epoch 5, batch 11030, batch avg loss 0.2473, total avg loss: 0.2571, batch size: 36
2021-10-14 10:38:59,029 INFO [train.py:451] Epoch 5, batch 11040, batch avg loss 0.2548, total avg loss: 0.2536, batch size: 35
2021-10-14 10:39:04,144 INFO [train.py:451] Epoch 5, batch 11050, batch avg loss 0.2669, total avg loss: 0.2511, batch size: 34
2021-10-14 10:39:08,978 INFO [train.py:451] Epoch 5, batch 11060, batch avg loss 0.2399, total avg loss: 0.2528, batch size: 36
2021-10-14 10:39:13,809 INFO [train.py:451] Epoch 5, batch 11070, batch avg loss 0.2548, total avg loss: 0.2535, batch size: 35
2021-10-14 10:39:18,808 INFO [train.py:451] Epoch 5, batch 11080, batch avg loss 0.2549, total avg loss: 0.2519, batch size: 38
2021-10-14 10:39:23,821 INFO [train.py:451] Epoch 5, batch 11090, batch avg loss 0.2519, total avg loss: 0.2518, batch size: 34
2021-10-14 10:39:28,668 INFO [train.py:451] Epoch 5, batch 11100, batch avg loss 0.2806, total avg loss: 0.2518, batch size: 74
2021-10-14 10:39:33,616 INFO [train.py:451] Epoch 5, batch 11110, batch avg loss 0.2709, total avg loss: 0.2524, batch size: 49
2021-10-14 10:39:38,486 INFO [train.py:451] Epoch 5, batch 11120, batch avg loss 0.2474, total avg loss: 0.2547, batch size: 49
2021-10-14 10:39:43,376 INFO [train.py:451] Epoch 5, batch 11130, batch avg loss 0.2234, total avg loss: 0.2534, batch size: 32
2021-10-14 10:39:48,331 INFO [train.py:451] Epoch 5, batch 11140, batch avg loss 0.2548, total avg loss: 0.2545, batch size: 36
2021-10-14 10:39:53,323 INFO [train.py:451] Epoch 5, batch 11150, batch avg loss 0.2637, total avg loss: 0.2551, batch size: 31
2021-10-14 10:39:58,331 INFO [train.py:451] Epoch 5, batch 11160, batch avg loss 0.2148, total avg loss: 0.2545, batch size: 31
2021-10-14 10:40:03,190 INFO [train.py:451] Epoch 5, batch 11170, batch avg loss 0.2606, total avg loss: 0.2550, batch size: 37
2021-10-14 10:40:07,935 INFO [train.py:451] Epoch 5, batch 11180, batch avg loss 0.1975, total avg loss: 0.2548, batch size: 28
2021-10-14 10:40:12,877 INFO [train.py:451] Epoch 5, batch 11190, batch avg loss 0.2437, total avg loss: 0.2546, batch size: 35
2021-10-14 10:40:18,154 INFO [train.py:451] Epoch 5, batch 11200, batch avg loss 0.2489, total avg loss: 0.2548, batch size: 37
2021-10-14 10:40:23,077 INFO [train.py:451] Epoch 5, batch 11210, batch avg loss 0.2165, total avg loss: 0.2594, batch size: 32
2021-10-14 10:40:27,894 INFO [train.py:451] Epoch 5, batch 11220, batch avg loss 0.2469, total avg loss: 0.2548, batch size: 36
2021-10-14 10:40:32,850 INFO [train.py:451] Epoch 5, batch 11230, batch avg loss 0.2139, total avg loss: 0.2519, batch size: 33
2021-10-14 10:40:37,763 INFO [train.py:451] Epoch 5, batch 11240, batch avg loss 0.2877, total avg loss: 0.2541, batch size: 57
2021-10-14 10:40:42,767 INFO [train.py:451] Epoch 5, batch 11250, batch avg loss 0.2074, total avg loss: 0.2523, batch size: 34
2021-10-14 10:40:47,626 INFO [train.py:451] Epoch 5, batch 11260, batch avg loss 0.2874, total avg loss: 0.2524, batch size: 72
2021-10-14 10:40:52,564 INFO [train.py:451] Epoch 5, batch 11270, batch avg loss 0.2283, total avg loss: 0.2493, batch size: 29
2021-10-14 10:40:57,466 INFO [train.py:451] Epoch 5, batch 11280, batch avg loss 0.2571, total avg loss: 0.2508, batch size: 39
2021-10-14 10:41:02,382 INFO [train.py:451] Epoch 5, batch 11290, batch avg loss 0.2248, total avg loss: 0.2504, batch size: 31
2021-10-14 10:41:07,395 INFO [train.py:451] Epoch 5, batch 11300, batch avg loss 0.2996, total avg loss: 0.2513, batch size: 49
2021-10-14 10:41:12,359 INFO [train.py:451] Epoch 5, batch 11310, batch avg loss 0.3161, total avg loss: 0.2551, batch size: 49
2021-10-14 10:41:17,370 INFO [train.py:451] Epoch 5, batch 11320, batch avg loss 0.1911, total avg loss: 0.2539, batch size: 27
2021-10-14 10:41:22,182 INFO [train.py:451] Epoch 5, batch 11330, batch avg loss 0.3778, total avg loss: 0.2535, batch size: 129
2021-10-14 10:41:27,177 INFO [train.py:451] Epoch 5, batch 11340, batch avg loss 0.2737, total avg loss: 0.2531, batch size: 36
2021-10-14 10:41:32,117 INFO [train.py:451] Epoch 5, batch 11350, batch avg loss 0.2275, total avg loss: 0.2511, batch size: 36
2021-10-14 10:41:36,987 INFO [train.py:451] Epoch 5, batch 11360, batch avg loss 0.2717, total avg loss: 0.2512, batch size: 38
2021-10-14 10:41:41,789 INFO [train.py:451] Epoch 5, batch 11370, batch avg loss 0.2531, total avg loss: 0.2512, batch size: 35
2021-10-14 10:41:46,944 INFO [train.py:451] Epoch 5, batch 11380, batch avg loss 0.2585, total avg loss: 0.2513, batch size: 32
2021-10-14 10:41:51,813 INFO [train.py:451] Epoch 5, batch 11390, batch avg loss 0.2848, total avg loss: 0.2521, batch size: 31
2021-10-14 10:41:56,815 INFO [train.py:451] Epoch 5, batch 11400, batch avg loss 0.1845, total avg loss: 0.2522, batch size: 28
2021-10-14 10:42:01,799 INFO [train.py:451] Epoch 5, batch 11410, batch avg loss 0.1747, total avg loss: 0.2507, batch size: 27
2021-10-14 10:42:06,743 INFO [train.py:451] Epoch 5, batch 11420, batch avg loss 0.2196, total avg loss: 0.2528, batch size: 28
2021-10-14 10:42:11,609 INFO [train.py:451] Epoch 5, batch 11430, batch avg loss 0.1921, total avg loss: 0.2534, batch size: 28
2021-10-14 10:42:16,767 INFO [train.py:451] Epoch 5, batch 11440, batch avg loss 0.2478, total avg loss: 0.2533, batch size: 27
2021-10-14 10:42:21,506 INFO [train.py:451] Epoch 5, batch 11450, batch avg loss 0.2719, total avg loss: 0.2558, batch size: 38
2021-10-14 10:42:26,367 INFO [train.py:451] Epoch 5, batch 11460, batch avg loss 0.2128, total avg loss: 0.2584, batch size: 27
2021-10-14 10:42:31,226 INFO [train.py:451] Epoch 5, batch 11470, batch avg loss 0.2410, total avg loss: 0.2583, batch size: 34
2021-10-14 10:42:35,955 INFO [train.py:451] Epoch 5, batch 11480, batch avg loss 0.2619, total avg loss: 0.2579, batch size: 32
2021-10-14 10:42:40,791 INFO [train.py:451] Epoch 5, batch 11490, batch avg loss 0.2566, total avg loss: 0.2591, batch size: 35
2021-10-14 10:42:45,828 INFO [train.py:451] Epoch 5, batch 11500, batch avg loss 0.2531, total avg loss: 0.2594, batch size: 34
2021-10-14 10:42:50,545 INFO [train.py:451] Epoch 5, batch 11510, batch avg loss 0.2911, total avg loss: 0.2617, batch size: 49
2021-10-14 10:42:55,338 INFO [train.py:451] Epoch 5, batch 11520, batch avg loss 0.3516, total avg loss: 0.2616, batch size: 129
2021-10-14 10:43:00,148 INFO [train.py:451] Epoch 5, batch 11530, batch avg loss 0.1832, total avg loss: 0.2611, batch size: 30
2021-10-14 10:43:05,059 INFO [train.py:451] Epoch 5, batch 11540, batch avg loss 0.2942, total avg loss: 0.2592, batch size: 57
2021-10-14 10:43:09,982 INFO [train.py:451] Epoch 5, batch 11550, batch avg loss 0.2828, total avg loss: 0.2602, batch size: 36
2021-10-14 10:43:14,916 INFO [train.py:451] Epoch 5, batch 11560, batch avg loss 0.3309, total avg loss: 0.2594, batch size: 36
2021-10-14 10:43:20,010 INFO [train.py:451] Epoch 5, batch 11570, batch avg loss 0.2736, total avg loss: 0.2587, batch size: 32
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2021-10-14 10:56:23,384 INFO [train.py:483] Epoch 5, valid loss 0.1817, best valid loss: 0.1817 best valid epoch: 5
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2021-10-14 11:23:08,461 INFO [train.py:483] Epoch 5, valid loss 0.1808, best valid loss: 0.1808 best valid epoch: 5
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2021-10-14 11:32:02,090 INFO [train.py:483] Epoch 5, valid loss 0.1804, best valid loss: 0.1804 best valid epoch: 5
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2021-10-14 11:49:53,459 INFO [train.py:483] Epoch 5, valid loss 0.1811, best valid loss: 0.1804 best valid epoch: 5
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2021-10-14 11:58:47,945 INFO [train.py:483] Epoch 5, valid loss 0.1804, best valid loss: 0.1804 best valid epoch: 5
2021-10-14 11:58:52,853 INFO [train.py:451] Epoch 5, batch 20010, batch avg loss 0.2719, total avg loss: 0.2634, batch size: 37
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2021-10-14 11:59:37,019 INFO [train.py:451] Epoch 5, batch 20100, batch avg loss 0.2207, total avg loss: 0.2517, batch size: 35
2021-10-14 11:59:41,998 INFO [train.py:451] Epoch 5, batch 20110, batch avg loss 0.2488, total avg loss: 0.2530, batch size: 35
2021-10-14 11:59:47,007 INFO [train.py:451] Epoch 5, batch 20120, batch avg loss 0.3056, total avg loss: 0.2528, batch size: 35
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2021-10-14 12:02:04,184 INFO [train.py:451] Epoch 5, batch 20400, batch avg loss 0.3035, total avg loss: 0.2560, batch size: 38
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2021-10-14 12:02:29,326 INFO [train.py:451] Epoch 5, batch 20450, batch avg loss 0.2405, total avg loss: 0.2520, batch size: 33
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2021-10-14 12:02:44,050 INFO [train.py:451] Epoch 5, batch 20480, batch avg loss 0.2090, total avg loss: 0.2512, batch size: 29
2021-10-14 12:02:49,005 INFO [train.py:451] Epoch 5, batch 20490, batch avg loss 0.3550, total avg loss: 0.2528, batch size: 125
2021-10-14 12:02:53,860 INFO [train.py:451] Epoch 5, batch 20500, batch avg loss 0.2633, total avg loss: 0.2530, batch size: 27
2021-10-14 12:02:58,710 INFO [train.py:451] Epoch 5, batch 20510, batch avg loss 0.2333, total avg loss: 0.2534, batch size: 34
2021-10-14 12:03:03,529 INFO [train.py:451] Epoch 5, batch 20520, batch avg loss 0.2763, total avg loss: 0.2534, batch size: 42
2021-10-14 12:03:08,485 INFO [train.py:451] Epoch 5, batch 20530, batch avg loss 0.2128, total avg loss: 0.2526, batch size: 33
2021-10-14 12:03:13,371 INFO [train.py:451] Epoch 5, batch 20540, batch avg loss 0.2543, total avg loss: 0.2519, batch size: 33
2021-10-14 12:03:18,335 INFO [train.py:451] Epoch 5, batch 20550, batch avg loss 0.2855, total avg loss: 0.2516, batch size: 36
2021-10-14 12:03:23,166 INFO [train.py:451] Epoch 5, batch 20560, batch avg loss 0.1996, total avg loss: 0.2509, batch size: 31
2021-10-14 12:03:28,132 INFO [train.py:451] Epoch 5, batch 20570, batch avg loss 0.2543, total avg loss: 0.2508, batch size: 37
2021-10-14 12:03:33,115 INFO [train.py:451] Epoch 5, batch 20580, batch avg loss 0.2299, total avg loss: 0.2511, batch size: 34
2021-10-14 12:03:37,985 INFO [train.py:451] Epoch 5, batch 20590, batch avg loss 0.3764, total avg loss: 0.2513, batch size: 133
2021-10-14 12:03:42,983 INFO [train.py:451] Epoch 5, batch 20600, batch avg loss 0.2455, total avg loss: 0.2510, batch size: 34
2021-10-14 12:03:47,759 INFO [train.py:451] Epoch 5, batch 20610, batch avg loss 0.3216, total avg loss: 0.2585, batch size: 74
2021-10-14 12:03:52,781 INFO [train.py:451] Epoch 5, batch 20620, batch avg loss 0.2416, total avg loss: 0.2526, batch size: 33
2021-10-14 12:03:57,557 INFO [train.py:451] Epoch 5, batch 20630, batch avg loss 0.1820, total avg loss: 0.2562, batch size: 28
2021-10-14 12:04:02,522 INFO [train.py:451] Epoch 5, batch 20640, batch avg loss 0.2445, total avg loss: 0.2528, batch size: 32
2021-10-14 12:04:07,404 INFO [train.py:451] Epoch 5, batch 20650, batch avg loss 0.2124, total avg loss: 0.2515, batch size: 33
2021-10-14 12:04:12,139 INFO [train.py:451] Epoch 5, batch 20660, batch avg loss 0.2650, total avg loss: 0.2524, batch size: 38
2021-10-14 12:04:17,073 INFO [train.py:451] Epoch 5, batch 20670, batch avg loss 0.2677, total avg loss: 0.2500, batch size: 36
2021-10-14 12:04:21,793 INFO [train.py:451] Epoch 5, batch 20680, batch avg loss 0.2434, total avg loss: 0.2514, batch size: 34
2021-10-14 12:04:26,659 INFO [train.py:451] Epoch 5, batch 20690, batch avg loss 0.2675, total avg loss: 0.2529, batch size: 38
2021-10-14 12:04:31,672 INFO [train.py:451] Epoch 5, batch 20700, batch avg loss 0.2848, total avg loss: 0.2512, batch size: 42
2021-10-14 12:04:36,527 INFO [train.py:451] Epoch 5, batch 20710, batch avg loss 0.2370, total avg loss: 0.2525, batch size: 39
2021-10-14 12:04:41,384 INFO [train.py:451] Epoch 5, batch 20720, batch avg loss 0.2562, total avg loss: 0.2547, batch size: 31
2021-10-14 12:04:46,457 INFO [train.py:451] Epoch 5, batch 20730, batch avg loss 0.2085, total avg loss: 0.2550, batch size: 27
2021-10-14 12:04:51,262 INFO [train.py:451] Epoch 5, batch 20740, batch avg loss 0.2900, total avg loss: 0.2569, batch size: 57
2021-10-14 12:04:56,061 INFO [train.py:451] Epoch 5, batch 20750, batch avg loss 0.2405, total avg loss: 0.2568, batch size: 38
2021-10-14 12:05:00,934 INFO [train.py:451] Epoch 5, batch 20760, batch avg loss 0.2926, total avg loss: 0.2573, batch size: 45
2021-10-14 12:05:05,711 INFO [train.py:451] Epoch 5, batch 20770, batch avg loss 0.2515, total avg loss: 0.2573, batch size: 34
2021-10-14 12:05:10,643 INFO [train.py:451] Epoch 5, batch 20780, batch avg loss 0.2711, total avg loss: 0.2564, batch size: 38
2021-10-14 12:05:15,724 INFO [train.py:451] Epoch 5, batch 20790, batch avg loss 0.2502, total avg loss: 0.2553, batch size: 28
2021-10-14 12:05:20,622 INFO [train.py:451] Epoch 5, batch 20800, batch avg loss 0.2305, total avg loss: 0.2551, batch size: 31
2021-10-14 12:05:25,371 INFO [train.py:451] Epoch 5, batch 20810, batch avg loss 0.2389, total avg loss: 0.2622, batch size: 29
2021-10-14 12:05:30,274 INFO [train.py:451] Epoch 5, batch 20820, batch avg loss 0.2171, total avg loss: 0.2597, batch size: 33
2021-10-14 12:05:35,023 INFO [train.py:451] Epoch 5, batch 20830, batch avg loss 0.2033, total avg loss: 0.2567, batch size: 31
2021-10-14 12:05:39,879 INFO [train.py:451] Epoch 5, batch 20840, batch avg loss 0.2878, total avg loss: 0.2543, batch size: 57
2021-10-14 12:05:44,614 INFO [train.py:451] Epoch 5, batch 20850, batch avg loss 0.2518, total avg loss: 0.2561, batch size: 37
2021-10-14 12:05:49,389 INFO [train.py:451] Epoch 5, batch 20860, batch avg loss 0.3001, total avg loss: 0.2553, batch size: 73
2021-10-14 12:05:54,305 INFO [train.py:451] Epoch 5, batch 20870, batch avg loss 0.2022, total avg loss: 0.2543, batch size: 31
2021-10-14 12:05:59,244 INFO [train.py:451] Epoch 5, batch 20880, batch avg loss 0.2385, total avg loss: 0.2553, batch size: 36
2021-10-14 12:06:04,203 INFO [train.py:451] Epoch 5, batch 20890, batch avg loss 0.2094, total avg loss: 0.2526, batch size: 32
2021-10-14 12:06:09,132 INFO [train.py:451] Epoch 5, batch 20900, batch avg loss 0.2410, total avg loss: 0.2491, batch size: 36
2021-10-14 12:06:14,048 INFO [train.py:451] Epoch 5, batch 20910, batch avg loss 0.2496, total avg loss: 0.2477, batch size: 39
2021-10-14 12:06:19,002 INFO [train.py:451] Epoch 5, batch 20920, batch avg loss 0.3324, total avg loss: 0.2477, batch size: 38
2021-10-14 12:06:23,882 INFO [train.py:451] Epoch 5, batch 20930, batch avg loss 0.2860, total avg loss: 0.2477, batch size: 36
2021-10-14 12:06:28,861 INFO [train.py:451] Epoch 5, batch 20940, batch avg loss 0.2615, total avg loss: 0.2479, batch size: 37
2021-10-14 12:06:33,823 INFO [train.py:451] Epoch 5, batch 20950, batch avg loss 0.3068, total avg loss: 0.2482, batch size: 45
2021-10-14 12:06:38,729 INFO [train.py:451] Epoch 5, batch 20960, batch avg loss 0.2510, total avg loss: 0.2479, batch size: 35
2021-10-14 12:06:43,545 INFO [train.py:451] Epoch 5, batch 20970, batch avg loss 0.2247, total avg loss: 0.2478, batch size: 30
2021-10-14 12:06:48,395 INFO [train.py:451] Epoch 5, batch 20980, batch avg loss 0.2774, total avg loss: 0.2483, batch size: 45
2021-10-14 12:06:53,283 INFO [train.py:451] Epoch 5, batch 20990, batch avg loss 0.2818, total avg loss: 0.2480, batch size: 73
2021-10-14 12:06:58,309 INFO [train.py:451] Epoch 5, batch 21000, batch avg loss 0.2807, total avg loss: 0.2476, batch size: 38
2021-10-14 12:07:38,250 INFO [train.py:483] Epoch 5, valid loss 0.1805, best valid loss: 0.1804 best valid epoch: 5
2021-10-14 12:07:43,176 INFO [train.py:451] Epoch 5, batch 21010, batch avg loss 0.2219, total avg loss: 0.2437, batch size: 30
2021-10-14 12:07:48,004 INFO [train.py:451] Epoch 5, batch 21020, batch avg loss 0.1876, total avg loss: 0.2450, batch size: 29
2021-10-14 12:07:52,931 INFO [train.py:451] Epoch 5, batch 21030, batch avg loss 0.2733, total avg loss: 0.2391, batch size: 39
2021-10-14 12:07:54,008 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "c6fca356-6408-9bb5-191a-734bfb2e1161" will not be mixed in.
2021-10-14 12:07:57,835 INFO [train.py:451] Epoch 5, batch 21040, batch avg loss 0.2872, total avg loss: 0.2434, batch size: 42
2021-10-14 12:08:02,784 INFO [train.py:451] Epoch 5, batch 21050, batch avg loss 0.2215, total avg loss: 0.2438, batch size: 31
2021-10-14 12:08:07,681 INFO [train.py:451] Epoch 5, batch 21060, batch avg loss 0.2070, total avg loss: 0.2461, batch size: 32
2021-10-14 12:08:12,358 INFO [train.py:451] Epoch 5, batch 21070, batch avg loss 0.2958, total avg loss: 0.2488, batch size: 32
2021-10-14 12:08:17,258 INFO [train.py:451] Epoch 5, batch 21080, batch avg loss 0.2481, total avg loss: 0.2495, batch size: 42
2021-10-14 12:08:22,088 INFO [train.py:451] Epoch 5, batch 21090, batch avg loss 0.2722, total avg loss: 0.2487, batch size: 35
2021-10-14 12:08:27,038 INFO [train.py:451] Epoch 5, batch 21100, batch avg loss 0.2306, total avg loss: 0.2463, batch size: 31
2021-10-14 12:08:31,839 INFO [train.py:451] Epoch 5, batch 21110, batch avg loss 0.2739, total avg loss: 0.2462, batch size: 73
2021-10-14 12:08:36,721 INFO [train.py:451] Epoch 5, batch 21120, batch avg loss 0.2506, total avg loss: 0.2462, batch size: 38
2021-10-14 12:08:41,554 INFO [train.py:451] Epoch 5, batch 21130, batch avg loss 0.2850, total avg loss: 0.2468, batch size: 72
2021-10-14 12:08:46,594 INFO [train.py:451] Epoch 5, batch 21140, batch avg loss 0.2237, total avg loss: 0.2471, batch size: 29
2021-10-14 12:08:51,757 INFO [train.py:451] Epoch 5, batch 21150, batch avg loss 0.2159, total avg loss: 0.2458, batch size: 32
2021-10-14 12:08:56,842 INFO [train.py:451] Epoch 5, batch 21160, batch avg loss 0.2298, total avg loss: 0.2446, batch size: 34
2021-10-14 12:09:01,633 INFO [train.py:451] Epoch 5, batch 21170, batch avg loss 0.2323, total avg loss: 0.2453, batch size: 38
2021-10-14 12:09:06,571 INFO [train.py:451] Epoch 5, batch 21180, batch avg loss 0.2742, total avg loss: 0.2459, batch size: 38
2021-10-14 12:09:11,689 INFO [checkpoint.py:62] Saving checkpoint to tdnn_lstm_ctc-v2/exp/epoch-5.pt
2021-10-14 12:09:12,507 INFO [train.py:564] epoch 6, lr: 0.00025
2021-10-14 12:09:16,886 INFO [train.py:451] Epoch 6, batch 0, batch avg loss 0.2507, total avg loss: 0.2507, batch size: 33
2021-10-14 12:09:22,147 INFO [train.py:451] Epoch 6, batch 10, batch avg loss 0.1865, total avg loss: 0.2439, batch size: 33
2021-10-14 12:09:27,324 INFO [train.py:451] Epoch 6, batch 20, batch avg loss 0.1820, total avg loss: 0.2412, batch size: 28
2021-10-14 12:09:32,181 INFO [train.py:451] Epoch 6, batch 30, batch avg loss 0.2565, total avg loss: 0.2483, batch size: 41
2021-10-14 12:09:37,176 INFO [train.py:451] Epoch 6, batch 40, batch avg loss 0.1872, total avg loss: 0.2468, batch size: 30
2021-10-14 12:09:42,098 INFO [train.py:451] Epoch 6, batch 50, batch avg loss 0.2574, total avg loss: 0.2464, batch size: 27
2021-10-14 12:09:47,042 INFO [train.py:451] Epoch 6, batch 60, batch avg loss 0.2749, total avg loss: 0.2440, batch size: 38
2021-10-14 12:09:51,972 INFO [train.py:451] Epoch 6, batch 70, batch avg loss 0.3067, total avg loss: 0.2469, batch size: 39
2021-10-14 12:09:56,833 INFO [train.py:451] Epoch 6, batch 80, batch avg loss 0.1803, total avg loss: 0.2471, batch size: 28
2021-10-14 12:10:01,940 INFO [train.py:451] Epoch 6, batch 90, batch avg loss 0.2472, total avg loss: 0.2472, batch size: 34
2021-10-14 12:10:06,831 INFO [train.py:451] Epoch 6, batch 100, batch avg loss 0.2661, total avg loss: 0.2464, batch size: 35
2021-10-14 12:10:11,912 INFO [train.py:451] Epoch 6, batch 110, batch avg loss 0.2543, total avg loss: 0.2466, batch size: 34
2021-10-14 12:10:16,821 INFO [train.py:451] Epoch 6, batch 120, batch avg loss 0.4278, total avg loss: 0.2487, batch size: 125
2021-10-14 12:10:21,781 INFO [train.py:451] Epoch 6, batch 130, batch avg loss 0.3250, total avg loss: 0.2496, batch size: 73
2021-10-14 12:10:26,788 INFO [train.py:451] Epoch 6, batch 140, batch avg loss 0.2156, total avg loss: 0.2480, batch size: 33
2021-10-14 12:10:31,521 INFO [train.py:451] Epoch 6, batch 150, batch avg loss 0.2427, total avg loss: 0.2492, batch size: 35
2021-10-14 12:10:36,549 INFO [train.py:451] Epoch 6, batch 160, batch avg loss 0.2508, total avg loss: 0.2483, batch size: 29
2021-10-14 12:10:41,561 INFO [train.py:451] Epoch 6, batch 170, batch avg loss 0.2401, total avg loss: 0.2480, batch size: 33
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2021-10-14 12:10:51,621 INFO [train.py:451] Epoch 6, batch 190, batch avg loss 0.2957, total avg loss: 0.2462, batch size: 35
2021-10-14 12:10:56,451 INFO [train.py:451] Epoch 6, batch 200, batch avg loss 0.2811, total avg loss: 0.2464, batch size: 49
2021-10-14 12:11:01,435 INFO [train.py:451] Epoch 6, batch 210, batch avg loss 0.3149, total avg loss: 0.2618, batch size: 34
2021-10-14 12:11:06,557 INFO [train.py:451] Epoch 6, batch 220, batch avg loss 0.2863, total avg loss: 0.2481, batch size: 41
2021-10-14 12:11:11,512 INFO [train.py:451] Epoch 6, batch 230, batch avg loss 0.2740, total avg loss: 0.2496, batch size: 32
2021-10-14 12:11:16,341 INFO [train.py:451] Epoch 6, batch 240, batch avg loss 0.2332, total avg loss: 0.2469, batch size: 42
2021-10-14 12:11:21,157 INFO [train.py:451] Epoch 6, batch 250, batch avg loss 0.2811, total avg loss: 0.2467, batch size: 38
2021-10-14 12:11:25,839 INFO [train.py:451] Epoch 6, batch 260, batch avg loss 0.2505, total avg loss: 0.2508, batch size: 57
2021-10-14 12:11:30,701 INFO [train.py:451] Epoch 6, batch 270, batch avg loss 0.2179, total avg loss: 0.2495, batch size: 33
2021-10-14 12:11:35,614 INFO [train.py:451] Epoch 6, batch 280, batch avg loss 0.2796, total avg loss: 0.2501, batch size: 33
2021-10-14 12:11:40,561 INFO [train.py:451] Epoch 6, batch 290, batch avg loss 0.2781, total avg loss: 0.2488, batch size: 72
2021-10-14 12:11:45,603 INFO [train.py:451] Epoch 6, batch 300, batch avg loss 0.2277, total avg loss: 0.2464, batch size: 29
2021-10-14 12:11:50,514 INFO [train.py:451] Epoch 6, batch 310, batch avg loss 0.2978, total avg loss: 0.2480, batch size: 49
2021-10-14 12:11:55,521 INFO [train.py:451] Epoch 6, batch 320, batch avg loss 0.1878, total avg loss: 0.2470, batch size: 27
2021-10-14 12:12:00,493 INFO [train.py:451] Epoch 6, batch 330, batch avg loss 0.2297, total avg loss: 0.2472, batch size: 33
2021-10-14 12:12:05,462 INFO [train.py:451] Epoch 6, batch 340, batch avg loss 0.2951, total avg loss: 0.2459, batch size: 38
2021-10-14 12:12:10,241 INFO [train.py:451] Epoch 6, batch 350, batch avg loss 0.2344, total avg loss: 0.2468, batch size: 32
2021-10-14 12:12:15,047 INFO [train.py:451] Epoch 6, batch 360, batch avg loss 0.2237, total avg loss: 0.2484, batch size: 31
2021-10-14 12:12:19,972 INFO [train.py:451] Epoch 6, batch 370, batch avg loss 0.2081, total avg loss: 0.2488, batch size: 32
2021-10-14 12:12:24,714 INFO [train.py:451] Epoch 6, batch 380, batch avg loss 0.2202, total avg loss: 0.2489, batch size: 35
2021-10-14 12:12:29,681 INFO [train.py:451] Epoch 6, batch 390, batch avg loss 0.3056, total avg loss: 0.2491, batch size: 49
2021-10-14 12:12:34,664 INFO [train.py:451] Epoch 6, batch 400, batch avg loss 0.2820, total avg loss: 0.2488, batch size: 39
2021-10-14 12:12:39,683 INFO [train.py:451] Epoch 6, batch 410, batch avg loss 0.2649, total avg loss: 0.2422, batch size: 37
2021-10-14 12:12:44,823 INFO [train.py:451] Epoch 6, batch 420, batch avg loss 0.2256, total avg loss: 0.2427, batch size: 42
2021-10-14 12:12:49,620 INFO [train.py:451] Epoch 6, batch 430, batch avg loss 0.2494, total avg loss: 0.2486, batch size: 42
2021-10-14 12:12:54,449 INFO [train.py:451] Epoch 6, batch 440, batch avg loss 0.2821, total avg loss: 0.2524, batch size: 41
2021-10-14 12:12:59,251 INFO [train.py:451] Epoch 6, batch 450, batch avg loss 0.2300, total avg loss: 0.2523, batch size: 38
2021-10-14 12:13:03,938 INFO [train.py:451] Epoch 6, batch 460, batch avg loss 0.2675, total avg loss: 0.2560, batch size: 36
2021-10-14 12:13:08,848 INFO [train.py:451] Epoch 6, batch 470, batch avg loss 0.2422, total avg loss: 0.2558, batch size: 34
2021-10-14 12:13:13,698 INFO [train.py:451] Epoch 6, batch 480, batch avg loss 0.2133, total avg loss: 0.2565, batch size: 34
2021-10-14 12:13:18,611 INFO [train.py:451] Epoch 6, batch 490, batch avg loss 0.2810, total avg loss: 0.2551, batch size: 37
2021-10-14 12:13:23,488 INFO [train.py:451] Epoch 6, batch 500, batch avg loss 0.2718, total avg loss: 0.2547, batch size: 49
2021-10-14 12:13:28,349 INFO [train.py:451] Epoch 6, batch 510, batch avg loss 0.2613, total avg loss: 0.2552, batch size: 38
2021-10-14 12:13:33,238 INFO [train.py:451] Epoch 6, batch 520, batch avg loss 0.2945, total avg loss: 0.2558, batch size: 35
2021-10-14 12:13:38,138 INFO [train.py:451] Epoch 6, batch 530, batch avg loss 0.2549, total avg loss: 0.2564, batch size: 32
2021-10-14 12:13:43,009 INFO [train.py:451] Epoch 6, batch 540, batch avg loss 0.2486, total avg loss: 0.2542, batch size: 38
2021-10-14 12:13:48,083 INFO [train.py:451] Epoch 6, batch 550, batch avg loss 0.3102, total avg loss: 0.2546, batch size: 72
2021-10-14 12:13:53,000 INFO [train.py:451] Epoch 6, batch 560, batch avg loss 0.2727, total avg loss: 0.2549, batch size: 45
2021-10-14 12:13:57,688 INFO [train.py:451] Epoch 6, batch 570, batch avg loss 0.3214, total avg loss: 0.2552, batch size: 135
2021-10-14 12:14:02,568 INFO [train.py:451] Epoch 6, batch 580, batch avg loss 0.2568, total avg loss: 0.2549, batch size: 32
2021-10-14 12:14:07,378 INFO [train.py:451] Epoch 6, batch 590, batch avg loss 0.2687, total avg loss: 0.2557, batch size: 34
2021-10-14 12:14:12,381 INFO [train.py:451] Epoch 6, batch 600, batch avg loss 0.2518, total avg loss: 0.2548, batch size: 35
2021-10-14 12:14:17,373 INFO [train.py:451] Epoch 6, batch 610, batch avg loss 0.2104, total avg loss: 0.2355, batch size: 29
2021-10-14 12:14:22,185 INFO [train.py:451] Epoch 6, batch 620, batch avg loss 0.3170, total avg loss: 0.2418, batch size: 38
2021-10-14 12:14:27,157 INFO [train.py:451] Epoch 6, batch 630, batch avg loss 0.2728, total avg loss: 0.2384, batch size: 35
2021-10-14 12:14:32,025 INFO [train.py:451] Epoch 6, batch 640, batch avg loss 0.2834, total avg loss: 0.2445, batch size: 32
2021-10-14 12:14:36,889 INFO [train.py:451] Epoch 6, batch 650, batch avg loss 0.2716, total avg loss: 0.2501, batch size: 35
2021-10-14 12:14:41,801 INFO [train.py:451] Epoch 6, batch 660, batch avg loss 0.3410, total avg loss: 0.2489, batch size: 133
2021-10-14 12:14:46,537 INFO [train.py:451] Epoch 6, batch 670, batch avg loss 0.3452, total avg loss: 0.2510, batch size: 128
2021-10-14 12:14:51,389 INFO [train.py:451] Epoch 6, batch 680, batch avg loss 0.2439, total avg loss: 0.2506, batch size: 34
2021-10-14 12:14:56,260 INFO [train.py:451] Epoch 6, batch 690, batch avg loss 0.2185, total avg loss: 0.2504, batch size: 30
2021-10-14 12:15:01,161 INFO [train.py:451] Epoch 6, batch 700, batch avg loss 0.2218, total avg loss: 0.2490, batch size: 31
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2021-10-14 12:15:25,873 INFO [train.py:451] Epoch 6, batch 750, batch avg loss 0.2760, total avg loss: 0.2478, batch size: 49
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2021-10-14 12:16:04,634 INFO [train.py:451] Epoch 6, batch 830, batch avg loss 0.2448, total avg loss: 0.2580, batch size: 38
2021-10-14 12:16:09,511 INFO [train.py:451] Epoch 6, batch 840, batch avg loss 0.3201, total avg loss: 0.2567, batch size: 72
2021-10-14 12:16:14,153 INFO [train.py:451] Epoch 6, batch 850, batch avg loss 0.2376, total avg loss: 0.2617, batch size: 35
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2021-10-14 12:16:38,514 INFO [train.py:451] Epoch 6, batch 900, batch avg loss 0.2356, total avg loss: 0.2575, batch size: 41
2021-10-14 12:16:43,465 INFO [train.py:451] Epoch 6, batch 910, batch avg loss 0.2646, total avg loss: 0.2560, batch size: 34
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2021-10-14 12:16:58,187 INFO [train.py:451] Epoch 6, batch 940, batch avg loss 0.2684, total avg loss: 0.2560, batch size: 33
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2021-10-14 12:17:17,504 INFO [train.py:451] Epoch 6, batch 980, batch avg loss 0.2575, total avg loss: 0.2572, batch size: 41
2021-10-14 12:17:22,571 INFO [train.py:451] Epoch 6, batch 990, batch avg loss 0.2747, total avg loss: 0.2565, batch size: 49
2021-10-14 12:17:27,450 INFO [train.py:451] Epoch 6, batch 1000, batch avg loss 0.2092, total avg loss: 0.2567, batch size: 31
2021-10-14 12:18:05,174 INFO [train.py:483] Epoch 6, valid loss 0.1804, best valid loss: 0.1804 best valid epoch: 5
2021-10-14 12:18:10,115 INFO [train.py:451] Epoch 6, batch 1010, batch avg loss 0.2600, total avg loss: 0.2411, batch size: 45
2021-10-14 12:18:15,253 INFO [train.py:451] Epoch 6, batch 1020, batch avg loss 0.2505, total avg loss: 0.2359, batch size: 34
2021-10-14 12:18:20,195 INFO [train.py:451] Epoch 6, batch 1030, batch avg loss 0.1989, total avg loss: 0.2396, batch size: 32
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2021-10-14 12:18:30,029 INFO [train.py:451] Epoch 6, batch 1050, batch avg loss 0.2081, total avg loss: 0.2419, batch size: 31
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2021-10-14 12:18:44,873 INFO [train.py:451] Epoch 6, batch 1080, batch avg loss 0.2322, total avg loss: 0.2459, batch size: 31
2021-10-14 12:18:49,725 INFO [train.py:451] Epoch 6, batch 1090, batch avg loss 0.2831, total avg loss: 0.2449, batch size: 57
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2021-10-14 12:19:48,150 INFO [train.py:451] Epoch 6, batch 1210, batch avg loss 0.2682, total avg loss: 0.2631, batch size: 45
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2021-10-14 12:20:02,764 INFO [train.py:451] Epoch 6, batch 1240, batch avg loss 0.2626, total avg loss: 0.2450, batch size: 38
2021-10-14 12:20:07,602 INFO [train.py:451] Epoch 6, batch 1250, batch avg loss 0.2369, total avg loss: 0.2481, batch size: 35
2021-10-14 12:20:12,524 INFO [train.py:451] Epoch 6, batch 1260, batch avg loss 0.2783, total avg loss: 0.2460, batch size: 41
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2021-10-14 12:22:11,338 INFO [train.py:451] Epoch 6, batch 1500, batch avg loss 0.2416, total avg loss: 0.2501, batch size: 33
2021-10-14 12:22:16,319 INFO [train.py:451] Epoch 6, batch 1510, batch avg loss 0.2555, total avg loss: 0.2499, batch size: 34
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2021-10-14 12:23:00,184 INFO [train.py:451] Epoch 6, batch 1600, batch avg loss 0.2647, total avg loss: 0.2535, batch size: 41
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2021-10-14 12:23:29,993 INFO [train.py:451] Epoch 6, batch 1660, batch avg loss 0.2125, total avg loss: 0.2411, batch size: 32
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2021-10-14 12:23:44,879 INFO [train.py:451] Epoch 6, batch 1690, batch avg loss 0.2422, total avg loss: 0.2406, batch size: 27
2021-10-14 12:23:49,721 INFO [train.py:451] Epoch 6, batch 1700, batch avg loss 0.2567, total avg loss: 0.2432, batch size: 37
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2021-10-14 12:24:24,300 INFO [train.py:451] Epoch 6, batch 1770, batch avg loss 0.2777, total avg loss: 0.2500, batch size: 31
2021-10-14 12:24:29,246 INFO [train.py:451] Epoch 6, batch 1780, batch avg loss 0.2201, total avg loss: 0.2493, batch size: 31
2021-10-14 12:24:34,200 INFO [train.py:451] Epoch 6, batch 1790, batch avg loss 0.3289, total avg loss: 0.2488, batch size: 39
2021-10-14 12:24:39,070 INFO [train.py:451] Epoch 6, batch 1800, batch avg loss 0.3069, total avg loss: 0.2499, batch size: 34
2021-10-14 12:24:43,968 INFO [train.py:451] Epoch 6, batch 1810, batch avg loss 0.2578, total avg loss: 0.2733, batch size: 27
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2021-10-14 12:26:56,473 INFO [train.py:483] Epoch 6, valid loss 0.1812, best valid loss: 0.1804 best valid epoch: 5
2021-10-14 12:27:01,435 INFO [train.py:451] Epoch 6, batch 2010, batch avg loss 0.2567, total avg loss: 0.2694, batch size: 35
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2021-10-14 12:28:19,607 INFO [train.py:451] Epoch 6, batch 2170, batch avg loss 0.2610, total avg loss: 0.2550, batch size: 34
2021-10-14 12:28:24,592 INFO [train.py:451] Epoch 6, batch 2180, batch avg loss 0.2892, total avg loss: 0.2544, batch size: 38
2021-10-14 12:28:29,459 INFO [train.py:451] Epoch 6, batch 2190, batch avg loss 0.2940, total avg loss: 0.2534, batch size: 72
2021-10-14 12:28:34,303 INFO [train.py:451] Epoch 6, batch 2200, batch avg loss 0.2479, total avg loss: 0.2536, batch size: 39
2021-10-14 12:28:39,162 INFO [train.py:451] Epoch 6, batch 2210, batch avg loss 0.2060, total avg loss: 0.2717, batch size: 33
2021-10-14 12:28:43,967 INFO [train.py:451] Epoch 6, batch 2220, batch avg loss 0.2342, total avg loss: 0.2614, batch size: 38
2021-10-14 12:28:48,885 INFO [train.py:451] Epoch 6, batch 2230, batch avg loss 0.2552, total avg loss: 0.2582, batch size: 45
2021-10-14 12:28:53,720 INFO [train.py:451] Epoch 6, batch 2240, batch avg loss 0.3279, total avg loss: 0.2576, batch size: 36
2021-10-14 12:28:58,873 INFO [train.py:451] Epoch 6, batch 2250, batch avg loss 0.2546, total avg loss: 0.2551, batch size: 33
2021-10-14 12:29:03,762 INFO [train.py:451] Epoch 6, batch 2260, batch avg loss 0.3526, total avg loss: 0.2556, batch size: 123
2021-10-14 12:29:08,545 INFO [train.py:451] Epoch 6, batch 2270, batch avg loss 0.2460, total avg loss: 0.2558, batch size: 37
2021-10-14 12:29:13,568 INFO [train.py:451] Epoch 6, batch 2280, batch avg loss 0.2745, total avg loss: 0.2528, batch size: 35
2021-10-14 12:29:18,396 INFO [train.py:451] Epoch 6, batch 2290, batch avg loss 0.2583, total avg loss: 0.2528, batch size: 39
2021-10-14 12:29:23,536 INFO [train.py:451] Epoch 6, batch 2300, batch avg loss 0.1774, total avg loss: 0.2511, batch size: 27
2021-10-14 12:29:28,576 INFO [train.py:451] Epoch 6, batch 2310, batch avg loss 0.2408, total avg loss: 0.2492, batch size: 31
2021-10-14 12:29:33,511 INFO [train.py:451] Epoch 6, batch 2320, batch avg loss 0.2750, total avg loss: 0.2500, batch size: 49
2021-10-14 12:29:38,329 INFO [train.py:451] Epoch 6, batch 2330, batch avg loss 0.2330, total avg loss: 0.2514, batch size: 32
2021-10-14 12:29:43,418 INFO [train.py:451] Epoch 6, batch 2340, batch avg loss 0.2348, total avg loss: 0.2508, batch size: 31
2021-10-14 12:29:48,448 INFO [train.py:451] Epoch 6, batch 2350, batch avg loss 0.2654, total avg loss: 0.2510, batch size: 41
2021-10-14 12:29:53,424 INFO [train.py:451] Epoch 6, batch 2360, batch avg loss 0.2812, total avg loss: 0.2504, batch size: 31
2021-10-14 12:29:58,567 INFO [train.py:451] Epoch 6, batch 2370, batch avg loss 0.2245, total avg loss: 0.2498, batch size: 33
2021-10-14 12:30:03,456 INFO [train.py:451] Epoch 6, batch 2380, batch avg loss 0.2411, total avg loss: 0.2510, batch size: 34
2021-10-14 12:30:08,198 INFO [train.py:451] Epoch 6, batch 2390, batch avg loss 0.2534, total avg loss: 0.2516, batch size: 42
2021-10-14 12:30:13,144 INFO [train.py:451] Epoch 6, batch 2400, batch avg loss 0.2312, total avg loss: 0.2522, batch size: 38
2021-10-14 12:30:18,230 INFO [train.py:451] Epoch 6, batch 2410, batch avg loss 0.2548, total avg loss: 0.2345, batch size: 45
2021-10-14 12:30:23,284 INFO [train.py:451] Epoch 6, batch 2420, batch avg loss 0.2691, total avg loss: 0.2320, batch size: 36
2021-10-14 12:30:28,260 INFO [train.py:451] Epoch 6, batch 2430, batch avg loss 0.2459, total avg loss: 0.2398, batch size: 35
2021-10-14 12:30:33,129 INFO [train.py:451] Epoch 6, batch 2440, batch avg loss 0.2430, total avg loss: 0.2515, batch size: 32
2021-10-14 12:30:38,027 INFO [train.py:451] Epoch 6, batch 2450, batch avg loss 0.2464, total avg loss: 0.2500, batch size: 27
2021-10-14 12:30:43,100 INFO [train.py:451] Epoch 6, batch 2460, batch avg loss 0.2262, total avg loss: 0.2470, batch size: 30
2021-10-14 12:30:47,980 INFO [train.py:451] Epoch 6, batch 2470, batch avg loss 0.2040, total avg loss: 0.2467, batch size: 29
2021-10-14 12:30:52,888 INFO [train.py:451] Epoch 6, batch 2480, batch avg loss 0.2850, total avg loss: 0.2471, batch size: 45
2021-10-14 12:30:57,687 INFO [train.py:451] Epoch 6, batch 2490, batch avg loss 0.2453, total avg loss: 0.2491, batch size: 35
2021-10-14 12:31:02,589 INFO [train.py:451] Epoch 6, batch 2500, batch avg loss 0.2321, total avg loss: 0.2493, batch size: 29
2021-10-14 12:31:07,536 INFO [train.py:451] Epoch 6, batch 2510, batch avg loss 0.2859, total avg loss: 0.2511, batch size: 73
2021-10-14 12:31:12,443 INFO [train.py:451] Epoch 6, batch 2520, batch avg loss 0.2265, total avg loss: 0.2502, batch size: 36
2021-10-14 12:31:17,480 INFO [train.py:451] Epoch 6, batch 2530, batch avg loss 0.2412, total avg loss: 0.2515, batch size: 34
2021-10-14 12:31:22,589 INFO [train.py:451] Epoch 6, batch 2540, batch avg loss 0.2506, total avg loss: 0.2509, batch size: 34
2021-10-14 12:31:27,459 INFO [train.py:451] Epoch 6, batch 2550, batch avg loss 0.2727, total avg loss: 0.2508, batch size: 39
2021-10-14 12:31:32,299 INFO [train.py:451] Epoch 6, batch 2560, batch avg loss 0.3128, total avg loss: 0.2515, batch size: 57
2021-10-14 12:31:37,271 INFO [train.py:451] Epoch 6, batch 2570, batch avg loss 0.2173, total avg loss: 0.2499, batch size: 31
2021-10-14 12:31:42,182 INFO [train.py:451] Epoch 6, batch 2580, batch avg loss 0.2904, total avg loss: 0.2503, batch size: 41
2021-10-14 12:31:47,138 INFO [train.py:451] Epoch 6, batch 2590, batch avg loss 0.2614, total avg loss: 0.2500, batch size: 41
2021-10-14 12:31:52,134 INFO [train.py:451] Epoch 6, batch 2600, batch avg loss 0.2049, total avg loss: 0.2504, batch size: 27
2021-10-14 12:31:55,940 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "6d0d9088-f98b-9991-6947-ad67aac7c0c5" will not be mixed in.
2021-10-14 12:31:56,876 INFO [train.py:451] Epoch 6, batch 2610, batch avg loss 0.1887, total avg loss: 0.2595, batch size: 29
2021-10-14 12:32:01,943 INFO [train.py:451] Epoch 6, batch 2620, batch avg loss 0.1919, total avg loss: 0.2466, batch size: 29
2021-10-14 12:32:06,754 INFO [train.py:451] Epoch 6, batch 2630, batch avg loss 0.2638, total avg loss: 0.2524, batch size: 36
2021-10-14 12:32:11,498 INFO [train.py:451] Epoch 6, batch 2640, batch avg loss 0.2034, total avg loss: 0.2588, batch size: 27
2021-10-14 12:32:16,366 INFO [train.py:451] Epoch 6, batch 2650, batch avg loss 0.2868, total avg loss: 0.2616, batch size: 34
2021-10-14 12:32:21,326 INFO [train.py:451] Epoch 6, batch 2660, batch avg loss 0.3652, total avg loss: 0.2600, batch size: 134
2021-10-14 12:32:26,209 INFO [train.py:451] Epoch 6, batch 2670, batch avg loss 0.1880, total avg loss: 0.2590, batch size: 29
2021-10-14 12:32:31,201 INFO [train.py:451] Epoch 6, batch 2680, batch avg loss 0.1997, total avg loss: 0.2561, batch size: 28
2021-10-14 12:32:36,144 INFO [train.py:451] Epoch 6, batch 2690, batch avg loss 0.2082, total avg loss: 0.2543, batch size: 33
2021-10-14 12:32:40,999 INFO [train.py:451] Epoch 6, batch 2700, batch avg loss 0.2241, total avg loss: 0.2536, batch size: 33
2021-10-14 12:32:44,628 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "8e2c0efa-18af-55df-de3e-a808b0dfa5e3" will not be mixed in.
2021-10-14 12:32:45,949 INFO [train.py:451] Epoch 6, batch 2710, batch avg loss 0.2263, total avg loss: 0.2522, batch size: 29
2021-10-14 12:32:50,850 INFO [train.py:451] Epoch 6, batch 2720, batch avg loss 0.2515, total avg loss: 0.2534, batch size: 49
2021-10-14 12:32:55,825 INFO [train.py:451] Epoch 6, batch 2730, batch avg loss 0.2539, total avg loss: 0.2537, batch size: 29
2021-10-14 12:33:00,711 INFO [train.py:451] Epoch 6, batch 2740, batch avg loss 0.2905, total avg loss: 0.2540, batch size: 56
2021-10-14 12:33:05,659 INFO [train.py:451] Epoch 6, batch 2750, batch avg loss 0.2199, total avg loss: 0.2544, batch size: 31
2021-10-14 12:33:10,539 INFO [train.py:451] Epoch 6, batch 2760, batch avg loss 0.2276, total avg loss: 0.2545, batch size: 38
2021-10-14 12:33:15,371 INFO [train.py:451] Epoch 6, batch 2770, batch avg loss 0.2492, total avg loss: 0.2542, batch size: 38
2021-10-14 12:33:27,812 INFO [train.py:451] Epoch 6, batch 2780, batch avg loss 0.2537, total avg loss: 0.2543, batch size: 34
2021-10-14 12:33:32,763 INFO [train.py:451] Epoch 6, batch 2790, batch avg loss 0.3054, total avg loss: 0.2546, batch size: 41
2021-10-14 12:33:37,643 INFO [train.py:451] Epoch 6, batch 2800, batch avg loss 0.3312, total avg loss: 0.2550, batch size: 74
2021-10-14 12:33:42,741 INFO [train.py:451] Epoch 6, batch 2810, batch avg loss 0.2075, total avg loss: 0.2360, batch size: 30
2021-10-14 12:33:47,681 INFO [train.py:451] Epoch 6, batch 2820, batch avg loss 0.1794, total avg loss: 0.2377, batch size: 29
2021-10-14 12:33:52,645 INFO [train.py:451] Epoch 6, batch 2830, batch avg loss 0.2909, total avg loss: 0.2426, batch size: 73
2021-10-14 12:33:57,750 INFO [train.py:451] Epoch 6, batch 2840, batch avg loss 0.2200, total avg loss: 0.2421, batch size: 33
2021-10-14 12:34:02,737 INFO [train.py:451] Epoch 6, batch 2850, batch avg loss 0.2350, total avg loss: 0.2420, batch size: 31
2021-10-14 12:34:07,747 INFO [train.py:451] Epoch 6, batch 2860, batch avg loss 0.2346, total avg loss: 0.2431, batch size: 36
2021-10-14 12:34:12,644 INFO [train.py:451] Epoch 6, batch 2870, batch avg loss 0.2150, total avg loss: 0.2425, batch size: 32
2021-10-14 12:34:17,575 INFO [train.py:451] Epoch 6, batch 2880, batch avg loss 0.2456, total avg loss: 0.2449, batch size: 32
2021-10-14 12:34:22,636 INFO [train.py:451] Epoch 6, batch 2890, batch avg loss 0.1964, total avg loss: 0.2433, batch size: 32
2021-10-14 12:34:27,562 INFO [train.py:451] Epoch 6, batch 2900, batch avg loss 0.2326, total avg loss: 0.2439, batch size: 37
2021-10-14 12:34:32,297 INFO [train.py:451] Epoch 6, batch 2910, batch avg loss 0.2819, total avg loss: 0.2462, batch size: 42
2021-10-14 12:34:37,184 INFO [train.py:451] Epoch 6, batch 2920, batch avg loss 0.2433, total avg loss: 0.2456, batch size: 41
2021-10-14 12:34:42,226 INFO [train.py:451] Epoch 6, batch 2930, batch avg loss 0.2274, total avg loss: 0.2469, batch size: 31
2021-10-14 12:34:47,162 INFO [train.py:451] Epoch 6, batch 2940, batch avg loss 0.2062, total avg loss: 0.2466, batch size: 31
2021-10-14 12:34:52,264 INFO [train.py:451] Epoch 6, batch 2950, batch avg loss 0.1803, total avg loss: 0.2453, batch size: 30
2021-10-14 12:34:57,104 INFO [train.py:451] Epoch 6, batch 2960, batch avg loss 0.2363, total avg loss: 0.2444, batch size: 34
2021-10-14 12:35:02,041 INFO [train.py:451] Epoch 6, batch 2970, batch avg loss 0.2557, total avg loss: 0.2449, batch size: 49
2021-10-14 12:35:07,047 INFO [train.py:451] Epoch 6, batch 2980, batch avg loss 0.2150, total avg loss: 0.2451, batch size: 28
2021-10-14 12:35:12,080 INFO [train.py:451] Epoch 6, batch 2990, batch avg loss 0.1928, total avg loss: 0.2455, batch size: 31
2021-10-14 12:35:16,854 INFO [train.py:451] Epoch 6, batch 3000, batch avg loss 0.2476, total avg loss: 0.2459, batch size: 49
2021-10-14 12:35:56,441 INFO [train.py:483] Epoch 6, valid loss 0.1798, best valid loss: 0.1798 best valid epoch: 6
2021-10-14 12:36:01,353 INFO [train.py:451] Epoch 6, batch 3010, batch avg loss 0.2655, total avg loss: 0.2604, batch size: 38
2021-10-14 12:36:06,264 INFO [train.py:451] Epoch 6, batch 3020, batch avg loss 0.2364, total avg loss: 0.2621, batch size: 34
2021-10-14 12:36:11,164 INFO [train.py:451] Epoch 6, batch 3030, batch avg loss 0.1986, total avg loss: 0.2562, batch size: 31
2021-10-14 12:36:16,123 INFO [train.py:451] Epoch 6, batch 3040, batch avg loss 0.3107, total avg loss: 0.2524, batch size: 36
2021-10-14 12:36:20,902 INFO [train.py:451] Epoch 6, batch 3050, batch avg loss 0.2739, total avg loss: 0.2528, batch size: 45
2021-10-14 12:36:25,745 INFO [train.py:451] Epoch 6, batch 3060, batch avg loss 0.2736, total avg loss: 0.2541, batch size: 34
2021-10-14 12:36:30,810 INFO [train.py:451] Epoch 6, batch 3070, batch avg loss 0.2427, total avg loss: 0.2511, batch size: 35
2021-10-14 12:36:35,781 INFO [train.py:451] Epoch 6, batch 3080, batch avg loss 0.2627, total avg loss: 0.2508, batch size: 33
2021-10-14 12:36:40,515 INFO [train.py:451] Epoch 6, batch 3090, batch avg loss 0.2521, total avg loss: 0.2524, batch size: 34
2021-10-14 12:36:45,689 INFO [train.py:451] Epoch 6, batch 3100, batch avg loss 0.2128, total avg loss: 0.2511, batch size: 31
2021-10-14 12:36:50,382 INFO [train.py:451] Epoch 6, batch 3110, batch avg loss 0.2929, total avg loss: 0.2520, batch size: 72
2021-10-14 12:36:55,266 INFO [train.py:451] Epoch 6, batch 3120, batch avg loss 0.2253, total avg loss: 0.2519, batch size: 31
2021-10-14 12:37:00,184 INFO [train.py:451] Epoch 6, batch 3130, batch avg loss 0.2445, total avg loss: 0.2523, batch size: 31
2021-10-14 12:37:05,234 INFO [train.py:451] Epoch 6, batch 3140, batch avg loss 0.2036, total avg loss: 0.2513, batch size: 31
2021-10-14 12:37:10,293 INFO [train.py:451] Epoch 6, batch 3150, batch avg loss 0.1762, total avg loss: 0.2507, batch size: 27
2021-10-14 12:37:15,359 INFO [train.py:451] Epoch 6, batch 3160, batch avg loss 0.3027, total avg loss: 0.2500, batch size: 41
2021-10-14 12:37:20,225 INFO [train.py:451] Epoch 6, batch 3170, batch avg loss 0.2122, total avg loss: 0.2497, batch size: 27
2021-10-14 12:37:25,273 INFO [train.py:451] Epoch 6, batch 3180, batch avg loss 0.2456, total avg loss: 0.2482, batch size: 33
2021-10-14 12:37:30,121 INFO [train.py:451] Epoch 6, batch 3190, batch avg loss 0.2394, total avg loss: 0.2473, batch size: 35
2021-10-14 12:37:34,835 INFO [train.py:451] Epoch 6, batch 3200, batch avg loss 0.3380, total avg loss: 0.2478, batch size: 72
2021-10-14 12:37:39,644 INFO [train.py:451] Epoch 6, batch 3210, batch avg loss 0.2945, total avg loss: 0.2608, batch size: 57
2021-10-14 12:37:44,565 INFO [train.py:451] Epoch 6, batch 3220, batch avg loss 0.2676, total avg loss: 0.2597, batch size: 56
2021-10-14 12:37:49,553 INFO [train.py:451] Epoch 6, batch 3230, batch avg loss 0.2693, total avg loss: 0.2570, batch size: 33
2021-10-14 12:37:54,482 INFO [train.py:451] Epoch 6, batch 3240, batch avg loss 0.2124, total avg loss: 0.2528, batch size: 33
2021-10-14 12:37:59,533 INFO [train.py:451] Epoch 6, batch 3250, batch avg loss 0.2161, total avg loss: 0.2527, batch size: 28
2021-10-14 12:38:04,609 INFO [train.py:451] Epoch 6, batch 3260, batch avg loss 0.2569, total avg loss: 0.2528, batch size: 38
2021-10-14 12:38:09,403 INFO [train.py:451] Epoch 6, batch 3270, batch avg loss 0.2413, total avg loss: 0.2550, batch size: 49
2021-10-14 12:38:14,195 INFO [train.py:451] Epoch 6, batch 3280, batch avg loss 0.2546, total avg loss: 0.2541, batch size: 38
2021-10-14 12:38:19,280 INFO [train.py:451] Epoch 6, batch 3290, batch avg loss 0.2099, total avg loss: 0.2535, batch size: 35
2021-10-14 12:38:24,132 INFO [train.py:451] Epoch 6, batch 3300, batch avg loss 0.2908, total avg loss: 0.2526, batch size: 73
2021-10-14 12:38:28,958 INFO [train.py:451] Epoch 6, batch 3310, batch avg loss 0.2389, total avg loss: 0.2544, batch size: 45
2021-10-14 12:38:33,997 INFO [train.py:451] Epoch 6, batch 3320, batch avg loss 0.2179, total avg loss: 0.2518, batch size: 35
2021-10-14 12:38:38,933 INFO [train.py:451] Epoch 6, batch 3330, batch avg loss 0.1850, total avg loss: 0.2504, batch size: 29
2021-10-14 12:38:43,993 INFO [train.py:451] Epoch 6, batch 3340, batch avg loss 0.2281, total avg loss: 0.2496, batch size: 33
2021-10-14 12:38:48,590 INFO [train.py:451] Epoch 6, batch 3350, batch avg loss 0.3604, total avg loss: 0.2524, batch size: 125
2021-10-14 12:38:53,596 INFO [train.py:451] Epoch 6, batch 3360, batch avg loss 0.2644, total avg loss: 0.2521, batch size: 34
2021-10-14 12:38:58,524 INFO [train.py:451] Epoch 6, batch 3370, batch avg loss 0.2533, total avg loss: 0.2525, batch size: 38
2021-10-14 12:39:03,669 INFO [train.py:451] Epoch 6, batch 3380, batch avg loss 0.2595, total avg loss: 0.2515, batch size: 35
2021-10-14 12:39:08,870 INFO [train.py:451] Epoch 6, batch 3390, batch avg loss 0.2528, total avg loss: 0.2506, batch size: 34
2021-10-14 12:39:13,751 INFO [train.py:451] Epoch 6, batch 3400, batch avg loss 0.2224, total avg loss: 0.2516, batch size: 34
2021-10-14 12:39:18,535 INFO [train.py:451] Epoch 6, batch 3410, batch avg loss 0.2393, total avg loss: 0.2593, batch size: 39
2021-10-14 12:39:23,553 INFO [train.py:451] Epoch 6, batch 3420, batch avg loss 0.3000, total avg loss: 0.2581, batch size: 34
2021-10-14 12:39:28,520 INFO [train.py:451] Epoch 6, batch 3430, batch avg loss 0.2465, total avg loss: 0.2524, batch size: 34
2021-10-14 12:39:33,491 INFO [train.py:451] Epoch 6, batch 3440, batch avg loss 0.3102, total avg loss: 0.2540, batch size: 73
2021-10-14 12:39:38,518 INFO [train.py:451] Epoch 6, batch 3450, batch avg loss 0.2367, total avg loss: 0.2530, batch size: 34
2021-10-14 12:39:43,249 INFO [train.py:451] Epoch 6, batch 3460, batch avg loss 0.2459, total avg loss: 0.2550, batch size: 27
2021-10-14 12:39:48,140 INFO [train.py:451] Epoch 6, batch 3470, batch avg loss 0.2584, total avg loss: 0.2540, batch size: 31
2021-10-14 12:39:53,241 INFO [train.py:451] Epoch 6, batch 3480, batch avg loss 0.2254, total avg loss: 0.2522, batch size: 32
2021-10-14 12:39:58,354 INFO [train.py:451] Epoch 6, batch 3490, batch avg loss 0.2466, total avg loss: 0.2502, batch size: 39
2021-10-14 12:40:03,301 INFO [train.py:451] Epoch 6, batch 3500, batch avg loss 0.2780, total avg loss: 0.2506, batch size: 38
2021-10-14 12:40:08,323 INFO [train.py:451] Epoch 6, batch 3510, batch avg loss 0.2583, total avg loss: 0.2510, batch size: 38
2021-10-14 12:40:13,233 INFO [train.py:451] Epoch 6, batch 3520, batch avg loss 0.3615, total avg loss: 0.2512, batch size: 125
2021-10-14 12:40:18,014 INFO [train.py:451] Epoch 6, batch 3530, batch avg loss 0.2010, total avg loss: 0.2516, batch size: 28
2021-10-14 12:40:23,057 INFO [train.py:451] Epoch 6, batch 3540, batch avg loss 0.2259, total avg loss: 0.2518, batch size: 42
2021-10-14 12:40:28,131 INFO [train.py:451] Epoch 6, batch 3550, batch avg loss 0.2193, total avg loss: 0.2508, batch size: 30
2021-10-14 12:40:33,136 INFO [train.py:451] Epoch 6, batch 3560, batch avg loss 0.2227, total avg loss: 0.2511, batch size: 36
2021-10-14 12:40:38,061 INFO [train.py:451] Epoch 6, batch 3570, batch avg loss 0.2293, total avg loss: 0.2516, batch size: 33
2021-10-14 12:40:42,769 INFO [train.py:451] Epoch 6, batch 3580, batch avg loss 0.2577, total avg loss: 0.2528, batch size: 34
2021-10-14 12:40:47,585 INFO [train.py:451] Epoch 6, batch 3590, batch avg loss 0.2805, total avg loss: 0.2537, batch size: 57
2021-10-14 12:40:52,369 INFO [train.py:451] Epoch 6, batch 3600, batch avg loss 0.2655, total avg loss: 0.2540, batch size: 36
2021-10-14 12:40:57,438 INFO [train.py:451] Epoch 6, batch 3610, batch avg loss 0.1913, total avg loss: 0.2423, batch size: 27
2021-10-14 12:41:02,373 INFO [train.py:451] Epoch 6, batch 3620, batch avg loss 0.2730, total avg loss: 0.2500, batch size: 35
2021-10-14 12:41:07,292 INFO [train.py:451] Epoch 6, batch 3630, batch avg loss 0.2105, total avg loss: 0.2443, batch size: 32
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2021-10-14 12:44:47,427 INFO [train.py:483] Epoch 6, valid loss 0.1796, best valid loss: 0.1796 best valid epoch: 6
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2021-10-14 12:53:41,343 INFO [train.py:483] Epoch 6, valid loss 0.1787, best valid loss: 0.1787 best valid epoch: 6
2021-10-14 12:53:46,385 INFO [train.py:451] Epoch 6, batch 5010, batch avg loss 0.2086, total avg loss: 0.2228, batch size: 33
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2021-10-14 13:02:33,998 INFO [train.py:483] Epoch 6, valid loss 0.1800, best valid loss: 0.1787 best valid epoch: 6
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2021-10-14 13:20:20,527 INFO [train.py:483] Epoch 6, valid loss 0.1805, best valid loss: 0.1787 best valid epoch: 6
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2021-10-14 13:29:11,359 INFO [train.py:483] Epoch 6, valid loss 0.1802, best valid loss: 0.1787 best valid epoch: 6
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2021-10-14 13:33:25,706 INFO [train.py:451] Epoch 6, batch 9520, batch avg loss 0.2154, total avg loss: 0.2498, batch size: 30
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2021-10-14 13:33:35,541 INFO [train.py:451] Epoch 6, batch 9540, batch avg loss 0.2008, total avg loss: 0.2489, batch size: 27
2021-10-14 13:33:40,346 INFO [train.py:451] Epoch 6, batch 9550, batch avg loss 0.2338, total avg loss: 0.2493, batch size: 36
2021-10-14 13:33:45,207 INFO [train.py:451] Epoch 6, batch 9560, batch avg loss 0.2062, total avg loss: 0.2495, batch size: 29
2021-10-14 13:33:50,090 INFO [train.py:451] Epoch 6, batch 9570, batch avg loss 0.2161, total avg loss: 0.2500, batch size: 31
2021-10-14 13:33:54,879 INFO [train.py:451] Epoch 6, batch 9580, batch avg loss 0.2282, total avg loss: 0.2500, batch size: 34
2021-10-14 13:33:59,813 INFO [train.py:451] Epoch 6, batch 9590, batch avg loss 0.2377, total avg loss: 0.2500, batch size: 38
2021-10-14 13:34:04,626 INFO [train.py:451] Epoch 6, batch 9600, batch avg loss 0.2697, total avg loss: 0.2501, batch size: 56
2021-10-14 13:34:09,499 INFO [train.py:451] Epoch 6, batch 9610, batch avg loss 0.2215, total avg loss: 0.2612, batch size: 29
2021-10-14 13:34:14,487 INFO [train.py:451] Epoch 6, batch 9620, batch avg loss 0.2482, total avg loss: 0.2501, batch size: 38
2021-10-14 13:34:19,536 INFO [train.py:451] Epoch 6, batch 9630, batch avg loss 0.2402, total avg loss: 0.2482, batch size: 32
2021-10-14 13:34:24,373 INFO [train.py:451] Epoch 6, batch 9640, batch avg loss 0.2167, total avg loss: 0.2504, batch size: 36
2021-10-14 13:34:29,097 INFO [train.py:451] Epoch 6, batch 9650, batch avg loss 0.3191, total avg loss: 0.2546, batch size: 72
2021-10-14 13:34:33,905 INFO [train.py:451] Epoch 6, batch 9660, batch avg loss 0.2316, total avg loss: 0.2529, batch size: 35
2021-10-14 13:34:38,810 INFO [train.py:451] Epoch 6, batch 9670, batch avg loss 0.2221, total avg loss: 0.2491, batch size: 30
2021-10-14 13:34:43,939 INFO [train.py:451] Epoch 6, batch 9680, batch avg loss 0.2595, total avg loss: 0.2468, batch size: 30
2021-10-14 13:34:48,891 INFO [train.py:451] Epoch 6, batch 9690, batch avg loss 0.2389, total avg loss: 0.2454, batch size: 32
2021-10-14 13:34:53,823 INFO [train.py:451] Epoch 6, batch 9700, batch avg loss 0.1864, total avg loss: 0.2431, batch size: 31
2021-10-14 13:34:58,762 INFO [train.py:451] Epoch 6, batch 9710, batch avg loss 0.2979, total avg loss: 0.2440, batch size: 35
2021-10-14 13:35:03,555 INFO [train.py:451] Epoch 6, batch 9720, batch avg loss 0.2469, total avg loss: 0.2459, batch size: 39
2021-10-14 13:35:08,464 INFO [train.py:451] Epoch 6, batch 9730, batch avg loss 0.2812, total avg loss: 0.2459, batch size: 35
2021-10-14 13:35:13,352 INFO [train.py:451] Epoch 6, batch 9740, batch avg loss 0.2546, total avg loss: 0.2468, batch size: 38
2021-10-14 13:35:18,253 INFO [train.py:451] Epoch 6, batch 9750, batch avg loss 0.2001, total avg loss: 0.2470, batch size: 31
2021-10-14 13:35:23,160 INFO [train.py:451] Epoch 6, batch 9760, batch avg loss 0.2640, total avg loss: 0.2475, batch size: 35
2021-10-14 13:35:28,193 INFO [train.py:451] Epoch 6, batch 9770, batch avg loss 0.2405, total avg loss: 0.2473, batch size: 38
2021-10-14 13:35:33,130 INFO [train.py:451] Epoch 6, batch 9780, batch avg loss 0.1978, total avg loss: 0.2473, batch size: 29
2021-10-14 13:35:38,013 INFO [train.py:451] Epoch 6, batch 9790, batch avg loss 0.2695, total avg loss: 0.2480, batch size: 41
2021-10-14 13:35:42,907 INFO [train.py:451] Epoch 6, batch 9800, batch avg loss 0.2039, total avg loss: 0.2484, batch size: 31
2021-10-14 13:35:47,730 INFO [train.py:451] Epoch 6, batch 9810, batch avg loss 0.2983, total avg loss: 0.2508, batch size: 36
2021-10-14 13:35:52,504 INFO [train.py:451] Epoch 6, batch 9820, batch avg loss 0.3078, total avg loss: 0.2585, batch size: 72
2021-10-14 13:35:57,392 INFO [train.py:451] Epoch 6, batch 9830, batch avg loss 0.2270, total avg loss: 0.2532, batch size: 27
2021-10-14 13:36:02,105 INFO [train.py:451] Epoch 6, batch 9840, batch avg loss 0.3657, total avg loss: 0.2625, batch size: 125
2021-10-14 13:36:06,951 INFO [train.py:451] Epoch 6, batch 9850, batch avg loss 0.2362, total avg loss: 0.2623, batch size: 34
2021-10-14 13:36:11,778 INFO [train.py:451] Epoch 6, batch 9860, batch avg loss 0.2436, total avg loss: 0.2628, batch size: 39
2021-10-14 13:36:16,661 INFO [train.py:451] Epoch 6, batch 9870, batch avg loss 0.2383, total avg loss: 0.2614, batch size: 34
2021-10-14 13:36:21,500 INFO [train.py:451] Epoch 6, batch 9880, batch avg loss 0.2181, total avg loss: 0.2591, batch size: 38
2021-10-14 13:36:26,292 INFO [train.py:451] Epoch 6, batch 9890, batch avg loss 0.2927, total avg loss: 0.2592, batch size: 39
2021-10-14 13:36:31,292 INFO [train.py:451] Epoch 6, batch 9900, batch avg loss 0.2500, total avg loss: 0.2563, batch size: 42
2021-10-14 13:36:36,084 INFO [train.py:451] Epoch 6, batch 9910, batch avg loss 0.2427, total avg loss: 0.2548, batch size: 42
2021-10-14 13:36:41,273 INFO [train.py:451] Epoch 6, batch 9920, batch avg loss 0.2580, total avg loss: 0.2527, batch size: 33
2021-10-14 13:36:43,433 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "eb3c90ae-798b-230e-1fe6-47b108e7fe18" will not be mixed in.
2021-10-14 13:36:46,387 INFO [train.py:451] Epoch 6, batch 9930, batch avg loss 0.2276, total avg loss: 0.2506, batch size: 33
2021-10-14 13:36:51,455 INFO [train.py:451] Epoch 6, batch 9940, batch avg loss 0.2442, total avg loss: 0.2511, batch size: 36
2021-10-14 13:36:56,367 INFO [train.py:451] Epoch 6, batch 9950, batch avg loss 0.2684, total avg loss: 0.2524, batch size: 45
2021-10-14 13:37:01,201 INFO [train.py:451] Epoch 6, batch 9960, batch avg loss 0.3798, total avg loss: 0.2535, batch size: 127
2021-10-14 13:37:06,128 INFO [train.py:451] Epoch 6, batch 9970, batch avg loss 0.2628, total avg loss: 0.2531, batch size: 49
2021-10-14 13:37:11,198 INFO [train.py:451] Epoch 6, batch 9980, batch avg loss 0.2681, total avg loss: 0.2525, batch size: 27
2021-10-14 13:37:16,092 INFO [train.py:451] Epoch 6, batch 9990, batch avg loss 0.2606, total avg loss: 0.2527, batch size: 32
2021-10-14 13:37:21,104 INFO [train.py:451] Epoch 6, batch 10000, batch avg loss 0.2658, total avg loss: 0.2515, batch size: 32
2021-10-14 13:37:59,138 INFO [train.py:483] Epoch 6, valid loss 0.1805, best valid loss: 0.1787 best valid epoch: 6
2021-10-14 13:38:03,928 INFO [train.py:451] Epoch 6, batch 10010, batch avg loss 0.2339, total avg loss: 0.2482, batch size: 36
2021-10-14 13:38:08,907 INFO [train.py:451] Epoch 6, batch 10020, batch avg loss 0.2228, total avg loss: 0.2487, batch size: 31
2021-10-14 13:38:13,695 INFO [train.py:451] Epoch 6, batch 10030, batch avg loss 0.3650, total avg loss: 0.2524, batch size: 126
2021-10-14 13:38:18,597 INFO [train.py:451] Epoch 6, batch 10040, batch avg loss 0.2396, total avg loss: 0.2544, batch size: 39
2021-10-14 13:38:23,582 INFO [train.py:451] Epoch 6, batch 10050, batch avg loss 0.2498, total avg loss: 0.2533, batch size: 37
2021-10-14 13:38:28,204 INFO [train.py:451] Epoch 6, batch 10060, batch avg loss 0.4038, total avg loss: 0.2584, batch size: 130
2021-10-14 13:38:33,112 INFO [train.py:451] Epoch 6, batch 10070, batch avg loss 0.2189, total avg loss: 0.2561, batch size: 30
2021-10-14 13:38:37,969 INFO [train.py:451] Epoch 6, batch 10080, batch avg loss 0.2570, total avg loss: 0.2554, batch size: 34
2021-10-14 13:38:42,845 INFO [train.py:451] Epoch 6, batch 10090, batch avg loss 0.3195, total avg loss: 0.2549, batch size: 42
2021-10-14 13:38:47,762 INFO [train.py:451] Epoch 6, batch 10100, batch avg loss 0.1867, total avg loss: 0.2538, batch size: 30
2021-10-14 13:38:52,585 INFO [train.py:451] Epoch 6, batch 10110, batch avg loss 0.2734, total avg loss: 0.2543, batch size: 34
2021-10-14 13:38:57,478 INFO [train.py:451] Epoch 6, batch 10120, batch avg loss 0.2346, total avg loss: 0.2535, batch size: 33
2021-10-14 13:39:02,279 INFO [train.py:451] Epoch 6, batch 10130, batch avg loss 0.2858, total avg loss: 0.2542, batch size: 35
2021-10-14 13:39:07,207 INFO [train.py:451] Epoch 6, batch 10140, batch avg loss 0.2135, total avg loss: 0.2540, batch size: 27
2021-10-14 13:39:12,172 INFO [train.py:451] Epoch 6, batch 10150, batch avg loss 0.2523, total avg loss: 0.2533, batch size: 32
2021-10-14 13:39:17,105 INFO [train.py:451] Epoch 6, batch 10160, batch avg loss 0.2182, total avg loss: 0.2526, batch size: 29
2021-10-14 13:39:21,738 INFO [train.py:451] Epoch 6, batch 10170, batch avg loss 0.3627, total avg loss: 0.2542, batch size: 129
2021-10-14 13:39:26,673 INFO [train.py:451] Epoch 6, batch 10180, batch avg loss 0.2361, total avg loss: 0.2534, batch size: 29
2021-10-14 13:39:31,689 INFO [train.py:451] Epoch 6, batch 10190, batch avg loss 0.1983, total avg loss: 0.2527, batch size: 27
2021-10-14 13:39:36,542 INFO [train.py:451] Epoch 6, batch 10200, batch avg loss 0.2533, total avg loss: 0.2531, batch size: 35
2021-10-14 13:39:41,476 INFO [train.py:451] Epoch 6, batch 10210, batch avg loss 0.2211, total avg loss: 0.2398, batch size: 31
2021-10-14 13:39:46,415 INFO [train.py:451] Epoch 6, batch 10220, batch avg loss 0.2145, total avg loss: 0.2357, batch size: 28
2021-10-14 13:39:51,453 INFO [train.py:451] Epoch 6, batch 10230, batch avg loss 0.1943, total avg loss: 0.2371, batch size: 30
2021-10-14 13:39:56,331 INFO [train.py:451] Epoch 6, batch 10240, batch avg loss 0.2089, total avg loss: 0.2359, batch size: 28
2021-10-14 13:40:01,283 INFO [train.py:451] Epoch 6, batch 10250, batch avg loss 0.2538, total avg loss: 0.2362, batch size: 33
2021-10-14 13:40:06,272 INFO [train.py:451] Epoch 6, batch 10260, batch avg loss 0.3459, total avg loss: 0.2403, batch size: 131
2021-10-14 13:40:11,153 INFO [train.py:451] Epoch 6, batch 10270, batch avg loss 0.2288, total avg loss: 0.2420, batch size: 33
2021-10-14 13:40:15,960 INFO [train.py:451] Epoch 6, batch 10280, batch avg loss 0.2681, total avg loss: 0.2430, batch size: 38
2021-10-14 13:40:20,793 INFO [train.py:451] Epoch 6, batch 10290, batch avg loss 0.2144, total avg loss: 0.2431, batch size: 28
2021-10-14 13:40:26,257 INFO [train.py:451] Epoch 6, batch 10300, batch avg loss 0.2394, total avg loss: 0.2432, batch size: 41
2021-10-14 13:40:31,248 INFO [train.py:451] Epoch 6, batch 10310, batch avg loss 0.1947, total avg loss: 0.2427, batch size: 31
2021-10-14 13:40:36,321 INFO [train.py:451] Epoch 6, batch 10320, batch avg loss 0.2399, total avg loss: 0.2412, batch size: 33
2021-10-14 13:40:41,285 INFO [train.py:451] Epoch 6, batch 10330, batch avg loss 0.2190, total avg loss: 0.2423, batch size: 31
2021-10-14 13:40:46,070 INFO [train.py:451] Epoch 6, batch 10340, batch avg loss 0.2289, total avg loss: 0.2440, batch size: 27
2021-10-14 13:40:51,148 INFO [train.py:451] Epoch 6, batch 10350, batch avg loss 0.2386, total avg loss: 0.2444, batch size: 29
2021-10-14 13:40:56,030 INFO [train.py:451] Epoch 6, batch 10360, batch avg loss 0.1706, total avg loss: 0.2448, batch size: 30
2021-10-14 13:41:00,901 INFO [train.py:451] Epoch 6, batch 10370, batch avg loss 0.2807, total avg loss: 0.2457, batch size: 71
2021-10-14 13:41:05,911 INFO [train.py:451] Epoch 6, batch 10380, batch avg loss 0.2263, total avg loss: 0.2456, batch size: 34
2021-10-14 13:41:10,729 INFO [train.py:451] Epoch 6, batch 10390, batch avg loss 0.2280, total avg loss: 0.2459, batch size: 35
2021-10-14 13:41:15,703 INFO [train.py:451] Epoch 6, batch 10400, batch avg loss 0.2919, total avg loss: 0.2458, batch size: 37
2021-10-14 13:41:20,521 INFO [train.py:451] Epoch 6, batch 10410, batch avg loss 0.2928, total avg loss: 0.2537, batch size: 57
2021-10-14 13:41:25,291 INFO [train.py:451] Epoch 6, batch 10420, batch avg loss 0.2745, total avg loss: 0.2587, batch size: 38
2021-10-14 13:41:30,228 INFO [train.py:451] Epoch 6, batch 10430, batch avg loss 0.1983, total avg loss: 0.2515, batch size: 29
2021-10-14 13:41:35,120 INFO [train.py:451] Epoch 6, batch 10440, batch avg loss 0.2037, total avg loss: 0.2467, batch size: 28
2021-10-14 13:41:40,474 INFO [train.py:451] Epoch 6, batch 10450, batch avg loss 0.2100, total avg loss: 0.2465, batch size: 29
2021-10-14 13:41:45,370 INFO [train.py:451] Epoch 6, batch 10460, batch avg loss 0.2677, total avg loss: 0.2501, batch size: 27
2021-10-14 13:41:50,326 INFO [train.py:451] Epoch 6, batch 10470, batch avg loss 0.2102, total avg loss: 0.2492, batch size: 31
2021-10-14 13:41:55,184 INFO [train.py:451] Epoch 6, batch 10480, batch avg loss 0.2592, total avg loss: 0.2485, batch size: 34
2021-10-14 13:41:59,996 INFO [train.py:451] Epoch 6, batch 10490, batch avg loss 0.2570, total avg loss: 0.2480, batch size: 45
2021-10-14 13:42:05,000 INFO [train.py:451] Epoch 6, batch 10500, batch avg loss 0.2693, total avg loss: 0.2468, batch size: 49
2021-10-14 13:42:09,931 INFO [train.py:451] Epoch 6, batch 10510, batch avg loss 0.2556, total avg loss: 0.2460, batch size: 33
2021-10-14 13:42:14,929 INFO [train.py:451] Epoch 6, batch 10520, batch avg loss 0.3012, total avg loss: 0.2466, batch size: 37
2021-10-14 13:42:19,875 INFO [train.py:451] Epoch 6, batch 10530, batch avg loss 0.2417, total avg loss: 0.2449, batch size: 42
2021-10-14 13:42:24,808 INFO [train.py:451] Epoch 6, batch 10540, batch avg loss 0.2573, total avg loss: 0.2452, batch size: 37
2021-10-14 13:42:29,633 INFO [train.py:451] Epoch 6, batch 10550, batch avg loss 0.2602, total avg loss: 0.2464, batch size: 31
2021-10-14 13:42:34,391 INFO [train.py:451] Epoch 6, batch 10560, batch avg loss 0.3437, total avg loss: 0.2471, batch size: 71
2021-10-14 13:42:39,321 INFO [train.py:451] Epoch 6, batch 10570, batch avg loss 0.2212, total avg loss: 0.2460, batch size: 30
2021-10-14 13:42:44,232 INFO [train.py:451] Epoch 6, batch 10580, batch avg loss 0.2525, total avg loss: 0.2460, batch size: 31
2021-10-14 13:42:49,190 INFO [train.py:451] Epoch 6, batch 10590, batch avg loss 0.1770, total avg loss: 0.2447, batch size: 30
2021-10-14 13:42:54,045 INFO [train.py:451] Epoch 6, batch 10600, batch avg loss 0.2915, total avg loss: 0.2454, batch size: 38
2021-10-14 13:42:58,837 INFO [train.py:451] Epoch 6, batch 10610, batch avg loss 0.2576, total avg loss: 0.2555, batch size: 36
2021-10-14 13:43:03,814 INFO [train.py:451] Epoch 6, batch 10620, batch avg loss 0.2367, total avg loss: 0.2483, batch size: 39
2021-10-14 13:43:08,753 INFO [train.py:451] Epoch 6, batch 10630, batch avg loss 0.2400, total avg loss: 0.2452, batch size: 33
2021-10-14 13:43:13,729 INFO [train.py:451] Epoch 6, batch 10640, batch avg loss 0.2299, total avg loss: 0.2415, batch size: 41
2021-10-14 13:43:18,409 INFO [train.py:451] Epoch 6, batch 10650, batch avg loss 0.3525, total avg loss: 0.2445, batch size: 125
2021-10-14 13:43:23,271 INFO [train.py:451] Epoch 6, batch 10660, batch avg loss 0.2511, total avg loss: 0.2447, batch size: 35
2021-10-14 13:43:28,000 INFO [train.py:451] Epoch 6, batch 10670, batch avg loss 0.2752, total avg loss: 0.2479, batch size: 42
2021-10-14 13:43:32,903 INFO [train.py:451] Epoch 6, batch 10680, batch avg loss 0.3342, total avg loss: 0.2501, batch size: 35
2021-10-14 13:43:37,769 INFO [train.py:451] Epoch 6, batch 10690, batch avg loss 0.2538, total avg loss: 0.2514, batch size: 34
2021-10-14 13:43:42,717 INFO [train.py:451] Epoch 6, batch 10700, batch avg loss 0.3079, total avg loss: 0.2503, batch size: 41
2021-10-14 13:43:47,556 INFO [train.py:451] Epoch 6, batch 10710, batch avg loss 0.2220, total avg loss: 0.2505, batch size: 30
2021-10-14 13:43:52,522 INFO [train.py:451] Epoch 6, batch 10720, batch avg loss 0.2460, total avg loss: 0.2519, batch size: 28
2021-10-14 13:43:57,314 INFO [train.py:451] Epoch 6, batch 10730, batch avg loss 0.2663, total avg loss: 0.2528, batch size: 45
2021-10-14 13:44:02,193 INFO [train.py:451] Epoch 6, batch 10740, batch avg loss 0.2579, total avg loss: 0.2513, batch size: 49
2021-10-14 13:44:07,056 INFO [train.py:451] Epoch 6, batch 10750, batch avg loss 0.2696, total avg loss: 0.2508, batch size: 73
2021-10-14 13:44:12,043 INFO [train.py:451] Epoch 6, batch 10760, batch avg loss 0.2977, total avg loss: 0.2496, batch size: 35
2021-10-14 13:44:16,706 INFO [train.py:451] Epoch 6, batch 10770, batch avg loss 0.1929, total avg loss: 0.2497, batch size: 29
2021-10-14 13:44:21,629 INFO [train.py:451] Epoch 6, batch 10780, batch avg loss 0.2039, total avg loss: 0.2487, batch size: 31
2021-10-14 13:44:26,362 INFO [train.py:451] Epoch 6, batch 10790, batch avg loss 0.2840, total avg loss: 0.2495, batch size: 32
2021-10-14 13:44:31,276 INFO [train.py:451] Epoch 6, batch 10800, batch avg loss 0.2209, total avg loss: 0.2499, batch size: 31
2021-10-14 13:44:36,328 INFO [train.py:451] Epoch 6, batch 10810, batch avg loss 0.2337, total avg loss: 0.2458, batch size: 34
2021-10-14 13:44:41,193 INFO [train.py:451] Epoch 6, batch 10820, batch avg loss 0.2149, total avg loss: 0.2495, batch size: 49
2021-10-14 13:44:45,975 INFO [train.py:451] Epoch 6, batch 10830, batch avg loss 0.2475, total avg loss: 0.2537, batch size: 36
2021-10-14 13:44:51,007 INFO [train.py:451] Epoch 6, batch 10840, batch avg loss 0.2449, total avg loss: 0.2476, batch size: 33
2021-10-14 13:44:55,858 INFO [train.py:451] Epoch 6, batch 10850, batch avg loss 0.2263, total avg loss: 0.2477, batch size: 39
2021-10-14 13:45:00,864 INFO [train.py:451] Epoch 6, batch 10860, batch avg loss 0.1849, total avg loss: 0.2451, batch size: 29
2021-10-14 13:45:05,786 INFO [train.py:451] Epoch 6, batch 10870, batch avg loss 0.2297, total avg loss: 0.2443, batch size: 41
2021-10-14 13:45:10,795 INFO [train.py:451] Epoch 6, batch 10880, batch avg loss 0.1677, total avg loss: 0.2438, batch size: 28
2021-10-14 13:45:15,632 INFO [train.py:451] Epoch 6, batch 10890, batch avg loss 0.4012, total avg loss: 0.2457, batch size: 124
2021-10-14 13:45:20,607 INFO [train.py:451] Epoch 6, batch 10900, batch avg loss 0.2775, total avg loss: 0.2443, batch size: 30
2021-10-14 13:45:25,495 INFO [train.py:451] Epoch 6, batch 10910, batch avg loss 0.3168, total avg loss: 0.2450, batch size: 39
2021-10-14 13:45:30,369 INFO [train.py:451] Epoch 6, batch 10920, batch avg loss 0.2073, total avg loss: 0.2454, batch size: 30
2021-10-14 13:45:35,287 INFO [train.py:451] Epoch 6, batch 10930, batch avg loss 0.2449, total avg loss: 0.2447, batch size: 36
2021-10-14 13:45:40,266 INFO [train.py:451] Epoch 6, batch 10940, batch avg loss 0.2607, total avg loss: 0.2448, batch size: 42
2021-10-14 13:45:45,293 INFO [train.py:451] Epoch 6, batch 10950, batch avg loss 0.1988, total avg loss: 0.2434, batch size: 29
2021-10-14 13:45:57,393 INFO [train.py:451] Epoch 6, batch 10960, batch avg loss 0.2567, total avg loss: 0.2437, batch size: 45
2021-10-14 13:46:02,317 INFO [train.py:451] Epoch 6, batch 10970, batch avg loss 0.2302, total avg loss: 0.2436, batch size: 33
2021-10-14 13:46:07,038 INFO [train.py:451] Epoch 6, batch 10980, batch avg loss 0.3075, total avg loss: 0.2440, batch size: 73
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2021-10-14 13:55:46,891 INFO [train.py:483] Epoch 6, valid loss 0.1784, best valid loss: 0.1784 best valid epoch: 6
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2021-10-14 14:04:35,959 INFO [train.py:483] Epoch 6, valid loss 0.1785, best valid loss: 0.1784 best valid epoch: 6
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2021-10-14 14:13:31,294 INFO [train.py:483] Epoch 6, valid loss 0.1799, best valid loss: 0.1784 best valid epoch: 6
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2021-10-14 14:22:25,619 INFO [train.py:483] Epoch 6, valid loss 0.1794, best valid loss: 0.1784 best valid epoch: 6
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2021-10-14 14:25:48,969 INFO [train.py:451] Epoch 6, batch 15410, batch avg loss 0.2438, total avg loss: 0.2514, batch size: 35
2021-10-14 14:25:53,917 INFO [train.py:451] Epoch 6, batch 15420, batch avg loss 0.2987, total avg loss: 0.2459, batch size: 39
2021-10-14 14:25:58,888 INFO [train.py:451] Epoch 6, batch 15430, batch avg loss 0.2427, total avg loss: 0.2505, batch size: 30
2021-10-14 14:26:03,852 INFO [train.py:451] Epoch 6, batch 15440, batch avg loss 0.2518, total avg loss: 0.2497, batch size: 38
2021-10-14 14:26:08,589 INFO [train.py:451] Epoch 6, batch 15450, batch avg loss 0.2403, total avg loss: 0.2535, batch size: 39
2021-10-14 14:26:13,345 INFO [train.py:451] Epoch 6, batch 15460, batch avg loss 0.2030, total avg loss: 0.2545, batch size: 35
2021-10-14 14:26:18,297 INFO [train.py:451] Epoch 6, batch 15470, batch avg loss 0.2880, total avg loss: 0.2518, batch size: 30
2021-10-14 14:26:19,467 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "e7bcb3e8-f03b-01a5-5e6d-084c438211b9" will not be mixed in.
2021-10-14 14:26:23,065 INFO [train.py:451] Epoch 6, batch 15480, batch avg loss 0.2636, total avg loss: 0.2519, batch size: 32
2021-10-14 14:26:28,237 INFO [train.py:451] Epoch 6, batch 15490, batch avg loss 0.2835, total avg loss: 0.2500, batch size: 40
2021-10-14 14:26:33,200 INFO [train.py:451] Epoch 6, batch 15500, batch avg loss 0.2444, total avg loss: 0.2488, batch size: 28
2021-10-14 14:26:38,152 INFO [train.py:451] Epoch 6, batch 15510, batch avg loss 0.2204, total avg loss: 0.2485, batch size: 35
2021-10-14 14:26:42,949 INFO [train.py:451] Epoch 6, batch 15520, batch avg loss 0.2236, total avg loss: 0.2490, batch size: 32
2021-10-14 14:26:47,958 INFO [train.py:451] Epoch 6, batch 15530, batch avg loss 0.3031, total avg loss: 0.2469, batch size: 72
2021-10-14 14:26:52,881 INFO [train.py:451] Epoch 6, batch 15540, batch avg loss 0.2444, total avg loss: 0.2472, batch size: 34
2021-10-14 14:26:57,819 INFO [train.py:451] Epoch 6, batch 15550, batch avg loss 0.2951, total avg loss: 0.2490, batch size: 37
2021-10-14 14:27:02,899 INFO [train.py:451] Epoch 6, batch 15560, batch avg loss 0.2367, total avg loss: 0.2491, batch size: 34
2021-10-14 14:27:07,898 INFO [train.py:451] Epoch 6, batch 15570, batch avg loss 0.2383, total avg loss: 0.2481, batch size: 35
2021-10-14 14:27:12,848 INFO [train.py:451] Epoch 6, batch 15580, batch avg loss 0.2629, total avg loss: 0.2481, batch size: 34
2021-10-14 14:27:17,782 INFO [train.py:451] Epoch 6, batch 15590, batch avg loss 0.2623, total avg loss: 0.2489, batch size: 41
2021-10-14 14:27:22,751 INFO [train.py:451] Epoch 6, batch 15600, batch avg loss 0.2453, total avg loss: 0.2493, batch size: 33
2021-10-14 14:27:27,721 INFO [train.py:451] Epoch 6, batch 15610, batch avg loss 0.2149, total avg loss: 0.2319, batch size: 33
2021-10-14 14:27:32,583 INFO [train.py:451] Epoch 6, batch 15620, batch avg loss 0.2730, total avg loss: 0.2399, batch size: 36
2021-10-14 14:27:37,560 INFO [train.py:451] Epoch 6, batch 15630, batch avg loss 0.2396, total avg loss: 0.2448, batch size: 34
2021-10-14 14:27:42,345 INFO [train.py:451] Epoch 6, batch 15640, batch avg loss 0.2448, total avg loss: 0.2421, batch size: 36
2021-10-14 14:27:47,228 INFO [train.py:451] Epoch 6, batch 15650, batch avg loss 0.3016, total avg loss: 0.2433, batch size: 56
2021-10-14 14:27:52,069 INFO [train.py:451] Epoch 6, batch 15660, batch avg loss 0.3045, total avg loss: 0.2452, batch size: 34
2021-10-14 14:27:57,050 INFO [train.py:451] Epoch 6, batch 15670, batch avg loss 0.2214, total avg loss: 0.2471, batch size: 36
2021-10-14 14:28:02,242 INFO [train.py:451] Epoch 6, batch 15680, batch avg loss 0.2753, total avg loss: 0.2484, batch size: 33
2021-10-14 14:28:07,152 INFO [train.py:451] Epoch 6, batch 15690, batch avg loss 0.2589, total avg loss: 0.2498, batch size: 38
2021-10-14 14:28:12,168 INFO [train.py:451] Epoch 6, batch 15700, batch avg loss 0.3543, total avg loss: 0.2501, batch size: 126
2021-10-14 14:28:17,226 INFO [train.py:451] Epoch 6, batch 15710, batch avg loss 0.3063, total avg loss: 0.2518, batch size: 36
2021-10-14 14:28:22,129 INFO [train.py:451] Epoch 6, batch 15720, batch avg loss 0.2062, total avg loss: 0.2515, batch size: 38
2021-10-14 14:28:27,081 INFO [train.py:451] Epoch 6, batch 15730, batch avg loss 0.2071, total avg loss: 0.2511, batch size: 33
2021-10-14 14:28:32,056 INFO [train.py:451] Epoch 6, batch 15740, batch avg loss 0.2925, total avg loss: 0.2510, batch size: 38
2021-10-14 14:28:36,974 INFO [train.py:451] Epoch 6, batch 15750, batch avg loss 0.2488, total avg loss: 0.2508, batch size: 49
2021-10-14 14:28:41,796 INFO [train.py:451] Epoch 6, batch 15760, batch avg loss 0.2797, total avg loss: 0.2516, batch size: 45
2021-10-14 14:28:46,722 INFO [train.py:451] Epoch 6, batch 15770, batch avg loss 0.2621, total avg loss: 0.2513, batch size: 27
2021-10-14 14:28:51,618 INFO [train.py:451] Epoch 6, batch 15780, batch avg loss 0.2051, total avg loss: 0.2518, batch size: 32
2021-10-14 14:28:56,652 INFO [train.py:451] Epoch 6, batch 15790, batch avg loss 0.2394, total avg loss: 0.2518, batch size: 31
2021-10-14 14:29:01,629 INFO [train.py:451] Epoch 6, batch 15800, batch avg loss 0.3027, total avg loss: 0.2511, batch size: 72
2021-10-14 14:29:06,565 INFO [train.py:451] Epoch 6, batch 15810, batch avg loss 0.2253, total avg loss: 0.2439, batch size: 35
2021-10-14 14:29:11,443 INFO [train.py:451] Epoch 6, batch 15820, batch avg loss 0.2537, total avg loss: 0.2552, batch size: 34
2021-10-14 14:29:16,397 INFO [train.py:451] Epoch 6, batch 15830, batch avg loss 0.2193, total avg loss: 0.2543, batch size: 36
2021-10-14 14:29:21,660 INFO [train.py:451] Epoch 6, batch 15840, batch avg loss 0.2868, total avg loss: 0.2502, batch size: 38
2021-10-14 14:29:26,718 INFO [train.py:451] Epoch 6, batch 15850, batch avg loss 0.2767, total avg loss: 0.2522, batch size: 38
2021-10-14 14:29:31,733 INFO [train.py:451] Epoch 6, batch 15860, batch avg loss 0.2485, total avg loss: 0.2520, batch size: 35
2021-10-14 14:29:36,823 INFO [train.py:451] Epoch 6, batch 15870, batch avg loss 0.2805, total avg loss: 0.2512, batch size: 38
2021-10-14 14:29:41,845 INFO [train.py:451] Epoch 6, batch 15880, batch avg loss 0.2346, total avg loss: 0.2508, batch size: 29
2021-10-14 14:29:46,529 INFO [train.py:451] Epoch 6, batch 15890, batch avg loss 0.3112, total avg loss: 0.2510, batch size: 57
2021-10-14 14:29:51,379 INFO [train.py:451] Epoch 6, batch 15900, batch avg loss 0.2680, total avg loss: 0.2495, batch size: 31
2021-10-14 14:29:56,245 INFO [train.py:451] Epoch 6, batch 15910, batch avg loss 0.2575, total avg loss: 0.2497, batch size: 30
2021-10-14 14:30:01,175 INFO [train.py:451] Epoch 6, batch 15920, batch avg loss 0.2172, total avg loss: 0.2502, batch size: 32
2021-10-14 14:30:06,023 INFO [train.py:451] Epoch 6, batch 15930, batch avg loss 0.2473, total avg loss: 0.2508, batch size: 33
2021-10-14 14:30:10,931 INFO [train.py:451] Epoch 6, batch 15940, batch avg loss 0.2653, total avg loss: 0.2507, batch size: 39
2021-10-14 14:30:15,786 INFO [train.py:451] Epoch 6, batch 15950, batch avg loss 0.2532, total avg loss: 0.2510, batch size: 35
2021-10-14 14:30:20,650 INFO [train.py:451] Epoch 6, batch 15960, batch avg loss 0.2358, total avg loss: 0.2519, batch size: 37
2021-10-14 14:30:25,446 INFO [train.py:451] Epoch 6, batch 15970, batch avg loss 0.2528, total avg loss: 0.2522, batch size: 38
2021-10-14 14:30:30,377 INFO [train.py:451] Epoch 6, batch 15980, batch avg loss 0.2200, total avg loss: 0.2517, batch size: 49
2021-10-14 14:30:35,158 INFO [train.py:451] Epoch 6, batch 15990, batch avg loss 0.2459, total avg loss: 0.2510, batch size: 41
2021-10-14 14:30:39,924 INFO [train.py:451] Epoch 6, batch 16000, batch avg loss 0.2984, total avg loss: 0.2513, batch size: 57
2021-10-14 14:31:20,546 INFO [train.py:483] Epoch 6, valid loss 0.1783, best valid loss: 0.1783 best valid epoch: 6
2021-10-14 14:31:25,427 INFO [train.py:451] Epoch 6, batch 16010, batch avg loss 0.2488, total avg loss: 0.2468, batch size: 32
2021-10-14 14:31:30,406 INFO [train.py:451] Epoch 6, batch 16020, batch avg loss 0.2886, total avg loss: 0.2509, batch size: 56
2021-10-14 14:31:35,394 INFO [train.py:451] Epoch 6, batch 16030, batch avg loss 0.2371, total avg loss: 0.2465, batch size: 34
2021-10-14 14:31:40,276 INFO [train.py:451] Epoch 6, batch 16040, batch avg loss 0.2526, total avg loss: 0.2459, batch size: 35
2021-10-14 14:31:45,297 INFO [train.py:451] Epoch 6, batch 16050, batch avg loss 0.1973, total avg loss: 0.2459, batch size: 33
2021-10-14 14:31:50,363 INFO [train.py:451] Epoch 6, batch 16060, batch avg loss 0.3073, total avg loss: 0.2462, batch size: 49
2021-10-14 14:31:55,262 INFO [train.py:451] Epoch 6, batch 16070, batch avg loss 0.2649, total avg loss: 0.2477, batch size: 36
2021-10-14 14:32:00,253 INFO [train.py:451] Epoch 6, batch 16080, batch avg loss 0.2404, total avg loss: 0.2483, batch size: 42
2021-10-14 14:32:05,020 INFO [train.py:451] Epoch 6, batch 16090, batch avg loss 0.2668, total avg loss: 0.2518, batch size: 34
2021-10-14 14:32:09,873 INFO [train.py:451] Epoch 6, batch 16100, batch avg loss 0.2981, total avg loss: 0.2530, batch size: 30
2021-10-14 14:32:14,671 INFO [train.py:451] Epoch 6, batch 16110, batch avg loss 0.2876, total avg loss: 0.2531, batch size: 31
2021-10-14 14:32:19,367 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "6577bb1b-701a-7db9-731b-20081661e69a" will not be mixed in.
2021-10-14 14:32:19,605 INFO [train.py:451] Epoch 6, batch 16120, batch avg loss 0.2645, total avg loss: 0.2538, batch size: 35
2021-10-14 14:32:24,245 INFO [train.py:451] Epoch 6, batch 16130, batch avg loss 0.2847, total avg loss: 0.2545, batch size: 57
2021-10-14 14:32:29,230 INFO [train.py:451] Epoch 6, batch 16140, batch avg loss 0.2267, total avg loss: 0.2527, batch size: 33
2021-10-14 14:32:34,244 INFO [train.py:451] Epoch 6, batch 16150, batch avg loss 0.3692, total avg loss: 0.2519, batch size: 134
2021-10-14 14:32:39,240 INFO [train.py:451] Epoch 6, batch 16160, batch avg loss 0.3065, total avg loss: 0.2523, batch size: 42
2021-10-14 14:32:43,989 INFO [train.py:451] Epoch 6, batch 16170, batch avg loss 0.1913, total avg loss: 0.2520, batch size: 32
2021-10-14 14:32:48,927 INFO [train.py:451] Epoch 6, batch 16180, batch avg loss 0.2019, total avg loss: 0.2519, batch size: 31
2021-10-14 14:32:53,797 INFO [train.py:451] Epoch 6, batch 16190, batch avg loss 0.2190, total avg loss: 0.2517, batch size: 32
2021-10-14 14:32:58,868 INFO [train.py:451] Epoch 6, batch 16200, batch avg loss 0.2503, total avg loss: 0.2510, batch size: 29
2021-10-14 14:33:03,728 INFO [train.py:451] Epoch 6, batch 16210, batch avg loss 0.2572, total avg loss: 0.2479, batch size: 73
2021-10-14 14:33:08,601 INFO [train.py:451] Epoch 6, batch 16220, batch avg loss 0.3853, total avg loss: 0.2556, batch size: 125
2021-10-14 14:33:13,500 INFO [train.py:451] Epoch 6, batch 16230, batch avg loss 0.3135, total avg loss: 0.2606, batch size: 49
2021-10-14 14:33:18,707 INFO [train.py:451] Epoch 6, batch 16240, batch avg loss 0.2255, total avg loss: 0.2516, batch size: 31
2021-10-14 14:33:23,952 INFO [train.py:451] Epoch 6, batch 16250, batch avg loss 0.2874, total avg loss: 0.2504, batch size: 39
2021-10-14 14:33:28,947 INFO [train.py:451] Epoch 6, batch 16260, batch avg loss 0.2345, total avg loss: 0.2501, batch size: 34
2021-10-14 14:33:33,951 INFO [train.py:451] Epoch 6, batch 16270, batch avg loss 0.2527, total avg loss: 0.2483, batch size: 39
2021-10-14 14:33:38,821 INFO [train.py:451] Epoch 6, batch 16280, batch avg loss 0.1965, total avg loss: 0.2502, batch size: 33
2021-10-14 14:33:43,759 INFO [train.py:451] Epoch 6, batch 16290, batch avg loss 0.3125, total avg loss: 0.2484, batch size: 34
2021-10-14 14:33:48,698 INFO [train.py:451] Epoch 6, batch 16300, batch avg loss 0.2823, total avg loss: 0.2486, batch size: 31
2021-10-14 14:33:53,579 INFO [train.py:451] Epoch 6, batch 16310, batch avg loss 0.2467, total avg loss: 0.2505, batch size: 34
2021-10-14 14:33:58,317 INFO [train.py:451] Epoch 6, batch 16320, batch avg loss 0.3195, total avg loss: 0.2503, batch size: 56
2021-10-14 14:34:03,315 INFO [train.py:451] Epoch 6, batch 16330, batch avg loss 0.1890, total avg loss: 0.2490, batch size: 28
2021-10-14 14:34:08,204 INFO [train.py:451] Epoch 6, batch 16340, batch avg loss 0.2995, total avg loss: 0.2482, batch size: 39
2021-10-14 14:34:12,995 INFO [train.py:451] Epoch 6, batch 16350, batch avg loss 0.2106, total avg loss: 0.2479, batch size: 34
2021-10-14 14:34:17,774 INFO [train.py:451] Epoch 6, batch 16360, batch avg loss 0.3500, total avg loss: 0.2485, batch size: 73
2021-10-14 14:34:22,844 INFO [train.py:451] Epoch 6, batch 16370, batch avg loss 0.2712, total avg loss: 0.2507, batch size: 42
2021-10-14 14:34:27,865 INFO [train.py:451] Epoch 6, batch 16380, batch avg loss 0.3002, total avg loss: 0.2509, batch size: 38
2021-10-14 14:34:32,963 INFO [train.py:451] Epoch 6, batch 16390, batch avg loss 0.2420, total avg loss: 0.2497, batch size: 31
2021-10-14 14:34:37,844 INFO [train.py:451] Epoch 6, batch 16400, batch avg loss 0.2477, total avg loss: 0.2492, batch size: 37
2021-10-14 14:34:42,849 INFO [train.py:451] Epoch 6, batch 16410, batch avg loss 0.2728, total avg loss: 0.2385, batch size: 38
2021-10-14 14:34:47,741 INFO [train.py:451] Epoch 6, batch 16420, batch avg loss 0.2384, total avg loss: 0.2456, batch size: 36
2021-10-14 14:34:52,629 INFO [train.py:451] Epoch 6, batch 16430, batch avg loss 0.2885, total avg loss: 0.2425, batch size: 36
2021-10-14 14:34:57,583 INFO [train.py:451] Epoch 6, batch 16440, batch avg loss 0.2322, total avg loss: 0.2485, batch size: 27
2021-10-14 14:35:02,649 INFO [train.py:451] Epoch 6, batch 16450, batch avg loss 0.2362, total avg loss: 0.2500, batch size: 42
2021-10-14 14:35:07,713 INFO [train.py:451] Epoch 6, batch 16460, batch avg loss 0.2198, total avg loss: 0.2491, batch size: 36
2021-10-14 14:35:12,801 INFO [train.py:451] Epoch 6, batch 16470, batch avg loss 0.2536, total avg loss: 0.2463, batch size: 35
2021-10-14 14:35:17,629 INFO [train.py:451] Epoch 6, batch 16480, batch avg loss 0.2623, total avg loss: 0.2491, batch size: 38
2021-10-14 14:35:22,494 INFO [train.py:451] Epoch 6, batch 16490, batch avg loss 0.2303, total avg loss: 0.2493, batch size: 42
2021-10-14 14:35:27,533 INFO [train.py:451] Epoch 6, batch 16500, batch avg loss 0.2501, total avg loss: 0.2485, batch size: 39
2021-10-14 14:35:32,490 INFO [train.py:451] Epoch 6, batch 16510, batch avg loss 0.3671, total avg loss: 0.2497, batch size: 131
2021-10-14 14:35:37,529 INFO [train.py:451] Epoch 6, batch 16520, batch avg loss 0.3150, total avg loss: 0.2509, batch size: 42
2021-10-14 14:35:42,566 INFO [train.py:451] Epoch 6, batch 16530, batch avg loss 0.2180, total avg loss: 0.2497, batch size: 29
2021-10-14 14:35:47,452 INFO [train.py:451] Epoch 6, batch 16540, batch avg loss 0.2391, total avg loss: 0.2502, batch size: 41
2021-10-14 14:35:52,242 INFO [train.py:451] Epoch 6, batch 16550, batch avg loss 0.2216, total avg loss: 0.2503, batch size: 30
2021-10-14 14:35:57,040 INFO [train.py:451] Epoch 6, batch 16560, batch avg loss 0.3339, total avg loss: 0.2525, batch size: 42
2021-10-14 14:36:02,183 INFO [train.py:451] Epoch 6, batch 16570, batch avg loss 0.1972, total avg loss: 0.2517, batch size: 28
2021-10-14 14:36:07,229 INFO [train.py:451] Epoch 6, batch 16580, batch avg loss 0.1958, total avg loss: 0.2515, batch size: 29
2021-10-14 14:36:12,326 INFO [train.py:451] Epoch 6, batch 16590, batch avg loss 0.2790, total avg loss: 0.2511, batch size: 38
2021-10-14 14:36:17,423 INFO [train.py:451] Epoch 6, batch 16600, batch avg loss 0.2931, total avg loss: 0.2507, batch size: 35
2021-10-14 14:36:22,692 INFO [train.py:451] Epoch 6, batch 16610, batch avg loss 0.2875, total avg loss: 0.2285, batch size: 45
2021-10-14 14:36:27,369 INFO [train.py:451] Epoch 6, batch 16620, batch avg loss 0.2102, total avg loss: 0.2419, batch size: 31
2021-10-14 14:36:32,380 INFO [train.py:451] Epoch 6, batch 16630, batch avg loss 0.2611, total avg loss: 0.2353, batch size: 49
2021-10-14 14:36:37,095 INFO [train.py:451] Epoch 6, batch 16640, batch avg loss 0.2341, total avg loss: 0.2396, batch size: 49
2021-10-14 14:36:42,191 INFO [train.py:451] Epoch 6, batch 16650, batch avg loss 0.2166, total avg loss: 0.2422, batch size: 29
2021-10-14 14:36:47,041 INFO [train.py:451] Epoch 6, batch 16660, batch avg loss 0.2473, total avg loss: 0.2433, batch size: 41
2021-10-14 14:36:51,731 INFO [train.py:451] Epoch 6, batch 16670, batch avg loss 0.3428, total avg loss: 0.2473, batch size: 73
2021-10-14 14:36:56,505 INFO [train.py:451] Epoch 6, batch 16680, batch avg loss 0.2912, total avg loss: 0.2506, batch size: 73
2021-10-14 14:37:01,407 INFO [train.py:451] Epoch 6, batch 16690, batch avg loss 0.2012, total avg loss: 0.2513, batch size: 33
2021-10-14 14:37:06,345 INFO [train.py:451] Epoch 6, batch 16700, batch avg loss 0.2556, total avg loss: 0.2500, batch size: 34
2021-10-14 14:37:11,292 INFO [train.py:451] Epoch 6, batch 16710, batch avg loss 0.2499, total avg loss: 0.2504, batch size: 30
2021-10-14 14:37:16,228 INFO [train.py:451] Epoch 6, batch 16720, batch avg loss 0.2143, total avg loss: 0.2504, batch size: 28
2021-10-14 14:37:21,093 INFO [train.py:451] Epoch 6, batch 16730, batch avg loss 0.2125, total avg loss: 0.2495, batch size: 29
2021-10-14 14:37:26,050 INFO [train.py:451] Epoch 6, batch 16740, batch avg loss 0.3005, total avg loss: 0.2500, batch size: 39
2021-10-14 14:37:30,890 INFO [train.py:451] Epoch 6, batch 16750, batch avg loss 0.2330, total avg loss: 0.2513, batch size: 42
2021-10-14 14:37:35,935 INFO [train.py:451] Epoch 6, batch 16760, batch avg loss 0.2020, total avg loss: 0.2513, batch size: 34
2021-10-14 14:37:41,108 INFO [train.py:451] Epoch 6, batch 16770, batch avg loss 0.2351, total avg loss: 0.2517, batch size: 28
2021-10-14 14:37:46,113 INFO [train.py:451] Epoch 6, batch 16780, batch avg loss 0.2191, total avg loss: 0.2508, batch size: 35
2021-10-14 14:37:51,028 INFO [train.py:451] Epoch 6, batch 16790, batch avg loss 0.2549, total avg loss: 0.2508, batch size: 41
2021-10-14 14:37:56,020 INFO [train.py:451] Epoch 6, batch 16800, batch avg loss 0.3163, total avg loss: 0.2504, batch size: 72
2021-10-14 14:38:01,153 INFO [train.py:451] Epoch 6, batch 16810, batch avg loss 0.2248, total avg loss: 0.2519, batch size: 32
2021-10-14 14:38:06,137 INFO [train.py:451] Epoch 6, batch 16820, batch avg loss 0.2792, total avg loss: 0.2533, batch size: 41
2021-10-14 14:38:11,238 INFO [train.py:451] Epoch 6, batch 16830, batch avg loss 0.2321, total avg loss: 0.2525, batch size: 38
2021-10-14 14:38:16,255 INFO [train.py:451] Epoch 6, batch 16840, batch avg loss 0.2471, total avg loss: 0.2461, batch size: 38
2021-10-14 14:38:21,178 INFO [train.py:451] Epoch 6, batch 16850, batch avg loss 0.2384, total avg loss: 0.2490, batch size: 38
2021-10-14 14:38:26,298 INFO [train.py:451] Epoch 6, batch 16860, batch avg loss 0.2257, total avg loss: 0.2479, batch size: 34
2021-10-14 14:38:31,305 INFO [train.py:451] Epoch 6, batch 16870, batch avg loss 0.2167, total avg loss: 0.2449, batch size: 38
2021-10-14 14:38:36,288 INFO [train.py:451] Epoch 6, batch 16880, batch avg loss 0.2530, total avg loss: 0.2452, batch size: 33
2021-10-14 14:38:41,316 INFO [train.py:451] Epoch 6, batch 16890, batch avg loss 0.2658, total avg loss: 0.2462, batch size: 35
2021-10-14 14:38:46,192 INFO [train.py:451] Epoch 6, batch 16900, batch avg loss 0.2651, total avg loss: 0.2477, batch size: 45
2021-10-14 14:38:51,212 INFO [train.py:451] Epoch 6, batch 16910, batch avg loss 0.2682, total avg loss: 0.2476, batch size: 45
2021-10-14 14:38:56,123 INFO [train.py:451] Epoch 6, batch 16920, batch avg loss 0.2990, total avg loss: 0.2481, batch size: 42
2021-10-14 14:39:01,024 INFO [train.py:451] Epoch 6, batch 16930, batch avg loss 0.2565, total avg loss: 0.2475, batch size: 38
2021-10-14 14:39:05,934 INFO [train.py:451] Epoch 6, batch 16940, batch avg loss 0.1991, total avg loss: 0.2489, batch size: 32
2021-10-14 14:39:11,112 INFO [train.py:451] Epoch 6, batch 16950, batch avg loss 0.2809, total avg loss: 0.2481, batch size: 38
2021-10-14 14:39:15,847 INFO [train.py:451] Epoch 6, batch 16960, batch avg loss 0.2829, total avg loss: 0.2505, batch size: 42
2021-10-14 14:39:20,626 INFO [train.py:451] Epoch 6, batch 16970, batch avg loss 0.2159, total avg loss: 0.2490, batch size: 29
2021-10-14 14:39:25,502 INFO [train.py:451] Epoch 6, batch 16980, batch avg loss 0.2552, total avg loss: 0.2484, batch size: 49
2021-10-14 14:39:30,319 INFO [train.py:451] Epoch 6, batch 16990, batch avg loss 0.2893, total avg loss: 0.2484, batch size: 72
2021-10-14 14:39:35,304 INFO [train.py:451] Epoch 6, batch 17000, batch avg loss 0.2525, total avg loss: 0.2488, batch size: 36
2021-10-14 14:40:16,724 INFO [train.py:483] Epoch 6, valid loss 0.1793, best valid loss: 0.1783 best valid epoch: 6
2021-10-14 14:40:21,707 INFO [train.py:451] Epoch 6, batch 17010, batch avg loss 0.2400, total avg loss: 0.2530, batch size: 36
2021-10-14 14:40:26,825 INFO [train.py:451] Epoch 6, batch 17020, batch avg loss 0.2358, total avg loss: 0.2394, batch size: 33
2021-10-14 14:40:31,736 INFO [train.py:451] Epoch 6, batch 17030, batch avg loss 0.2279, total avg loss: 0.2472, batch size: 32
2021-10-14 14:40:36,862 INFO [train.py:451] Epoch 6, batch 17040, batch avg loss 0.3319, total avg loss: 0.2469, batch size: 35
2021-10-14 14:40:41,803 INFO [train.py:451] Epoch 6, batch 17050, batch avg loss 0.2061, total avg loss: 0.2487, batch size: 31
2021-10-14 14:40:46,673 INFO [train.py:451] Epoch 6, batch 17060, batch avg loss 0.2787, total avg loss: 0.2517, batch size: 38
2021-10-14 14:40:51,847 INFO [train.py:451] Epoch 6, batch 17070, batch avg loss 0.2848, total avg loss: 0.2484, batch size: 32
2021-10-14 14:40:56,925 INFO [train.py:451] Epoch 6, batch 17080, batch avg loss 0.2708, total avg loss: 0.2484, batch size: 37
2021-10-14 14:41:01,918 INFO [train.py:451] Epoch 6, batch 17090, batch avg loss 0.2313, total avg loss: 0.2495, batch size: 38
2021-10-14 14:41:06,673 INFO [train.py:451] Epoch 6, batch 17100, batch avg loss 0.2171, total avg loss: 0.2512, batch size: 37
2021-10-14 14:41:11,688 INFO [train.py:451] Epoch 6, batch 17110, batch avg loss 0.2178, total avg loss: 0.2478, batch size: 28
2021-10-14 14:41:16,693 INFO [train.py:451] Epoch 6, batch 17120, batch avg loss 0.2000, total avg loss: 0.2480, batch size: 29
2021-10-14 14:41:21,487 INFO [train.py:451] Epoch 6, batch 17130, batch avg loss 0.2621, total avg loss: 0.2480, batch size: 37
2021-10-14 14:41:26,382 INFO [train.py:451] Epoch 6, batch 17140, batch avg loss 0.2724, total avg loss: 0.2480, batch size: 57
2021-10-14 14:41:31,318 INFO [train.py:451] Epoch 6, batch 17150, batch avg loss 0.2473, total avg loss: 0.2482, batch size: 31
2021-10-14 14:41:36,263 INFO [train.py:451] Epoch 6, batch 17160, batch avg loss 0.2373, total avg loss: 0.2470, batch size: 39
2021-10-14 14:41:41,199 INFO [train.py:451] Epoch 6, batch 17170, batch avg loss 0.2605, total avg loss: 0.2477, batch size: 34
2021-10-14 14:41:45,885 INFO [train.py:451] Epoch 6, batch 17180, batch avg loss 0.3232, total avg loss: 0.2486, batch size: 44
2021-10-14 14:41:50,962 INFO [train.py:451] Epoch 6, batch 17190, batch avg loss 0.1900, total avg loss: 0.2478, batch size: 27
2021-10-14 14:41:55,885 INFO [train.py:451] Epoch 6, batch 17200, batch avg loss 0.2275, total avg loss: 0.2473, batch size: 33
2021-10-14 14:42:01,090 INFO [train.py:451] Epoch 6, batch 17210, batch avg loss 0.2306, total avg loss: 0.2462, batch size: 33
2021-10-14 14:42:05,992 INFO [train.py:451] Epoch 6, batch 17220, batch avg loss 0.2855, total avg loss: 0.2524, batch size: 36
2021-10-14 14:42:10,936 INFO [train.py:451] Epoch 6, batch 17230, batch avg loss 0.2290, total avg loss: 0.2473, batch size: 34
2021-10-14 14:42:15,891 INFO [train.py:451] Epoch 6, batch 17240, batch avg loss 0.2428, total avg loss: 0.2504, batch size: 34
2021-10-14 14:42:20,904 INFO [train.py:451] Epoch 6, batch 17250, batch avg loss 0.2962, total avg loss: 0.2501, batch size: 73
2021-10-14 14:42:25,787 INFO [train.py:451] Epoch 6, batch 17260, batch avg loss 0.2504, total avg loss: 0.2511, batch size: 34
2021-10-14 14:42:30,716 INFO [train.py:451] Epoch 6, batch 17270, batch avg loss 0.2944, total avg loss: 0.2501, batch size: 41
2021-10-14 14:42:35,725 INFO [train.py:451] Epoch 6, batch 17280, batch avg loss 0.2638, total avg loss: 0.2480, batch size: 33
2021-10-14 14:42:40,549 INFO [train.py:451] Epoch 6, batch 17290, batch avg loss 0.2357, total avg loss: 0.2513, batch size: 34
2021-10-14 14:42:45,511 INFO [train.py:451] Epoch 6, batch 17300, batch avg loss 0.2915, total avg loss: 0.2499, batch size: 49
2021-10-14 14:42:50,455 INFO [train.py:451] Epoch 6, batch 17310, batch avg loss 0.2353, total avg loss: 0.2484, batch size: 36
2021-10-14 14:42:55,480 INFO [train.py:451] Epoch 6, batch 17320, batch avg loss 0.1864, total avg loss: 0.2471, batch size: 30
2021-10-14 14:43:00,375 INFO [train.py:451] Epoch 6, batch 17330, batch avg loss 0.2090, total avg loss: 0.2460, batch size: 30
2021-10-14 14:43:05,295 INFO [train.py:451] Epoch 6, batch 17340, batch avg loss 0.2023, total avg loss: 0.2458, batch size: 34
2021-10-14 14:43:10,154 INFO [train.py:451] Epoch 6, batch 17350, batch avg loss 0.2669, total avg loss: 0.2460, batch size: 41
2021-10-14 14:43:15,122 INFO [train.py:451] Epoch 6, batch 17360, batch avg loss 0.2488, total avg loss: 0.2457, batch size: 36
2021-10-14 14:43:20,058 INFO [train.py:451] Epoch 6, batch 17370, batch avg loss 0.2142, total avg loss: 0.2454, batch size: 29
2021-10-14 14:43:24,876 INFO [train.py:451] Epoch 6, batch 17380, batch avg loss 0.2919, total avg loss: 0.2467, batch size: 38
2021-10-14 14:43:29,692 INFO [train.py:451] Epoch 6, batch 17390, batch avg loss 0.3307, total avg loss: 0.2476, batch size: 73
2021-10-14 14:43:34,699 INFO [train.py:451] Epoch 6, batch 17400, batch avg loss 0.2498, total avg loss: 0.2487, batch size: 30
2021-10-14 14:43:39,639 INFO [train.py:451] Epoch 6, batch 17410, batch avg loss 0.2705, total avg loss: 0.2424, batch size: 42
2021-10-14 14:43:44,545 INFO [train.py:451] Epoch 6, batch 17420, batch avg loss 0.2058, total avg loss: 0.2434, batch size: 29
2021-10-14 14:43:49,414 INFO [train.py:451] Epoch 6, batch 17430, batch avg loss 0.3020, total avg loss: 0.2468, batch size: 56
2021-10-14 14:43:54,224 INFO [train.py:451] Epoch 6, batch 17440, batch avg loss 0.2158, total avg loss: 0.2508, batch size: 29
2021-10-14 14:43:59,120 INFO [train.py:451] Epoch 6, batch 17450, batch avg loss 0.2221, total avg loss: 0.2503, batch size: 34
2021-10-14 14:44:03,940 INFO [train.py:451] Epoch 6, batch 17460, batch avg loss 0.2707, total avg loss: 0.2533, batch size: 45
2021-10-14 14:44:08,824 INFO [train.py:451] Epoch 6, batch 17470, batch avg loss 0.2386, total avg loss: 0.2531, batch size: 35
2021-10-14 14:44:13,572 INFO [train.py:451] Epoch 6, batch 17480, batch avg loss 0.2909, total avg loss: 0.2543, batch size: 72
2021-10-14 14:44:18,142 INFO [train.py:451] Epoch 6, batch 17490, batch avg loss 0.3323, total avg loss: 0.2568, batch size: 72
2021-10-14 14:44:23,075 INFO [train.py:451] Epoch 6, batch 17500, batch avg loss 0.2630, total avg loss: 0.2568, batch size: 35
2021-10-14 14:44:35,135 INFO [train.py:451] Epoch 6, batch 17510, batch avg loss 0.2092, total avg loss: 0.2532, batch size: 31
2021-10-14 14:44:40,052 INFO [train.py:451] Epoch 6, batch 17520, batch avg loss 0.2333, total avg loss: 0.2532, batch size: 30
2021-10-14 14:44:45,159 INFO [train.py:451] Epoch 6, batch 17530, batch avg loss 0.2318, total avg loss: 0.2519, batch size: 29
2021-10-14 14:44:50,097 INFO [train.py:451] Epoch 6, batch 17540, batch avg loss 0.1900, total avg loss: 0.2519, batch size: 30
2021-10-14 14:44:54,985 INFO [train.py:451] Epoch 6, batch 17550, batch avg loss 0.2822, total avg loss: 0.2509, batch size: 49
2021-10-14 14:45:00,182 INFO [train.py:451] Epoch 6, batch 17560, batch avg loss 0.2694, total avg loss: 0.2520, batch size: 37
2021-10-14 14:45:05,071 INFO [train.py:451] Epoch 6, batch 17570, batch avg loss 0.2658, total avg loss: 0.2523, batch size: 33
2021-10-14 14:45:09,867 INFO [train.py:451] Epoch 6, batch 17580, batch avg loss 0.2766, total avg loss: 0.2525, batch size: 38
2021-10-14 14:45:14,824 INFO [train.py:451] Epoch 6, batch 17590, batch avg loss 0.2789, total avg loss: 0.2529, batch size: 35
2021-10-14 14:45:19,697 INFO [train.py:451] Epoch 6, batch 17600, batch avg loss 0.2698, total avg loss: 0.2522, batch size: 37
2021-10-14 14:45:24,505 INFO [train.py:451] Epoch 6, batch 17610, batch avg loss 0.2078, total avg loss: 0.2512, batch size: 33
2021-10-14 14:45:29,551 INFO [train.py:451] Epoch 6, batch 17620, batch avg loss 0.2320, total avg loss: 0.2491, batch size: 30
2021-10-14 14:45:34,681 INFO [train.py:451] Epoch 6, batch 17630, batch avg loss 0.2413, total avg loss: 0.2445, batch size: 38
2021-10-14 14:45:39,632 INFO [train.py:451] Epoch 6, batch 17640, batch avg loss 0.2358, total avg loss: 0.2456, batch size: 31
2021-10-14 14:45:44,561 INFO [train.py:451] Epoch 6, batch 17650, batch avg loss 0.3306, total avg loss: 0.2436, batch size: 128
2021-10-14 14:45:49,508 INFO [train.py:451] Epoch 6, batch 17660, batch avg loss 0.2422, total avg loss: 0.2444, batch size: 38
2021-10-14 14:45:54,420 INFO [train.py:451] Epoch 6, batch 17670, batch avg loss 0.2011, total avg loss: 0.2429, batch size: 30
2021-10-14 14:45:59,456 INFO [train.py:451] Epoch 6, batch 17680, batch avg loss 0.2770, total avg loss: 0.2412, batch size: 34
2021-10-14 14:46:04,180 INFO [train.py:451] Epoch 6, batch 17690, batch avg loss 0.2784, total avg loss: 0.2430, batch size: 49
2021-10-14 14:46:09,057 INFO [train.py:451] Epoch 6, batch 17700, batch avg loss 0.2266, total avg loss: 0.2422, batch size: 30
2021-10-14 14:46:13,938 INFO [train.py:451] Epoch 6, batch 17710, batch avg loss 0.2524, total avg loss: 0.2435, batch size: 34
2021-10-14 14:46:18,944 INFO [train.py:451] Epoch 6, batch 17720, batch avg loss 0.2442, total avg loss: 0.2423, batch size: 42
2021-10-14 14:46:23,661 INFO [train.py:451] Epoch 6, batch 17730, batch avg loss 0.2811, total avg loss: 0.2438, batch size: 34
2021-10-14 14:46:28,434 INFO [train.py:451] Epoch 6, batch 17740, batch avg loss 0.2522, total avg loss: 0.2457, batch size: 45
2021-10-14 14:46:33,413 INFO [train.py:451] Epoch 6, batch 17750, batch avg loss 0.2435, total avg loss: 0.2449, batch size: 34
2021-10-14 14:46:38,205 INFO [train.py:451] Epoch 6, batch 17760, batch avg loss 0.2132, total avg loss: 0.2442, batch size: 27
2021-10-14 14:46:43,176 INFO [train.py:451] Epoch 6, batch 17770, batch avg loss 0.2542, total avg loss: 0.2438, batch size: 45
2021-10-14 14:46:48,474 INFO [train.py:451] Epoch 6, batch 17780, batch avg loss 0.2542, total avg loss: 0.2436, batch size: 33
2021-10-14 14:46:53,447 INFO [train.py:451] Epoch 6, batch 17790, batch avg loss 0.2302, total avg loss: 0.2431, batch size: 33
2021-10-14 14:46:58,267 INFO [train.py:451] Epoch 6, batch 17800, batch avg loss 0.2858, total avg loss: 0.2456, batch size: 49
2021-10-14 14:47:03,112 INFO [train.py:451] Epoch 6, batch 17810, batch avg loss 0.2059, total avg loss: 0.2630, batch size: 28
2021-10-14 14:47:08,191 INFO [train.py:451] Epoch 6, batch 17820, batch avg loss 0.3029, total avg loss: 0.2548, batch size: 33
2021-10-14 14:47:19,972 INFO [train.py:451] Epoch 6, batch 17830, batch avg loss 0.2363, total avg loss: 0.2529, batch size: 34
2021-10-14 14:47:24,885 INFO [train.py:451] Epoch 6, batch 17840, batch avg loss 0.2311, total avg loss: 0.2530, batch size: 39
2021-10-14 14:47:29,674 INFO [train.py:451] Epoch 6, batch 17850, batch avg loss 0.2555, total avg loss: 0.2554, batch size: 38
2021-10-14 14:47:34,476 INFO [train.py:451] Epoch 6, batch 17860, batch avg loss 0.2693, total avg loss: 0.2544, batch size: 49
2021-10-14 14:47:39,442 INFO [train.py:451] Epoch 6, batch 17870, batch avg loss 0.2394, total avg loss: 0.2520, batch size: 49
2021-10-14 14:47:44,186 INFO [train.py:451] Epoch 6, batch 17880, batch avg loss 0.3633, total avg loss: 0.2541, batch size: 128
2021-10-14 14:47:48,987 INFO [train.py:451] Epoch 6, batch 17890, batch avg loss 0.3903, total avg loss: 0.2556, batch size: 125
2021-10-14 14:47:54,008 INFO [train.py:451] Epoch 6, batch 17900, batch avg loss 0.2851, total avg loss: 0.2534, batch size: 34
2021-10-14 14:47:58,951 INFO [train.py:451] Epoch 6, batch 17910, batch avg loss 0.2653, total avg loss: 0.2551, batch size: 56
2021-10-14 14:48:03,885 INFO [train.py:451] Epoch 6, batch 17920, batch avg loss 0.2536, total avg loss: 0.2561, batch size: 33
2021-10-14 14:48:08,933 INFO [train.py:451] Epoch 6, batch 17930, batch avg loss 0.3755, total avg loss: 0.2558, batch size: 126
2021-10-14 14:48:13,848 INFO [train.py:451] Epoch 6, batch 17940, batch avg loss 0.2743, total avg loss: 0.2553, batch size: 57
2021-10-14 14:48:18,632 INFO [train.py:451] Epoch 6, batch 17950, batch avg loss 0.2981, total avg loss: 0.2552, batch size: 34
2021-10-14 14:48:23,538 INFO [train.py:451] Epoch 6, batch 17960, batch avg loss 0.2651, total avg loss: 0.2562, batch size: 33
2021-10-14 14:48:28,489 INFO [train.py:451] Epoch 6, batch 17970, batch avg loss 0.2478, total avg loss: 0.2559, batch size: 28
2021-10-14 14:48:33,572 INFO [train.py:451] Epoch 6, batch 17980, batch avg loss 0.3817, total avg loss: 0.2565, batch size: 132
2021-10-14 14:48:38,371 INFO [train.py:451] Epoch 6, batch 17990, batch avg loss 0.2453, total avg loss: 0.2558, batch size: 56
2021-10-14 14:48:43,233 INFO [train.py:451] Epoch 6, batch 18000, batch avg loss 0.3075, total avg loss: 0.2558, batch size: 57
2021-10-14 14:49:21,426 INFO [train.py:483] Epoch 6, valid loss 0.1776, best valid loss: 0.1776 best valid epoch: 6
2021-10-14 14:49:26,406 INFO [train.py:451] Epoch 6, batch 18010, batch avg loss 0.2455, total avg loss: 0.2341, batch size: 35
2021-10-14 14:49:31,457 INFO [train.py:451] Epoch 6, batch 18020, batch avg loss 0.2275, total avg loss: 0.2318, batch size: 33
2021-10-14 14:49:36,288 INFO [train.py:451] Epoch 6, batch 18030, batch avg loss 0.2573, total avg loss: 0.2386, batch size: 30
2021-10-14 14:49:40,336 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "5c767916-b0b5-9dfb-bb12-c211ddbf9c44" will not be mixed in.
2021-10-14 14:49:41,276 INFO [train.py:451] Epoch 6, batch 18040, batch avg loss 0.1762, total avg loss: 0.2329, batch size: 27
2021-10-14 14:49:46,172 INFO [train.py:451] Epoch 6, batch 18050, batch avg loss 0.2598, total avg loss: 0.2342, batch size: 39
2021-10-14 14:49:51,112 INFO [train.py:451] Epoch 6, batch 18060, batch avg loss 0.2579, total avg loss: 0.2366, batch size: 33
2021-10-14 14:49:55,966 INFO [train.py:451] Epoch 6, batch 18070, batch avg loss 0.3056, total avg loss: 0.2404, batch size: 49
2021-10-14 14:50:00,836 INFO [train.py:451] Epoch 6, batch 18080, batch avg loss 0.2645, total avg loss: 0.2419, batch size: 49
2021-10-14 14:50:05,908 INFO [train.py:451] Epoch 6, batch 18090, batch avg loss 0.2637, total avg loss: 0.2403, batch size: 34
2021-10-14 14:50:10,869 INFO [train.py:451] Epoch 6, batch 18100, batch avg loss 0.2751, total avg loss: 0.2419, batch size: 39
2021-10-14 14:50:16,044 INFO [train.py:451] Epoch 6, batch 18110, batch avg loss 0.2804, total avg loss: 0.2407, batch size: 35
2021-10-14 14:50:20,882 INFO [train.py:451] Epoch 6, batch 18120, batch avg loss 0.2707, total avg loss: 0.2410, batch size: 42
2021-10-14 14:50:25,793 INFO [train.py:451] Epoch 6, batch 18130, batch avg loss 0.2039, total avg loss: 0.2410, batch size: 32
2021-10-14 14:50:30,497 INFO [train.py:451] Epoch 6, batch 18140, batch avg loss 0.2962, total avg loss: 0.2424, batch size: 57
2021-10-14 14:50:35,336 INFO [train.py:451] Epoch 6, batch 18150, batch avg loss 0.2798, total avg loss: 0.2431, batch size: 41
2021-10-14 14:50:40,247 INFO [train.py:451] Epoch 6, batch 18160, batch avg loss 0.2886, total avg loss: 0.2432, batch size: 36
2021-10-14 14:50:45,194 INFO [train.py:451] Epoch 6, batch 18170, batch avg loss 0.2605, total avg loss: 0.2430, batch size: 34
2021-10-14 14:50:50,116 INFO [train.py:451] Epoch 6, batch 18180, batch avg loss 0.2374, total avg loss: 0.2422, batch size: 42
2021-10-14 14:50:55,023 INFO [train.py:451] Epoch 6, batch 18190, batch avg loss 0.2029, total avg loss: 0.2422, batch size: 30
2021-10-14 14:51:00,077 INFO [train.py:451] Epoch 6, batch 18200, batch avg loss 0.2613, total avg loss: 0.2422, batch size: 30
2021-10-14 14:51:04,926 INFO [train.py:451] Epoch 6, batch 18210, batch avg loss 0.2053, total avg loss: 0.2527, batch size: 29
2021-10-14 14:51:10,001 INFO [train.py:451] Epoch 6, batch 18220, batch avg loss 0.2616, total avg loss: 0.2506, batch size: 33
2021-10-14 14:51:14,739 INFO [train.py:451] Epoch 6, batch 18230, batch avg loss 0.2531, total avg loss: 0.2547, batch size: 49
2021-10-14 14:51:19,741 INFO [train.py:451] Epoch 6, batch 18240, batch avg loss 0.2229, total avg loss: 0.2527, batch size: 36
2021-10-14 14:51:24,959 INFO [train.py:451] Epoch 6, batch 18250, batch avg loss 0.2448, total avg loss: 0.2518, batch size: 34
2021-10-14 14:51:30,045 INFO [train.py:451] Epoch 6, batch 18260, batch avg loss 0.2677, total avg loss: 0.2493, batch size: 34
2021-10-14 14:51:34,917 INFO [train.py:451] Epoch 6, batch 18270, batch avg loss 0.2501, total avg loss: 0.2498, batch size: 42
2021-10-14 14:51:39,823 INFO [train.py:451] Epoch 6, batch 18280, batch avg loss 0.3879, total avg loss: 0.2509, batch size: 131
2021-10-14 14:51:44,761 INFO [train.py:451] Epoch 6, batch 18290, batch avg loss 0.2351, total avg loss: 0.2508, batch size: 32
2021-10-14 14:51:49,668 INFO [train.py:451] Epoch 6, batch 18300, batch avg loss 0.2955, total avg loss: 0.2509, batch size: 74
2021-10-14 14:51:54,427 INFO [train.py:451] Epoch 6, batch 18310, batch avg loss 0.2997, total avg loss: 0.2516, batch size: 57
2021-10-14 14:51:59,251 INFO [train.py:451] Epoch 6, batch 18320, batch avg loss 0.2385, total avg loss: 0.2534, batch size: 33
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2021-10-14 14:58:17,289 INFO [train.py:483] Epoch 6, valid loss 0.1773, best valid loss: 0.1773 best valid epoch: 6
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2021-10-14 15:05:24,033 INFO [train.py:451] Epoch 6, batch 19850, batch avg loss 0.2713, total avg loss: 0.2457, batch size: 38
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2021-10-14 15:05:49,177 INFO [train.py:451] Epoch 6, batch 19900, batch avg loss 0.1984, total avg loss: 0.2424, batch size: 32
2021-10-14 15:05:54,352 INFO [train.py:451] Epoch 6, batch 19910, batch avg loss 0.2469, total avg loss: 0.2424, batch size: 34
2021-10-14 15:05:59,396 INFO [train.py:451] Epoch 6, batch 19920, batch avg loss 0.2290, total avg loss: 0.2408, batch size: 39
2021-10-14 15:06:04,344 INFO [train.py:451] Epoch 6, batch 19930, batch avg loss 0.2496, total avg loss: 0.2404, batch size: 49
2021-10-14 15:06:09,461 INFO [train.py:451] Epoch 6, batch 19940, batch avg loss 0.2358, total avg loss: 0.2399, batch size: 34
2021-10-14 15:06:14,693 INFO [train.py:451] Epoch 6, batch 19950, batch avg loss 0.2288, total avg loss: 0.2394, batch size: 35
2021-10-14 15:06:19,827 INFO [train.py:451] Epoch 6, batch 19960, batch avg loss 0.2244, total avg loss: 0.2390, batch size: 29
2021-10-14 15:06:24,648 INFO [train.py:451] Epoch 6, batch 19970, batch avg loss 0.2894, total avg loss: 0.2392, batch size: 49
2021-10-14 15:06:29,507 INFO [train.py:451] Epoch 6, batch 19980, batch avg loss 0.2675, total avg loss: 0.2398, batch size: 49
2021-10-14 15:06:34,311 INFO [train.py:451] Epoch 6, batch 19990, batch avg loss 0.3575, total avg loss: 0.2410, batch size: 129
2021-10-14 15:06:39,195 INFO [train.py:451] Epoch 6, batch 20000, batch avg loss 0.2193, total avg loss: 0.2419, batch size: 35
2021-10-14 15:07:17,430 INFO [train.py:483] Epoch 6, valid loss 0.1771, best valid loss: 0.1771 best valid epoch: 6
2021-10-14 15:07:22,210 INFO [train.py:451] Epoch 6, batch 20010, batch avg loss 0.2443, total avg loss: 0.2603, batch size: 32
2021-10-14 15:07:27,340 INFO [train.py:451] Epoch 6, batch 20020, batch avg loss 0.1921, total avg loss: 0.2433, batch size: 33
2021-10-14 15:07:32,130 INFO [train.py:451] Epoch 6, batch 20030, batch avg loss 0.1774, total avg loss: 0.2406, batch size: 30
2021-10-14 15:07:36,987 INFO [train.py:451] Epoch 6, batch 20040, batch avg loss 0.2546, total avg loss: 0.2436, batch size: 38
2021-10-14 15:07:40,967 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "fded83dd-e4c1-14df-2572-6d7d0ca99609" will not be mixed in.
2021-10-14 15:07:41,729 INFO [train.py:451] Epoch 6, batch 20050, batch avg loss 0.2751, total avg loss: 0.2471, batch size: 49
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2021-10-14 15:08:06,267 INFO [train.py:451] Epoch 6, batch 20100, batch avg loss 0.2708, total avg loss: 0.2482, batch size: 36
2021-10-14 15:08:10,994 INFO [train.py:451] Epoch 6, batch 20110, batch avg loss 0.3002, total avg loss: 0.2483, batch size: 73
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2021-10-14 15:09:44,195 INFO [train.py:451] Epoch 6, batch 20300, batch avg loss 0.1708, total avg loss: 0.2475, batch size: 28
2021-10-14 15:09:49,254 INFO [train.py:451] Epoch 6, batch 20310, batch avg loss 0.2853, total avg loss: 0.2469, batch size: 72
2021-10-14 15:09:54,031 INFO [train.py:451] Epoch 6, batch 20320, batch avg loss 0.2329, total avg loss: 0.2489, batch size: 30
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2021-10-14 15:10:38,420 INFO [train.py:451] Epoch 6, batch 20410, batch avg loss 0.2208, total avg loss: 0.2495, batch size: 27
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2021-10-14 15:10:53,527 INFO [train.py:451] Epoch 6, batch 20440, batch avg loss 0.1585, total avg loss: 0.2455, batch size: 30
2021-10-14 15:10:58,290 INFO [train.py:451] Epoch 6, batch 20450, batch avg loss 0.2041, total avg loss: 0.2446, batch size: 32
2021-10-14 15:11:03,168 INFO [train.py:451] Epoch 6, batch 20460, batch avg loss 0.2660, total avg loss: 0.2451, batch size: 45
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2021-10-14 15:11:27,600 INFO [train.py:451] Epoch 6, batch 20510, batch avg loss 0.1951, total avg loss: 0.2476, batch size: 30
2021-10-14 15:11:32,650 INFO [train.py:451] Epoch 6, batch 20520, batch avg loss 0.2005, total avg loss: 0.2462, batch size: 27
2021-10-14 15:11:37,625 INFO [train.py:451] Epoch 6, batch 20530, batch avg loss 0.2722, total avg loss: 0.2464, batch size: 42
2021-10-14 15:11:42,722 INFO [train.py:451] Epoch 6, batch 20540, batch avg loss 0.2398, total avg loss: 0.2463, batch size: 34
2021-10-14 15:11:47,547 INFO [train.py:451] Epoch 6, batch 20550, batch avg loss 0.2144, total avg loss: 0.2458, batch size: 30
2021-10-14 15:11:52,483 INFO [train.py:451] Epoch 6, batch 20560, batch avg loss 0.1996, total avg loss: 0.2450, batch size: 31
2021-10-14 15:11:57,340 INFO [train.py:451] Epoch 6, batch 20570, batch avg loss 0.2344, total avg loss: 0.2449, batch size: 38
2021-10-14 15:12:02,142 INFO [train.py:451] Epoch 6, batch 20580, batch avg loss 0.3020, total avg loss: 0.2459, batch size: 42
2021-10-14 15:12:07,145 INFO [train.py:451] Epoch 6, batch 20590, batch avg loss 0.2135, total avg loss: 0.2455, batch size: 34
2021-10-14 15:12:12,101 INFO [train.py:451] Epoch 6, batch 20600, batch avg loss 0.2551, total avg loss: 0.2461, batch size: 38
2021-10-14 15:12:17,143 INFO [train.py:451] Epoch 6, batch 20610, batch avg loss 0.2351, total avg loss: 0.2423, batch size: 33
2021-10-14 15:12:21,968 INFO [train.py:451] Epoch 6, batch 20620, batch avg loss 0.2070, total avg loss: 0.2482, batch size: 30
2021-10-14 15:12:26,992 INFO [train.py:451] Epoch 6, batch 20630, batch avg loss 0.2324, total avg loss: 0.2473, batch size: 34
2021-10-14 15:12:31,757 INFO [train.py:451] Epoch 6, batch 20640, batch avg loss 0.2397, total avg loss: 0.2493, batch size: 49
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2021-10-14 15:13:01,641 INFO [train.py:451] Epoch 6, batch 20700, batch avg loss 0.2865, total avg loss: 0.2425, batch size: 35
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2021-10-14 15:13:11,481 INFO [train.py:451] Epoch 6, batch 20720, batch avg loss 0.2501, total avg loss: 0.2427, batch size: 37
2021-10-14 15:13:16,380 INFO [train.py:451] Epoch 6, batch 20730, batch avg loss 0.2377, total avg loss: 0.2426, batch size: 38
2021-10-14 15:13:21,363 INFO [train.py:451] Epoch 6, batch 20740, batch avg loss 0.2532, total avg loss: 0.2431, batch size: 30
2021-10-14 15:13:26,116 INFO [train.py:451] Epoch 6, batch 20750, batch avg loss 0.1980, total avg loss: 0.2432, batch size: 29
2021-10-14 15:13:30,943 INFO [train.py:451] Epoch 6, batch 20760, batch avg loss 0.2221, total avg loss: 0.2430, batch size: 38
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2021-10-14 15:13:41,000 INFO [train.py:451] Epoch 6, batch 20780, batch avg loss 0.2207, total avg loss: 0.2444, batch size: 33
2021-10-14 15:13:46,008 INFO [train.py:451] Epoch 6, batch 20790, batch avg loss 0.2364, total avg loss: 0.2439, batch size: 41
2021-10-14 15:13:51,053 INFO [train.py:451] Epoch 6, batch 20800, batch avg loss 0.2736, total avg loss: 0.2444, batch size: 56
2021-10-14 15:13:56,124 INFO [train.py:451] Epoch 6, batch 20810, batch avg loss 0.2537, total avg loss: 0.2458, batch size: 45
2021-10-14 15:14:01,089 INFO [train.py:451] Epoch 6, batch 20820, batch avg loss 0.2493, total avg loss: 0.2426, batch size: 41
2021-10-14 15:14:05,745 INFO [train.py:451] Epoch 6, batch 20830, batch avg loss 0.2299, total avg loss: 0.2517, batch size: 33
2021-10-14 15:14:10,947 INFO [train.py:451] Epoch 6, batch 20840, batch avg loss 0.1839, total avg loss: 0.2470, batch size: 27
2021-10-14 15:14:15,833 INFO [train.py:451] Epoch 6, batch 20850, batch avg loss 0.2290, total avg loss: 0.2471, batch size: 41
2021-10-14 15:14:20,854 INFO [train.py:451] Epoch 6, batch 20860, batch avg loss 0.2135, total avg loss: 0.2450, batch size: 33
2021-10-14 15:14:25,962 INFO [train.py:451] Epoch 6, batch 20870, batch avg loss 0.2657, total avg loss: 0.2436, batch size: 38
2021-10-14 15:14:30,946 INFO [train.py:451] Epoch 6, batch 20880, batch avg loss 0.2350, total avg loss: 0.2427, batch size: 39
2021-10-14 15:14:35,749 INFO [train.py:451] Epoch 6, batch 20890, batch avg loss 0.2111, total avg loss: 0.2429, batch size: 32
2021-10-14 15:14:40,653 INFO [train.py:451] Epoch 6, batch 20900, batch avg loss 0.2781, total avg loss: 0.2444, batch size: 42
2021-10-14 15:14:45,454 INFO [train.py:451] Epoch 6, batch 20910, batch avg loss 0.2417, total avg loss: 0.2456, batch size: 30
2021-10-14 15:14:50,318 INFO [train.py:451] Epoch 6, batch 20920, batch avg loss 0.2500, total avg loss: 0.2452, batch size: 49
2021-10-14 15:14:55,312 INFO [train.py:451] Epoch 6, batch 20930, batch avg loss 0.2428, total avg loss: 0.2456, batch size: 45
2021-10-14 15:15:00,196 INFO [train.py:451] Epoch 6, batch 20940, batch avg loss 0.2868, total avg loss: 0.2452, batch size: 58
2021-10-14 15:15:05,179 INFO [train.py:451] Epoch 6, batch 20950, batch avg loss 0.2442, total avg loss: 0.2451, batch size: 32
2021-10-14 15:15:09,942 INFO [train.py:451] Epoch 6, batch 20960, batch avg loss 0.2901, total avg loss: 0.2456, batch size: 45
2021-10-14 15:15:14,833 INFO [train.py:451] Epoch 6, batch 20970, batch avg loss 0.2515, total avg loss: 0.2447, batch size: 39
2021-10-14 15:15:19,627 INFO [train.py:451] Epoch 6, batch 20980, batch avg loss 0.2233, total avg loss: 0.2460, batch size: 32
2021-10-14 15:15:24,525 INFO [train.py:451] Epoch 6, batch 20990, batch avg loss 0.2654, total avg loss: 0.2465, batch size: 38
2021-10-14 15:15:29,323 INFO [train.py:451] Epoch 6, batch 21000, batch avg loss 0.2702, total avg loss: 0.2470, batch size: 57
2021-10-14 15:16:09,074 INFO [train.py:483] Epoch 6, valid loss 0.1773, best valid loss: 0.1771 best valid epoch: 6
2021-10-14 15:16:13,923 INFO [train.py:451] Epoch 6, batch 21010, batch avg loss 0.2604, total avg loss: 0.2508, batch size: 35
2021-10-14 15:16:18,602 INFO [train.py:451] Epoch 6, batch 21020, batch avg loss 0.2451, total avg loss: 0.2549, batch size: 34
2021-10-14 15:16:23,397 INFO [train.py:451] Epoch 6, batch 21030, batch avg loss 0.2404, total avg loss: 0.2501, batch size: 36
2021-10-14 15:16:28,342 INFO [train.py:451] Epoch 6, batch 21040, batch avg loss 0.2885, total avg loss: 0.2488, batch size: 36
2021-10-14 15:16:33,324 INFO [train.py:451] Epoch 6, batch 21050, batch avg loss 0.2735, total avg loss: 0.2447, batch size: 39
2021-10-14 15:16:38,298 INFO [train.py:451] Epoch 6, batch 21060, batch avg loss 0.2515, total avg loss: 0.2459, batch size: 37
2021-10-14 15:16:43,180 INFO [train.py:451] Epoch 6, batch 21070, batch avg loss 0.2623, total avg loss: 0.2476, batch size: 41
2021-10-14 15:16:48,105 INFO [train.py:451] Epoch 6, batch 21080, batch avg loss 0.2547, total avg loss: 0.2488, batch size: 35
2021-10-14 15:16:52,941 INFO [train.py:451] Epoch 6, batch 21090, batch avg loss 0.3044, total avg loss: 0.2504, batch size: 34
2021-10-14 15:16:57,938 INFO [train.py:451] Epoch 6, batch 21100, batch avg loss 0.2280, total avg loss: 0.2510, batch size: 34
2021-10-14 15:17:02,736 INFO [train.py:451] Epoch 6, batch 21110, batch avg loss 0.2781, total avg loss: 0.2513, batch size: 42
2021-10-14 15:17:07,627 INFO [train.py:451] Epoch 6, batch 21120, batch avg loss 0.2140, total avg loss: 0.2507, batch size: 30
2021-10-14 15:17:12,649 INFO [train.py:451] Epoch 6, batch 21130, batch avg loss 0.2327, total avg loss: 0.2477, batch size: 32
2021-10-14 15:17:17,509 INFO [train.py:451] Epoch 6, batch 21140, batch avg loss 0.2524, total avg loss: 0.2488, batch size: 36
2021-10-14 15:17:22,280 INFO [train.py:451] Epoch 6, batch 21150, batch avg loss 0.2510, total avg loss: 0.2496, batch size: 30
2021-10-14 15:17:27,011 INFO [train.py:451] Epoch 6, batch 21160, batch avg loss 0.2218, total avg loss: 0.2485, batch size: 34
2021-10-14 15:17:31,828 INFO [train.py:451] Epoch 6, batch 21170, batch avg loss 0.3092, total avg loss: 0.2495, batch size: 45
2021-10-14 15:17:36,481 INFO [train.py:451] Epoch 6, batch 21180, batch avg loss 0.2084, total avg loss: 0.2504, batch size: 36
2021-10-14 15:17:41,427 INFO [checkpoint.py:62] Saving checkpoint to tdnn_lstm_ctc-v2/exp/epoch-6.pt
2021-10-14 15:17:42,248 INFO [train.py:564] epoch 7, lr: 0.00025
2021-10-14 15:17:46,766 INFO [train.py:451] Epoch 7, batch 0, batch avg loss 0.2670, total avg loss: 0.2670, batch size: 37
2021-10-14 15:17:51,549 INFO [train.py:451] Epoch 7, batch 10, batch avg loss 0.2397, total avg loss: 0.2539, batch size: 29
2021-10-14 15:17:56,397 INFO [train.py:451] Epoch 7, batch 20, batch avg loss 0.2134, total avg loss: 0.2493, batch size: 42
2021-10-14 15:18:01,234 INFO [train.py:451] Epoch 7, batch 30, batch avg loss 0.2862, total avg loss: 0.2514, batch size: 34
2021-10-14 15:18:05,903 INFO [train.py:451] Epoch 7, batch 40, batch avg loss 0.2236, total avg loss: 0.2510, batch size: 45
2021-10-14 15:18:10,727 INFO [train.py:451] Epoch 7, batch 50, batch avg loss 0.2755, total avg loss: 0.2503, batch size: 42
2021-10-14 15:18:15,709 INFO [train.py:451] Epoch 7, batch 60, batch avg loss 0.2763, total avg loss: 0.2483, batch size: 41
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2021-10-14 15:24:14,487 INFO [train.py:451] Epoch 7, batch 790, batch avg loss 0.2347, total avg loss: 0.2442, batch size: 31
2021-10-14 15:24:19,394 INFO [train.py:451] Epoch 7, batch 800, batch avg loss 0.3776, total avg loss: 0.2447, batch size: 127
2021-10-14 15:24:24,625 INFO [train.py:451] Epoch 7, batch 810, batch avg loss 0.1867, total avg loss: 0.2367, batch size: 29
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2021-10-14 15:24:34,592 INFO [train.py:451] Epoch 7, batch 830, batch avg loss 0.3121, total avg loss: 0.2448, batch size: 73
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2021-10-14 15:24:44,379 INFO [train.py:451] Epoch 7, batch 850, batch avg loss 0.2685, total avg loss: 0.2490, batch size: 39
2021-10-14 15:24:49,395 INFO [train.py:451] Epoch 7, batch 860, batch avg loss 0.2842, total avg loss: 0.2483, batch size: 37
2021-10-14 15:24:54,393 INFO [train.py:451] Epoch 7, batch 870, batch avg loss 0.2377, total avg loss: 0.2458, batch size: 29
2021-10-14 15:24:59,353 INFO [train.py:451] Epoch 7, batch 880, batch avg loss 0.2574, total avg loss: 0.2470, batch size: 35
2021-10-14 15:25:04,233 INFO [train.py:451] Epoch 7, batch 890, batch avg loss 0.2579, total avg loss: 0.2469, batch size: 41
2021-10-14 15:25:09,047 INFO [train.py:451] Epoch 7, batch 900, batch avg loss 0.2536, total avg loss: 0.2471, batch size: 38
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2021-10-14 15:25:19,006 INFO [train.py:451] Epoch 7, batch 920, batch avg loss 0.2733, total avg loss: 0.2455, batch size: 35
2021-10-14 15:25:23,895 INFO [train.py:451] Epoch 7, batch 930, batch avg loss 0.2891, total avg loss: 0.2464, batch size: 42
2021-10-14 15:25:28,753 INFO [train.py:451] Epoch 7, batch 940, batch avg loss 0.2554, total avg loss: 0.2463, batch size: 49
2021-10-14 15:25:33,501 INFO [train.py:451] Epoch 7, batch 950, batch avg loss 0.2708, total avg loss: 0.2483, batch size: 35
2021-10-14 15:25:38,407 INFO [train.py:451] Epoch 7, batch 960, batch avg loss 0.1992, total avg loss: 0.2488, batch size: 30
2021-10-14 15:25:43,208 INFO [train.py:451] Epoch 7, batch 970, batch avg loss 0.2932, total avg loss: 0.2490, batch size: 73
2021-10-14 15:25:48,256 INFO [train.py:451] Epoch 7, batch 980, batch avg loss 0.2595, total avg loss: 0.2478, batch size: 34
2021-10-14 15:25:53,159 INFO [train.py:451] Epoch 7, batch 990, batch avg loss 0.2690, total avg loss: 0.2476, batch size: 42
2021-10-14 15:25:58,045 INFO [train.py:451] Epoch 7, batch 1000, batch avg loss 0.2384, total avg loss: 0.2473, batch size: 31
2021-10-14 15:26:37,755 INFO [train.py:483] Epoch 7, valid loss 0.1779, best valid loss: 0.1771 best valid epoch: 6
2021-10-14 15:26:42,791 INFO [train.py:451] Epoch 7, batch 1010, batch avg loss 0.2052, total avg loss: 0.2330, batch size: 31
2021-10-14 15:26:47,879 INFO [train.py:451] Epoch 7, batch 1020, batch avg loss 0.2501, total avg loss: 0.2377, batch size: 41
2021-10-14 15:26:52,559 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "f91f78c3-328d-909e-96b9-697aa3c8c7c4" will not be mixed in.
2021-10-14 15:26:52,803 INFO [train.py:451] Epoch 7, batch 1030, batch avg loss 0.2532, total avg loss: 0.2397, batch size: 39
2021-10-14 15:26:57,639 INFO [train.py:451] Epoch 7, batch 1040, batch avg loss 0.2097, total avg loss: 0.2413, batch size: 38
2021-10-14 15:27:02,526 INFO [train.py:451] Epoch 7, batch 1050, batch avg loss 0.3425, total avg loss: 0.2426, batch size: 124
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2021-10-14 15:27:17,166 INFO [train.py:451] Epoch 7, batch 1080, batch avg loss 0.2186, total avg loss: 0.2485, batch size: 28
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2021-10-14 15:27:26,918 INFO [train.py:451] Epoch 7, batch 1100, batch avg loss 0.2018, total avg loss: 0.2495, batch size: 34
2021-10-14 15:27:31,824 INFO [train.py:451] Epoch 7, batch 1110, batch avg loss 0.2497, total avg loss: 0.2485, batch size: 45
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2021-10-14 15:27:46,356 INFO [train.py:451] Epoch 7, batch 1140, batch avg loss 0.2786, total avg loss: 0.2481, batch size: 30
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2021-10-14 15:28:15,931 INFO [train.py:451] Epoch 7, batch 1200, batch avg loss 0.2395, total avg loss: 0.2448, batch size: 39
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2021-10-14 15:28:35,909 INFO [train.py:451] Epoch 7, batch 1240, batch avg loss 0.2955, total avg loss: 0.2434, batch size: 42
2021-10-14 15:28:40,949 INFO [train.py:451] Epoch 7, batch 1250, batch avg loss 0.1996, total avg loss: 0.2435, batch size: 29
2021-10-14 15:28:45,970 INFO [train.py:451] Epoch 7, batch 1260, batch avg loss 0.2836, total avg loss: 0.2414, batch size: 40
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2021-10-14 15:28:55,631 INFO [train.py:451] Epoch 7, batch 1280, batch avg loss 0.2285, total avg loss: 0.2452, batch size: 32
2021-10-14 15:29:00,424 INFO [train.py:451] Epoch 7, batch 1290, batch avg loss 0.2905, total avg loss: 0.2455, batch size: 31
2021-10-14 15:29:05,288 INFO [train.py:451] Epoch 7, batch 1300, batch avg loss 0.4040, total avg loss: 0.2484, batch size: 131
2021-10-14 15:29:10,122 INFO [train.py:451] Epoch 7, batch 1310, batch avg loss 0.2287, total avg loss: 0.2480, batch size: 38
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2021-10-14 15:29:54,830 INFO [train.py:451] Epoch 7, batch 1400, batch avg loss 0.2418, total avg loss: 0.2464, batch size: 38
2021-10-14 15:29:59,748 INFO [train.py:451] Epoch 7, batch 1410, batch avg loss 0.2625, total avg loss: 0.2382, batch size: 38
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2021-10-14 15:30:09,465 INFO [train.py:451] Epoch 7, batch 1430, batch avg loss 0.2696, total avg loss: 0.2484, batch size: 34
2021-10-14 15:30:14,241 INFO [train.py:451] Epoch 7, batch 1440, batch avg loss 0.2253, total avg loss: 0.2480, batch size: 35
2021-10-14 15:30:19,282 INFO [train.py:451] Epoch 7, batch 1450, batch avg loss 0.2793, total avg loss: 0.2455, batch size: 57
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2021-10-14 15:30:29,173 INFO [train.py:451] Epoch 7, batch 1470, batch avg loss 0.2584, total avg loss: 0.2457, batch size: 41
2021-10-14 15:30:34,188 INFO [train.py:451] Epoch 7, batch 1480, batch avg loss 0.2483, total avg loss: 0.2423, batch size: 34
2021-10-14 15:30:38,967 INFO [train.py:451] Epoch 7, batch 1490, batch avg loss 0.2624, total avg loss: 0.2454, batch size: 33
2021-10-14 15:30:43,918 INFO [train.py:451] Epoch 7, batch 1500, batch avg loss 0.2214, total avg loss: 0.2450, batch size: 35
2021-10-14 15:30:48,958 INFO [train.py:451] Epoch 7, batch 1510, batch avg loss 0.3011, total avg loss: 0.2445, batch size: 33
2021-10-14 15:30:53,857 INFO [train.py:451] Epoch 7, batch 1520, batch avg loss 0.2376, total avg loss: 0.2450, batch size: 39
2021-10-14 15:30:58,915 INFO [train.py:451] Epoch 7, batch 1530, batch avg loss 0.1931, total avg loss: 0.2435, batch size: 34
2021-10-14 15:31:03,853 INFO [train.py:451] Epoch 7, batch 1540, batch avg loss 0.2280, total avg loss: 0.2448, batch size: 36
2021-10-14 15:31:08,006 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "37bd18c5-6217-0a18-1f5f-a3b4c7d0a26c" will not be mixed in.
2021-10-14 15:31:08,826 INFO [train.py:451] Epoch 7, batch 1550, batch avg loss 0.2382, total avg loss: 0.2451, batch size: 33
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2021-10-14 15:32:57,697 INFO [train.py:451] Epoch 7, batch 1770, batch avg loss 0.2774, total avg loss: 0.2421, batch size: 39
2021-10-14 15:33:02,712 INFO [train.py:451] Epoch 7, batch 1780, batch avg loss 0.3056, total avg loss: 0.2420, batch size: 38
2021-10-14 15:33:07,699 INFO [train.py:451] Epoch 7, batch 1790, batch avg loss 0.2387, total avg loss: 0.2422, batch size: 38
2021-10-14 15:33:12,824 INFO [train.py:451] Epoch 7, batch 1800, batch avg loss 0.2512, total avg loss: 0.2427, batch size: 34
2021-10-14 15:33:17,885 INFO [train.py:451] Epoch 7, batch 1810, batch avg loss 0.2523, total avg loss: 0.2573, batch size: 28
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2021-10-14 15:33:27,777 INFO [train.py:451] Epoch 7, batch 1830, batch avg loss 0.2898, total avg loss: 0.2520, batch size: 34
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2021-10-14 15:34:07,177 INFO [train.py:451] Epoch 7, batch 1910, batch avg loss 0.1890, total avg loss: 0.2452, batch size: 29
2021-10-14 15:34:12,147 INFO [train.py:451] Epoch 7, batch 1920, batch avg loss 0.2518, total avg loss: 0.2442, batch size: 37
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2021-10-14 15:34:36,460 INFO [train.py:451] Epoch 7, batch 1970, batch avg loss 0.2781, total avg loss: 0.2457, batch size: 49
2021-10-14 15:34:41,279 INFO [train.py:451] Epoch 7, batch 1980, batch avg loss 0.3210, total avg loss: 0.2466, batch size: 49
2021-10-14 15:34:46,188 INFO [train.py:451] Epoch 7, batch 1990, batch avg loss 0.1915, total avg loss: 0.2468, batch size: 27
2021-10-14 15:34:50,954 INFO [train.py:451] Epoch 7, batch 2000, batch avg loss 0.2595, total avg loss: 0.2467, batch size: 34
2021-10-14 15:35:31,486 INFO [train.py:483] Epoch 7, valid loss 0.1775, best valid loss: 0.1771 best valid epoch: 6
2021-10-14 15:35:36,358 INFO [train.py:451] Epoch 7, batch 2010, batch avg loss 0.2377, total avg loss: 0.2442, batch size: 42
2021-10-14 15:35:41,160 INFO [train.py:451] Epoch 7, batch 2020, batch avg loss 0.2438, total avg loss: 0.2606, batch size: 34
2021-10-14 15:35:46,168 INFO [train.py:451] Epoch 7, batch 2030, batch avg loss 0.2652, total avg loss: 0.2513, batch size: 34
2021-10-14 15:35:50,970 INFO [train.py:451] Epoch 7, batch 2040, batch avg loss 0.2448, total avg loss: 0.2522, batch size: 38
2021-10-14 15:35:55,879 INFO [train.py:451] Epoch 7, batch 2050, batch avg loss 0.2122, total avg loss: 0.2500, batch size: 32
2021-10-14 15:36:00,863 INFO [train.py:451] Epoch 7, batch 2060, batch avg loss 0.2036, total avg loss: 0.2490, batch size: 27
2021-10-14 15:36:05,664 INFO [train.py:451] Epoch 7, batch 2070, batch avg loss 0.2248, total avg loss: 0.2474, batch size: 41
2021-10-14 15:36:10,468 INFO [train.py:451] Epoch 7, batch 2080, batch avg loss 0.2697, total avg loss: 0.2471, batch size: 38
2021-10-14 15:36:15,312 INFO [train.py:451] Epoch 7, batch 2090, batch avg loss 0.2861, total avg loss: 0.2459, batch size: 57
2021-10-14 15:36:20,102 INFO [train.py:451] Epoch 7, batch 2100, batch avg loss 0.1994, total avg loss: 0.2464, batch size: 27
2021-10-14 15:36:25,043 INFO [train.py:451] Epoch 7, batch 2110, batch avg loss 0.1938, total avg loss: 0.2446, batch size: 30
2021-10-14 15:36:30,225 INFO [train.py:451] Epoch 7, batch 2120, batch avg loss 0.2289, total avg loss: 0.2435, batch size: 31
2021-10-14 15:36:35,124 INFO [train.py:451] Epoch 7, batch 2130, batch avg loss 0.2448, total avg loss: 0.2427, batch size: 37
2021-10-14 15:36:39,935 INFO [train.py:451] Epoch 7, batch 2140, batch avg loss 0.3107, total avg loss: 0.2431, batch size: 57
2021-10-14 15:36:44,841 INFO [train.py:451] Epoch 7, batch 2150, batch avg loss 0.2262, total avg loss: 0.2425, batch size: 27
2021-10-14 15:36:49,735 INFO [train.py:451] Epoch 7, batch 2160, batch avg loss 0.2154, total avg loss: 0.2419, batch size: 33
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2021-10-14 15:37:04,225 INFO [train.py:451] Epoch 7, batch 2190, batch avg loss 0.2452, total avg loss: 0.2428, batch size: 32
2021-10-14 15:37:09,229 INFO [train.py:451] Epoch 7, batch 2200, batch avg loss 0.1715, total avg loss: 0.2416, batch size: 32
2021-10-14 15:37:14,185 INFO [train.py:451] Epoch 7, batch 2210, batch avg loss 0.2612, total avg loss: 0.2434, batch size: 35
2021-10-14 15:37:19,225 INFO [train.py:451] Epoch 7, batch 2220, batch avg loss 0.2266, total avg loss: 0.2411, batch size: 32
2021-10-14 15:37:24,301 INFO [train.py:451] Epoch 7, batch 2230, batch avg loss 0.3012, total avg loss: 0.2484, batch size: 36
2021-10-14 15:37:29,441 INFO [train.py:451] Epoch 7, batch 2240, batch avg loss 0.2049, total avg loss: 0.2506, batch size: 34
2021-10-14 15:37:34,265 INFO [train.py:451] Epoch 7, batch 2250, batch avg loss 0.2897, total avg loss: 0.2530, batch size: 38
2021-10-14 15:37:39,149 INFO [train.py:451] Epoch 7, batch 2260, batch avg loss 0.2100, total avg loss: 0.2497, batch size: 31
2021-10-14 15:37:44,093 INFO [train.py:451] Epoch 7, batch 2270, batch avg loss 0.2436, total avg loss: 0.2471, batch size: 36
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2021-10-14 15:37:54,077 INFO [train.py:451] Epoch 7, batch 2290, batch avg loss 0.2209, total avg loss: 0.2459, batch size: 32
2021-10-14 15:37:59,164 INFO [train.py:451] Epoch 7, batch 2300, batch avg loss 0.2506, total avg loss: 0.2475, batch size: 34
2021-10-14 15:38:04,350 INFO [train.py:451] Epoch 7, batch 2310, batch avg loss 0.2832, total avg loss: 0.2461, batch size: 38
2021-10-14 15:38:09,364 INFO [train.py:451] Epoch 7, batch 2320, batch avg loss 0.2664, total avg loss: 0.2475, batch size: 34
2021-10-14 15:38:14,306 INFO [train.py:451] Epoch 7, batch 2330, batch avg loss 0.1970, total avg loss: 0.2480, batch size: 29
2021-10-14 15:38:19,338 INFO [train.py:451] Epoch 7, batch 2340, batch avg loss 0.3330, total avg loss: 0.2481, batch size: 131
2021-10-14 15:38:24,391 INFO [train.py:451] Epoch 7, batch 2350, batch avg loss 0.2711, total avg loss: 0.2475, batch size: 31
2021-10-14 15:38:29,737 INFO [train.py:451] Epoch 7, batch 2360, batch avg loss 0.1575, total avg loss: 0.2462, batch size: 29
2021-10-14 15:38:34,664 INFO [train.py:451] Epoch 7, batch 2370, batch avg loss 0.2663, total avg loss: 0.2465, batch size: 57
2021-10-14 15:38:39,579 INFO [train.py:451] Epoch 7, batch 2380, batch avg loss 0.2863, total avg loss: 0.2469, batch size: 34
2021-10-14 15:38:44,770 INFO [train.py:451] Epoch 7, batch 2390, batch avg loss 0.2927, total avg loss: 0.2462, batch size: 34
2021-10-14 15:38:49,870 INFO [train.py:451] Epoch 7, batch 2400, batch avg loss 0.2360, total avg loss: 0.2460, batch size: 38
2021-10-14 15:38:54,776 INFO [train.py:451] Epoch 7, batch 2410, batch avg loss 0.2227, total avg loss: 0.2603, batch size: 36
2021-10-14 15:38:59,761 INFO [train.py:451] Epoch 7, batch 2420, batch avg loss 0.2490, total avg loss: 0.2563, batch size: 37
2021-10-14 15:39:04,851 INFO [train.py:451] Epoch 7, batch 2430, batch avg loss 0.2246, total avg loss: 0.2511, batch size: 34
2021-10-14 15:39:09,723 INFO [train.py:451] Epoch 7, batch 2440, batch avg loss 0.1707, total avg loss: 0.2520, batch size: 29
2021-10-14 15:39:14,765 INFO [train.py:451] Epoch 7, batch 2450, batch avg loss 0.1982, total avg loss: 0.2505, batch size: 29
2021-10-14 15:39:19,656 INFO [train.py:451] Epoch 7, batch 2460, batch avg loss 0.2347, total avg loss: 0.2483, batch size: 34
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2021-10-14 15:39:29,813 INFO [train.py:451] Epoch 7, batch 2480, batch avg loss 0.2978, total avg loss: 0.2476, batch size: 73
2021-10-14 15:39:34,872 INFO [train.py:451] Epoch 7, batch 2490, batch avg loss 0.2731, total avg loss: 0.2453, batch size: 39
2021-10-14 15:39:39,882 INFO [train.py:451] Epoch 7, batch 2500, batch avg loss 0.2905, total avg loss: 0.2461, batch size: 38
2021-10-14 15:39:44,764 INFO [train.py:451] Epoch 7, batch 2510, batch avg loss 0.2116, total avg loss: 0.2455, batch size: 33
2021-10-14 15:39:49,692 INFO [train.py:451] Epoch 7, batch 2520, batch avg loss 0.2387, total avg loss: 0.2448, batch size: 39
2021-10-14 15:39:54,870 INFO [train.py:451] Epoch 7, batch 2530, batch avg loss 0.2418, total avg loss: 0.2445, batch size: 39
2021-10-14 15:39:59,860 INFO [train.py:451] Epoch 7, batch 2540, batch avg loss 0.2126, total avg loss: 0.2445, batch size: 29
2021-10-14 15:40:04,814 INFO [train.py:451] Epoch 7, batch 2550, batch avg loss 0.2488, total avg loss: 0.2451, batch size: 35
2021-10-14 15:40:09,697 INFO [train.py:451] Epoch 7, batch 2560, batch avg loss 0.2673, total avg loss: 0.2450, batch size: 45
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2021-10-14 15:40:19,303 INFO [train.py:451] Epoch 7, batch 2580, batch avg loss 0.3087, total avg loss: 0.2468, batch size: 73
2021-10-14 15:40:24,149 INFO [train.py:451] Epoch 7, batch 2590, batch avg loss 0.2447, total avg loss: 0.2465, batch size: 38
2021-10-14 15:40:29,142 INFO [train.py:451] Epoch 7, batch 2600, batch avg loss 0.4101, total avg loss: 0.2468, batch size: 132
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2021-10-14 15:40:39,150 INFO [train.py:451] Epoch 7, batch 2620, batch avg loss 0.2313, total avg loss: 0.2525, batch size: 42
2021-10-14 15:40:43,880 INFO [train.py:451] Epoch 7, batch 2630, batch avg loss 0.2861, total avg loss: 0.2585, batch size: 42
2021-10-14 15:40:48,825 INFO [train.py:451] Epoch 7, batch 2640, batch avg loss 0.2479, total avg loss: 0.2498, batch size: 35
2021-10-14 15:40:53,886 INFO [train.py:451] Epoch 7, batch 2650, batch avg loss 0.2456, total avg loss: 0.2474, batch size: 39
2021-10-14 15:40:58,760 INFO [train.py:451] Epoch 7, batch 2660, batch avg loss 0.2688, total avg loss: 0.2527, batch size: 34
2021-10-14 15:41:03,604 INFO [train.py:451] Epoch 7, batch 2670, batch avg loss 0.2349, total avg loss: 0.2520, batch size: 31
2021-10-14 15:41:08,623 INFO [train.py:451] Epoch 7, batch 2680, batch avg loss 0.2636, total avg loss: 0.2501, batch size: 34
2021-10-14 15:41:13,607 INFO [train.py:451] Epoch 7, batch 2690, batch avg loss 0.2181, total avg loss: 0.2479, batch size: 33
2021-10-14 15:41:18,430 INFO [train.py:451] Epoch 7, batch 2700, batch avg loss 0.2262, total avg loss: 0.2468, batch size: 38
2021-10-14 15:41:23,688 INFO [train.py:451] Epoch 7, batch 2710, batch avg loss 0.2228, total avg loss: 0.2454, batch size: 39
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2021-10-14 15:41:33,463 INFO [train.py:451] Epoch 7, batch 2730, batch avg loss 0.2077, total avg loss: 0.2456, batch size: 33
2021-10-14 15:41:38,457 INFO [train.py:451] Epoch 7, batch 2740, batch avg loss 0.2556, total avg loss: 0.2458, batch size: 35
2021-10-14 15:41:43,442 INFO [train.py:451] Epoch 7, batch 2750, batch avg loss 0.3355, total avg loss: 0.2467, batch size: 71
2021-10-14 15:41:48,424 INFO [train.py:451] Epoch 7, batch 2760, batch avg loss 0.2497, total avg loss: 0.2466, batch size: 34
2021-10-14 15:41:53,417 INFO [train.py:451] Epoch 7, batch 2770, batch avg loss 0.2579, total avg loss: 0.2467, batch size: 33
2021-10-14 15:41:58,464 INFO [train.py:451] Epoch 7, batch 2780, batch avg loss 0.2760, total avg loss: 0.2475, batch size: 36
2021-10-14 15:42:03,430 INFO [train.py:451] Epoch 7, batch 2790, batch avg loss 0.2321, total avg loss: 0.2475, batch size: 38
2021-10-14 15:42:08,405 INFO [train.py:451] Epoch 7, batch 2800, batch avg loss 0.2646, total avg loss: 0.2479, batch size: 39
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2021-10-14 15:42:23,236 INFO [train.py:451] Epoch 7, batch 2830, batch avg loss 0.2320, total avg loss: 0.2431, batch size: 34
2021-10-14 15:42:28,318 INFO [train.py:451] Epoch 7, batch 2840, batch avg loss 0.2370, total avg loss: 0.2440, batch size: 36
2021-10-14 15:42:33,263 INFO [train.py:451] Epoch 7, batch 2850, batch avg loss 0.1792, total avg loss: 0.2445, batch size: 28
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2021-10-14 15:42:48,129 INFO [train.py:451] Epoch 7, batch 2880, batch avg loss 0.2437, total avg loss: 0.2442, batch size: 45
2021-10-14 15:42:52,987 INFO [train.py:451] Epoch 7, batch 2890, batch avg loss 0.2635, total avg loss: 0.2457, batch size: 36
2021-10-14 15:42:58,070 INFO [train.py:451] Epoch 7, batch 2900, batch avg loss 0.2257, total avg loss: 0.2449, batch size: 33
2021-10-14 15:43:03,104 INFO [train.py:451] Epoch 7, batch 2910, batch avg loss 0.1780, total avg loss: 0.2453, batch size: 28
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2021-10-14 15:43:12,865 INFO [train.py:451] Epoch 7, batch 2930, batch avg loss 0.2662, total avg loss: 0.2455, batch size: 72
2021-10-14 15:43:17,807 INFO [train.py:451] Epoch 7, batch 2940, batch avg loss 0.2678, total avg loss: 0.2443, batch size: 45
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2021-10-14 15:43:32,470 INFO [train.py:451] Epoch 7, batch 2970, batch avg loss 0.2310, total avg loss: 0.2457, batch size: 45
2021-10-14 15:43:37,330 INFO [train.py:451] Epoch 7, batch 2980, batch avg loss 0.2250, total avg loss: 0.2462, batch size: 31
2021-10-14 15:43:42,120 INFO [train.py:451] Epoch 7, batch 2990, batch avg loss 0.2422, total avg loss: 0.2468, batch size: 28
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2021-10-14 15:44:25,302 INFO [train.py:483] Epoch 7, valid loss 0.1782, best valid loss: 0.1771 best valid epoch: 6
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2021-10-14 15:53:21,106 INFO [train.py:483] Epoch 7, valid loss 0.1763, best valid loss: 0.1763 best valid epoch: 7
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2021-10-14 15:57:25,178 INFO [train.py:451] Epoch 7, batch 4490, batch avg loss 0.2250, total avg loss: 0.2413, batch size: 33
2021-10-14 15:57:30,041 INFO [train.py:451] Epoch 7, batch 4500, batch avg loss 0.2188, total avg loss: 0.2424, batch size: 35
2021-10-14 15:57:34,797 INFO [train.py:451] Epoch 7, batch 4510, batch avg loss 0.2459, total avg loss: 0.2434, batch size: 49
2021-10-14 15:57:39,584 INFO [train.py:451] Epoch 7, batch 4520, batch avg loss 0.2984, total avg loss: 0.2450, batch size: 73
2021-10-14 15:57:44,634 INFO [train.py:451] Epoch 7, batch 4530, batch avg loss 0.2806, total avg loss: 0.2444, batch size: 36
2021-10-14 15:57:49,545 INFO [train.py:451] Epoch 7, batch 4540, batch avg loss 0.2010, total avg loss: 0.2441, batch size: 32
2021-10-14 15:57:54,524 INFO [train.py:451] Epoch 7, batch 4550, batch avg loss 0.1984, total avg loss: 0.2441, batch size: 32
2021-10-14 15:57:55,611 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "89af7506-38b8-59f7-b53a-8645d5e45b2d" will not be mixed in.
2021-10-14 15:57:59,309 INFO [train.py:451] Epoch 7, batch 4560, batch avg loss 0.2407, total avg loss: 0.2447, batch size: 49
2021-10-14 15:58:04,362 INFO [train.py:451] Epoch 7, batch 4570, batch avg loss 0.2136, total avg loss: 0.2438, batch size: 27
2021-10-14 15:58:09,248 INFO [train.py:451] Epoch 7, batch 4580, batch avg loss 0.2493, total avg loss: 0.2441, batch size: 49
2021-10-14 15:58:14,145 INFO [train.py:451] Epoch 7, batch 4590, batch avg loss 0.2354, total avg loss: 0.2439, batch size: 31
2021-10-14 15:58:18,968 INFO [train.py:451] Epoch 7, batch 4600, batch avg loss 0.2157, total avg loss: 0.2449, batch size: 27
2021-10-14 15:58:23,771 INFO [train.py:451] Epoch 7, batch 4610, batch avg loss 0.2052, total avg loss: 0.2305, batch size: 30
2021-10-14 15:58:28,751 INFO [train.py:451] Epoch 7, batch 4620, batch avg loss 0.2035, total avg loss: 0.2344, batch size: 33
2021-10-14 15:58:33,791 INFO [train.py:451] Epoch 7, batch 4630, batch avg loss 0.2845, total avg loss: 0.2442, batch size: 34
2021-10-14 15:58:38,749 INFO [train.py:451] Epoch 7, batch 4640, batch avg loss 0.2898, total avg loss: 0.2436, batch size: 56
2021-10-14 15:58:43,795 INFO [train.py:451] Epoch 7, batch 4650, batch avg loss 0.1912, total avg loss: 0.2429, batch size: 31
2021-10-14 15:58:48,588 INFO [train.py:451] Epoch 7, batch 4660, batch avg loss 0.2764, total avg loss: 0.2448, batch size: 57
2021-10-14 15:58:53,568 INFO [train.py:451] Epoch 7, batch 4670, batch avg loss 0.2265, total avg loss: 0.2401, batch size: 42
2021-10-14 15:58:58,768 INFO [train.py:451] Epoch 7, batch 4680, batch avg loss 0.2131, total avg loss: 0.2409, batch size: 33
2021-10-14 15:59:03,692 INFO [train.py:451] Epoch 7, batch 4690, batch avg loss 0.2374, total avg loss: 0.2430, batch size: 36
2021-10-14 15:59:08,919 INFO [train.py:451] Epoch 7, batch 4700, batch avg loss 0.2119, total avg loss: 0.2409, batch size: 32
2021-10-14 15:59:13,756 INFO [train.py:451] Epoch 7, batch 4710, batch avg loss 0.2198, total avg loss: 0.2407, batch size: 36
2021-10-14 15:59:18,499 INFO [train.py:451] Epoch 7, batch 4720, batch avg loss 0.2854, total avg loss: 0.2406, batch size: 48
2021-10-14 15:59:23,375 INFO [train.py:451] Epoch 7, batch 4730, batch avg loss 0.1813, total avg loss: 0.2405, batch size: 28
2021-10-14 15:59:28,307 INFO [train.py:451] Epoch 7, batch 4740, batch avg loss 0.2374, total avg loss: 0.2412, batch size: 31
2021-10-14 15:59:33,365 INFO [train.py:451] Epoch 7, batch 4750, batch avg loss 0.2128, total avg loss: 0.2408, batch size: 28
2021-10-14 15:59:38,469 INFO [train.py:451] Epoch 7, batch 4760, batch avg loss 0.2350, total avg loss: 0.2413, batch size: 37
2021-10-14 15:59:43,489 INFO [train.py:451] Epoch 7, batch 4770, batch avg loss 0.2514, total avg loss: 0.2404, batch size: 41
2021-10-14 15:59:48,498 INFO [train.py:451] Epoch 7, batch 4780, batch avg loss 0.2803, total avg loss: 0.2404, batch size: 32
2021-10-14 15:59:53,686 INFO [train.py:451] Epoch 7, batch 4790, batch avg loss 0.1954, total avg loss: 0.2401, batch size: 31
2021-10-14 15:59:58,705 INFO [train.py:451] Epoch 7, batch 4800, batch avg loss 0.2354, total avg loss: 0.2404, batch size: 37
2021-10-14 16:00:03,521 INFO [train.py:451] Epoch 7, batch 4810, batch avg loss 0.2883, total avg loss: 0.2547, batch size: 36
2021-10-14 16:00:08,391 INFO [train.py:451] Epoch 7, batch 4820, batch avg loss 0.2140, total avg loss: 0.2498, batch size: 35
2021-10-14 16:00:13,410 INFO [train.py:451] Epoch 7, batch 4830, batch avg loss 0.2219, total avg loss: 0.2457, batch size: 32
2021-10-14 16:00:18,277 INFO [train.py:451] Epoch 7, batch 4840, batch avg loss 0.1854, total avg loss: 0.2483, batch size: 33
2021-10-14 16:00:23,150 INFO [train.py:451] Epoch 7, batch 4850, batch avg loss 0.2216, total avg loss: 0.2492, batch size: 27
2021-10-14 16:00:27,979 INFO [train.py:451] Epoch 7, batch 4860, batch avg loss 0.2758, total avg loss: 0.2477, batch size: 36
2021-10-14 16:00:33,076 INFO [train.py:451] Epoch 7, batch 4870, batch avg loss 0.2128, total avg loss: 0.2464, batch size: 35
2021-10-14 16:00:37,904 INFO [train.py:451] Epoch 7, batch 4880, batch avg loss 0.2718, total avg loss: 0.2467, batch size: 45
2021-10-14 16:00:42,854 INFO [train.py:451] Epoch 7, batch 4890, batch avg loss 0.1848, total avg loss: 0.2445, batch size: 29
2021-10-14 16:00:47,709 INFO [train.py:451] Epoch 7, batch 4900, batch avg loss 0.2734, total avg loss: 0.2450, batch size: 32
2021-10-14 16:00:52,908 INFO [train.py:451] Epoch 7, batch 4910, batch avg loss 0.2321, total avg loss: 0.2450, batch size: 35
2021-10-14 16:00:58,149 INFO [train.py:451] Epoch 7, batch 4920, batch avg loss 0.2430, total avg loss: 0.2438, batch size: 34
2021-10-14 16:01:02,982 INFO [train.py:451] Epoch 7, batch 4930, batch avg loss 0.2525, total avg loss: 0.2444, batch size: 36
2021-10-14 16:01:07,924 INFO [train.py:451] Epoch 7, batch 4940, batch avg loss 0.2551, total avg loss: 0.2435, batch size: 37
2021-10-14 16:01:12,788 INFO [train.py:451] Epoch 7, batch 4950, batch avg loss 0.2149, total avg loss: 0.2428, batch size: 41
2021-10-14 16:01:17,714 INFO [train.py:451] Epoch 7, batch 4960, batch avg loss 0.3776, total avg loss: 0.2438, batch size: 35
2021-10-14 16:01:22,682 INFO [train.py:451] Epoch 7, batch 4970, batch avg loss 0.3456, total avg loss: 0.2433, batch size: 125
2021-10-14 16:01:27,461 INFO [train.py:451] Epoch 7, batch 4980, batch avg loss 0.2648, total avg loss: 0.2445, batch size: 49
2021-10-14 16:01:32,250 INFO [train.py:451] Epoch 7, batch 4990, batch avg loss 0.2394, total avg loss: 0.2452, batch size: 42
2021-10-14 16:01:37,266 INFO [train.py:451] Epoch 7, batch 5000, batch avg loss 0.2165, total avg loss: 0.2453, batch size: 27
2021-10-14 16:02:17,465 INFO [train.py:483] Epoch 7, valid loss 0.1766, best valid loss: 0.1763 best valid epoch: 7
2021-10-14 16:02:22,348 INFO [train.py:451] Epoch 7, batch 5010, batch avg loss 0.2919, total avg loss: 0.2488, batch size: 37
2021-10-14 16:02:27,208 INFO [train.py:451] Epoch 7, batch 5020, batch avg loss 0.2753, total avg loss: 0.2466, batch size: 72
2021-10-14 16:02:32,092 INFO [train.py:451] Epoch 7, batch 5030, batch avg loss 0.2828, total avg loss: 0.2504, batch size: 39
2021-10-14 16:02:36,937 INFO [train.py:451] Epoch 7, batch 5040, batch avg loss 0.3238, total avg loss: 0.2514, batch size: 73
2021-10-14 16:02:41,717 INFO [train.py:451] Epoch 7, batch 5050, batch avg loss 0.2447, total avg loss: 0.2523, batch size: 28
2021-10-14 16:02:46,522 INFO [train.py:451] Epoch 7, batch 5060, batch avg loss 0.2211, total avg loss: 0.2535, batch size: 32
2021-10-14 16:02:51,420 INFO [train.py:451] Epoch 7, batch 5070, batch avg loss 0.2478, total avg loss: 0.2532, batch size: 37
2021-10-14 16:02:56,620 INFO [train.py:451] Epoch 7, batch 5080, batch avg loss 0.2045, total avg loss: 0.2508, batch size: 29
2021-10-14 16:03:01,621 INFO [train.py:451] Epoch 7, batch 5090, batch avg loss 0.2212, total avg loss: 0.2496, batch size: 30
2021-10-14 16:03:06,445 INFO [train.py:451] Epoch 7, batch 5100, batch avg loss 0.1809, total avg loss: 0.2503, batch size: 28
2021-10-14 16:03:11,486 INFO [train.py:451] Epoch 7, batch 5110, batch avg loss 0.2217, total avg loss: 0.2502, batch size: 38
2021-10-14 16:03:16,590 INFO [train.py:451] Epoch 7, batch 5120, batch avg loss 0.2028, total avg loss: 0.2501, batch size: 27
2021-10-14 16:03:21,500 INFO [train.py:451] Epoch 7, batch 5130, batch avg loss 0.2333, total avg loss: 0.2516, batch size: 32
2021-10-14 16:03:26,549 INFO [train.py:451] Epoch 7, batch 5140, batch avg loss 0.2298, total avg loss: 0.2510, batch size: 36
2021-10-14 16:03:31,642 INFO [train.py:451] Epoch 7, batch 5150, batch avg loss 0.1785, total avg loss: 0.2498, batch size: 27
2021-10-14 16:03:36,597 INFO [train.py:451] Epoch 7, batch 5160, batch avg loss 0.2344, total avg loss: 0.2504, batch size: 34
2021-10-14 16:03:41,424 INFO [train.py:451] Epoch 7, batch 5170, batch avg loss 0.2840, total avg loss: 0.2507, batch size: 37
2021-10-14 16:03:46,394 INFO [train.py:451] Epoch 7, batch 5180, batch avg loss 0.2697, total avg loss: 0.2506, batch size: 37
2021-10-14 16:03:51,293 INFO [train.py:451] Epoch 7, batch 5190, batch avg loss 0.2202, total avg loss: 0.2506, batch size: 30
2021-10-14 16:03:56,240 INFO [train.py:451] Epoch 7, batch 5200, batch avg loss 0.2322, total avg loss: 0.2502, batch size: 33
2021-10-14 16:04:01,150 INFO [train.py:451] Epoch 7, batch 5210, batch avg loss 0.2958, total avg loss: 0.2458, batch size: 34
2021-10-14 16:04:06,222 INFO [train.py:451] Epoch 7, batch 5220, batch avg loss 0.2551, total avg loss: 0.2367, batch size: 35
2021-10-14 16:04:11,083 INFO [train.py:451] Epoch 7, batch 5230, batch avg loss 0.2286, total avg loss: 0.2436, batch size: 35
2021-10-14 16:04:16,139 INFO [train.py:451] Epoch 7, batch 5240, batch avg loss 0.2010, total avg loss: 0.2433, batch size: 31
2021-10-14 16:04:21,150 INFO [train.py:451] Epoch 7, batch 5250, batch avg loss 0.2712, total avg loss: 0.2417, batch size: 37
2021-10-14 16:04:26,392 INFO [train.py:451] Epoch 7, batch 5260, batch avg loss 0.2604, total avg loss: 0.2414, batch size: 38
2021-10-14 16:04:31,309 INFO [train.py:451] Epoch 7, batch 5270, batch avg loss 0.2480, total avg loss: 0.2421, batch size: 45
2021-10-14 16:04:36,347 INFO [train.py:451] Epoch 7, batch 5280, batch avg loss 0.2080, total avg loss: 0.2399, batch size: 30
2021-10-14 16:04:41,167 INFO [train.py:451] Epoch 7, batch 5290, batch avg loss 0.2425, total avg loss: 0.2427, batch size: 34
2021-10-14 16:04:46,000 INFO [train.py:451] Epoch 7, batch 5300, batch avg loss 0.3065, total avg loss: 0.2445, batch size: 38
2021-10-14 16:04:50,907 INFO [train.py:451] Epoch 7, batch 5310, batch avg loss 0.2260, total avg loss: 0.2433, batch size: 31
2021-10-14 16:04:55,784 INFO [train.py:451] Epoch 7, batch 5320, batch avg loss 0.2608, total avg loss: 0.2438, batch size: 32
2021-10-14 16:05:00,650 INFO [train.py:451] Epoch 7, batch 5330, batch avg loss 0.1731, total avg loss: 0.2446, batch size: 27
2021-10-14 16:05:05,677 INFO [train.py:451] Epoch 7, batch 5340, batch avg loss 0.2446, total avg loss: 0.2453, batch size: 33
2021-10-14 16:05:10,646 INFO [train.py:451] Epoch 7, batch 5350, batch avg loss 0.2903, total avg loss: 0.2453, batch size: 37
2021-10-14 16:05:15,706 INFO [train.py:451] Epoch 7, batch 5360, batch avg loss 0.2268, total avg loss: 0.2444, batch size: 28
2021-10-14 16:05:20,686 INFO [train.py:451] Epoch 7, batch 5370, batch avg loss 0.1925, total avg loss: 0.2428, batch size: 34
2021-10-14 16:05:25,522 INFO [train.py:451] Epoch 7, batch 5380, batch avg loss 0.2306, total avg loss: 0.2431, batch size: 29
2021-10-14 16:05:30,499 INFO [train.py:451] Epoch 7, batch 5390, batch avg loss 0.2087, total avg loss: 0.2434, batch size: 32
2021-10-14 16:05:35,559 INFO [train.py:451] Epoch 7, batch 5400, batch avg loss 0.2476, total avg loss: 0.2431, batch size: 41
2021-10-14 16:05:40,530 INFO [train.py:451] Epoch 7, batch 5410, batch avg loss 0.2913, total avg loss: 0.2467, batch size: 34
2021-10-14 16:05:45,345 INFO [train.py:451] Epoch 7, batch 5420, batch avg loss 0.2165, total avg loss: 0.2479, batch size: 29
2021-10-14 16:05:50,190 INFO [train.py:451] Epoch 7, batch 5430, batch avg loss 0.2280, total avg loss: 0.2504, batch size: 32
2021-10-14 16:05:55,028 INFO [train.py:451] Epoch 7, batch 5440, batch avg loss 0.3039, total avg loss: 0.2525, batch size: 37
2021-10-14 16:05:59,986 INFO [train.py:451] Epoch 7, batch 5450, batch avg loss 0.2714, total avg loss: 0.2513, batch size: 29
2021-10-14 16:06:05,042 INFO [train.py:451] Epoch 7, batch 5460, batch avg loss 0.2194, total avg loss: 0.2504, batch size: 33
2021-10-14 16:06:09,906 INFO [train.py:451] Epoch 7, batch 5470, batch avg loss 0.1767, total avg loss: 0.2503, batch size: 31
2021-10-14 16:06:14,803 INFO [train.py:451] Epoch 7, batch 5480, batch avg loss 0.3043, total avg loss: 0.2528, batch size: 36
2021-10-14 16:06:19,611 INFO [train.py:451] Epoch 7, batch 5490, batch avg loss 0.2159, total avg loss: 0.2518, batch size: 31
2021-10-14 16:06:24,691 INFO [train.py:451] Epoch 7, batch 5500, batch avg loss 0.2205, total avg loss: 0.2509, batch size: 29
2021-10-14 16:06:29,808 INFO [train.py:451] Epoch 7, batch 5510, batch avg loss 0.2107, total avg loss: 0.2500, batch size: 30
2021-10-14 16:06:34,627 INFO [train.py:451] Epoch 7, batch 5520, batch avg loss 0.2151, total avg loss: 0.2501, batch size: 32
2021-10-14 16:06:39,598 INFO [train.py:451] Epoch 7, batch 5530, batch avg loss 0.2364, total avg loss: 0.2484, batch size: 30
2021-10-14 16:06:44,585 INFO [train.py:451] Epoch 7, batch 5540, batch avg loss 0.2059, total avg loss: 0.2485, batch size: 36
2021-10-14 16:06:49,472 INFO [train.py:451] Epoch 7, batch 5550, batch avg loss 0.2564, total avg loss: 0.2490, batch size: 36
2021-10-14 16:06:54,451 INFO [train.py:451] Epoch 7, batch 5560, batch avg loss 0.2128, total avg loss: 0.2487, batch size: 34
2021-10-14 16:06:59,293 INFO [train.py:451] Epoch 7, batch 5570, batch avg loss 0.2999, total avg loss: 0.2487, batch size: 74
2021-10-14 16:07:04,244 INFO [train.py:451] Epoch 7, batch 5580, batch avg loss 0.2248, total avg loss: 0.2488, batch size: 39
2021-10-14 16:07:09,171 INFO [train.py:451] Epoch 7, batch 5590, batch avg loss 0.2591, total avg loss: 0.2479, batch size: 36
2021-10-14 16:07:14,240 INFO [train.py:451] Epoch 7, batch 5600, batch avg loss 0.1920, total avg loss: 0.2478, batch size: 27
2021-10-14 16:07:19,211 INFO [train.py:451] Epoch 7, batch 5610, batch avg loss 0.2468, total avg loss: 0.2503, batch size: 41
2021-10-14 16:07:24,121 INFO [train.py:451] Epoch 7, batch 5620, batch avg loss 0.1848, total avg loss: 0.2386, batch size: 28
2021-10-14 16:07:29,030 INFO [train.py:451] Epoch 7, batch 5630, batch avg loss 0.1838, total avg loss: 0.2368, batch size: 32
2021-10-14 16:07:34,039 INFO [train.py:451] Epoch 7, batch 5640, batch avg loss 0.2161, total avg loss: 0.2372, batch size: 36
2021-10-14 16:07:38,850 INFO [train.py:451] Epoch 7, batch 5650, batch avg loss 0.2598, total avg loss: 0.2400, batch size: 45
2021-10-14 16:07:43,829 INFO [train.py:451] Epoch 7, batch 5660, batch avg loss 0.1791, total avg loss: 0.2420, batch size: 33
2021-10-14 16:07:48,667 INFO [train.py:451] Epoch 7, batch 5670, batch avg loss 0.2027, total avg loss: 0.2448, batch size: 29
2021-10-14 16:07:53,873 INFO [train.py:451] Epoch 7, batch 5680, batch avg loss 0.2454, total avg loss: 0.2428, batch size: 30
2021-10-14 16:07:58,789 INFO [train.py:451] Epoch 7, batch 5690, batch avg loss 0.2549, total avg loss: 0.2427, batch size: 35
2021-10-14 16:08:03,829 INFO [train.py:451] Epoch 7, batch 5700, batch avg loss 0.2886, total avg loss: 0.2443, batch size: 45
2021-10-14 16:08:08,944 INFO [train.py:451] Epoch 7, batch 5710, batch avg loss 0.2388, total avg loss: 0.2447, batch size: 29
2021-10-14 16:08:13,942 INFO [train.py:451] Epoch 7, batch 5720, batch avg loss 0.2490, total avg loss: 0.2460, batch size: 34
2021-10-14 16:08:19,032 INFO [train.py:451] Epoch 7, batch 5730, batch avg loss 0.2231, total avg loss: 0.2451, batch size: 32
2021-10-14 16:08:23,923 INFO [train.py:451] Epoch 7, batch 5740, batch avg loss 0.2255, total avg loss: 0.2462, batch size: 41
2021-10-14 16:08:29,044 INFO [train.py:451] Epoch 7, batch 5750, batch avg loss 0.1946, total avg loss: 0.2456, batch size: 28
2021-10-14 16:08:34,087 INFO [train.py:451] Epoch 7, batch 5760, batch avg loss 0.2843, total avg loss: 0.2449, batch size: 35
2021-10-14 16:08:38,976 INFO [train.py:451] Epoch 7, batch 5770, batch avg loss 0.2918, total avg loss: 0.2454, batch size: 72
2021-10-14 16:08:44,091 INFO [train.py:451] Epoch 7, batch 5780, batch avg loss 0.2316, total avg loss: 0.2448, batch size: 32
2021-10-14 16:08:49,198 INFO [train.py:451] Epoch 7, batch 5790, batch avg loss 0.2258, total avg loss: 0.2447, batch size: 28
2021-10-14 16:08:54,144 INFO [train.py:451] Epoch 7, batch 5800, batch avg loss 0.2179, total avg loss: 0.2446, batch size: 29
2021-10-14 16:08:59,153 INFO [train.py:451] Epoch 7, batch 5810, batch avg loss 0.2135, total avg loss: 0.2219, batch size: 34
2021-10-14 16:09:04,216 INFO [train.py:451] Epoch 7, batch 5820, batch avg loss 0.2524, total avg loss: 0.2244, batch size: 35
2021-10-14 16:09:09,280 INFO [train.py:451] Epoch 7, batch 5830, batch avg loss 0.2693, total avg loss: 0.2294, batch size: 36
2021-10-14 16:09:14,257 INFO [train.py:451] Epoch 7, batch 5840, batch avg loss 0.4162, total avg loss: 0.2415, batch size: 127
2021-10-14 16:09:19,171 INFO [train.py:451] Epoch 7, batch 5850, batch avg loss 0.2021, total avg loss: 0.2440, batch size: 32
2021-10-14 16:09:24,317 INFO [train.py:451] Epoch 7, batch 5860, batch avg loss 0.2531, total avg loss: 0.2431, batch size: 33
2021-10-14 16:09:29,580 INFO [train.py:451] Epoch 7, batch 5870, batch avg loss 0.2852, total avg loss: 0.2415, batch size: 34
2021-10-14 16:09:34,555 INFO [train.py:451] Epoch 7, batch 5880, batch avg loss 0.2357, total avg loss: 0.2428, batch size: 32
2021-10-14 16:09:39,442 INFO [train.py:451] Epoch 7, batch 5890, batch avg loss 0.2280, total avg loss: 0.2456, batch size: 34
2021-10-14 16:09:44,361 INFO [train.py:451] Epoch 7, batch 5900, batch avg loss 0.2278, total avg loss: 0.2472, batch size: 28
2021-10-14 16:09:49,355 INFO [train.py:451] Epoch 7, batch 5910, batch avg loss 0.2000, total avg loss: 0.2455, batch size: 34
2021-10-14 16:09:54,212 INFO [train.py:451] Epoch 7, batch 5920, batch avg loss 0.2145, total avg loss: 0.2454, batch size: 32
2021-10-14 16:09:59,021 INFO [train.py:451] Epoch 7, batch 5930, batch avg loss 0.3088, total avg loss: 0.2474, batch size: 41
2021-10-14 16:10:04,065 INFO [train.py:451] Epoch 7, batch 5940, batch avg loss 0.2320, total avg loss: 0.2468, batch size: 34
2021-10-14 16:10:09,109 INFO [train.py:451] Epoch 7, batch 5950, batch avg loss 0.2375, total avg loss: 0.2450, batch size: 34
2021-10-14 16:10:14,063 INFO [train.py:451] Epoch 7, batch 5960, batch avg loss 0.3720, total avg loss: 0.2460, batch size: 129
2021-10-14 16:10:19,159 INFO [train.py:451] Epoch 7, batch 5970, batch avg loss 0.2256, total avg loss: 0.2460, batch size: 27
2021-10-14 16:10:24,333 INFO [train.py:451] Epoch 7, batch 5980, batch avg loss 0.2673, total avg loss: 0.2459, batch size: 38
2021-10-14 16:10:29,330 INFO [train.py:451] Epoch 7, batch 5990, batch avg loss 0.2511, total avg loss: 0.2459, batch size: 31
2021-10-14 16:10:34,474 INFO [train.py:451] Epoch 7, batch 6000, batch avg loss 0.2112, total avg loss: 0.2454, batch size: 31
2021-10-14 16:11:14,625 INFO [train.py:483] Epoch 7, valid loss 0.1773, best valid loss: 0.1763 best valid epoch: 7
2021-10-14 16:11:19,424 INFO [train.py:451] Epoch 7, batch 6010, batch avg loss 0.2798, total avg loss: 0.2459, batch size: 39
2021-10-14 16:11:24,491 INFO [train.py:451] Epoch 7, batch 6020, batch avg loss 0.2797, total avg loss: 0.2402, batch size: 33
2021-10-14 16:11:29,388 INFO [train.py:451] Epoch 7, batch 6030, batch avg loss 0.2431, total avg loss: 0.2429, batch size: 35
2021-10-14 16:11:34,355 INFO [train.py:451] Epoch 7, batch 6040, batch avg loss 0.2327, total avg loss: 0.2412, batch size: 45
2021-10-14 16:11:39,070 INFO [train.py:451] Epoch 7, batch 6050, batch avg loss 0.2509, total avg loss: 0.2429, batch size: 38
2021-10-14 16:11:44,078 INFO [train.py:451] Epoch 7, batch 6060, batch avg loss 0.2118, total avg loss: 0.2439, batch size: 33
2021-10-14 16:11:49,003 INFO [train.py:451] Epoch 7, batch 6070, batch avg loss 0.2458, total avg loss: 0.2446, batch size: 30
2021-10-14 16:11:53,861 INFO [train.py:451] Epoch 7, batch 6080, batch avg loss 0.2621, total avg loss: 0.2439, batch size: 49
2021-10-14 16:11:58,720 INFO [train.py:451] Epoch 7, batch 6090, batch avg loss 0.2620, total avg loss: 0.2423, batch size: 45
2021-10-14 16:12:03,432 INFO [train.py:451] Epoch 7, batch 6100, batch avg loss 0.2495, total avg loss: 0.2456, batch size: 45
2021-10-14 16:12:08,474 INFO [train.py:451] Epoch 7, batch 6110, batch avg loss 0.2528, total avg loss: 0.2446, batch size: 35
2021-10-14 16:12:13,330 INFO [train.py:451] Epoch 7, batch 6120, batch avg loss 0.2167, total avg loss: 0.2444, batch size: 31
2021-10-14 16:12:18,308 INFO [train.py:451] Epoch 7, batch 6130, batch avg loss 0.2184, total avg loss: 0.2437, batch size: 32
2021-10-14 16:12:23,189 INFO [train.py:451] Epoch 7, batch 6140, batch avg loss 0.2038, total avg loss: 0.2436, batch size: 33
2021-10-14 16:12:27,984 INFO [train.py:451] Epoch 7, batch 6150, batch avg loss 0.1802, total avg loss: 0.2438, batch size: 30
2021-10-14 16:12:32,921 INFO [train.py:451] Epoch 7, batch 6160, batch avg loss 0.3565, total avg loss: 0.2442, batch size: 125
2021-10-14 16:12:37,950 INFO [train.py:451] Epoch 7, batch 6170, batch avg loss 0.2805, total avg loss: 0.2452, batch size: 38
2021-10-14 16:12:42,827 INFO [train.py:451] Epoch 7, batch 6180, batch avg loss 0.3434, total avg loss: 0.2448, batch size: 131
2021-10-14 16:12:47,780 INFO [train.py:451] Epoch 7, batch 6190, batch avg loss 0.2760, total avg loss: 0.2456, batch size: 29
2021-10-14 16:12:52,701 INFO [train.py:451] Epoch 7, batch 6200, batch avg loss 0.2025, total avg loss: 0.2461, batch size: 29
2021-10-14 16:12:57,667 INFO [train.py:451] Epoch 7, batch 6210, batch avg loss 0.2066, total avg loss: 0.2276, batch size: 29
2021-10-14 16:13:02,630 INFO [train.py:451] Epoch 7, batch 6220, batch avg loss 0.3414, total avg loss: 0.2308, batch size: 74
2021-10-14 16:13:07,578 INFO [train.py:451] Epoch 7, batch 6230, batch avg loss 0.2645, total avg loss: 0.2323, batch size: 35
2021-10-14 16:13:12,666 INFO [train.py:451] Epoch 7, batch 6240, batch avg loss 0.1835, total avg loss: 0.2330, batch size: 29
2021-10-14 16:13:17,579 INFO [train.py:451] Epoch 7, batch 6250, batch avg loss 0.2694, total avg loss: 0.2339, batch size: 36
2021-10-14 16:13:22,634 INFO [train.py:451] Epoch 7, batch 6260, batch avg loss 0.2402, total avg loss: 0.2353, batch size: 34
2021-10-14 16:13:27,542 INFO [train.py:451] Epoch 7, batch 6270, batch avg loss 0.2290, total avg loss: 0.2366, batch size: 31
2021-10-14 16:13:32,342 INFO [train.py:451] Epoch 7, batch 6280, batch avg loss 0.2828, total avg loss: 0.2387, batch size: 57
2021-10-14 16:13:37,241 INFO [train.py:451] Epoch 7, batch 6290, batch avg loss 0.2416, total avg loss: 0.2416, batch size: 32
2021-10-14 16:13:50,142 INFO [train.py:451] Epoch 7, batch 6300, batch avg loss 0.2537, total avg loss: 0.2428, batch size: 34
2021-10-14 16:13:55,163 INFO [train.py:451] Epoch 7, batch 6310, batch avg loss 0.2274, total avg loss: 0.2442, batch size: 34
2021-10-14 16:14:00,024 INFO [train.py:451] Epoch 7, batch 6320, batch avg loss 0.2549, total avg loss: 0.2454, batch size: 37
2021-10-14 16:14:05,117 INFO [train.py:451] Epoch 7, batch 6330, batch avg loss 0.2283, total avg loss: 0.2459, batch size: 33
2021-10-14 16:14:10,186 INFO [train.py:451] Epoch 7, batch 6340, batch avg loss 0.2023, total avg loss: 0.2452, batch size: 27
2021-10-14 16:14:15,151 INFO [train.py:451] Epoch 7, batch 6350, batch avg loss 0.2972, total avg loss: 0.2440, batch size: 73
2021-10-14 16:14:20,293 INFO [train.py:451] Epoch 7, batch 6360, batch avg loss 0.1851, total avg loss: 0.2437, batch size: 28
2021-10-14 16:14:25,189 INFO [train.py:451] Epoch 7, batch 6370, batch avg loss 0.2867, total avg loss: 0.2447, batch size: 28
2021-10-14 16:14:30,343 INFO [train.py:451] Epoch 7, batch 6380, batch avg loss 0.2794, total avg loss: 0.2447, batch size: 36
2021-10-14 16:14:35,391 INFO [train.py:451] Epoch 7, batch 6390, batch avg loss 0.2889, total avg loss: 0.2449, batch size: 45
2021-10-14 16:14:40,290 INFO [train.py:451] Epoch 7, batch 6400, batch avg loss 0.2554, total avg loss: 0.2452, batch size: 34
2021-10-14 16:14:45,077 INFO [train.py:451] Epoch 7, batch 6410, batch avg loss 0.2520, total avg loss: 0.2444, batch size: 32
2021-10-14 16:14:50,053 INFO [train.py:451] Epoch 7, batch 6420, batch avg loss 0.2587, total avg loss: 0.2468, batch size: 38
2021-10-14 16:14:54,999 INFO [train.py:451] Epoch 7, batch 6430, batch avg loss 0.2414, total avg loss: 0.2448, batch size: 45
2021-10-14 16:14:59,923 INFO [train.py:451] Epoch 7, batch 6440, batch avg loss 0.2716, total avg loss: 0.2425, batch size: 49
2021-10-14 16:15:04,733 INFO [train.py:451] Epoch 7, batch 6450, batch avg loss 0.3058, total avg loss: 0.2441, batch size: 73
2021-10-14 16:15:09,636 INFO [train.py:451] Epoch 7, batch 6460, batch avg loss 0.2076, total avg loss: 0.2417, batch size: 33
2021-10-14 16:15:14,538 INFO [train.py:451] Epoch 7, batch 6470, batch avg loss 0.2929, total avg loss: 0.2402, batch size: 38
2021-10-14 16:15:19,575 INFO [train.py:451] Epoch 7, batch 6480, batch avg loss 0.2109, total avg loss: 0.2400, batch size: 35
2021-10-14 16:15:20,854 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "df8cacc1-7f02-b6fc-2acb-aad3e029221e" will not be mixed in.
2021-10-14 16:15:24,468 INFO [train.py:451] Epoch 7, batch 6490, batch avg loss 0.2377, total avg loss: 0.2415, batch size: 29
2021-10-14 16:15:29,386 INFO [train.py:451] Epoch 7, batch 6500, batch avg loss 0.2430, total avg loss: 0.2418, batch size: 36
2021-10-14 16:15:34,565 INFO [train.py:451] Epoch 7, batch 6510, batch avg loss 0.2561, total avg loss: 0.2428, batch size: 34
2021-10-14 16:15:39,439 INFO [train.py:451] Epoch 7, batch 6520, batch avg loss 0.1885, total avg loss: 0.2436, batch size: 27
2021-10-14 16:15:44,198 INFO [train.py:451] Epoch 7, batch 6530, batch avg loss 0.2889, total avg loss: 0.2450, batch size: 57
2021-10-14 16:15:49,218 INFO [train.py:451] Epoch 7, batch 6540, batch avg loss 0.2668, total avg loss: 0.2449, batch size: 36
2021-10-14 16:15:54,170 INFO [train.py:451] Epoch 7, batch 6550, batch avg loss 0.2037, total avg loss: 0.2429, batch size: 41
2021-10-14 16:15:59,120 INFO [train.py:451] Epoch 7, batch 6560, batch avg loss 0.2336, total avg loss: 0.2430, batch size: 27
2021-10-14 16:16:04,086 INFO [train.py:451] Epoch 7, batch 6570, batch avg loss 0.1949, total avg loss: 0.2432, batch size: 34
2021-10-14 16:16:08,937 INFO [train.py:451] Epoch 7, batch 6580, batch avg loss 0.3085, total avg loss: 0.2449, batch size: 41
2021-10-14 16:16:13,939 INFO [train.py:451] Epoch 7, batch 6590, batch avg loss 0.2359, total avg loss: 0.2447, batch size: 30
2021-10-14 16:16:18,943 INFO [train.py:451] Epoch 7, batch 6600, batch avg loss 0.2338, total avg loss: 0.2444, batch size: 27
2021-10-14 16:16:23,938 INFO [train.py:451] Epoch 7, batch 6610, batch avg loss 0.2128, total avg loss: 0.2317, batch size: 31
2021-10-14 16:16:28,860 INFO [train.py:451] Epoch 7, batch 6620, batch avg loss 0.1935, total avg loss: 0.2315, batch size: 30
2021-10-14 16:16:33,852 INFO [train.py:451] Epoch 7, batch 6630, batch avg loss 0.2761, total avg loss: 0.2396, batch size: 36
2021-10-14 16:16:38,643 INFO [train.py:451] Epoch 7, batch 6640, batch avg loss 0.2833, total avg loss: 0.2432, batch size: 45
2021-10-14 16:16:43,501 INFO [train.py:451] Epoch 7, batch 6650, batch avg loss 0.2503, total avg loss: 0.2498, batch size: 38
2021-10-14 16:16:48,454 INFO [train.py:451] Epoch 7, batch 6660, batch avg loss 0.2235, total avg loss: 0.2478, batch size: 38
2021-10-14 16:16:53,619 INFO [train.py:451] Epoch 7, batch 6670, batch avg loss 0.2284, total avg loss: 0.2455, batch size: 37
2021-10-14 16:16:58,595 INFO [train.py:451] Epoch 7, batch 6680, batch avg loss 0.2384, total avg loss: 0.2451, batch size: 38
2021-10-14 16:17:03,589 INFO [train.py:451] Epoch 7, batch 6690, batch avg loss 0.2376, total avg loss: 0.2441, batch size: 29
2021-10-14 16:17:08,605 INFO [train.py:451] Epoch 7, batch 6700, batch avg loss 0.2389, total avg loss: 0.2456, batch size: 34
2021-10-14 16:17:13,517 INFO [train.py:451] Epoch 7, batch 6710, batch avg loss 0.2452, total avg loss: 0.2462, batch size: 37
2021-10-14 16:17:18,607 INFO [train.py:451] Epoch 7, batch 6720, batch avg loss 0.2388, total avg loss: 0.2468, batch size: 45
2021-10-14 16:17:23,618 INFO [train.py:451] Epoch 7, batch 6730, batch avg loss 0.2018, total avg loss: 0.2466, batch size: 36
2021-10-14 16:17:25,817 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "bc248f5e-e0cb-2dd3-e2e9-c2ad3c6e2628" will not be mixed in.
2021-10-14 16:17:28,466 INFO [train.py:451] Epoch 7, batch 6740, batch avg loss 0.2464, total avg loss: 0.2479, batch size: 35
2021-10-14 16:17:33,315 INFO [train.py:451] Epoch 7, batch 6750, batch avg loss 0.2415, total avg loss: 0.2496, batch size: 31
2021-10-14 16:17:38,163 INFO [train.py:451] Epoch 7, batch 6760, batch avg loss 0.2571, total avg loss: 0.2495, batch size: 35
2021-10-14 16:17:43,209 INFO [train.py:451] Epoch 7, batch 6770, batch avg loss 0.2265, total avg loss: 0.2479, batch size: 33
2021-10-14 16:17:47,997 INFO [train.py:451] Epoch 7, batch 6780, batch avg loss 0.2422, total avg loss: 0.2482, batch size: 36
2021-10-14 16:17:52,962 INFO [train.py:451] Epoch 7, batch 6790, batch avg loss 0.2276, total avg loss: 0.2476, batch size: 31
2021-10-14 16:17:57,788 INFO [train.py:451] Epoch 7, batch 6800, batch avg loss 0.2077, total avg loss: 0.2475, batch size: 34
2021-10-14 16:18:02,664 INFO [train.py:451] Epoch 7, batch 6810, batch avg loss 0.2104, total avg loss: 0.2665, batch size: 31
2021-10-14 16:18:07,413 INFO [train.py:451] Epoch 7, batch 6820, batch avg loss 0.2531, total avg loss: 0.2647, batch size: 34
2021-10-14 16:18:12,265 INFO [train.py:451] Epoch 7, batch 6830, batch avg loss 0.2829, total avg loss: 0.2607, batch size: 45
2021-10-14 16:18:17,048 INFO [train.py:451] Epoch 7, batch 6840, batch avg loss 0.2145, total avg loss: 0.2585, batch size: 29
2021-10-14 16:18:22,007 INFO [train.py:451] Epoch 7, batch 6850, batch avg loss 0.2628, total avg loss: 0.2561, batch size: 33
2021-10-14 16:18:26,930 INFO [train.py:451] Epoch 7, batch 6860, batch avg loss 0.2909, total avg loss: 0.2542, batch size: 37
2021-10-14 16:18:31,977 INFO [train.py:451] Epoch 7, batch 6870, batch avg loss 0.2599, total avg loss: 0.2539, batch size: 30
2021-10-14 16:18:36,994 INFO [train.py:451] Epoch 7, batch 6880, batch avg loss 0.3032, total avg loss: 0.2516, batch size: 57
2021-10-14 16:18:41,966 INFO [train.py:451] Epoch 7, batch 6890, batch avg loss 0.2723, total avg loss: 0.2513, batch size: 37
2021-10-14 16:18:46,844 INFO [train.py:451] Epoch 7, batch 6900, batch avg loss 0.2752, total avg loss: 0.2534, batch size: 45
2021-10-14 16:18:51,816 INFO [train.py:451] Epoch 7, batch 6910, batch avg loss 0.2674, total avg loss: 0.2522, batch size: 42
2021-10-14 16:18:56,676 INFO [train.py:451] Epoch 7, batch 6920, batch avg loss 0.1993, total avg loss: 0.2520, batch size: 32
2021-10-14 16:19:01,653 INFO [train.py:451] Epoch 7, batch 6930, batch avg loss 0.2506, total avg loss: 0.2505, batch size: 49
2021-10-14 16:19:06,386 INFO [train.py:451] Epoch 7, batch 6940, batch avg loss 0.1909, total avg loss: 0.2512, batch size: 31
2021-10-14 16:19:11,262 INFO [train.py:451] Epoch 7, batch 6950, batch avg loss 0.2331, total avg loss: 0.2504, batch size: 35
2021-10-14 16:19:16,141 INFO [train.py:451] Epoch 7, batch 6960, batch avg loss 0.2566, total avg loss: 0.2497, batch size: 33
2021-10-14 16:19:20,945 INFO [train.py:451] Epoch 7, batch 6970, batch avg loss 0.2788, total avg loss: 0.2495, batch size: 39
2021-10-14 16:19:25,814 INFO [train.py:451] Epoch 7, batch 6980, batch avg loss 0.2331, total avg loss: 0.2498, batch size: 37
2021-10-14 16:19:30,893 INFO [train.py:451] Epoch 7, batch 6990, batch avg loss 0.1931, total avg loss: 0.2493, batch size: 28
2021-10-14 16:19:35,852 INFO [train.py:451] Epoch 7, batch 7000, batch avg loss 0.2977, total avg loss: 0.2495, batch size: 39
2021-10-14 16:20:13,968 INFO [train.py:483] Epoch 7, valid loss 0.1771, best valid loss: 0.1763 best valid epoch: 7
2021-10-14 16:20:18,850 INFO [train.py:451] Epoch 7, batch 7010, batch avg loss 0.2888, total avg loss: 0.2544, batch size: 49
2021-10-14 16:20:23,760 INFO [train.py:451] Epoch 7, batch 7020, batch avg loss 0.2733, total avg loss: 0.2529, batch size: 37
2021-10-14 16:20:28,723 INFO [train.py:451] Epoch 7, batch 7030, batch avg loss 0.2514, total avg loss: 0.2526, batch size: 35
2021-10-14 16:20:33,619 INFO [train.py:451] Epoch 7, batch 7040, batch avg loss 0.2141, total avg loss: 0.2520, batch size: 34
2021-10-14 16:20:38,518 INFO [train.py:451] Epoch 7, batch 7050, batch avg loss 0.2708, total avg loss: 0.2508, batch size: 41
2021-10-14 16:20:43,605 INFO [train.py:451] Epoch 7, batch 7060, batch avg loss 0.2466, total avg loss: 0.2493, batch size: 31
2021-10-14 16:20:48,547 INFO [train.py:451] Epoch 7, batch 7070, batch avg loss 0.2774, total avg loss: 0.2478, batch size: 34
2021-10-14 16:20:53,598 INFO [train.py:451] Epoch 7, batch 7080, batch avg loss 0.1935, total avg loss: 0.2437, batch size: 29
2021-10-14 16:20:58,693 INFO [train.py:451] Epoch 7, batch 7090, batch avg loss 0.2600, total avg loss: 0.2433, batch size: 39
2021-10-14 16:21:03,658 INFO [train.py:451] Epoch 7, batch 7100, batch avg loss 0.1989, total avg loss: 0.2416, batch size: 32
2021-10-14 16:21:08,577 INFO [train.py:451] Epoch 7, batch 7110, batch avg loss 0.2238, total avg loss: 0.2416, batch size: 37
2021-10-14 16:21:13,542 INFO [train.py:451] Epoch 7, batch 7120, batch avg loss 0.2149, total avg loss: 0.2432, batch size: 36
2021-10-14 16:21:18,429 INFO [train.py:451] Epoch 7, batch 7130, batch avg loss 0.1923, total avg loss: 0.2428, batch size: 31
2021-10-14 16:21:23,377 INFO [train.py:451] Epoch 7, batch 7140, batch avg loss 0.1821, total avg loss: 0.2420, batch size: 29
2021-10-14 16:21:28,337 INFO [train.py:451] Epoch 7, batch 7150, batch avg loss 0.2149, total avg loss: 0.2429, batch size: 29
2021-10-14 16:21:33,294 INFO [train.py:451] Epoch 7, batch 7160, batch avg loss 0.2467, total avg loss: 0.2436, batch size: 31
2021-10-14 16:21:38,285 INFO [train.py:451] Epoch 7, batch 7170, batch avg loss 0.2130, total avg loss: 0.2432, batch size: 27
2021-10-14 16:21:43,330 INFO [train.py:451] Epoch 7, batch 7180, batch avg loss 0.1884, total avg loss: 0.2418, batch size: 30
2021-10-14 16:21:48,326 INFO [train.py:451] Epoch 7, batch 7190, batch avg loss 0.2484, total avg loss: 0.2416, batch size: 27
2021-10-14 16:21:53,308 INFO [train.py:451] Epoch 7, batch 7200, batch avg loss 0.2183, total avg loss: 0.2414, batch size: 28
2021-10-14 16:21:58,219 INFO [train.py:451] Epoch 7, batch 7210, batch avg loss 0.2624, total avg loss: 0.2230, batch size: 45
2021-10-14 16:22:03,003 INFO [train.py:451] Epoch 7, batch 7220, batch avg loss 0.2622, total avg loss: 0.2329, batch size: 38
2021-10-14 16:22:07,920 INFO [train.py:451] Epoch 7, batch 7230, batch avg loss 0.2874, total avg loss: 0.2365, batch size: 34
2021-10-14 16:22:12,676 INFO [train.py:451] Epoch 7, batch 7240, batch avg loss 0.2423, total avg loss: 0.2405, batch size: 31
2021-10-14 16:22:17,603 INFO [train.py:451] Epoch 7, batch 7250, batch avg loss 0.2072, total avg loss: 0.2399, batch size: 31
2021-10-14 16:22:22,511 INFO [train.py:451] Epoch 7, batch 7260, batch avg loss 0.2770, total avg loss: 0.2416, batch size: 35
2021-10-14 16:22:27,435 INFO [train.py:451] Epoch 7, batch 7270, batch avg loss 0.2030, total avg loss: 0.2423, batch size: 33
2021-10-14 16:22:32,285 INFO [train.py:451] Epoch 7, batch 7280, batch avg loss 0.1850, total avg loss: 0.2447, batch size: 32
2021-10-14 16:22:37,277 INFO [train.py:451] Epoch 7, batch 7290, batch avg loss 0.2746, total avg loss: 0.2460, batch size: 36
2021-10-14 16:22:42,090 INFO [train.py:451] Epoch 7, batch 7300, batch avg loss 0.2045, total avg loss: 0.2464, batch size: 30
2021-10-14 16:22:46,913 INFO [train.py:451] Epoch 7, batch 7310, batch avg loss 0.2769, total avg loss: 0.2466, batch size: 73
2021-10-14 16:22:51,819 INFO [train.py:451] Epoch 7, batch 7320, batch avg loss 0.2111, total avg loss: 0.2466, batch size: 31
2021-10-14 16:22:56,646 INFO [train.py:451] Epoch 7, batch 7330, batch avg loss 0.2343, total avg loss: 0.2463, batch size: 49
2021-10-14 16:23:01,595 INFO [train.py:451] Epoch 7, batch 7340, batch avg loss 0.2219, total avg loss: 0.2441, batch size: 37
2021-10-14 16:23:06,573 INFO [train.py:451] Epoch 7, batch 7350, batch avg loss 0.2164, total avg loss: 0.2441, batch size: 33
2021-10-14 16:23:11,330 INFO [train.py:451] Epoch 7, batch 7360, batch avg loss 0.2129, total avg loss: 0.2445, batch size: 30
2021-10-14 16:23:16,353 INFO [train.py:451] Epoch 7, batch 7370, batch avg loss 0.1874, total avg loss: 0.2441, batch size: 27
2021-10-14 16:23:21,361 INFO [train.py:451] Epoch 7, batch 7380, batch avg loss 0.2609, total avg loss: 0.2441, batch size: 37
2021-10-14 16:23:26,269 INFO [train.py:451] Epoch 7, batch 7390, batch avg loss 0.2450, total avg loss: 0.2446, batch size: 33
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2021-10-14 16:36:22,296 INFO [train.py:451] Epoch 7, batch 8880, batch avg loss 0.2205, total avg loss: 0.2474, batch size: 32
2021-10-14 16:36:27,124 INFO [train.py:451] Epoch 7, batch 8890, batch avg loss 0.2572, total avg loss: 0.2482, batch size: 41
2021-10-14 16:36:32,125 INFO [train.py:451] Epoch 7, batch 8900, batch avg loss 0.2275, total avg loss: 0.2468, batch size: 42
2021-10-14 16:36:37,002 INFO [train.py:451] Epoch 7, batch 8910, batch avg loss 0.2002, total avg loss: 0.2470, batch size: 32
2021-10-14 16:36:41,949 INFO [train.py:451] Epoch 7, batch 8920, batch avg loss 0.2784, total avg loss: 0.2462, batch size: 56
2021-10-14 16:36:47,012 INFO [train.py:451] Epoch 7, batch 8930, batch avg loss 0.2156, total avg loss: 0.2469, batch size: 35
2021-10-14 16:36:51,690 INFO [train.py:451] Epoch 7, batch 8940, batch avg loss 0.1916, total avg loss: 0.2492, batch size: 33
2021-10-14 16:36:56,786 INFO [train.py:451] Epoch 7, batch 8950, batch avg loss 0.2635, total avg loss: 0.2476, batch size: 34
2021-10-14 16:37:01,678 INFO [train.py:451] Epoch 7, batch 8960, batch avg loss 0.2423, total avg loss: 0.2466, batch size: 34
2021-10-14 16:37:06,555 INFO [train.py:451] Epoch 7, batch 8970, batch avg loss 0.2843, total avg loss: 0.2477, batch size: 37
2021-10-14 16:37:11,340 INFO [train.py:451] Epoch 7, batch 8980, batch avg loss 0.2229, total avg loss: 0.2470, batch size: 39
2021-10-14 16:37:16,391 INFO [train.py:451] Epoch 7, batch 8990, batch avg loss 0.2319, total avg loss: 0.2468, batch size: 27
2021-10-14 16:37:21,440 INFO [train.py:451] Epoch 7, batch 9000, batch avg loss 0.2166, total avg loss: 0.2458, batch size: 32
2021-10-14 16:38:01,074 INFO [train.py:483] Epoch 7, valid loss 0.1781, best valid loss: 0.1763 best valid epoch: 7
2021-10-14 16:38:05,969 INFO [train.py:451] Epoch 7, batch 9010, batch avg loss 0.2166, total avg loss: 0.2512, batch size: 30
2021-10-14 16:38:10,947 INFO [train.py:451] Epoch 7, batch 9020, batch avg loss 0.1974, total avg loss: 0.2469, batch size: 30
2021-10-14 16:38:15,985 INFO [train.py:451] Epoch 7, batch 9030, batch avg loss 0.2258, total avg loss: 0.2457, batch size: 34
2021-10-14 16:38:20,911 INFO [train.py:451] Epoch 7, batch 9040, batch avg loss 0.2043, total avg loss: 0.2425, batch size: 32
2021-10-14 16:38:25,882 INFO [train.py:451] Epoch 7, batch 9050, batch avg loss 0.2577, total avg loss: 0.2425, batch size: 39
2021-10-14 16:38:30,775 INFO [train.py:451] Epoch 7, batch 9060, batch avg loss 0.2445, total avg loss: 0.2432, batch size: 39
2021-10-14 16:38:35,585 INFO [train.py:451] Epoch 7, batch 9070, batch avg loss 0.2581, total avg loss: 0.2440, batch size: 72
2021-10-14 16:38:40,488 INFO [train.py:451] Epoch 7, batch 9080, batch avg loss 0.2959, total avg loss: 0.2446, batch size: 38
2021-10-14 16:38:45,547 INFO [train.py:451] Epoch 7, batch 9090, batch avg loss 0.2617, total avg loss: 0.2435, batch size: 33
2021-10-14 16:38:50,674 INFO [train.py:451] Epoch 7, batch 9100, batch avg loss 0.2059, total avg loss: 0.2423, batch size: 30
2021-10-14 16:38:55,659 INFO [train.py:451] Epoch 7, batch 9110, batch avg loss 0.2360, total avg loss: 0.2427, batch size: 36
2021-10-14 16:39:00,498 INFO [train.py:451] Epoch 7, batch 9120, batch avg loss 0.1931, total avg loss: 0.2419, batch size: 32
2021-10-14 16:39:05,293 INFO [train.py:451] Epoch 7, batch 9130, batch avg loss 0.2121, total avg loss: 0.2422, batch size: 37
2021-10-14 16:39:10,058 INFO [train.py:451] Epoch 7, batch 9140, batch avg loss 0.2438, total avg loss: 0.2428, batch size: 42
2021-10-14 16:39:14,882 INFO [train.py:451] Epoch 7, batch 9150, batch avg loss 0.2630, total avg loss: 0.2439, batch size: 30
2021-10-14 16:39:19,897 INFO [train.py:451] Epoch 7, batch 9160, batch avg loss 0.3288, total avg loss: 0.2452, batch size: 36
2021-10-14 16:39:24,818 INFO [train.py:451] Epoch 7, batch 9170, batch avg loss 0.2546, total avg loss: 0.2445, batch size: 45
2021-10-14 16:39:29,649 INFO [train.py:451] Epoch 7, batch 9180, batch avg loss 0.1935, total avg loss: 0.2447, batch size: 29
2021-10-14 16:39:34,533 INFO [train.py:451] Epoch 7, batch 9190, batch avg loss 0.2634, total avg loss: 0.2447, batch size: 36
2021-10-14 16:39:39,452 INFO [train.py:451] Epoch 7, batch 9200, batch avg loss 0.2674, total avg loss: 0.2450, batch size: 35
2021-10-14 16:39:44,102 INFO [train.py:451] Epoch 7, batch 9210, batch avg loss 0.4023, total avg loss: 0.2780, batch size: 127
2021-10-14 16:39:49,005 INFO [train.py:451] Epoch 7, batch 9220, batch avg loss 0.2553, total avg loss: 0.2627, batch size: 42
2021-10-14 16:39:54,134 INFO [train.py:451] Epoch 7, batch 9230, batch avg loss 0.3566, total avg loss: 0.2562, batch size: 128
2021-10-14 16:39:58,971 INFO [train.py:451] Epoch 7, batch 9240, batch avg loss 0.1884, total avg loss: 0.2558, batch size: 32
2021-10-14 16:40:03,824 INFO [train.py:451] Epoch 7, batch 9250, batch avg loss 0.2014, total avg loss: 0.2515, batch size: 33
2021-10-14 16:40:08,859 INFO [train.py:451] Epoch 7, batch 9260, batch avg loss 0.2018, total avg loss: 0.2496, batch size: 30
2021-10-14 16:40:13,791 INFO [train.py:451] Epoch 7, batch 9270, batch avg loss 0.2982, total avg loss: 0.2516, batch size: 37
2021-10-14 16:40:18,671 INFO [train.py:451] Epoch 7, batch 9280, batch avg loss 0.2917, total avg loss: 0.2512, batch size: 49
2021-10-14 16:40:23,710 INFO [train.py:451] Epoch 7, batch 9290, batch avg loss 0.2307, total avg loss: 0.2492, batch size: 32
2021-10-14 16:40:28,650 INFO [train.py:451] Epoch 7, batch 9300, batch avg loss 0.2711, total avg loss: 0.2501, batch size: 39
2021-10-14 16:40:33,557 INFO [train.py:451] Epoch 7, batch 9310, batch avg loss 0.2332, total avg loss: 0.2495, batch size: 35
2021-10-14 16:40:38,442 INFO [train.py:451] Epoch 7, batch 9320, batch avg loss 0.2490, total avg loss: 0.2482, batch size: 31
2021-10-14 16:40:43,414 INFO [train.py:451] Epoch 7, batch 9330, batch avg loss 0.2722, total avg loss: 0.2468, batch size: 49
2021-10-14 16:40:48,165 INFO [train.py:451] Epoch 7, batch 9340, batch avg loss 0.3316, total avg loss: 0.2467, batch size: 72
2021-10-14 16:40:53,068 INFO [train.py:451] Epoch 7, batch 9350, batch avg loss 0.2022, total avg loss: 0.2470, batch size: 33
2021-10-14 16:40:58,009 INFO [train.py:451] Epoch 7, batch 9360, batch avg loss 0.2655, total avg loss: 0.2467, batch size: 36
2021-10-14 16:41:02,842 INFO [train.py:451] Epoch 7, batch 9370, batch avg loss 0.3748, total avg loss: 0.2478, batch size: 125
2021-10-14 16:41:07,764 INFO [train.py:451] Epoch 7, batch 9380, batch avg loss 0.2316, total avg loss: 0.2478, batch size: 34
2021-10-14 16:41:12,716 INFO [train.py:451] Epoch 7, batch 9390, batch avg loss 0.2250, total avg loss: 0.2476, batch size: 38
2021-10-14 16:41:17,469 INFO [train.py:451] Epoch 7, batch 9400, batch avg loss 0.2963, total avg loss: 0.2491, batch size: 45
2021-10-14 16:41:22,304 INFO [train.py:451] Epoch 7, batch 9410, batch avg loss 0.2094, total avg loss: 0.2668, batch size: 30
2021-10-14 16:41:27,224 INFO [train.py:451] Epoch 7, batch 9420, batch avg loss 0.2710, total avg loss: 0.2548, batch size: 34
2021-10-14 16:41:32,196 INFO [train.py:451] Epoch 7, batch 9430, batch avg loss 0.1839, total avg loss: 0.2553, batch size: 29
2021-10-14 16:41:37,178 INFO [train.py:451] Epoch 7, batch 9440, batch avg loss 0.2238, total avg loss: 0.2479, batch size: 34
2021-10-14 16:41:41,973 INFO [train.py:451] Epoch 7, batch 9450, batch avg loss 0.2461, total avg loss: 0.2490, batch size: 34
2021-10-14 16:41:46,896 INFO [train.py:451] Epoch 7, batch 9460, batch avg loss 0.2129, total avg loss: 0.2531, batch size: 36
2021-10-14 16:41:51,846 INFO [train.py:451] Epoch 7, batch 9470, batch avg loss 0.2408, total avg loss: 0.2529, batch size: 36
2021-10-14 16:41:56,716 INFO [train.py:451] Epoch 7, batch 9480, batch avg loss 0.2647, total avg loss: 0.2513, batch size: 49
2021-10-14 16:42:01,497 INFO [train.py:451] Epoch 7, batch 9490, batch avg loss 0.2145, total avg loss: 0.2527, batch size: 32
2021-10-14 16:42:06,492 INFO [train.py:451] Epoch 7, batch 9500, batch avg loss 0.2476, total avg loss: 0.2527, batch size: 32
2021-10-14 16:42:11,277 INFO [train.py:451] Epoch 7, batch 9510, batch avg loss 0.2370, total avg loss: 0.2525, batch size: 42
2021-10-14 16:42:16,143 INFO [train.py:451] Epoch 7, batch 9520, batch avg loss 0.3026, total avg loss: 0.2528, batch size: 74
2021-10-14 16:42:20,996 INFO [train.py:451] Epoch 7, batch 9530, batch avg loss 0.2074, total avg loss: 0.2508, batch size: 31
2021-10-14 16:42:25,763 INFO [train.py:451] Epoch 7, batch 9540, batch avg loss 0.2779, total avg loss: 0.2501, batch size: 72
2021-10-14 16:42:30,628 INFO [train.py:451] Epoch 7, batch 9550, batch avg loss 0.2757, total avg loss: 0.2513, batch size: 38
2021-10-14 16:42:35,610 INFO [train.py:451] Epoch 7, batch 9560, batch avg loss 0.2136, total avg loss: 0.2498, batch size: 29
2021-10-14 16:42:40,611 INFO [train.py:451] Epoch 7, batch 9570, batch avg loss 0.2388, total avg loss: 0.2493, batch size: 42
2021-10-14 16:42:45,699 INFO [train.py:451] Epoch 7, batch 9580, batch avg loss 0.2211, total avg loss: 0.2483, batch size: 30
2021-10-14 16:42:50,764 INFO [train.py:451] Epoch 7, batch 9590, batch avg loss 0.2224, total avg loss: 0.2473, batch size: 29
2021-10-14 16:42:55,770 INFO [train.py:451] Epoch 7, batch 9600, batch avg loss 0.2966, total avg loss: 0.2465, batch size: 57
2021-10-14 16:43:00,792 INFO [train.py:451] Epoch 7, batch 9610, batch avg loss 0.2258, total avg loss: 0.2449, batch size: 31
2021-10-14 16:43:05,493 INFO [train.py:451] Epoch 7, batch 9620, batch avg loss 0.2407, total avg loss: 0.2497, batch size: 34
2021-10-14 16:43:10,326 INFO [train.py:451] Epoch 7, batch 9630, batch avg loss 0.3514, total avg loss: 0.2504, batch size: 129
2021-10-14 16:43:15,345 INFO [train.py:451] Epoch 7, batch 9640, batch avg loss 0.2163, total avg loss: 0.2477, batch size: 32
2021-10-14 16:43:20,261 INFO [train.py:451] Epoch 7, batch 9650, batch avg loss 0.2457, total avg loss: 0.2488, batch size: 35
2021-10-14 16:43:25,090 INFO [train.py:451] Epoch 7, batch 9660, batch avg loss 0.2347, total avg loss: 0.2503, batch size: 49
2021-10-14 16:43:29,836 INFO [train.py:451] Epoch 7, batch 9670, batch avg loss 0.2342, total avg loss: 0.2526, batch size: 36
2021-10-14 16:43:34,737 INFO [train.py:451] Epoch 7, batch 9680, batch avg loss 0.2125, total avg loss: 0.2504, batch size: 32
2021-10-14 16:43:39,689 INFO [train.py:451] Epoch 7, batch 9690, batch avg loss 0.2286, total avg loss: 0.2502, batch size: 34
2021-10-14 16:43:44,690 INFO [train.py:451] Epoch 7, batch 9700, batch avg loss 0.2393, total avg loss: 0.2498, batch size: 39
2021-10-14 16:43:49,736 INFO [train.py:451] Epoch 7, batch 9710, batch avg loss 0.2128, total avg loss: 0.2473, batch size: 29
2021-10-14 16:43:54,556 INFO [train.py:451] Epoch 7, batch 9720, batch avg loss 0.2433, total avg loss: 0.2472, batch size: 32
2021-10-14 16:43:59,536 INFO [train.py:451] Epoch 7, batch 9730, batch avg loss 0.2602, total avg loss: 0.2466, batch size: 36
2021-10-14 16:44:04,507 INFO [train.py:451] Epoch 7, batch 9740, batch avg loss 0.2603, total avg loss: 0.2465, batch size: 38
2021-10-14 16:44:09,290 INFO [train.py:451] Epoch 7, batch 9750, batch avg loss 0.2567, total avg loss: 0.2476, batch size: 34
2021-10-14 16:44:14,303 INFO [train.py:451] Epoch 7, batch 9760, batch avg loss 0.2441, total avg loss: 0.2476, batch size: 34
2021-10-14 16:44:19,247 INFO [train.py:451] Epoch 7, batch 9770, batch avg loss 0.2431, total avg loss: 0.2475, batch size: 29
2021-10-14 16:44:24,180 INFO [train.py:451] Epoch 7, batch 9780, batch avg loss 0.2146, total avg loss: 0.2474, batch size: 35
2021-10-14 16:44:28,959 INFO [train.py:451] Epoch 7, batch 9790, batch avg loss 0.2145, total avg loss: 0.2475, batch size: 33
2021-10-14 16:44:33,770 INFO [train.py:451] Epoch 7, batch 9800, batch avg loss 0.2214, total avg loss: 0.2477, batch size: 32
2021-10-14 16:44:38,698 INFO [train.py:451] Epoch 7, batch 9810, batch avg loss 0.2559, total avg loss: 0.2530, batch size: 34
2021-10-14 16:44:43,608 INFO [train.py:451] Epoch 7, batch 9820, batch avg loss 0.2450, total avg loss: 0.2418, batch size: 45
2021-10-14 16:44:48,392 INFO [train.py:451] Epoch 7, batch 9830, batch avg loss 0.3782, total avg loss: 0.2486, batch size: 126
2021-10-14 16:44:53,393 INFO [train.py:451] Epoch 7, batch 9840, batch avg loss 0.3045, total avg loss: 0.2483, batch size: 39
2021-10-14 16:44:58,495 INFO [train.py:451] Epoch 7, batch 9850, batch avg loss 0.3410, total avg loss: 0.2467, batch size: 131
2021-10-14 16:45:03,398 INFO [train.py:451] Epoch 7, batch 9860, batch avg loss 0.2321, total avg loss: 0.2439, batch size: 36
2021-10-14 16:45:08,289 INFO [train.py:451] Epoch 7, batch 9870, batch avg loss 0.2323, total avg loss: 0.2455, batch size: 36
2021-10-14 16:45:13,311 INFO [train.py:451] Epoch 7, batch 9880, batch avg loss 0.2275, total avg loss: 0.2437, batch size: 35
2021-10-14 16:45:18,365 INFO [train.py:451] Epoch 7, batch 9890, batch avg loss 0.2793, total avg loss: 0.2446, batch size: 37
2021-10-14 16:45:23,438 INFO [train.py:451] Epoch 7, batch 9900, batch avg loss 0.2784, total avg loss: 0.2447, batch size: 41
2021-10-14 16:45:28,338 INFO [train.py:451] Epoch 7, batch 9910, batch avg loss 0.1666, total avg loss: 0.2442, batch size: 29
2021-10-14 16:45:33,511 INFO [train.py:451] Epoch 7, batch 9920, batch avg loss 0.2077, total avg loss: 0.2429, batch size: 29
2021-10-14 16:45:38,386 INFO [train.py:451] Epoch 7, batch 9930, batch avg loss 0.1949, total avg loss: 0.2420, batch size: 29
2021-10-14 16:45:43,330 INFO [train.py:451] Epoch 7, batch 9940, batch avg loss 0.2593, total avg loss: 0.2444, batch size: 37
2021-10-14 16:45:48,215 INFO [train.py:451] Epoch 7, batch 9950, batch avg loss 0.1851, total avg loss: 0.2439, batch size: 28
2021-10-14 16:45:53,063 INFO [train.py:451] Epoch 7, batch 9960, batch avg loss 0.2832, total avg loss: 0.2455, batch size: 57
2021-10-14 16:45:58,226 INFO [train.py:451] Epoch 7, batch 9970, batch avg loss 0.2433, total avg loss: 0.2438, batch size: 36
2021-10-14 16:46:03,101 INFO [train.py:451] Epoch 7, batch 9980, batch avg loss 0.2564, total avg loss: 0.2449, batch size: 35
2021-10-14 16:46:08,120 INFO [train.py:451] Epoch 7, batch 9990, batch avg loss 0.2730, total avg loss: 0.2440, batch size: 36
2021-10-14 16:46:13,156 INFO [train.py:451] Epoch 7, batch 10000, batch avg loss 0.2229, total avg loss: 0.2434, batch size: 38
2021-10-14 16:46:52,515 INFO [train.py:483] Epoch 7, valid loss 0.1787, best valid loss: 0.1763 best valid epoch: 7
2021-10-14 16:46:57,436 INFO [train.py:451] Epoch 7, batch 10010, batch avg loss 0.2263, total avg loss: 0.2507, batch size: 30
2021-10-14 16:47:02,324 INFO [train.py:451] Epoch 7, batch 10020, batch avg loss 0.2542, total avg loss: 0.2488, batch size: 35
2021-10-14 16:47:07,119 INFO [train.py:451] Epoch 7, batch 10030, batch avg loss 0.2204, total avg loss: 0.2493, batch size: 29
2021-10-14 16:47:11,938 INFO [train.py:451] Epoch 7, batch 10040, batch avg loss 0.2517, total avg loss: 0.2512, batch size: 34
2021-10-14 16:47:16,875 INFO [train.py:451] Epoch 7, batch 10050, batch avg loss 0.2144, total avg loss: 0.2499, batch size: 31
2021-10-14 16:47:21,702 INFO [train.py:451] Epoch 7, batch 10060, batch avg loss 0.2154, total avg loss: 0.2513, batch size: 32
2021-10-14 16:47:26,608 INFO [train.py:451] Epoch 7, batch 10070, batch avg loss 0.2452, total avg loss: 0.2511, batch size: 32
2021-10-14 16:47:31,723 INFO [train.py:451] Epoch 7, batch 10080, batch avg loss 0.2160, total avg loss: 0.2491, batch size: 45
2021-10-14 16:47:36,787 INFO [train.py:451] Epoch 7, batch 10090, batch avg loss 0.2231, total avg loss: 0.2463, batch size: 27
2021-10-14 16:47:41,716 INFO [train.py:451] Epoch 7, batch 10100, batch avg loss 0.2723, total avg loss: 0.2473, batch size: 57
2021-10-14 16:47:46,498 INFO [train.py:451] Epoch 7, batch 10110, batch avg loss 0.2862, total avg loss: 0.2462, batch size: 45
2021-10-14 16:47:51,615 INFO [train.py:451] Epoch 7, batch 10120, batch avg loss 0.3162, total avg loss: 0.2462, batch size: 39
2021-10-14 16:47:56,696 INFO [train.py:451] Epoch 7, batch 10130, batch avg loss 0.2143, total avg loss: 0.2452, batch size: 30
2021-10-14 16:48:01,647 INFO [train.py:451] Epoch 7, batch 10140, batch avg loss 0.2429, total avg loss: 0.2462, batch size: 31
2021-10-14 16:48:06,524 INFO [train.py:451] Epoch 7, batch 10150, batch avg loss 0.2801, total avg loss: 0.2469, batch size: 38
2021-10-14 16:48:11,645 INFO [train.py:451] Epoch 7, batch 10160, batch avg loss 0.2601, total avg loss: 0.2462, batch size: 35
2021-10-14 16:48:16,741 INFO [train.py:451] Epoch 7, batch 10170, batch avg loss 0.2680, total avg loss: 0.2470, batch size: 39
2021-10-14 16:48:21,496 INFO [train.py:451] Epoch 7, batch 10180, batch avg loss 0.2055, total avg loss: 0.2472, batch size: 31
2021-10-14 16:48:26,417 INFO [train.py:451] Epoch 7, batch 10190, batch avg loss 0.2553, total avg loss: 0.2475, batch size: 31
2021-10-14 16:48:31,708 INFO [train.py:451] Epoch 7, batch 10200, batch avg loss 0.2248, total avg loss: 0.2468, batch size: 34
2021-10-14 16:48:36,733 INFO [train.py:451] Epoch 7, batch 10210, batch avg loss 0.1997, total avg loss: 0.2413, batch size: 30
2021-10-14 16:48:41,594 INFO [train.py:451] Epoch 7, batch 10220, batch avg loss 0.2287, total avg loss: 0.2424, batch size: 41
2021-10-14 16:48:42,086 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "f8f6119b-de9e-d114-9537-29aeefafc7fe" will not be mixed in.
2021-10-14 16:48:46,642 INFO [train.py:451] Epoch 7, batch 10230, batch avg loss 0.2419, total avg loss: 0.2395, batch size: 34
2021-10-14 16:48:51,895 INFO [train.py:451] Epoch 7, batch 10240, batch avg loss 0.2503, total avg loss: 0.2359, batch size: 36
2021-10-14 16:48:56,839 INFO [train.py:451] Epoch 7, batch 10250, batch avg loss 0.3122, total avg loss: 0.2402, batch size: 36
2021-10-14 16:49:01,818 INFO [train.py:451] Epoch 7, batch 10260, batch avg loss 0.2209, total avg loss: 0.2407, batch size: 30
2021-10-14 16:49:06,776 INFO [train.py:451] Epoch 7, batch 10270, batch avg loss 0.2312, total avg loss: 0.2424, batch size: 34
2021-10-14 16:49:11,587 INFO [train.py:451] Epoch 7, batch 10280, batch avg loss 0.2380, total avg loss: 0.2466, batch size: 35
2021-10-14 16:49:16,556 INFO [train.py:451] Epoch 7, batch 10290, batch avg loss 0.1968, total avg loss: 0.2482, batch size: 33
2021-10-14 16:49:21,462 INFO [train.py:451] Epoch 7, batch 10300, batch avg loss 0.2451, total avg loss: 0.2469, batch size: 45
2021-10-14 16:49:26,486 INFO [train.py:451] Epoch 7, batch 10310, batch avg loss 0.2777, total avg loss: 0.2459, batch size: 39
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2021-10-14 17:40:18,147 INFO [train.py:483] Epoch 7, valid loss 0.1765, best valid loss: 0.1759 best valid epoch: 7
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2021-10-14 18:06:51,585 INFO [train.py:483] Epoch 7, valid loss 0.1758, best valid loss: 0.1758 best valid epoch: 7
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2021-10-14 18:09:24,630 INFO [train.py:451] Epoch 7, batch 19310, batch avg loss 0.2731, total avg loss: 0.2470, batch size: 32
2021-10-14 18:09:29,503 INFO [train.py:451] Epoch 7, batch 19320, batch avg loss 0.2257, total avg loss: 0.2473, batch size: 34
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2021-10-14 18:10:04,788 INFO [train.py:451] Epoch 7, batch 19390, batch avg loss 0.1978, total avg loss: 0.2463, batch size: 27
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2021-10-14 18:10:15,259 INFO [train.py:451] Epoch 7, batch 19410, batch avg loss 0.2588, total avg loss: 0.2275, batch size: 26
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2021-10-14 18:10:39,912 INFO [train.py:451] Epoch 7, batch 19460, batch avg loss 0.2221, total avg loss: 0.2427, batch size: 28
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2021-10-14 18:10:59,618 INFO [train.py:451] Epoch 7, batch 19500, batch avg loss 0.2724, total avg loss: 0.2442, batch size: 38
2021-10-14 18:11:04,636 INFO [train.py:451] Epoch 7, batch 19510, batch avg loss 0.2319, total avg loss: 0.2459, batch size: 31
2021-10-14 18:11:09,512 INFO [train.py:451] Epoch 7, batch 19520, batch avg loss 0.2395, total avg loss: 0.2459, batch size: 42
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2021-10-14 18:11:33,828 INFO [train.py:451] Epoch 7, batch 19570, batch avg loss 0.2606, total avg loss: 0.2479, batch size: 31
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2021-10-14 18:11:43,984 INFO [train.py:451] Epoch 7, batch 19590, batch avg loss 0.3209, total avg loss: 0.2469, batch size: 129
2021-10-14 18:11:48,994 INFO [train.py:451] Epoch 7, batch 19600, batch avg loss 0.2030, total avg loss: 0.2467, batch size: 38
2021-10-14 18:11:53,807 INFO [train.py:451] Epoch 7, batch 19610, batch avg loss 0.2317, total avg loss: 0.2408, batch size: 34
2021-10-14 18:11:58,697 INFO [train.py:451] Epoch 7, batch 19620, batch avg loss 0.2458, total avg loss: 0.2453, batch size: 34
2021-10-14 18:12:03,577 INFO [train.py:451] Epoch 7, batch 19630, batch avg loss 0.2556, total avg loss: 0.2420, batch size: 42
2021-10-14 18:12:08,524 INFO [train.py:451] Epoch 7, batch 19640, batch avg loss 0.2454, total avg loss: 0.2431, batch size: 36
2021-10-14 18:12:13,484 INFO [train.py:451] Epoch 7, batch 19650, batch avg loss 0.2267, total avg loss: 0.2461, batch size: 30
2021-10-14 18:12:18,522 INFO [train.py:451] Epoch 7, batch 19660, batch avg loss 0.2334, total avg loss: 0.2437, batch size: 32
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2021-10-14 18:12:33,466 INFO [train.py:451] Epoch 7, batch 19690, batch avg loss 0.2252, total avg loss: 0.2442, batch size: 30
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2021-10-14 18:12:43,226 INFO [train.py:451] Epoch 7, batch 19710, batch avg loss 0.2593, total avg loss: 0.2439, batch size: 45
2021-10-14 18:12:48,180 INFO [train.py:451] Epoch 7, batch 19720, batch avg loss 0.2116, total avg loss: 0.2421, batch size: 30
2021-10-14 18:12:52,969 INFO [train.py:451] Epoch 7, batch 19730, batch avg loss 0.2635, total avg loss: 0.2427, batch size: 41
2021-10-14 18:12:57,906 INFO [train.py:451] Epoch 7, batch 19740, batch avg loss 0.1948, total avg loss: 0.2415, batch size: 33
2021-10-14 18:13:02,767 INFO [train.py:451] Epoch 7, batch 19750, batch avg loss 0.1773, total avg loss: 0.2409, batch size: 33
2021-10-14 18:13:07,674 INFO [train.py:451] Epoch 7, batch 19760, batch avg loss 0.1970, total avg loss: 0.2412, batch size: 28
2021-10-14 18:13:12,561 INFO [train.py:451] Epoch 7, batch 19770, batch avg loss 0.2800, total avg loss: 0.2411, batch size: 41
2021-10-14 18:13:17,488 INFO [train.py:451] Epoch 7, batch 19780, batch avg loss 0.2009, total avg loss: 0.2403, batch size: 28
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2021-10-14 18:13:27,699 INFO [train.py:451] Epoch 7, batch 19800, batch avg loss 0.1780, total avg loss: 0.2393, batch size: 27
2021-10-14 18:13:32,814 INFO [train.py:451] Epoch 7, batch 19810, batch avg loss 0.2143, total avg loss: 0.2308, batch size: 30
2021-10-14 18:13:37,723 INFO [train.py:451] Epoch 7, batch 19820, batch avg loss 0.2495, total avg loss: 0.2347, batch size: 39
2021-10-14 18:13:42,725 INFO [train.py:451] Epoch 7, batch 19830, batch avg loss 0.2100, total avg loss: 0.2387, batch size: 30
2021-10-14 18:13:47,744 INFO [train.py:451] Epoch 7, batch 19840, batch avg loss 0.1959, total avg loss: 0.2406, batch size: 27
2021-10-14 18:13:52,660 INFO [train.py:451] Epoch 7, batch 19850, batch avg loss 0.2892, total avg loss: 0.2381, batch size: 73
2021-10-14 18:13:57,430 INFO [train.py:451] Epoch 7, batch 19860, batch avg loss 0.2409, total avg loss: 0.2397, batch size: 36
2021-10-14 18:14:02,283 INFO [train.py:451] Epoch 7, batch 19870, batch avg loss 0.2345, total avg loss: 0.2412, batch size: 34
2021-10-14 18:14:07,176 INFO [train.py:451] Epoch 7, batch 19880, batch avg loss 0.2259, total avg loss: 0.2428, batch size: 31
2021-10-14 18:14:12,031 INFO [train.py:451] Epoch 7, batch 19890, batch avg loss 0.2068, total avg loss: 0.2440, batch size: 32
2021-10-14 18:14:16,830 INFO [train.py:451] Epoch 7, batch 19900, batch avg loss 0.2532, total avg loss: 0.2431, batch size: 35
2021-10-14 18:14:21,726 INFO [train.py:451] Epoch 7, batch 19910, batch avg loss 0.2330, total avg loss: 0.2434, batch size: 33
2021-10-14 18:14:26,596 INFO [train.py:451] Epoch 7, batch 19920, batch avg loss 0.2187, total avg loss: 0.2432, batch size: 30
2021-10-14 18:14:31,626 INFO [train.py:451] Epoch 7, batch 19930, batch avg loss 0.2245, total avg loss: 0.2421, batch size: 31
2021-10-14 18:14:36,486 INFO [train.py:451] Epoch 7, batch 19940, batch avg loss 0.2674, total avg loss: 0.2430, batch size: 38
2021-10-14 18:14:41,478 INFO [train.py:451] Epoch 7, batch 19950, batch avg loss 0.2017, total avg loss: 0.2422, batch size: 35
2021-10-14 18:14:46,341 INFO [train.py:451] Epoch 7, batch 19960, batch avg loss 0.2641, total avg loss: 0.2423, batch size: 57
2021-10-14 18:14:47,509 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "98f7ebec-d806-1943-3ce0-4ee126e66872" will not be mixed in.
2021-10-14 18:14:51,233 INFO [train.py:451] Epoch 7, batch 19970, batch avg loss 0.2666, total avg loss: 0.2432, batch size: 36
2021-10-14 18:14:55,953 INFO [train.py:451] Epoch 7, batch 19980, batch avg loss 0.3122, total avg loss: 0.2440, batch size: 57
2021-10-14 18:15:00,880 INFO [train.py:451] Epoch 7, batch 19990, batch avg loss 0.2657, total avg loss: 0.2447, batch size: 34
2021-10-14 18:15:05,782 INFO [train.py:451] Epoch 7, batch 20000, batch avg loss 0.2522, total avg loss: 0.2447, batch size: 37
2021-10-14 18:15:45,328 INFO [train.py:483] Epoch 7, valid loss 0.1745, best valid loss: 0.1745 best valid epoch: 7
2021-10-14 18:15:50,228 INFO [train.py:451] Epoch 7, batch 20010, batch avg loss 0.2300, total avg loss: 0.2494, batch size: 37
2021-10-14 18:15:55,109 INFO [train.py:451] Epoch 7, batch 20020, batch avg loss 0.2977, total avg loss: 0.2517, batch size: 49
2021-10-14 18:16:00,048 INFO [train.py:451] Epoch 7, batch 20030, batch avg loss 0.2110, total avg loss: 0.2485, batch size: 32
2021-10-14 18:16:05,077 INFO [train.py:451] Epoch 7, batch 20040, batch avg loss 0.2478, total avg loss: 0.2478, batch size: 37
2021-10-14 18:16:10,057 INFO [train.py:451] Epoch 7, batch 20050, batch avg loss 0.2387, total avg loss: 0.2452, batch size: 38
2021-10-14 18:16:14,932 INFO [train.py:451] Epoch 7, batch 20060, batch avg loss 0.1740, total avg loss: 0.2464, batch size: 30
2021-10-14 18:16:19,900 INFO [train.py:451] Epoch 7, batch 20070, batch avg loss 0.2293, total avg loss: 0.2468, batch size: 32
2021-10-14 18:16:24,760 INFO [train.py:451] Epoch 7, batch 20080, batch avg loss 0.2713, total avg loss: 0.2457, batch size: 49
2021-10-14 18:16:29,706 INFO [train.py:451] Epoch 7, batch 20090, batch avg loss 0.2445, total avg loss: 0.2451, batch size: 38
2021-10-14 18:16:34,540 INFO [train.py:451] Epoch 7, batch 20100, batch avg loss 0.2220, total avg loss: 0.2456, batch size: 45
2021-10-14 18:16:39,551 INFO [train.py:451] Epoch 7, batch 20110, batch avg loss 0.2652, total avg loss: 0.2463, batch size: 34
2021-10-14 18:16:44,621 INFO [train.py:451] Epoch 7, batch 20120, batch avg loss 0.2558, total avg loss: 0.2455, batch size: 38
2021-10-14 18:16:49,640 INFO [train.py:451] Epoch 7, batch 20130, batch avg loss 0.2095, total avg loss: 0.2453, batch size: 29
2021-10-14 18:16:54,529 INFO [train.py:451] Epoch 7, batch 20140, batch avg loss 0.2333, total avg loss: 0.2459, batch size: 33
2021-10-14 18:16:59,387 INFO [train.py:451] Epoch 7, batch 20150, batch avg loss 0.2352, total avg loss: 0.2472, batch size: 42
2021-10-14 18:17:04,314 INFO [train.py:451] Epoch 7, batch 20160, batch avg loss 0.2758, total avg loss: 0.2473, batch size: 32
2021-10-14 18:17:09,262 INFO [train.py:451] Epoch 7, batch 20170, batch avg loss 0.2245, total avg loss: 0.2462, batch size: 38
2021-10-14 18:17:14,409 INFO [train.py:451] Epoch 7, batch 20180, batch avg loss 0.2050, total avg loss: 0.2459, batch size: 28
2021-10-14 18:17:19,464 INFO [train.py:451] Epoch 7, batch 20190, batch avg loss 0.2782, total avg loss: 0.2454, batch size: 39
2021-10-14 18:17:24,478 INFO [train.py:451] Epoch 7, batch 20200, batch avg loss 0.2343, total avg loss: 0.2456, batch size: 41
2021-10-14 18:17:29,567 INFO [train.py:451] Epoch 7, batch 20210, batch avg loss 0.2329, total avg loss: 0.2537, batch size: 33
2021-10-14 18:17:34,675 INFO [train.py:451] Epoch 7, batch 20220, batch avg loss 0.2077, total avg loss: 0.2396, batch size: 31
2021-10-14 18:17:39,757 INFO [train.py:451] Epoch 7, batch 20230, batch avg loss 0.2774, total avg loss: 0.2443, batch size: 41
2021-10-14 18:17:44,870 INFO [train.py:451] Epoch 7, batch 20240, batch avg loss 0.2769, total avg loss: 0.2491, batch size: 38
2021-10-14 18:17:49,745 INFO [train.py:451] Epoch 7, batch 20250, batch avg loss 0.2788, total avg loss: 0.2498, batch size: 57
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2021-10-14 18:17:59,350 INFO [train.py:451] Epoch 7, batch 20270, batch avg loss 0.2401, total avg loss: 0.2533, batch size: 30
2021-10-14 18:18:04,314 INFO [train.py:451] Epoch 7, batch 20280, batch avg loss 0.1991, total avg loss: 0.2523, batch size: 31
2021-10-14 18:18:09,368 INFO [train.py:451] Epoch 7, batch 20290, batch avg loss 0.1516, total avg loss: 0.2507, batch size: 27
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2021-10-14 18:18:19,601 INFO [train.py:451] Epoch 7, batch 20310, batch avg loss 0.2239, total avg loss: 0.2501, batch size: 32
2021-10-14 18:18:24,737 INFO [train.py:451] Epoch 7, batch 20320, batch avg loss 0.2137, total avg loss: 0.2491, batch size: 30
2021-10-14 18:18:29,580 INFO [train.py:451] Epoch 7, batch 20330, batch avg loss 0.4033, total avg loss: 0.2505, batch size: 130
2021-10-14 18:18:34,711 INFO [train.py:451] Epoch 7, batch 20340, batch avg loss 0.2691, total avg loss: 0.2508, batch size: 36
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2021-10-14 18:18:44,784 INFO [train.py:451] Epoch 7, batch 20360, batch avg loss 0.2290, total avg loss: 0.2497, batch size: 35
2021-10-14 18:18:49,670 INFO [train.py:451] Epoch 7, batch 20370, batch avg loss 0.2172, total avg loss: 0.2490, batch size: 41
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2021-10-14 18:18:59,587 INFO [train.py:451] Epoch 7, batch 20390, batch avg loss 0.2901, total avg loss: 0.2490, batch size: 36
2021-10-14 18:19:04,375 INFO [train.py:451] Epoch 7, batch 20400, batch avg loss 0.3480, total avg loss: 0.2504, batch size: 127
2021-10-14 18:19:09,015 INFO [train.py:451] Epoch 7, batch 20410, batch avg loss 0.2531, total avg loss: 0.2641, batch size: 34
2021-10-14 18:19:13,866 INFO [train.py:451] Epoch 7, batch 20420, batch avg loss 0.2889, total avg loss: 0.2463, batch size: 36
2021-10-14 18:19:18,614 INFO [train.py:451] Epoch 7, batch 20430, batch avg loss 0.2196, total avg loss: 0.2445, batch size: 31
2021-10-14 18:19:23,256 INFO [train.py:451] Epoch 7, batch 20440, batch avg loss 0.2983, total avg loss: 0.2467, batch size: 72
2021-10-14 18:19:28,248 INFO [train.py:451] Epoch 7, batch 20450, batch avg loss 0.1915, total avg loss: 0.2421, batch size: 29
2021-10-14 18:19:33,048 INFO [train.py:451] Epoch 7, batch 20460, batch avg loss 0.2937, total avg loss: 0.2437, batch size: 45
2021-10-14 18:19:37,868 INFO [train.py:451] Epoch 7, batch 20470, batch avg loss 0.2322, total avg loss: 0.2467, batch size: 32
2021-10-14 18:19:42,837 INFO [train.py:451] Epoch 7, batch 20480, batch avg loss 0.2176, total avg loss: 0.2452, batch size: 35
2021-10-14 18:19:47,734 INFO [train.py:451] Epoch 7, batch 20490, batch avg loss 0.1910, total avg loss: 0.2450, batch size: 32
2021-10-14 18:19:52,675 INFO [train.py:451] Epoch 7, batch 20500, batch avg loss 0.2110, total avg loss: 0.2449, batch size: 29
2021-10-14 18:19:57,212 INFO [train.py:451] Epoch 7, batch 20510, batch avg loss 0.3205, total avg loss: 0.2477, batch size: 72
2021-10-14 18:20:02,161 INFO [train.py:451] Epoch 7, batch 20520, batch avg loss 0.2206, total avg loss: 0.2481, batch size: 30
2021-10-14 18:20:06,956 INFO [train.py:451] Epoch 7, batch 20530, batch avg loss 0.2020, total avg loss: 0.2484, batch size: 29
2021-10-14 18:20:11,936 INFO [train.py:451] Epoch 7, batch 20540, batch avg loss 0.2357, total avg loss: 0.2484, batch size: 42
2021-10-14 18:20:17,011 INFO [train.py:451] Epoch 7, batch 20550, batch avg loss 0.2590, total avg loss: 0.2481, batch size: 42
2021-10-14 18:20:21,873 INFO [train.py:451] Epoch 7, batch 20560, batch avg loss 0.1986, total avg loss: 0.2464, batch size: 30
2021-10-14 18:20:26,630 INFO [train.py:451] Epoch 7, batch 20570, batch avg loss 0.3741, total avg loss: 0.2487, batch size: 122
2021-10-14 18:20:31,568 INFO [train.py:451] Epoch 7, batch 20580, batch avg loss 0.2518, total avg loss: 0.2484, batch size: 37
2021-10-14 18:20:36,897 INFO [train.py:451] Epoch 7, batch 20590, batch avg loss 0.2169, total avg loss: 0.2495, batch size: 27
2021-10-14 18:20:42,069 INFO [train.py:451] Epoch 7, batch 20600, batch avg loss 0.1939, total avg loss: 0.2492, batch size: 29
2021-10-14 18:20:47,090 INFO [train.py:451] Epoch 7, batch 20610, batch avg loss 0.2432, total avg loss: 0.2177, batch size: 39
2021-10-14 18:20:51,778 INFO [train.py:451] Epoch 7, batch 20620, batch avg loss 0.2865, total avg loss: 0.2291, batch size: 73
2021-10-14 18:20:56,794 INFO [train.py:451] Epoch 7, batch 20630, batch avg loss 0.2532, total avg loss: 0.2307, batch size: 57
2021-10-14 18:21:01,696 INFO [train.py:451] Epoch 7, batch 20640, batch avg loss 0.2370, total avg loss: 0.2360, batch size: 36
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2021-10-14 18:24:39,418 INFO [train.py:483] Epoch 7, valid loss 0.1747, best valid loss: 0.1745 best valid epoch: 7
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2021-10-14 18:26:11,788 INFO [checkpoint.py:62] Saving checkpoint to tdnn_lstm_ctc-v2/exp/epoch-7.pt
2021-10-14 18:26:12,612 INFO [train.py:564] epoch 8, lr: 2.5e-05
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2021-10-14 18:30:29,294 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "589dcfeb-a5e3-11a7-15c0-69d1ebf286a2" will not be mixed in.
2021-10-14 18:30:31,372 INFO [train.py:451] Epoch 8, batch 520, batch avg loss 0.2642, total avg loss: 0.2436, batch size: 70
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2021-10-14 18:35:03,702 INFO [train.py:483] Epoch 8, valid loss 0.1704, best valid loss: 0.1704 best valid epoch: 8
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2021-10-14 18:44:01,691 INFO [train.py:483] Epoch 8, valid loss 0.1695, best valid loss: 0.1695 best valid epoch: 8
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2021-10-14 18:47:13,809 INFO [train.py:451] Epoch 8, batch 2390, batch avg loss 0.2934, total avg loss: 0.2317, batch size: 37
2021-10-14 18:47:18,613 INFO [train.py:451] Epoch 8, batch 2400, batch avg loss 0.2253, total avg loss: 0.2323, batch size: 39
2021-10-14 18:47:23,545 INFO [train.py:451] Epoch 8, batch 2410, batch avg loss 0.2236, total avg loss: 0.2215, batch size: 34
2021-10-14 18:47:28,566 INFO [train.py:451] Epoch 8, batch 2420, batch avg loss 0.2284, total avg loss: 0.2256, batch size: 34
2021-10-14 18:47:33,540 INFO [train.py:451] Epoch 8, batch 2430, batch avg loss 0.1925, total avg loss: 0.2265, batch size: 29
2021-10-14 18:47:38,520 INFO [train.py:451] Epoch 8, batch 2440, batch avg loss 0.2178, total avg loss: 0.2242, batch size: 30
2021-10-14 18:47:43,474 INFO [train.py:451] Epoch 8, batch 2450, batch avg loss 0.2616, total avg loss: 0.2262, batch size: 73
2021-10-14 18:47:48,357 INFO [train.py:451] Epoch 8, batch 2460, batch avg loss 0.2046, total avg loss: 0.2287, batch size: 29
2021-10-14 18:47:53,208 INFO [train.py:451] Epoch 8, batch 2470, batch avg loss 0.2429, total avg loss: 0.2294, batch size: 35
2021-10-14 18:47:58,162 INFO [train.py:451] Epoch 8, batch 2480, batch avg loss 0.2087, total avg loss: 0.2304, batch size: 34
2021-10-14 18:48:03,044 INFO [train.py:451] Epoch 8, batch 2490, batch avg loss 0.2148, total avg loss: 0.2298, batch size: 32
2021-10-14 18:48:08,053 INFO [train.py:451] Epoch 8, batch 2500, batch avg loss 0.3494, total avg loss: 0.2292, batch size: 137
2021-10-14 18:48:12,994 INFO [train.py:451] Epoch 8, batch 2510, batch avg loss 0.2520, total avg loss: 0.2286, batch size: 38
2021-10-14 18:48:17,856 INFO [train.py:451] Epoch 8, batch 2520, batch avg loss 0.1818, total avg loss: 0.2288, batch size: 30
2021-10-14 18:48:22,798 INFO [train.py:451] Epoch 8, batch 2530, batch avg loss 0.2241, total avg loss: 0.2283, batch size: 36
2021-10-14 18:48:27,679 INFO [train.py:451] Epoch 8, batch 2540, batch avg loss 0.3067, total avg loss: 0.2299, batch size: 36
2021-10-14 18:48:32,652 INFO [train.py:451] Epoch 8, batch 2550, batch avg loss 0.2006, total avg loss: 0.2294, batch size: 30
2021-10-14 18:48:37,780 INFO [train.py:451] Epoch 8, batch 2560, batch avg loss 0.2578, total avg loss: 0.2298, batch size: 42
2021-10-14 18:48:42,636 INFO [train.py:451] Epoch 8, batch 2570, batch avg loss 0.2196, total avg loss: 0.2301, batch size: 32
2021-10-14 18:48:47,533 INFO [train.py:451] Epoch 8, batch 2580, batch avg loss 0.2355, total avg loss: 0.2306, batch size: 38
2021-10-14 18:48:52,502 INFO [train.py:451] Epoch 8, batch 2590, batch avg loss 0.3768, total avg loss: 0.2313, batch size: 138
2021-10-14 18:48:57,510 INFO [train.py:451] Epoch 8, batch 2600, batch avg loss 0.3272, total avg loss: 0.2313, batch size: 124
2021-10-14 18:49:02,267 INFO [train.py:451] Epoch 8, batch 2610, batch avg loss 0.2450, total avg loss: 0.2471, batch size: 32
2021-10-14 18:49:07,242 INFO [train.py:451] Epoch 8, batch 2620, batch avg loss 0.1553, total avg loss: 0.2323, batch size: 30
2021-10-14 18:49:12,051 INFO [train.py:451] Epoch 8, batch 2630, batch avg loss 0.2907, total avg loss: 0.2358, batch size: 73
2021-10-14 18:49:16,837 INFO [train.py:451] Epoch 8, batch 2640, batch avg loss 0.2429, total avg loss: 0.2389, batch size: 49
2021-10-14 18:49:21,816 INFO [train.py:451] Epoch 8, batch 2650, batch avg loss 0.2153, total avg loss: 0.2413, batch size: 33
2021-10-14 18:49:26,586 INFO [train.py:451] Epoch 8, batch 2660, batch avg loss 0.3514, total avg loss: 0.2424, batch size: 127
2021-10-14 18:49:31,549 INFO [train.py:451] Epoch 8, batch 2670, batch avg loss 0.1836, total avg loss: 0.2396, batch size: 34
2021-10-14 18:49:36,338 INFO [train.py:451] Epoch 8, batch 2680, batch avg loss 0.2810, total avg loss: 0.2404, batch size: 41
2021-10-14 18:49:41,517 INFO [train.py:451] Epoch 8, batch 2690, batch avg loss 0.1919, total avg loss: 0.2386, batch size: 27
2021-10-14 18:49:46,330 INFO [train.py:451] Epoch 8, batch 2700, batch avg loss 0.2412, total avg loss: 0.2392, batch size: 57
2021-10-14 18:49:51,101 INFO [train.py:451] Epoch 8, batch 2710, batch avg loss 0.2398, total avg loss: 0.2403, batch size: 35
2021-10-14 18:49:56,157 INFO [train.py:451] Epoch 8, batch 2720, batch avg loss 0.2103, total avg loss: 0.2381, batch size: 33
2021-10-14 18:50:00,995 INFO [train.py:451] Epoch 8, batch 2730, batch avg loss 0.2379, total avg loss: 0.2376, batch size: 38
2021-10-14 18:50:05,791 INFO [train.py:451] Epoch 8, batch 2740, batch avg loss 0.2149, total avg loss: 0.2384, batch size: 49
2021-10-14 18:50:10,662 INFO [train.py:451] Epoch 8, batch 2750, batch avg loss 0.2211, total avg loss: 0.2380, batch size: 42
2021-10-14 18:50:15,674 INFO [train.py:451] Epoch 8, batch 2760, batch avg loss 0.1995, total avg loss: 0.2363, batch size: 35
2021-10-14 18:50:20,796 INFO [train.py:451] Epoch 8, batch 2770, batch avg loss 0.2811, total avg loss: 0.2360, batch size: 34
2021-10-14 18:50:25,872 INFO [train.py:451] Epoch 8, batch 2780, batch avg loss 0.2012, total avg loss: 0.2356, batch size: 29
2021-10-14 18:50:30,821 INFO [train.py:451] Epoch 8, batch 2790, batch avg loss 0.1922, total avg loss: 0.2353, batch size: 28
2021-10-14 18:50:35,896 INFO [train.py:451] Epoch 8, batch 2800, batch avg loss 0.1844, total avg loss: 0.2350, batch size: 30
2021-10-14 18:50:40,743 INFO [train.py:451] Epoch 8, batch 2810, batch avg loss 0.2239, total avg loss: 0.2229, batch size: 34
2021-10-14 18:50:45,661 INFO [train.py:451] Epoch 8, batch 2820, batch avg loss 0.1872, total avg loss: 0.2244, batch size: 28
2021-10-14 18:50:50,576 INFO [train.py:451] Epoch 8, batch 2830, batch avg loss 0.2091, total avg loss: 0.2232, batch size: 33
2021-10-14 18:50:55,400 INFO [train.py:451] Epoch 8, batch 2840, batch avg loss 0.2529, total avg loss: 0.2259, batch size: 39
2021-10-14 18:51:00,502 INFO [train.py:451] Epoch 8, batch 2850, batch avg loss 0.2764, total avg loss: 0.2250, batch size: 56
2021-10-14 18:51:05,388 INFO [train.py:451] Epoch 8, batch 2860, batch avg loss 0.2215, total avg loss: 0.2268, batch size: 28
2021-10-14 18:51:10,325 INFO [train.py:451] Epoch 8, batch 2870, batch avg loss 0.2164, total avg loss: 0.2268, batch size: 36
2021-10-14 18:51:15,522 INFO [train.py:451] Epoch 8, batch 2880, batch avg loss 0.1989, total avg loss: 0.2252, batch size: 33
2021-10-14 18:51:20,302 INFO [train.py:451] Epoch 8, batch 2890, batch avg loss 0.2328, total avg loss: 0.2268, batch size: 36
2021-10-14 18:51:25,108 INFO [train.py:451] Epoch 8, batch 2900, batch avg loss 0.2015, total avg loss: 0.2264, batch size: 33
2021-10-14 18:51:29,992 INFO [train.py:451] Epoch 8, batch 2910, batch avg loss 0.2529, total avg loss: 0.2260, batch size: 39
2021-10-14 18:51:34,733 INFO [train.py:451] Epoch 8, batch 2920, batch avg loss 0.2216, total avg loss: 0.2261, batch size: 38
2021-10-14 18:51:39,654 INFO [train.py:451] Epoch 8, batch 2930, batch avg loss 0.2485, total avg loss: 0.2256, batch size: 57
2021-10-14 18:51:44,453 INFO [train.py:451] Epoch 8, batch 2940, batch avg loss 0.2209, total avg loss: 0.2259, batch size: 42
2021-10-14 18:51:49,347 INFO [train.py:451] Epoch 8, batch 2950, batch avg loss 0.2129, total avg loss: 0.2271, batch size: 32
2021-10-14 18:51:54,150 INFO [train.py:451] Epoch 8, batch 2960, batch avg loss 0.2314, total avg loss: 0.2278, batch size: 30
2021-10-14 18:51:59,055 INFO [train.py:451] Epoch 8, batch 2970, batch avg loss 0.2065, total avg loss: 0.2273, batch size: 27
2021-10-14 18:52:03,940 INFO [train.py:451] Epoch 8, batch 2980, batch avg loss 0.3124, total avg loss: 0.2281, batch size: 129
2021-10-14 18:52:08,691 INFO [train.py:451] Epoch 8, batch 2990, batch avg loss 0.2484, total avg loss: 0.2280, batch size: 32
2021-10-14 18:52:13,349 INFO [train.py:451] Epoch 8, batch 3000, batch avg loss 0.2601, total avg loss: 0.2297, batch size: 39
2021-10-14 18:52:51,181 INFO [train.py:483] Epoch 8, valid loss 0.1682, best valid loss: 0.1682 best valid epoch: 8
2021-10-14 18:52:56,090 INFO [train.py:451] Epoch 8, batch 3010, batch avg loss 0.2448, total avg loss: 0.2307, batch size: 49
2021-10-14 18:53:00,935 INFO [train.py:451] Epoch 8, batch 3020, batch avg loss 0.2504, total avg loss: 0.2310, batch size: 37
2021-10-14 18:53:05,816 INFO [train.py:451] Epoch 8, batch 3030, batch avg loss 0.1744, total avg loss: 0.2278, batch size: 29
2021-10-14 18:53:10,726 INFO [train.py:451] Epoch 8, batch 3040, batch avg loss 0.2123, total avg loss: 0.2275, batch size: 36
2021-10-14 18:53:15,656 INFO [train.py:451] Epoch 8, batch 3050, batch avg loss 0.2075, total avg loss: 0.2250, batch size: 35
2021-10-14 18:53:20,668 INFO [train.py:451] Epoch 8, batch 3060, batch avg loss 0.1970, total avg loss: 0.2263, batch size: 30
2021-10-14 18:53:20,824 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "f0a4b423-3f70-3892-92d9-bf6862bb630e" will not be mixed in.
2021-10-14 18:53:25,512 INFO [train.py:451] Epoch 8, batch 3070, batch avg loss 0.2737, total avg loss: 0.2271, batch size: 71
2021-10-14 18:53:30,489 INFO [train.py:451] Epoch 8, batch 3080, batch avg loss 0.2045, total avg loss: 0.2286, batch size: 27
2021-10-14 18:53:35,373 INFO [train.py:451] Epoch 8, batch 3090, batch avg loss 0.1890, total avg loss: 0.2300, batch size: 31
2021-10-14 18:53:40,211 INFO [train.py:451] Epoch 8, batch 3100, batch avg loss 0.2339, total avg loss: 0.2309, batch size: 31
2021-10-14 18:53:45,130 INFO [train.py:451] Epoch 8, batch 3110, batch avg loss 0.1993, total avg loss: 0.2294, batch size: 36
2021-10-14 18:53:49,975 INFO [train.py:451] Epoch 8, batch 3120, batch avg loss 0.2726, total avg loss: 0.2304, batch size: 72
2021-10-14 18:53:54,967 INFO [train.py:451] Epoch 8, batch 3130, batch avg loss 0.2654, total avg loss: 0.2298, batch size: 49
2021-10-14 18:53:59,719 INFO [train.py:451] Epoch 8, batch 3140, batch avg loss 0.2468, total avg loss: 0.2311, batch size: 42
2021-10-14 18:54:04,579 INFO [train.py:451] Epoch 8, batch 3150, batch avg loss 0.1979, total avg loss: 0.2305, batch size: 32
2021-10-14 18:54:09,499 INFO [train.py:451] Epoch 8, batch 3160, batch avg loss 0.2224, total avg loss: 0.2301, batch size: 38
2021-10-14 18:54:14,507 INFO [train.py:451] Epoch 8, batch 3170, batch avg loss 0.2363, total avg loss: 0.2301, batch size: 35
2021-10-14 18:54:19,440 INFO [train.py:451] Epoch 8, batch 3180, batch avg loss 0.2056, total avg loss: 0.2301, batch size: 31
2021-10-14 18:54:24,498 INFO [train.py:451] Epoch 8, batch 3190, batch avg loss 0.1941, total avg loss: 0.2288, batch size: 31
2021-10-14 18:54:29,495 INFO [train.py:451] Epoch 8, batch 3200, batch avg loss 0.2190, total avg loss: 0.2292, batch size: 34
2021-10-14 18:54:34,274 INFO [train.py:451] Epoch 8, batch 3210, batch avg loss 0.3837, total avg loss: 0.2513, batch size: 129
2021-10-14 18:54:39,240 INFO [train.py:451] Epoch 8, batch 3220, batch avg loss 0.2346, total avg loss: 0.2375, batch size: 36
2021-10-14 18:54:44,217 INFO [train.py:451] Epoch 8, batch 3230, batch avg loss 0.2311, total avg loss: 0.2324, batch size: 49
2021-10-14 18:54:49,352 INFO [train.py:451] Epoch 8, batch 3240, batch avg loss 0.1858, total avg loss: 0.2274, batch size: 27
2021-10-14 18:54:54,222 INFO [train.py:451] Epoch 8, batch 3250, batch avg loss 0.2270, total avg loss: 0.2285, batch size: 29
2021-10-14 18:54:59,191 INFO [train.py:451] Epoch 8, batch 3260, batch avg loss 0.2417, total avg loss: 0.2270, batch size: 45
2021-10-14 18:55:04,149 INFO [train.py:451] Epoch 8, batch 3270, batch avg loss 0.3627, total avg loss: 0.2278, batch size: 125
2021-10-14 18:55:09,109 INFO [train.py:451] Epoch 8, batch 3280, batch avg loss 0.1889, total avg loss: 0.2283, batch size: 34
2021-10-14 18:55:14,132 INFO [train.py:451] Epoch 8, batch 3290, batch avg loss 0.2279, total avg loss: 0.2261, batch size: 35
2021-10-14 18:55:19,092 INFO [train.py:451] Epoch 8, batch 3300, batch avg loss 0.1829, total avg loss: 0.2293, batch size: 29
2021-10-14 18:55:23,972 INFO [train.py:451] Epoch 8, batch 3310, batch avg loss 0.1932, total avg loss: 0.2299, batch size: 36
2021-10-14 18:55:28,900 INFO [train.py:451] Epoch 8, batch 3320, batch avg loss 0.2386, total avg loss: 0.2302, batch size: 29
2021-10-14 18:55:33,681 INFO [train.py:451] Epoch 8, batch 3330, batch avg loss 0.2561, total avg loss: 0.2309, batch size: 57
2021-10-14 18:55:38,624 INFO [train.py:451] Epoch 8, batch 3340, batch avg loss 0.2368, total avg loss: 0.2306, batch size: 35
2021-10-14 18:55:43,632 INFO [train.py:451] Epoch 8, batch 3350, batch avg loss 0.2778, total avg loss: 0.2307, batch size: 37
2021-10-14 18:55:48,482 INFO [train.py:451] Epoch 8, batch 3360, batch avg loss 0.1836, total avg loss: 0.2301, batch size: 34
2021-10-14 18:55:53,543 INFO [train.py:451] Epoch 8, batch 3370, batch avg loss 0.2279, total avg loss: 0.2288, batch size: 49
2021-10-14 18:55:58,433 INFO [train.py:451] Epoch 8, batch 3380, batch avg loss 0.2232, total avg loss: 0.2290, batch size: 35
2021-10-14 18:56:03,551 INFO [train.py:451] Epoch 8, batch 3390, batch avg loss 0.2807, total avg loss: 0.2298, batch size: 36
2021-10-14 18:56:08,527 INFO [train.py:451] Epoch 8, batch 3400, batch avg loss 0.2253, total avg loss: 0.2298, batch size: 36
2021-10-14 18:56:13,566 INFO [train.py:451] Epoch 8, batch 3410, batch avg loss 0.2532, total avg loss: 0.2181, batch size: 36
2021-10-14 18:56:18,536 INFO [train.py:451] Epoch 8, batch 3420, batch avg loss 0.2139, total avg loss: 0.2204, batch size: 34
2021-10-14 18:56:23,514 INFO [train.py:451] Epoch 8, batch 3430, batch avg loss 0.2034, total avg loss: 0.2275, batch size: 30
2021-10-14 18:56:28,592 INFO [train.py:451] Epoch 8, batch 3440, batch avg loss 0.1763, total avg loss: 0.2226, batch size: 31
2021-10-14 18:56:33,638 INFO [train.py:451] Epoch 8, batch 3450, batch avg loss 0.3134, total avg loss: 0.2275, batch size: 71
2021-10-14 18:56:38,680 INFO [train.py:451] Epoch 8, batch 3460, batch avg loss 0.2488, total avg loss: 0.2270, batch size: 49
2021-10-14 18:56:43,490 INFO [train.py:451] Epoch 8, batch 3470, batch avg loss 0.1974, total avg loss: 0.2296, batch size: 30
2021-10-14 18:56:48,351 INFO [train.py:451] Epoch 8, batch 3480, batch avg loss 0.2737, total avg loss: 0.2310, batch size: 72
2021-10-14 18:56:53,221 INFO [train.py:451] Epoch 8, batch 3490, batch avg loss 0.1995, total avg loss: 0.2311, batch size: 33
2021-10-14 18:56:58,223 INFO [train.py:451] Epoch 8, batch 3500, batch avg loss 0.2360, total avg loss: 0.2294, batch size: 49
2021-10-14 18:57:03,100 INFO [train.py:451] Epoch 8, batch 3510, batch avg loss 0.2536, total avg loss: 0.2317, batch size: 41
2021-10-14 18:57:08,063 INFO [train.py:451] Epoch 8, batch 3520, batch avg loss 0.2119, total avg loss: 0.2303, batch size: 33
2021-10-14 18:57:12,905 INFO [train.py:451] Epoch 8, batch 3530, batch avg loss 0.2423, total avg loss: 0.2303, batch size: 37
2021-10-14 18:57:17,545 INFO [train.py:451] Epoch 8, batch 3540, batch avg loss 0.2506, total avg loss: 0.2308, batch size: 41
2021-10-14 18:57:22,487 INFO [train.py:451] Epoch 8, batch 3550, batch avg loss 0.2801, total avg loss: 0.2315, batch size: 36
2021-10-14 18:57:27,495 INFO [train.py:451] Epoch 8, batch 3560, batch avg loss 0.2367, total avg loss: 0.2310, batch size: 42
2021-10-14 18:57:32,462 INFO [train.py:451] Epoch 8, batch 3570, batch avg loss 0.2438, total avg loss: 0.2310, batch size: 39
2021-10-14 18:57:37,437 INFO [train.py:451] Epoch 8, batch 3580, batch avg loss 0.1607, total avg loss: 0.2307, batch size: 29
2021-10-14 18:57:42,279 INFO [train.py:451] Epoch 8, batch 3590, batch avg loss 0.2790, total avg loss: 0.2322, batch size: 41
2021-10-14 18:57:47,094 INFO [train.py:451] Epoch 8, batch 3600, batch avg loss 0.3604, total avg loss: 0.2338, batch size: 135
2021-10-14 18:57:51,942 INFO [train.py:451] Epoch 8, batch 3610, batch avg loss 0.1963, total avg loss: 0.2431, batch size: 30
2021-10-14 18:57:56,663 INFO [train.py:451] Epoch 8, batch 3620, batch avg loss 0.3227, total avg loss: 0.2517, batch size: 133
2021-10-14 18:58:01,752 INFO [train.py:451] Epoch 8, batch 3630, batch avg loss 0.1684, total avg loss: 0.2401, batch size: 29
2021-10-14 18:58:06,556 INFO [train.py:451] Epoch 8, batch 3640, batch avg loss 0.2194, total avg loss: 0.2389, batch size: 31
2021-10-14 18:58:11,377 INFO [train.py:451] Epoch 8, batch 3650, batch avg loss 0.2368, total avg loss: 0.2402, batch size: 35
2021-10-14 18:58:16,350 INFO [train.py:451] Epoch 8, batch 3660, batch avg loss 0.2384, total avg loss: 0.2404, batch size: 34
2021-10-14 18:58:21,302 INFO [train.py:451] Epoch 8, batch 3670, batch avg loss 0.1672, total avg loss: 0.2381, batch size: 29
2021-10-14 18:58:26,063 INFO [train.py:451] Epoch 8, batch 3680, batch avg loss 0.3181, total avg loss: 0.2369, batch size: 126
2021-10-14 18:58:31,129 INFO [train.py:451] Epoch 8, batch 3690, batch avg loss 0.2105, total avg loss: 0.2363, batch size: 34
2021-10-14 18:58:35,992 INFO [train.py:451] Epoch 8, batch 3700, batch avg loss 0.2122, total avg loss: 0.2363, batch size: 32
2021-10-14 18:58:40,893 INFO [train.py:451] Epoch 8, batch 3710, batch avg loss 0.1603, total avg loss: 0.2357, batch size: 27
2021-10-14 18:58:45,772 INFO [train.py:451] Epoch 8, batch 3720, batch avg loss 0.2352, total avg loss: 0.2364, batch size: 36
2021-10-14 18:58:50,615 INFO [train.py:451] Epoch 8, batch 3730, batch avg loss 0.2049, total avg loss: 0.2373, batch size: 34
2021-10-14 18:58:55,468 INFO [train.py:451] Epoch 8, batch 3740, batch avg loss 0.1922, total avg loss: 0.2371, batch size: 34
2021-10-14 18:59:00,305 INFO [train.py:451] Epoch 8, batch 3750, batch avg loss 0.2622, total avg loss: 0.2372, batch size: 49
2021-10-14 18:59:05,357 INFO [train.py:451] Epoch 8, batch 3760, batch avg loss 0.1966, total avg loss: 0.2363, batch size: 32
2021-10-14 18:59:10,344 INFO [train.py:451] Epoch 8, batch 3770, batch avg loss 0.2105, total avg loss: 0.2354, batch size: 34
2021-10-14 18:59:15,260 INFO [train.py:451] Epoch 8, batch 3780, batch avg loss 0.2592, total avg loss: 0.2346, batch size: 39
2021-10-14 18:59:19,977 INFO [train.py:451] Epoch 8, batch 3790, batch avg loss 0.2578, total avg loss: 0.2351, batch size: 45
2021-10-14 18:59:24,897 INFO [train.py:451] Epoch 8, batch 3800, batch avg loss 0.2188, total avg loss: 0.2344, batch size: 34
2021-10-14 18:59:29,942 INFO [train.py:451] Epoch 8, batch 3810, batch avg loss 0.2532, total avg loss: 0.2307, batch size: 31
2021-10-14 18:59:34,831 INFO [train.py:451] Epoch 8, batch 3820, batch avg loss 0.2191, total avg loss: 0.2318, batch size: 32
2021-10-14 18:59:39,775 INFO [train.py:451] Epoch 8, batch 3830, batch avg loss 0.2214, total avg loss: 0.2250, batch size: 38
2021-10-14 18:59:44,640 INFO [train.py:451] Epoch 8, batch 3840, batch avg loss 0.1972, total avg loss: 0.2284, batch size: 34
2021-10-14 18:59:49,533 INFO [train.py:451] Epoch 8, batch 3850, batch avg loss 0.2589, total avg loss: 0.2295, batch size: 45
2021-10-14 18:59:54,598 INFO [train.py:451] Epoch 8, batch 3860, batch avg loss 0.2530, total avg loss: 0.2279, batch size: 36
2021-10-14 18:59:59,504 INFO [train.py:451] Epoch 8, batch 3870, batch avg loss 0.1951, total avg loss: 0.2268, batch size: 45
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2021-10-14 19:01:43,207 INFO [train.py:483] Epoch 8, valid loss 0.1686, best valid loss: 0.1682 best valid epoch: 8
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2021-10-14 19:10:37,866 INFO [train.py:483] Epoch 8, valid loss 0.1671, best valid loss: 0.1671 best valid epoch: 8
2021-10-14 19:10:42,895 INFO [train.py:451] Epoch 8, batch 5010, batch avg loss 0.2309, total avg loss: 0.2177, batch size: 28
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2021-10-14 19:19:29,223 INFO [train.py:483] Epoch 8, valid loss 0.1672, best valid loss: 0.1671 best valid epoch: 8
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2021-10-14 19:28:20,419 INFO [train.py:483] Epoch 8, valid loss 0.1661, best valid loss: 0.1661 best valid epoch: 8
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2021-10-14 19:37:13,020 INFO [train.py:483] Epoch 8, valid loss 0.1669, best valid loss: 0.1661 best valid epoch: 8
2021-10-14 19:37:17,940 INFO [train.py:451] Epoch 8, batch 8010, batch avg loss 0.2417, total avg loss: 0.2452, batch size: 42
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2021-10-14 19:46:01,481 INFO [train.py:483] Epoch 8, valid loss 0.1666, best valid loss: 0.1661 best valid epoch: 8
2021-10-14 19:46:06,420 INFO [train.py:451] Epoch 8, batch 9010, batch avg loss 0.2009, total avg loss: 0.2185, batch size: 31
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2021-10-14 20:03:59,061 INFO [train.py:483] Epoch 8, valid loss 0.1658, best valid loss: 0.1658 best valid epoch: 8
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2021-10-14 20:21:46,232 INFO [train.py:483] Epoch 8, valid loss 0.1656, best valid loss: 0.1656 best valid epoch: 8
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2021-10-14 20:30:40,801 INFO [train.py:483] Epoch 8, valid loss 0.1655, best valid loss: 0.1655 best valid epoch: 8
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2021-10-14 20:39:31,356 INFO [train.py:483] Epoch 8, valid loss 0.1653, best valid loss: 0.1653 best valid epoch: 8
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2021-10-14 20:57:20,722 INFO [train.py:483] Epoch 8, valid loss 0.1656, best valid loss: 0.1649 best valid epoch: 8
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2021-10-14 20:58:33,969 INFO [train.py:451] Epoch 8, batch 17150, batch avg loss 0.2212, total avg loss: 0.2204, batch size: 29
2021-10-14 20:58:39,065 INFO [train.py:451] Epoch 8, batch 17160, batch avg loss 0.2156, total avg loss: 0.2203, batch size: 33
2021-10-14 20:58:43,971 INFO [train.py:451] Epoch 8, batch 17170, batch avg loss 0.1786, total avg loss: 0.2192, batch size: 33
2021-10-14 20:58:48,668 INFO [train.py:451] Epoch 8, batch 17180, batch avg loss 0.1872, total avg loss: 0.2210, batch size: 31
2021-10-14 20:58:53,507 INFO [train.py:451] Epoch 8, batch 17190, batch avg loss 0.2497, total avg loss: 0.2206, batch size: 32
2021-10-14 20:58:58,427 INFO [train.py:451] Epoch 8, batch 17200, batch avg loss 0.2513, total avg loss: 0.2214, batch size: 35
2021-10-14 20:59:03,400 INFO [train.py:451] Epoch 8, batch 17210, batch avg loss 0.1609, total avg loss: 0.2139, batch size: 29
2021-10-14 20:59:08,188 INFO [train.py:451] Epoch 8, batch 17220, batch avg loss 0.3449, total avg loss: 0.2296, batch size: 122
2021-10-14 20:59:12,966 INFO [train.py:451] Epoch 8, batch 17230, batch avg loss 0.2622, total avg loss: 0.2250, batch size: 37
2021-10-14 20:59:17,670 INFO [train.py:451] Epoch 8, batch 17240, batch avg loss 0.2359, total avg loss: 0.2254, batch size: 33
2021-10-14 20:59:22,433 INFO [train.py:451] Epoch 8, batch 17250, batch avg loss 0.2792, total avg loss: 0.2277, batch size: 42
2021-10-14 20:59:27,438 INFO [train.py:451] Epoch 8, batch 17260, batch avg loss 0.1440, total avg loss: 0.2274, batch size: 29
2021-10-14 20:59:32,433 INFO [train.py:451] Epoch 8, batch 17270, batch avg loss 0.2003, total avg loss: 0.2264, batch size: 34
2021-10-14 20:59:37,249 INFO [train.py:451] Epoch 8, batch 17280, batch avg loss 0.2807, total avg loss: 0.2267, batch size: 45
2021-10-14 20:59:42,068 INFO [train.py:451] Epoch 8, batch 17290, batch avg loss 0.2038, total avg loss: 0.2261, batch size: 36
2021-10-14 20:59:47,007 INFO [train.py:451] Epoch 8, batch 17300, batch avg loss 0.2298, total avg loss: 0.2263, batch size: 35
2021-10-14 20:59:52,001 INFO [train.py:451] Epoch 8, batch 17310, batch avg loss 0.2932, total avg loss: 0.2267, batch size: 37
2021-10-14 20:59:56,845 INFO [train.py:451] Epoch 8, batch 17320, batch avg loss 0.1934, total avg loss: 0.2263, batch size: 34
2021-10-14 21:00:01,879 INFO [train.py:451] Epoch 8, batch 17330, batch avg loss 0.2008, total avg loss: 0.2266, batch size: 34
2021-10-14 21:00:06,750 INFO [train.py:451] Epoch 8, batch 17340, batch avg loss 0.2625, total avg loss: 0.2271, batch size: 35
2021-10-14 21:00:11,597 INFO [train.py:451] Epoch 8, batch 17350, batch avg loss 0.1781, total avg loss: 0.2262, batch size: 31
2021-10-14 21:00:16,526 INFO [train.py:451] Epoch 8, batch 17360, batch avg loss 0.2563, total avg loss: 0.2258, batch size: 49
2021-10-14 21:00:21,512 INFO [train.py:451] Epoch 8, batch 17370, batch avg loss 0.1887, total avg loss: 0.2246, batch size: 32
2021-10-14 21:00:26,398 INFO [train.py:451] Epoch 8, batch 17380, batch avg loss 0.2342, total avg loss: 0.2242, batch size: 30
2021-10-14 21:00:31,309 INFO [train.py:451] Epoch 8, batch 17390, batch avg loss 0.2038, total avg loss: 0.2243, batch size: 30
2021-10-14 21:00:36,511 INFO [train.py:451] Epoch 8, batch 17400, batch avg loss 0.2007, total avg loss: 0.2237, batch size: 31
2021-10-14 21:00:41,508 INFO [train.py:451] Epoch 8, batch 17410, batch avg loss 0.2236, total avg loss: 0.2212, batch size: 30
2021-10-14 21:00:46,472 INFO [train.py:451] Epoch 8, batch 17420, batch avg loss 0.2355, total avg loss: 0.2210, batch size: 38
2021-10-14 21:00:51,238 INFO [train.py:451] Epoch 8, batch 17430, batch avg loss 0.2196, total avg loss: 0.2293, batch size: 38
2021-10-14 21:00:56,343 INFO [train.py:451] Epoch 8, batch 17440, batch avg loss 0.2024, total avg loss: 0.2256, batch size: 35
2021-10-14 21:01:01,300 INFO [train.py:451] Epoch 8, batch 17450, batch avg loss 0.2682, total avg loss: 0.2237, batch size: 49
2021-10-14 21:01:06,278 INFO [train.py:451] Epoch 8, batch 17460, batch avg loss 0.2571, total avg loss: 0.2204, batch size: 36
2021-10-14 21:01:11,117 INFO [train.py:451] Epoch 8, batch 17470, batch avg loss 0.1873, total avg loss: 0.2230, batch size: 31
2021-10-14 21:01:15,807 INFO [train.py:451] Epoch 8, batch 17480, batch avg loss 0.2771, total avg loss: 0.2269, batch size: 73
2021-10-14 21:01:20,646 INFO [train.py:451] Epoch 8, batch 17490, batch avg loss 0.1999, total avg loss: 0.2280, batch size: 38
2021-10-14 21:01:25,293 INFO [train.py:451] Epoch 8, batch 17500, batch avg loss 0.2767, total avg loss: 0.2294, batch size: 57
2021-10-14 21:01:30,346 INFO [train.py:451] Epoch 8, batch 17510, batch avg loss 0.1729, total avg loss: 0.2286, batch size: 29
2021-10-14 21:01:35,224 INFO [train.py:451] Epoch 8, batch 17520, batch avg loss 0.2123, total avg loss: 0.2263, batch size: 31
2021-10-14 21:01:40,358 INFO [train.py:451] Epoch 8, batch 17530, batch avg loss 0.1773, total avg loss: 0.2249, batch size: 27
2021-10-14 21:01:45,276 INFO [train.py:451] Epoch 8, batch 17540, batch avg loss 0.2104, total avg loss: 0.2229, batch size: 38
2021-10-14 21:01:50,124 INFO [train.py:451] Epoch 8, batch 17550, batch avg loss 0.3116, total avg loss: 0.2243, batch size: 38
2021-10-14 21:01:55,044 INFO [train.py:451] Epoch 8, batch 17560, batch avg loss 0.2274, total avg loss: 0.2237, batch size: 36
2021-10-14 21:01:59,977 INFO [train.py:451] Epoch 8, batch 17570, batch avg loss 0.2755, total avg loss: 0.2231, batch size: 72
2021-10-14 21:02:04,915 INFO [train.py:451] Epoch 8, batch 17580, batch avg loss 0.2305, total avg loss: 0.2238, batch size: 49
2021-10-14 21:02:09,775 INFO [train.py:451] Epoch 8, batch 17590, batch avg loss 0.2183, total avg loss: 0.2239, batch size: 35
2021-10-14 21:02:14,695 INFO [train.py:451] Epoch 8, batch 17600, batch avg loss 0.2245, total avg loss: 0.2242, batch size: 34
2021-10-14 21:02:19,658 INFO [train.py:451] Epoch 8, batch 17610, batch avg loss 0.2090, total avg loss: 0.2166, batch size: 35
2021-10-14 21:02:24,649 INFO [train.py:451] Epoch 8, batch 17620, batch avg loss 0.2038, total avg loss: 0.2186, batch size: 34
2021-10-14 21:02:29,608 INFO [train.py:451] Epoch 8, batch 17630, batch avg loss 0.1998, total avg loss: 0.2181, batch size: 33
2021-10-14 21:02:34,565 INFO [train.py:451] Epoch 8, batch 17640, batch avg loss 0.1804, total avg loss: 0.2186, batch size: 33
2021-10-14 21:02:39,380 INFO [train.py:451] Epoch 8, batch 17650, batch avg loss 0.3234, total avg loss: 0.2202, batch size: 130
2021-10-14 21:02:44,246 INFO [train.py:451] Epoch 8, batch 17660, batch avg loss 0.2448, total avg loss: 0.2205, batch size: 38
2021-10-14 21:02:49,023 INFO [train.py:451] Epoch 8, batch 17670, batch avg loss 0.2253, total avg loss: 0.2210, batch size: 38
2021-10-14 21:02:54,016 INFO [train.py:451] Epoch 8, batch 17680, batch avg loss 0.2709, total avg loss: 0.2193, batch size: 39
2021-10-14 21:02:59,008 INFO [train.py:451] Epoch 8, batch 17690, batch avg loss 0.2561, total avg loss: 0.2171, batch size: 35
2021-10-14 21:03:03,848 INFO [train.py:451] Epoch 8, batch 17700, batch avg loss 0.2310, total avg loss: 0.2191, batch size: 34
2021-10-14 21:03:08,711 INFO [train.py:451] Epoch 8, batch 17710, batch avg loss 0.2078, total avg loss: 0.2200, batch size: 30
2021-10-14 21:03:13,665 INFO [train.py:451] Epoch 8, batch 17720, batch avg loss 0.2628, total avg loss: 0.2198, batch size: 35
2021-10-14 21:03:18,621 INFO [train.py:451] Epoch 8, batch 17730, batch avg loss 0.2702, total avg loss: 0.2200, batch size: 42
2021-10-14 21:03:28,980 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "2730a55d-cf72-26f3-099d-a0ace9787247" will not be mixed in.
2021-10-14 21:03:30,599 INFO [train.py:451] Epoch 8, batch 17740, batch avg loss 0.2308, total avg loss: 0.2217, batch size: 37
2021-10-14 21:03:35,526 INFO [train.py:451] Epoch 8, batch 17750, batch avg loss 0.2214, total avg loss: 0.2214, batch size: 42
2021-10-14 21:03:40,358 INFO [train.py:451] Epoch 8, batch 17760, batch avg loss 0.2413, total avg loss: 0.2217, batch size: 38
2021-10-14 21:03:45,398 INFO [train.py:451] Epoch 8, batch 17770, batch avg loss 0.3103, total avg loss: 0.2217, batch size: 74
2021-10-14 21:03:50,308 INFO [train.py:451] Epoch 8, batch 17780, batch avg loss 0.2311, total avg loss: 0.2205, batch size: 32
2021-10-14 21:03:55,467 INFO [train.py:451] Epoch 8, batch 17790, batch avg loss 0.1578, total avg loss: 0.2198, batch size: 27
2021-10-14 21:04:00,469 INFO [train.py:451] Epoch 8, batch 17800, batch avg loss 0.1961, total avg loss: 0.2193, batch size: 28
2021-10-14 21:04:05,439 INFO [train.py:451] Epoch 8, batch 17810, batch avg loss 0.2054, total avg loss: 0.2299, batch size: 29
2021-10-14 21:04:10,324 INFO [train.py:451] Epoch 8, batch 17820, batch avg loss 0.2579, total avg loss: 0.2270, batch size: 42
2021-10-14 21:04:15,166 INFO [train.py:451] Epoch 8, batch 17830, batch avg loss 0.2414, total avg loss: 0.2265, batch size: 35
2021-10-14 21:04:20,032 INFO [train.py:451] Epoch 8, batch 17840, batch avg loss 0.2281, total avg loss: 0.2290, batch size: 34
2021-10-14 21:04:24,928 INFO [train.py:451] Epoch 8, batch 17850, batch avg loss 0.2286, total avg loss: 0.2289, batch size: 45
2021-10-14 21:04:29,828 INFO [train.py:451] Epoch 8, batch 17860, batch avg loss 0.2519, total avg loss: 0.2293, batch size: 39
2021-10-14 21:04:34,773 INFO [train.py:451] Epoch 8, batch 17870, batch avg loss 0.2426, total avg loss: 0.2306, batch size: 38
2021-10-14 21:04:39,665 INFO [train.py:451] Epoch 8, batch 17880, batch avg loss 0.1711, total avg loss: 0.2272, batch size: 30
2021-10-14 21:04:44,455 INFO [train.py:451] Epoch 8, batch 17890, batch avg loss 0.2329, total avg loss: 0.2279, batch size: 34
2021-10-14 21:04:49,264 INFO [train.py:451] Epoch 8, batch 17900, batch avg loss 0.2335, total avg loss: 0.2292, batch size: 49
2021-10-14 21:04:54,078 INFO [train.py:451] Epoch 8, batch 17910, batch avg loss 0.1925, total avg loss: 0.2289, batch size: 32
2021-10-14 21:04:58,936 INFO [train.py:451] Epoch 8, batch 17920, batch avg loss 0.1997, total avg loss: 0.2303, batch size: 33
2021-10-14 21:05:03,783 INFO [train.py:451] Epoch 8, batch 17930, batch avg loss 0.2659, total avg loss: 0.2309, batch size: 72
2021-10-14 21:05:08,856 INFO [train.py:451] Epoch 8, batch 17940, batch avg loss 0.2203, total avg loss: 0.2296, batch size: 39
2021-10-14 21:05:13,871 INFO [train.py:451] Epoch 8, batch 17950, batch avg loss 0.1884, total avg loss: 0.2295, batch size: 28
2021-10-14 21:05:18,523 INFO [train.py:451] Epoch 8, batch 17960, batch avg loss 0.2619, total avg loss: 0.2302, batch size: 32
2021-10-14 21:05:23,336 INFO [train.py:451] Epoch 8, batch 17970, batch avg loss 0.2340, total avg loss: 0.2313, batch size: 45
2021-10-14 21:05:28,305 INFO [train.py:451] Epoch 8, batch 17980, batch avg loss 0.2038, total avg loss: 0.2309, batch size: 31
2021-10-14 21:05:33,189 INFO [train.py:451] Epoch 8, batch 17990, batch avg loss 0.2287, total avg loss: 0.2311, batch size: 31
2021-10-14 21:05:38,115 INFO [train.py:451] Epoch 8, batch 18000, batch avg loss 0.2335, total avg loss: 0.2306, batch size: 33
2021-10-14 21:06:16,024 INFO [train.py:483] Epoch 8, valid loss 0.1648, best valid loss: 0.1648 best valid epoch: 8
2021-10-14 21:06:20,988 INFO [train.py:451] Epoch 8, batch 18010, batch avg loss 0.2478, total avg loss: 0.2153, batch size: 38
2021-10-14 21:06:26,061 INFO [train.py:451] Epoch 8, batch 18020, batch avg loss 0.2508, total avg loss: 0.2146, batch size: 35
2021-10-14 21:06:30,862 INFO [train.py:451] Epoch 8, batch 18030, batch avg loss 0.2102, total avg loss: 0.2207, batch size: 38
2021-10-14 21:06:35,933 INFO [train.py:451] Epoch 8, batch 18040, batch avg loss 0.2472, total avg loss: 0.2150, batch size: 38
2021-10-14 21:06:40,750 INFO [train.py:451] Epoch 8, batch 18050, batch avg loss 0.1969, total avg loss: 0.2145, batch size: 28
2021-10-14 21:06:45,785 INFO [train.py:451] Epoch 8, batch 18060, batch avg loss 0.1850, total avg loss: 0.2169, batch size: 30
2021-10-14 21:06:50,698 INFO [train.py:451] Epoch 8, batch 18070, batch avg loss 0.2006, total avg loss: 0.2208, batch size: 34
2021-10-14 21:06:55,707 INFO [train.py:451] Epoch 8, batch 18080, batch avg loss 0.2049, total avg loss: 0.2194, batch size: 36
2021-10-14 21:07:00,658 INFO [train.py:451] Epoch 8, batch 18090, batch avg loss 0.2286, total avg loss: 0.2202, batch size: 31
2021-10-14 21:07:05,567 INFO [train.py:451] Epoch 8, batch 18100, batch avg loss 0.2654, total avg loss: 0.2192, batch size: 57
2021-10-14 21:07:10,324 INFO [train.py:451] Epoch 8, batch 18110, batch avg loss 0.2447, total avg loss: 0.2193, batch size: 57
2021-10-14 21:07:15,206 INFO [train.py:451] Epoch 8, batch 18120, batch avg loss 0.2688, total avg loss: 0.2194, batch size: 57
2021-10-14 21:07:20,117 INFO [train.py:451] Epoch 8, batch 18130, batch avg loss 0.2053, total avg loss: 0.2200, batch size: 32
2021-10-14 21:07:24,953 INFO [train.py:451] Epoch 8, batch 18140, batch avg loss 0.2697, total avg loss: 0.2223, batch size: 38
2021-10-14 21:07:30,047 INFO [train.py:451] Epoch 8, batch 18150, batch avg loss 0.2237, total avg loss: 0.2220, batch size: 31
2021-10-14 21:07:34,913 INFO [train.py:451] Epoch 8, batch 18160, batch avg loss 0.2097, total avg loss: 0.2221, batch size: 34
2021-10-14 21:07:39,775 INFO [train.py:451] Epoch 8, batch 18170, batch avg loss 0.2156, total avg loss: 0.2229, batch size: 31
2021-10-14 21:07:44,692 INFO [train.py:451] Epoch 8, batch 18180, batch avg loss 0.2071, total avg loss: 0.2229, batch size: 28
2021-10-14 21:07:49,527 INFO [train.py:451] Epoch 8, batch 18190, batch avg loss 0.1532, total avg loss: 0.2229, batch size: 30
2021-10-14 21:07:54,415 INFO [train.py:451] Epoch 8, batch 18200, batch avg loss 0.1957, total avg loss: 0.2229, batch size: 34
2021-10-14 21:07:59,478 INFO [train.py:451] Epoch 8, batch 18210, batch avg loss 0.2391, total avg loss: 0.2255, batch size: 39
2021-10-14 21:08:04,335 INFO [train.py:451] Epoch 8, batch 18220, batch avg loss 0.2338, total avg loss: 0.2244, batch size: 36
2021-10-14 21:08:09,252 INFO [train.py:451] Epoch 8, batch 18230, batch avg loss 0.2604, total avg loss: 0.2210, batch size: 57
2021-10-14 21:08:14,191 INFO [train.py:451] Epoch 8, batch 18240, batch avg loss 0.2395, total avg loss: 0.2201, batch size: 38
2021-10-14 21:08:19,073 INFO [train.py:451] Epoch 8, batch 18250, batch avg loss 0.2128, total avg loss: 0.2245, batch size: 30
2021-10-14 21:08:24,022 INFO [train.py:451] Epoch 8, batch 18260, batch avg loss 0.3458, total avg loss: 0.2254, batch size: 129
2021-10-14 21:08:28,756 INFO [train.py:451] Epoch 8, batch 18270, batch avg loss 0.2372, total avg loss: 0.2263, batch size: 49
2021-10-14 21:08:33,707 INFO [train.py:451] Epoch 8, batch 18280, batch avg loss 0.1715, total avg loss: 0.2245, batch size: 33
2021-10-14 21:08:38,620 INFO [train.py:451] Epoch 8, batch 18290, batch avg loss 0.1905, total avg loss: 0.2242, batch size: 35
2021-10-14 21:08:43,711 INFO [train.py:451] Epoch 8, batch 18300, batch avg loss 0.2188, total avg loss: 0.2237, batch size: 33
2021-10-14 21:08:48,606 INFO [train.py:451] Epoch 8, batch 18310, batch avg loss 0.1812, total avg loss: 0.2248, batch size: 33
2021-10-14 21:08:53,530 INFO [train.py:451] Epoch 8, batch 18320, batch avg loss 0.2350, total avg loss: 0.2235, batch size: 38
2021-10-14 21:08:58,626 INFO [train.py:451] Epoch 8, batch 18330, batch avg loss 0.2180, total avg loss: 0.2222, batch size: 30
2021-10-14 21:09:03,636 INFO [train.py:451] Epoch 8, batch 18340, batch avg loss 0.2028, total avg loss: 0.2227, batch size: 34
2021-10-14 21:09:08,543 INFO [train.py:451] Epoch 8, batch 18350, batch avg loss 0.2122, total avg loss: 0.2222, batch size: 33
2021-10-14 21:09:13,545 INFO [train.py:451] Epoch 8, batch 18360, batch avg loss 0.2052, total avg loss: 0.2217, batch size: 34
2021-10-14 21:09:18,416 INFO [train.py:451] Epoch 8, batch 18370, batch avg loss 0.2318, total avg loss: 0.2222, batch size: 35
2021-10-14 21:09:23,205 INFO [train.py:451] Epoch 8, batch 18380, batch avg loss 0.2158, total avg loss: 0.2218, batch size: 35
2021-10-14 21:09:28,255 INFO [train.py:451] Epoch 8, batch 18390, batch avg loss 0.2348, total avg loss: 0.2206, batch size: 45
2021-10-14 21:09:33,015 INFO [train.py:451] Epoch 8, batch 18400, batch avg loss 0.2600, total avg loss: 0.2211, batch size: 39
2021-10-14 21:09:37,908 INFO [train.py:451] Epoch 8, batch 18410, batch avg loss 0.1845, total avg loss: 0.2167, batch size: 32
2021-10-14 21:09:42,937 INFO [train.py:451] Epoch 8, batch 18420, batch avg loss 0.2225, total avg loss: 0.2230, batch size: 31
2021-10-14 21:09:47,889 INFO [train.py:451] Epoch 8, batch 18430, batch avg loss 0.1950, total avg loss: 0.2279, batch size: 29
2021-10-14 21:09:52,837 INFO [train.py:451] Epoch 8, batch 18440, batch avg loss 0.2260, total avg loss: 0.2256, batch size: 38
2021-10-14 21:09:57,582 INFO [train.py:451] Epoch 8, batch 18450, batch avg loss 0.2270, total avg loss: 0.2287, batch size: 34
2021-10-14 21:10:02,470 INFO [train.py:451] Epoch 8, batch 18460, batch avg loss 0.2117, total avg loss: 0.2293, batch size: 35
2021-10-14 21:10:07,208 INFO [train.py:451] Epoch 8, batch 18470, batch avg loss 0.2039, total avg loss: 0.2274, batch size: 39
2021-10-14 21:10:12,024 INFO [train.py:451] Epoch 8, batch 18480, batch avg loss 0.1927, total avg loss: 0.2278, batch size: 29
2021-10-14 21:10:16,901 INFO [train.py:451] Epoch 8, batch 18490, batch avg loss 0.2640, total avg loss: 0.2260, batch size: 49
2021-10-14 21:10:21,894 INFO [train.py:451] Epoch 8, batch 18500, batch avg loss 0.2125, total avg loss: 0.2248, batch size: 41
2021-10-14 21:10:26,850 INFO [train.py:451] Epoch 8, batch 18510, batch avg loss 0.2419, total avg loss: 0.2256, batch size: 35
2021-10-14 21:10:32,129 INFO [train.py:451] Epoch 8, batch 18520, batch avg loss 0.2626, total avg loss: 0.2259, batch size: 35
2021-10-14 21:10:37,109 INFO [train.py:451] Epoch 8, batch 18530, batch avg loss 0.2005, total avg loss: 0.2257, batch size: 31
2021-10-14 21:10:42,047 INFO [train.py:451] Epoch 8, batch 18540, batch avg loss 0.2150, total avg loss: 0.2258, batch size: 33
2021-10-14 21:10:46,991 INFO [train.py:451] Epoch 8, batch 18550, batch avg loss 0.2275, total avg loss: 0.2253, batch size: 45
2021-10-14 21:10:51,983 INFO [train.py:451] Epoch 8, batch 18560, batch avg loss 0.1966, total avg loss: 0.2243, batch size: 29
2021-10-14 21:10:56,937 INFO [train.py:451] Epoch 8, batch 18570, batch avg loss 0.2285, total avg loss: 0.2236, batch size: 34
2021-10-14 21:11:01,760 INFO [train.py:451] Epoch 8, batch 18580, batch avg loss 0.2300, total avg loss: 0.2235, batch size: 36
2021-10-14 21:11:06,688 INFO [train.py:451] Epoch 8, batch 18590, batch avg loss 0.2461, total avg loss: 0.2239, batch size: 49
2021-10-14 21:11:11,717 INFO [train.py:451] Epoch 8, batch 18600, batch avg loss 0.2021, total avg loss: 0.2232, batch size: 42
2021-10-14 21:11:16,635 INFO [train.py:451] Epoch 8, batch 18610, batch avg loss 0.2556, total avg loss: 0.2224, batch size: 38
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2021-10-14 21:15:09,847 INFO [train.py:483] Epoch 8, valid loss 0.1646, best valid loss: 0.1646 best valid epoch: 8
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2021-10-14 21:24:00,476 INFO [train.py:483] Epoch 8, valid loss 0.1648, best valid loss: 0.1646 best valid epoch: 8
2021-10-14 21:24:05,328 INFO [train.py:451] Epoch 8, batch 20010, batch avg loss 0.2254, total avg loss: 0.2162, batch size: 36
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2021-10-14 21:34:27,145 INFO [checkpoint.py:62] Saving checkpoint to tdnn_lstm_ctc-v2/exp/epoch-8.pt
2021-10-14 21:34:27,964 INFO [train.py:564] epoch 9, lr: 2.5e-05
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2021-10-14 21:52:11,164 INFO [train.py:483] Epoch 9, valid loss 0.1641, best valid loss: 0.1640 best valid epoch: 9
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2021-10-14 22:01:01,353 INFO [train.py:483] Epoch 9, valid loss 0.1639, best valid loss: 0.1639 best valid epoch: 9
2021-10-14 22:01:06,190 INFO [train.py:451] Epoch 9, batch 3010, batch avg loss 0.1998, total avg loss: 0.2280, batch size: 30
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2021-10-14 22:03:50,156 INFO [train.py:451] Epoch 9, batch 3340, batch avg loss 0.1542, total avg loss: 0.2238, batch size: 29
2021-10-14 22:03:54,943 INFO [train.py:451] Epoch 9, batch 3350, batch avg loss 0.2071, total avg loss: 0.2243, batch size: 34
2021-10-14 22:04:00,006 INFO [train.py:451] Epoch 9, batch 3360, batch avg loss 0.1733, total avg loss: 0.2236, batch size: 34
2021-10-14 22:04:05,005 INFO [train.py:451] Epoch 9, batch 3370, batch avg loss 0.2653, total avg loss: 0.2232, batch size: 32
2021-10-14 22:04:10,019 INFO [train.py:451] Epoch 9, batch 3380, batch avg loss 0.2287, total avg loss: 0.2221, batch size: 38
2021-10-14 22:04:14,947 INFO [train.py:451] Epoch 9, batch 3390, batch avg loss 0.2375, total avg loss: 0.2222, batch size: 49
2021-10-14 22:04:19,682 INFO [train.py:451] Epoch 9, batch 3400, batch avg loss 0.2025, total avg loss: 0.2232, batch size: 36
2021-10-14 22:04:24,524 INFO [train.py:451] Epoch 9, batch 3410, batch avg loss 0.2467, total avg loss: 0.2311, batch size: 37
2021-10-14 22:04:29,379 INFO [train.py:451] Epoch 9, batch 3420, batch avg loss 0.1905, total avg loss: 0.2306, batch size: 34
2021-10-14 22:04:34,399 INFO [train.py:451] Epoch 9, batch 3430, batch avg loss 0.2565, total avg loss: 0.2271, batch size: 34
2021-10-14 22:04:39,362 INFO [train.py:451] Epoch 9, batch 3440, batch avg loss 0.2651, total avg loss: 0.2262, batch size: 38
2021-10-14 22:04:44,257 INFO [train.py:451] Epoch 9, batch 3450, batch avg loss 0.1970, total avg loss: 0.2258, batch size: 31
2021-10-14 22:04:49,300 INFO [train.py:451] Epoch 9, batch 3460, batch avg loss 0.2309, total avg loss: 0.2236, batch size: 31
2021-10-14 22:04:54,183 INFO [train.py:451] Epoch 9, batch 3470, batch avg loss 0.2393, total avg loss: 0.2244, batch size: 39
2021-10-14 22:04:59,134 INFO [train.py:451] Epoch 9, batch 3480, batch avg loss 0.1747, total avg loss: 0.2243, batch size: 34
2021-10-14 22:05:03,927 INFO [train.py:451] Epoch 9, batch 3490, batch avg loss 0.2332, total avg loss: 0.2251, batch size: 45
2021-10-14 22:05:08,928 INFO [train.py:451] Epoch 9, batch 3500, batch avg loss 0.2145, total avg loss: 0.2239, batch size: 36
2021-10-14 22:05:13,699 INFO [train.py:451] Epoch 9, batch 3510, batch avg loss 0.2116, total avg loss: 0.2231, batch size: 38
2021-10-14 22:05:18,304 INFO [train.py:451] Epoch 9, batch 3520, batch avg loss 0.3302, total avg loss: 0.2243, batch size: 126
2021-10-14 22:05:23,241 INFO [train.py:451] Epoch 9, batch 3530, batch avg loss 0.1929, total avg loss: 0.2223, batch size: 33
2021-10-14 22:05:28,154 INFO [train.py:451] Epoch 9, batch 3540, batch avg loss 0.2527, total avg loss: 0.2220, batch size: 33
2021-10-14 22:05:33,144 INFO [train.py:451] Epoch 9, batch 3550, batch avg loss 0.2420, total avg loss: 0.2226, batch size: 33
2021-10-14 22:05:37,933 INFO [train.py:451] Epoch 9, batch 3560, batch avg loss 0.2411, total avg loss: 0.2229, batch size: 36
2021-10-14 22:05:42,821 INFO [train.py:451] Epoch 9, batch 3570, batch avg loss 0.2130, total avg loss: 0.2226, batch size: 35
2021-10-14 22:05:47,684 INFO [train.py:451] Epoch 9, batch 3580, batch avg loss 0.2363, total avg loss: 0.2230, batch size: 32
2021-10-14 22:05:52,636 INFO [train.py:451] Epoch 9, batch 3590, batch avg loss 0.2169, total avg loss: 0.2221, batch size: 32
2021-10-14 22:05:57,632 INFO [train.py:451] Epoch 9, batch 3600, batch avg loss 0.2060, total avg loss: 0.2219, batch size: 35
2021-10-14 22:06:02,446 INFO [train.py:451] Epoch 9, batch 3610, batch avg loss 0.2237, total avg loss: 0.2432, batch size: 33
2021-10-14 22:06:07,126 INFO [train.py:451] Epoch 9, batch 3620, batch avg loss 0.2168, total avg loss: 0.2462, batch size: 29
2021-10-14 22:06:12,090 INFO [train.py:451] Epoch 9, batch 3630, batch avg loss 0.2042, total avg loss: 0.2360, batch size: 36
2021-10-14 22:06:16,886 INFO [train.py:451] Epoch 9, batch 3640, batch avg loss 0.2283, total avg loss: 0.2358, batch size: 42
2021-10-14 22:06:21,766 INFO [train.py:451] Epoch 9, batch 3650, batch avg loss 0.2195, total avg loss: 0.2331, batch size: 29
2021-10-14 22:06:26,800 INFO [train.py:451] Epoch 9, batch 3660, batch avg loss 0.1891, total avg loss: 0.2322, batch size: 29
2021-10-14 22:06:31,636 INFO [train.py:451] Epoch 9, batch 3670, batch avg loss 0.1769, total avg loss: 0.2332, batch size: 30
2021-10-14 22:06:36,527 INFO [train.py:451] Epoch 9, batch 3680, batch avg loss 0.2667, total avg loss: 0.2330, batch size: 45
2021-10-14 22:06:41,444 INFO [train.py:451] Epoch 9, batch 3690, batch avg loss 0.2269, total avg loss: 0.2314, batch size: 49
2021-10-14 22:06:46,372 INFO [train.py:451] Epoch 9, batch 3700, batch avg loss 0.2355, total avg loss: 0.2303, batch size: 38
2021-10-14 22:06:51,321 INFO [train.py:451] Epoch 9, batch 3710, batch avg loss 0.2228, total avg loss: 0.2302, batch size: 36
2021-10-14 22:06:56,145 INFO [train.py:451] Epoch 9, batch 3720, batch avg loss 0.2203, total avg loss: 0.2296, batch size: 37
2021-10-14 22:07:01,114 INFO [train.py:451] Epoch 9, batch 3730, batch avg loss 0.2607, total avg loss: 0.2295, batch size: 45
2021-10-14 22:07:06,048 INFO [train.py:451] Epoch 9, batch 3740, batch avg loss 0.1852, total avg loss: 0.2278, batch size: 30
2021-10-14 22:07:11,053 INFO [train.py:451] Epoch 9, batch 3750, batch avg loss 0.2349, total avg loss: 0.2271, batch size: 34
2021-10-14 22:07:16,077 INFO [train.py:451] Epoch 9, batch 3760, batch avg loss 0.2645, total avg loss: 0.2263, batch size: 36
2021-10-14 22:07:21,107 INFO [train.py:451] Epoch 9, batch 3770, batch avg loss 0.1998, total avg loss: 0.2258, batch size: 32
2021-10-14 22:07:25,882 INFO [train.py:451] Epoch 9, batch 3780, batch avg loss 0.3061, total avg loss: 0.2259, batch size: 71
2021-10-14 22:07:30,815 INFO [train.py:451] Epoch 9, batch 3790, batch avg loss 0.2685, total avg loss: 0.2255, batch size: 41
2021-10-14 22:07:35,818 INFO [train.py:451] Epoch 9, batch 3800, batch avg loss 0.2125, total avg loss: 0.2251, batch size: 29
2021-10-14 22:07:40,916 INFO [train.py:451] Epoch 9, batch 3810, batch avg loss 0.2009, total avg loss: 0.2133, batch size: 31
2021-10-14 22:07:45,688 INFO [train.py:451] Epoch 9, batch 3820, batch avg loss 0.2042, total avg loss: 0.2194, batch size: 34
2021-10-14 22:07:50,486 INFO [train.py:451] Epoch 9, batch 3830, batch avg loss 0.2044, total avg loss: 0.2251, batch size: 31
2021-10-14 22:07:55,354 INFO [train.py:451] Epoch 9, batch 3840, batch avg loss 0.2123, total avg loss: 0.2266, batch size: 29
2021-10-14 22:08:00,141 INFO [train.py:451] Epoch 9, batch 3850, batch avg loss 0.3125, total avg loss: 0.2293, batch size: 129
2021-10-14 22:08:05,185 INFO [train.py:451] Epoch 9, batch 3860, batch avg loss 0.1895, total avg loss: 0.2276, batch size: 28
2021-10-14 22:08:10,381 INFO [train.py:451] Epoch 9, batch 3870, batch avg loss 0.1895, total avg loss: 0.2237, batch size: 33
2021-10-14 22:08:15,290 INFO [train.py:451] Epoch 9, batch 3880, batch avg loss 0.2115, total avg loss: 0.2244, batch size: 38
2021-10-14 22:08:20,134 INFO [train.py:451] Epoch 9, batch 3890, batch avg loss 0.1932, total avg loss: 0.2240, batch size: 35
2021-10-14 22:08:24,840 INFO [train.py:451] Epoch 9, batch 3900, batch avg loss 0.1825, total avg loss: 0.2272, batch size: 29
2021-10-14 22:08:29,959 INFO [train.py:451] Epoch 9, batch 3910, batch avg loss 0.2457, total avg loss: 0.2264, batch size: 38
2021-10-14 22:08:34,961 INFO [train.py:451] Epoch 9, batch 3920, batch avg loss 0.2127, total avg loss: 0.2263, batch size: 35
2021-10-14 22:08:39,883 INFO [train.py:451] Epoch 9, batch 3930, batch avg loss 0.3507, total avg loss: 0.2276, batch size: 130
2021-10-14 22:08:44,675 INFO [train.py:451] Epoch 9, batch 3940, batch avg loss 0.3231, total avg loss: 0.2286, batch size: 125
2021-10-14 22:08:49,683 INFO [train.py:451] Epoch 9, batch 3950, batch avg loss 0.2269, total avg loss: 0.2282, batch size: 34
2021-10-14 22:08:54,609 INFO [train.py:451] Epoch 9, batch 3960, batch avg loss 0.2235, total avg loss: 0.2269, batch size: 38
2021-10-14 22:08:59,501 INFO [train.py:451] Epoch 9, batch 3970, batch avg loss 0.1890, total avg loss: 0.2272, batch size: 34
2021-10-14 22:09:04,560 INFO [train.py:451] Epoch 9, batch 3980, batch avg loss 0.2071, total avg loss: 0.2271, batch size: 32
2021-10-14 22:09:09,420 INFO [train.py:451] Epoch 9, batch 3990, batch avg loss 0.1881, total avg loss: 0.2269, batch size: 33
2021-10-14 22:09:14,655 INFO [train.py:451] Epoch 9, batch 4000, batch avg loss 0.2186, total avg loss: 0.2261, batch size: 34
2021-10-14 22:09:54,958 INFO [train.py:483] Epoch 9, valid loss 0.1640, best valid loss: 0.1639 best valid epoch: 9
2021-10-14 22:09:59,827 INFO [train.py:451] Epoch 9, batch 4010, batch avg loss 0.2144, total avg loss: 0.2227, batch size: 33
2021-10-14 22:10:04,664 INFO [train.py:451] Epoch 9, batch 4020, batch avg loss 0.1940, total avg loss: 0.2271, batch size: 45
2021-10-14 22:10:09,620 INFO [train.py:451] Epoch 9, batch 4030, batch avg loss 0.1929, total avg loss: 0.2187, batch size: 29
2021-10-14 22:10:14,484 INFO [train.py:451] Epoch 9, batch 4040, batch avg loss 0.2027, total avg loss: 0.2149, batch size: 42
2021-10-14 22:10:19,488 INFO [train.py:451] Epoch 9, batch 4050, batch avg loss 0.1947, total avg loss: 0.2121, batch size: 28
2021-10-14 22:10:24,371 INFO [train.py:451] Epoch 9, batch 4060, batch avg loss 0.1708, total avg loss: 0.2110, batch size: 30
2021-10-14 22:10:29,204 INFO [train.py:451] Epoch 9, batch 4070, batch avg loss 0.2268, total avg loss: 0.2124, batch size: 38
2021-10-14 22:10:34,125 INFO [train.py:451] Epoch 9, batch 4080, batch avg loss 0.1842, total avg loss: 0.2108, batch size: 30
2021-10-14 22:10:39,000 INFO [train.py:451] Epoch 9, batch 4090, batch avg loss 0.2441, total avg loss: 0.2122, batch size: 41
2021-10-14 22:10:44,072 INFO [train.py:451] Epoch 9, batch 4100, batch avg loss 0.2442, total avg loss: 0.2133, batch size: 34
2021-10-14 22:10:49,026 INFO [train.py:451] Epoch 9, batch 4110, batch avg loss 0.1683, total avg loss: 0.2128, batch size: 32
2021-10-14 22:10:54,141 INFO [train.py:451] Epoch 9, batch 4120, batch avg loss 0.2235, total avg loss: 0.2126, batch size: 32
2021-10-14 22:10:59,221 INFO [train.py:451] Epoch 9, batch 4130, batch avg loss 0.2683, total avg loss: 0.2128, batch size: 38
2021-10-14 22:11:04,321 INFO [train.py:451] Epoch 9, batch 4140, batch avg loss 0.2175, total avg loss: 0.2131, batch size: 33
2021-10-14 22:11:09,263 INFO [train.py:451] Epoch 9, batch 4150, batch avg loss 0.2128, total avg loss: 0.2133, batch size: 36
2021-10-14 22:11:14,184 INFO [train.py:451] Epoch 9, batch 4160, batch avg loss 0.2194, total avg loss: 0.2140, batch size: 49
2021-10-14 22:11:18,975 INFO [train.py:451] Epoch 9, batch 4170, batch avg loss 0.2007, total avg loss: 0.2147, batch size: 45
2021-10-14 22:11:23,946 INFO [train.py:451] Epoch 9, batch 4180, batch avg loss 0.1906, total avg loss: 0.2150, batch size: 35
2021-10-14 22:11:28,969 INFO [train.py:451] Epoch 9, batch 4190, batch avg loss 0.2061, total avg loss: 0.2150, batch size: 27
2021-10-14 22:11:33,961 INFO [train.py:451] Epoch 9, batch 4200, batch avg loss 0.2002, total avg loss: 0.2137, batch size: 41
2021-10-14 22:11:38,764 INFO [train.py:451] Epoch 9, batch 4210, batch avg loss 0.1931, total avg loss: 0.2236, batch size: 30
2021-10-14 22:11:43,684 INFO [train.py:451] Epoch 9, batch 4220, batch avg loss 0.1829, total avg loss: 0.2212, batch size: 33
2021-10-14 22:11:48,543 INFO [train.py:451] Epoch 9, batch 4230, batch avg loss 0.2024, total avg loss: 0.2213, batch size: 31
2021-10-14 22:11:53,392 INFO [train.py:451] Epoch 9, batch 4240, batch avg loss 0.2158, total avg loss: 0.2222, batch size: 35
2021-10-14 22:11:58,338 INFO [train.py:451] Epoch 9, batch 4250, batch avg loss 0.2172, total avg loss: 0.2216, batch size: 49
2021-10-14 22:12:03,251 INFO [train.py:451] Epoch 9, batch 4260, batch avg loss 0.2567, total avg loss: 0.2204, batch size: 73
2021-10-14 22:12:08,173 INFO [train.py:451] Epoch 9, batch 4270, batch avg loss 0.2492, total avg loss: 0.2218, batch size: 34
2021-10-14 22:12:13,123 INFO [train.py:451] Epoch 9, batch 4280, batch avg loss 0.2670, total avg loss: 0.2213, batch size: 33
2021-10-14 22:12:17,987 INFO [train.py:451] Epoch 9, batch 4290, batch avg loss 0.2450, total avg loss: 0.2206, batch size: 42
2021-10-14 22:12:23,032 INFO [train.py:451] Epoch 9, batch 4300, batch avg loss 0.2453, total avg loss: 0.2197, batch size: 30
2021-10-14 22:12:27,987 INFO [train.py:451] Epoch 9, batch 4310, batch avg loss 0.2150, total avg loss: 0.2202, batch size: 36
2021-10-14 22:12:33,041 INFO [train.py:451] Epoch 9, batch 4320, batch avg loss 0.2952, total avg loss: 0.2202, batch size: 131
2021-10-14 22:12:37,927 INFO [train.py:451] Epoch 9, batch 4330, batch avg loss 0.2288, total avg loss: 0.2200, batch size: 39
2021-10-14 22:12:43,112 INFO [train.py:451] Epoch 9, batch 4340, batch avg loss 0.1488, total avg loss: 0.2196, batch size: 27
2021-10-14 22:12:48,062 INFO [train.py:451] Epoch 9, batch 4350, batch avg loss 0.2702, total avg loss: 0.2197, batch size: 38
2021-10-14 22:12:53,145 INFO [train.py:451] Epoch 9, batch 4360, batch avg loss 0.2390, total avg loss: 0.2189, batch size: 41
2021-10-14 22:12:58,205 INFO [train.py:451] Epoch 9, batch 4370, batch avg loss 0.1901, total avg loss: 0.2186, batch size: 32
2021-10-14 22:13:03,149 INFO [train.py:451] Epoch 9, batch 4380, batch avg loss 0.2174, total avg loss: 0.2187, batch size: 41
2021-10-14 22:13:08,090 INFO [train.py:451] Epoch 9, batch 4390, batch avg loss 0.2457, total avg loss: 0.2185, batch size: 37
2021-10-14 22:13:13,057 INFO [train.py:451] Epoch 9, batch 4400, batch avg loss 0.1817, total avg loss: 0.2182, batch size: 29
2021-10-14 22:13:17,902 INFO [train.py:451] Epoch 9, batch 4410, batch avg loss 0.2417, total avg loss: 0.2308, batch size: 56
2021-10-14 22:13:22,816 INFO [train.py:451] Epoch 9, batch 4420, batch avg loss 0.1923, total avg loss: 0.2249, batch size: 28
2021-10-14 22:13:27,798 INFO [train.py:451] Epoch 9, batch 4430, batch avg loss 0.2014, total avg loss: 0.2237, batch size: 32
2021-10-14 22:13:32,694 INFO [train.py:451] Epoch 9, batch 4440, batch avg loss 0.2651, total avg loss: 0.2241, batch size: 38
2021-10-14 22:13:37,521 INFO [train.py:451] Epoch 9, batch 4450, batch avg loss 0.1860, total avg loss: 0.2270, batch size: 38
2021-10-14 22:13:42,231 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "28eeeb82-79e2-1d56-930e-d904fe7187e6" will not be mixed in.
2021-10-14 22:13:42,622 INFO [train.py:451] Epoch 9, batch 4460, batch avg loss 0.2195, total avg loss: 0.2286, batch size: 39
2021-10-14 22:13:47,629 INFO [train.py:451] Epoch 9, batch 4470, batch avg loss 0.2522, total avg loss: 0.2300, batch size: 33
2021-10-14 22:13:52,797 INFO [train.py:451] Epoch 9, batch 4480, batch avg loss 0.2255, total avg loss: 0.2272, batch size: 36
2021-10-14 22:13:57,908 INFO [train.py:451] Epoch 9, batch 4490, batch avg loss 0.2029, total avg loss: 0.2250, batch size: 33
2021-10-14 22:14:02,850 INFO [train.py:451] Epoch 9, batch 4500, batch avg loss 0.3153, total avg loss: 0.2266, batch size: 125
2021-10-14 22:14:07,726 INFO [train.py:451] Epoch 9, batch 4510, batch avg loss 0.2090, total avg loss: 0.2260, batch size: 32
2021-10-14 22:14:12,643 INFO [train.py:451] Epoch 9, batch 4520, batch avg loss 0.2252, total avg loss: 0.2260, batch size: 27
2021-10-14 22:14:17,618 INFO [train.py:451] Epoch 9, batch 4530, batch avg loss 0.1768, total avg loss: 0.2247, batch size: 30
2021-10-14 22:14:22,721 INFO [train.py:451] Epoch 9, batch 4540, batch avg loss 0.1726, total avg loss: 0.2232, batch size: 33
2021-10-14 22:14:27,731 INFO [train.py:451] Epoch 9, batch 4550, batch avg loss 0.2096, total avg loss: 0.2224, batch size: 34
2021-10-14 22:14:32,675 INFO [train.py:451] Epoch 9, batch 4560, batch avg loss 0.2071, total avg loss: 0.2228, batch size: 34
2021-10-14 22:14:37,430 INFO [train.py:451] Epoch 9, batch 4570, batch avg loss 0.2454, total avg loss: 0.2233, batch size: 37
2021-10-14 22:14:42,366 INFO [train.py:451] Epoch 9, batch 4580, batch avg loss 0.3010, total avg loss: 0.2235, batch size: 41
2021-10-14 22:14:47,102 INFO [train.py:451] Epoch 9, batch 4590, batch avg loss 0.2131, total avg loss: 0.2242, batch size: 49
2021-10-14 22:14:52,117 INFO [train.py:451] Epoch 9, batch 4600, batch avg loss 0.1831, total avg loss: 0.2243, batch size: 31
2021-10-14 22:14:56,912 INFO [train.py:451] Epoch 9, batch 4610, batch avg loss 0.2277, total avg loss: 0.2351, batch size: 41
2021-10-14 22:15:01,801 INFO [train.py:451] Epoch 9, batch 4620, batch avg loss 0.2417, total avg loss: 0.2306, batch size: 35
2021-10-14 22:15:06,700 INFO [train.py:451] Epoch 9, batch 4630, batch avg loss 0.2898, total avg loss: 0.2271, batch size: 38
2021-10-14 22:15:11,593 INFO [train.py:451] Epoch 9, batch 4640, batch avg loss 0.3475, total avg loss: 0.2285, batch size: 129
2021-10-14 22:15:16,607 INFO [train.py:451] Epoch 9, batch 4650, batch avg loss 0.1768, total avg loss: 0.2241, batch size: 27
2021-10-14 22:15:21,635 INFO [train.py:451] Epoch 9, batch 4660, batch avg loss 0.2204, total avg loss: 0.2219, batch size: 34
2021-10-14 22:15:26,620 INFO [train.py:451] Epoch 9, batch 4670, batch avg loss 0.2379, total avg loss: 0.2245, batch size: 33
2021-10-14 22:15:31,786 INFO [train.py:451] Epoch 9, batch 4680, batch avg loss 0.2402, total avg loss: 0.2218, batch size: 29
2021-10-14 22:15:36,981 INFO [train.py:451] Epoch 9, batch 4690, batch avg loss 0.2658, total avg loss: 0.2210, batch size: 27
2021-10-14 22:15:41,973 INFO [train.py:451] Epoch 9, batch 4700, batch avg loss 0.2259, total avg loss: 0.2211, batch size: 49
2021-10-14 22:15:46,940 INFO [train.py:451] Epoch 9, batch 4710, batch avg loss 0.1785, total avg loss: 0.2219, batch size: 29
2021-10-14 22:15:52,162 INFO [train.py:451] Epoch 9, batch 4720, batch avg loss 0.2056, total avg loss: 0.2209, batch size: 29
2021-10-14 22:15:57,180 INFO [train.py:451] Epoch 9, batch 4730, batch avg loss 0.2244, total avg loss: 0.2202, batch size: 35
2021-10-14 22:16:02,410 INFO [train.py:451] Epoch 9, batch 4740, batch avg loss 0.1934, total avg loss: 0.2192, batch size: 27
2021-10-14 22:16:07,285 INFO [train.py:451] Epoch 9, batch 4750, batch avg loss 0.1952, total avg loss: 0.2194, batch size: 30
2021-10-14 22:16:12,012 INFO [train.py:451] Epoch 9, batch 4760, batch avg loss 0.2118, total avg loss: 0.2193, batch size: 29
2021-10-14 22:16:16,919 INFO [train.py:451] Epoch 9, batch 4770, batch avg loss 0.1902, total avg loss: 0.2195, batch size: 27
2021-10-14 22:16:29,200 INFO [train.py:451] Epoch 9, batch 4780, batch avg loss 0.1855, total avg loss: 0.2199, batch size: 32
2021-10-14 22:16:34,295 INFO [train.py:451] Epoch 9, batch 4790, batch avg loss 0.2425, total avg loss: 0.2204, batch size: 36
2021-10-14 22:16:39,351 INFO [train.py:451] Epoch 9, batch 4800, batch avg loss 0.1964, total avg loss: 0.2208, batch size: 33
2021-10-14 22:16:44,369 INFO [train.py:451] Epoch 9, batch 4810, batch avg loss 0.2019, total avg loss: 0.2186, batch size: 34
2021-10-14 22:16:49,355 INFO [train.py:451] Epoch 9, batch 4820, batch avg loss 0.2298, total avg loss: 0.2198, batch size: 49
2021-10-14 22:16:54,137 INFO [train.py:451] Epoch 9, batch 4830, batch avg loss 0.2551, total avg loss: 0.2166, batch size: 49
2021-10-14 22:16:59,112 INFO [train.py:451] Epoch 9, batch 4840, batch avg loss 0.1958, total avg loss: 0.2154, batch size: 38
2021-10-14 22:17:03,858 INFO [train.py:451] Epoch 9, batch 4850, batch avg loss 0.2248, total avg loss: 0.2199, batch size: 49
2021-10-14 22:17:08,696 INFO [train.py:451] Epoch 9, batch 4860, batch avg loss 0.2155, total avg loss: 0.2202, batch size: 39
2021-10-14 22:17:13,532 INFO [train.py:451] Epoch 9, batch 4870, batch avg loss 0.1722, total avg loss: 0.2209, batch size: 31
2021-10-14 22:17:18,585 INFO [train.py:451] Epoch 9, batch 4880, batch avg loss 0.2319, total avg loss: 0.2203, batch size: 34
2021-10-14 22:17:23,354 INFO [train.py:451] Epoch 9, batch 4890, batch avg loss 0.3305, total avg loss: 0.2222, batch size: 132
2021-10-14 22:17:28,066 INFO [train.py:451] Epoch 9, batch 4900, batch avg loss 0.2884, total avg loss: 0.2239, batch size: 73
2021-10-14 22:17:33,169 INFO [train.py:451] Epoch 9, batch 4910, batch avg loss 0.1868, total avg loss: 0.2212, batch size: 32
2021-10-14 22:17:37,865 INFO [train.py:451] Epoch 9, batch 4920, batch avg loss 0.2392, total avg loss: 0.2217, batch size: 42
2021-10-14 22:17:42,801 INFO [train.py:451] Epoch 9, batch 4930, batch avg loss 0.2401, total avg loss: 0.2208, batch size: 45
2021-10-14 22:17:47,765 INFO [train.py:451] Epoch 9, batch 4940, batch avg loss 0.1953, total avg loss: 0.2209, batch size: 29
2021-10-14 22:17:52,663 INFO [train.py:451] Epoch 9, batch 4950, batch avg loss 0.1881, total avg loss: 0.2217, batch size: 38
2021-10-14 22:17:57,439 INFO [train.py:451] Epoch 9, batch 4960, batch avg loss 0.3708, total avg loss: 0.2230, batch size: 133
2021-10-14 22:18:02,348 INFO [train.py:451] Epoch 9, batch 4970, batch avg loss 0.1841, total avg loss: 0.2228, batch size: 29
2021-10-14 22:18:07,302 INFO [train.py:451] Epoch 9, batch 4980, batch avg loss 0.2241, total avg loss: 0.2220, batch size: 38
2021-10-14 22:18:12,190 INFO [train.py:451] Epoch 9, batch 4990, batch avg loss 0.2214, total avg loss: 0.2212, batch size: 57
2021-10-14 22:18:17,114 INFO [train.py:451] Epoch 9, batch 5000, batch avg loss 0.2222, total avg loss: 0.2213, batch size: 37
2021-10-14 22:18:56,763 INFO [train.py:483] Epoch 9, valid loss 0.1639, best valid loss: 0.1639 best valid epoch: 9
2021-10-14 22:19:01,913 INFO [train.py:451] Epoch 9, batch 5010, batch avg loss 0.1862, total avg loss: 0.2184, batch size: 28
2021-10-14 22:19:06,820 INFO [train.py:451] Epoch 9, batch 5020, batch avg loss 0.1646, total avg loss: 0.2192, batch size: 30
2021-10-14 22:19:11,927 INFO [train.py:451] Epoch 9, batch 5030, batch avg loss 0.2075, total avg loss: 0.2229, batch size: 30
2021-10-14 22:19:17,025 INFO [train.py:451] Epoch 9, batch 5040, batch avg loss 0.2128, total avg loss: 0.2221, batch size: 37
2021-10-14 22:19:21,807 INFO [train.py:451] Epoch 9, batch 5050, batch avg loss 0.2489, total avg loss: 0.2259, batch size: 33
2021-10-14 22:19:26,643 INFO [train.py:451] Epoch 9, batch 5060, batch avg loss 0.2153, total avg loss: 0.2266, batch size: 30
2021-10-14 22:19:31,553 INFO [train.py:451] Epoch 9, batch 5070, batch avg loss 0.1915, total avg loss: 0.2307, batch size: 28
2021-10-14 22:19:36,546 INFO [train.py:451] Epoch 9, batch 5080, batch avg loss 0.2325, total avg loss: 0.2294, batch size: 31
2021-10-14 22:19:41,380 INFO [train.py:451] Epoch 9, batch 5090, batch avg loss 0.3366, total avg loss: 0.2284, batch size: 128
2021-10-14 22:19:46,067 INFO [train.py:451] Epoch 9, batch 5100, batch avg loss 0.2316, total avg loss: 0.2293, batch size: 49
2021-10-14 22:19:50,999 INFO [train.py:451] Epoch 9, batch 5110, batch avg loss 0.3414, total avg loss: 0.2290, batch size: 125
2021-10-14 22:19:56,089 INFO [train.py:451] Epoch 9, batch 5120, batch avg loss 0.2095, total avg loss: 0.2265, batch size: 32
2021-10-14 22:20:01,031 INFO [train.py:451] Epoch 9, batch 5130, batch avg loss 0.2396, total avg loss: 0.2239, batch size: 38
2021-10-14 22:20:05,754 INFO [train.py:451] Epoch 9, batch 5140, batch avg loss 0.2471, total avg loss: 0.2254, batch size: 41
2021-10-14 22:20:10,709 INFO [train.py:451] Epoch 9, batch 5150, batch avg loss 0.2008, total avg loss: 0.2256, batch size: 31
2021-10-14 22:20:15,657 INFO [train.py:451] Epoch 9, batch 5160, batch avg loss 0.2648, total avg loss: 0.2264, batch size: 35
2021-10-14 22:20:20,468 INFO [train.py:451] Epoch 9, batch 5170, batch avg loss 0.2191, total avg loss: 0.2265, batch size: 28
2021-10-14 22:20:25,337 INFO [train.py:451] Epoch 9, batch 5180, batch avg loss 0.1880, total avg loss: 0.2261, batch size: 33
2021-10-14 22:20:30,232 INFO [train.py:451] Epoch 9, batch 5190, batch avg loss 0.2072, total avg loss: 0.2267, batch size: 41
2021-10-14 22:20:35,236 INFO [train.py:451] Epoch 9, batch 5200, batch avg loss 0.2259, total avg loss: 0.2263, batch size: 30
2021-10-14 22:20:39,971 INFO [train.py:451] Epoch 9, batch 5210, batch avg loss 0.2280, total avg loss: 0.2537, batch size: 35
2021-10-14 22:20:44,833 INFO [train.py:451] Epoch 9, batch 5220, batch avg loss 0.3093, total avg loss: 0.2502, batch size: 36
2021-10-14 22:20:49,955 INFO [train.py:451] Epoch 9, batch 5230, batch avg loss 0.1957, total avg loss: 0.2390, batch size: 33
2021-10-14 22:20:54,817 INFO [train.py:451] Epoch 9, batch 5240, batch avg loss 0.2242, total avg loss: 0.2334, batch size: 31
2021-10-14 22:20:59,598 INFO [train.py:451] Epoch 9, batch 5250, batch avg loss 0.2107, total avg loss: 0.2322, batch size: 45
2021-10-14 22:21:04,585 INFO [train.py:451] Epoch 9, batch 5260, batch avg loss 0.2200, total avg loss: 0.2306, batch size: 41
2021-10-14 22:21:09,471 INFO [train.py:451] Epoch 9, batch 5270, batch avg loss 0.2366, total avg loss: 0.2285, batch size: 34
2021-10-14 22:21:14,454 INFO [train.py:451] Epoch 9, batch 5280, batch avg loss 0.1751, total avg loss: 0.2245, batch size: 29
2021-10-14 22:21:19,352 INFO [train.py:451] Epoch 9, batch 5290, batch avg loss 0.2879, total avg loss: 0.2239, batch size: 73
2021-10-14 22:21:24,328 INFO [train.py:451] Epoch 9, batch 5300, batch avg loss 0.2485, total avg loss: 0.2223, batch size: 35
2021-10-14 22:21:29,036 INFO [train.py:451] Epoch 9, batch 5310, batch avg loss 0.2494, total avg loss: 0.2236, batch size: 41
2021-10-14 22:21:33,826 INFO [train.py:451] Epoch 9, batch 5320, batch avg loss 0.2813, total avg loss: 0.2254, batch size: 45
2021-10-14 22:21:38,759 INFO [train.py:451] Epoch 9, batch 5330, batch avg loss 0.2574, total avg loss: 0.2251, batch size: 49
2021-10-14 22:21:43,794 INFO [train.py:451] Epoch 9, batch 5340, batch avg loss 0.2338, total avg loss: 0.2246, batch size: 36
2021-10-14 22:21:48,620 INFO [train.py:451] Epoch 9, batch 5350, batch avg loss 0.1907, total avg loss: 0.2254, batch size: 29
2021-10-14 22:21:53,363 INFO [train.py:451] Epoch 9, batch 5360, batch avg loss 0.1740, total avg loss: 0.2253, batch size: 30
2021-10-14 22:21:58,125 INFO [train.py:451] Epoch 9, batch 5370, batch avg loss 0.2928, total avg loss: 0.2257, batch size: 72
2021-10-14 22:22:03,099 INFO [train.py:451] Epoch 9, batch 5380, batch avg loss 0.2795, total avg loss: 0.2246, batch size: 38
2021-10-14 22:22:07,946 INFO [train.py:451] Epoch 9, batch 5390, batch avg loss 0.2199, total avg loss: 0.2246, batch size: 35
2021-10-14 22:22:12,916 INFO [train.py:451] Epoch 9, batch 5400, batch avg loss 0.1816, total avg loss: 0.2240, batch size: 32
2021-10-14 22:22:17,742 INFO [train.py:451] Epoch 9, batch 5410, batch avg loss 0.1966, total avg loss: 0.2257, batch size: 31
2021-10-14 22:22:22,703 INFO [train.py:451] Epoch 9, batch 5420, batch avg loss 0.2057, total avg loss: 0.2170, batch size: 37
2021-10-14 22:22:27,482 INFO [train.py:451] Epoch 9, batch 5430, batch avg loss 0.2383, total avg loss: 0.2247, batch size: 49
2021-10-14 22:22:32,209 INFO [train.py:451] Epoch 9, batch 5440, batch avg loss 0.1679, total avg loss: 0.2267, batch size: 31
2021-10-14 22:22:37,012 INFO [train.py:451] Epoch 9, batch 5450, batch avg loss 0.2665, total avg loss: 0.2276, batch size: 37
2021-10-14 22:22:42,016 INFO [train.py:451] Epoch 9, batch 5460, batch avg loss 0.1891, total avg loss: 0.2226, batch size: 31
2021-10-14 22:22:46,843 INFO [train.py:451] Epoch 9, batch 5470, batch avg loss 0.1772, total avg loss: 0.2245, batch size: 33
2021-10-14 22:22:51,714 INFO [train.py:451] Epoch 9, batch 5480, batch avg loss 0.2145, total avg loss: 0.2226, batch size: 34
2021-10-14 22:22:56,474 INFO [train.py:451] Epoch 9, batch 5490, batch avg loss 0.2662, total avg loss: 0.2235, batch size: 57
2021-10-14 22:23:01,524 INFO [train.py:451] Epoch 9, batch 5500, batch avg loss 0.1588, total avg loss: 0.2212, batch size: 29
2021-10-14 22:23:06,539 INFO [train.py:451] Epoch 9, batch 5510, batch avg loss 0.2562, total avg loss: 0.2223, batch size: 36
2021-10-14 22:23:11,637 INFO [train.py:451] Epoch 9, batch 5520, batch avg loss 0.2036, total avg loss: 0.2218, batch size: 37
2021-10-14 22:23:16,525 INFO [train.py:451] Epoch 9, batch 5530, batch avg loss 0.1950, total avg loss: 0.2214, batch size: 33
2021-10-14 22:23:21,454 INFO [train.py:451] Epoch 9, batch 5540, batch avg loss 0.2386, total avg loss: 0.2209, batch size: 34
2021-10-14 22:23:26,284 INFO [train.py:451] Epoch 9, batch 5550, batch avg loss 0.2508, total avg loss: 0.2221, batch size: 39
2021-10-14 22:23:31,053 INFO [train.py:451] Epoch 9, batch 5560, batch avg loss 0.2689, total avg loss: 0.2237, batch size: 45
2021-10-14 22:23:35,713 INFO [train.py:451] Epoch 9, batch 5570, batch avg loss 0.2672, total avg loss: 0.2254, batch size: 38
2021-10-14 22:23:40,634 INFO [train.py:451] Epoch 9, batch 5580, batch avg loss 0.1750, total avg loss: 0.2247, batch size: 30
2021-10-14 22:23:45,496 INFO [train.py:451] Epoch 9, batch 5590, batch avg loss 0.2065, total avg loss: 0.2254, batch size: 35
2021-10-14 22:23:50,308 INFO [train.py:451] Epoch 9, batch 5600, batch avg loss 0.1536, total avg loss: 0.2249, batch size: 30
2021-10-14 22:23:55,323 INFO [train.py:451] Epoch 9, batch 5610, batch avg loss 0.2147, total avg loss: 0.2224, batch size: 34
2021-10-14 22:24:00,034 INFO [train.py:451] Epoch 9, batch 5620, batch avg loss 0.2484, total avg loss: 0.2276, batch size: 49
2021-10-14 22:24:05,003 INFO [train.py:451] Epoch 9, batch 5630, batch avg loss 0.2259, total avg loss: 0.2261, batch size: 35
2021-10-14 22:24:09,922 INFO [train.py:451] Epoch 9, batch 5640, batch avg loss 0.2068, total avg loss: 0.2229, batch size: 34
2021-10-14 22:24:14,827 INFO [train.py:451] Epoch 9, batch 5650, batch avg loss 0.1795, total avg loss: 0.2244, batch size: 30
2021-10-14 22:24:19,822 INFO [train.py:451] Epoch 9, batch 5660, batch avg loss 0.2057, total avg loss: 0.2231, batch size: 32
2021-10-14 22:24:24,622 INFO [train.py:451] Epoch 9, batch 5670, batch avg loss 0.1563, total avg loss: 0.2228, batch size: 30
2021-10-14 22:24:29,472 INFO [train.py:451] Epoch 9, batch 5680, batch avg loss 0.2053, total avg loss: 0.2221, batch size: 34
2021-10-14 22:24:34,311 INFO [train.py:451] Epoch 9, batch 5690, batch avg loss 0.1877, total avg loss: 0.2222, batch size: 31
2021-10-14 22:24:39,296 INFO [train.py:451] Epoch 9, batch 5700, batch avg loss 0.2013, total avg loss: 0.2216, batch size: 32
2021-10-14 22:24:44,161 INFO [train.py:451] Epoch 9, batch 5710, batch avg loss 0.2335, total avg loss: 0.2218, batch size: 49
2021-10-14 22:24:49,156 INFO [train.py:451] Epoch 9, batch 5720, batch avg loss 0.2138, total avg loss: 0.2220, batch size: 41
2021-10-14 22:24:54,035 INFO [train.py:451] Epoch 9, batch 5730, batch avg loss 0.1737, total avg loss: 0.2217, batch size: 30
2021-10-14 22:24:58,842 INFO [train.py:451] Epoch 9, batch 5740, batch avg loss 0.1736, total avg loss: 0.2220, batch size: 29
2021-10-14 22:25:03,847 INFO [train.py:451] Epoch 9, batch 5750, batch avg loss 0.1887, total avg loss: 0.2222, batch size: 29
2021-10-14 22:25:08,726 INFO [train.py:451] Epoch 9, batch 5760, batch avg loss 0.2155, total avg loss: 0.2212, batch size: 42
2021-10-14 22:25:13,519 INFO [train.py:451] Epoch 9, batch 5770, batch avg loss 0.2156, total avg loss: 0.2219, batch size: 36
2021-10-14 22:25:18,534 INFO [train.py:451] Epoch 9, batch 5780, batch avg loss 0.1996, total avg loss: 0.2216, batch size: 34
2021-10-14 22:25:23,556 INFO [train.py:451] Epoch 9, batch 5790, batch avg loss 0.2253, total avg loss: 0.2210, batch size: 49
2021-10-14 22:25:28,556 INFO [train.py:451] Epoch 9, batch 5800, batch avg loss 0.2356, total avg loss: 0.2218, batch size: 35
2021-10-14 22:25:33,479 INFO [train.py:451] Epoch 9, batch 5810, batch avg loss 0.2169, total avg loss: 0.2267, batch size: 32
2021-10-14 22:25:38,313 INFO [train.py:451] Epoch 9, batch 5820, batch avg loss 0.2438, total avg loss: 0.2359, batch size: 34
2021-10-14 22:25:43,181 INFO [train.py:451] Epoch 9, batch 5830, batch avg loss 0.2844, total avg loss: 0.2322, batch size: 73
2021-10-14 22:25:48,017 INFO [train.py:451] Epoch 9, batch 5840, batch avg loss 0.2079, total avg loss: 0.2296, batch size: 41
2021-10-14 22:25:52,822 INFO [train.py:451] Epoch 9, batch 5850, batch avg loss 0.2666, total avg loss: 0.2294, batch size: 32
2021-10-14 22:25:57,814 INFO [train.py:451] Epoch 9, batch 5860, batch avg loss 0.1879, total avg loss: 0.2276, batch size: 27
2021-10-14 22:26:02,883 INFO [train.py:451] Epoch 9, batch 5870, batch avg loss 0.2560, total avg loss: 0.2276, batch size: 56
2021-10-14 22:26:07,607 INFO [train.py:451] Epoch 9, batch 5880, batch avg loss 0.2709, total avg loss: 0.2291, batch size: 49
2021-10-14 22:26:12,597 INFO [train.py:451] Epoch 9, batch 5890, batch avg loss 0.2532, total avg loss: 0.2291, batch size: 33
2021-10-14 22:26:17,555 INFO [train.py:451] Epoch 9, batch 5900, batch avg loss 0.2321, total avg loss: 0.2287, batch size: 34
2021-10-14 22:26:19,234 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "4c031532-0e0c-ccf9-ac4d-551bf810c8f0" will not be mixed in.
2021-10-14 22:26:22,488 INFO [train.py:451] Epoch 9, batch 5910, batch avg loss 0.1966, total avg loss: 0.2271, batch size: 32
2021-10-14 22:26:27,792 INFO [train.py:451] Epoch 9, batch 5920, batch avg loss 0.2518, total avg loss: 0.2258, batch size: 39
2021-10-14 22:26:32,801 INFO [train.py:451] Epoch 9, batch 5930, batch avg loss 0.3057, total avg loss: 0.2250, batch size: 128
2021-10-14 22:26:37,818 INFO [train.py:451] Epoch 9, batch 5940, batch avg loss 0.1952, total avg loss: 0.2246, batch size: 30
2021-10-14 22:26:42,972 INFO [train.py:451] Epoch 9, batch 5950, batch avg loss 0.3731, total avg loss: 0.2243, batch size: 133
2021-10-14 22:26:48,072 INFO [train.py:451] Epoch 9, batch 5960, batch avg loss 0.3340, total avg loss: 0.2246, batch size: 129
2021-10-14 22:26:53,089 INFO [train.py:451] Epoch 9, batch 5970, batch avg loss 0.1878, total avg loss: 0.2243, batch size: 31
2021-10-14 22:26:58,103 INFO [train.py:451] Epoch 9, batch 5980, batch avg loss 0.2250, total avg loss: 0.2240, batch size: 33
2021-10-14 22:27:03,114 INFO [train.py:451] Epoch 9, batch 5990, batch avg loss 0.2685, total avg loss: 0.2248, batch size: 33
2021-10-14 22:27:08,258 INFO [train.py:451] Epoch 9, batch 6000, batch avg loss 0.2075, total avg loss: 0.2242, batch size: 32
2021-10-14 22:27:47,977 INFO [train.py:483] Epoch 9, valid loss 0.1644, best valid loss: 0.1639 best valid epoch: 9
2021-10-14 22:27:52,893 INFO [train.py:451] Epoch 9, batch 6010, batch avg loss 0.2087, total avg loss: 0.2116, batch size: 31
2021-10-14 22:27:57,791 INFO [train.py:451] Epoch 9, batch 6020, batch avg loss 0.2083, total avg loss: 0.2146, batch size: 32
2021-10-14 22:28:02,728 INFO [train.py:451] Epoch 9, batch 6030, batch avg loss 0.2242, total avg loss: 0.2198, batch size: 33
2021-10-14 22:28:07,818 INFO [train.py:451] Epoch 9, batch 6040, batch avg loss 0.2178, total avg loss: 0.2202, batch size: 36
2021-10-14 22:28:12,679 INFO [train.py:451] Epoch 9, batch 6050, batch avg loss 0.2285, total avg loss: 0.2208, batch size: 42
2021-10-14 22:28:17,530 INFO [train.py:451] Epoch 9, batch 6060, batch avg loss 0.2548, total avg loss: 0.2208, batch size: 56
2021-10-14 22:28:22,368 INFO [train.py:451] Epoch 9, batch 6070, batch avg loss 0.2113, total avg loss: 0.2192, batch size: 37
2021-10-14 22:28:27,404 INFO [train.py:451] Epoch 9, batch 6080, batch avg loss 0.1795, total avg loss: 0.2184, batch size: 31
2021-10-14 22:28:32,185 INFO [train.py:451] Epoch 9, batch 6090, batch avg loss 0.2021, total avg loss: 0.2196, batch size: 36
2021-10-14 22:28:37,145 INFO [train.py:451] Epoch 9, batch 6100, batch avg loss 0.2615, total avg loss: 0.2183, batch size: 35
2021-10-14 22:28:41,946 INFO [train.py:451] Epoch 9, batch 6110, batch avg loss 0.2221, total avg loss: 0.2194, batch size: 36
2021-10-14 22:28:46,805 INFO [train.py:451] Epoch 9, batch 6120, batch avg loss 0.1969, total avg loss: 0.2206, batch size: 34
2021-10-14 22:28:51,725 INFO [train.py:451] Epoch 9, batch 6130, batch avg loss 0.2029, total avg loss: 0.2209, batch size: 30
2021-10-14 22:28:56,702 INFO [train.py:451] Epoch 9, batch 6140, batch avg loss 0.2319, total avg loss: 0.2204, batch size: 57
2021-10-14 22:29:01,586 INFO [train.py:451] Epoch 9, batch 6150, batch avg loss 0.1896, total avg loss: 0.2199, batch size: 31
2021-10-14 22:29:06,589 INFO [train.py:451] Epoch 9, batch 6160, batch avg loss 0.2018, total avg loss: 0.2197, batch size: 34
2021-10-14 22:29:11,285 INFO [train.py:451] Epoch 9, batch 6170, batch avg loss 0.3625, total avg loss: 0.2213, batch size: 129
2021-10-14 22:29:16,331 INFO [train.py:451] Epoch 9, batch 6180, batch avg loss 0.1540, total avg loss: 0.2202, batch size: 32
2021-10-14 22:29:21,259 INFO [train.py:451] Epoch 9, batch 6190, batch avg loss 0.2216, total avg loss: 0.2206, batch size: 49
2021-10-14 22:29:26,334 INFO [train.py:451] Epoch 9, batch 6200, batch avg loss 0.2317, total avg loss: 0.2201, batch size: 33
2021-10-14 22:29:31,270 INFO [train.py:451] Epoch 9, batch 6210, batch avg loss 0.1698, total avg loss: 0.2313, batch size: 30
2021-10-14 22:29:36,160 INFO [train.py:451] Epoch 9, batch 6220, batch avg loss 0.2225, total avg loss: 0.2255, batch size: 38
2021-10-14 22:29:41,051 INFO [train.py:451] Epoch 9, batch 6230, batch avg loss 0.2412, total avg loss: 0.2210, batch size: 37
2021-10-14 22:29:45,955 INFO [train.py:451] Epoch 9, batch 6240, batch avg loss 0.2399, total avg loss: 0.2189, batch size: 42
2021-10-14 22:29:50,833 INFO [train.py:451] Epoch 9, batch 6250, batch avg loss 0.2350, total avg loss: 0.2175, batch size: 56
2021-10-14 22:29:55,759 INFO [train.py:451] Epoch 9, batch 6260, batch avg loss 0.1962, total avg loss: 0.2165, batch size: 38
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2021-10-14 22:36:38,834 INFO [train.py:483] Epoch 9, valid loss 0.1636, best valid loss: 0.1636 best valid epoch: 9
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2021-10-14 22:45:29,996 INFO [train.py:483] Epoch 9, valid loss 0.1634, best valid loss: 0.1634 best valid epoch: 9
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2021-10-14 22:54:19,878 INFO [train.py:483] Epoch 9, valid loss 0.1642, best valid loss: 0.1634 best valid epoch: 9
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2021-10-14 22:55:58,874 INFO [train.py:451] Epoch 9, batch 9200, batch avg loss 0.2006, total avg loss: 0.2141, batch size: 34
2021-10-14 22:56:03,840 INFO [train.py:451] Epoch 9, batch 9210, batch avg loss 0.2343, total avg loss: 0.2195, batch size: 31
2021-10-14 22:56:08,780 INFO [train.py:451] Epoch 9, batch 9220, batch avg loss 0.1702, total avg loss: 0.2135, batch size: 29
2021-10-14 22:56:13,562 INFO [train.py:451] Epoch 9, batch 9230, batch avg loss 0.2300, total avg loss: 0.2186, batch size: 34
2021-10-14 22:56:18,529 INFO [train.py:451] Epoch 9, batch 9240, batch avg loss 0.1753, total avg loss: 0.2156, batch size: 34
2021-10-14 22:56:23,384 INFO [train.py:451] Epoch 9, batch 9250, batch avg loss 0.2636, total avg loss: 0.2172, batch size: 36
2021-10-14 22:56:28,393 INFO [train.py:451] Epoch 9, batch 9260, batch avg loss 0.1858, total avg loss: 0.2148, batch size: 36
2021-10-14 22:56:33,122 INFO [train.py:451] Epoch 9, batch 9270, batch avg loss 0.2179, total avg loss: 0.2151, batch size: 31
2021-10-14 22:56:37,858 INFO [train.py:451] Epoch 9, batch 9280, batch avg loss 0.2192, total avg loss: 0.2149, batch size: 73
2021-10-14 22:56:42,742 INFO [train.py:451] Epoch 9, batch 9290, batch avg loss 0.2558, total avg loss: 0.2156, batch size: 72
2021-10-14 22:56:47,657 INFO [train.py:451] Epoch 9, batch 9300, batch avg loss 0.2474, total avg loss: 0.2160, batch size: 49
2021-10-14 22:56:52,477 INFO [train.py:451] Epoch 9, batch 9310, batch avg loss 0.2825, total avg loss: 0.2179, batch size: 35
2021-10-14 22:56:57,462 INFO [train.py:451] Epoch 9, batch 9320, batch avg loss 0.2471, total avg loss: 0.2167, batch size: 35
2021-10-14 22:57:02,524 INFO [train.py:451] Epoch 9, batch 9330, batch avg loss 0.2005, total avg loss: 0.2168, batch size: 34
2021-10-14 22:57:07,165 INFO [train.py:451] Epoch 9, batch 9340, batch avg loss 0.2572, total avg loss: 0.2176, batch size: 42
2021-10-14 22:57:11,920 INFO [train.py:451] Epoch 9, batch 9350, batch avg loss 0.1510, total avg loss: 0.2180, batch size: 30
2021-10-14 22:57:16,816 INFO [train.py:451] Epoch 9, batch 9360, batch avg loss 0.2007, total avg loss: 0.2180, batch size: 38
2021-10-14 22:57:21,720 INFO [train.py:451] Epoch 9, batch 9370, batch avg loss 0.2086, total avg loss: 0.2179, batch size: 35
2021-10-14 22:57:26,627 INFO [train.py:451] Epoch 9, batch 9380, batch avg loss 0.2222, total avg loss: 0.2186, batch size: 33
2021-10-14 22:57:31,640 INFO [train.py:451] Epoch 9, batch 9390, batch avg loss 0.1888, total avg loss: 0.2183, batch size: 32
2021-10-14 22:57:36,518 INFO [train.py:451] Epoch 9, batch 9400, batch avg loss 0.1768, total avg loss: 0.2181, batch size: 30
2021-10-14 22:57:41,570 INFO [train.py:451] Epoch 9, batch 9410, batch avg loss 0.2313, total avg loss: 0.2131, batch size: 34
2021-10-14 22:57:46,466 INFO [train.py:451] Epoch 9, batch 9420, batch avg loss 0.3007, total avg loss: 0.2185, batch size: 49
2021-10-14 22:57:51,335 INFO [train.py:451] Epoch 9, batch 9430, batch avg loss 0.1558, total avg loss: 0.2209, batch size: 29
2021-10-14 22:57:56,036 INFO [train.py:451] Epoch 9, batch 9440, batch avg loss 0.3471, total avg loss: 0.2251, batch size: 128
2021-10-14 22:58:01,173 INFO [train.py:451] Epoch 9, batch 9450, batch avg loss 0.2663, total avg loss: 0.2230, batch size: 39
2021-10-14 22:58:06,096 INFO [train.py:451] Epoch 9, batch 9460, batch avg loss 0.2306, total avg loss: 0.2205, batch size: 45
2021-10-14 22:58:11,226 INFO [train.py:451] Epoch 9, batch 9470, batch avg loss 0.1741, total avg loss: 0.2198, batch size: 30
2021-10-14 22:58:16,214 INFO [train.py:451] Epoch 9, batch 9480, batch avg loss 0.1995, total avg loss: 0.2186, batch size: 30
2021-10-14 22:58:21,131 INFO [train.py:451] Epoch 9, batch 9490, batch avg loss 0.2333, total avg loss: 0.2185, batch size: 36
2021-10-14 22:58:26,016 INFO [train.py:451] Epoch 9, batch 9500, batch avg loss 0.2116, total avg loss: 0.2168, batch size: 34
2021-10-14 22:58:30,863 INFO [train.py:451] Epoch 9, batch 9510, batch avg loss 0.1718, total avg loss: 0.2166, batch size: 30
2021-10-14 22:58:35,833 INFO [train.py:451] Epoch 9, batch 9520, batch avg loss 0.2336, total avg loss: 0.2160, batch size: 33
2021-10-14 22:58:40,844 INFO [train.py:451] Epoch 9, batch 9530, batch avg loss 0.1718, total avg loss: 0.2161, batch size: 31
2021-10-14 22:58:45,883 INFO [train.py:451] Epoch 9, batch 9540, batch avg loss 0.2407, total avg loss: 0.2163, batch size: 33
2021-10-14 22:58:50,640 INFO [train.py:451] Epoch 9, batch 9550, batch avg loss 0.2218, total avg loss: 0.2184, batch size: 41
2021-10-14 22:58:55,629 INFO [train.py:451] Epoch 9, batch 9560, batch avg loss 0.2356, total avg loss: 0.2184, batch size: 41
2021-10-14 22:59:00,518 INFO [train.py:451] Epoch 9, batch 9570, batch avg loss 0.2524, total avg loss: 0.2178, batch size: 42
2021-10-14 22:59:05,322 INFO [train.py:451] Epoch 9, batch 9580, batch avg loss 0.1856, total avg loss: 0.2182, batch size: 45
2021-10-14 22:59:10,347 INFO [train.py:451] Epoch 9, batch 9590, batch avg loss 0.2086, total avg loss: 0.2175, batch size: 36
2021-10-14 22:59:15,192 INFO [train.py:451] Epoch 9, batch 9600, batch avg loss 0.1600, total avg loss: 0.2179, batch size: 30
2021-10-14 22:59:20,016 INFO [train.py:451] Epoch 9, batch 9610, batch avg loss 0.2522, total avg loss: 0.2277, batch size: 35
2021-10-14 22:59:24,941 INFO [train.py:451] Epoch 9, batch 9620, batch avg loss 0.1538, total avg loss: 0.2222, batch size: 28
2021-10-14 22:59:29,860 INFO [train.py:451] Epoch 9, batch 9630, batch avg loss 0.2446, total avg loss: 0.2247, batch size: 35
2021-10-14 22:59:34,884 INFO [train.py:451] Epoch 9, batch 9640, batch avg loss 0.2239, total avg loss: 0.2245, batch size: 31
2021-10-14 22:59:39,825 INFO [train.py:451] Epoch 9, batch 9650, batch avg loss 0.2285, total avg loss: 0.2238, batch size: 35
2021-10-14 22:59:44,636 INFO [train.py:451] Epoch 9, batch 9660, batch avg loss 0.2280, total avg loss: 0.2246, batch size: 49
2021-10-14 22:59:49,624 INFO [train.py:451] Epoch 9, batch 9670, batch avg loss 0.1541, total avg loss: 0.2228, batch size: 27
2021-10-14 22:59:54,826 INFO [train.py:451] Epoch 9, batch 9680, batch avg loss 0.2312, total avg loss: 0.2237, batch size: 29
2021-10-14 22:59:59,612 INFO [train.py:451] Epoch 9, batch 9690, batch avg loss 0.2451, total avg loss: 0.2228, batch size: 49
2021-10-14 23:00:04,453 INFO [train.py:451] Epoch 9, batch 9700, batch avg loss 0.1957, total avg loss: 0.2218, batch size: 36
2021-10-14 23:00:09,329 INFO [train.py:451] Epoch 9, batch 9710, batch avg loss 0.2056, total avg loss: 0.2212, batch size: 39
2021-10-14 23:00:14,370 INFO [train.py:451] Epoch 9, batch 9720, batch avg loss 0.2176, total avg loss: 0.2195, batch size: 34
2021-10-14 23:00:19,334 INFO [train.py:451] Epoch 9, batch 9730, batch avg loss 0.1766, total avg loss: 0.2195, batch size: 29
2021-10-14 23:00:24,340 INFO [train.py:451] Epoch 9, batch 9740, batch avg loss 0.2024, total avg loss: 0.2191, batch size: 34
2021-10-14 23:00:29,185 INFO [train.py:451] Epoch 9, batch 9750, batch avg loss 0.1708, total avg loss: 0.2185, batch size: 33
2021-10-14 23:00:34,030 INFO [train.py:451] Epoch 9, batch 9760, batch avg loss 0.2001, total avg loss: 0.2190, batch size: 38
2021-10-14 23:00:38,883 INFO [train.py:451] Epoch 9, batch 9770, batch avg loss 0.2501, total avg loss: 0.2181, batch size: 42
2021-10-14 23:00:43,840 INFO [train.py:451] Epoch 9, batch 9780, batch avg loss 0.2018, total avg loss: 0.2183, batch size: 32
2021-10-14 23:00:48,669 INFO [train.py:451] Epoch 9, batch 9790, batch avg loss 0.2519, total avg loss: 0.2192, batch size: 57
2021-10-14 23:00:53,646 INFO [train.py:451] Epoch 9, batch 9800, batch avg loss 0.2036, total avg loss: 0.2189, batch size: 36
2021-10-14 23:00:58,596 INFO [train.py:451] Epoch 9, batch 9810, batch avg loss 0.1766, total avg loss: 0.2129, batch size: 32
2021-10-14 23:01:03,491 INFO [train.py:451] Epoch 9, batch 9820, batch avg loss 0.2144, total avg loss: 0.2122, batch size: 42
2021-10-14 23:01:08,320 INFO [train.py:451] Epoch 9, batch 9830, batch avg loss 0.1999, total avg loss: 0.2169, batch size: 36
2021-10-14 23:01:13,313 INFO [train.py:451] Epoch 9, batch 9840, batch avg loss 0.2433, total avg loss: 0.2162, batch size: 37
2021-10-14 23:01:18,144 INFO [train.py:451] Epoch 9, batch 9850, batch avg loss 0.2702, total avg loss: 0.2199, batch size: 35
2021-10-14 23:01:23,100 INFO [train.py:451] Epoch 9, batch 9860, batch avg loss 0.2482, total avg loss: 0.2205, batch size: 49
2021-10-14 23:01:27,999 INFO [train.py:451] Epoch 9, batch 9870, batch avg loss 0.1773, total avg loss: 0.2210, batch size: 41
2021-10-14 23:01:33,042 INFO [train.py:451] Epoch 9, batch 9880, batch avg loss 0.2113, total avg loss: 0.2192, batch size: 37
2021-10-14 23:01:38,152 INFO [train.py:451] Epoch 9, batch 9890, batch avg loss 0.2394, total avg loss: 0.2196, batch size: 34
2021-10-14 23:01:43,162 INFO [train.py:451] Epoch 9, batch 9900, batch avg loss 0.2304, total avg loss: 0.2181, batch size: 38
2021-10-14 23:01:47,892 INFO [train.py:451] Epoch 9, batch 9910, batch avg loss 0.1798, total avg loss: 0.2189, batch size: 31
2021-10-14 23:01:52,773 INFO [train.py:451] Epoch 9, batch 9920, batch avg loss 0.2177, total avg loss: 0.2199, batch size: 34
2021-10-14 23:01:57,879 INFO [train.py:451] Epoch 9, batch 9930, batch avg loss 0.2154, total avg loss: 0.2193, batch size: 29
2021-10-14 23:02:02,914 INFO [train.py:451] Epoch 9, batch 9940, batch avg loss 0.1994, total avg loss: 0.2173, batch size: 42
2021-10-14 23:02:08,032 INFO [train.py:451] Epoch 9, batch 9950, batch avg loss 0.2159, total avg loss: 0.2170, batch size: 36
2021-10-14 23:02:12,899 INFO [train.py:451] Epoch 9, batch 9960, batch avg loss 0.2568, total avg loss: 0.2182, batch size: 41
2021-10-14 23:02:13,655 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "6697c14a-dc7e-762e-6055-16dd68e2807f" will not be mixed in.
2021-10-14 23:02:17,764 INFO [train.py:451] Epoch 9, batch 9970, batch avg loss 0.3089, total avg loss: 0.2198, batch size: 126
2021-10-14 23:02:22,639 INFO [train.py:451] Epoch 9, batch 9980, batch avg loss 0.1977, total avg loss: 0.2205, batch size: 36
2021-10-14 23:02:27,398 INFO [train.py:451] Epoch 9, batch 9990, batch avg loss 0.2493, total avg loss: 0.2208, batch size: 71
2021-10-14 23:02:32,320 INFO [train.py:451] Epoch 9, batch 10000, batch avg loss 0.1753, total avg loss: 0.2211, batch size: 28
2021-10-14 23:03:11,849 INFO [train.py:483] Epoch 9, valid loss 0.1629, best valid loss: 0.1629 best valid epoch: 9
2021-10-14 23:03:16,617 INFO [train.py:451] Epoch 9, batch 10010, batch avg loss 0.1786, total avg loss: 0.2331, batch size: 29
2021-10-14 23:03:21,404 INFO [train.py:451] Epoch 9, batch 10020, batch avg loss 0.2196, total avg loss: 0.2264, batch size: 33
2021-10-14 23:03:26,426 INFO [train.py:451] Epoch 9, batch 10030, batch avg loss 0.1957, total avg loss: 0.2192, batch size: 33
2021-10-14 23:03:31,218 INFO [train.py:451] Epoch 9, batch 10040, batch avg loss 0.2758, total avg loss: 0.2236, batch size: 74
2021-10-14 23:03:36,222 INFO [train.py:451] Epoch 9, batch 10050, batch avg loss 0.1649, total avg loss: 0.2211, batch size: 28
2021-10-14 23:03:41,058 INFO [train.py:451] Epoch 9, batch 10060, batch avg loss 0.2253, total avg loss: 0.2242, batch size: 35
2021-10-14 23:03:45,677 INFO [train.py:451] Epoch 9, batch 10070, batch avg loss 0.2361, total avg loss: 0.2249, batch size: 39
2021-10-14 23:03:50,357 INFO [train.py:451] Epoch 9, batch 10080, batch avg loss 0.2565, total avg loss: 0.2273, batch size: 56
2021-10-14 23:03:55,323 INFO [train.py:451] Epoch 9, batch 10090, batch avg loss 0.2204, total avg loss: 0.2249, batch size: 35
2021-10-14 23:04:00,073 INFO [train.py:451] Epoch 9, batch 10100, batch avg loss 0.1867, total avg loss: 0.2271, batch size: 32
2021-10-14 23:04:05,245 INFO [train.py:451] Epoch 9, batch 10110, batch avg loss 0.1923, total avg loss: 0.2244, batch size: 30
2021-10-14 23:04:10,140 INFO [train.py:451] Epoch 9, batch 10120, batch avg loss 0.1858, total avg loss: 0.2242, batch size: 34
2021-10-14 23:04:15,005 INFO [train.py:451] Epoch 9, batch 10130, batch avg loss 0.2517, total avg loss: 0.2243, batch size: 49
2021-10-14 23:04:19,843 INFO [train.py:451] Epoch 9, batch 10140, batch avg loss 0.2505, total avg loss: 0.2253, batch size: 45
2021-10-14 23:04:24,645 INFO [train.py:451] Epoch 9, batch 10150, batch avg loss 0.2608, total avg loss: 0.2255, batch size: 41
2021-10-14 23:04:29,468 INFO [train.py:451] Epoch 9, batch 10160, batch avg loss 0.3253, total avg loss: 0.2252, batch size: 128
2021-10-14 23:04:34,349 INFO [train.py:451] Epoch 9, batch 10170, batch avg loss 0.2019, total avg loss: 0.2242, batch size: 29
2021-10-14 23:04:39,186 INFO [train.py:451] Epoch 9, batch 10180, batch avg loss 0.2568, total avg loss: 0.2243, batch size: 35
2021-10-14 23:04:44,026 INFO [train.py:451] Epoch 9, batch 10190, batch avg loss 0.2136, total avg loss: 0.2242, batch size: 32
2021-10-14 23:04:48,883 INFO [train.py:451] Epoch 9, batch 10200, batch avg loss 0.2733, total avg loss: 0.2242, batch size: 73
2021-10-14 23:04:53,859 INFO [train.py:451] Epoch 9, batch 10210, batch avg loss 0.2286, total avg loss: 0.2097, batch size: 34
2021-10-14 23:04:59,133 INFO [train.py:451] Epoch 9, batch 10220, batch avg loss 0.1696, total avg loss: 0.2104, batch size: 30
2021-10-14 23:05:03,795 INFO [train.py:451] Epoch 9, batch 10230, batch avg loss 0.2866, total avg loss: 0.2196, batch size: 74
2021-10-14 23:05:08,596 INFO [train.py:451] Epoch 9, batch 10240, batch avg loss 0.2974, total avg loss: 0.2245, batch size: 57
2021-10-14 23:05:13,436 INFO [train.py:451] Epoch 9, batch 10250, batch avg loss 0.2387, total avg loss: 0.2235, batch size: 73
2021-10-14 23:05:18,421 INFO [train.py:451] Epoch 9, batch 10260, batch avg loss 0.1793, total avg loss: 0.2220, batch size: 32
2021-10-14 23:05:23,342 INFO [train.py:451] Epoch 9, batch 10270, batch avg loss 0.1986, total avg loss: 0.2221, batch size: 42
2021-10-14 23:05:28,393 INFO [train.py:451] Epoch 9, batch 10280, batch avg loss 0.2800, total avg loss: 0.2218, batch size: 35
2021-10-14 23:05:33,505 INFO [train.py:451] Epoch 9, batch 10290, batch avg loss 0.1906, total avg loss: 0.2197, batch size: 30
2021-10-14 23:05:38,424 INFO [train.py:451] Epoch 9, batch 10300, batch avg loss 0.2451, total avg loss: 0.2197, batch size: 49
2021-10-14 23:05:43,141 INFO [train.py:451] Epoch 9, batch 10310, batch avg loss 0.2652, total avg loss: 0.2199, batch size: 73
2021-10-14 23:05:48,121 INFO [train.py:451] Epoch 9, batch 10320, batch avg loss 0.1895, total avg loss: 0.2196, batch size: 30
2021-10-14 23:05:52,884 INFO [train.py:451] Epoch 9, batch 10330, batch avg loss 0.2374, total avg loss: 0.2200, batch size: 73
2021-10-14 23:05:57,671 INFO [train.py:451] Epoch 9, batch 10340, batch avg loss 0.1895, total avg loss: 0.2210, batch size: 34
2021-10-14 23:06:02,605 INFO [train.py:451] Epoch 9, batch 10350, batch avg loss 0.2171, total avg loss: 0.2211, batch size: 38
2021-10-14 23:06:07,607 INFO [train.py:451] Epoch 9, batch 10360, batch avg loss 0.2349, total avg loss: 0.2214, batch size: 34
2021-10-14 23:06:12,671 INFO [train.py:451] Epoch 9, batch 10370, batch avg loss 0.1939, total avg loss: 0.2204, batch size: 27
2021-10-14 23:06:17,719 INFO [train.py:451] Epoch 9, batch 10380, batch avg loss 0.2319, total avg loss: 0.2201, batch size: 34
2021-10-14 23:06:22,690 INFO [train.py:451] Epoch 9, batch 10390, batch avg loss 0.3163, total avg loss: 0.2199, batch size: 126
2021-10-14 23:06:27,640 INFO [train.py:451] Epoch 9, batch 10400, batch avg loss 0.2253, total avg loss: 0.2202, batch size: 41
2021-10-14 23:06:32,482 INFO [train.py:451] Epoch 9, batch 10410, batch avg loss 0.2214, total avg loss: 0.2284, batch size: 35
2021-10-14 23:06:37,560 INFO [train.py:451] Epoch 9, batch 10420, batch avg loss 0.2569, total avg loss: 0.2157, batch size: 36
2021-10-14 23:06:42,537 INFO [train.py:451] Epoch 9, batch 10430, batch avg loss 0.2522, total avg loss: 0.2197, batch size: 35
2021-10-14 23:06:47,613 INFO [train.py:451] Epoch 9, batch 10440, batch avg loss 0.2354, total avg loss: 0.2183, batch size: 35
2021-10-14 23:06:52,738 INFO [train.py:451] Epoch 9, batch 10450, batch avg loss 0.1865, total avg loss: 0.2153, batch size: 36
2021-10-14 23:06:57,532 INFO [train.py:451] Epoch 9, batch 10460, batch avg loss 0.2239, total avg loss: 0.2206, batch size: 30
2021-10-14 23:07:02,428 INFO [train.py:451] Epoch 9, batch 10470, batch avg loss 0.2366, total avg loss: 0.2205, batch size: 42
2021-10-14 23:07:07,382 INFO [train.py:451] Epoch 9, batch 10480, batch avg loss 0.2943, total avg loss: 0.2184, batch size: 129
2021-10-14 23:07:12,420 INFO [train.py:451] Epoch 9, batch 10490, batch avg loss 0.1843, total avg loss: 0.2193, batch size: 33
2021-10-14 23:07:17,458 INFO [train.py:451] Epoch 9, batch 10500, batch avg loss 0.2510, total avg loss: 0.2201, batch size: 56
2021-10-14 23:07:22,438 INFO [train.py:451] Epoch 9, batch 10510, batch avg loss 0.2431, total avg loss: 0.2205, batch size: 35
2021-10-14 23:07:27,493 INFO [train.py:451] Epoch 9, batch 10520, batch avg loss 0.2203, total avg loss: 0.2201, batch size: 33
2021-10-14 23:07:32,612 INFO [train.py:451] Epoch 9, batch 10530, batch avg loss 0.2575, total avg loss: 0.2189, batch size: 35
2021-10-14 23:07:37,403 INFO [train.py:451] Epoch 9, batch 10540, batch avg loss 0.2362, total avg loss: 0.2199, batch size: 45
2021-10-14 23:07:42,235 INFO [train.py:451] Epoch 9, batch 10550, batch avg loss 0.2100, total avg loss: 0.2198, batch size: 32
2021-10-14 23:07:46,933 INFO [train.py:451] Epoch 9, batch 10560, batch avg loss 0.2318, total avg loss: 0.2202, batch size: 42
2021-10-14 23:07:51,798 INFO [train.py:451] Epoch 9, batch 10570, batch avg loss 0.2428, total avg loss: 0.2211, batch size: 38
2021-10-14 23:07:56,615 INFO [train.py:451] Epoch 9, batch 10580, batch avg loss 0.2411, total avg loss: 0.2207, batch size: 38
2021-10-14 23:08:01,527 INFO [train.py:451] Epoch 9, batch 10590, batch avg loss 0.2219, total avg loss: 0.2210, batch size: 33
2021-10-14 23:08:06,432 INFO [train.py:451] Epoch 9, batch 10600, batch avg loss 0.2245, total avg loss: 0.2208, batch size: 36
2021-10-14 23:08:11,283 INFO [train.py:451] Epoch 9, batch 10610, batch avg loss 0.3098, total avg loss: 0.2347, batch size: 129
2021-10-14 23:08:16,003 INFO [train.py:451] Epoch 9, batch 10620, batch avg loss 0.2466, total avg loss: 0.2358, batch size: 39
2021-10-14 23:08:20,804 INFO [train.py:451] Epoch 9, batch 10630, batch avg loss 0.1901, total avg loss: 0.2317, batch size: 30
2021-10-14 23:08:25,864 INFO [train.py:451] Epoch 9, batch 10640, batch avg loss 0.1848, total avg loss: 0.2273, batch size: 36
2021-10-14 23:08:30,650 INFO [train.py:451] Epoch 9, batch 10650, batch avg loss 0.2016, total avg loss: 0.2254, batch size: 35
2021-10-14 23:08:35,538 INFO [train.py:451] Epoch 9, batch 10660, batch avg loss 0.1742, total avg loss: 0.2244, batch size: 34
2021-10-14 23:08:40,450 INFO [train.py:451] Epoch 9, batch 10670, batch avg loss 0.2361, total avg loss: 0.2240, batch size: 36
2021-10-14 23:08:45,253 INFO [train.py:451] Epoch 9, batch 10680, batch avg loss 0.1806, total avg loss: 0.2223, batch size: 29
2021-10-14 23:08:50,159 INFO [train.py:451] Epoch 9, batch 10690, batch avg loss 0.2318, total avg loss: 0.2209, batch size: 29
2021-10-14 23:08:55,016 INFO [train.py:451] Epoch 9, batch 10700, batch avg loss 0.2025, total avg loss: 0.2223, batch size: 33
2021-10-14 23:08:59,902 INFO [train.py:451] Epoch 9, batch 10710, batch avg loss 0.2581, total avg loss: 0.2228, batch size: 35
2021-10-14 23:09:04,902 INFO [train.py:451] Epoch 9, batch 10720, batch avg loss 0.2078, total avg loss: 0.2232, batch size: 33
2021-10-14 23:09:09,929 INFO [train.py:451] Epoch 9, batch 10730, batch avg loss 0.2011, total avg loss: 0.2227, batch size: 34
2021-10-14 23:09:15,027 INFO [train.py:451] Epoch 9, batch 10740, batch avg loss 0.2557, total avg loss: 0.2232, batch size: 33
2021-10-14 23:09:20,026 INFO [train.py:451] Epoch 9, batch 10750, batch avg loss 0.2712, total avg loss: 0.2227, batch size: 39
2021-10-14 23:09:24,908 INFO [train.py:451] Epoch 9, batch 10760, batch avg loss 0.1856, total avg loss: 0.2224, batch size: 33
2021-10-14 23:09:29,739 INFO [train.py:451] Epoch 9, batch 10770, batch avg loss 0.2221, total avg loss: 0.2227, batch size: 57
2021-10-14 23:09:34,821 INFO [train.py:451] Epoch 9, batch 10780, batch avg loss 0.2239, total avg loss: 0.2224, batch size: 41
2021-10-14 23:09:39,683 INFO [train.py:451] Epoch 9, batch 10790, batch avg loss 0.2370, total avg loss: 0.2230, batch size: 38
2021-10-14 23:09:44,645 INFO [train.py:451] Epoch 9, batch 10800, batch avg loss 0.1726, total avg loss: 0.2226, batch size: 33
2021-10-14 23:09:49,828 INFO [train.py:451] Epoch 9, batch 10810, batch avg loss 0.2487, total avg loss: 0.2149, batch size: 31
2021-10-14 23:09:54,713 INFO [train.py:451] Epoch 9, batch 10820, batch avg loss 0.1937, total avg loss: 0.2134, batch size: 33
2021-10-14 23:09:59,632 INFO [train.py:451] Epoch 9, batch 10830, batch avg loss 0.1804, total avg loss: 0.2138, batch size: 30
2021-10-14 23:10:04,823 INFO [train.py:451] Epoch 9, batch 10840, batch avg loss 0.1710, total avg loss: 0.2091, batch size: 27
2021-10-14 23:10:09,734 INFO [train.py:451] Epoch 9, batch 10850, batch avg loss 0.2609, total avg loss: 0.2139, batch size: 42
2021-10-14 23:10:14,584 INFO [train.py:451] Epoch 9, batch 10860, batch avg loss 0.3410, total avg loss: 0.2210, batch size: 126
2021-10-14 23:10:19,427 INFO [train.py:451] Epoch 9, batch 10870, batch avg loss 0.2166, total avg loss: 0.2189, batch size: 38
2021-10-14 23:10:24,347 INFO [train.py:451] Epoch 9, batch 10880, batch avg loss 0.2168, total avg loss: 0.2201, batch size: 32
2021-10-14 23:10:29,200 INFO [train.py:451] Epoch 9, batch 10890, batch avg loss 0.2591, total avg loss: 0.2199, batch size: 36
2021-10-14 23:10:34,048 INFO [train.py:451] Epoch 9, batch 10900, batch avg loss 0.2652, total avg loss: 0.2200, batch size: 57
2021-10-14 23:10:38,914 INFO [train.py:451] Epoch 9, batch 10910, batch avg loss 0.1759, total avg loss: 0.2202, batch size: 30
2021-10-14 23:10:43,713 INFO [train.py:451] Epoch 9, batch 10920, batch avg loss 0.2113, total avg loss: 0.2203, batch size: 38
2021-10-14 23:10:48,723 INFO [train.py:451] Epoch 9, batch 10930, batch avg loss 0.2036, total avg loss: 0.2198, batch size: 32
2021-10-14 23:10:53,819 INFO [train.py:451] Epoch 9, batch 10940, batch avg loss 0.1767, total avg loss: 0.2195, batch size: 28
2021-10-14 23:10:58,786 INFO [train.py:451] Epoch 9, batch 10950, batch avg loss 0.2432, total avg loss: 0.2209, batch size: 34
2021-10-14 23:11:03,760 INFO [train.py:451] Epoch 9, batch 10960, batch avg loss 0.2330, total avg loss: 0.2210, batch size: 42
2021-10-14 23:11:08,684 INFO [train.py:451] Epoch 9, batch 10970, batch avg loss 0.2273, total avg loss: 0.2203, batch size: 36
2021-10-14 23:11:13,721 INFO [train.py:451] Epoch 9, batch 10980, batch avg loss 0.2482, total avg loss: 0.2196, batch size: 39
2021-10-14 23:11:18,757 INFO [train.py:451] Epoch 9, batch 10990, batch avg loss 0.2397, total avg loss: 0.2195, batch size: 37
2021-10-14 23:11:23,729 INFO [train.py:451] Epoch 9, batch 11000, batch avg loss 0.2083, total avg loss: 0.2193, batch size: 31
2021-10-14 23:12:03,330 INFO [train.py:483] Epoch 9, valid loss 0.1643, best valid loss: 0.1629 best valid epoch: 9
2021-10-14 23:12:08,266 INFO [train.py:451] Epoch 9, batch 11010, batch avg loss 0.1747, total avg loss: 0.2138, batch size: 29
2021-10-14 23:12:13,229 INFO [train.py:451] Epoch 9, batch 11020, batch avg loss 0.1748, total avg loss: 0.2213, batch size: 29
2021-10-14 23:12:18,191 INFO [train.py:451] Epoch 9, batch 11030, batch avg loss 0.2203, total avg loss: 0.2175, batch size: 36
2021-10-14 23:12:23,203 INFO [train.py:451] Epoch 9, batch 11040, batch avg loss 0.1626, total avg loss: 0.2180, batch size: 30
2021-10-14 23:12:28,095 INFO [train.py:451] Epoch 9, batch 11050, batch avg loss 0.2539, total avg loss: 0.2210, batch size: 34
2021-10-14 23:12:33,278 INFO [train.py:451] Epoch 9, batch 11060, batch avg loss 0.1643, total avg loss: 0.2213, batch size: 33
2021-10-14 23:12:38,238 INFO [train.py:451] Epoch 9, batch 11070, batch avg loss 0.1862, total avg loss: 0.2209, batch size: 30
2021-10-14 23:12:43,280 INFO [train.py:451] Epoch 9, batch 11080, batch avg loss 0.1991, total avg loss: 0.2197, batch size: 33
2021-10-14 23:12:48,184 INFO [train.py:451] Epoch 9, batch 11090, batch avg loss 0.2754, total avg loss: 0.2197, batch size: 57
2021-10-14 23:12:53,102 INFO [train.py:451] Epoch 9, batch 11100, batch avg loss 0.2935, total avg loss: 0.2202, batch size: 73
2021-10-14 23:12:57,871 INFO [train.py:451] Epoch 9, batch 11110, batch avg loss 0.2417, total avg loss: 0.2216, batch size: 39
2021-10-14 23:13:02,654 INFO [train.py:451] Epoch 9, batch 11120, batch avg loss 0.1554, total avg loss: 0.2221, batch size: 28
2021-10-14 23:13:07,646 INFO [train.py:451] Epoch 9, batch 11130, batch avg loss 0.1693, total avg loss: 0.2221, batch size: 31
2021-10-14 23:13:12,636 INFO [train.py:451] Epoch 9, batch 11140, batch avg loss 0.1961, total avg loss: 0.2213, batch size: 31
2021-10-14 23:13:17,483 INFO [train.py:451] Epoch 9, batch 11150, batch avg loss 0.1894, total avg loss: 0.2214, batch size: 31
2021-10-14 23:13:22,431 INFO [train.py:451] Epoch 9, batch 11160, batch avg loss 0.2283, total avg loss: 0.2206, batch size: 34
2021-10-14 23:13:27,426 INFO [train.py:451] Epoch 9, batch 11170, batch avg loss 0.2140, total avg loss: 0.2205, batch size: 34
2021-10-14 23:13:32,293 INFO [train.py:451] Epoch 9, batch 11180, batch avg loss 0.3077, total avg loss: 0.2202, batch size: 72
2021-10-14 23:13:37,090 INFO [train.py:451] Epoch 9, batch 11190, batch avg loss 0.2096, total avg loss: 0.2204, batch size: 37
2021-10-14 23:13:42,100 INFO [train.py:451] Epoch 9, batch 11200, batch avg loss 0.1495, total avg loss: 0.2195, batch size: 27
2021-10-14 23:13:47,085 INFO [train.py:451] Epoch 9, batch 11210, batch avg loss 0.2159, total avg loss: 0.2000, batch size: 32
2021-10-14 23:13:52,054 INFO [train.py:451] Epoch 9, batch 11220, batch avg loss 0.2017, total avg loss: 0.2115, batch size: 34
2021-10-14 23:13:57,114 INFO [train.py:451] Epoch 9, batch 11230, batch avg loss 0.2351, total avg loss: 0.2158, batch size: 35
2021-10-14 23:14:02,012 INFO [train.py:451] Epoch 9, batch 11240, batch avg loss 0.2310, total avg loss: 0.2215, batch size: 38
2021-10-14 23:14:06,926 INFO [train.py:451] Epoch 9, batch 11250, batch avg loss 0.2446, total avg loss: 0.2244, batch size: 41
2021-10-14 23:14:11,957 INFO [train.py:451] Epoch 9, batch 11260, batch avg loss 0.2006, total avg loss: 0.2237, batch size: 34
2021-10-14 23:14:16,886 INFO [train.py:451] Epoch 9, batch 11270, batch avg loss 0.1796, total avg loss: 0.2239, batch size: 31
2021-10-14 23:14:21,928 INFO [train.py:451] Epoch 9, batch 11280, batch avg loss 0.2200, total avg loss: 0.2228, batch size: 33
2021-10-14 23:14:26,960 INFO [train.py:451] Epoch 9, batch 11290, batch avg loss 0.2578, total avg loss: 0.2213, batch size: 72
2021-10-14 23:14:32,298 INFO [train.py:451] Epoch 9, batch 11300, batch avg loss 0.3187, total avg loss: 0.2218, batch size: 127
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2021-10-14 23:14:42,000 INFO [train.py:451] Epoch 9, batch 11320, batch avg loss 0.2317, total avg loss: 0.2200, batch size: 72
2021-10-14 23:14:46,874 INFO [train.py:451] Epoch 9, batch 11330, batch avg loss 0.2133, total avg loss: 0.2197, batch size: 32
2021-10-14 23:14:51,723 INFO [train.py:451] Epoch 9, batch 11340, batch avg loss 0.1752, total avg loss: 0.2184, batch size: 31
2021-10-14 23:14:56,661 INFO [train.py:451] Epoch 9, batch 11350, batch avg loss 0.1848, total avg loss: 0.2190, batch size: 28
2021-10-14 23:15:01,569 INFO [train.py:451] Epoch 9, batch 11360, batch avg loss 0.2031, total avg loss: 0.2187, batch size: 36
2021-10-14 23:15:06,504 INFO [train.py:451] Epoch 9, batch 11370, batch avg loss 0.2447, total avg loss: 0.2193, batch size: 39
2021-10-14 23:15:11,238 INFO [train.py:451] Epoch 9, batch 11380, batch avg loss 0.2511, total avg loss: 0.2206, batch size: 57
2021-10-14 23:15:16,218 INFO [train.py:451] Epoch 9, batch 11390, batch avg loss 0.2428, total avg loss: 0.2199, batch size: 33
2021-10-14 23:15:21,002 INFO [train.py:451] Epoch 9, batch 11400, batch avg loss 0.1989, total avg loss: 0.2204, batch size: 32
2021-10-14 23:15:25,760 INFO [train.py:451] Epoch 9, batch 11410, batch avg loss 0.2308, total avg loss: 0.2532, batch size: 38
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2021-10-14 23:15:35,616 INFO [train.py:451] Epoch 9, batch 11430, batch avg loss 0.2749, total avg loss: 0.2383, batch size: 73
2021-10-14 23:15:40,598 INFO [train.py:451] Epoch 9, batch 11440, batch avg loss 0.2328, total avg loss: 0.2319, batch size: 29
2021-10-14 23:15:45,622 INFO [train.py:451] Epoch 9, batch 11450, batch avg loss 0.2253, total avg loss: 0.2289, batch size: 34
2021-10-14 23:15:50,756 INFO [train.py:451] Epoch 9, batch 11460, batch avg loss 0.2148, total avg loss: 0.2265, batch size: 33
2021-10-14 23:15:55,865 INFO [train.py:451] Epoch 9, batch 11470, batch avg loss 0.2058, total avg loss: 0.2250, batch size: 39
2021-10-14 23:16:00,868 INFO [train.py:451] Epoch 9, batch 11480, batch avg loss 0.1975, total avg loss: 0.2229, batch size: 29
2021-10-14 23:16:05,768 INFO [train.py:451] Epoch 9, batch 11490, batch avg loss 0.2321, total avg loss: 0.2243, batch size: 31
2021-10-14 23:16:10,687 INFO [train.py:451] Epoch 9, batch 11500, batch avg loss 0.2161, total avg loss: 0.2227, batch size: 35
2021-10-14 23:16:15,658 INFO [train.py:451] Epoch 9, batch 11510, batch avg loss 0.1957, total avg loss: 0.2217, batch size: 33
2021-10-14 23:16:20,496 INFO [train.py:451] Epoch 9, batch 11520, batch avg loss 0.2568, total avg loss: 0.2217, batch size: 35
2021-10-14 23:16:25,426 INFO [train.py:451] Epoch 9, batch 11530, batch avg loss 0.2717, total avg loss: 0.2217, batch size: 35
2021-10-14 23:16:30,359 INFO [train.py:451] Epoch 9, batch 11540, batch avg loss 0.1673, total avg loss: 0.2210, batch size: 31
2021-10-14 23:16:35,329 INFO [train.py:451] Epoch 9, batch 11550, batch avg loss 0.2546, total avg loss: 0.2216, batch size: 45
2021-10-14 23:16:40,056 INFO [train.py:451] Epoch 9, batch 11560, batch avg loss 0.2550, total avg loss: 0.2226, batch size: 35
2021-10-14 23:16:44,948 INFO [train.py:451] Epoch 9, batch 11570, batch avg loss 0.2044, total avg loss: 0.2224, batch size: 57
2021-10-14 23:16:49,815 INFO [train.py:451] Epoch 9, batch 11580, batch avg loss 0.1903, total avg loss: 0.2220, batch size: 36
2021-10-14 23:16:54,837 INFO [train.py:451] Epoch 9, batch 11590, batch avg loss 0.2917, total avg loss: 0.2220, batch size: 38
2021-10-14 23:16:59,694 INFO [train.py:451] Epoch 9, batch 11600, batch avg loss 0.1624, total avg loss: 0.2217, batch size: 29
2021-10-14 23:17:04,359 INFO [train.py:451] Epoch 9, batch 11610, batch avg loss 0.2432, total avg loss: 0.2402, batch size: 38
2021-10-14 23:17:09,163 INFO [train.py:451] Epoch 9, batch 11620, batch avg loss 0.2063, total avg loss: 0.2355, batch size: 32
2021-10-14 23:17:14,004 INFO [train.py:451] Epoch 9, batch 11630, batch avg loss 0.2260, total avg loss: 0.2302, batch size: 37
2021-10-14 23:17:18,801 INFO [train.py:451] Epoch 9, batch 11640, batch avg loss 0.1939, total avg loss: 0.2277, batch size: 31
2021-10-14 23:17:23,754 INFO [train.py:451] Epoch 9, batch 11650, batch avg loss 0.2293, total avg loss: 0.2265, batch size: 39
2021-10-14 23:17:28,712 INFO [train.py:451] Epoch 9, batch 11660, batch avg loss 0.2109, total avg loss: 0.2275, batch size: 33
2021-10-14 23:17:33,666 INFO [train.py:451] Epoch 9, batch 11670, batch avg loss 0.2080, total avg loss: 0.2268, batch size: 28
2021-10-14 23:17:38,574 INFO [train.py:451] Epoch 9, batch 11680, batch avg loss 0.1725, total avg loss: 0.2271, batch size: 28
2021-10-14 23:17:43,528 INFO [train.py:451] Epoch 9, batch 11690, batch avg loss 0.1835, total avg loss: 0.2257, batch size: 31
2021-10-14 23:17:48,457 INFO [train.py:451] Epoch 9, batch 11700, batch avg loss 0.2000, total avg loss: 0.2260, batch size: 29
2021-10-14 23:17:53,513 INFO [train.py:451] Epoch 9, batch 11710, batch avg loss 0.2122, total avg loss: 0.2262, batch size: 33
2021-10-14 23:17:58,408 INFO [train.py:451] Epoch 9, batch 11720, batch avg loss 0.2399, total avg loss: 0.2258, batch size: 36
2021-10-14 23:18:03,300 INFO [train.py:451] Epoch 9, batch 11730, batch avg loss 0.2597, total avg loss: 0.2260, batch size: 35
2021-10-14 23:18:08,203 INFO [train.py:451] Epoch 9, batch 11740, batch avg loss 0.2391, total avg loss: 0.2260, batch size: 34
2021-10-14 23:18:13,168 INFO [train.py:451] Epoch 9, batch 11750, batch avg loss 0.2017, total avg loss: 0.2257, batch size: 34
2021-10-14 23:18:18,175 INFO [train.py:451] Epoch 9, batch 11760, batch avg loss 0.2142, total avg loss: 0.2250, batch size: 38
2021-10-14 23:18:23,019 INFO [train.py:451] Epoch 9, batch 11770, batch avg loss 0.2193, total avg loss: 0.2253, batch size: 27
2021-10-14 23:18:27,886 INFO [train.py:451] Epoch 9, batch 11780, batch avg loss 0.2870, total avg loss: 0.2247, batch size: 38
2021-10-14 23:18:32,885 INFO [train.py:451] Epoch 9, batch 11790, batch avg loss 0.2535, total avg loss: 0.2245, batch size: 38
2021-10-14 23:18:37,782 INFO [train.py:451] Epoch 9, batch 11800, batch avg loss 0.2941, total avg loss: 0.2253, batch size: 35
2021-10-14 23:18:42,694 INFO [train.py:451] Epoch 9, batch 11810, batch avg loss 0.1898, total avg loss: 0.2185, batch size: 31
2021-10-14 23:18:47,731 INFO [train.py:451] Epoch 9, batch 11820, batch avg loss 0.1945, total avg loss: 0.2085, batch size: 30
2021-10-14 23:18:52,760 INFO [train.py:451] Epoch 9, batch 11830, batch avg loss 0.1863, total avg loss: 0.2145, batch size: 30
2021-10-14 23:18:57,760 INFO [train.py:451] Epoch 9, batch 11840, batch avg loss 0.2192, total avg loss: 0.2136, batch size: 37
2021-10-14 23:19:02,607 INFO [train.py:451] Epoch 9, batch 11850, batch avg loss 0.2763, total avg loss: 0.2157, batch size: 33
2021-10-14 23:19:07,605 INFO [train.py:451] Epoch 9, batch 11860, batch avg loss 0.1969, total avg loss: 0.2157, batch size: 35
2021-10-14 23:19:12,449 INFO [train.py:451] Epoch 9, batch 11870, batch avg loss 0.2410, total avg loss: 0.2172, batch size: 73
2021-10-14 23:19:17,567 INFO [train.py:451] Epoch 9, batch 11880, batch avg loss 0.3194, total avg loss: 0.2179, batch size: 128
2021-10-14 23:19:22,535 INFO [train.py:451] Epoch 9, batch 11890, batch avg loss 0.1910, total avg loss: 0.2166, batch size: 33
2021-10-14 23:19:27,536 INFO [train.py:451] Epoch 9, batch 11900, batch avg loss 0.2333, total avg loss: 0.2168, batch size: 35
2021-10-14 23:19:32,455 INFO [train.py:451] Epoch 9, batch 11910, batch avg loss 0.2534, total avg loss: 0.2178, batch size: 32
2021-10-14 23:19:37,416 INFO [train.py:451] Epoch 9, batch 11920, batch avg loss 0.2518, total avg loss: 0.2176, batch size: 39
2021-10-14 23:19:42,204 INFO [train.py:451] Epoch 9, batch 11930, batch avg loss 0.2400, total avg loss: 0.2176, batch size: 56
2021-10-14 23:19:47,047 INFO [train.py:451] Epoch 9, batch 11940, batch avg loss 0.2752, total avg loss: 0.2183, batch size: 45
2021-10-14 23:19:52,004 INFO [train.py:451] Epoch 9, batch 11950, batch avg loss 0.2048, total avg loss: 0.2188, batch size: 36
2021-10-14 23:19:56,946 INFO [train.py:451] Epoch 9, batch 11960, batch avg loss 0.2102, total avg loss: 0.2188, batch size: 32
2021-10-14 23:20:01,742 INFO [train.py:451] Epoch 9, batch 11970, batch avg loss 0.2359, total avg loss: 0.2195, batch size: 37
2021-10-14 23:20:06,578 INFO [train.py:451] Epoch 9, batch 11980, batch avg loss 0.2659, total avg loss: 0.2196, batch size: 49
2021-10-14 23:20:11,536 INFO [train.py:451] Epoch 9, batch 11990, batch avg loss 0.1920, total avg loss: 0.2183, batch size: 33
2021-10-14 23:20:11,752 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "41500a84-92d2-34b3-da4e-7632209d8b97" will not be mixed in.
2021-10-14 23:20:16,516 INFO [train.py:451] Epoch 9, batch 12000, batch avg loss 0.2170, total avg loss: 0.2183, batch size: 37
2021-10-14 23:20:54,322 INFO [train.py:483] Epoch 9, valid loss 0.1631, best valid loss: 0.1629 best valid epoch: 9
2021-10-14 23:20:59,343 INFO [train.py:451] Epoch 9, batch 12010, batch avg loss 0.2483, total avg loss: 0.2119, batch size: 38
2021-10-14 23:21:04,188 INFO [train.py:451] Epoch 9, batch 12020, batch avg loss 0.2424, total avg loss: 0.2160, batch size: 35
2021-10-14 23:21:09,026 INFO [train.py:451] Epoch 9, batch 12030, batch avg loss 0.2093, total avg loss: 0.2209, batch size: 33
2021-10-14 23:21:13,785 INFO [train.py:451] Epoch 9, batch 12040, batch avg loss 0.1745, total avg loss: 0.2227, batch size: 27
2021-10-14 23:21:18,554 INFO [train.py:451] Epoch 9, batch 12050, batch avg loss 0.2331, total avg loss: 0.2258, batch size: 39
2021-10-14 23:21:23,462 INFO [train.py:451] Epoch 9, batch 12060, batch avg loss 0.2876, total avg loss: 0.2251, batch size: 73
2021-10-14 23:21:28,383 INFO [train.py:451] Epoch 9, batch 12070, batch avg loss 0.1691, total avg loss: 0.2239, batch size: 28
2021-10-14 23:21:33,200 INFO [train.py:451] Epoch 9, batch 12080, batch avg loss 0.2659, total avg loss: 0.2229, batch size: 41
2021-10-14 23:21:38,278 INFO [train.py:451] Epoch 9, batch 12090, batch avg loss 0.1812, total avg loss: 0.2217, batch size: 34
2021-10-14 23:21:43,002 INFO [train.py:451] Epoch 9, batch 12100, batch avg loss 0.2191, total avg loss: 0.2222, batch size: 45
2021-10-14 23:21:48,029 INFO [train.py:451] Epoch 9, batch 12110, batch avg loss 0.2237, total avg loss: 0.2230, batch size: 30
2021-10-14 23:21:52,994 INFO [train.py:451] Epoch 9, batch 12120, batch avg loss 0.1905, total avg loss: 0.2220, batch size: 34
2021-10-14 23:21:57,898 INFO [train.py:451] Epoch 9, batch 12130, batch avg loss 0.2166, total avg loss: 0.2221, batch size: 38
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2021-10-14 23:29:56,395 INFO [train.py:483] Epoch 9, valid loss 0.1637, best valid loss: 0.1629 best valid epoch: 9
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2021-10-14 23:38:50,439 INFO [train.py:483] Epoch 9, valid loss 0.1629, best valid loss: 0.1629 best valid epoch: 9
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2021-10-14 23:47:43,067 INFO [train.py:483] Epoch 9, valid loss 0.1633, best valid loss: 0.1629 best valid epoch: 9
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2021-10-14 23:56:36,431 INFO [train.py:483] Epoch 9, valid loss 0.1635, best valid loss: 0.1629 best valid epoch: 9
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2021-10-15 00:14:26,137 INFO [train.py:483] Epoch 9, valid loss 0.1633, best valid loss: 0.1629 best valid epoch: 9
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2021-10-15 00:15:14,947 INFO [train.py:451] Epoch 9, batch 18100, batch avg loss 0.2250, total avg loss: 0.2216, batch size: 36
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2021-10-15 00:15:24,920 INFO [train.py:451] Epoch 9, batch 18120, batch avg loss 0.2247, total avg loss: 0.2224, batch size: 36
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2021-10-15 00:15:34,540 INFO [train.py:451] Epoch 9, batch 18140, batch avg loss 0.2056, total avg loss: 0.2235, batch size: 31
2021-10-15 00:15:39,393 INFO [train.py:451] Epoch 9, batch 18150, batch avg loss 0.1757, total avg loss: 0.2231, batch size: 30
2021-10-15 00:15:44,247 INFO [train.py:451] Epoch 9, batch 18160, batch avg loss 0.2115, total avg loss: 0.2233, batch size: 42
2021-10-15 00:15:49,226 INFO [train.py:451] Epoch 9, batch 18170, batch avg loss 0.2098, total avg loss: 0.2222, batch size: 34
2021-10-15 00:15:54,066 INFO [train.py:451] Epoch 9, batch 18180, batch avg loss 0.1612, total avg loss: 0.2226, batch size: 29
2021-10-15 00:15:59,081 INFO [train.py:451] Epoch 9, batch 18190, batch avg loss 0.2755, total avg loss: 0.2222, batch size: 74
2021-10-15 00:16:04,098 INFO [train.py:451] Epoch 9, batch 18200, batch avg loss 0.2264, total avg loss: 0.2221, batch size: 41
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2021-10-15 00:16:14,134 INFO [train.py:451] Epoch 9, batch 18220, batch avg loss 0.1565, total avg loss: 0.2116, batch size: 29
2021-10-15 00:16:19,166 INFO [train.py:451] Epoch 9, batch 18230, batch avg loss 0.2044, total avg loss: 0.2134, batch size: 33
2021-10-15 00:16:24,051 INFO [train.py:451] Epoch 9, batch 18240, batch avg loss 0.1802, total avg loss: 0.2168, batch size: 32
2021-10-15 00:16:28,956 INFO [train.py:451] Epoch 9, batch 18250, batch avg loss 0.2146, total avg loss: 0.2195, batch size: 34
2021-10-15 00:16:33,738 INFO [train.py:451] Epoch 9, batch 18260, batch avg loss 0.3292, total avg loss: 0.2224, batch size: 130
2021-10-15 00:16:38,620 INFO [train.py:451] Epoch 9, batch 18270, batch avg loss 0.2591, total avg loss: 0.2237, batch size: 31
2021-10-15 00:16:43,553 INFO [train.py:451] Epoch 9, batch 18280, batch avg loss 0.1980, total avg loss: 0.2234, batch size: 29
2021-10-15 00:16:48,193 INFO [train.py:451] Epoch 9, batch 18290, batch avg loss 0.2718, total avg loss: 0.2267, batch size: 73
2021-10-15 00:16:53,266 INFO [train.py:451] Epoch 9, batch 18300, batch avg loss 0.2363, total avg loss: 0.2239, batch size: 38
2021-10-15 00:16:58,055 INFO [train.py:451] Epoch 9, batch 18310, batch avg loss 0.1656, total avg loss: 0.2230, batch size: 32
2021-10-15 00:17:03,028 INFO [train.py:451] Epoch 9, batch 18320, batch avg loss 0.2294, total avg loss: 0.2218, batch size: 41
2021-10-15 00:17:07,835 INFO [train.py:451] Epoch 9, batch 18330, batch avg loss 0.1942, total avg loss: 0.2226, batch size: 38
2021-10-15 00:17:12,637 INFO [train.py:451] Epoch 9, batch 18340, batch avg loss 0.1719, total avg loss: 0.2215, batch size: 32
2021-10-15 00:17:17,569 INFO [train.py:451] Epoch 9, batch 18350, batch avg loss 0.2316, total avg loss: 0.2207, batch size: 41
2021-10-15 00:17:22,528 INFO [train.py:451] Epoch 9, batch 18360, batch avg loss 0.2309, total avg loss: 0.2202, batch size: 39
2021-10-15 00:17:27,251 INFO [train.py:451] Epoch 9, batch 18370, batch avg loss 0.2609, total avg loss: 0.2205, batch size: 73
2021-10-15 00:17:32,176 INFO [train.py:451] Epoch 9, batch 18380, batch avg loss 0.2107, total avg loss: 0.2208, batch size: 32
2021-10-15 00:17:36,902 INFO [train.py:451] Epoch 9, batch 18390, batch avg loss 0.2381, total avg loss: 0.2209, batch size: 39
2021-10-15 00:17:41,841 INFO [train.py:451] Epoch 9, batch 18400, batch avg loss 0.1721, total avg loss: 0.2204, batch size: 30
2021-10-15 00:17:46,785 INFO [train.py:451] Epoch 9, batch 18410, batch avg loss 0.2509, total avg loss: 0.2196, batch size: 49
2021-10-15 00:17:51,755 INFO [train.py:451] Epoch 9, batch 18420, batch avg loss 0.2241, total avg loss: 0.2191, batch size: 34
2021-10-15 00:17:56,636 INFO [train.py:451] Epoch 9, batch 18430, batch avg loss 0.2311, total avg loss: 0.2191, batch size: 33
2021-10-15 00:18:01,609 INFO [train.py:451] Epoch 9, batch 18440, batch avg loss 0.1970, total avg loss: 0.2128, batch size: 36
2021-10-15 00:18:06,497 INFO [train.py:451] Epoch 9, batch 18450, batch avg loss 0.2923, total avg loss: 0.2138, batch size: 42
2021-10-15 00:18:11,404 INFO [train.py:451] Epoch 9, batch 18460, batch avg loss 0.2435, total avg loss: 0.2129, batch size: 39
2021-10-15 00:18:16,433 INFO [train.py:451] Epoch 9, batch 18470, batch avg loss 0.2024, total avg loss: 0.2131, batch size: 38
2021-10-15 00:18:21,297 INFO [train.py:451] Epoch 9, batch 18480, batch avg loss 0.2232, total avg loss: 0.2150, batch size: 35
2021-10-15 00:18:26,237 INFO [train.py:451] Epoch 9, batch 18490, batch avg loss 0.1467, total avg loss: 0.2136, batch size: 31
2021-10-15 00:18:31,072 INFO [train.py:451] Epoch 9, batch 18500, batch avg loss 0.1913, total avg loss: 0.2155, batch size: 34
2021-10-15 00:18:36,056 INFO [train.py:451] Epoch 9, batch 18510, batch avg loss 0.1981, total avg loss: 0.2154, batch size: 36
2021-10-15 00:18:41,002 INFO [train.py:451] Epoch 9, batch 18520, batch avg loss 0.2178, total avg loss: 0.2152, batch size: 35
2021-10-15 00:18:45,811 INFO [train.py:451] Epoch 9, batch 18530, batch avg loss 0.3228, total avg loss: 0.2159, batch size: 127
2021-10-15 00:18:50,830 INFO [train.py:451] Epoch 9, batch 18540, batch avg loss 0.2625, total avg loss: 0.2154, batch size: 42
2021-10-15 00:18:55,824 INFO [train.py:451] Epoch 9, batch 18550, batch avg loss 0.2699, total avg loss: 0.2159, batch size: 38
2021-10-15 00:19:00,853 INFO [train.py:451] Epoch 9, batch 18560, batch avg loss 0.2119, total avg loss: 0.2150, batch size: 35
2021-10-15 00:19:05,791 INFO [train.py:451] Epoch 9, batch 18570, batch avg loss 0.2487, total avg loss: 0.2152, batch size: 30
2021-10-15 00:19:10,690 INFO [train.py:451] Epoch 9, batch 18580, batch avg loss 0.1854, total avg loss: 0.2158, batch size: 38
2021-10-15 00:19:15,544 INFO [train.py:451] Epoch 9, batch 18590, batch avg loss 0.2219, total avg loss: 0.2165, batch size: 40
2021-10-15 00:19:20,550 INFO [train.py:451] Epoch 9, batch 18600, batch avg loss 0.2359, total avg loss: 0.2158, batch size: 49
2021-10-15 00:19:25,405 INFO [train.py:451] Epoch 9, batch 18610, batch avg loss 0.2700, total avg loss: 0.2518, batch size: 32
2021-10-15 00:19:30,167 INFO [train.py:451] Epoch 9, batch 18620, batch avg loss 0.1683, total avg loss: 0.2473, batch size: 29
2021-10-15 00:19:35,234 INFO [train.py:451] Epoch 9, batch 18630, batch avg loss 0.2416, total avg loss: 0.2357, batch size: 38
2021-10-15 00:19:40,235 INFO [train.py:451] Epoch 9, batch 18640, batch avg loss 0.1981, total avg loss: 0.2327, batch size: 34
2021-10-15 00:19:44,954 INFO [train.py:451] Epoch 9, batch 18650, batch avg loss 0.2815, total avg loss: 0.2319, batch size: 39
2021-10-15 00:19:49,927 INFO [train.py:451] Epoch 9, batch 18660, batch avg loss 0.2497, total avg loss: 0.2288, batch size: 38
2021-10-15 00:19:54,929 INFO [train.py:451] Epoch 9, batch 18670, batch avg loss 0.1976, total avg loss: 0.2259, batch size: 35
2021-10-15 00:19:59,824 INFO [train.py:451] Epoch 9, batch 18680, batch avg loss 0.1575, total avg loss: 0.2243, batch size: 29
2021-10-15 00:20:04,913 INFO [train.py:451] Epoch 9, batch 18690, batch avg loss 0.2009, total avg loss: 0.2225, batch size: 30
2021-10-15 00:20:09,846 INFO [train.py:451] Epoch 9, batch 18700, batch avg loss 0.2410, total avg loss: 0.2227, batch size: 35
2021-10-15 00:20:14,611 INFO [train.py:451] Epoch 9, batch 18710, batch avg loss 0.3275, total avg loss: 0.2238, batch size: 130
2021-10-15 00:20:19,362 INFO [train.py:451] Epoch 9, batch 18720, batch avg loss 0.1845, total avg loss: 0.2240, batch size: 32
2021-10-15 00:20:24,211 INFO [train.py:451] Epoch 9, batch 18730, batch avg loss 0.2139, total avg loss: 0.2241, batch size: 33
2021-10-15 00:20:29,023 INFO [train.py:451] Epoch 9, batch 18740, batch avg loss 0.2380, total avg loss: 0.2241, batch size: 34
2021-10-15 00:20:34,059 INFO [train.py:451] Epoch 9, batch 18750, batch avg loss 0.1886, total avg loss: 0.2238, batch size: 27
2021-10-15 00:20:39,025 INFO [train.py:451] Epoch 9, batch 18760, batch avg loss 0.2343, total avg loss: 0.2228, batch size: 45
2021-10-15 00:20:44,093 INFO [train.py:451] Epoch 9, batch 18770, batch avg loss 0.2455, total avg loss: 0.2228, batch size: 42
2021-10-15 00:20:48,966 INFO [train.py:451] Epoch 9, batch 18780, batch avg loss 0.2020, total avg loss: 0.2234, batch size: 34
2021-10-15 00:20:53,911 INFO [train.py:451] Epoch 9, batch 18790, batch avg loss 0.2613, total avg loss: 0.2224, batch size: 37
2021-10-15 00:20:58,739 INFO [train.py:451] Epoch 9, batch 18800, batch avg loss 0.2364, total avg loss: 0.2217, batch size: 57
2021-10-15 00:21:03,659 INFO [train.py:451] Epoch 9, batch 18810, batch avg loss 0.1702, total avg loss: 0.2052, batch size: 29
2021-10-15 00:21:08,467 INFO [train.py:451] Epoch 9, batch 18820, batch avg loss 0.2553, total avg loss: 0.2165, batch size: 73
2021-10-15 00:21:13,394 INFO [train.py:451] Epoch 9, batch 18830, batch avg loss 0.1899, total avg loss: 0.2120, batch size: 32
2021-10-15 00:21:18,340 INFO [train.py:451] Epoch 9, batch 18840, batch avg loss 0.2006, total avg loss: 0.2153, batch size: 30
2021-10-15 00:21:23,264 INFO [train.py:451] Epoch 9, batch 18850, batch avg loss 0.1802, total avg loss: 0.2151, batch size: 34
2021-10-15 00:21:28,124 INFO [train.py:451] Epoch 9, batch 18860, batch avg loss 0.1980, total avg loss: 0.2227, batch size: 33
2021-10-15 00:21:33,066 INFO [train.py:451] Epoch 9, batch 18870, batch avg loss 0.2451, total avg loss: 0.2221, batch size: 35
2021-10-15 00:21:37,854 INFO [train.py:451] Epoch 9, batch 18880, batch avg loss 0.2261, total avg loss: 0.2268, batch size: 35
2021-10-15 00:21:42,710 INFO [train.py:451] Epoch 9, batch 18890, batch avg loss 0.3381, total avg loss: 0.2278, batch size: 128
2021-10-15 00:21:47,749 INFO [train.py:451] Epoch 9, batch 18900, batch avg loss 0.2792, total avg loss: 0.2276, batch size: 34
2021-10-15 00:21:52,556 INFO [train.py:451] Epoch 9, batch 18910, batch avg loss 0.1537, total avg loss: 0.2278, batch size: 29
2021-10-15 00:21:57,281 INFO [train.py:451] Epoch 9, batch 18920, batch avg loss 0.1839, total avg loss: 0.2275, batch size: 32
2021-10-15 00:21:59,437 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "6b669619-b4bf-47ca-1078-c7628b927f8a" will not be mixed in.
2021-10-15 00:22:02,144 INFO [train.py:451] Epoch 9, batch 18930, batch avg loss 0.2449, total avg loss: 0.2270, batch size: 38
2021-10-15 00:22:06,926 INFO [train.py:451] Epoch 9, batch 18940, batch avg loss 0.2678, total avg loss: 0.2268, batch size: 74
2021-10-15 00:22:11,788 INFO [train.py:451] Epoch 9, batch 18950, batch avg loss 0.1810, total avg loss: 0.2262, batch size: 29
2021-10-15 00:22:16,744 INFO [train.py:451] Epoch 9, batch 18960, batch avg loss 0.1489, total avg loss: 0.2256, batch size: 28
2021-10-15 00:22:21,774 INFO [train.py:451] Epoch 9, batch 18970, batch avg loss 0.2304, total avg loss: 0.2248, batch size: 35
2021-10-15 00:22:26,590 INFO [train.py:451] Epoch 9, batch 18980, batch avg loss 0.1949, total avg loss: 0.2251, batch size: 29
2021-10-15 00:22:31,536 INFO [train.py:451] Epoch 9, batch 18990, batch avg loss 0.2287, total avg loss: 0.2254, batch size: 35
2021-10-15 00:22:36,472 INFO [train.py:451] Epoch 9, batch 19000, batch avg loss 0.1802, total avg loss: 0.2249, batch size: 27
2021-10-15 00:23:16,584 INFO [train.py:483] Epoch 9, valid loss 0.1628, best valid loss: 0.1628 best valid epoch: 9
2021-10-15 00:23:21,544 INFO [train.py:451] Epoch 9, batch 19010, batch avg loss 0.2482, total avg loss: 0.2175, batch size: 37
2021-10-15 00:23:26,372 INFO [train.py:451] Epoch 9, batch 19020, batch avg loss 0.2379, total avg loss: 0.2167, batch size: 36
2021-10-15 00:23:31,362 INFO [train.py:451] Epoch 9, batch 19030, batch avg loss 0.2077, total avg loss: 0.2191, batch size: 34
2021-10-15 00:23:36,333 INFO [train.py:451] Epoch 9, batch 19040, batch avg loss 0.1546, total avg loss: 0.2175, batch size: 27
2021-10-15 00:23:41,223 INFO [train.py:451] Epoch 9, batch 19050, batch avg loss 0.2317, total avg loss: 0.2154, batch size: 36
2021-10-15 00:23:46,057 INFO [train.py:451] Epoch 9, batch 19060, batch avg loss 0.2849, total avg loss: 0.2175, batch size: 72
2021-10-15 00:23:50,782 INFO [train.py:451] Epoch 9, batch 19070, batch avg loss 0.2335, total avg loss: 0.2202, batch size: 30
2021-10-15 00:23:55,574 INFO [train.py:451] Epoch 9, batch 19080, batch avg loss 0.3460, total avg loss: 0.2243, batch size: 127
2021-10-15 00:24:00,384 INFO [train.py:451] Epoch 9, batch 19090, batch avg loss 0.2269, total avg loss: 0.2234, batch size: 31
2021-10-15 00:24:05,443 INFO [train.py:451] Epoch 9, batch 19100, batch avg loss 0.2097, total avg loss: 0.2239, batch size: 38
2021-10-15 00:24:10,627 INFO [train.py:451] Epoch 9, batch 19110, batch avg loss 0.1477, total avg loss: 0.2210, batch size: 29
2021-10-15 00:24:15,580 INFO [train.py:451] Epoch 9, batch 19120, batch avg loss 0.2017, total avg loss: 0.2199, batch size: 30
2021-10-15 00:24:20,435 INFO [train.py:451] Epoch 9, batch 19130, batch avg loss 0.3560, total avg loss: 0.2205, batch size: 130
2021-10-15 00:24:25,408 INFO [train.py:451] Epoch 9, batch 19140, batch avg loss 0.2440, total avg loss: 0.2199, batch size: 42
2021-10-15 00:24:30,353 INFO [train.py:451] Epoch 9, batch 19150, batch avg loss 0.1923, total avg loss: 0.2189, batch size: 34
2021-10-15 00:24:35,169 INFO [train.py:451] Epoch 9, batch 19160, batch avg loss 0.2391, total avg loss: 0.2186, batch size: 35
2021-10-15 00:24:40,467 INFO [train.py:451] Epoch 9, batch 19170, batch avg loss 0.2256, total avg loss: 0.2181, batch size: 39
2021-10-15 00:24:45,439 INFO [train.py:451] Epoch 9, batch 19180, batch avg loss 0.1806, total avg loss: 0.2179, batch size: 31
2021-10-15 00:24:50,541 INFO [train.py:451] Epoch 9, batch 19190, batch avg loss 0.1709, total avg loss: 0.2171, batch size: 29
2021-10-15 00:24:55,512 INFO [train.py:451] Epoch 9, batch 19200, batch avg loss 0.1680, total avg loss: 0.2160, batch size: 32
2021-10-15 00:25:00,673 INFO [train.py:451] Epoch 9, batch 19210, batch avg loss 0.2393, total avg loss: 0.2175, batch size: 39
2021-10-15 00:25:05,679 INFO [train.py:451] Epoch 9, batch 19220, batch avg loss 0.1937, total avg loss: 0.2202, batch size: 29
2021-10-15 00:25:10,735 INFO [train.py:451] Epoch 9, batch 19230, batch avg loss 0.1769, total avg loss: 0.2158, batch size: 36
2021-10-15 00:25:15,665 INFO [train.py:451] Epoch 9, batch 19240, batch avg loss 0.1679, total avg loss: 0.2190, batch size: 30
2021-10-15 00:25:20,771 INFO [train.py:451] Epoch 9, batch 19250, batch avg loss 0.1843, total avg loss: 0.2158, batch size: 27
2021-10-15 00:25:25,696 INFO [train.py:451] Epoch 9, batch 19260, batch avg loss 0.2083, total avg loss: 0.2140, batch size: 57
2021-10-15 00:25:30,637 INFO [train.py:451] Epoch 9, batch 19270, batch avg loss 0.2071, total avg loss: 0.2145, batch size: 41
2021-10-15 00:25:35,601 INFO [train.py:451] Epoch 9, batch 19280, batch avg loss 0.1929, total avg loss: 0.2142, batch size: 27
2021-10-15 00:25:40,520 INFO [train.py:451] Epoch 9, batch 19290, batch avg loss 0.2142, total avg loss: 0.2140, batch size: 41
2021-10-15 00:25:45,307 INFO [train.py:451] Epoch 9, batch 19300, batch avg loss 0.2293, total avg loss: 0.2151, batch size: 31
2021-10-15 00:25:50,334 INFO [train.py:451] Epoch 9, batch 19310, batch avg loss 0.2019, total avg loss: 0.2158, batch size: 30
2021-10-15 00:25:55,339 INFO [train.py:451] Epoch 9, batch 19320, batch avg loss 0.2105, total avg loss: 0.2161, batch size: 38
2021-10-15 00:26:00,307 INFO [train.py:451] Epoch 9, batch 19330, batch avg loss 0.2643, total avg loss: 0.2166, batch size: 32
2021-10-15 00:26:05,305 INFO [train.py:451] Epoch 9, batch 19340, batch avg loss 0.1568, total avg loss: 0.2157, batch size: 28
2021-10-15 00:26:10,164 INFO [train.py:451] Epoch 9, batch 19350, batch avg loss 0.2321, total avg loss: 0.2171, batch size: 36
2021-10-15 00:26:15,103 INFO [train.py:451] Epoch 9, batch 19360, batch avg loss 0.2128, total avg loss: 0.2172, batch size: 30
2021-10-15 00:26:20,137 INFO [train.py:451] Epoch 9, batch 19370, batch avg loss 0.1910, total avg loss: 0.2161, batch size: 28
2021-10-15 00:26:25,112 INFO [train.py:451] Epoch 9, batch 19380, batch avg loss 0.2941, total avg loss: 0.2155, batch size: 72
2021-10-15 00:26:30,086 INFO [train.py:451] Epoch 9, batch 19390, batch avg loss 0.1547, total avg loss: 0.2154, batch size: 29
2021-10-15 00:26:35,159 INFO [train.py:451] Epoch 9, batch 19400, batch avg loss 0.1974, total avg loss: 0.2161, batch size: 34
2021-10-15 00:26:40,212 INFO [train.py:451] Epoch 9, batch 19410, batch avg loss 0.1863, total avg loss: 0.2135, batch size: 32
2021-10-15 00:26:45,183 INFO [train.py:451] Epoch 9, batch 19420, batch avg loss 0.2582, total avg loss: 0.2095, batch size: 74
2021-10-15 00:26:50,324 INFO [train.py:451] Epoch 9, batch 19430, batch avg loss 0.1611, total avg loss: 0.2062, batch size: 29
2021-10-15 00:26:55,190 INFO [train.py:451] Epoch 9, batch 19440, batch avg loss 0.1880, total avg loss: 0.2121, batch size: 31
2021-10-15 00:27:00,186 INFO [train.py:451] Epoch 9, batch 19450, batch avg loss 0.2100, total avg loss: 0.2133, batch size: 34
2021-10-15 00:27:05,156 INFO [train.py:451] Epoch 9, batch 19460, batch avg loss 0.2635, total avg loss: 0.2157, batch size: 45
2021-10-15 00:27:10,195 INFO [train.py:451] Epoch 9, batch 19470, batch avg loss 0.2068, total avg loss: 0.2148, batch size: 32
2021-10-15 00:27:15,061 INFO [train.py:451] Epoch 9, batch 19480, batch avg loss 0.1784, total avg loss: 0.2147, batch size: 30
2021-10-15 00:27:19,954 INFO [train.py:451] Epoch 9, batch 19490, batch avg loss 0.2687, total avg loss: 0.2140, batch size: 73
2021-10-15 00:27:24,769 INFO [train.py:451] Epoch 9, batch 19500, batch avg loss 0.2166, total avg loss: 0.2160, batch size: 36
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2021-10-15 00:32:12,214 INFO [train.py:483] Epoch 9, valid loss 0.1631, best valid loss: 0.1628 best valid epoch: 9
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2021-10-15 00:42:41,938 INFO [checkpoint.py:62] Saving checkpoint to tdnn_lstm_ctc-v2/exp/epoch-9.pt
2021-10-15 00:42:42,748 INFO [train.py:564] epoch 10, lr: 2.5e-05
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2021-10-15 00:51:41,151 INFO [train.py:483] Epoch 10, valid loss 0.1631, best valid loss: 0.1628 best valid epoch: 9
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2021-10-15 00:53:53,280 INFO [train.py:451] Epoch 10, batch 1270, batch avg loss 0.2956, total avg loss: 0.2187, batch size: 38
2021-10-15 00:53:58,121 INFO [train.py:451] Epoch 10, batch 1280, batch avg loss 0.2856, total avg loss: 0.2187, batch size: 42
2021-10-15 00:54:02,823 INFO [train.py:451] Epoch 10, batch 1290, batch avg loss 0.2089, total avg loss: 0.2194, batch size: 49
2021-10-15 00:54:07,589 INFO [train.py:451] Epoch 10, batch 1300, batch avg loss 0.2183, total avg loss: 0.2206, batch size: 34
2021-10-15 00:54:12,421 INFO [train.py:451] Epoch 10, batch 1310, batch avg loss 0.2026, total avg loss: 0.2196, batch size: 42
2021-10-15 00:54:17,279 INFO [train.py:451] Epoch 10, batch 1320, batch avg loss 0.2496, total avg loss: 0.2206, batch size: 42
2021-10-15 00:54:22,178 INFO [train.py:451] Epoch 10, batch 1330, batch avg loss 0.2541, total avg loss: 0.2203, batch size: 45
2021-10-15 00:54:27,056 INFO [train.py:451] Epoch 10, batch 1340, batch avg loss 0.1962, total avg loss: 0.2208, batch size: 33
2021-10-15 00:54:31,858 INFO [train.py:451] Epoch 10, batch 1350, batch avg loss 0.2964, total avg loss: 0.2216, batch size: 73
2021-10-15 00:54:36,714 INFO [train.py:451] Epoch 10, batch 1360, batch avg loss 0.2614, total avg loss: 0.2225, batch size: 35
2021-10-15 00:54:41,570 INFO [train.py:451] Epoch 10, batch 1370, batch avg loss 0.2017, total avg loss: 0.2225, batch size: 36
2021-10-15 00:54:46,532 INFO [train.py:451] Epoch 10, batch 1380, batch avg loss 0.1830, total avg loss: 0.2223, batch size: 29
2021-10-15 00:54:51,342 INFO [train.py:451] Epoch 10, batch 1390, batch avg loss 0.2056, total avg loss: 0.2225, batch size: 33
2021-10-15 00:54:56,013 INFO [train.py:451] Epoch 10, batch 1400, batch avg loss 0.2817, total avg loss: 0.2229, batch size: 57
2021-10-15 00:55:01,073 INFO [train.py:451] Epoch 10, batch 1410, batch avg loss 0.1708, total avg loss: 0.2199, batch size: 34
2021-10-15 00:55:06,048 INFO [train.py:451] Epoch 10, batch 1420, batch avg loss 0.2089, total avg loss: 0.2251, batch size: 37
2021-10-15 00:55:11,099 INFO [train.py:451] Epoch 10, batch 1430, batch avg loss 0.2077, total avg loss: 0.2194, batch size: 29
2021-10-15 00:55:16,096 INFO [train.py:451] Epoch 10, batch 1440, batch avg loss 0.2184, total avg loss: 0.2168, batch size: 35
2021-10-15 00:55:21,002 INFO [train.py:451] Epoch 10, batch 1450, batch avg loss 0.2178, total avg loss: 0.2201, batch size: 34
2021-10-15 00:55:25,698 INFO [train.py:451] Epoch 10, batch 1460, batch avg loss 0.2776, total avg loss: 0.2220, batch size: 72
2021-10-15 00:55:30,622 INFO [train.py:451] Epoch 10, batch 1470, batch avg loss 0.2206, total avg loss: 0.2213, batch size: 35
2021-10-15 00:55:35,447 INFO [train.py:451] Epoch 10, batch 1480, batch avg loss 0.2301, total avg loss: 0.2229, batch size: 39
2021-10-15 00:55:40,302 INFO [train.py:451] Epoch 10, batch 1490, batch avg loss 0.2176, total avg loss: 0.2245, batch size: 45
2021-10-15 00:55:45,296 INFO [train.py:451] Epoch 10, batch 1500, batch avg loss 0.2431, total avg loss: 0.2219, batch size: 35
2021-10-15 00:55:50,134 INFO [train.py:451] Epoch 10, batch 1510, batch avg loss 0.2203, total avg loss: 0.2216, batch size: 34
2021-10-15 00:55:54,989 INFO [train.py:451] Epoch 10, batch 1520, batch avg loss 0.2123, total avg loss: 0.2214, batch size: 31
2021-10-15 00:56:00,063 INFO [train.py:451] Epoch 10, batch 1530, batch avg loss 0.2011, total avg loss: 0.2214, batch size: 37
2021-10-15 00:56:04,948 INFO [train.py:451] Epoch 10, batch 1540, batch avg loss 0.2185, total avg loss: 0.2218, batch size: 39
2021-10-15 00:56:09,863 INFO [train.py:451] Epoch 10, batch 1550, batch avg loss 0.2632, total avg loss: 0.2211, batch size: 57
2021-10-15 00:56:13,476 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "1e8954f7-7815-9c6a-9333-93204a2f99fe" will not be mixed in.
2021-10-15 00:56:14,788 INFO [train.py:451] Epoch 10, batch 1560, batch avg loss 0.2160, total avg loss: 0.2210, batch size: 38
2021-10-15 00:56:19,803 INFO [train.py:451] Epoch 10, batch 1570, batch avg loss 0.1783, total avg loss: 0.2195, batch size: 31
2021-10-15 00:56:24,877 INFO [train.py:451] Epoch 10, batch 1580, batch avg loss 0.2212, total avg loss: 0.2185, batch size: 34
2021-10-15 00:56:29,752 INFO [train.py:451] Epoch 10, batch 1590, batch avg loss 0.2581, total avg loss: 0.2190, batch size: 74
2021-10-15 00:56:34,651 INFO [train.py:451] Epoch 10, batch 1600, batch avg loss 0.1813, total avg loss: 0.2183, batch size: 32
2021-10-15 00:56:39,607 INFO [train.py:451] Epoch 10, batch 1610, batch avg loss 0.2096, total avg loss: 0.2078, batch size: 41
2021-10-15 00:56:44,563 INFO [train.py:451] Epoch 10, batch 1620, batch avg loss 0.1737, total avg loss: 0.2131, batch size: 27
2021-10-15 00:56:49,627 INFO [train.py:451] Epoch 10, batch 1630, batch avg loss 0.2059, total avg loss: 0.2124, batch size: 32
2021-10-15 00:56:54,706 INFO [train.py:451] Epoch 10, batch 1640, batch avg loss 0.1912, total avg loss: 0.2080, batch size: 36
2021-10-15 00:56:59,450 INFO [train.py:451] Epoch 10, batch 1650, batch avg loss 0.2182, total avg loss: 0.2116, batch size: 31
2021-10-15 00:57:04,334 INFO [train.py:451] Epoch 10, batch 1660, batch avg loss 0.2397, total avg loss: 0.2125, batch size: 49
2021-10-15 00:57:09,092 INFO [train.py:451] Epoch 10, batch 1670, batch avg loss 0.2176, total avg loss: 0.2152, batch size: 38
2021-10-15 00:57:13,853 INFO [train.py:451] Epoch 10, batch 1680, batch avg loss 0.2491, total avg loss: 0.2155, batch size: 49
2021-10-15 00:57:19,005 INFO [train.py:451] Epoch 10, batch 1690, batch avg loss 0.2088, total avg loss: 0.2132, batch size: 34
2021-10-15 00:57:23,990 INFO [train.py:451] Epoch 10, batch 1700, batch avg loss 0.2537, total avg loss: 0.2130, batch size: 49
2021-10-15 00:57:28,756 INFO [train.py:451] Epoch 10, batch 1710, batch avg loss 0.2511, total avg loss: 0.2148, batch size: 37
2021-10-15 00:57:33,583 INFO [train.py:451] Epoch 10, batch 1720, batch avg loss 0.2536, total avg loss: 0.2160, batch size: 36
2021-10-15 00:57:38,304 INFO [train.py:451] Epoch 10, batch 1730, batch avg loss 0.2048, total avg loss: 0.2165, batch size: 38
2021-10-15 00:57:43,055 INFO [train.py:451] Epoch 10, batch 1740, batch avg loss 0.2059, total avg loss: 0.2161, batch size: 42
2021-10-15 00:57:47,782 INFO [train.py:451] Epoch 10, batch 1750, batch avg loss 0.2258, total avg loss: 0.2168, batch size: 38
2021-10-15 00:57:52,692 INFO [train.py:451] Epoch 10, batch 1760, batch avg loss 0.2074, total avg loss: 0.2174, batch size: 36
2021-10-15 00:57:57,779 INFO [train.py:451] Epoch 10, batch 1770, batch avg loss 0.2119, total avg loss: 0.2178, batch size: 31
2021-10-15 00:58:02,746 INFO [train.py:451] Epoch 10, batch 1780, batch avg loss 0.2165, total avg loss: 0.2179, batch size: 37
2021-10-15 00:58:07,630 INFO [train.py:451] Epoch 10, batch 1790, batch avg loss 0.2638, total avg loss: 0.2177, batch size: 39
2021-10-15 00:58:12,518 INFO [train.py:451] Epoch 10, batch 1800, batch avg loss 0.2383, total avg loss: 0.2174, batch size: 57
2021-10-15 00:58:17,501 INFO [train.py:451] Epoch 10, batch 1810, batch avg loss 0.1821, total avg loss: 0.2052, batch size: 30
2021-10-15 00:58:22,376 INFO [train.py:451] Epoch 10, batch 1820, batch avg loss 0.1588, total avg loss: 0.2168, batch size: 28
2021-10-15 00:58:27,268 INFO [train.py:451] Epoch 10, batch 1830, batch avg loss 0.2314, total avg loss: 0.2157, batch size: 72
2021-10-15 00:58:32,211 INFO [train.py:451] Epoch 10, batch 1840, batch avg loss 0.2177, total avg loss: 0.2172, batch size: 45
2021-10-15 00:58:37,162 INFO [train.py:451] Epoch 10, batch 1850, batch avg loss 0.2449, total avg loss: 0.2153, batch size: 49
2021-10-15 00:58:42,019 INFO [train.py:451] Epoch 10, batch 1860, batch avg loss 0.1978, total avg loss: 0.2158, batch size: 39
2021-10-15 00:58:46,896 INFO [train.py:451] Epoch 10, batch 1870, batch avg loss 0.2019, total avg loss: 0.2168, batch size: 34
2021-10-15 00:58:51,677 INFO [train.py:451] Epoch 10, batch 1880, batch avg loss 0.1901, total avg loss: 0.2189, batch size: 33
2021-10-15 00:58:56,590 INFO [train.py:451] Epoch 10, batch 1890, batch avg loss 0.2199, total avg loss: 0.2172, batch size: 32
2021-10-15 00:59:01,590 INFO [train.py:451] Epoch 10, batch 1900, batch avg loss 0.2147, total avg loss: 0.2167, batch size: 36
2021-10-15 00:59:06,640 INFO [train.py:451] Epoch 10, batch 1910, batch avg loss 0.2059, total avg loss: 0.2163, batch size: 30
2021-10-15 00:59:11,690 INFO [train.py:451] Epoch 10, batch 1920, batch avg loss 0.2010, total avg loss: 0.2156, batch size: 38
2021-10-15 00:59:16,621 INFO [train.py:451] Epoch 10, batch 1930, batch avg loss 0.1839, total avg loss: 0.2162, batch size: 34
2021-10-15 00:59:21,416 INFO [train.py:451] Epoch 10, batch 1940, batch avg loss 0.2074, total avg loss: 0.2171, batch size: 32
2021-10-15 00:59:26,391 INFO [train.py:451] Epoch 10, batch 1950, batch avg loss 0.2681, total avg loss: 0.2168, batch size: 38
2021-10-15 00:59:31,201 INFO [train.py:451] Epoch 10, batch 1960, batch avg loss 0.2226, total avg loss: 0.2187, batch size: 35
2021-10-15 00:59:36,147 INFO [train.py:451] Epoch 10, batch 1970, batch avg loss 0.2274, total avg loss: 0.2208, batch size: 38
2021-10-15 00:59:41,044 INFO [train.py:451] Epoch 10, batch 1980, batch avg loss 0.1840, total avg loss: 0.2205, batch size: 31
2021-10-15 00:59:46,066 INFO [train.py:451] Epoch 10, batch 1990, batch avg loss 0.2005, total avg loss: 0.2200, batch size: 35
2021-10-15 00:59:50,825 INFO [train.py:451] Epoch 10, batch 2000, batch avg loss 0.1995, total avg loss: 0.2206, batch size: 29
2021-10-15 01:00:30,463 INFO [train.py:483] Epoch 10, valid loss 0.1627, best valid loss: 0.1627 best valid epoch: 10
2021-10-15 01:00:35,169 INFO [train.py:451] Epoch 10, batch 2010, batch avg loss 0.1970, total avg loss: 0.2273, batch size: 38
2021-10-15 01:00:40,051 INFO [train.py:451] Epoch 10, batch 2020, batch avg loss 0.1574, total avg loss: 0.2264, batch size: 27
2021-10-15 01:00:44,989 INFO [train.py:451] Epoch 10, batch 2030, batch avg loss 0.2370, total avg loss: 0.2248, batch size: 39
2021-10-15 01:00:49,932 INFO [train.py:451] Epoch 10, batch 2040, batch avg loss 0.2341, total avg loss: 0.2219, batch size: 36
2021-10-15 01:00:54,840 INFO [train.py:451] Epoch 10, batch 2050, batch avg loss 0.2344, total avg loss: 0.2221, batch size: 38
2021-10-15 01:00:59,818 INFO [train.py:451] Epoch 10, batch 2060, batch avg loss 0.2265, total avg loss: 0.2202, batch size: 38
2021-10-15 01:01:04,829 INFO [train.py:451] Epoch 10, batch 2070, batch avg loss 0.1884, total avg loss: 0.2191, batch size: 33
2021-10-15 01:01:09,745 INFO [train.py:451] Epoch 10, batch 2080, batch avg loss 0.1559, total avg loss: 0.2167, batch size: 30
2021-10-15 01:01:14,527 INFO [train.py:451] Epoch 10, batch 2090, batch avg loss 0.1977, total avg loss: 0.2162, batch size: 30
2021-10-15 01:01:19,378 INFO [train.py:451] Epoch 10, batch 2100, batch avg loss 0.2502, total avg loss: 0.2179, batch size: 37
2021-10-15 01:01:24,218 INFO [train.py:451] Epoch 10, batch 2110, batch avg loss 0.2058, total avg loss: 0.2189, batch size: 31
2021-10-15 01:01:29,143 INFO [train.py:451] Epoch 10, batch 2120, batch avg loss 0.2024, total avg loss: 0.2189, batch size: 34
2021-10-15 01:01:34,326 INFO [train.py:451] Epoch 10, batch 2130, batch avg loss 0.1818, total avg loss: 0.2176, batch size: 32
2021-10-15 01:01:39,312 INFO [train.py:451] Epoch 10, batch 2140, batch avg loss 0.1959, total avg loss: 0.2178, batch size: 27
2021-10-15 01:01:44,263 INFO [train.py:451] Epoch 10, batch 2150, batch avg loss 0.2198, total avg loss: 0.2180, batch size: 30
2021-10-15 01:01:49,161 INFO [train.py:451] Epoch 10, batch 2160, batch avg loss 0.2132, total avg loss: 0.2190, batch size: 30
2021-10-15 01:01:54,165 INFO [train.py:451] Epoch 10, batch 2170, batch avg loss 0.2026, total avg loss: 0.2185, batch size: 32
2021-10-15 01:01:59,149 INFO [train.py:451] Epoch 10, batch 2180, batch avg loss 0.1786, total avg loss: 0.2179, batch size: 30
2021-10-15 01:02:04,172 INFO [train.py:451] Epoch 10, batch 2190, batch avg loss 0.2245, total avg loss: 0.2173, batch size: 31
2021-10-15 01:02:09,007 INFO [train.py:451] Epoch 10, batch 2200, batch avg loss 0.2015, total avg loss: 0.2185, batch size: 37
2021-10-15 01:02:14,110 INFO [train.py:451] Epoch 10, batch 2210, batch avg loss 0.1667, total avg loss: 0.2104, batch size: 29
2021-10-15 01:02:19,124 INFO [train.py:451] Epoch 10, batch 2220, batch avg loss 0.2121, total avg loss: 0.2086, batch size: 29
2021-10-15 01:02:24,134 INFO [train.py:451] Epoch 10, batch 2230, batch avg loss 0.1898, total avg loss: 0.2114, batch size: 27
2021-10-15 01:02:28,946 INFO [train.py:451] Epoch 10, batch 2240, batch avg loss 0.2236, total avg loss: 0.2127, batch size: 45
2021-10-15 01:02:33,996 INFO [train.py:451] Epoch 10, batch 2250, batch avg loss 0.2039, total avg loss: 0.2123, batch size: 29
2021-10-15 01:02:39,154 INFO [train.py:451] Epoch 10, batch 2260, batch avg loss 0.3416, total avg loss: 0.2156, batch size: 135
2021-10-15 01:02:43,874 INFO [train.py:451] Epoch 10, batch 2270, batch avg loss 0.3022, total avg loss: 0.2209, batch size: 72
2021-10-15 01:02:48,669 INFO [train.py:451] Epoch 10, batch 2280, batch avg loss 0.2296, total avg loss: 0.2197, batch size: 57
2021-10-15 01:02:53,504 INFO [train.py:451] Epoch 10, batch 2290, batch avg loss 0.2849, total avg loss: 0.2198, batch size: 73
2021-10-15 01:02:58,495 INFO [train.py:451] Epoch 10, batch 2300, batch avg loss 0.2133, total avg loss: 0.2190, batch size: 35
2021-10-15 01:03:03,364 INFO [train.py:451] Epoch 10, batch 2310, batch avg loss 0.2282, total avg loss: 0.2176, batch size: 45
2021-10-15 01:03:08,416 INFO [train.py:451] Epoch 10, batch 2320, batch avg loss 0.1806, total avg loss: 0.2160, batch size: 29
2021-10-15 01:03:13,244 INFO [train.py:451] Epoch 10, batch 2330, batch avg loss 0.1924, total avg loss: 0.2161, batch size: 34
2021-10-15 01:03:18,144 INFO [train.py:451] Epoch 10, batch 2340, batch avg loss 0.2337, total avg loss: 0.2162, batch size: 57
2021-10-15 01:03:23,125 INFO [train.py:451] Epoch 10, batch 2350, batch avg loss 0.1784, total avg loss: 0.2149, batch size: 30
2021-10-15 01:03:27,869 INFO [train.py:451] Epoch 10, batch 2360, batch avg loss 0.2094, total avg loss: 0.2147, batch size: 31
2021-10-15 01:03:29,507 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "1f46a479-74e6-4bdc-83b9-2df72a9e9732" will not be mixed in.
2021-10-15 01:03:32,835 INFO [train.py:451] Epoch 10, batch 2370, batch avg loss 0.1719, total avg loss: 0.2150, batch size: 28
2021-10-15 01:03:37,664 INFO [train.py:451] Epoch 10, batch 2380, batch avg loss 0.3077, total avg loss: 0.2169, batch size: 73
2021-10-15 01:03:42,525 INFO [train.py:451] Epoch 10, batch 2390, batch avg loss 0.1765, total avg loss: 0.2171, batch size: 29
2021-10-15 01:03:47,360 INFO [train.py:451] Epoch 10, batch 2400, batch avg loss 0.2399, total avg loss: 0.2173, batch size: 35
2021-10-15 01:03:52,194 INFO [train.py:451] Epoch 10, batch 2410, batch avg loss 0.1934, total avg loss: 0.2199, batch size: 41
2021-10-15 01:03:57,152 INFO [train.py:451] Epoch 10, batch 2420, batch avg loss 0.2211, total avg loss: 0.2151, batch size: 37
2021-10-15 01:04:01,850 INFO [train.py:451] Epoch 10, batch 2430, batch avg loss 0.2531, total avg loss: 0.2235, batch size: 57
2021-10-15 01:04:06,551 INFO [train.py:451] Epoch 10, batch 2440, batch avg loss 0.3135, total avg loss: 0.2256, batch size: 129
2021-10-15 01:04:11,422 INFO [train.py:451] Epoch 10, batch 2450, batch avg loss 0.1822, total avg loss: 0.2243, batch size: 31
2021-10-15 01:04:16,242 INFO [train.py:451] Epoch 10, batch 2460, batch avg loss 0.2310, total avg loss: 0.2274, batch size: 35
2021-10-15 01:04:21,261 INFO [train.py:451] Epoch 10, batch 2470, batch avg loss 0.2535, total avg loss: 0.2244, batch size: 45
2021-10-15 01:04:26,043 INFO [train.py:451] Epoch 10, batch 2480, batch avg loss 0.2353, total avg loss: 0.2242, batch size: 41
2021-10-15 01:04:30,935 INFO [train.py:451] Epoch 10, batch 2490, batch avg loss 0.1941, total avg loss: 0.2227, batch size: 31
2021-10-15 01:04:35,919 INFO [train.py:451] Epoch 10, batch 2500, batch avg loss 0.1926, total avg loss: 0.2211, batch size: 30
2021-10-15 01:04:40,932 INFO [train.py:451] Epoch 10, batch 2510, batch avg loss 0.2164, total avg loss: 0.2207, batch size: 39
2021-10-15 01:04:46,027 INFO [train.py:451] Epoch 10, batch 2520, batch avg loss 0.1646, total avg loss: 0.2193, batch size: 30
2021-10-15 01:04:50,912 INFO [train.py:451] Epoch 10, batch 2530, batch avg loss 0.2182, total avg loss: 0.2193, batch size: 42
2021-10-15 01:04:55,908 INFO [train.py:451] Epoch 10, batch 2540, batch avg loss 0.1848, total avg loss: 0.2189, batch size: 33
2021-10-15 01:05:00,981 INFO [train.py:451] Epoch 10, batch 2550, batch avg loss 0.2170, total avg loss: 0.2197, batch size: 32
2021-10-15 01:05:05,929 INFO [train.py:451] Epoch 10, batch 2560, batch avg loss 0.2323, total avg loss: 0.2207, batch size: 49
2021-10-15 01:05:11,054 INFO [train.py:451] Epoch 10, batch 2570, batch avg loss 0.2127, total avg loss: 0.2204, batch size: 30
2021-10-15 01:05:15,921 INFO [train.py:451] Epoch 10, batch 2580, batch avg loss 0.2539, total avg loss: 0.2200, batch size: 56
2021-10-15 01:05:20,922 INFO [train.py:451] Epoch 10, batch 2590, batch avg loss 0.2563, total avg loss: 0.2197, batch size: 35
2021-10-15 01:05:25,799 INFO [train.py:451] Epoch 10, batch 2600, batch avg loss 0.1927, total avg loss: 0.2194, batch size: 32
2021-10-15 01:05:30,635 INFO [train.py:451] Epoch 10, batch 2610, batch avg loss 0.2420, total avg loss: 0.2246, batch size: 36
2021-10-15 01:05:35,553 INFO [train.py:451] Epoch 10, batch 2620, batch avg loss 0.2787, total avg loss: 0.2176, batch size: 42
2021-10-15 01:05:40,457 INFO [train.py:451] Epoch 10, batch 2630, batch avg loss 0.2371, total avg loss: 0.2183, batch size: 45
2021-10-15 01:05:45,220 INFO [train.py:451] Epoch 10, batch 2640, batch avg loss 0.1842, total avg loss: 0.2194, batch size: 28
2021-10-15 01:05:50,137 INFO [train.py:451] Epoch 10, batch 2650, batch avg loss 0.2126, total avg loss: 0.2208, batch size: 41
2021-10-15 01:05:55,007 INFO [train.py:451] Epoch 10, batch 2660, batch avg loss 0.2397, total avg loss: 0.2196, batch size: 38
2021-10-15 01:05:59,981 INFO [train.py:451] Epoch 10, batch 2670, batch avg loss 0.2432, total avg loss: 0.2189, batch size: 33
2021-10-15 01:06:04,913 INFO [train.py:451] Epoch 10, batch 2680, batch avg loss 0.2067, total avg loss: 0.2180, batch size: 38
2021-10-15 01:06:09,836 INFO [train.py:451] Epoch 10, batch 2690, batch avg loss 0.2421, total avg loss: 0.2173, batch size: 38
2021-10-15 01:06:14,772 INFO [train.py:451] Epoch 10, batch 2700, batch avg loss 0.1863, total avg loss: 0.2165, batch size: 31
2021-10-15 01:06:19,584 INFO [train.py:451] Epoch 10, batch 2710, batch avg loss 0.1942, total avg loss: 0.2176, batch size: 30
2021-10-15 01:06:24,416 INFO [train.py:451] Epoch 10, batch 2720, batch avg loss 0.2301, total avg loss: 0.2191, batch size: 41
2021-10-15 01:06:29,389 INFO [train.py:451] Epoch 10, batch 2730, batch avg loss 0.2169, total avg loss: 0.2182, batch size: 35
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2021-10-15 01:06:53,798 INFO [train.py:451] Epoch 10, batch 2780, batch avg loss 0.2385, total avg loss: 0.2189, batch size: 57
2021-10-15 01:06:58,709 INFO [train.py:451] Epoch 10, batch 2790, batch avg loss 0.2387, total avg loss: 0.2194, batch size: 41
2021-10-15 01:07:03,673 INFO [train.py:451] Epoch 10, batch 2800, batch avg loss 0.1843, total avg loss: 0.2185, batch size: 31
2021-10-15 01:07:08,718 INFO [train.py:451] Epoch 10, batch 2810, batch avg loss 0.2039, total avg loss: 0.2076, batch size: 30
2021-10-15 01:07:13,631 INFO [train.py:451] Epoch 10, batch 2820, batch avg loss 0.2521, total avg loss: 0.2141, batch size: 45
2021-10-15 01:07:18,427 INFO [train.py:451] Epoch 10, batch 2830, batch avg loss 0.2737, total avg loss: 0.2175, batch size: 56
2021-10-15 01:07:23,219 INFO [train.py:451] Epoch 10, batch 2840, batch avg loss 0.2160, total avg loss: 0.2220, batch size: 45
2021-10-15 01:07:28,055 INFO [train.py:451] Epoch 10, batch 2850, batch avg loss 0.2752, total avg loss: 0.2227, batch size: 72
2021-10-15 01:07:32,934 INFO [train.py:451] Epoch 10, batch 2860, batch avg loss 0.2431, total avg loss: 0.2221, batch size: 34
2021-10-15 01:07:37,851 INFO [train.py:451] Epoch 10, batch 2870, batch avg loss 0.2037, total avg loss: 0.2213, batch size: 29
2021-10-15 01:07:42,834 INFO [train.py:451] Epoch 10, batch 2880, batch avg loss 0.2515, total avg loss: 0.2220, batch size: 37
2021-10-15 01:07:47,537 INFO [train.py:451] Epoch 10, batch 2890, batch avg loss 0.2330, total avg loss: 0.2221, batch size: 42
2021-10-15 01:07:52,423 INFO [train.py:451] Epoch 10, batch 2900, batch avg loss 0.2205, total avg loss: 0.2205, batch size: 49
2021-10-15 01:07:57,255 INFO [train.py:451] Epoch 10, batch 2910, batch avg loss 0.1725, total avg loss: 0.2219, batch size: 28
2021-10-15 01:08:02,121 INFO [train.py:451] Epoch 10, batch 2920, batch avg loss 0.3301, total avg loss: 0.2226, batch size: 126
2021-10-15 01:08:07,167 INFO [train.py:451] Epoch 10, batch 2930, batch avg loss 0.1955, total avg loss: 0.2221, batch size: 41
2021-10-15 01:08:11,910 INFO [train.py:451] Epoch 10, batch 2940, batch avg loss 0.2158, total avg loss: 0.2225, batch size: 45
2021-10-15 01:08:16,839 INFO [train.py:451] Epoch 10, batch 2950, batch avg loss 0.2375, total avg loss: 0.2209, batch size: 31
2021-10-15 01:08:21,749 INFO [train.py:451] Epoch 10, batch 2960, batch avg loss 0.2147, total avg loss: 0.2205, batch size: 39
2021-10-15 01:08:26,594 INFO [train.py:451] Epoch 10, batch 2970, batch avg loss 0.2323, total avg loss: 0.2212, batch size: 28
2021-10-15 01:08:31,628 INFO [train.py:451] Epoch 10, batch 2980, batch avg loss 0.2043, total avg loss: 0.2205, batch size: 34
2021-10-15 01:08:36,714 INFO [train.py:451] Epoch 10, batch 2990, batch avg loss 0.2218, total avg loss: 0.2208, batch size: 38
2021-10-15 01:08:41,769 INFO [train.py:451] Epoch 10, batch 3000, batch avg loss 0.1798, total avg loss: 0.2207, batch size: 27
2021-10-15 01:09:20,083 INFO [train.py:483] Epoch 10, valid loss 0.1630, best valid loss: 0.1627 best valid epoch: 10
2021-10-15 01:09:24,911 INFO [train.py:451] Epoch 10, batch 3010, batch avg loss 0.2590, total avg loss: 0.2242, batch size: 49
2021-10-15 01:09:29,872 INFO [train.py:451] Epoch 10, batch 3020, batch avg loss 0.2395, total avg loss: 0.2172, batch size: 35
2021-10-15 01:09:34,848 INFO [train.py:451] Epoch 10, batch 3030, batch avg loss 0.2520, total avg loss: 0.2185, batch size: 35
2021-10-15 01:09:39,685 INFO [train.py:451] Epoch 10, batch 3040, batch avg loss 0.1884, total avg loss: 0.2208, batch size: 31
2021-10-15 01:09:44,791 INFO [train.py:451] Epoch 10, batch 3050, batch avg loss 0.1899, total avg loss: 0.2187, batch size: 28
2021-10-15 01:09:49,784 INFO [train.py:451] Epoch 10, batch 3060, batch avg loss 0.1639, total avg loss: 0.2172, batch size: 29
2021-10-15 01:09:54,780 INFO [train.py:451] Epoch 10, batch 3070, batch avg loss 0.1971, total avg loss: 0.2176, batch size: 35
2021-10-15 01:09:59,679 INFO [train.py:451] Epoch 10, batch 3080, batch avg loss 0.2050, total avg loss: 0.2163, batch size: 34
2021-10-15 01:10:04,597 INFO [train.py:451] Epoch 10, batch 3090, batch avg loss 0.2061, total avg loss: 0.2153, batch size: 38
2021-10-15 01:10:09,405 INFO [train.py:451] Epoch 10, batch 3100, batch avg loss 0.1874, total avg loss: 0.2150, batch size: 38
2021-10-15 01:10:14,161 INFO [train.py:451] Epoch 10, batch 3110, batch avg loss 0.1970, total avg loss: 0.2158, batch size: 33
2021-10-15 01:10:18,899 INFO [train.py:451] Epoch 10, batch 3120, batch avg loss 0.1936, total avg loss: 0.2174, batch size: 29
2021-10-15 01:10:23,799 INFO [train.py:451] Epoch 10, batch 3130, batch avg loss 0.1654, total avg loss: 0.2162, batch size: 28
2021-10-15 01:10:28,605 INFO [train.py:451] Epoch 10, batch 3140, batch avg loss 0.1956, total avg loss: 0.2180, batch size: 30
2021-10-15 01:10:33,497 INFO [train.py:451] Epoch 10, batch 3150, batch avg loss 0.2183, total avg loss: 0.2192, batch size: 30
2021-10-15 01:10:38,577 INFO [train.py:451] Epoch 10, batch 3160, batch avg loss 0.1966, total avg loss: 0.2191, batch size: 33
2021-10-15 01:10:43,509 INFO [train.py:451] Epoch 10, batch 3170, batch avg loss 0.1692, total avg loss: 0.2186, batch size: 32
2021-10-15 01:10:48,647 INFO [train.py:451] Epoch 10, batch 3180, batch avg loss 0.2530, total avg loss: 0.2175, batch size: 42
2021-10-15 01:10:53,512 INFO [train.py:451] Epoch 10, batch 3190, batch avg loss 0.2406, total avg loss: 0.2175, batch size: 36
2021-10-15 01:10:58,439 INFO [train.py:451] Epoch 10, batch 3200, batch avg loss 0.2884, total avg loss: 0.2169, batch size: 130
2021-10-15 01:11:03,370 INFO [train.py:451] Epoch 10, batch 3210, batch avg loss 0.1698, total avg loss: 0.2159, batch size: 32
2021-10-15 01:11:08,280 INFO [train.py:451] Epoch 10, batch 3220, batch avg loss 0.2035, total avg loss: 0.2218, batch size: 36
2021-10-15 01:11:13,369 INFO [train.py:451] Epoch 10, batch 3230, batch avg loss 0.2083, total avg loss: 0.2160, batch size: 28
2021-10-15 01:11:18,242 INFO [train.py:451] Epoch 10, batch 3240, batch avg loss 0.2366, total avg loss: 0.2149, batch size: 37
2021-10-15 01:11:23,236 INFO [train.py:451] Epoch 10, batch 3250, batch avg loss 0.1987, total avg loss: 0.2157, batch size: 29
2021-10-15 01:11:28,063 INFO [train.py:451] Epoch 10, batch 3260, batch avg loss 0.2563, total avg loss: 0.2177, batch size: 35
2021-10-15 01:11:32,870 INFO [train.py:451] Epoch 10, batch 3270, batch avg loss 0.2627, total avg loss: 0.2199, batch size: 38
2021-10-15 01:11:37,752 INFO [train.py:451] Epoch 10, batch 3280, batch avg loss 0.2187, total avg loss: 0.2194, batch size: 36
2021-10-15 01:11:42,774 INFO [train.py:451] Epoch 10, batch 3290, batch avg loss 0.2305, total avg loss: 0.2180, batch size: 49
2021-10-15 01:11:48,014 INFO [train.py:451] Epoch 10, batch 3300, batch avg loss 0.2145, total avg loss: 0.2157, batch size: 34
2021-10-15 01:11:52,927 INFO [train.py:451] Epoch 10, batch 3310, batch avg loss 0.2678, total avg loss: 0.2180, batch size: 38
2021-10-15 01:11:57,840 INFO [train.py:451] Epoch 10, batch 3320, batch avg loss 0.1650, total avg loss: 0.2176, batch size: 30
2021-10-15 01:12:02,694 INFO [train.py:451] Epoch 10, batch 3330, batch avg loss 0.2298, total avg loss: 0.2186, batch size: 35
2021-10-15 01:12:07,391 INFO [train.py:451] Epoch 10, batch 3340, batch avg loss 0.2133, total avg loss: 0.2204, batch size: 32
2021-10-15 01:12:12,521 INFO [train.py:451] Epoch 10, batch 3350, batch avg loss 0.2176, total avg loss: 0.2190, batch size: 31
2021-10-15 01:12:17,345 INFO [train.py:451] Epoch 10, batch 3360, batch avg loss 0.2807, total avg loss: 0.2203, batch size: 73
2021-10-15 01:12:22,387 INFO [train.py:451] Epoch 10, batch 3370, batch avg loss 0.2207, total avg loss: 0.2206, batch size: 37
2021-10-15 01:12:27,373 INFO [train.py:451] Epoch 10, batch 3380, batch avg loss 0.1850, total avg loss: 0.2207, batch size: 32
2021-10-15 01:12:32,192 INFO [train.py:451] Epoch 10, batch 3390, batch avg loss 0.3054, total avg loss: 0.2208, batch size: 126
2021-10-15 01:12:37,249 INFO [train.py:451] Epoch 10, batch 3400, batch avg loss 0.2119, total avg loss: 0.2201, batch size: 32
2021-10-15 01:12:42,123 INFO [train.py:451] Epoch 10, batch 3410, batch avg loss 0.2096, total avg loss: 0.2174, batch size: 39
2021-10-15 01:12:47,018 INFO [train.py:451] Epoch 10, batch 3420, batch avg loss 0.2222, total avg loss: 0.2110, batch size: 34
2021-10-15 01:12:51,857 INFO [train.py:451] Epoch 10, batch 3430, batch avg loss 0.1728, total avg loss: 0.2079, batch size: 30
2021-10-15 01:12:56,784 INFO [train.py:451] Epoch 10, batch 3440, batch avg loss 0.1943, total avg loss: 0.2101, batch size: 31
2021-10-15 01:13:01,827 INFO [train.py:451] Epoch 10, batch 3450, batch avg loss 0.1869, total avg loss: 0.2096, batch size: 35
2021-10-15 01:13:07,018 INFO [train.py:451] Epoch 10, batch 3460, batch avg loss 0.2320, total avg loss: 0.2125, batch size: 36
2021-10-15 01:13:12,027 INFO [train.py:451] Epoch 10, batch 3470, batch avg loss 0.1717, total avg loss: 0.2107, batch size: 30
2021-10-15 01:13:17,227 INFO [train.py:451] Epoch 10, batch 3480, batch avg loss 0.2257, total avg loss: 0.2107, batch size: 42
2021-10-15 01:13:22,094 INFO [train.py:451] Epoch 10, batch 3490, batch avg loss 0.2324, total avg loss: 0.2106, batch size: 36
2021-10-15 01:13:26,947 INFO [train.py:451] Epoch 10, batch 3500, batch avg loss 0.1835, total avg loss: 0.2106, batch size: 32
2021-10-15 01:13:31,749 INFO [train.py:451] Epoch 10, batch 3510, batch avg loss 0.2417, total avg loss: 0.2119, batch size: 73
2021-10-15 01:13:36,683 INFO [train.py:451] Epoch 10, batch 3520, batch avg loss 0.2101, total avg loss: 0.2119, batch size: 35
2021-10-15 01:13:41,544 INFO [train.py:451] Epoch 10, batch 3530, batch avg loss 0.2207, total avg loss: 0.2142, batch size: 28
2021-10-15 01:13:46,491 INFO [train.py:451] Epoch 10, batch 3540, batch avg loss 0.2586, total avg loss: 0.2142, batch size: 39
2021-10-15 01:13:51,374 INFO [train.py:451] Epoch 10, batch 3550, batch avg loss 0.2207, total avg loss: 0.2141, batch size: 38
2021-10-15 01:13:56,363 INFO [train.py:451] Epoch 10, batch 3560, batch avg loss 0.1798, total avg loss: 0.2136, batch size: 33
2021-10-15 01:14:01,165 INFO [train.py:451] Epoch 10, batch 3570, batch avg loss 0.3223, total avg loss: 0.2150, batch size: 124
2021-10-15 01:14:06,003 INFO [train.py:451] Epoch 10, batch 3580, batch avg loss 0.2455, total avg loss: 0.2158, batch size: 71
2021-10-15 01:14:10,969 INFO [train.py:451] Epoch 10, batch 3590, batch avg loss 0.2072, total avg loss: 0.2151, batch size: 34
2021-10-15 01:14:15,821 INFO [train.py:451] Epoch 10, batch 3600, batch avg loss 0.1953, total avg loss: 0.2158, batch size: 36
2021-10-15 01:14:20,664 INFO [train.py:451] Epoch 10, batch 3610, batch avg loss 0.2541, total avg loss: 0.2317, batch size: 39
2021-10-15 01:14:25,677 INFO [train.py:451] Epoch 10, batch 3620, batch avg loss 0.1698, total avg loss: 0.2163, batch size: 29
2021-10-15 01:14:30,456 INFO [train.py:451] Epoch 10, batch 3630, batch avg loss 0.2815, total avg loss: 0.2230, batch size: 56
2021-10-15 01:14:35,514 INFO [train.py:451] Epoch 10, batch 3640, batch avg loss 0.2116, total avg loss: 0.2188, batch size: 42
2021-10-15 01:14:40,446 INFO [train.py:451] Epoch 10, batch 3650, batch avg loss 0.2052, total avg loss: 0.2165, batch size: 42
2021-10-15 01:14:45,331 INFO [train.py:451] Epoch 10, batch 3660, batch avg loss 0.1909, total avg loss: 0.2139, batch size: 30
2021-10-15 01:14:50,187 INFO [train.py:451] Epoch 10, batch 3670, batch avg loss 0.2453, total avg loss: 0.2165, batch size: 45
2021-10-15 01:14:55,019 INFO [train.py:451] Epoch 10, batch 3680, batch avg loss 0.2059, total avg loss: 0.2165, batch size: 42
2021-10-15 01:15:00,215 INFO [train.py:451] Epoch 10, batch 3690, batch avg loss 0.1845, total avg loss: 0.2166, batch size: 30
2021-10-15 01:15:05,144 INFO [train.py:451] Epoch 10, batch 3700, batch avg loss 0.2040, total avg loss: 0.2166, batch size: 49
2021-10-15 01:15:10,246 INFO [train.py:451] Epoch 10, batch 3710, batch avg loss 0.2515, total avg loss: 0.2173, batch size: 49
2021-10-15 01:15:15,258 INFO [train.py:451] Epoch 10, batch 3720, batch avg loss 0.2342, total avg loss: 0.2176, batch size: 49
2021-10-15 01:15:20,282 INFO [train.py:451] Epoch 10, batch 3730, batch avg loss 0.2023, total avg loss: 0.2166, batch size: 33
2021-10-15 01:15:25,278 INFO [train.py:451] Epoch 10, batch 3740, batch avg loss 0.2972, total avg loss: 0.2178, batch size: 45
2021-10-15 01:15:30,096 INFO [train.py:451] Epoch 10, batch 3750, batch avg loss 0.2167, total avg loss: 0.2179, batch size: 37
2021-10-15 01:15:34,928 INFO [train.py:451] Epoch 10, batch 3760, batch avg loss 0.2378, total avg loss: 0.2187, batch size: 36
2021-10-15 01:15:39,800 INFO [train.py:451] Epoch 10, batch 3770, batch avg loss 0.2186, total avg loss: 0.2191, batch size: 38
2021-10-15 01:15:45,274 INFO [train.py:451] Epoch 10, batch 3780, batch avg loss 0.1828, total avg loss: 0.2180, batch size: 35
2021-10-15 01:15:50,329 INFO [train.py:451] Epoch 10, batch 3790, batch avg loss 0.2066, total avg loss: 0.2179, batch size: 35
2021-10-15 01:15:55,301 INFO [train.py:451] Epoch 10, batch 3800, batch avg loss 0.2306, total avg loss: 0.2185, batch size: 42
2021-10-15 01:16:00,157 INFO [train.py:451] Epoch 10, batch 3810, batch avg loss 0.2148, total avg loss: 0.2287, batch size: 45
2021-10-15 01:16:05,057 INFO [train.py:451] Epoch 10, batch 3820, batch avg loss 0.3744, total avg loss: 0.2329, batch size: 130
2021-10-15 01:16:10,099 INFO [train.py:451] Epoch 10, batch 3830, batch avg loss 0.2009, total avg loss: 0.2227, batch size: 32
2021-10-15 01:16:15,040 INFO [train.py:451] Epoch 10, batch 3840, batch avg loss 0.2146, total avg loss: 0.2193, batch size: 34
2021-10-15 01:16:19,734 INFO [train.py:451] Epoch 10, batch 3850, batch avg loss 0.1955, total avg loss: 0.2190, batch size: 41
2021-10-15 01:16:24,569 INFO [train.py:451] Epoch 10, batch 3860, batch avg loss 0.1740, total avg loss: 0.2185, batch size: 30
2021-10-15 01:16:29,265 INFO [train.py:451] Epoch 10, batch 3870, batch avg loss 0.2584, total avg loss: 0.2228, batch size: 57
2021-10-15 01:16:34,299 INFO [train.py:451] Epoch 10, batch 3880, batch avg loss 0.1933, total avg loss: 0.2203, batch size: 29
2021-10-15 01:16:39,172 INFO [train.py:451] Epoch 10, batch 3890, batch avg loss 0.1824, total avg loss: 0.2201, batch size: 39
2021-10-15 01:16:44,226 INFO [train.py:451] Epoch 10, batch 3900, batch avg loss 0.1748, total avg loss: 0.2184, batch size: 28
2021-10-15 01:16:49,067 INFO [train.py:451] Epoch 10, batch 3910, batch avg loss 0.2542, total avg loss: 0.2181, batch size: 49
2021-10-15 01:16:53,884 INFO [train.py:451] Epoch 10, batch 3920, batch avg loss 0.2777, total avg loss: 0.2191, batch size: 71
2021-10-15 01:16:58,821 INFO [train.py:451] Epoch 10, batch 3930, batch avg loss 0.2012, total avg loss: 0.2199, batch size: 33
2021-10-15 01:17:03,916 INFO [train.py:451] Epoch 10, batch 3940, batch avg loss 0.1563, total avg loss: 0.2194, batch size: 28
2021-10-15 01:17:08,733 INFO [train.py:451] Epoch 10, batch 3950, batch avg loss 0.1750, total avg loss: 0.2188, batch size: 31
2021-10-15 01:17:13,622 INFO [train.py:451] Epoch 10, batch 3960, batch avg loss 0.2037, total avg loss: 0.2190, batch size: 38
2021-10-15 01:17:18,473 INFO [train.py:451] Epoch 10, batch 3970, batch avg loss 0.2388, total avg loss: 0.2198, batch size: 37
2021-10-15 01:17:23,423 INFO [train.py:451] Epoch 10, batch 3980, batch avg loss 0.1767, total avg loss: 0.2195, batch size: 33
2021-10-15 01:17:28,258 INFO [train.py:451] Epoch 10, batch 3990, batch avg loss 0.1997, total avg loss: 0.2192, batch size: 31
2021-10-15 01:17:33,170 INFO [train.py:451] Epoch 10, batch 4000, batch avg loss 0.1581, total avg loss: 0.2192, batch size: 31
2021-10-15 01:18:10,895 INFO [train.py:483] Epoch 10, valid loss 0.1628, best valid loss: 0.1627 best valid epoch: 10
2021-10-15 01:18:15,776 INFO [train.py:451] Epoch 10, batch 4010, batch avg loss 0.2296, total avg loss: 0.2272, batch size: 35
2021-10-15 01:18:20,731 INFO [train.py:451] Epoch 10, batch 4020, batch avg loss 0.1843, total avg loss: 0.2233, batch size: 33
2021-10-15 01:18:25,492 INFO [train.py:451] Epoch 10, batch 4030, batch avg loss 0.2073, total avg loss: 0.2208, batch size: 30
2021-10-15 01:18:30,368 INFO [train.py:451] Epoch 10, batch 4040, batch avg loss 0.2094, total avg loss: 0.2190, batch size: 28
2021-10-15 01:18:35,434 INFO [train.py:451] Epoch 10, batch 4050, batch avg loss 0.2298, total avg loss: 0.2178, batch size: 31
2021-10-15 01:18:40,354 INFO [train.py:451] Epoch 10, batch 4060, batch avg loss 0.2355, total avg loss: 0.2170, batch size: 29
2021-10-15 01:18:45,368 INFO [train.py:451] Epoch 10, batch 4070, batch avg loss 0.2413, total avg loss: 0.2175, batch size: 33
2021-10-15 01:18:50,350 INFO [train.py:451] Epoch 10, batch 4080, batch avg loss 0.2323, total avg loss: 0.2173, batch size: 33
2021-10-15 01:18:55,282 INFO [train.py:451] Epoch 10, batch 4090, batch avg loss 0.2782, total avg loss: 0.2170, batch size: 56
2021-10-15 01:19:00,143 INFO [train.py:451] Epoch 10, batch 4100, batch avg loss 0.2289, total avg loss: 0.2169, batch size: 38
2021-10-15 01:19:04,924 INFO [train.py:451] Epoch 10, batch 4110, batch avg loss 0.2204, total avg loss: 0.2174, batch size: 35
2021-10-15 01:19:09,922 INFO [train.py:451] Epoch 10, batch 4120, batch avg loss 0.1756, total avg loss: 0.2166, batch size: 27
2021-10-15 01:19:15,048 INFO [train.py:451] Epoch 10, batch 4130, batch avg loss 0.2284, total avg loss: 0.2169, batch size: 33
2021-10-15 01:19:19,952 INFO [train.py:451] Epoch 10, batch 4140, batch avg loss 0.2054, total avg loss: 0.2178, batch size: 31
2021-10-15 01:19:25,025 INFO [train.py:451] Epoch 10, batch 4150, batch avg loss 0.2334, total avg loss: 0.2182, batch size: 36
2021-10-15 01:19:30,009 INFO [train.py:451] Epoch 10, batch 4160, batch avg loss 0.2029, total avg loss: 0.2186, batch size: 34
2021-10-15 01:19:34,916 INFO [train.py:451] Epoch 10, batch 4170, batch avg loss 0.2303, total avg loss: 0.2181, batch size: 34
2021-10-15 01:19:39,926 INFO [train.py:451] Epoch 10, batch 4180, batch avg loss 0.1839, total avg loss: 0.2174, batch size: 30
2021-10-15 01:19:44,548 INFO [train.py:451] Epoch 10, batch 4190, batch avg loss 0.2973, total avg loss: 0.2185, batch size: 57
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2021-10-15 01:21:27,047 INFO [train.py:451] Epoch 10, batch 4400, batch avg loss 0.3195, total avg loss: 0.2246, batch size: 128
2021-10-15 01:21:31,831 INFO [train.py:451] Epoch 10, batch 4410, batch avg loss 0.2175, total avg loss: 0.2340, batch size: 36
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2021-10-15 01:21:41,997 INFO [train.py:451] Epoch 10, batch 4430, batch avg loss 0.2263, total avg loss: 0.2185, batch size: 45
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2021-10-15 01:21:51,904 INFO [train.py:451] Epoch 10, batch 4450, batch avg loss 0.2401, total avg loss: 0.2128, batch size: 32
2021-10-15 01:21:56,829 INFO [train.py:451] Epoch 10, batch 4460, batch avg loss 0.3063, total avg loss: 0.2119, batch size: 126
2021-10-15 01:22:01,682 INFO [train.py:451] Epoch 10, batch 4470, batch avg loss 0.2480, total avg loss: 0.2113, batch size: 45
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2021-10-15 01:22:11,391 INFO [train.py:451] Epoch 10, batch 4490, batch avg loss 0.2271, total avg loss: 0.2137, batch size: 31
2021-10-15 01:22:16,305 INFO [train.py:451] Epoch 10, batch 4500, batch avg loss 0.1815, total avg loss: 0.2145, batch size: 30
2021-10-15 01:22:21,220 INFO [train.py:451] Epoch 10, batch 4510, batch avg loss 0.1928, total avg loss: 0.2158, batch size: 34
2021-10-15 01:22:26,184 INFO [train.py:451] Epoch 10, batch 4520, batch avg loss 0.1530, total avg loss: 0.2147, batch size: 28
2021-10-15 01:22:31,078 INFO [train.py:451] Epoch 10, batch 4530, batch avg loss 0.2173, total avg loss: 0.2141, batch size: 37
2021-10-15 01:22:36,117 INFO [train.py:451] Epoch 10, batch 4540, batch avg loss 0.2191, total avg loss: 0.2140, batch size: 42
2021-10-15 01:22:41,006 INFO [train.py:451] Epoch 10, batch 4550, batch avg loss 0.3469, total avg loss: 0.2157, batch size: 128
2021-10-15 01:22:46,266 INFO [train.py:451] Epoch 10, batch 4560, batch avg loss 0.2308, total avg loss: 0.2153, batch size: 42
2021-10-15 01:22:51,213 INFO [train.py:451] Epoch 10, batch 4570, batch avg loss 0.1818, total avg loss: 0.2163, batch size: 32
2021-10-15 01:22:56,309 INFO [train.py:451] Epoch 10, batch 4580, batch avg loss 0.1890, total avg loss: 0.2157, batch size: 33
2021-10-15 01:23:01,329 INFO [train.py:451] Epoch 10, batch 4590, batch avg loss 0.2435, total avg loss: 0.2156, batch size: 34
2021-10-15 01:23:06,232 INFO [train.py:451] Epoch 10, batch 4600, batch avg loss 0.2475, total avg loss: 0.2162, batch size: 73
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2021-10-15 01:23:21,290 INFO [train.py:451] Epoch 10, batch 4630, batch avg loss 0.1894, total avg loss: 0.2082, batch size: 45
2021-10-15 01:23:26,312 INFO [train.py:451] Epoch 10, batch 4640, batch avg loss 0.3181, total avg loss: 0.2136, batch size: 129
2021-10-15 01:23:31,285 INFO [train.py:451] Epoch 10, batch 4650, batch avg loss 0.1891, total avg loss: 0.2144, batch size: 28
2021-10-15 01:23:36,182 INFO [train.py:451] Epoch 10, batch 4660, batch avg loss 0.2456, total avg loss: 0.2140, batch size: 73
2021-10-15 01:23:41,416 INFO [train.py:451] Epoch 10, batch 4670, batch avg loss 0.1836, total avg loss: 0.2119, batch size: 34
2021-10-15 01:23:46,277 INFO [train.py:451] Epoch 10, batch 4680, batch avg loss 0.2169, total avg loss: 0.2139, batch size: 39
2021-10-15 01:23:51,357 INFO [train.py:451] Epoch 10, batch 4690, batch avg loss 0.2093, total avg loss: 0.2126, batch size: 32
2021-10-15 01:23:56,262 INFO [train.py:451] Epoch 10, batch 4700, batch avg loss 0.1685, total avg loss: 0.2126, batch size: 34
2021-10-15 01:24:01,301 INFO [train.py:451] Epoch 10, batch 4710, batch avg loss 0.1619, total avg loss: 0.2122, batch size: 27
2021-10-15 01:24:06,224 INFO [train.py:451] Epoch 10, batch 4720, batch avg loss 0.1916, total avg loss: 0.2118, batch size: 29
2021-10-15 01:24:11,187 INFO [train.py:451] Epoch 10, batch 4730, batch avg loss 0.2252, total avg loss: 0.2107, batch size: 31
2021-10-15 01:24:16,248 INFO [train.py:451] Epoch 10, batch 4740, batch avg loss 0.1726, total avg loss: 0.2101, batch size: 30
2021-10-15 01:24:21,071 INFO [train.py:451] Epoch 10, batch 4750, batch avg loss 0.2844, total avg loss: 0.2121, batch size: 38
2021-10-15 01:24:25,881 INFO [train.py:451] Epoch 10, batch 4760, batch avg loss 0.2824, total avg loss: 0.2129, batch size: 57
2021-10-15 01:24:30,964 INFO [train.py:451] Epoch 10, batch 4770, batch avg loss 0.2111, total avg loss: 0.2132, batch size: 49
2021-10-15 01:24:35,889 INFO [train.py:451] Epoch 10, batch 4780, batch avg loss 0.2008, total avg loss: 0.2141, batch size: 27
2021-10-15 01:24:40,805 INFO [train.py:451] Epoch 10, batch 4790, batch avg loss 0.3249, total avg loss: 0.2149, batch size: 127
2021-10-15 01:24:45,811 INFO [train.py:451] Epoch 10, batch 4800, batch avg loss 0.2165, total avg loss: 0.2149, batch size: 35
2021-10-15 01:24:50,851 INFO [train.py:451] Epoch 10, batch 4810, batch avg loss 0.1823, total avg loss: 0.2050, batch size: 36
2021-10-15 01:24:55,672 INFO [train.py:451] Epoch 10, batch 4820, batch avg loss 0.2993, total avg loss: 0.2105, batch size: 129
2021-10-15 01:25:00,625 INFO [train.py:451] Epoch 10, batch 4830, batch avg loss 0.2602, total avg loss: 0.2128, batch size: 34
2021-10-15 01:25:05,604 INFO [train.py:451] Epoch 10, batch 4840, batch avg loss 0.1979, total avg loss: 0.2133, batch size: 32
2021-10-15 01:25:10,474 INFO [train.py:451] Epoch 10, batch 4850, batch avg loss 0.2152, total avg loss: 0.2133, batch size: 41
2021-10-15 01:25:15,322 INFO [train.py:451] Epoch 10, batch 4860, batch avg loss 0.2448, total avg loss: 0.2161, batch size: 41
2021-10-15 01:25:20,010 INFO [train.py:451] Epoch 10, batch 4870, batch avg loss 0.2597, total avg loss: 0.2225, batch size: 49
2021-10-15 01:25:24,861 INFO [train.py:451] Epoch 10, batch 4880, batch avg loss 0.1908, total avg loss: 0.2220, batch size: 33
2021-10-15 01:25:29,697 INFO [train.py:451] Epoch 10, batch 4890, batch avg loss 0.2325, total avg loss: 0.2222, batch size: 41
2021-10-15 01:25:34,474 INFO [train.py:451] Epoch 10, batch 4900, batch avg loss 0.2717, total avg loss: 0.2220, batch size: 72
2021-10-15 01:25:39,420 INFO [train.py:451] Epoch 10, batch 4910, batch avg loss 0.1799, total avg loss: 0.2191, batch size: 36
2021-10-15 01:25:44,022 INFO [train.py:451] Epoch 10, batch 4920, batch avg loss 0.2025, total avg loss: 0.2202, batch size: 39
2021-10-15 01:25:48,996 INFO [train.py:451] Epoch 10, batch 4930, batch avg loss 0.2415, total avg loss: 0.2193, batch size: 45
2021-10-15 01:25:53,816 INFO [train.py:451] Epoch 10, batch 4940, batch avg loss 0.2184, total avg loss: 0.2192, batch size: 31
2021-10-15 01:25:58,806 INFO [train.py:451] Epoch 10, batch 4950, batch avg loss 0.1849, total avg loss: 0.2176, batch size: 31
2021-10-15 01:26:03,743 INFO [train.py:451] Epoch 10, batch 4960, batch avg loss 0.2174, total avg loss: 0.2175, batch size: 39
2021-10-15 01:26:08,753 INFO [train.py:451] Epoch 10, batch 4970, batch avg loss 0.2059, total avg loss: 0.2161, batch size: 33
2021-10-15 01:26:13,626 INFO [train.py:451] Epoch 10, batch 4980, batch avg loss 0.2033, total avg loss: 0.2162, batch size: 31
2021-10-15 01:26:18,730 INFO [train.py:451] Epoch 10, batch 4990, batch avg loss 0.2317, total avg loss: 0.2158, batch size: 35
2021-10-15 01:26:23,710 INFO [train.py:451] Epoch 10, batch 5000, batch avg loss 0.2227, total avg loss: 0.2161, batch size: 34
2021-10-15 01:27:04,506 INFO [train.py:483] Epoch 10, valid loss 0.1626, best valid loss: 0.1626 best valid epoch: 10
2021-10-15 01:27:09,574 INFO [train.py:451] Epoch 10, batch 5010, batch avg loss 0.1433, total avg loss: 0.2027, batch size: 28
2021-10-15 01:27:14,568 INFO [train.py:451] Epoch 10, batch 5020, batch avg loss 0.2114, total avg loss: 0.2066, batch size: 34
2021-10-15 01:27:19,472 INFO [train.py:451] Epoch 10, batch 5030, batch avg loss 0.3327, total avg loss: 0.2108, batch size: 133
2021-10-15 01:27:24,183 INFO [train.py:451] Epoch 10, batch 5040, batch avg loss 0.1959, total avg loss: 0.2159, batch size: 32
2021-10-15 01:27:29,029 INFO [train.py:451] Epoch 10, batch 5050, batch avg loss 0.2128, total avg loss: 0.2178, batch size: 31
2021-10-15 01:27:34,021 INFO [train.py:451] Epoch 10, batch 5060, batch avg loss 0.1779, total avg loss: 0.2166, batch size: 28
2021-10-15 01:27:39,013 INFO [train.py:451] Epoch 10, batch 5070, batch avg loss 0.2513, total avg loss: 0.2166, batch size: 42
2021-10-15 01:27:43,955 INFO [train.py:451] Epoch 10, batch 5080, batch avg loss 0.1911, total avg loss: 0.2167, batch size: 34
2021-10-15 01:27:48,909 INFO [train.py:451] Epoch 10, batch 5090, batch avg loss 0.2120, total avg loss: 0.2178, batch size: 38
2021-10-15 01:27:53,536 INFO [train.py:451] Epoch 10, batch 5100, batch avg loss 0.2251, total avg loss: 0.2193, batch size: 42
2021-10-15 01:27:58,537 INFO [train.py:451] Epoch 10, batch 5110, batch avg loss 0.1862, total avg loss: 0.2184, batch size: 34
2021-10-15 01:28:03,475 INFO [train.py:451] Epoch 10, batch 5120, batch avg loss 0.2014, total avg loss: 0.2171, batch size: 36
2021-10-15 01:28:08,472 INFO [train.py:451] Epoch 10, batch 5130, batch avg loss 0.3602, total avg loss: 0.2178, batch size: 128
2021-10-15 01:28:13,454 INFO [train.py:451] Epoch 10, batch 5140, batch avg loss 0.2399, total avg loss: 0.2175, batch size: 31
2021-10-15 01:28:18,530 INFO [train.py:451] Epoch 10, batch 5150, batch avg loss 0.1899, total avg loss: 0.2167, batch size: 35
2021-10-15 01:28:23,364 INFO [train.py:451] Epoch 10, batch 5160, batch avg loss 0.2223, total avg loss: 0.2185, batch size: 33
2021-10-15 01:28:28,382 INFO [train.py:451] Epoch 10, batch 5170, batch avg loss 0.2002, total avg loss: 0.2175, batch size: 34
2021-10-15 01:28:33,378 INFO [train.py:451] Epoch 10, batch 5180, batch avg loss 0.2079, total avg loss: 0.2184, batch size: 27
2021-10-15 01:28:38,400 INFO [train.py:451] Epoch 10, batch 5190, batch avg loss 0.2341, total avg loss: 0.2188, batch size: 33
2021-10-15 01:28:43,367 INFO [train.py:451] Epoch 10, batch 5200, batch avg loss 0.2103, total avg loss: 0.2190, batch size: 34
2021-10-15 01:28:48,390 INFO [train.py:451] Epoch 10, batch 5210, batch avg loss 0.1766, total avg loss: 0.2031, batch size: 31
2021-10-15 01:28:53,223 INFO [train.py:451] Epoch 10, batch 5220, batch avg loss 0.3122, total avg loss: 0.2070, batch size: 129
2021-10-15 01:28:58,215 INFO [train.py:451] Epoch 10, batch 5230, batch avg loss 0.2114, total avg loss: 0.2114, batch size: 34
2021-10-15 01:29:03,189 INFO [train.py:451] Epoch 10, batch 5240, batch avg loss 0.2259, total avg loss: 0.2110, batch size: 38
2021-10-15 01:29:08,038 INFO [train.py:451] Epoch 10, batch 5250, batch avg loss 0.2174, total avg loss: 0.2119, batch size: 34
2021-10-15 01:29:12,834 INFO [train.py:451] Epoch 10, batch 5260, batch avg loss 0.2685, total avg loss: 0.2161, batch size: 30
2021-10-15 01:29:17,543 INFO [train.py:451] Epoch 10, batch 5270, batch avg loss 0.2719, total avg loss: 0.2162, batch size: 73
2021-10-15 01:29:22,317 INFO [train.py:451] Epoch 10, batch 5280, batch avg loss 0.1935, total avg loss: 0.2154, batch size: 34
2021-10-15 01:29:27,274 INFO [train.py:451] Epoch 10, batch 5290, batch avg loss 0.2423, total avg loss: 0.2150, batch size: 38
2021-10-15 01:29:32,124 INFO [train.py:451] Epoch 10, batch 5300, batch avg loss 0.2151, total avg loss: 0.2140, batch size: 34
2021-10-15 01:29:37,036 INFO [train.py:451] Epoch 10, batch 5310, batch avg loss 0.2428, total avg loss: 0.2147, batch size: 39
2021-10-15 01:29:41,948 INFO [train.py:451] Epoch 10, batch 5320, batch avg loss 0.2195, total avg loss: 0.2161, batch size: 34
2021-10-15 01:29:46,809 INFO [train.py:451] Epoch 10, batch 5330, batch avg loss 0.1963, total avg loss: 0.2175, batch size: 28
2021-10-15 01:29:51,569 INFO [train.py:451] Epoch 10, batch 5340, batch avg loss 0.2133, total avg loss: 0.2182, batch size: 38
2021-10-15 01:29:56,499 INFO [train.py:451] Epoch 10, batch 5350, batch avg loss 0.2966, total avg loss: 0.2175, batch size: 72
2021-10-15 01:30:01,263 INFO [train.py:451] Epoch 10, batch 5360, batch avg loss 0.2521, total avg loss: 0.2182, batch size: 72
2021-10-15 01:30:06,118 INFO [train.py:451] Epoch 10, batch 5370, batch avg loss 0.1783, total avg loss: 0.2169, batch size: 29
2021-10-15 01:30:11,215 INFO [train.py:451] Epoch 10, batch 5380, batch avg loss 0.1888, total avg loss: 0.2174, batch size: 27
2021-10-15 01:30:16,243 INFO [train.py:451] Epoch 10, batch 5390, batch avg loss 0.1785, total avg loss: 0.2175, batch size: 28
2021-10-15 01:30:21,180 INFO [train.py:451] Epoch 10, batch 5400, batch avg loss 0.2443, total avg loss: 0.2173, batch size: 49
2021-10-15 01:30:25,945 INFO [train.py:451] Epoch 10, batch 5410, batch avg loss 0.2036, total avg loss: 0.2429, batch size: 36
2021-10-15 01:30:31,168 INFO [train.py:451] Epoch 10, batch 5420, batch avg loss 0.2011, total avg loss: 0.2272, batch size: 34
2021-10-15 01:30:36,155 INFO [train.py:451] Epoch 10, batch 5430, batch avg loss 0.3759, total avg loss: 0.2285, batch size: 124
2021-10-15 01:30:40,938 INFO [train.py:451] Epoch 10, batch 5440, batch avg loss 0.2341, total avg loss: 0.2296, batch size: 36
2021-10-15 01:30:45,702 INFO [train.py:451] Epoch 10, batch 5450, batch avg loss 0.1980, total avg loss: 0.2291, batch size: 38
2021-10-15 01:30:50,496 INFO [train.py:451] Epoch 10, batch 5460, batch avg loss 0.1789, total avg loss: 0.2309, batch size: 31
2021-10-15 01:30:55,429 INFO [train.py:451] Epoch 10, batch 5470, batch avg loss 0.2220, total avg loss: 0.2306, batch size: 38
2021-10-15 01:31:00,437 INFO [train.py:451] Epoch 10, batch 5480, batch avg loss 0.1942, total avg loss: 0.2283, batch size: 29
2021-10-15 01:31:05,716 INFO [train.py:451] Epoch 10, batch 5490, batch avg loss 0.1981, total avg loss: 0.2255, batch size: 35
2021-10-15 01:31:10,894 INFO [train.py:451] Epoch 10, batch 5500, batch avg loss 0.2566, total avg loss: 0.2239, batch size: 37
2021-10-15 01:31:15,771 INFO [train.py:451] Epoch 10, batch 5510, batch avg loss 0.1860, total avg loss: 0.2245, batch size: 31
2021-10-15 01:31:20,685 INFO [train.py:451] Epoch 10, batch 5520, batch avg loss 0.2188, total avg loss: 0.2234, batch size: 30
2021-10-15 01:31:25,613 INFO [train.py:451] Epoch 10, batch 5530, batch avg loss 0.2698, total avg loss: 0.2226, batch size: 41
2021-10-15 01:31:30,673 INFO [train.py:451] Epoch 10, batch 5540, batch avg loss 0.2085, total avg loss: 0.2219, batch size: 29
2021-10-15 01:31:35,639 INFO [train.py:451] Epoch 10, batch 5550, batch avg loss 0.2562, total avg loss: 0.2214, batch size: 49
2021-10-15 01:31:40,557 INFO [train.py:451] Epoch 10, batch 5560, batch avg loss 0.1765, total avg loss: 0.2218, batch size: 28
2021-10-15 01:31:45,410 INFO [train.py:451] Epoch 10, batch 5570, batch avg loss 0.2010, total avg loss: 0.2229, batch size: 39
2021-10-15 01:31:50,151 INFO [train.py:451] Epoch 10, batch 5580, batch avg loss 0.2307, total avg loss: 0.2239, batch size: 72
2021-10-15 01:31:55,272 INFO [train.py:451] Epoch 10, batch 5590, batch avg loss 0.2158, total avg loss: 0.2234, batch size: 35
2021-10-15 01:32:00,194 INFO [train.py:451] Epoch 10, batch 5600, batch avg loss 0.1858, total avg loss: 0.2236, batch size: 30
2021-10-15 01:32:05,155 INFO [train.py:451] Epoch 10, batch 5610, batch avg loss 0.1952, total avg loss: 0.2137, batch size: 35
2021-10-15 01:32:10,027 INFO [train.py:451] Epoch 10, batch 5620, batch avg loss 0.1816, total avg loss: 0.2144, batch size: 33
2021-10-15 01:32:14,923 INFO [train.py:451] Epoch 10, batch 5630, batch avg loss 0.1587, total avg loss: 0.2157, batch size: 33
2021-10-15 01:32:19,777 INFO [train.py:451] Epoch 10, batch 5640, batch avg loss 0.1859, total avg loss: 0.2227, batch size: 32
2021-10-15 01:32:24,557 INFO [train.py:451] Epoch 10, batch 5650, batch avg loss 0.2350, total avg loss: 0.2246, batch size: 57
2021-10-15 01:32:29,393 INFO [train.py:451] Epoch 10, batch 5660, batch avg loss 0.2572, total avg loss: 0.2261, batch size: 38
2021-10-15 01:32:34,408 INFO [train.py:451] Epoch 10, batch 5670, batch avg loss 0.2864, total avg loss: 0.2278, batch size: 38
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2021-10-15 01:35:17,084 INFO [train.py:451] Epoch 10, batch 6000, batch avg loss 0.1977, total avg loss: 0.2186, batch size: 29
2021-10-15 01:35:55,234 INFO [train.py:483] Epoch 10, valid loss 0.1621, best valid loss: 0.1621 best valid epoch: 10
2021-10-15 01:36:00,212 INFO [train.py:451] Epoch 10, batch 6010, batch avg loss 0.2343, total avg loss: 0.2265, batch size: 35
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2021-10-15 01:37:38,677 INFO [train.py:451] Epoch 10, batch 6210, batch avg loss 0.2500, total avg loss: 0.2280, batch size: 37
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2021-10-15 01:38:56,964 INFO [train.py:451] Epoch 10, batch 6370, batch avg loss 0.2178, total avg loss: 0.2271, batch size: 33
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2021-10-15 01:39:12,130 INFO [train.py:451] Epoch 10, batch 6400, batch avg loss 0.2041, total avg loss: 0.2244, batch size: 34
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2021-10-15 01:39:41,749 INFO [train.py:451] Epoch 10, batch 6460, batch avg loss 0.2854, total avg loss: 0.2221, batch size: 72
2021-10-15 01:39:46,521 INFO [train.py:451] Epoch 10, batch 6470, batch avg loss 0.2597, total avg loss: 0.2233, batch size: 35
2021-10-15 01:39:51,441 INFO [train.py:451] Epoch 10, batch 6480, batch avg loss 0.1953, total avg loss: 0.2231, batch size: 38
2021-10-15 01:39:56,494 INFO [train.py:451] Epoch 10, batch 6490, batch avg loss 0.1864, total avg loss: 0.2219, batch size: 34
2021-10-15 01:40:01,414 INFO [train.py:451] Epoch 10, batch 6500, batch avg loss 0.2938, total avg loss: 0.2235, batch size: 49
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2021-10-15 01:40:50,453 INFO [train.py:451] Epoch 10, batch 6600, batch avg loss 0.3292, total avg loss: 0.2237, batch size: 129
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2021-10-15 01:41:19,864 INFO [train.py:451] Epoch 10, batch 6660, batch avg loss 0.1892, total avg loss: 0.2271, batch size: 31
2021-10-15 01:41:24,578 INFO [train.py:451] Epoch 10, batch 6670, batch avg loss 0.2717, total avg loss: 0.2272, batch size: 38
2021-10-15 01:41:29,440 INFO [train.py:451] Epoch 10, batch 6680, batch avg loss 0.2050, total avg loss: 0.2242, batch size: 34
2021-10-15 01:41:34,476 INFO [train.py:451] Epoch 10, batch 6690, batch avg loss 0.2263, total avg loss: 0.2242, batch size: 36
2021-10-15 01:41:39,613 INFO [train.py:451] Epoch 10, batch 6700, batch avg loss 0.2120, total avg loss: 0.2233, batch size: 31
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2021-10-15 01:42:09,227 INFO [train.py:451] Epoch 10, batch 6760, batch avg loss 0.2109, total avg loss: 0.2222, batch size: 34
2021-10-15 01:42:14,115 INFO [train.py:451] Epoch 10, batch 6770, batch avg loss 0.1819, total avg loss: 0.2219, batch size: 32
2021-10-15 01:42:19,230 INFO [train.py:451] Epoch 10, batch 6780, batch avg loss 0.2452, total avg loss: 0.2211, batch size: 33
2021-10-15 01:42:24,013 INFO [train.py:451] Epoch 10, batch 6790, batch avg loss 0.2102, total avg loss: 0.2210, batch size: 39
2021-10-15 01:42:28,816 INFO [train.py:451] Epoch 10, batch 6800, batch avg loss 0.2313, total avg loss: 0.2209, batch size: 56
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2021-10-15 01:42:43,570 INFO [train.py:451] Epoch 10, batch 6830, batch avg loss 0.2407, total avg loss: 0.2224, batch size: 58
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2021-10-15 01:43:02,961 INFO [train.py:451] Epoch 10, batch 6870, batch avg loss 0.2028, total avg loss: 0.2188, batch size: 35
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2021-10-15 01:43:17,930 INFO [train.py:451] Epoch 10, batch 6900, batch avg loss 0.1681, total avg loss: 0.2194, batch size: 30
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2021-10-15 01:44:02,563 INFO [train.py:451] Epoch 10, batch 6990, batch avg loss 0.2015, total avg loss: 0.2195, batch size: 33
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2021-10-15 01:44:47,703 INFO [train.py:483] Epoch 10, valid loss 0.1624, best valid loss: 0.1621 best valid epoch: 10
2021-10-15 01:44:52,676 INFO [train.py:451] Epoch 10, batch 7010, batch avg loss 0.2197, total avg loss: 0.2263, batch size: 34
2021-10-15 01:44:57,628 INFO [train.py:451] Epoch 10, batch 7020, batch avg loss 0.1983, total avg loss: 0.2233, batch size: 38
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2021-10-15 01:45:36,916 INFO [train.py:451] Epoch 10, batch 7100, batch avg loss 0.2344, total avg loss: 0.2207, batch size: 41
2021-10-15 01:45:41,660 INFO [train.py:451] Epoch 10, batch 7110, batch avg loss 0.2669, total avg loss: 0.2199, batch size: 73
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2021-10-15 01:50:25,612 INFO [train.py:451] Epoch 10, batch 7690, batch avg loss 0.2481, total avg loss: 0.2213, batch size: 39
2021-10-15 01:50:30,486 INFO [train.py:451] Epoch 10, batch 7700, batch avg loss 0.1897, total avg loss: 0.2214, batch size: 30
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2021-10-15 01:52:35,125 INFO [train.py:451] Epoch 10, batch 7940, batch avg loss 0.1796, total avg loss: 0.2195, batch size: 29
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2021-10-15 01:53:43,724 INFO [train.py:483] Epoch 10, valid loss 0.1627, best valid loss: 0.1621 best valid epoch: 10
2021-10-15 01:53:48,576 INFO [train.py:451] Epoch 10, batch 8010, batch avg loss 0.1858, total avg loss: 0.2238, batch size: 32
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2021-10-15 01:54:52,716 INFO [train.py:451] Epoch 10, batch 8140, batch avg loss 0.1980, total avg loss: 0.2210, batch size: 33
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2021-10-15 01:55:41,827 INFO [train.py:451] Epoch 10, batch 8240, batch avg loss 0.2512, total avg loss: 0.2244, batch size: 41
2021-10-15 01:55:46,685 INFO [train.py:451] Epoch 10, batch 8250, batch avg loss 0.2382, total avg loss: 0.2257, batch size: 32
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2021-10-15 01:55:56,570 INFO [train.py:451] Epoch 10, batch 8270, batch avg loss 0.1869, total avg loss: 0.2248, batch size: 37
2021-10-15 01:56:01,339 INFO [train.py:451] Epoch 10, batch 8280, batch avg loss 0.1643, total avg loss: 0.2245, batch size: 29
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2021-10-15 01:58:02,839 INFO [train.py:451] Epoch 10, batch 8530, batch avg loss 0.2040, total avg loss: 0.2193, batch size: 31
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2021-10-15 01:58:22,381 INFO [train.py:451] Epoch 10, batch 8570, batch avg loss 0.3218, total avg loss: 0.2195, batch size: 124
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2021-10-15 01:58:36,952 INFO [train.py:451] Epoch 10, batch 8600, batch avg loss 0.2066, total avg loss: 0.2200, batch size: 32
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2021-10-15 01:59:25,477 INFO [train.py:451] Epoch 10, batch 8700, batch avg loss 0.2028, total avg loss: 0.2231, batch size: 36
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2021-10-15 01:59:49,992 INFO [train.py:451] Epoch 10, batch 8750, batch avg loss 0.1737, total avg loss: 0.2239, batch size: 30
2021-10-15 01:59:54,780 INFO [train.py:451] Epoch 10, batch 8760, batch avg loss 0.2168, total avg loss: 0.2243, batch size: 39
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2021-10-15 02:00:04,482 INFO [train.py:451] Epoch 10, batch 8780, batch avg loss 0.1815, total avg loss: 0.2243, batch size: 33
2021-10-15 02:00:09,423 INFO [train.py:451] Epoch 10, batch 8790, batch avg loss 0.1890, total avg loss: 0.2238, batch size: 32
2021-10-15 02:00:14,305 INFO [train.py:451] Epoch 10, batch 8800, batch avg loss 0.2453, total avg loss: 0.2243, batch size: 49
2021-10-15 02:00:19,029 INFO [train.py:451] Epoch 10, batch 8810, batch avg loss 0.2002, total avg loss: 0.2312, batch size: 39
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2021-10-15 02:00:34,007 INFO [train.py:451] Epoch 10, batch 8840, batch avg loss 0.2116, total avg loss: 0.2226, batch size: 30
2021-10-15 02:00:38,724 INFO [train.py:451] Epoch 10, batch 8850, batch avg loss 0.1900, total avg loss: 0.2232, batch size: 41
2021-10-15 02:00:43,528 INFO [train.py:451] Epoch 10, batch 8860, batch avg loss 0.2039, total avg loss: 0.2242, batch size: 32
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2021-10-15 02:01:03,441 INFO [train.py:451] Epoch 10, batch 8900, batch avg loss 0.2520, total avg loss: 0.2218, batch size: 35
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2021-10-15 02:01:33,156 INFO [train.py:451] Epoch 10, batch 8960, batch avg loss 0.1837, total avg loss: 0.2187, batch size: 31
2021-10-15 02:01:37,877 INFO [train.py:451] Epoch 10, batch 8970, batch avg loss 0.2208, total avg loss: 0.2202, batch size: 36
2021-10-15 02:01:42,627 INFO [train.py:451] Epoch 10, batch 8980, batch avg loss 0.3433, total avg loss: 0.2209, batch size: 124
2021-10-15 02:01:47,571 INFO [train.py:451] Epoch 10, batch 8990, batch avg loss 0.2538, total avg loss: 0.2210, batch size: 36
2021-10-15 02:01:52,600 INFO [train.py:451] Epoch 10, batch 9000, batch avg loss 0.2315, total avg loss: 0.2205, batch size: 34
2021-10-15 02:02:31,729 INFO [train.py:483] Epoch 10, valid loss 0.1627, best valid loss: 0.1621 best valid epoch: 10
2021-10-15 02:02:36,708 INFO [train.py:451] Epoch 10, batch 9010, batch avg loss 0.1760, total avg loss: 0.2218, batch size: 34
2021-10-15 02:02:41,523 INFO [train.py:451] Epoch 10, batch 9020, batch avg loss 0.2221, total avg loss: 0.2231, batch size: 38
2021-10-15 02:02:46,383 INFO [train.py:451] Epoch 10, batch 9030, batch avg loss 0.2450, total avg loss: 0.2214, batch size: 57
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2021-10-15 02:03:01,234 INFO [train.py:451] Epoch 10, batch 9060, batch avg loss 0.1883, total avg loss: 0.2202, batch size: 28
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2021-10-15 02:03:11,150 INFO [train.py:451] Epoch 10, batch 9080, batch avg loss 0.2226, total avg loss: 0.2196, batch size: 31
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2021-10-15 02:03:21,184 INFO [train.py:451] Epoch 10, batch 9100, batch avg loss 0.2183, total avg loss: 0.2182, batch size: 34
2021-10-15 02:03:26,113 INFO [train.py:451] Epoch 10, batch 9110, batch avg loss 0.2476, total avg loss: 0.2188, batch size: 35
2021-10-15 02:03:30,876 INFO [train.py:451] Epoch 10, batch 9120, batch avg loss 0.2130, total avg loss: 0.2193, batch size: 37
2021-10-15 02:03:35,770 INFO [train.py:451] Epoch 10, batch 9130, batch avg loss 0.1771, total avg loss: 0.2189, batch size: 30
2021-10-15 02:03:40,647 INFO [train.py:451] Epoch 10, batch 9140, batch avg loss 0.1819, total avg loss: 0.2179, batch size: 33
2021-10-15 02:03:45,417 INFO [train.py:451] Epoch 10, batch 9150, batch avg loss 0.2922, total avg loss: 0.2171, batch size: 41
2021-10-15 02:03:50,472 INFO [train.py:451] Epoch 10, batch 9160, batch avg loss 0.1760, total avg loss: 0.2177, batch size: 30
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2021-10-15 02:04:00,244 INFO [train.py:451] Epoch 10, batch 9180, batch avg loss 0.2181, total avg loss: 0.2178, batch size: 29
2021-10-15 02:04:05,141 INFO [train.py:451] Epoch 10, batch 9190, batch avg loss 0.1931, total avg loss: 0.2181, batch size: 34
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2021-10-15 02:04:15,113 INFO [train.py:451] Epoch 10, batch 9210, batch avg loss 0.2039, total avg loss: 0.2239, batch size: 36
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2021-10-15 02:04:29,994 INFO [train.py:451] Epoch 10, batch 9240, batch avg loss 0.1961, total avg loss: 0.2275, batch size: 36
2021-10-15 02:04:34,866 INFO [train.py:451] Epoch 10, batch 9250, batch avg loss 0.3166, total avg loss: 0.2251, batch size: 127
2021-10-15 02:04:39,796 INFO [train.py:451] Epoch 10, batch 9260, batch avg loss 0.1789, total avg loss: 0.2242, batch size: 32
2021-10-15 02:04:44,704 INFO [train.py:451] Epoch 10, batch 9270, batch avg loss 0.2135, total avg loss: 0.2236, batch size: 31
2021-10-15 02:04:49,636 INFO [train.py:451] Epoch 10, batch 9280, batch avg loss 0.2495, total avg loss: 0.2236, batch size: 35
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2021-10-15 02:04:59,215 INFO [train.py:451] Epoch 10, batch 9300, batch avg loss 0.2180, total avg loss: 0.2242, batch size: 34
2021-10-15 02:05:04,036 INFO [train.py:451] Epoch 10, batch 9310, batch avg loss 0.2345, total avg loss: 0.2246, batch size: 34
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2021-10-15 02:05:33,269 INFO [train.py:451] Epoch 10, batch 9370, batch avg loss 0.2195, total avg loss: 0.2229, batch size: 32
2021-10-15 02:05:38,245 INFO [train.py:451] Epoch 10, batch 9380, batch avg loss 0.1997, total avg loss: 0.2221, batch size: 34
2021-10-15 02:05:43,042 INFO [train.py:451] Epoch 10, batch 9390, batch avg loss 0.2378, total avg loss: 0.2230, batch size: 32
2021-10-15 02:05:47,994 INFO [train.py:451] Epoch 10, batch 9400, batch avg loss 0.1686, total avg loss: 0.2220, batch size: 31
2021-10-15 02:05:52,964 INFO [train.py:451] Epoch 10, batch 9410, batch avg loss 0.2103, total avg loss: 0.2213, batch size: 38
2021-10-15 02:05:57,681 INFO [train.py:451] Epoch 10, batch 9420, batch avg loss 0.2020, total avg loss: 0.2274, batch size: 34
2021-10-15 02:06:02,590 INFO [train.py:451] Epoch 10, batch 9430, batch avg loss 0.3227, total avg loss: 0.2292, batch size: 125
2021-10-15 02:06:07,476 INFO [train.py:451] Epoch 10, batch 9440, batch avg loss 0.1974, total avg loss: 0.2269, batch size: 29
2021-10-15 02:06:12,459 INFO [train.py:451] Epoch 10, batch 9450, batch avg loss 0.1936, total avg loss: 0.2278, batch size: 34
2021-10-15 02:06:17,350 INFO [train.py:451] Epoch 10, batch 9460, batch avg loss 0.1440, total avg loss: 0.2238, batch size: 30
2021-10-15 02:06:22,316 INFO [train.py:451] Epoch 10, batch 9470, batch avg loss 0.2414, total avg loss: 0.2200, batch size: 34
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2021-10-15 02:06:31,948 INFO [train.py:451] Epoch 10, batch 9490, batch avg loss 0.2171, total avg loss: 0.2202, batch size: 45
2021-10-15 02:06:36,861 INFO [train.py:451] Epoch 10, batch 9500, batch avg loss 0.1765, total avg loss: 0.2198, batch size: 32
2021-10-15 02:06:41,813 INFO [train.py:451] Epoch 10, batch 9510, batch avg loss 0.2009, total avg loss: 0.2197, batch size: 32
2021-10-15 02:06:46,813 INFO [train.py:451] Epoch 10, batch 9520, batch avg loss 0.2639, total avg loss: 0.2209, batch size: 49
2021-10-15 02:06:51,788 INFO [train.py:451] Epoch 10, batch 9530, batch avg loss 0.1997, total avg loss: 0.2207, batch size: 32
2021-10-15 02:06:56,742 INFO [train.py:451] Epoch 10, batch 9540, batch avg loss 0.2211, total avg loss: 0.2205, batch size: 36
2021-10-15 02:07:01,685 INFO [train.py:451] Epoch 10, batch 9550, batch avg loss 0.2058, total avg loss: 0.2215, batch size: 33
2021-10-15 02:07:06,729 INFO [train.py:451] Epoch 10, batch 9560, batch avg loss 0.2814, total avg loss: 0.2219, batch size: 45
2021-10-15 02:07:11,584 INFO [train.py:451] Epoch 10, batch 9570, batch avg loss 0.1937, total avg loss: 0.2224, batch size: 32
2021-10-15 02:07:16,497 INFO [train.py:451] Epoch 10, batch 9580, batch avg loss 0.1814, total avg loss: 0.2222, batch size: 39
2021-10-15 02:07:21,425 INFO [train.py:451] Epoch 10, batch 9590, batch avg loss 0.1808, total avg loss: 0.2228, batch size: 27
2021-10-15 02:07:26,305 INFO [train.py:451] Epoch 10, batch 9600, batch avg loss 0.2296, total avg loss: 0.2223, batch size: 30
2021-10-15 02:07:31,288 INFO [train.py:451] Epoch 10, batch 9610, batch avg loss 0.1852, total avg loss: 0.2207, batch size: 33
2021-10-15 02:07:36,179 INFO [train.py:451] Epoch 10, batch 9620, batch avg loss 0.1962, total avg loss: 0.2234, batch size: 35
2021-10-15 02:07:41,021 INFO [train.py:451] Epoch 10, batch 9630, batch avg loss 0.2278, total avg loss: 0.2232, batch size: 38
2021-10-15 02:07:46,176 INFO [train.py:451] Epoch 10, batch 9640, batch avg loss 0.2080, total avg loss: 0.2182, batch size: 36
2021-10-15 02:07:51,218 INFO [train.py:451] Epoch 10, batch 9650, batch avg loss 0.2266, total avg loss: 0.2188, batch size: 29
2021-10-15 02:07:56,101 INFO [train.py:451] Epoch 10, batch 9660, batch avg loss 0.1848, total avg loss: 0.2190, batch size: 30
2021-10-15 02:08:00,969 INFO [train.py:451] Epoch 10, batch 9670, batch avg loss 0.1851, total avg loss: 0.2175, batch size: 34
2021-10-15 02:08:06,012 INFO [train.py:451] Epoch 10, batch 9680, batch avg loss 0.1757, total avg loss: 0.2165, batch size: 37
2021-10-15 02:08:10,796 INFO [train.py:451] Epoch 10, batch 9690, batch avg loss 0.2384, total avg loss: 0.2187, batch size: 32
2021-10-15 02:08:15,785 INFO [train.py:451] Epoch 10, batch 9700, batch avg loss 0.2708, total avg loss: 0.2172, batch size: 57
2021-10-15 02:08:20,804 INFO [train.py:451] Epoch 10, batch 9710, batch avg loss 0.1851, total avg loss: 0.2167, batch size: 35
2021-10-15 02:08:25,873 INFO [train.py:451] Epoch 10, batch 9720, batch avg loss 0.1968, total avg loss: 0.2154, batch size: 32
2021-10-15 02:08:30,809 INFO [train.py:451] Epoch 10, batch 9730, batch avg loss 0.2498, total avg loss: 0.2165, batch size: 45
2021-10-15 02:08:35,483 INFO [train.py:451] Epoch 10, batch 9740, batch avg loss 0.2075, total avg loss: 0.2191, batch size: 38
2021-10-15 02:08:40,535 INFO [train.py:451] Epoch 10, batch 9750, batch avg loss 0.2190, total avg loss: 0.2185, batch size: 45
2021-10-15 02:08:45,615 INFO [train.py:451] Epoch 10, batch 9760, batch avg loss 0.2357, total avg loss: 0.2188, batch size: 35
2021-10-15 02:08:50,516 INFO [train.py:451] Epoch 10, batch 9770, batch avg loss 0.1984, total avg loss: 0.2195, batch size: 36
2021-10-15 02:08:55,631 INFO [train.py:451] Epoch 10, batch 9780, batch avg loss 0.2158, total avg loss: 0.2195, batch size: 36
2021-10-15 02:09:00,732 INFO [train.py:451] Epoch 10, batch 9790, batch avg loss 0.1992, total avg loss: 0.2186, batch size: 33
2021-10-15 02:09:05,743 INFO [train.py:451] Epoch 10, batch 9800, batch avg loss 0.1609, total avg loss: 0.2177, batch size: 30
2021-10-15 02:09:10,579 INFO [train.py:451] Epoch 10, batch 9810, batch avg loss 0.2265, total avg loss: 0.2165, batch size: 29
2021-10-15 02:09:15,272 INFO [train.py:451] Epoch 10, batch 9820, batch avg loss 0.2096, total avg loss: 0.2327, batch size: 41
2021-10-15 02:09:20,109 INFO [train.py:451] Epoch 10, batch 9830, batch avg loss 0.2769, total avg loss: 0.2287, batch size: 42
2021-10-15 02:09:25,042 INFO [train.py:451] Epoch 10, batch 9840, batch avg loss 0.2347, total avg loss: 0.2228, batch size: 35
2021-10-15 02:09:29,734 INFO [train.py:451] Epoch 10, batch 9850, batch avg loss 0.1594, total avg loss: 0.2285, batch size: 28
2021-10-15 02:09:34,684 INFO [train.py:451] Epoch 10, batch 9860, batch avg loss 0.2341, total avg loss: 0.2265, batch size: 35
2021-10-15 02:09:39,766 INFO [train.py:451] Epoch 10, batch 9870, batch avg loss 0.3941, total avg loss: 0.2258, batch size: 131
2021-10-15 02:09:44,739 INFO [train.py:451] Epoch 10, batch 9880, batch avg loss 0.2149, total avg loss: 0.2237, batch size: 36
2021-10-15 02:09:49,706 INFO [train.py:451] Epoch 10, batch 9890, batch avg loss 0.2152, total avg loss: 0.2221, batch size: 35
2021-10-15 02:09:54,641 INFO [train.py:451] Epoch 10, batch 9900, batch avg loss 0.2460, total avg loss: 0.2220, batch size: 33
2021-10-15 02:09:59,617 INFO [train.py:451] Epoch 10, batch 9910, batch avg loss 0.2509, total avg loss: 0.2213, batch size: 35
2021-10-15 02:10:04,575 INFO [train.py:451] Epoch 10, batch 9920, batch avg loss 0.2224, total avg loss: 0.2213, batch size: 31
2021-10-15 02:10:09,692 INFO [train.py:451] Epoch 10, batch 9930, batch avg loss 0.2215, total avg loss: 0.2203, batch size: 32
2021-10-15 02:10:14,815 INFO [train.py:451] Epoch 10, batch 9940, batch avg loss 0.2093, total avg loss: 0.2190, batch size: 35
2021-10-15 02:10:19,531 INFO [train.py:451] Epoch 10, batch 9950, batch avg loss 0.1792, total avg loss: 0.2185, batch size: 38
2021-10-15 02:10:24,179 INFO [train.py:451] Epoch 10, batch 9960, batch avg loss 0.1950, total avg loss: 0.2192, batch size: 39
2021-10-15 02:10:28,964 INFO [train.py:451] Epoch 10, batch 9970, batch avg loss 0.2017, total avg loss: 0.2195, batch size: 29
2021-10-15 02:10:33,923 INFO [train.py:451] Epoch 10, batch 9980, batch avg loss 0.2792, total avg loss: 0.2193, batch size: 38
2021-10-15 02:10:38,834 INFO [train.py:451] Epoch 10, batch 9990, batch avg loss 0.3130, total avg loss: 0.2202, batch size: 132
2021-10-15 02:10:43,831 INFO [train.py:451] Epoch 10, batch 10000, batch avg loss 0.1963, total avg loss: 0.2197, batch size: 30
2021-10-15 02:11:21,810 INFO [train.py:483] Epoch 10, valid loss 0.1623, best valid loss: 0.1621 best valid epoch: 10
2021-10-15 02:11:26,736 INFO [train.py:451] Epoch 10, batch 10010, batch avg loss 0.2200, total avg loss: 0.2322, batch size: 36
2021-10-15 02:11:31,513 INFO [train.py:451] Epoch 10, batch 10020, batch avg loss 0.2211, total avg loss: 0.2318, batch size: 31
2021-10-15 02:11:36,193 INFO [train.py:451] Epoch 10, batch 10030, batch avg loss 0.2207, total avg loss: 0.2331, batch size: 41
2021-10-15 02:11:41,021 INFO [train.py:451] Epoch 10, batch 10040, batch avg loss 0.2420, total avg loss: 0.2319, batch size: 34
2021-10-15 02:11:45,829 INFO [train.py:451] Epoch 10, batch 10050, batch avg loss 0.2888, total avg loss: 0.2307, batch size: 49
2021-10-15 02:11:50,741 INFO [train.py:451] Epoch 10, batch 10060, batch avg loss 0.1902, total avg loss: 0.2235, batch size: 29
2021-10-15 02:11:55,621 INFO [train.py:451] Epoch 10, batch 10070, batch avg loss 0.3597, total avg loss: 0.2263, batch size: 129
2021-10-15 02:12:00,641 INFO [train.py:451] Epoch 10, batch 10080, batch avg loss 0.1995, total avg loss: 0.2240, batch size: 30
2021-10-15 02:12:05,677 INFO [train.py:451] Epoch 10, batch 10090, batch avg loss 0.2151, total avg loss: 0.2253, batch size: 33
2021-10-15 02:12:10,450 INFO [train.py:451] Epoch 10, batch 10100, batch avg loss 0.2604, total avg loss: 0.2256, batch size: 45
2021-10-15 02:12:15,497 INFO [train.py:451] Epoch 10, batch 10110, batch avg loss 0.1951, total avg loss: 0.2239, batch size: 41
2021-10-15 02:12:20,601 INFO [train.py:451] Epoch 10, batch 10120, batch avg loss 0.1785, total avg loss: 0.2237, batch size: 30
2021-10-15 02:12:25,655 INFO [train.py:451] Epoch 10, batch 10130, batch avg loss 0.1951, total avg loss: 0.2225, batch size: 34
2021-10-15 02:12:30,412 INFO [train.py:451] Epoch 10, batch 10140, batch avg loss 0.2729, total avg loss: 0.2243, batch size: 35
2021-10-15 02:12:35,247 INFO [train.py:451] Epoch 10, batch 10150, batch avg loss 0.2096, total avg loss: 0.2245, batch size: 29
2021-10-15 02:12:40,042 INFO [train.py:451] Epoch 10, batch 10160, batch avg loss 0.2478, total avg loss: 0.2255, batch size: 72
2021-10-15 02:12:44,749 INFO [train.py:451] Epoch 10, batch 10170, batch avg loss 0.2798, total avg loss: 0.2259, batch size: 74
2021-10-15 02:12:49,683 INFO [train.py:451] Epoch 10, batch 10180, batch avg loss 0.2345, total avg loss: 0.2258, batch size: 57
2021-10-15 02:12:54,378 INFO [train.py:451] Epoch 10, batch 10190, batch avg loss 0.1394, total avg loss: 0.2264, batch size: 30
2021-10-15 02:12:59,170 INFO [train.py:451] Epoch 10, batch 10200, batch avg loss 0.2501, total avg loss: 0.2258, batch size: 45
2021-10-15 02:13:04,077 INFO [train.py:451] Epoch 10, batch 10210, batch avg loss 0.1850, total avg loss: 0.2218, batch size: 29
2021-10-15 02:13:09,004 INFO [train.py:451] Epoch 10, batch 10220, batch avg loss 0.2304, total avg loss: 0.2223, batch size: 31
2021-10-15 02:13:13,980 INFO [train.py:451] Epoch 10, batch 10230, batch avg loss 0.1551, total avg loss: 0.2245, batch size: 29
2021-10-15 02:13:18,985 INFO [train.py:451] Epoch 10, batch 10240, batch avg loss 0.1724, total avg loss: 0.2239, batch size: 27
2021-10-15 02:13:24,161 INFO [train.py:451] Epoch 10, batch 10250, batch avg loss 0.1653, total avg loss: 0.2170, batch size: 27
2021-10-15 02:13:28,893 INFO [train.py:451] Epoch 10, batch 10260, batch avg loss 0.2635, total avg loss: 0.2186, batch size: 49
2021-10-15 02:13:33,810 INFO [train.py:451] Epoch 10, batch 10270, batch avg loss 0.1564, total avg loss: 0.2172, batch size: 30
2021-10-15 02:13:38,739 INFO [train.py:451] Epoch 10, batch 10280, batch avg loss 0.2230, total avg loss: 0.2185, batch size: 39
2021-10-15 02:13:43,685 INFO [train.py:451] Epoch 10, batch 10290, batch avg loss 0.2195, total avg loss: 0.2171, batch size: 31
2021-10-15 02:13:48,529 INFO [train.py:451] Epoch 10, batch 10300, batch avg loss 0.2525, total avg loss: 0.2182, batch size: 39
2021-10-15 02:13:53,514 INFO [train.py:451] Epoch 10, batch 10310, batch avg loss 0.1550, total avg loss: 0.2183, batch size: 29
2021-10-15 02:13:58,365 INFO [train.py:451] Epoch 10, batch 10320, batch avg loss 0.2167, total avg loss: 0.2187, batch size: 41
2021-10-15 02:14:03,484 INFO [train.py:451] Epoch 10, batch 10330, batch avg loss 0.2044, total avg loss: 0.2180, batch size: 35
2021-10-15 02:14:08,427 INFO [train.py:451] Epoch 10, batch 10340, batch avg loss 0.2027, total avg loss: 0.2179, batch size: 31
2021-10-15 02:14:13,374 INFO [train.py:451] Epoch 10, batch 10350, batch avg loss 0.3513, total avg loss: 0.2202, batch size: 131
2021-10-15 02:14:18,173 INFO [train.py:451] Epoch 10, batch 10360, batch avg loss 0.2317, total avg loss: 0.2207, batch size: 41
2021-10-15 02:14:23,059 INFO [train.py:451] Epoch 10, batch 10370, batch avg loss 0.1701, total avg loss: 0.2199, batch size: 33
2021-10-15 02:14:27,979 INFO [train.py:451] Epoch 10, batch 10380, batch avg loss 0.2515, total avg loss: 0.2198, batch size: 39
2021-10-15 02:14:30,192 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "4c44df35-7498-828c-3b32-7f328f3d8042" will not be mixed in.
2021-10-15 02:14:32,880 INFO [train.py:451] Epoch 10, batch 10390, batch avg loss 0.2039, total avg loss: 0.2196, batch size: 33
2021-10-15 02:14:37,852 INFO [train.py:451] Epoch 10, batch 10400, batch avg loss 0.2020, total avg loss: 0.2189, batch size: 36
2021-10-15 02:14:42,817 INFO [train.py:451] Epoch 10, batch 10410, batch avg loss 0.2045, total avg loss: 0.2148, batch size: 33
2021-10-15 02:14:47,594 INFO [train.py:451] Epoch 10, batch 10420, batch avg loss 0.2076, total avg loss: 0.2234, batch size: 42
2021-10-15 02:14:52,599 INFO [train.py:451] Epoch 10, batch 10430, batch avg loss 0.2864, total avg loss: 0.2218, batch size: 45
2021-10-15 02:14:57,460 INFO [train.py:451] Epoch 10, batch 10440, batch avg loss 0.2494, total avg loss: 0.2214, batch size: 73
2021-10-15 02:15:02,379 INFO [train.py:451] Epoch 10, batch 10450, batch avg loss 0.2372, total avg loss: 0.2208, batch size: 29
2021-10-15 02:15:07,274 INFO [train.py:451] Epoch 10, batch 10460, batch avg loss 0.2025, total avg loss: 0.2188, batch size: 32
2021-10-15 02:15:12,064 INFO [train.py:451] Epoch 10, batch 10470, batch avg loss 0.1833, total avg loss: 0.2180, batch size: 30
2021-10-15 02:15:16,939 INFO [train.py:451] Epoch 10, batch 10480, batch avg loss 0.1737, total avg loss: 0.2177, batch size: 27
2021-10-15 02:15:22,008 INFO [train.py:451] Epoch 10, batch 10490, batch avg loss 0.1842, total avg loss: 0.2185, batch size: 29
2021-10-15 02:15:26,905 INFO [train.py:451] Epoch 10, batch 10500, batch avg loss 0.1923, total avg loss: 0.2174, batch size: 27
2021-10-15 02:15:31,843 INFO [train.py:451] Epoch 10, batch 10510, batch avg loss 0.2091, total avg loss: 0.2172, batch size: 34
2021-10-15 02:15:36,797 INFO [train.py:451] Epoch 10, batch 10520, batch avg loss 0.2554, total avg loss: 0.2161, batch size: 71
2021-10-15 02:15:41,637 INFO [train.py:451] Epoch 10, batch 10530, batch avg loss 0.2095, total avg loss: 0.2152, batch size: 39
2021-10-15 02:15:46,521 INFO [train.py:451] Epoch 10, batch 10540, batch avg loss 0.2138, total avg loss: 0.2152, batch size: 31
2021-10-15 02:15:51,489 INFO [train.py:451] Epoch 10, batch 10550, batch avg loss 0.2204, total avg loss: 0.2151, batch size: 32
2021-10-15 02:15:56,305 INFO [train.py:451] Epoch 10, batch 10560, batch avg loss 0.2422, total avg loss: 0.2159, batch size: 38
2021-10-15 02:16:01,227 INFO [train.py:451] Epoch 10, batch 10570, batch avg loss 0.2005, total avg loss: 0.2177, batch size: 30
2021-10-15 02:16:06,266 INFO [train.py:451] Epoch 10, batch 10580, batch avg loss 0.1918, total avg loss: 0.2167, batch size: 36
2021-10-15 02:16:11,114 INFO [train.py:451] Epoch 10, batch 10590, batch avg loss 0.2069, total avg loss: 0.2166, batch size: 31
2021-10-15 02:16:16,315 INFO [train.py:451] Epoch 10, batch 10600, batch avg loss 0.1720, total avg loss: 0.2166, batch size: 28
2021-10-15 02:16:21,367 INFO [train.py:451] Epoch 10, batch 10610, batch avg loss 0.2140, total avg loss: 0.2143, batch size: 29
2021-10-15 02:16:26,333 INFO [train.py:451] Epoch 10, batch 10620, batch avg loss 0.1986, total avg loss: 0.2074, batch size: 35
2021-10-15 02:16:31,247 INFO [train.py:451] Epoch 10, batch 10630, batch avg loss 0.2195, total avg loss: 0.2160, batch size: 33
2021-10-15 02:16:36,037 INFO [train.py:451] Epoch 10, batch 10640, batch avg loss 0.1903, total avg loss: 0.2169, batch size: 34
2021-10-15 02:16:40,902 INFO [train.py:451] Epoch 10, batch 10650, batch avg loss 0.1602, total avg loss: 0.2194, batch size: 29
2021-10-15 02:16:45,661 INFO [train.py:451] Epoch 10, batch 10660, batch avg loss 0.2287, total avg loss: 0.2203, batch size: 33
2021-10-15 02:16:50,504 INFO [train.py:451] Epoch 10, batch 10670, batch avg loss 0.2647, total avg loss: 0.2228, batch size: 35
2021-10-15 02:16:55,376 INFO [train.py:451] Epoch 10, batch 10680, batch avg loss 0.2143, total avg loss: 0.2219, batch size: 31
2021-10-15 02:17:00,249 INFO [train.py:451] Epoch 10, batch 10690, batch avg loss 0.1636, total avg loss: 0.2222, batch size: 30
2021-10-15 02:17:05,051 INFO [train.py:451] Epoch 10, batch 10700, batch avg loss 0.2038, total avg loss: 0.2220, batch size: 38
2021-10-15 02:17:09,889 INFO [train.py:451] Epoch 10, batch 10710, batch avg loss 0.2436, total avg loss: 0.2218, batch size: 36
2021-10-15 02:17:14,759 INFO [train.py:451] Epoch 10, batch 10720, batch avg loss 0.1876, total avg loss: 0.2217, batch size: 32
2021-10-15 02:17:19,666 INFO [train.py:451] Epoch 10, batch 10730, batch avg loss 0.3299, total avg loss: 0.2217, batch size: 131
2021-10-15 02:17:24,711 INFO [train.py:451] Epoch 10, batch 10740, batch avg loss 0.1643, total avg loss: 0.2207, batch size: 30
2021-10-15 02:17:29,573 INFO [train.py:451] Epoch 10, batch 10750, batch avg loss 0.2204, total avg loss: 0.2207, batch size: 36
2021-10-15 02:17:34,556 INFO [train.py:451] Epoch 10, batch 10760, batch avg loss 0.2520, total avg loss: 0.2209, batch size: 41
2021-10-15 02:17:39,710 INFO [train.py:451] Epoch 10, batch 10770, batch avg loss 0.1935, total avg loss: 0.2197, batch size: 27
2021-10-15 02:17:44,726 INFO [train.py:451] Epoch 10, batch 10780, batch avg loss 0.2228, total avg loss: 0.2197, batch size: 49
2021-10-15 02:17:49,675 INFO [train.py:451] Epoch 10, batch 10790, batch avg loss 0.1857, total avg loss: 0.2197, batch size: 32
2021-10-15 02:17:54,759 INFO [train.py:451] Epoch 10, batch 10800, batch avg loss 0.2155, total avg loss: 0.2191, batch size: 31
2021-10-15 02:17:59,760 INFO [train.py:451] Epoch 10, batch 10810, batch avg loss 0.2178, total avg loss: 0.2191, batch size: 38
2021-10-15 02:18:04,560 INFO [train.py:451] Epoch 10, batch 10820, batch avg loss 0.1901, total avg loss: 0.2260, batch size: 34
2021-10-15 02:18:09,316 INFO [train.py:451] Epoch 10, batch 10830, batch avg loss 0.2597, total avg loss: 0.2273, batch size: 37
2021-10-15 02:18:14,099 INFO [train.py:451] Epoch 10, batch 10840, batch avg loss 0.2460, total avg loss: 0.2272, batch size: 73
2021-10-15 02:18:18,999 INFO [train.py:451] Epoch 10, batch 10850, batch avg loss 0.2795, total avg loss: 0.2256, batch size: 42
2021-10-15 02:18:24,015 INFO [train.py:451] Epoch 10, batch 10860, batch avg loss 0.1737, total avg loss: 0.2206, batch size: 29
2021-10-15 02:18:29,004 INFO [train.py:451] Epoch 10, batch 10870, batch avg loss 0.2264, total avg loss: 0.2200, batch size: 32
2021-10-15 02:18:34,020 INFO [train.py:451] Epoch 10, batch 10880, batch avg loss 0.2120, total avg loss: 0.2195, batch size: 33
2021-10-15 02:18:39,083 INFO [train.py:451] Epoch 10, batch 10890, batch avg loss 0.2494, total avg loss: 0.2178, batch size: 38
2021-10-15 02:18:43,963 INFO [train.py:451] Epoch 10, batch 10900, batch avg loss 0.2581, total avg loss: 0.2182, batch size: 58
2021-10-15 02:18:48,900 INFO [train.py:451] Epoch 10, batch 10910, batch avg loss 0.2793, total avg loss: 0.2173, batch size: 41
2021-10-15 02:18:53,731 INFO [train.py:451] Epoch 10, batch 10920, batch avg loss 0.2431, total avg loss: 0.2178, batch size: 41
2021-10-15 02:18:58,655 INFO [train.py:451] Epoch 10, batch 10930, batch avg loss 0.2576, total avg loss: 0.2171, batch size: 34
2021-10-15 02:19:03,536 INFO [train.py:451] Epoch 10, batch 10940, batch avg loss 0.2370, total avg loss: 0.2179, batch size: 35
2021-10-15 02:19:08,265 INFO [train.py:451] Epoch 10, batch 10950, batch avg loss 0.2491, total avg loss: 0.2188, batch size: 35
2021-10-15 02:19:13,228 INFO [train.py:451] Epoch 10, batch 10960, batch avg loss 0.2007, total avg loss: 0.2183, batch size: 32
2021-10-15 02:19:18,214 INFO [train.py:451] Epoch 10, batch 10970, batch avg loss 0.2839, total avg loss: 0.2184, batch size: 45
2021-10-15 02:19:23,111 INFO [train.py:451] Epoch 10, batch 10980, batch avg loss 0.2856, total avg loss: 0.2189, batch size: 73
2021-10-15 02:19:27,920 INFO [train.py:451] Epoch 10, batch 10990, batch avg loss 0.2086, total avg loss: 0.2195, batch size: 34
2021-10-15 02:19:32,557 INFO [train.py:451] Epoch 10, batch 11000, batch avg loss 0.2060, total avg loss: 0.2208, batch size: 36
2021-10-15 02:20:10,310 INFO [train.py:483] Epoch 10, valid loss 0.1617, best valid loss: 0.1617 best valid epoch: 10
2021-10-15 02:20:15,171 INFO [train.py:451] Epoch 10, batch 11010, batch avg loss 0.2469, total avg loss: 0.2302, batch size: 36
2021-10-15 02:20:20,007 INFO [train.py:451] Epoch 10, batch 11020, batch avg loss 0.2549, total avg loss: 0.2392, batch size: 41
2021-10-15 02:20:24,848 INFO [train.py:451] Epoch 10, batch 11030, batch avg loss 0.2083, total avg loss: 0.2390, batch size: 34
2021-10-15 02:20:29,702 INFO [train.py:451] Epoch 10, batch 11040, batch avg loss 0.2221, total avg loss: 0.2339, batch size: 45
2021-10-15 02:20:34,525 INFO [train.py:451] Epoch 10, batch 11050, batch avg loss 0.2063, total avg loss: 0.2314, batch size: 30
2021-10-15 02:20:39,376 INFO [train.py:451] Epoch 10, batch 11060, batch avg loss 0.1685, total avg loss: 0.2304, batch size: 29
2021-10-15 02:20:44,241 INFO [train.py:451] Epoch 10, batch 11070, batch avg loss 0.3046, total avg loss: 0.2272, batch size: 129
2021-10-15 02:20:49,033 INFO [train.py:451] Epoch 10, batch 11080, batch avg loss 0.1875, total avg loss: 0.2281, batch size: 33
2021-10-15 02:20:53,957 INFO [train.py:451] Epoch 10, batch 11090, batch avg loss 0.1901, total avg loss: 0.2265, batch size: 37
2021-10-15 02:20:58,883 INFO [train.py:451] Epoch 10, batch 11100, batch avg loss 0.2172, total avg loss: 0.2272, batch size: 35
2021-10-15 02:21:03,811 INFO [train.py:451] Epoch 10, batch 11110, batch avg loss 0.1684, total avg loss: 0.2254, batch size: 29
2021-10-15 02:21:08,562 INFO [train.py:451] Epoch 10, batch 11120, batch avg loss 0.2302, total avg loss: 0.2257, batch size: 36
2021-10-15 02:21:13,568 INFO [train.py:451] Epoch 10, batch 11130, batch avg loss 0.2477, total avg loss: 0.2241, batch size: 49
2021-10-15 02:21:18,414 INFO [train.py:451] Epoch 10, batch 11140, batch avg loss 0.2786, total avg loss: 0.2237, batch size: 73
2021-10-15 02:21:23,585 INFO [train.py:451] Epoch 10, batch 11150, batch avg loss 0.2070, total avg loss: 0.2228, batch size: 37
2021-10-15 02:21:28,716 INFO [train.py:451] Epoch 10, batch 11160, batch avg loss 0.2471, total avg loss: 0.2217, batch size: 41
2021-10-15 02:21:33,710 INFO [train.py:451] Epoch 10, batch 11170, batch avg loss 0.3330, total avg loss: 0.2218, batch size: 130
2021-10-15 02:21:38,741 INFO [train.py:451] Epoch 10, batch 11180, batch avg loss 0.1854, total avg loss: 0.2212, batch size: 32
2021-10-15 02:21:43,787 INFO [train.py:451] Epoch 10, batch 11190, batch avg loss 0.2220, total avg loss: 0.2208, batch size: 34
2021-10-15 02:21:48,740 INFO [train.py:451] Epoch 10, batch 11200, batch avg loss 0.2252, total avg loss: 0.2209, batch size: 30
2021-10-15 02:21:53,694 INFO [train.py:451] Epoch 10, batch 11210, batch avg loss 0.2724, total avg loss: 0.2172, batch size: 57
2021-10-15 02:21:58,507 INFO [train.py:451] Epoch 10, batch 11220, batch avg loss 0.2351, total avg loss: 0.2219, batch size: 41
2021-10-15 02:22:03,680 INFO [train.py:451] Epoch 10, batch 11230, batch avg loss 0.1639, total avg loss: 0.2188, batch size: 29
2021-10-15 02:22:08,624 INFO [train.py:451] Epoch 10, batch 11240, batch avg loss 0.1992, total avg loss: 0.2190, batch size: 30
2021-10-15 02:22:13,325 INFO [train.py:451] Epoch 10, batch 11250, batch avg loss 0.2366, total avg loss: 0.2224, batch size: 41
2021-10-15 02:22:18,408 INFO [train.py:451] Epoch 10, batch 11260, batch avg loss 0.2099, total avg loss: 0.2192, batch size: 34
2021-10-15 02:22:23,066 INFO [train.py:451] Epoch 10, batch 11270, batch avg loss 0.2291, total avg loss: 0.2249, batch size: 34
2021-10-15 02:22:27,987 INFO [train.py:451] Epoch 10, batch 11280, batch avg loss 0.2266, total avg loss: 0.2271, batch size: 38
2021-10-15 02:22:32,917 INFO [train.py:451] Epoch 10, batch 11290, batch avg loss 0.1885, total avg loss: 0.2256, batch size: 33
2021-10-15 02:22:37,742 INFO [train.py:451] Epoch 10, batch 11300, batch avg loss 0.2175, total avg loss: 0.2256, batch size: 35
2021-10-15 02:22:42,734 INFO [train.py:451] Epoch 10, batch 11310, batch avg loss 0.2052, total avg loss: 0.2250, batch size: 34
2021-10-15 02:22:47,712 INFO [train.py:451] Epoch 10, batch 11320, batch avg loss 0.2086, total avg loss: 0.2241, batch size: 36
2021-10-15 02:22:52,718 INFO [train.py:451] Epoch 10, batch 11330, batch avg loss 0.1848, total avg loss: 0.2240, batch size: 34
2021-10-15 02:22:57,704 INFO [train.py:451] Epoch 10, batch 11340, batch avg loss 0.2455, total avg loss: 0.2233, batch size: 39
2021-10-15 02:23:02,705 INFO [train.py:451] Epoch 10, batch 11350, batch avg loss 0.2487, total avg loss: 0.2228, batch size: 34
2021-10-15 02:23:07,634 INFO [train.py:451] Epoch 10, batch 11360, batch avg loss 0.2054, total avg loss: 0.2230, batch size: 31
2021-10-15 02:23:12,698 INFO [train.py:451] Epoch 10, batch 11370, batch avg loss 0.1994, total avg loss: 0.2220, batch size: 35
2021-10-15 02:23:17,526 INFO [train.py:451] Epoch 10, batch 11380, batch avg loss 0.2377, total avg loss: 0.2222, batch size: 35
2021-10-15 02:23:22,562 INFO [train.py:451] Epoch 10, batch 11390, batch avg loss 0.2358, total avg loss: 0.2220, batch size: 34
2021-10-15 02:23:27,462 INFO [train.py:451] Epoch 10, batch 11400, batch avg loss 0.2006, total avg loss: 0.2212, batch size: 32
2021-10-15 02:23:32,295 INFO [train.py:451] Epoch 10, batch 11410, batch avg loss 0.2110, total avg loss: 0.2151, batch size: 37
2021-10-15 02:23:37,267 INFO [train.py:451] Epoch 10, batch 11420, batch avg loss 0.2325, total avg loss: 0.2051, batch size: 36
2021-10-15 02:23:42,175 INFO [train.py:451] Epoch 10, batch 11430, batch avg loss 0.2322, total avg loss: 0.2044, batch size: 37
2021-10-15 02:23:47,049 INFO [train.py:451] Epoch 10, batch 11440, batch avg loss 0.1664, total avg loss: 0.2017, batch size: 27
2021-10-15 02:23:52,058 INFO [train.py:451] Epoch 10, batch 11450, batch avg loss 0.1662, total avg loss: 0.2056, batch size: 27
2021-10-15 02:23:56,902 INFO [train.py:451] Epoch 10, batch 11460, batch avg loss 0.1780, total avg loss: 0.2063, batch size: 35
2021-10-15 02:24:02,014 INFO [train.py:451] Epoch 10, batch 11470, batch avg loss 0.2267, total avg loss: 0.2074, batch size: 42
2021-10-15 02:24:07,240 INFO [train.py:451] Epoch 10, batch 11480, batch avg loss 0.2425, total avg loss: 0.2082, batch size: 38
2021-10-15 02:24:12,197 INFO [train.py:451] Epoch 10, batch 11490, batch avg loss 0.2249, total avg loss: 0.2100, batch size: 36
2021-10-15 02:24:17,139 INFO [train.py:451] Epoch 10, batch 11500, batch avg loss 0.2370, total avg loss: 0.2107, batch size: 41
2021-10-15 02:24:22,261 INFO [train.py:451] Epoch 10, batch 11510, batch avg loss 0.1981, total avg loss: 0.2106, batch size: 29
2021-10-15 02:24:27,118 INFO [train.py:451] Epoch 10, batch 11520, batch avg loss 0.2540, total avg loss: 0.2117, batch size: 34
2021-10-15 02:24:32,150 INFO [train.py:451] Epoch 10, batch 11530, batch avg loss 0.1780, total avg loss: 0.2110, batch size: 33
2021-10-15 02:24:37,057 INFO [train.py:451] Epoch 10, batch 11540, batch avg loss 0.2022, total avg loss: 0.2111, batch size: 32
2021-10-15 02:24:41,941 INFO [train.py:451] Epoch 10, batch 11550, batch avg loss 0.2168, total avg loss: 0.2114, batch size: 36
2021-10-15 02:24:46,846 INFO [train.py:451] Epoch 10, batch 11560, batch avg loss 0.2218, total avg loss: 0.2124, batch size: 38
2021-10-15 02:24:51,778 INFO [train.py:451] Epoch 10, batch 11570, batch avg loss 0.1996, total avg loss: 0.2119, batch size: 36
2021-10-15 02:24:56,809 INFO [train.py:451] Epoch 10, batch 11580, batch avg loss 0.1805, total avg loss: 0.2119, batch size: 34
2021-10-15 02:25:01,751 INFO [train.py:451] Epoch 10, batch 11590, batch avg loss 0.2231, total avg loss: 0.2129, batch size: 39
2021-10-15 02:25:06,618 INFO [train.py:451] Epoch 10, batch 11600, batch avg loss 0.1906, total avg loss: 0.2128, batch size: 30
2021-10-15 02:25:11,620 INFO [train.py:451] Epoch 10, batch 11610, batch avg loss 0.1768, total avg loss: 0.2087, batch size: 30
2021-10-15 02:25:16,592 INFO [train.py:451] Epoch 10, batch 11620, batch avg loss 0.1792, total avg loss: 0.2085, batch size: 33
2021-10-15 02:25:21,428 INFO [train.py:451] Epoch 10, batch 11630, batch avg loss 0.2237, total avg loss: 0.2102, batch size: 49
2021-10-15 02:25:26,160 INFO [train.py:451] Epoch 10, batch 11640, batch avg loss 0.2031, total avg loss: 0.2130, batch size: 49
2021-10-15 02:25:30,906 INFO [train.py:451] Epoch 10, batch 11650, batch avg loss 0.1882, total avg loss: 0.2151, batch size: 35
2021-10-15 02:25:35,968 INFO [train.py:451] Epoch 10, batch 11660, batch avg loss 0.2201, total avg loss: 0.2162, batch size: 31
2021-10-15 02:25:41,000 INFO [train.py:451] Epoch 10, batch 11670, batch avg loss 0.1672, total avg loss: 0.2154, batch size: 30
2021-10-15 02:25:45,908 INFO [train.py:451] Epoch 10, batch 11680, batch avg loss 0.1702, total avg loss: 0.2143, batch size: 36
2021-10-15 02:25:50,725 INFO [train.py:451] Epoch 10, batch 11690, batch avg loss 0.2872, total avg loss: 0.2166, batch size: 73
2021-10-15 02:25:55,585 INFO [train.py:451] Epoch 10, batch 11700, batch avg loss 0.2601, total avg loss: 0.2163, batch size: 41
2021-10-15 02:26:00,747 INFO [train.py:451] Epoch 10, batch 11710, batch avg loss 0.2192, total avg loss: 0.2153, batch size: 34
2021-10-15 02:26:05,731 INFO [train.py:451] Epoch 10, batch 11720, batch avg loss 0.1402, total avg loss: 0.2132, batch size: 27
2021-10-15 02:26:10,701 INFO [train.py:451] Epoch 10, batch 11730, batch avg loss 0.2397, total avg loss: 0.2146, batch size: 57
2021-10-15 02:26:15,831 INFO [train.py:451] Epoch 10, batch 11740, batch avg loss 0.2097, total avg loss: 0.2136, batch size: 56
2021-10-15 02:26:20,937 INFO [train.py:451] Epoch 10, batch 11750, batch avg loss 0.2230, total avg loss: 0.2126, batch size: 33
2021-10-15 02:26:25,865 INFO [train.py:451] Epoch 10, batch 11760, batch avg loss 0.2157, total avg loss: 0.2132, batch size: 34
2021-10-15 02:26:30,865 INFO [train.py:451] Epoch 10, batch 11770, batch avg loss 0.1931, total avg loss: 0.2126, batch size: 30
2021-10-15 02:26:35,619 INFO [train.py:451] Epoch 10, batch 11780, batch avg loss 0.1865, total avg loss: 0.2127, batch size: 35
2021-10-15 02:26:40,634 INFO [train.py:451] Epoch 10, batch 11790, batch avg loss 0.1624, total avg loss: 0.2116, batch size: 27
2021-10-15 02:26:45,383 INFO [train.py:451] Epoch 10, batch 11800, batch avg loss 0.2336, total avg loss: 0.2122, batch size: 45
2021-10-15 02:26:50,250 INFO [train.py:451] Epoch 10, batch 11810, batch avg loss 0.2391, total avg loss: 0.2210, batch size: 45
2021-10-15 02:26:55,169 INFO [train.py:451] Epoch 10, batch 11820, batch avg loss 0.2135, total avg loss: 0.2256, batch size: 38
2021-10-15 02:27:00,057 INFO [train.py:451] Epoch 10, batch 11830, batch avg loss 0.3511, total avg loss: 0.2249, batch size: 128
2021-10-15 02:27:05,010 INFO [train.py:451] Epoch 10, batch 11840, batch avg loss 0.1966, total avg loss: 0.2222, batch size: 28
2021-10-15 02:27:10,032 INFO [train.py:451] Epoch 10, batch 11850, batch avg loss 0.1626, total avg loss: 0.2211, batch size: 33
2021-10-15 02:27:15,114 INFO [train.py:451] Epoch 10, batch 11860, batch avg loss 0.1751, total avg loss: 0.2181, batch size: 28
2021-10-15 02:27:20,017 INFO [train.py:451] Epoch 10, batch 11870, batch avg loss 0.1860, total avg loss: 0.2184, batch size: 33
2021-10-15 02:27:24,924 INFO [train.py:451] Epoch 10, batch 11880, batch avg loss 0.2555, total avg loss: 0.2178, batch size: 39
2021-10-15 02:27:29,818 INFO [train.py:451] Epoch 10, batch 11890, batch avg loss 0.1795, total avg loss: 0.2190, batch size: 29
2021-10-15 02:27:34,608 INFO [train.py:451] Epoch 10, batch 11900, batch avg loss 0.3414, total avg loss: 0.2190, batch size: 131
2021-10-15 02:27:39,573 INFO [train.py:451] Epoch 10, batch 11910, batch avg loss 0.3351, total avg loss: 0.2185, batch size: 133
2021-10-15 02:27:44,517 INFO [train.py:451] Epoch 10, batch 11920, batch avg loss 0.1814, total avg loss: 0.2180, batch size: 29
2021-10-15 02:27:49,470 INFO [train.py:451] Epoch 10, batch 11930, batch avg loss 0.1856, total avg loss: 0.2185, batch size: 27
2021-10-15 02:27:54,327 INFO [train.py:451] Epoch 10, batch 11940, batch avg loss 0.1628, total avg loss: 0.2188, batch size: 30
2021-10-15 02:27:59,486 INFO [train.py:451] Epoch 10, batch 11950, batch avg loss 0.2166, total avg loss: 0.2190, batch size: 41
2021-10-15 02:28:04,308 INFO [train.py:451] Epoch 10, batch 11960, batch avg loss 0.2310, total avg loss: 0.2192, batch size: 35
2021-10-15 02:28:09,006 INFO [train.py:451] Epoch 10, batch 11970, batch avg loss 0.2252, total avg loss: 0.2206, batch size: 38
2021-10-15 02:28:13,887 INFO [train.py:451] Epoch 10, batch 11980, batch avg loss 0.2514, total avg loss: 0.2206, batch size: 49
2021-10-15 02:28:18,659 INFO [train.py:451] Epoch 10, batch 11990, batch avg loss 0.2502, total avg loss: 0.2202, batch size: 45
2021-10-15 02:28:23,868 INFO [train.py:451] Epoch 10, batch 12000, batch avg loss 0.2536, total avg loss: 0.2195, batch size: 45
2021-10-15 02:29:04,355 INFO [train.py:483] Epoch 10, valid loss 0.1623, best valid loss: 0.1617 best valid epoch: 10
2021-10-15 02:29:09,197 INFO [train.py:451] Epoch 10, batch 12010, batch avg loss 0.2257, total avg loss: 0.2222, batch size: 57
2021-10-15 02:29:14,060 INFO [train.py:451] Epoch 10, batch 12020, batch avg loss 0.2806, total avg loss: 0.2226, batch size: 74
2021-10-15 02:29:19,029 INFO [train.py:451] Epoch 10, batch 12030, batch avg loss 0.2110, total avg loss: 0.2185, batch size: 34
2021-10-15 02:29:23,846 INFO [train.py:451] Epoch 10, batch 12040, batch avg loss 0.2115, total avg loss: 0.2201, batch size: 37
2021-10-15 02:29:28,938 INFO [train.py:451] Epoch 10, batch 12050, batch avg loss 0.2281, total avg loss: 0.2186, batch size: 37
2021-10-15 02:29:34,002 INFO [train.py:451] Epoch 10, batch 12060, batch avg loss 0.1962, total avg loss: 0.2191, batch size: 41
2021-10-15 02:29:39,110 INFO [train.py:451] Epoch 10, batch 12070, batch avg loss 0.2209, total avg loss: 0.2164, batch size: 45
2021-10-15 02:29:43,831 INFO [train.py:451] Epoch 10, batch 12080, batch avg loss 0.2213, total avg loss: 0.2172, batch size: 41
2021-10-15 02:29:48,668 INFO [train.py:451] Epoch 10, batch 12090, batch avg loss 0.2019, total avg loss: 0.2158, batch size: 33
2021-10-15 02:29:53,600 INFO [train.py:451] Epoch 10, batch 12100, batch avg loss 0.2498, total avg loss: 0.2183, batch size: 34
2021-10-15 02:29:58,533 INFO [train.py:451] Epoch 10, batch 12110, batch avg loss 0.1975, total avg loss: 0.2192, batch size: 35
2021-10-15 02:30:03,615 INFO [train.py:451] Epoch 10, batch 12120, batch avg loss 0.2010, total avg loss: 0.2179, batch size: 36
2021-10-15 02:30:08,570 INFO [train.py:451] Epoch 10, batch 12130, batch avg loss 0.2201, total avg loss: 0.2183, batch size: 33
2021-10-15 02:30:13,599 INFO [train.py:451] Epoch 10, batch 12140, batch avg loss 0.1502, total avg loss: 0.2182, batch size: 27
2021-10-15 02:30:18,447 INFO [train.py:451] Epoch 10, batch 12150, batch avg loss 0.1884, total avg loss: 0.2184, batch size: 32
2021-10-15 02:30:23,395 INFO [train.py:451] Epoch 10, batch 12160, batch avg loss 0.2653, total avg loss: 0.2186, batch size: 45
2021-10-15 02:30:28,179 INFO [train.py:451] Epoch 10, batch 12170, batch avg loss 0.1826, total avg loss: 0.2179, batch size: 32
2021-10-15 02:30:33,062 INFO [train.py:451] Epoch 10, batch 12180, batch avg loss 0.2574, total avg loss: 0.2178, batch size: 73
2021-10-15 02:30:37,951 INFO [train.py:451] Epoch 10, batch 12190, batch avg loss 0.1977, total avg loss: 0.2172, batch size: 32
2021-10-15 02:30:43,029 INFO [train.py:451] Epoch 10, batch 12200, batch avg loss 0.2101, total avg loss: 0.2168, batch size: 33
2021-10-15 02:30:47,990 INFO [train.py:451] Epoch 10, batch 12210, batch avg loss 0.2140, total avg loss: 0.2175, batch size: 33
2021-10-15 02:30:52,827 INFO [train.py:451] Epoch 10, batch 12220, batch avg loss 0.1796, total avg loss: 0.2333, batch size: 36
2021-10-15 02:30:57,642 INFO [train.py:451] Epoch 10, batch 12230, batch avg loss 0.2521, total avg loss: 0.2335, batch size: 49
2021-10-15 02:31:02,458 INFO [train.py:451] Epoch 10, batch 12240, batch avg loss 0.2112, total avg loss: 0.2323, batch size: 34
2021-10-15 02:31:07,364 INFO [train.py:451] Epoch 10, batch 12250, batch avg loss 0.2770, total avg loss: 0.2333, batch size: 57
2021-10-15 02:31:12,256 INFO [train.py:451] Epoch 10, batch 12260, batch avg loss 0.2391, total avg loss: 0.2307, batch size: 73
2021-10-15 02:31:17,130 INFO [train.py:451] Epoch 10, batch 12270, batch avg loss 0.2221, total avg loss: 0.2287, batch size: 34
2021-10-15 02:31:22,022 INFO [train.py:451] Epoch 10, batch 12280, batch avg loss 0.2478, total avg loss: 0.2276, batch size: 38
2021-10-15 02:31:26,798 INFO [train.py:451] Epoch 10, batch 12290, batch avg loss 0.1594, total avg loss: 0.2255, batch size: 30
2021-10-15 02:31:31,789 INFO [train.py:451] Epoch 10, batch 12300, batch avg loss 0.1645, total avg loss: 0.2240, batch size: 30
2021-10-15 02:31:36,688 INFO [train.py:451] Epoch 10, batch 12310, batch avg loss 0.2279, total avg loss: 0.2239, batch size: 72
2021-10-15 02:31:41,683 INFO [train.py:451] Epoch 10, batch 12320, batch avg loss 0.2077, total avg loss: 0.2222, batch size: 39
2021-10-15 02:31:46,639 INFO [train.py:451] Epoch 10, batch 12330, batch avg loss 0.2098, total avg loss: 0.2222, batch size: 38
2021-10-15 02:31:51,588 INFO [train.py:451] Epoch 10, batch 12340, batch avg loss 0.2436, total avg loss: 0.2233, batch size: 29
2021-10-15 02:31:56,624 INFO [train.py:451] Epoch 10, batch 12350, batch avg loss 0.2090, total avg loss: 0.2226, batch size: 36
2021-10-15 02:32:01,693 INFO [train.py:451] Epoch 10, batch 12360, batch avg loss 0.1916, total avg loss: 0.2221, batch size: 30
2021-10-15 02:32:06,740 INFO [train.py:451] Epoch 10, batch 12370, batch avg loss 0.1932, total avg loss: 0.2214, batch size: 33
2021-10-15 02:32:11,664 INFO [train.py:451] Epoch 10, batch 12380, batch avg loss 0.2885, total avg loss: 0.2218, batch size: 57
2021-10-15 02:32:16,484 INFO [train.py:451] Epoch 10, batch 12390, batch avg loss 0.2192, total avg loss: 0.2238, batch size: 32
2021-10-15 02:32:21,648 INFO [train.py:451] Epoch 10, batch 12400, batch avg loss 0.1962, total avg loss: 0.2229, batch size: 35
2021-10-15 02:32:26,609 INFO [train.py:451] Epoch 10, batch 12410, batch avg loss 0.1822, total avg loss: 0.2209, batch size: 29
2021-10-15 02:32:31,493 INFO [train.py:451] Epoch 10, batch 12420, batch avg loss 0.2014, total avg loss: 0.2250, batch size: 32
2021-10-15 02:32:36,466 INFO [train.py:451] Epoch 10, batch 12430, batch avg loss 0.1977, total avg loss: 0.2192, batch size: 34
2021-10-15 02:32:41,210 INFO [train.py:451] Epoch 10, batch 12440, batch avg loss 0.2682, total avg loss: 0.2163, batch size: 72
2021-10-15 02:32:46,205 INFO [train.py:451] Epoch 10, batch 12450, batch avg loss 0.1764, total avg loss: 0.2143, batch size: 32
2021-10-15 02:32:51,060 INFO [train.py:451] Epoch 10, batch 12460, batch avg loss 0.2825, total avg loss: 0.2160, batch size: 45
2021-10-15 02:32:55,876 INFO [train.py:451] Epoch 10, batch 12470, batch avg loss 0.2326, total avg loss: 0.2153, batch size: 30
2021-10-15 02:33:00,761 INFO [train.py:451] Epoch 10, batch 12480, batch avg loss 0.2350, total avg loss: 0.2171, batch size: 38
2021-10-15 02:33:05,540 INFO [train.py:451] Epoch 10, batch 12490, batch avg loss 0.2326, total avg loss: 0.2171, batch size: 42
2021-10-15 02:33:10,505 INFO [train.py:451] Epoch 10, batch 12500, batch avg loss 0.1693, total avg loss: 0.2149, batch size: 32
2021-10-15 02:33:15,501 INFO [train.py:451] Epoch 10, batch 12510, batch avg loss 0.2145, total avg loss: 0.2131, batch size: 34
2021-10-15 02:33:20,409 INFO [train.py:451] Epoch 10, batch 12520, batch avg loss 0.2240, total avg loss: 0.2141, batch size: 36
2021-10-15 02:33:25,362 INFO [train.py:451] Epoch 10, batch 12530, batch avg loss 0.1946, total avg loss: 0.2138, batch size: 28
2021-10-15 02:33:30,148 INFO [train.py:451] Epoch 10, batch 12540, batch avg loss 0.2811, total avg loss: 0.2143, batch size: 74
2021-10-15 02:33:35,033 INFO [train.py:451] Epoch 10, batch 12550, batch avg loss 0.2180, total avg loss: 0.2149, batch size: 34
2021-10-15 02:33:39,930 INFO [train.py:451] Epoch 10, batch 12560, batch avg loss 0.2410, total avg loss: 0.2151, batch size: 42
2021-10-15 02:33:44,738 INFO [train.py:451] Epoch 10, batch 12570, batch avg loss 0.2561, total avg loss: 0.2152, batch size: 57
2021-10-15 02:33:49,643 INFO [train.py:451] Epoch 10, batch 12580, batch avg loss 0.1860, total avg loss: 0.2154, batch size: 31
2021-10-15 02:33:54,680 INFO [train.py:451] Epoch 10, batch 12590, batch avg loss 0.2258, total avg loss: 0.2147, batch size: 34
2021-10-15 02:33:59,493 INFO [train.py:451] Epoch 10, batch 12600, batch avg loss 0.2590, total avg loss: 0.2149, batch size: 72
2021-10-15 02:34:04,302 INFO [train.py:451] Epoch 10, batch 12610, batch avg loss 0.1709, total avg loss: 0.2198, batch size: 27
2021-10-15 02:34:09,140 INFO [train.py:451] Epoch 10, batch 12620, batch avg loss 0.2171, total avg loss: 0.2271, batch size: 35
2021-10-15 02:34:14,055 INFO [train.py:451] Epoch 10, batch 12630, batch avg loss 0.2802, total avg loss: 0.2264, batch size: 49
2021-10-15 02:34:19,091 INFO [train.py:451] Epoch 10, batch 12640, batch avg loss 0.1955, total avg loss: 0.2226, batch size: 33
2021-10-15 02:34:24,014 INFO [train.py:451] Epoch 10, batch 12650, batch avg loss 0.2363, total avg loss: 0.2233, batch size: 38
2021-10-15 02:34:29,075 INFO [train.py:451] Epoch 10, batch 12660, batch avg loss 0.2202, total avg loss: 0.2207, batch size: 35
2021-10-15 02:34:33,975 INFO [train.py:451] Epoch 10, batch 12670, batch avg loss 0.2283, total avg loss: 0.2225, batch size: 38
2021-10-15 02:34:38,784 INFO [train.py:451] Epoch 10, batch 12680, batch avg loss 0.2451, total avg loss: 0.2253, batch size: 41
2021-10-15 02:34:43,578 INFO [train.py:451] Epoch 10, batch 12690, batch avg loss 0.2687, total avg loss: 0.2263, batch size: 35
2021-10-15 02:34:48,473 INFO [train.py:451] Epoch 10, batch 12700, batch avg loss 0.1908, total avg loss: 0.2249, batch size: 31
2021-10-15 02:34:53,427 INFO [train.py:451] Epoch 10, batch 12710, batch avg loss 0.3555, total avg loss: 0.2252, batch size: 131
2021-10-15 02:34:58,193 INFO [train.py:451] Epoch 10, batch 12720, batch avg loss 0.3291, total avg loss: 0.2263, batch size: 124
2021-10-15 02:35:01,335 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "37945ddd-f303-6ab2-c8f2-f5530b4b4271" will not be mixed in.
2021-10-15 02:35:03,144 INFO [train.py:451] Epoch 10, batch 12730, batch avg loss 0.1983, total avg loss: 0.2268, batch size: 35
2021-10-15 02:35:07,846 INFO [train.py:451] Epoch 10, batch 12740, batch avg loss 0.2664, total avg loss: 0.2273, batch size: 57
2021-10-15 02:35:12,728 INFO [train.py:451] Epoch 10, batch 12750, batch avg loss 0.2087, total avg loss: 0.2261, batch size: 33
2021-10-15 02:35:17,792 INFO [train.py:451] Epoch 10, batch 12760, batch avg loss 0.2459, total avg loss: 0.2264, batch size: 34
2021-10-15 02:35:22,859 INFO [train.py:451] Epoch 10, batch 12770, batch avg loss 0.2383, total avg loss: 0.2260, batch size: 32
2021-10-15 02:35:27,895 INFO [train.py:451] Epoch 10, batch 12780, batch avg loss 0.2146, total avg loss: 0.2255, batch size: 35
2021-10-15 02:35:32,992 INFO [train.py:451] Epoch 10, batch 12790, batch avg loss 0.2238, total avg loss: 0.2244, batch size: 36
2021-10-15 02:35:38,023 INFO [train.py:451] Epoch 10, batch 12800, batch avg loss 0.2034, total avg loss: 0.2235, batch size: 31
2021-10-15 02:35:42,806 INFO [train.py:451] Epoch 10, batch 12810, batch avg loss 0.2537, total avg loss: 0.2281, batch size: 71
2021-10-15 02:35:47,662 INFO [train.py:451] Epoch 10, batch 12820, batch avg loss 0.1976, total avg loss: 0.2308, batch size: 30
2021-10-15 02:35:52,187 INFO [train.py:451] Epoch 10, batch 12830, batch avg loss 0.2114, total avg loss: 0.2349, batch size: 36
2021-10-15 02:35:57,153 INFO [train.py:451] Epoch 10, batch 12840, batch avg loss 0.2329, total avg loss: 0.2324, batch size: 42
2021-10-15 02:36:02,026 INFO [train.py:451] Epoch 10, batch 12850, batch avg loss 0.2037, total avg loss: 0.2299, batch size: 30
2021-10-15 02:36:07,047 INFO [train.py:451] Epoch 10, batch 12860, batch avg loss 0.2106, total avg loss: 0.2271, batch size: 41
2021-10-15 02:36:11,983 INFO [train.py:451] Epoch 10, batch 12870, batch avg loss 0.2030, total avg loss: 0.2229, batch size: 34
2021-10-15 02:36:16,846 INFO [train.py:451] Epoch 10, batch 12880, batch avg loss 0.2222, total avg loss: 0.2211, batch size: 45
2021-10-15 02:36:21,604 INFO [train.py:451] Epoch 10, batch 12890, batch avg loss 0.2702, total avg loss: 0.2215, batch size: 38
2021-10-15 02:36:26,713 INFO [train.py:451] Epoch 10, batch 12900, batch avg loss 0.2182, total avg loss: 0.2200, batch size: 45
2021-10-15 02:36:31,820 INFO [train.py:451] Epoch 10, batch 12910, batch avg loss 0.2255, total avg loss: 0.2203, batch size: 28
2021-10-15 02:36:36,716 INFO [train.py:451] Epoch 10, batch 12920, batch avg loss 0.2136, total avg loss: 0.2197, batch size: 39
2021-10-15 02:36:41,429 INFO [train.py:451] Epoch 10, batch 12930, batch avg loss 0.1741, total avg loss: 0.2218, batch size: 32
2021-10-15 02:36:46,395 INFO [train.py:451] Epoch 10, batch 12940, batch avg loss 0.1822, total avg loss: 0.2199, batch size: 32
2021-10-15 02:36:51,282 INFO [train.py:451] Epoch 10, batch 12950, batch avg loss 0.1885, total avg loss: 0.2202, batch size: 33
2021-10-15 02:36:56,146 INFO [train.py:451] Epoch 10, batch 12960, batch avg loss 0.1677, total avg loss: 0.2204, batch size: 37
2021-10-15 02:37:01,010 INFO [train.py:451] Epoch 10, batch 12970, batch avg loss 0.2130, total avg loss: 0.2208, batch size: 37
2021-10-15 02:37:06,031 INFO [train.py:451] Epoch 10, batch 12980, batch avg loss 0.2183, total avg loss: 0.2203, batch size: 34
2021-10-15 02:37:11,211 INFO [train.py:451] Epoch 10, batch 12990, batch avg loss 0.2399, total avg loss: 0.2199, batch size: 41
2021-10-15 02:37:16,205 INFO [train.py:451] Epoch 10, batch 13000, batch avg loss 0.2092, total avg loss: 0.2196, batch size: 38
2021-10-15 02:37:57,892 INFO [train.py:483] Epoch 10, valid loss 0.1629, best valid loss: 0.1617 best valid epoch: 10
2021-10-15 02:38:02,960 INFO [train.py:451] Epoch 10, batch 13010, batch avg loss 0.2256, total avg loss: 0.2055, batch size: 42
2021-10-15 02:38:07,983 INFO [train.py:451] Epoch 10, batch 13020, batch avg loss 0.1959, total avg loss: 0.2027, batch size: 34
2021-10-15 02:38:12,720 INFO [train.py:451] Epoch 10, batch 13030, batch avg loss 0.2615, total avg loss: 0.2138, batch size: 49
2021-10-15 02:38:17,826 INFO [train.py:451] Epoch 10, batch 13040, batch avg loss 0.2162, total avg loss: 0.2116, batch size: 30
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2021-10-15 02:46:55,125 INFO [train.py:483] Epoch 10, valid loss 0.1623, best valid loss: 0.1617 best valid epoch: 10
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2021-10-15 02:51:06,097 INFO [train.py:451] Epoch 10, batch 14510, batch avg loss 0.2302, total avg loss: 0.2171, batch size: 33
2021-10-15 02:51:10,985 INFO [train.py:451] Epoch 10, batch 14520, batch avg loss 0.3229, total avg loss: 0.2191, batch size: 124
2021-10-15 02:51:15,953 INFO [train.py:451] Epoch 10, batch 14530, batch avg loss 0.2372, total avg loss: 0.2200, batch size: 49
2021-10-15 02:51:20,908 INFO [train.py:451] Epoch 10, batch 14540, batch avg loss 0.2078, total avg loss: 0.2206, batch size: 29
2021-10-15 02:51:25,891 INFO [train.py:451] Epoch 10, batch 14550, batch avg loss 0.3174, total avg loss: 0.2202, batch size: 131
2021-10-15 02:51:30,981 INFO [train.py:451] Epoch 10, batch 14560, batch avg loss 0.2341, total avg loss: 0.2196, batch size: 33
2021-10-15 02:51:36,009 INFO [train.py:451] Epoch 10, batch 14570, batch avg loss 0.2098, total avg loss: 0.2192, batch size: 30
2021-10-15 02:51:41,024 INFO [train.py:451] Epoch 10, batch 14580, batch avg loss 0.2401, total avg loss: 0.2188, batch size: 30
2021-10-15 02:51:46,099 INFO [train.py:451] Epoch 10, batch 14590, batch avg loss 0.2011, total avg loss: 0.2189, batch size: 35
2021-10-15 02:51:50,978 INFO [train.py:451] Epoch 10, batch 14600, batch avg loss 0.1685, total avg loss: 0.2186, batch size: 28
2021-10-15 02:51:55,776 INFO [train.py:451] Epoch 10, batch 14610, batch avg loss 0.2009, total avg loss: 0.2237, batch size: 33
2021-10-15 02:52:00,849 INFO [train.py:451] Epoch 10, batch 14620, batch avg loss 0.2387, total avg loss: 0.2124, batch size: 57
2021-10-15 02:52:05,623 INFO [train.py:451] Epoch 10, batch 14630, batch avg loss 0.2003, total avg loss: 0.2200, batch size: 29
2021-10-15 02:52:10,529 INFO [train.py:451] Epoch 10, batch 14640, batch avg loss 0.2625, total avg loss: 0.2229, batch size: 37
2021-10-15 02:52:15,446 INFO [train.py:451] Epoch 10, batch 14650, batch avg loss 0.1845, total avg loss: 0.2199, batch size: 28
2021-10-15 02:52:20,269 INFO [train.py:451] Epoch 10, batch 14660, batch avg loss 0.3564, total avg loss: 0.2205, batch size: 133
2021-10-15 02:52:25,094 INFO [train.py:451] Epoch 10, batch 14670, batch avg loss 0.2385, total avg loss: 0.2212, batch size: 36
2021-10-15 02:52:30,043 INFO [train.py:451] Epoch 10, batch 14680, batch avg loss 0.2455, total avg loss: 0.2231, batch size: 32
2021-10-15 02:52:35,231 INFO [train.py:451] Epoch 10, batch 14690, batch avg loss 0.2592, total avg loss: 0.2217, batch size: 39
2021-10-15 02:52:40,110 INFO [train.py:451] Epoch 10, batch 14700, batch avg loss 0.2608, total avg loss: 0.2219, batch size: 38
2021-10-15 02:52:44,963 INFO [train.py:451] Epoch 10, batch 14710, batch avg loss 0.2403, total avg loss: 0.2214, batch size: 36
2021-10-15 02:52:49,919 INFO [train.py:451] Epoch 10, batch 14720, batch avg loss 0.2632, total avg loss: 0.2203, batch size: 32
2021-10-15 02:52:54,865 INFO [train.py:451] Epoch 10, batch 14730, batch avg loss 0.1825, total avg loss: 0.2207, batch size: 38
2021-10-15 02:52:59,788 INFO [train.py:451] Epoch 10, batch 14740, batch avg loss 0.2316, total avg loss: 0.2202, batch size: 34
2021-10-15 02:53:04,996 INFO [train.py:451] Epoch 10, batch 14750, batch avg loss 0.1929, total avg loss: 0.2192, batch size: 29
2021-10-15 02:53:09,905 INFO [train.py:451] Epoch 10, batch 14760, batch avg loss 0.3263, total avg loss: 0.2196, batch size: 130
2021-10-15 02:53:15,110 INFO [train.py:451] Epoch 10, batch 14770, batch avg loss 0.1930, total avg loss: 0.2186, batch size: 30
2021-10-15 02:53:19,991 INFO [train.py:451] Epoch 10, batch 14780, batch avg loss 0.2056, total avg loss: 0.2194, batch size: 42
2021-10-15 02:53:24,792 INFO [train.py:451] Epoch 10, batch 14790, batch avg loss 0.2281, total avg loss: 0.2197, batch size: 38
2021-10-15 02:53:29,725 INFO [train.py:451] Epoch 10, batch 14800, batch avg loss 0.2039, total avg loss: 0.2189, batch size: 34
2021-10-15 02:53:34,691 INFO [train.py:451] Epoch 10, batch 14810, batch avg loss 0.2173, total avg loss: 0.2183, batch size: 38
2021-10-15 02:53:39,606 INFO [train.py:451] Epoch 10, batch 14820, batch avg loss 0.1859, total avg loss: 0.2175, batch size: 29
2021-10-15 02:53:44,477 INFO [train.py:451] Epoch 10, batch 14830, batch avg loss 0.2275, total avg loss: 0.2166, batch size: 34
2021-10-15 02:53:49,323 INFO [train.py:451] Epoch 10, batch 14840, batch avg loss 0.1771, total avg loss: 0.2176, batch size: 32
2021-10-15 02:53:54,196 INFO [train.py:451] Epoch 10, batch 14850, batch avg loss 0.1809, total avg loss: 0.2151, batch size: 33
2021-10-15 02:53:58,952 INFO [train.py:451] Epoch 10, batch 14860, batch avg loss 0.2810, total avg loss: 0.2180, batch size: 49
2021-10-15 02:54:03,845 INFO [train.py:451] Epoch 10, batch 14870, batch avg loss 0.2473, total avg loss: 0.2190, batch size: 35
2021-10-15 02:54:08,808 INFO [train.py:451] Epoch 10, batch 14880, batch avg loss 0.1898, total avg loss: 0.2191, batch size: 33
2021-10-15 02:54:13,714 INFO [train.py:451] Epoch 10, batch 14890, batch avg loss 0.2989, total avg loss: 0.2203, batch size: 57
2021-10-15 02:54:18,627 INFO [train.py:451] Epoch 10, batch 14900, batch avg loss 0.2108, total avg loss: 0.2188, batch size: 32
2021-10-15 02:54:23,442 INFO [train.py:451] Epoch 10, batch 14910, batch avg loss 0.1783, total avg loss: 0.2190, batch size: 29
2021-10-15 02:54:28,340 INFO [train.py:451] Epoch 10, batch 14920, batch avg loss 0.2744, total avg loss: 0.2179, batch size: 73
2021-10-15 02:54:33,266 INFO [train.py:451] Epoch 10, batch 14930, batch avg loss 0.1863, total avg loss: 0.2171, batch size: 32
2021-10-15 02:54:38,174 INFO [train.py:451] Epoch 10, batch 14940, batch avg loss 0.1736, total avg loss: 0.2172, batch size: 33
2021-10-15 02:54:43,091 INFO [train.py:451] Epoch 10, batch 14950, batch avg loss 0.2148, total avg loss: 0.2169, batch size: 32
2021-10-15 02:54:48,087 INFO [train.py:451] Epoch 10, batch 14960, batch avg loss 0.2013, total avg loss: 0.2157, batch size: 34
2021-10-15 02:54:53,176 INFO [train.py:451] Epoch 10, batch 14970, batch avg loss 0.1938, total avg loss: 0.2148, batch size: 34
2021-10-15 02:54:58,117 INFO [train.py:451] Epoch 10, batch 14980, batch avg loss 0.2121, total avg loss: 0.2153, batch size: 34
2021-10-15 02:55:02,985 INFO [train.py:451] Epoch 10, batch 14990, batch avg loss 0.1666, total avg loss: 0.2156, batch size: 33
2021-10-15 02:55:07,829 INFO [train.py:451] Epoch 10, batch 15000, batch avg loss 0.3077, total avg loss: 0.2157, batch size: 34
2021-10-15 02:55:47,790 INFO [train.py:483] Epoch 10, valid loss 0.1624, best valid loss: 0.1617 best valid epoch: 10
2021-10-15 02:55:52,782 INFO [train.py:451] Epoch 10, batch 15010, batch avg loss 0.1983, total avg loss: 0.2094, batch size: 37
2021-10-15 02:55:57,649 INFO [train.py:451] Epoch 10, batch 15020, batch avg loss 0.2481, total avg loss: 0.2117, batch size: 37
2021-10-15 02:56:02,382 INFO [train.py:451] Epoch 10, batch 15030, batch avg loss 0.2366, total avg loss: 0.2198, batch size: 36
2021-10-15 02:56:07,256 INFO [train.py:451] Epoch 10, batch 15040, batch avg loss 0.2207, total avg loss: 0.2241, batch size: 57
2021-10-15 02:56:12,061 INFO [train.py:451] Epoch 10, batch 15050, batch avg loss 0.3007, total avg loss: 0.2262, batch size: 72
2021-10-15 02:56:17,004 INFO [train.py:451] Epoch 10, batch 15060, batch avg loss 0.3231, total avg loss: 0.2283, batch size: 134
2021-10-15 02:56:21,994 INFO [train.py:451] Epoch 10, batch 15070, batch avg loss 0.2373, total avg loss: 0.2253, batch size: 45
2021-10-15 02:56:27,052 INFO [train.py:451] Epoch 10, batch 15080, batch avg loss 0.1692, total avg loss: 0.2223, batch size: 29
2021-10-15 02:56:32,172 INFO [train.py:451] Epoch 10, batch 15090, batch avg loss 0.1748, total avg loss: 0.2206, batch size: 27
2021-10-15 02:56:37,089 INFO [train.py:451] Epoch 10, batch 15100, batch avg loss 0.2198, total avg loss: 0.2208, batch size: 56
2021-10-15 02:56:42,059 INFO [train.py:451] Epoch 10, batch 15110, batch avg loss 0.2147, total avg loss: 0.2213, batch size: 36
2021-10-15 02:56:46,947 INFO [train.py:451] Epoch 10, batch 15120, batch avg loss 0.2353, total avg loss: 0.2215, batch size: 49
2021-10-15 02:56:52,012 INFO [train.py:451] Epoch 10, batch 15130, batch avg loss 0.1758, total avg loss: 0.2204, batch size: 35
2021-10-15 02:56:56,930 INFO [train.py:451] Epoch 10, batch 15140, batch avg loss 0.1846, total avg loss: 0.2215, batch size: 30
2021-10-15 02:57:01,722 INFO [train.py:451] Epoch 10, batch 15150, batch avg loss 0.2889, total avg loss: 0.2228, batch size: 56
2021-10-15 02:57:06,693 INFO [train.py:451] Epoch 10, batch 15160, batch avg loss 0.2122, total avg loss: 0.2220, batch size: 38
2021-10-15 02:57:11,536 INFO [train.py:451] Epoch 10, batch 15170, batch avg loss 0.2589, total avg loss: 0.2213, batch size: 38
2021-10-15 02:57:16,566 INFO [train.py:451] Epoch 10, batch 15180, batch avg loss 0.2033, total avg loss: 0.2217, batch size: 33
2021-10-15 02:57:21,474 INFO [train.py:451] Epoch 10, batch 15190, batch avg loss 0.1707, total avg loss: 0.2219, batch size: 32
2021-10-15 02:57:26,496 INFO [train.py:451] Epoch 10, batch 15200, batch avg loss 0.2214, total avg loss: 0.2218, batch size: 37
2021-10-15 02:57:31,226 INFO [train.py:451] Epoch 10, batch 15210, batch avg loss 0.1816, total avg loss: 0.2435, batch size: 30
2021-10-15 02:57:36,244 INFO [train.py:451] Epoch 10, batch 15220, batch avg loss 0.2049, total avg loss: 0.2277, batch size: 34
2021-10-15 02:57:41,178 INFO [train.py:451] Epoch 10, batch 15230, batch avg loss 0.2249, total avg loss: 0.2330, batch size: 45
2021-10-15 02:57:46,060 INFO [train.py:451] Epoch 10, batch 15240, batch avg loss 0.1944, total avg loss: 0.2306, batch size: 31
2021-10-15 02:57:51,055 INFO [train.py:451] Epoch 10, batch 15250, batch avg loss 0.1818, total avg loss: 0.2270, batch size: 29
2021-10-15 02:57:56,156 INFO [train.py:451] Epoch 10, batch 15260, batch avg loss 0.2103, total avg loss: 0.2243, batch size: 31
2021-10-15 02:58:01,047 INFO [train.py:451] Epoch 10, batch 15270, batch avg loss 0.2058, total avg loss: 0.2229, batch size: 41
2021-10-15 02:58:06,018 INFO [train.py:451] Epoch 10, batch 15280, batch avg loss 0.1900, total avg loss: 0.2227, batch size: 35
2021-10-15 02:58:10,819 INFO [train.py:451] Epoch 10, batch 15290, batch avg loss 0.1677, total avg loss: 0.2224, batch size: 28
2021-10-15 02:58:15,761 INFO [train.py:451] Epoch 10, batch 15300, batch avg loss 0.2200, total avg loss: 0.2216, batch size: 33
2021-10-15 02:58:20,638 INFO [train.py:451] Epoch 10, batch 15310, batch avg loss 0.1992, total avg loss: 0.2214, batch size: 30
2021-10-15 02:58:25,719 INFO [train.py:451] Epoch 10, batch 15320, batch avg loss 0.1988, total avg loss: 0.2206, batch size: 34
2021-10-15 02:58:30,808 INFO [train.py:451] Epoch 10, batch 15330, batch avg loss 0.2032, total avg loss: 0.2200, batch size: 27
2021-10-15 02:58:35,841 INFO [train.py:451] Epoch 10, batch 15340, batch avg loss 0.2182, total avg loss: 0.2209, batch size: 36
2021-10-15 02:58:40,686 INFO [train.py:451] Epoch 10, batch 15350, batch avg loss 0.2755, total avg loss: 0.2203, batch size: 72
2021-10-15 02:58:45,665 INFO [train.py:451] Epoch 10, batch 15360, batch avg loss 0.2019, total avg loss: 0.2210, batch size: 30
2021-10-15 02:58:50,696 INFO [train.py:451] Epoch 10, batch 15370, batch avg loss 0.2099, total avg loss: 0.2204, batch size: 34
2021-10-15 02:58:55,512 INFO [train.py:451] Epoch 10, batch 15380, batch avg loss 0.2305, total avg loss: 0.2191, batch size: 57
2021-10-15 02:59:00,212 INFO [train.py:451] Epoch 10, batch 15390, batch avg loss 0.2505, total avg loss: 0.2194, batch size: 39
2021-10-15 02:59:04,865 INFO [train.py:451] Epoch 10, batch 15400, batch avg loss 0.2317, total avg loss: 0.2194, batch size: 42
2021-10-15 02:59:09,791 INFO [train.py:451] Epoch 10, batch 15410, batch avg loss 0.2136, total avg loss: 0.2065, batch size: 38
2021-10-15 02:59:14,641 INFO [train.py:451] Epoch 10, batch 15420, batch avg loss 0.1703, total avg loss: 0.2128, batch size: 28
2021-10-15 02:59:19,297 INFO [train.py:451] Epoch 10, batch 15430, batch avg loss 0.2206, total avg loss: 0.2244, batch size: 35
2021-10-15 02:59:24,175 INFO [train.py:451] Epoch 10, batch 15440, batch avg loss 0.2008, total avg loss: 0.2236, batch size: 27
2021-10-15 02:59:29,124 INFO [train.py:451] Epoch 10, batch 15450, batch avg loss 0.1784, total avg loss: 0.2202, batch size: 34
2021-10-15 02:59:33,913 INFO [train.py:451] Epoch 10, batch 15460, batch avg loss 0.1916, total avg loss: 0.2176, batch size: 34
2021-10-15 02:59:38,784 INFO [train.py:451] Epoch 10, batch 15470, batch avg loss 0.2263, total avg loss: 0.2184, batch size: 42
2021-10-15 02:59:43,680 INFO [train.py:451] Epoch 10, batch 15480, batch avg loss 0.2610, total avg loss: 0.2191, batch size: 41
2021-10-15 02:59:48,802 INFO [train.py:451] Epoch 10, batch 15490, batch avg loss 0.2143, total avg loss: 0.2178, batch size: 36
2021-10-15 02:59:53,850 INFO [train.py:451] Epoch 10, batch 15500, batch avg loss 0.2184, total avg loss: 0.2179, batch size: 49
2021-10-15 02:59:58,775 INFO [train.py:451] Epoch 10, batch 15510, batch avg loss 0.2406, total avg loss: 0.2181, batch size: 36
2021-10-15 03:00:03,745 INFO [train.py:451] Epoch 10, batch 15520, batch avg loss 0.2306, total avg loss: 0.2178, batch size: 35
2021-10-15 03:00:08,484 INFO [train.py:451] Epoch 10, batch 15530, batch avg loss 0.2166, total avg loss: 0.2180, batch size: 37
2021-10-15 03:00:13,342 INFO [train.py:451] Epoch 10, batch 15540, batch avg loss 0.2202, total avg loss: 0.2178, batch size: 31
2021-10-15 03:00:18,181 INFO [train.py:451] Epoch 10, batch 15550, batch avg loss 0.1428, total avg loss: 0.2173, batch size: 28
2021-10-15 03:00:22,956 INFO [train.py:451] Epoch 10, batch 15560, batch avg loss 0.2823, total avg loss: 0.2179, batch size: 73
2021-10-15 03:00:27,864 INFO [train.py:451] Epoch 10, batch 15570, batch avg loss 0.2096, total avg loss: 0.2188, batch size: 34
2021-10-15 03:00:32,730 INFO [train.py:451] Epoch 10, batch 15580, batch avg loss 0.2044, total avg loss: 0.2194, batch size: 34
2021-10-15 03:00:34,815 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "0952f2f6-2eb2-e2f2-8760-5a9db3138b96" will not be mixed in.
2021-10-15 03:00:37,586 INFO [train.py:451] Epoch 10, batch 15590, batch avg loss 0.2014, total avg loss: 0.2196, batch size: 32
2021-10-15 03:00:42,749 INFO [train.py:451] Epoch 10, batch 15600, batch avg loss 0.1367, total avg loss: 0.2191, batch size: 29
2021-10-15 03:00:47,707 INFO [train.py:451] Epoch 10, batch 15610, batch avg loss 0.1823, total avg loss: 0.2164, batch size: 31
2021-10-15 03:00:52,767 INFO [train.py:451] Epoch 10, batch 15620, batch avg loss 0.1858, total avg loss: 0.2103, batch size: 30
2021-10-15 03:00:54,939 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "0abb13fe-30f1-c6ab-da45-dcff38b5ee64" will not be mixed in.
2021-10-15 03:00:57,808 INFO [train.py:451] Epoch 10, batch 15630, batch avg loss 0.1833, total avg loss: 0.2144, batch size: 30
2021-10-15 03:01:02,847 INFO [train.py:451] Epoch 10, batch 15640, batch avg loss 0.2027, total avg loss: 0.2130, batch size: 34
2021-10-15 03:01:07,863 INFO [train.py:451] Epoch 10, batch 15650, batch avg loss 0.1941, total avg loss: 0.2115, batch size: 31
2021-10-15 03:01:12,696 INFO [train.py:451] Epoch 10, batch 15660, batch avg loss 0.2016, total avg loss: 0.2132, batch size: 36
2021-10-15 03:01:17,618 INFO [train.py:451] Epoch 10, batch 15670, batch avg loss 0.2627, total avg loss: 0.2157, batch size: 39
2021-10-15 03:01:22,536 INFO [train.py:451] Epoch 10, batch 15680, batch avg loss 0.2153, total avg loss: 0.2158, batch size: 34
2021-10-15 03:01:27,853 INFO [train.py:451] Epoch 10, batch 15690, batch avg loss 0.2015, total avg loss: 0.2163, batch size: 34
2021-10-15 03:01:32,938 INFO [train.py:451] Epoch 10, batch 15700, batch avg loss 0.2149, total avg loss: 0.2155, batch size: 31
2021-10-15 03:01:38,062 INFO [train.py:451] Epoch 10, batch 15710, batch avg loss 0.2277, total avg loss: 0.2177, batch size: 39
2021-10-15 03:01:43,088 INFO [train.py:451] Epoch 10, batch 15720, batch avg loss 0.2040, total avg loss: 0.2175, batch size: 31
2021-10-15 03:01:48,288 INFO [train.py:451] Epoch 10, batch 15730, batch avg loss 0.2271, total avg loss: 0.2165, batch size: 35
2021-10-15 03:01:53,021 INFO [train.py:451] Epoch 10, batch 15740, batch avg loss 0.2493, total avg loss: 0.2179, batch size: 37
2021-10-15 03:01:57,801 INFO [train.py:451] Epoch 10, batch 15750, batch avg loss 0.2463, total avg loss: 0.2181, batch size: 32
2021-10-15 03:02:02,730 INFO [train.py:451] Epoch 10, batch 15760, batch avg loss 0.2209, total avg loss: 0.2181, batch size: 35
2021-10-15 03:02:07,615 INFO [train.py:451] Epoch 10, batch 15770, batch avg loss 0.2287, total avg loss: 0.2175, batch size: 41
2021-10-15 03:02:12,350 INFO [train.py:451] Epoch 10, batch 15780, batch avg loss 0.1848, total avg loss: 0.2173, batch size: 31
2021-10-15 03:02:17,347 INFO [train.py:451] Epoch 10, batch 15790, batch avg loss 0.2286, total avg loss: 0.2167, batch size: 41
2021-10-15 03:02:22,169 INFO [train.py:451] Epoch 10, batch 15800, batch avg loss 0.2038, total avg loss: 0.2160, batch size: 35
2021-10-15 03:02:27,124 INFO [train.py:451] Epoch 10, batch 15810, batch avg loss 0.2388, total avg loss: 0.2406, batch size: 42
2021-10-15 03:02:31,831 INFO [train.py:451] Epoch 10, batch 15820, batch avg loss 0.2282, total avg loss: 0.2426, batch size: 39
2021-10-15 03:02:36,517 INFO [train.py:451] Epoch 10, batch 15830, batch avg loss 0.2510, total avg loss: 0.2433, batch size: 45
2021-10-15 03:02:41,588 INFO [train.py:451] Epoch 10, batch 15840, batch avg loss 0.1879, total avg loss: 0.2404, batch size: 33
2021-10-15 03:02:46,661 INFO [train.py:451] Epoch 10, batch 15850, batch avg loss 0.2092, total avg loss: 0.2321, batch size: 34
2021-10-15 03:02:51,590 INFO [train.py:451] Epoch 10, batch 15860, batch avg loss 0.2132, total avg loss: 0.2293, batch size: 31
2021-10-15 03:02:56,671 INFO [train.py:451] Epoch 10, batch 15870, batch avg loss 0.2208, total avg loss: 0.2258, batch size: 45
2021-10-15 03:03:01,896 INFO [train.py:451] Epoch 10, batch 15880, batch avg loss 0.2011, total avg loss: 0.2216, batch size: 34
2021-10-15 03:03:06,792 INFO [train.py:451] Epoch 10, batch 15890, batch avg loss 0.2148, total avg loss: 0.2216, batch size: 32
2021-10-15 03:03:11,745 INFO [train.py:451] Epoch 10, batch 15900, batch avg loss 0.1890, total avg loss: 0.2216, batch size: 33
2021-10-15 03:03:16,674 INFO [train.py:451] Epoch 10, batch 15910, batch avg loss 0.2360, total avg loss: 0.2201, batch size: 72
2021-10-15 03:03:21,576 INFO [train.py:451] Epoch 10, batch 15920, batch avg loss 0.2486, total avg loss: 0.2202, batch size: 57
2021-10-15 03:03:26,674 INFO [train.py:451] Epoch 10, batch 15930, batch avg loss 0.2193, total avg loss: 0.2203, batch size: 33
2021-10-15 03:03:38,953 INFO [train.py:451] Epoch 10, batch 15940, batch avg loss 0.2331, total avg loss: 0.2210, batch size: 32
2021-10-15 03:03:44,032 INFO [train.py:451] Epoch 10, batch 15950, batch avg loss 0.2007, total avg loss: 0.2201, batch size: 31
2021-10-15 03:03:49,167 INFO [train.py:451] Epoch 10, batch 15960, batch avg loss 0.2280, total avg loss: 0.2197, batch size: 42
2021-10-15 03:03:54,142 INFO [train.py:451] Epoch 10, batch 15970, batch avg loss 0.1625, total avg loss: 0.2191, batch size: 29
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2021-10-15 03:13:44,231 INFO [train.py:483] Epoch 10, valid loss 0.1621, best valid loss: 0.1615 best valid epoch: 10
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2021-10-15 03:17:40,812 INFO [train.py:451] Epoch 10, batch 17480, batch avg loss 0.2149, total avg loss: 0.2209, batch size: 34
2021-10-15 03:17:45,794 INFO [train.py:451] Epoch 10, batch 17490, batch avg loss 0.2179, total avg loss: 0.2217, batch size: 27
2021-10-15 03:17:50,889 INFO [train.py:451] Epoch 10, batch 17500, batch avg loss 0.1684, total avg loss: 0.2194, batch size: 34
2021-10-15 03:17:55,661 INFO [train.py:451] Epoch 10, batch 17510, batch avg loss 0.2157, total avg loss: 0.2205, batch size: 32
2021-10-15 03:18:00,674 INFO [train.py:451] Epoch 10, batch 17520, batch avg loss 0.2336, total avg loss: 0.2211, batch size: 45
2021-10-15 03:18:05,773 INFO [train.py:451] Epoch 10, batch 17530, batch avg loss 0.1838, total avg loss: 0.2203, batch size: 36
2021-10-15 03:18:10,812 INFO [train.py:451] Epoch 10, batch 17540, batch avg loss 0.2963, total avg loss: 0.2202, batch size: 124
2021-10-15 03:18:15,732 INFO [train.py:451] Epoch 10, batch 17550, batch avg loss 0.1793, total avg loss: 0.2192, batch size: 28
2021-10-15 03:18:20,721 INFO [train.py:451] Epoch 10, batch 17560, batch avg loss 0.2018, total avg loss: 0.2194, batch size: 38
2021-10-15 03:18:25,672 INFO [train.py:451] Epoch 10, batch 17570, batch avg loss 0.2226, total avg loss: 0.2200, batch size: 34
2021-10-15 03:18:30,840 INFO [train.py:451] Epoch 10, batch 17580, batch avg loss 0.1930, total avg loss: 0.2186, batch size: 29
2021-10-15 03:18:35,921 INFO [train.py:451] Epoch 10, batch 17590, batch avg loss 0.2246, total avg loss: 0.2184, batch size: 38
2021-10-15 03:18:40,798 INFO [train.py:451] Epoch 10, batch 17600, batch avg loss 0.2413, total avg loss: 0.2192, batch size: 38
2021-10-15 03:18:45,861 INFO [train.py:451] Epoch 10, batch 17610, batch avg loss 0.2836, total avg loss: 0.2293, batch size: 34
2021-10-15 03:18:50,667 INFO [train.py:451] Epoch 10, batch 17620, batch avg loss 0.2378, total avg loss: 0.2333, batch size: 38
2021-10-15 03:18:55,517 INFO [train.py:451] Epoch 10, batch 17630, batch avg loss 0.2477, total avg loss: 0.2340, batch size: 33
2021-10-15 03:19:00,308 INFO [train.py:451] Epoch 10, batch 17640, batch avg loss 0.2059, total avg loss: 0.2309, batch size: 33
2021-10-15 03:19:05,291 INFO [train.py:451] Epoch 10, batch 17650, batch avg loss 0.1549, total avg loss: 0.2270, batch size: 30
2021-10-15 03:19:10,183 INFO [train.py:451] Epoch 10, batch 17660, batch avg loss 0.2619, total avg loss: 0.2289, batch size: 36
2021-10-15 03:19:15,242 INFO [train.py:451] Epoch 10, batch 17670, batch avg loss 0.2127, total avg loss: 0.2264, batch size: 38
2021-10-15 03:19:20,153 INFO [train.py:451] Epoch 10, batch 17680, batch avg loss 0.2229, total avg loss: 0.2291, batch size: 38
2021-10-15 03:19:25,185 INFO [train.py:451] Epoch 10, batch 17690, batch avg loss 0.1889, total avg loss: 0.2289, batch size: 31
2021-10-15 03:19:30,215 INFO [train.py:451] Epoch 10, batch 17700, batch avg loss 0.2101, total avg loss: 0.2285, batch size: 35
2021-10-15 03:19:35,189 INFO [train.py:451] Epoch 10, batch 17710, batch avg loss 0.1754, total avg loss: 0.2267, batch size: 30
2021-10-15 03:19:40,309 INFO [train.py:451] Epoch 10, batch 17720, batch avg loss 0.2175, total avg loss: 0.2250, batch size: 31
2021-10-15 03:19:45,262 INFO [train.py:451] Epoch 10, batch 17730, batch avg loss 0.1659, total avg loss: 0.2239, batch size: 29
2021-10-15 03:19:50,239 INFO [train.py:451] Epoch 10, batch 17740, batch avg loss 0.2250, total avg loss: 0.2248, batch size: 29
2021-10-15 03:19:55,229 INFO [train.py:451] Epoch 10, batch 17750, batch avg loss 0.1821, total avg loss: 0.2234, batch size: 30
2021-10-15 03:20:00,256 INFO [train.py:451] Epoch 10, batch 17760, batch avg loss 0.2160, total avg loss: 0.2225, batch size: 32
2021-10-15 03:20:05,247 INFO [train.py:451] Epoch 10, batch 17770, batch avg loss 0.2393, total avg loss: 0.2220, batch size: 33
2021-10-15 03:20:10,270 INFO [train.py:451] Epoch 10, batch 17780, batch avg loss 0.2123, total avg loss: 0.2216, batch size: 39
2021-10-15 03:20:15,445 INFO [train.py:451] Epoch 10, batch 17790, batch avg loss 0.2529, total avg loss: 0.2214, batch size: 33
2021-10-15 03:20:20,364 INFO [train.py:451] Epoch 10, batch 17800, batch avg loss 0.1834, total avg loss: 0.2217, batch size: 30
2021-10-15 03:20:25,361 INFO [train.py:451] Epoch 10, batch 17810, batch avg loss 0.3282, total avg loss: 0.2133, batch size: 134
2021-10-15 03:20:30,154 INFO [train.py:451] Epoch 10, batch 17820, batch avg loss 0.1885, total avg loss: 0.2242, batch size: 34
2021-10-15 03:20:35,126 INFO [train.py:451] Epoch 10, batch 17830, batch avg loss 0.2156, total avg loss: 0.2232, batch size: 33
2021-10-15 03:20:40,038 INFO [train.py:451] Epoch 10, batch 17840, batch avg loss 0.2057, total avg loss: 0.2239, batch size: 33
2021-10-15 03:20:44,933 INFO [train.py:451] Epoch 10, batch 17850, batch avg loss 0.2327, total avg loss: 0.2212, batch size: 33
2021-10-15 03:20:49,903 INFO [train.py:451] Epoch 10, batch 17860, batch avg loss 0.1891, total avg loss: 0.2208, batch size: 38
2021-10-15 03:20:54,780 INFO [train.py:451] Epoch 10, batch 17870, batch avg loss 0.1993, total avg loss: 0.2223, batch size: 38
2021-10-15 03:20:59,748 INFO [train.py:451] Epoch 10, batch 17880, batch avg loss 0.2129, total avg loss: 0.2208, batch size: 32
2021-10-15 03:21:04,626 INFO [train.py:451] Epoch 10, batch 17890, batch avg loss 0.2068, total avg loss: 0.2194, batch size: 37
2021-10-15 03:21:09,362 INFO [train.py:451] Epoch 10, batch 17900, batch avg loss 0.2567, total avg loss: 0.2211, batch size: 72
2021-10-15 03:21:14,384 INFO [train.py:451] Epoch 10, batch 17910, batch avg loss 0.2232, total avg loss: 0.2185, batch size: 38
2021-10-15 03:21:19,196 INFO [train.py:451] Epoch 10, batch 17920, batch avg loss 0.2357, total avg loss: 0.2201, batch size: 30
2021-10-15 03:21:24,109 INFO [train.py:451] Epoch 10, batch 17930, batch avg loss 0.2015, total avg loss: 0.2196, batch size: 33
2021-10-15 03:21:29,020 INFO [train.py:451] Epoch 10, batch 17940, batch avg loss 0.2335, total avg loss: 0.2191, batch size: 45
2021-10-15 03:21:34,014 INFO [train.py:451] Epoch 10, batch 17950, batch avg loss 0.1964, total avg loss: 0.2194, batch size: 33
2021-10-15 03:21:39,102 INFO [train.py:451] Epoch 10, batch 17960, batch avg loss 0.2422, total avg loss: 0.2183, batch size: 33
2021-10-15 03:21:44,229 INFO [train.py:451] Epoch 10, batch 17970, batch avg loss 0.2001, total avg loss: 0.2183, batch size: 34
2021-10-15 03:21:49,271 INFO [train.py:451] Epoch 10, batch 17980, batch avg loss 0.2713, total avg loss: 0.2182, batch size: 39
2021-10-15 03:21:54,266 INFO [train.py:451] Epoch 10, batch 17990, batch avg loss 0.1853, total avg loss: 0.2180, batch size: 28
2021-10-15 03:21:59,016 INFO [train.py:451] Epoch 10, batch 18000, batch avg loss 0.1973, total avg loss: 0.2188, batch size: 30
2021-10-15 03:22:39,682 INFO [train.py:483] Epoch 10, valid loss 0.1618, best valid loss: 0.1615 best valid epoch: 10
2021-10-15 03:22:44,439 INFO [train.py:451] Epoch 10, batch 18010, batch avg loss 0.2689, total avg loss: 0.2148, batch size: 72
2021-10-15 03:22:49,154 INFO [train.py:451] Epoch 10, batch 18020, batch avg loss 0.2107, total avg loss: 0.2221, batch size: 34
2021-10-15 03:22:53,994 INFO [train.py:451] Epoch 10, batch 18030, batch avg loss 0.1974, total avg loss: 0.2167, batch size: 39
2021-10-15 03:22:58,880 INFO [train.py:451] Epoch 10, batch 18040, batch avg loss 0.2946, total avg loss: 0.2197, batch size: 72
2021-10-15 03:23:03,772 INFO [train.py:451] Epoch 10, batch 18050, batch avg loss 0.1844, total avg loss: 0.2170, batch size: 27
2021-10-15 03:23:08,717 INFO [train.py:451] Epoch 10, batch 18060, batch avg loss 0.2048, total avg loss: 0.2188, batch size: 38
2021-10-15 03:23:13,650 INFO [train.py:451] Epoch 10, batch 18070, batch avg loss 0.1814, total avg loss: 0.2188, batch size: 29
2021-10-15 03:23:18,514 INFO [train.py:451] Epoch 10, batch 18080, batch avg loss 0.2167, total avg loss: 0.2183, batch size: 45
2021-10-15 03:23:23,332 INFO [train.py:451] Epoch 10, batch 18090, batch avg loss 0.2275, total avg loss: 0.2189, batch size: 31
2021-10-15 03:23:28,215 INFO [train.py:451] Epoch 10, batch 18100, batch avg loss 0.2711, total avg loss: 0.2191, batch size: 41
2021-10-15 03:23:33,162 INFO [train.py:451] Epoch 10, batch 18110, batch avg loss 0.1787, total avg loss: 0.2190, batch size: 30
2021-10-15 03:23:38,093 INFO [train.py:451] Epoch 10, batch 18120, batch avg loss 0.1972, total avg loss: 0.2173, batch size: 32
2021-10-15 03:23:42,853 INFO [train.py:451] Epoch 10, batch 18130, batch avg loss 0.2131, total avg loss: 0.2165, batch size: 32
2021-10-15 03:23:47,615 INFO [train.py:451] Epoch 10, batch 18140, batch avg loss 0.2352, total avg loss: 0.2168, batch size: 38
2021-10-15 03:23:52,410 INFO [train.py:451] Epoch 10, batch 18150, batch avg loss 0.2198, total avg loss: 0.2172, batch size: 35
2021-10-15 03:23:57,381 INFO [train.py:451] Epoch 10, batch 18160, batch avg loss 0.2312, total avg loss: 0.2161, batch size: 29
2021-10-15 03:24:02,367 INFO [train.py:451] Epoch 10, batch 18170, batch avg loss 0.2307, total avg loss: 0.2159, batch size: 36
2021-10-15 03:24:07,129 INFO [train.py:451] Epoch 10, batch 18180, batch avg loss 0.2220, total avg loss: 0.2161, batch size: 56
2021-10-15 03:24:12,037 INFO [train.py:451] Epoch 10, batch 18190, batch avg loss 0.2555, total avg loss: 0.2160, batch size: 39
2021-10-15 03:24:16,898 INFO [train.py:451] Epoch 10, batch 18200, batch avg loss 0.2274, total avg loss: 0.2155, batch size: 41
2021-10-15 03:24:21,794 INFO [train.py:451] Epoch 10, batch 18210, batch avg loss 0.1906, total avg loss: 0.2154, batch size: 34
2021-10-15 03:24:26,683 INFO [train.py:451] Epoch 10, batch 18220, batch avg loss 0.1910, total avg loss: 0.2144, batch size: 31
2021-10-15 03:24:31,580 INFO [train.py:451] Epoch 10, batch 18230, batch avg loss 0.1940, total avg loss: 0.2181, batch size: 34
2021-10-15 03:24:36,612 INFO [train.py:451] Epoch 10, batch 18240, batch avg loss 0.2659, total avg loss: 0.2166, batch size: 37
2021-10-15 03:24:41,514 INFO [train.py:451] Epoch 10, batch 18250, batch avg loss 0.2332, total avg loss: 0.2170, batch size: 39
2021-10-15 03:24:46,346 INFO [train.py:451] Epoch 10, batch 18260, batch avg loss 0.2433, total avg loss: 0.2177, batch size: 56
2021-10-15 03:24:51,159 INFO [train.py:451] Epoch 10, batch 18270, batch avg loss 0.3358, total avg loss: 0.2215, batch size: 128
2021-10-15 03:24:55,884 INFO [train.py:451] Epoch 10, batch 18280, batch avg loss 0.2495, total avg loss: 0.2216, batch size: 42
2021-10-15 03:24:56,639 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "63328ac4-6895-0077-7c12-99ed0e4ec7b6" will not be mixed in.
2021-10-15 03:25:00,738 INFO [train.py:451] Epoch 10, batch 18290, batch avg loss 0.2212, total avg loss: 0.2205, batch size: 29
2021-10-15 03:25:05,540 INFO [train.py:451] Epoch 10, batch 18300, batch avg loss 0.3264, total avg loss: 0.2208, batch size: 126
2021-10-15 03:25:10,640 INFO [train.py:451] Epoch 10, batch 18310, batch avg loss 0.1621, total avg loss: 0.2196, batch size: 27
2021-10-15 03:25:15,554 INFO [train.py:451] Epoch 10, batch 18320, batch avg loss 0.2182, total avg loss: 0.2177, batch size: 32
2021-10-15 03:25:20,501 INFO [train.py:451] Epoch 10, batch 18330, batch avg loss 0.2219, total avg loss: 0.2176, batch size: 35
2021-10-15 03:25:25,345 INFO [train.py:451] Epoch 10, batch 18340, batch avg loss 0.1850, total avg loss: 0.2179, batch size: 36
2021-10-15 03:25:30,268 INFO [train.py:451] Epoch 10, batch 18350, batch avg loss 0.1613, total avg loss: 0.2178, batch size: 31
2021-10-15 03:25:35,340 INFO [train.py:451] Epoch 10, batch 18360, batch avg loss 0.2229, total avg loss: 0.2166, batch size: 35
2021-10-15 03:25:40,110 INFO [train.py:451] Epoch 10, batch 18370, batch avg loss 0.2225, total avg loss: 0.2174, batch size: 37
2021-10-15 03:25:45,018 INFO [train.py:451] Epoch 10, batch 18380, batch avg loss 0.1869, total avg loss: 0.2161, batch size: 31
2021-10-15 03:25:49,835 INFO [train.py:451] Epoch 10, batch 18390, batch avg loss 0.2612, total avg loss: 0.2157, batch size: 73
2021-10-15 03:25:54,800 INFO [train.py:451] Epoch 10, batch 18400, batch avg loss 0.2369, total avg loss: 0.2158, batch size: 42
2021-10-15 03:25:59,726 INFO [train.py:451] Epoch 10, batch 18410, batch avg loss 0.1930, total avg loss: 0.2512, batch size: 34
2021-10-15 03:26:04,651 INFO [train.py:451] Epoch 10, batch 18420, batch avg loss 0.1767, total avg loss: 0.2429, batch size: 29
2021-10-15 03:26:09,607 INFO [train.py:451] Epoch 10, batch 18430, batch avg loss 0.2542, total avg loss: 0.2394, batch size: 36
2021-10-15 03:26:14,685 INFO [train.py:451] Epoch 10, batch 18440, batch avg loss 0.2708, total avg loss: 0.2369, batch size: 34
2021-10-15 03:26:19,567 INFO [train.py:451] Epoch 10, batch 18450, batch avg loss 0.2344, total avg loss: 0.2367, batch size: 34
2021-10-15 03:26:24,589 INFO [train.py:451] Epoch 10, batch 18460, batch avg loss 0.2292, total avg loss: 0.2338, batch size: 33
2021-10-15 03:26:29,712 INFO [train.py:451] Epoch 10, batch 18470, batch avg loss 0.1846, total avg loss: 0.2307, batch size: 28
2021-10-15 03:26:34,867 INFO [train.py:451] Epoch 10, batch 18480, batch avg loss 0.2001, total avg loss: 0.2272, batch size: 28
2021-10-15 03:26:39,894 INFO [train.py:451] Epoch 10, batch 18490, batch avg loss 0.2009, total avg loss: 0.2251, batch size: 29
2021-10-15 03:26:45,013 INFO [train.py:451] Epoch 10, batch 18500, batch avg loss 0.1834, total avg loss: 0.2237, batch size: 34
2021-10-15 03:26:49,890 INFO [train.py:451] Epoch 10, batch 18510, batch avg loss 0.2087, total avg loss: 0.2239, batch size: 42
2021-10-15 03:26:54,655 INFO [train.py:451] Epoch 10, batch 18520, batch avg loss 0.2312, total avg loss: 0.2246, batch size: 37
2021-10-15 03:26:59,578 INFO [train.py:451] Epoch 10, batch 18530, batch avg loss 0.1673, total avg loss: 0.2230, batch size: 28
2021-10-15 03:27:04,373 INFO [train.py:451] Epoch 10, batch 18540, batch avg loss 0.2330, total avg loss: 0.2224, batch size: 41
2021-10-15 03:27:09,382 INFO [train.py:451] Epoch 10, batch 18550, batch avg loss 0.2695, total avg loss: 0.2224, batch size: 30
2021-10-15 03:27:14,233 INFO [train.py:451] Epoch 10, batch 18560, batch avg loss 0.2157, total avg loss: 0.2222, batch size: 42
2021-10-15 03:27:19,151 INFO [train.py:451] Epoch 10, batch 18570, batch avg loss 0.2144, total avg loss: 0.2212, batch size: 42
2021-10-15 03:27:24,106 INFO [train.py:451] Epoch 10, batch 18580, batch avg loss 0.2066, total avg loss: 0.2202, batch size: 30
2021-10-15 03:27:29,020 INFO [train.py:451] Epoch 10, batch 18590, batch avg loss 0.2554, total avg loss: 0.2194, batch size: 57
2021-10-15 03:27:33,978 INFO [train.py:451] Epoch 10, batch 18600, batch avg loss 0.2078, total avg loss: 0.2206, batch size: 27
2021-10-15 03:27:38,904 INFO [train.py:451] Epoch 10, batch 18610, batch avg loss 0.2023, total avg loss: 0.2232, batch size: 38
2021-10-15 03:27:43,739 INFO [train.py:451] Epoch 10, batch 18620, batch avg loss 0.2358, total avg loss: 0.2273, batch size: 28
2021-10-15 03:27:48,596 INFO [train.py:451] Epoch 10, batch 18630, batch avg loss 0.1883, total avg loss: 0.2190, batch size: 29
2021-10-15 03:27:53,579 INFO [train.py:451] Epoch 10, batch 18640, batch avg loss 0.2706, total avg loss: 0.2174, batch size: 57
2021-10-15 03:27:58,480 INFO [train.py:451] Epoch 10, batch 18650, batch avg loss 0.1950, total avg loss: 0.2148, batch size: 38
2021-10-15 03:28:03,350 INFO [train.py:451] Epoch 10, batch 18660, batch avg loss 0.2094, total avg loss: 0.2150, batch size: 34
2021-10-15 03:28:08,047 INFO [train.py:451] Epoch 10, batch 18670, batch avg loss 0.2230, total avg loss: 0.2182, batch size: 34
2021-10-15 03:28:12,984 INFO [train.py:451] Epoch 10, batch 18680, batch avg loss 0.2262, total avg loss: 0.2176, batch size: 37
2021-10-15 03:28:17,959 INFO [train.py:451] Epoch 10, batch 18690, batch avg loss 0.1782, total avg loss: 0.2171, batch size: 31
2021-10-15 03:28:23,002 INFO [train.py:451] Epoch 10, batch 18700, batch avg loss 0.2064, total avg loss: 0.2162, batch size: 28
2021-10-15 03:28:27,950 INFO [train.py:451] Epoch 10, batch 18710, batch avg loss 0.2289, total avg loss: 0.2189, batch size: 41
2021-10-15 03:28:32,953 INFO [train.py:451] Epoch 10, batch 18720, batch avg loss 0.2116, total avg loss: 0.2191, batch size: 38
2021-10-15 03:28:37,889 INFO [train.py:451] Epoch 10, batch 18730, batch avg loss 0.1874, total avg loss: 0.2199, batch size: 36
2021-10-15 03:28:42,812 INFO [train.py:451] Epoch 10, batch 18740, batch avg loss 0.2252, total avg loss: 0.2196, batch size: 35
2021-10-15 03:28:47,899 INFO [train.py:451] Epoch 10, batch 18750, batch avg loss 0.1990, total avg loss: 0.2188, batch size: 30
2021-10-15 03:28:52,900 INFO [train.py:451] Epoch 10, batch 18760, batch avg loss 0.2629, total avg loss: 0.2199, batch size: 57
2021-10-15 03:28:57,965 INFO [train.py:451] Epoch 10, batch 18770, batch avg loss 0.2418, total avg loss: 0.2189, batch size: 35
2021-10-15 03:29:02,871 INFO [train.py:451] Epoch 10, batch 18780, batch avg loss 0.2838, total avg loss: 0.2193, batch size: 71
2021-10-15 03:29:07,875 INFO [train.py:451] Epoch 10, batch 18790, batch avg loss 0.2174, total avg loss: 0.2195, batch size: 32
2021-10-15 03:29:12,752 INFO [train.py:451] Epoch 10, batch 18800, batch avg loss 0.2841, total avg loss: 0.2191, batch size: 38
2021-10-15 03:29:17,626 INFO [train.py:451] Epoch 10, batch 18810, batch avg loss 0.2013, total avg loss: 0.2235, batch size: 27
2021-10-15 03:29:22,605 INFO [train.py:451] Epoch 10, batch 18820, batch avg loss 0.2226, total avg loss: 0.2230, batch size: 35
2021-10-15 03:29:27,588 INFO [train.py:451] Epoch 10, batch 18830, batch avg loss 0.2061, total avg loss: 0.2186, batch size: 39
2021-10-15 03:29:32,644 INFO [train.py:451] Epoch 10, batch 18840, batch avg loss 0.2106, total avg loss: 0.2150, batch size: 28
2021-10-15 03:29:37,474 INFO [train.py:451] Epoch 10, batch 18850, batch avg loss 0.2140, total avg loss: 0.2143, batch size: 45
2021-10-15 03:29:42,361 INFO [train.py:451] Epoch 10, batch 18860, batch avg loss 0.1682, total avg loss: 0.2137, batch size: 32
2021-10-15 03:29:47,238 INFO [train.py:451] Epoch 10, batch 18870, batch avg loss 0.2344, total avg loss: 0.2141, batch size: 34
2021-10-15 03:29:52,186 INFO [train.py:451] Epoch 10, batch 18880, batch avg loss 0.2702, total avg loss: 0.2151, batch size: 41
2021-10-15 03:29:57,059 INFO [train.py:451] Epoch 10, batch 18890, batch avg loss 0.2240, total avg loss: 0.2155, batch size: 38
2021-10-15 03:30:02,188 INFO [train.py:451] Epoch 10, batch 18900, batch avg loss 0.2266, total avg loss: 0.2143, batch size: 36
2021-10-15 03:30:07,149 INFO [train.py:451] Epoch 10, batch 18910, batch avg loss 0.3076, total avg loss: 0.2161, batch size: 35
2021-10-15 03:30:11,959 INFO [train.py:451] Epoch 10, batch 18920, batch avg loss 0.2270, total avg loss: 0.2171, batch size: 33
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2021-10-15 03:31:31,589 INFO [train.py:483] Epoch 10, valid loss 0.1615, best valid loss: 0.1615 best valid epoch: 10
2021-10-15 03:31:36,498 INFO [train.py:451] Epoch 10, batch 19010, batch avg loss 0.1639, total avg loss: 0.2186, batch size: 29
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2021-10-15 03:37:11,279 INFO [train.py:451] Epoch 10, batch 19690, batch avg loss 0.2238, total avg loss: 0.2105, batch size: 34
2021-10-15 03:37:16,302 INFO [train.py:451] Epoch 10, batch 19700, batch avg loss 0.2084, total avg loss: 0.2121, batch size: 35
2021-10-15 03:37:21,285 INFO [train.py:451] Epoch 10, batch 19710, batch avg loss 0.2280, total avg loss: 0.2121, batch size: 30
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2021-10-15 03:38:10,928 INFO [train.py:451] Epoch 10, batch 19810, batch avg loss 0.1920, total avg loss: 0.2091, batch size: 32
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2021-10-15 03:38:56,138 INFO [train.py:451] Epoch 10, batch 19900, batch avg loss 0.2495, total avg loss: 0.2173, batch size: 33
2021-10-15 03:39:01,043 INFO [train.py:451] Epoch 10, batch 19910, batch avg loss 0.2224, total avg loss: 0.2183, batch size: 34
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2021-10-15 03:39:10,617 INFO [train.py:451] Epoch 10, batch 19930, batch avg loss 0.2466, total avg loss: 0.2205, batch size: 72
2021-10-15 03:39:15,538 INFO [train.py:451] Epoch 10, batch 19940, batch avg loss 0.2410, total avg loss: 0.2203, batch size: 38
2021-10-15 03:39:20,406 INFO [train.py:451] Epoch 10, batch 19950, batch avg loss 0.1859, total avg loss: 0.2195, batch size: 28
2021-10-15 03:39:25,145 INFO [train.py:451] Epoch 10, batch 19960, batch avg loss 0.2259, total avg loss: 0.2205, batch size: 42
2021-10-15 03:39:30,200 INFO [train.py:451] Epoch 10, batch 19970, batch avg loss 0.2109, total avg loss: 0.2204, batch size: 35
2021-10-15 03:39:35,120 INFO [train.py:451] Epoch 10, batch 19980, batch avg loss 0.2320, total avg loss: 0.2205, batch size: 49
2021-10-15 03:39:40,039 INFO [train.py:451] Epoch 10, batch 19990, batch avg loss 0.1944, total avg loss: 0.2210, batch size: 32
2021-10-15 03:39:44,736 INFO [train.py:451] Epoch 10, batch 20000, batch avg loss 0.2505, total avg loss: 0.2215, batch size: 38
2021-10-15 03:40:24,732 INFO [train.py:483] Epoch 10, valid loss 0.1614, best valid loss: 0.1614 best valid epoch: 10
2021-10-15 03:40:29,518 INFO [train.py:451] Epoch 10, batch 20010, batch avg loss 0.2569, total avg loss: 0.2464, batch size: 32
2021-10-15 03:40:34,575 INFO [train.py:451] Epoch 10, batch 20020, batch avg loss 0.2419, total avg loss: 0.2403, batch size: 73
2021-10-15 03:40:39,448 INFO [train.py:451] Epoch 10, batch 20030, batch avg loss 0.2214, total avg loss: 0.2274, batch size: 29
2021-10-15 03:40:44,191 INFO [train.py:451] Epoch 10, batch 20040, batch avg loss 0.2179, total avg loss: 0.2278, batch size: 37
2021-10-15 03:40:49,151 INFO [train.py:451] Epoch 10, batch 20050, batch avg loss 0.2153, total avg loss: 0.2257, batch size: 32
2021-10-15 03:40:54,088 INFO [train.py:451] Epoch 10, batch 20060, batch avg loss 0.1862, total avg loss: 0.2220, batch size: 30
2021-10-15 03:40:58,998 INFO [train.py:451] Epoch 10, batch 20070, batch avg loss 0.2331, total avg loss: 0.2220, batch size: 34
2021-10-15 03:41:03,999 INFO [train.py:451] Epoch 10, batch 20080, batch avg loss 0.2168, total avg loss: 0.2218, batch size: 32
2021-10-15 03:41:08,845 INFO [train.py:451] Epoch 10, batch 20090, batch avg loss 0.2573, total avg loss: 0.2213, batch size: 38
2021-10-15 03:41:13,777 INFO [train.py:451] Epoch 10, batch 20100, batch avg loss 0.1745, total avg loss: 0.2189, batch size: 27
2021-10-15 03:41:18,800 INFO [train.py:451] Epoch 10, batch 20110, batch avg loss 0.2300, total avg loss: 0.2205, batch size: 41
2021-10-15 03:41:23,663 INFO [train.py:451] Epoch 10, batch 20120, batch avg loss 0.2501, total avg loss: 0.2195, batch size: 57
2021-10-15 03:41:28,471 INFO [train.py:451] Epoch 10, batch 20130, batch avg loss 0.2453, total avg loss: 0.2201, batch size: 42
2021-10-15 03:41:33,297 INFO [train.py:451] Epoch 10, batch 20140, batch avg loss 0.2371, total avg loss: 0.2223, batch size: 49
2021-10-15 03:41:38,233 INFO [train.py:451] Epoch 10, batch 20150, batch avg loss 0.3334, total avg loss: 0.2229, batch size: 132
2021-10-15 03:41:43,241 INFO [train.py:451] Epoch 10, batch 20160, batch avg loss 0.2368, total avg loss: 0.2225, batch size: 32
2021-10-15 03:41:48,315 INFO [train.py:451] Epoch 10, batch 20170, batch avg loss 0.1595, total avg loss: 0.2220, batch size: 28
2021-10-15 03:41:53,341 INFO [train.py:451] Epoch 10, batch 20180, batch avg loss 0.2269, total avg loss: 0.2220, batch size: 33
2021-10-15 03:41:58,220 INFO [train.py:451] Epoch 10, batch 20190, batch avg loss 0.1818, total avg loss: 0.2216, batch size: 31
2021-10-15 03:42:03,135 INFO [train.py:451] Epoch 10, batch 20200, batch avg loss 0.2315, total avg loss: 0.2207, batch size: 41
2021-10-15 03:42:08,160 INFO [train.py:451] Epoch 10, batch 20210, batch avg loss 0.2258, total avg loss: 0.2137, batch size: 31
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2021-10-15 03:42:18,157 INFO [train.py:451] Epoch 10, batch 20230, batch avg loss 0.2021, total avg loss: 0.2135, batch size: 28
2021-10-15 03:42:23,098 INFO [train.py:451] Epoch 10, batch 20240, batch avg loss 0.2256, total avg loss: 0.2170, batch size: 37
2021-10-15 03:42:28,143 INFO [train.py:451] Epoch 10, batch 20250, batch avg loss 0.2240, total avg loss: 0.2171, batch size: 31
2021-10-15 03:42:32,926 INFO [train.py:451] Epoch 10, batch 20260, batch avg loss 0.2379, total avg loss: 0.2158, batch size: 42
2021-10-15 03:42:37,851 INFO [train.py:451] Epoch 10, batch 20270, batch avg loss 0.2000, total avg loss: 0.2174, batch size: 33
2021-10-15 03:42:42,831 INFO [train.py:451] Epoch 10, batch 20280, batch avg loss 0.1823, total avg loss: 0.2179, batch size: 31
2021-10-15 03:42:47,766 INFO [train.py:451] Epoch 10, batch 20290, batch avg loss 0.2203, total avg loss: 0.2183, batch size: 38
2021-10-15 03:42:52,586 INFO [train.py:451] Epoch 10, batch 20300, batch avg loss 0.1576, total avg loss: 0.2193, batch size: 32
2021-10-15 03:42:57,697 INFO [train.py:451] Epoch 10, batch 20310, batch avg loss 0.1881, total avg loss: 0.2174, batch size: 32
2021-10-15 03:43:02,670 INFO [train.py:451] Epoch 10, batch 20320, batch avg loss 0.2292, total avg loss: 0.2181, batch size: 34
2021-10-15 03:43:07,595 INFO [train.py:451] Epoch 10, batch 20330, batch avg loss 0.2434, total avg loss: 0.2169, batch size: 34
2021-10-15 03:43:12,604 INFO [train.py:451] Epoch 10, batch 20340, batch avg loss 0.1814, total avg loss: 0.2159, batch size: 33
2021-10-15 03:43:17,357 INFO [train.py:451] Epoch 10, batch 20350, batch avg loss 0.2386, total avg loss: 0.2167, batch size: 39
2021-10-15 03:43:22,291 INFO [train.py:451] Epoch 10, batch 20360, batch avg loss 0.2266, total avg loss: 0.2166, batch size: 38
2021-10-15 03:43:27,131 INFO [train.py:451] Epoch 10, batch 20370, batch avg loss 0.1664, total avg loss: 0.2174, batch size: 30
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2021-10-15 03:49:17,050 INFO [train.py:483] Epoch 10, valid loss 0.1619, best valid loss: 0.1614 best valid epoch: 10
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2021-10-15 03:50:50,430 INFO [checkpoint.py:62] Saving checkpoint to tdnn_lstm_ctc-v2/exp/epoch-10.pt
2021-10-15 03:50:51,257 INFO [train.py:564] epoch 11, lr: 2.5e-05
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2021-10-15 03:56:35,310 INFO [train.py:451] Epoch 11, batch 690, batch avg loss 0.2203, total avg loss: 0.2200, batch size: 41
2021-10-15 03:56:40,136 INFO [train.py:451] Epoch 11, batch 700, batch avg loss 0.1792, total avg loss: 0.2186, batch size: 34
2021-10-15 03:56:44,944 INFO [train.py:451] Epoch 11, batch 710, batch avg loss 0.1770, total avg loss: 0.2192, batch size: 32
2021-10-15 03:56:49,932 INFO [train.py:451] Epoch 11, batch 720, batch avg loss 0.2364, total avg loss: 0.2182, batch size: 42
2021-10-15 03:56:54,779 INFO [train.py:451] Epoch 11, batch 730, batch avg loss 0.2375, total avg loss: 0.2186, batch size: 36
2021-10-15 03:56:59,673 INFO [train.py:451] Epoch 11, batch 740, batch avg loss 0.2321, total avg loss: 0.2192, batch size: 38
2021-10-15 03:57:04,558 INFO [train.py:451] Epoch 11, batch 750, batch avg loss 0.2104, total avg loss: 0.2203, batch size: 38
2021-10-15 03:57:09,437 INFO [train.py:451] Epoch 11, batch 760, batch avg loss 0.1734, total avg loss: 0.2207, batch size: 27
2021-10-15 03:57:14,397 INFO [train.py:451] Epoch 11, batch 770, batch avg loss 0.2290, total avg loss: 0.2205, batch size: 32
2021-10-15 03:57:19,182 INFO [train.py:451] Epoch 11, batch 780, batch avg loss 0.2188, total avg loss: 0.2210, batch size: 37
2021-10-15 03:57:24,052 INFO [train.py:451] Epoch 11, batch 790, batch avg loss 0.2428, total avg loss: 0.2209, batch size: 45
2021-10-15 03:57:28,940 INFO [train.py:451] Epoch 11, batch 800, batch avg loss 0.3035, total avg loss: 0.2209, batch size: 72
2021-10-15 03:57:33,898 INFO [train.py:451] Epoch 11, batch 810, batch avg loss 0.1907, total avg loss: 0.2244, batch size: 27
2021-10-15 03:57:38,692 INFO [train.py:451] Epoch 11, batch 820, batch avg loss 0.2674, total avg loss: 0.2253, batch size: 34
2021-10-15 03:57:43,576 INFO [train.py:451] Epoch 11, batch 830, batch avg loss 0.2448, total avg loss: 0.2257, batch size: 34
2021-10-15 03:57:48,535 INFO [train.py:451] Epoch 11, batch 840, batch avg loss 0.2363, total avg loss: 0.2225, batch size: 38
2021-10-15 03:57:53,331 INFO [train.py:451] Epoch 11, batch 850, batch avg loss 0.1774, total avg loss: 0.2224, batch size: 31
2021-10-15 03:57:58,169 INFO [train.py:451] Epoch 11, batch 860, batch avg loss 0.1841, total avg loss: 0.2217, batch size: 32
2021-10-15 03:58:03,015 INFO [train.py:451] Epoch 11, batch 870, batch avg loss 0.1864, total avg loss: 0.2231, batch size: 29
2021-10-15 03:58:07,821 INFO [train.py:451] Epoch 11, batch 880, batch avg loss 0.1998, total avg loss: 0.2232, batch size: 41
2021-10-15 03:58:12,731 INFO [train.py:451] Epoch 11, batch 890, batch avg loss 0.1895, total avg loss: 0.2230, batch size: 28
2021-10-15 03:58:17,584 INFO [train.py:451] Epoch 11, batch 900, batch avg loss 0.2602, total avg loss: 0.2227, batch size: 42
2021-10-15 03:58:22,547 INFO [train.py:451] Epoch 11, batch 910, batch avg loss 0.3476, total avg loss: 0.2220, batch size: 125
2021-10-15 03:58:27,551 INFO [train.py:451] Epoch 11, batch 920, batch avg loss 0.1862, total avg loss: 0.2212, batch size: 32
2021-10-15 03:58:32,404 INFO [train.py:451] Epoch 11, batch 930, batch avg loss 0.2120, total avg loss: 0.2215, batch size: 34
2021-10-15 03:58:37,298 INFO [train.py:451] Epoch 11, batch 940, batch avg loss 0.2501, total avg loss: 0.2213, batch size: 38
2021-10-15 03:58:42,104 INFO [train.py:451] Epoch 11, batch 950, batch avg loss 0.2899, total avg loss: 0.2216, batch size: 56
2021-10-15 03:58:47,207 INFO [train.py:451] Epoch 11, batch 960, batch avg loss 0.1833, total avg loss: 0.2204, batch size: 30
2021-10-15 03:58:52,046 INFO [train.py:451] Epoch 11, batch 970, batch avg loss 0.2146, total avg loss: 0.2206, batch size: 34
2021-10-15 03:58:57,009 INFO [train.py:451] Epoch 11, batch 980, batch avg loss 0.2181, total avg loss: 0.2206, batch size: 35
2021-10-15 03:59:01,957 INFO [train.py:451] Epoch 11, batch 990, batch avg loss 0.2398, total avg loss: 0.2199, batch size: 35
2021-10-15 03:59:06,827 INFO [train.py:451] Epoch 11, batch 1000, batch avg loss 0.2518, total avg loss: 0.2204, batch size: 41
2021-10-15 03:59:44,436 INFO [train.py:483] Epoch 11, valid loss 0.1618, best valid loss: 0.1614 best valid epoch: 10
2021-10-15 03:59:49,410 INFO [train.py:451] Epoch 11, batch 1010, batch avg loss 0.2095, total avg loss: 0.2049, batch size: 38
2021-10-15 03:59:54,337 INFO [train.py:451] Epoch 11, batch 1020, batch avg loss 0.1620, total avg loss: 0.2052, batch size: 27
2021-10-15 03:59:59,231 INFO [train.py:451] Epoch 11, batch 1030, batch avg loss 0.2419, total avg loss: 0.2089, batch size: 41
2021-10-15 04:00:04,197 INFO [train.py:451] Epoch 11, batch 1040, batch avg loss 0.1928, total avg loss: 0.2072, batch size: 35
2021-10-15 04:00:09,097 INFO [train.py:451] Epoch 11, batch 1050, batch avg loss 0.2030, total avg loss: 0.2079, batch size: 37
2021-10-15 04:00:13,990 INFO [train.py:451] Epoch 11, batch 1060, batch avg loss 0.2025, total avg loss: 0.2104, batch size: 29
2021-10-15 04:00:18,940 INFO [train.py:451] Epoch 11, batch 1070, batch avg loss 0.1898, total avg loss: 0.2109, batch size: 32
2021-10-15 04:00:23,889 INFO [train.py:451] Epoch 11, batch 1080, batch avg loss 0.2344, total avg loss: 0.2125, batch size: 36
2021-10-15 04:00:28,833 INFO [train.py:451] Epoch 11, batch 1090, batch avg loss 0.2086, total avg loss: 0.2133, batch size: 45
2021-10-15 04:00:33,846 INFO [train.py:451] Epoch 11, batch 1100, batch avg loss 0.2315, total avg loss: 0.2124, batch size: 57
2021-10-15 04:00:38,780 INFO [train.py:451] Epoch 11, batch 1110, batch avg loss 0.1617, total avg loss: 0.2124, batch size: 32
2021-10-15 04:00:43,679 INFO [train.py:451] Epoch 11, batch 1120, batch avg loss 0.1925, total avg loss: 0.2131, batch size: 34
2021-10-15 04:00:48,598 INFO [train.py:451] Epoch 11, batch 1130, batch avg loss 0.2713, total avg loss: 0.2150, batch size: 27
2021-10-15 04:00:53,746 INFO [train.py:451] Epoch 11, batch 1140, batch avg loss 0.1653, total avg loss: 0.2148, batch size: 27
2021-10-15 04:00:58,558 INFO [train.py:451] Epoch 11, batch 1150, batch avg loss 0.2191, total avg loss: 0.2156, batch size: 36
2021-10-15 04:01:03,388 INFO [train.py:451] Epoch 11, batch 1160, batch avg loss 0.1902, total avg loss: 0.2165, batch size: 31
2021-10-15 04:01:08,315 INFO [train.py:451] Epoch 11, batch 1170, batch avg loss 0.2355, total avg loss: 0.2162, batch size: 38
2021-10-15 04:01:13,109 INFO [train.py:451] Epoch 11, batch 1180, batch avg loss 0.3124, total avg loss: 0.2167, batch size: 129
2021-10-15 04:01:17,900 INFO [train.py:451] Epoch 11, batch 1190, batch avg loss 0.2291, total avg loss: 0.2166, batch size: 35
2021-10-15 04:01:22,745 INFO [train.py:451] Epoch 11, batch 1200, batch avg loss 0.2107, total avg loss: 0.2161, batch size: 35
2021-10-15 04:01:27,646 INFO [train.py:451] Epoch 11, batch 1210, batch avg loss 0.2137, total avg loss: 0.2169, batch size: 30
2021-10-15 04:01:32,458 INFO [train.py:451] Epoch 11, batch 1220, batch avg loss 0.1566, total avg loss: 0.2166, batch size: 31
2021-10-15 04:01:37,304 INFO [train.py:451] Epoch 11, batch 1230, batch avg loss 0.2186, total avg loss: 0.2224, batch size: 32
2021-10-15 04:01:42,103 INFO [train.py:451] Epoch 11, batch 1240, batch avg loss 0.2791, total avg loss: 0.2248, batch size: 35
2021-10-15 04:01:47,205 INFO [train.py:451] Epoch 11, batch 1250, batch avg loss 0.2583, total avg loss: 0.2199, batch size: 33
2021-10-15 04:01:52,024 INFO [train.py:451] Epoch 11, batch 1260, batch avg loss 0.2076, total avg loss: 0.2209, batch size: 36
2021-10-15 04:01:56,934 INFO [train.py:451] Epoch 11, batch 1270, batch avg loss 0.2088, total avg loss: 0.2194, batch size: 35
2021-10-15 04:02:01,929 INFO [train.py:451] Epoch 11, batch 1280, batch avg loss 0.1760, total avg loss: 0.2196, batch size: 34
2021-10-15 04:02:06,843 INFO [train.py:451] Epoch 11, batch 1290, batch avg loss 0.2266, total avg loss: 0.2188, batch size: 36
2021-10-15 04:02:11,894 INFO [train.py:451] Epoch 11, batch 1300, batch avg loss 0.2425, total avg loss: 0.2180, batch size: 33
2021-10-15 04:02:16,764 INFO [train.py:451] Epoch 11, batch 1310, batch avg loss 0.2005, total avg loss: 0.2176, batch size: 34
2021-10-15 04:02:21,578 INFO [train.py:451] Epoch 11, batch 1320, batch avg loss 0.3250, total avg loss: 0.2188, batch size: 129
2021-10-15 04:02:26,617 INFO [train.py:451] Epoch 11, batch 1330, batch avg loss 0.1958, total avg loss: 0.2187, batch size: 30
2021-10-15 04:02:31,716 INFO [train.py:451] Epoch 11, batch 1340, batch avg loss 0.1798, total avg loss: 0.2184, batch size: 27
2021-10-15 04:02:36,640 INFO [train.py:451] Epoch 11, batch 1350, batch avg loss 0.2024, total avg loss: 0.2191, batch size: 34
2021-10-15 04:02:41,549 INFO [train.py:451] Epoch 11, batch 1360, batch avg loss 0.1926, total avg loss: 0.2196, batch size: 34
2021-10-15 04:02:46,361 INFO [train.py:451] Epoch 11, batch 1370, batch avg loss 0.2286, total avg loss: 0.2193, batch size: 49
2021-10-15 04:02:51,286 INFO [train.py:451] Epoch 11, batch 1380, batch avg loss 0.1840, total avg loss: 0.2189, batch size: 32
2021-10-15 04:02:56,161 INFO [train.py:451] Epoch 11, batch 1390, batch avg loss 0.2300, total avg loss: 0.2192, batch size: 41
2021-10-15 04:03:01,044 INFO [train.py:451] Epoch 11, batch 1400, batch avg loss 0.2452, total avg loss: 0.2195, batch size: 42
2021-10-15 04:03:05,889 INFO [train.py:451] Epoch 11, batch 1410, batch avg loss 0.1582, total avg loss: 0.2093, batch size: 30
2021-10-15 04:03:10,849 INFO [train.py:451] Epoch 11, batch 1420, batch avg loss 0.1671, total avg loss: 0.2161, batch size: 27
2021-10-15 04:03:15,866 INFO [train.py:451] Epoch 11, batch 1430, batch avg loss 0.1526, total avg loss: 0.2139, batch size: 28
2021-10-15 04:03:20,741 INFO [train.py:451] Epoch 11, batch 1440, batch avg loss 0.1863, total avg loss: 0.2186, batch size: 31
2021-10-15 04:03:25,680 INFO [train.py:451] Epoch 11, batch 1450, batch avg loss 0.1697, total avg loss: 0.2165, batch size: 32
2021-10-15 04:03:30,474 INFO [train.py:451] Epoch 11, batch 1460, batch avg loss 0.3126, total avg loss: 0.2186, batch size: 73
2021-10-15 04:03:35,335 INFO [train.py:451] Epoch 11, batch 1470, batch avg loss 0.1815, total avg loss: 0.2203, batch size: 27
2021-10-15 04:03:40,399 INFO [train.py:451] Epoch 11, batch 1480, batch avg loss 0.2498, total avg loss: 0.2179, batch size: 36
2021-10-15 04:03:45,192 INFO [train.py:451] Epoch 11, batch 1490, batch avg loss 0.2370, total avg loss: 0.2170, batch size: 45
2021-10-15 04:03:49,916 INFO [train.py:451] Epoch 11, batch 1500, batch avg loss 0.2152, total avg loss: 0.2187, batch size: 33
2021-10-15 04:03:54,816 INFO [train.py:451] Epoch 11, batch 1510, batch avg loss 0.2197, total avg loss: 0.2190, batch size: 35
2021-10-15 04:03:59,723 INFO [train.py:451] Epoch 11, batch 1520, batch avg loss 0.2393, total avg loss: 0.2192, batch size: 35
2021-10-15 04:04:04,805 INFO [train.py:451] Epoch 11, batch 1530, batch avg loss 0.1809, total avg loss: 0.2177, batch size: 33
2021-10-15 04:04:09,682 INFO [train.py:451] Epoch 11, batch 1540, batch avg loss 0.1923, total avg loss: 0.2174, batch size: 27
2021-10-15 04:04:14,607 INFO [train.py:451] Epoch 11, batch 1550, batch avg loss 0.2321, total avg loss: 0.2174, batch size: 33
2021-10-15 04:04:19,412 INFO [train.py:451] Epoch 11, batch 1560, batch avg loss 0.2018, total avg loss: 0.2189, batch size: 37
2021-10-15 04:04:24,262 INFO [train.py:451] Epoch 11, batch 1570, batch avg loss 0.1615, total avg loss: 0.2195, batch size: 28
2021-10-15 04:04:29,253 INFO [train.py:451] Epoch 11, batch 1580, batch avg loss 0.1690, total avg loss: 0.2188, batch size: 28
2021-10-15 04:04:34,141 INFO [train.py:451] Epoch 11, batch 1590, batch avg loss 0.2030, total avg loss: 0.2187, batch size: 36
2021-10-15 04:04:39,165 INFO [train.py:451] Epoch 11, batch 1600, batch avg loss 0.2016, total avg loss: 0.2181, batch size: 30
2021-10-15 04:04:43,876 INFO [train.py:451] Epoch 11, batch 1610, batch avg loss 0.2580, total avg loss: 0.2424, batch size: 38
2021-10-15 04:04:48,752 INFO [train.py:451] Epoch 11, batch 1620, batch avg loss 0.2479, total avg loss: 0.2295, batch size: 49
2021-10-15 04:04:53,689 INFO [train.py:451] Epoch 11, batch 1630, batch avg loss 0.2240, total avg loss: 0.2259, batch size: 38
2021-10-15 04:04:58,592 INFO [train.py:451] Epoch 11, batch 1640, batch avg loss 0.2360, total avg loss: 0.2225, batch size: 37
2021-10-15 04:05:03,429 INFO [train.py:451] Epoch 11, batch 1650, batch avg loss 0.2423, total avg loss: 0.2271, batch size: 49
2021-10-15 04:05:08,273 INFO [train.py:451] Epoch 11, batch 1660, batch avg loss 0.2935, total avg loss: 0.2271, batch size: 132
2021-10-15 04:05:13,208 INFO [train.py:451] Epoch 11, batch 1670, batch avg loss 0.2149, total avg loss: 0.2266, batch size: 31
2021-10-15 04:05:18,074 INFO [train.py:451] Epoch 11, batch 1680, batch avg loss 0.2007, total avg loss: 0.2256, batch size: 29
2021-10-15 04:05:22,905 INFO [train.py:451] Epoch 11, batch 1690, batch avg loss 0.2430, total avg loss: 0.2236, batch size: 38
2021-10-15 04:05:27,777 INFO [train.py:451] Epoch 11, batch 1700, batch avg loss 0.1997, total avg loss: 0.2232, batch size: 37
2021-10-15 04:05:32,675 INFO [train.py:451] Epoch 11, batch 1710, batch avg loss 0.1873, total avg loss: 0.2218, batch size: 31
2021-10-15 04:05:37,624 INFO [train.py:451] Epoch 11, batch 1720, batch avg loss 0.1870, total avg loss: 0.2213, batch size: 33
2021-10-15 04:05:42,665 INFO [train.py:451] Epoch 11, batch 1730, batch avg loss 0.2059, total avg loss: 0.2203, batch size: 32
2021-10-15 04:05:47,433 INFO [train.py:451] Epoch 11, batch 1740, batch avg loss 0.1945, total avg loss: 0.2217, batch size: 33
2021-10-15 04:05:52,452 INFO [train.py:451] Epoch 11, batch 1750, batch avg loss 0.1825, total avg loss: 0.2203, batch size: 31
2021-10-15 04:05:57,430 INFO [train.py:451] Epoch 11, batch 1760, batch avg loss 0.1999, total avg loss: 0.2194, batch size: 32
2021-10-15 04:06:02,486 INFO [train.py:451] Epoch 11, batch 1770, batch avg loss 0.2098, total avg loss: 0.2183, batch size: 29
2021-10-15 04:06:07,437 INFO [train.py:451] Epoch 11, batch 1780, batch avg loss 0.2475, total avg loss: 0.2179, batch size: 38
2021-10-15 04:06:12,279 INFO [train.py:451] Epoch 11, batch 1790, batch avg loss 0.2094, total avg loss: 0.2180, batch size: 29
2021-10-15 04:06:17,358 INFO [train.py:451] Epoch 11, batch 1800, batch avg loss 0.2072, total avg loss: 0.2178, batch size: 34
2021-10-15 04:06:22,371 INFO [train.py:451] Epoch 11, batch 1810, batch avg loss 0.2459, total avg loss: 0.2064, batch size: 49
2021-10-15 04:06:27,408 INFO [train.py:451] Epoch 11, batch 1820, batch avg loss 0.2602, total avg loss: 0.2152, batch size: 35
2021-10-15 04:06:32,224 INFO [train.py:451] Epoch 11, batch 1830, batch avg loss 0.3038, total avg loss: 0.2240, batch size: 132
2021-10-15 04:06:37,103 INFO [train.py:451] Epoch 11, batch 1840, batch avg loss 0.2239, total avg loss: 0.2238, batch size: 35
2021-10-15 04:06:42,371 INFO [train.py:451] Epoch 11, batch 1850, batch avg loss 0.2654, total avg loss: 0.2239, batch size: 34
2021-10-15 04:06:47,019 INFO [train.py:451] Epoch 11, batch 1860, batch avg loss 0.2197, total avg loss: 0.2235, batch size: 42
2021-10-15 04:06:51,833 INFO [train.py:451] Epoch 11, batch 1870, batch avg loss 0.2163, total avg loss: 0.2232, batch size: 30
2021-10-15 04:06:56,779 INFO [train.py:451] Epoch 11, batch 1880, batch avg loss 0.2073, total avg loss: 0.2199, batch size: 30
2021-10-15 04:07:01,728 INFO [train.py:451] Epoch 11, batch 1890, batch avg loss 0.2186, total avg loss: 0.2198, batch size: 33
2021-10-15 04:07:06,599 INFO [train.py:451] Epoch 11, batch 1900, batch avg loss 0.2320, total avg loss: 0.2195, batch size: 49
2021-10-15 04:07:11,552 INFO [train.py:451] Epoch 11, batch 1910, batch avg loss 0.1747, total avg loss: 0.2192, batch size: 30
2021-10-15 04:07:16,521 INFO [train.py:451] Epoch 11, batch 1920, batch avg loss 0.2506, total avg loss: 0.2199, batch size: 37
2021-10-15 04:07:21,545 INFO [train.py:451] Epoch 11, batch 1930, batch avg loss 0.2327, total avg loss: 0.2200, batch size: 34
2021-10-15 04:07:26,396 INFO [train.py:451] Epoch 11, batch 1940, batch avg loss 0.2367, total avg loss: 0.2186, batch size: 38
2021-10-15 04:07:31,490 INFO [train.py:451] Epoch 11, batch 1950, batch avg loss 0.2027, total avg loss: 0.2185, batch size: 35
2021-10-15 04:07:36,661 INFO [train.py:451] Epoch 11, batch 1960, batch avg loss 0.2322, total avg loss: 0.2178, batch size: 35
2021-10-15 04:07:41,476 INFO [train.py:451] Epoch 11, batch 1970, batch avg loss 0.1953, total avg loss: 0.2197, batch size: 36
2021-10-15 04:07:46,463 INFO [train.py:451] Epoch 11, batch 1980, batch avg loss 0.2599, total avg loss: 0.2196, batch size: 39
2021-10-15 04:07:51,251 INFO [train.py:451] Epoch 11, batch 1990, batch avg loss 0.2352, total avg loss: 0.2208, batch size: 38
2021-10-15 04:07:56,131 INFO [train.py:451] Epoch 11, batch 2000, batch avg loss 0.3161, total avg loss: 0.2208, batch size: 129
2021-10-15 04:08:35,974 INFO [train.py:483] Epoch 11, valid loss 0.1615, best valid loss: 0.1614 best valid epoch: 10
2021-10-15 04:08:40,912 INFO [train.py:451] Epoch 11, batch 2010, batch avg loss 0.2568, total avg loss: 0.2171, batch size: 31
2021-10-15 04:08:45,948 INFO [train.py:451] Epoch 11, batch 2020, batch avg loss 0.1795, total avg loss: 0.2061, batch size: 39
2021-10-15 04:08:50,828 INFO [train.py:451] Epoch 11, batch 2030, batch avg loss 0.2362, total avg loss: 0.2073, batch size: 57
2021-10-15 04:08:55,770 INFO [train.py:451] Epoch 11, batch 2040, batch avg loss 0.2221, total avg loss: 0.2112, batch size: 32
2021-10-15 04:09:00,795 INFO [train.py:451] Epoch 11, batch 2050, batch avg loss 0.2493, total avg loss: 0.2138, batch size: 34
2021-10-15 04:09:05,810 INFO [train.py:451] Epoch 11, batch 2060, batch avg loss 0.2041, total avg loss: 0.2159, batch size: 34
2021-10-15 04:09:06,435 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "fcf6e676-6477-23e1-3a72-de78326d3ad2" will not be mixed in.
2021-10-15 04:09:10,835 INFO [train.py:451] Epoch 11, batch 2070, batch avg loss 0.2025, total avg loss: 0.2158, batch size: 29
2021-10-15 04:09:15,915 INFO [train.py:451] Epoch 11, batch 2080, batch avg loss 0.1965, total avg loss: 0.2148, batch size: 31
2021-10-15 04:09:20,722 INFO [train.py:451] Epoch 11, batch 2090, batch avg loss 0.2116, total avg loss: 0.2142, batch size: 36
2021-10-15 04:09:25,599 INFO [train.py:451] Epoch 11, batch 2100, batch avg loss 0.2220, total avg loss: 0.2151, batch size: 36
2021-10-15 04:09:30,564 INFO [train.py:451] Epoch 11, batch 2110, batch avg loss 0.2079, total avg loss: 0.2153, batch size: 31
2021-10-15 04:09:35,369 INFO [train.py:451] Epoch 11, batch 2120, batch avg loss 0.1902, total avg loss: 0.2149, batch size: 27
2021-10-15 04:09:40,396 INFO [train.py:451] Epoch 11, batch 2130, batch avg loss 0.1859, total avg loss: 0.2153, batch size: 35
2021-10-15 04:09:45,271 INFO [train.py:451] Epoch 11, batch 2140, batch avg loss 0.2674, total avg loss: 0.2156, batch size: 72
2021-10-15 04:09:50,157 INFO [train.py:451] Epoch 11, batch 2150, batch avg loss 0.2270, total avg loss: 0.2149, batch size: 42
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2021-10-15 04:17:28,229 INFO [train.py:483] Epoch 11, valid loss 0.1610, best valid loss: 0.1610 best valid epoch: 11
2021-10-15 04:17:33,068 INFO [train.py:451] Epoch 11, batch 3010, batch avg loss 0.3411, total avg loss: 0.2139, batch size: 130
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2021-10-15 04:22:40,844 INFO [train.py:451] Epoch 11, batch 3620, batch avg loss 0.1585, total avg loss: 0.2061, batch size: 30
2021-10-15 04:22:45,794 INFO [train.py:451] Epoch 11, batch 3630, batch avg loss 0.1849, total avg loss: 0.2057, batch size: 30
2021-10-15 04:22:50,652 INFO [train.py:451] Epoch 11, batch 3640, batch avg loss 0.2514, total avg loss: 0.2083, batch size: 56
2021-10-15 04:22:55,407 INFO [train.py:451] Epoch 11, batch 3650, batch avg loss 0.1912, total avg loss: 0.2128, batch size: 33
2021-10-15 04:23:00,358 INFO [train.py:451] Epoch 11, batch 3660, batch avg loss 0.1833, total avg loss: 0.2140, batch size: 30
2021-10-15 04:23:05,386 INFO [train.py:451] Epoch 11, batch 3670, batch avg loss 0.2308, total avg loss: 0.2129, batch size: 49
2021-10-15 04:23:10,328 INFO [train.py:451] Epoch 11, batch 3680, batch avg loss 0.1602, total avg loss: 0.2145, batch size: 30
2021-10-15 04:23:15,292 INFO [train.py:451] Epoch 11, batch 3690, batch avg loss 0.1710, total avg loss: 0.2136, batch size: 31
2021-10-15 04:23:20,126 INFO [train.py:451] Epoch 11, batch 3700, batch avg loss 0.1880, total avg loss: 0.2150, batch size: 34
2021-10-15 04:23:25,038 INFO [train.py:451] Epoch 11, batch 3710, batch avg loss 0.2070, total avg loss: 0.2171, batch size: 35
2021-10-15 04:23:30,101 INFO [train.py:451] Epoch 11, batch 3720, batch avg loss 0.1875, total avg loss: 0.2161, batch size: 31
2021-10-15 04:23:35,113 INFO [train.py:451] Epoch 11, batch 3730, batch avg loss 0.1886, total avg loss: 0.2151, batch size: 32
2021-10-15 04:23:40,019 INFO [train.py:451] Epoch 11, batch 3740, batch avg loss 0.2468, total avg loss: 0.2141, batch size: 72
2021-10-15 04:23:44,908 INFO [train.py:451] Epoch 11, batch 3750, batch avg loss 0.1906, total avg loss: 0.2148, batch size: 29
2021-10-15 04:23:50,049 INFO [train.py:451] Epoch 11, batch 3760, batch avg loss 0.1743, total avg loss: 0.2138, batch size: 34
2021-10-15 04:23:55,102 INFO [train.py:451] Epoch 11, batch 3770, batch avg loss 0.2090, total avg loss: 0.2132, batch size: 32
2021-10-15 04:23:59,840 INFO [train.py:451] Epoch 11, batch 3780, batch avg loss 0.2095, total avg loss: 0.2132, batch size: 49
2021-10-15 04:24:04,670 INFO [train.py:451] Epoch 11, batch 3790, batch avg loss 0.3414, total avg loss: 0.2146, batch size: 131
2021-10-15 04:24:09,586 INFO [train.py:451] Epoch 11, batch 3800, batch avg loss 0.2017, total avg loss: 0.2145, batch size: 30
2021-10-15 04:24:14,467 INFO [train.py:451] Epoch 11, batch 3810, batch avg loss 0.1822, total avg loss: 0.2163, batch size: 32
2021-10-15 04:24:19,275 INFO [train.py:451] Epoch 11, batch 3820, batch avg loss 0.1941, total avg loss: 0.2156, batch size: 35
2021-10-15 04:24:24,055 INFO [train.py:451] Epoch 11, batch 3830, batch avg loss 0.2209, total avg loss: 0.2214, batch size: 36
2021-10-15 04:24:29,092 INFO [train.py:451] Epoch 11, batch 3840, batch avg loss 0.2033, total avg loss: 0.2174, batch size: 34
2021-10-15 04:24:34,088 INFO [train.py:451] Epoch 11, batch 3850, batch avg loss 0.2531, total avg loss: 0.2199, batch size: 49
2021-10-15 04:24:38,963 INFO [train.py:451] Epoch 11, batch 3860, batch avg loss 0.2263, total avg loss: 0.2215, batch size: 28
2021-10-15 04:24:43,853 INFO [train.py:451] Epoch 11, batch 3870, batch avg loss 0.1853, total avg loss: 0.2194, batch size: 30
2021-10-15 04:24:48,763 INFO [train.py:451] Epoch 11, batch 3880, batch avg loss 0.2182, total avg loss: 0.2183, batch size: 33
2021-10-15 04:24:53,737 INFO [train.py:451] Epoch 11, batch 3890, batch avg loss 0.2471, total avg loss: 0.2178, batch size: 41
2021-10-15 04:24:58,710 INFO [train.py:451] Epoch 11, batch 3900, batch avg loss 0.2003, total avg loss: 0.2165, batch size: 31
2021-10-15 04:25:03,837 INFO [train.py:451] Epoch 11, batch 3910, batch avg loss 0.2182, total avg loss: 0.2159, batch size: 33
2021-10-15 04:25:08,859 INFO [train.py:451] Epoch 11, batch 3920, batch avg loss 0.1888, total avg loss: 0.2156, batch size: 34
2021-10-15 04:25:13,695 INFO [train.py:451] Epoch 11, batch 3930, batch avg loss 0.3562, total avg loss: 0.2152, batch size: 125
2021-10-15 04:25:18,618 INFO [train.py:451] Epoch 11, batch 3940, batch avg loss 0.1830, total avg loss: 0.2149, batch size: 29
2021-10-15 04:25:23,685 INFO [train.py:451] Epoch 11, batch 3950, batch avg loss 0.1865, total avg loss: 0.2150, batch size: 30
2021-10-15 04:25:28,475 INFO [train.py:451] Epoch 11, batch 3960, batch avg loss 0.1940, total avg loss: 0.2150, batch size: 30
2021-10-15 04:25:33,321 INFO [train.py:451] Epoch 11, batch 3970, batch avg loss 0.1805, total avg loss: 0.2144, batch size: 30
2021-10-15 04:25:38,286 INFO [train.py:451] Epoch 11, batch 3980, batch avg loss 0.2117, total avg loss: 0.2144, batch size: 34
2021-10-15 04:25:43,294 INFO [train.py:451] Epoch 11, batch 3990, batch avg loss 0.1999, total avg loss: 0.2137, batch size: 32
2021-10-15 04:25:48,023 INFO [train.py:451] Epoch 11, batch 4000, batch avg loss 0.2167, total avg loss: 0.2141, batch size: 31
2021-10-15 04:26:25,521 INFO [train.py:483] Epoch 11, valid loss 0.1612, best valid loss: 0.1610 best valid epoch: 11
2021-10-15 04:26:30,474 INFO [train.py:451] Epoch 11, batch 4010, batch avg loss 0.1886, total avg loss: 0.2153, batch size: 32
2021-10-15 04:26:35,430 INFO [train.py:451] Epoch 11, batch 4020, batch avg loss 0.2103, total avg loss: 0.2150, batch size: 32
2021-10-15 04:26:40,076 INFO [train.py:451] Epoch 11, batch 4030, batch avg loss 0.2571, total avg loss: 0.2249, batch size: 44
2021-10-15 04:26:45,056 INFO [train.py:451] Epoch 11, batch 4040, batch avg loss 0.1501, total avg loss: 0.2191, batch size: 30
2021-10-15 04:26:49,838 INFO [train.py:451] Epoch 11, batch 4050, batch avg loss 0.1571, total avg loss: 0.2197, batch size: 27
2021-10-15 04:26:54,818 INFO [train.py:451] Epoch 11, batch 4060, batch avg loss 0.2102, total avg loss: 0.2175, batch size: 34
2021-10-15 04:26:59,732 INFO [train.py:451] Epoch 11, batch 4070, batch avg loss 0.2429, total avg loss: 0.2144, batch size: 35
2021-10-15 04:27:04,671 INFO [train.py:451] Epoch 11, batch 4080, batch avg loss 0.3613, total avg loss: 0.2162, batch size: 128
2021-10-15 04:27:09,641 INFO [train.py:451] Epoch 11, batch 4090, batch avg loss 0.2229, total avg loss: 0.2170, batch size: 35
2021-10-15 04:27:14,673 INFO [train.py:451] Epoch 11, batch 4100, batch avg loss 0.2323, total avg loss: 0.2172, batch size: 36
2021-10-15 04:27:19,435 INFO [train.py:451] Epoch 11, batch 4110, batch avg loss 0.2296, total avg loss: 0.2177, batch size: 31
2021-10-15 04:27:24,211 INFO [train.py:451] Epoch 11, batch 4120, batch avg loss 0.2285, total avg loss: 0.2183, batch size: 38
2021-10-15 04:27:29,122 INFO [train.py:451] Epoch 11, batch 4130, batch avg loss 0.2048, total avg loss: 0.2177, batch size: 31
2021-10-15 04:27:34,136 INFO [train.py:451] Epoch 11, batch 4140, batch avg loss 0.2283, total avg loss: 0.2184, batch size: 30
2021-10-15 04:27:38,859 INFO [train.py:451] Epoch 11, batch 4150, batch avg loss 0.2282, total avg loss: 0.2185, batch size: 35
2021-10-15 04:27:43,734 INFO [train.py:451] Epoch 11, batch 4160, batch avg loss 0.2231, total avg loss: 0.2184, batch size: 36
2021-10-15 04:27:48,767 INFO [train.py:451] Epoch 11, batch 4170, batch avg loss 0.1956, total avg loss: 0.2177, batch size: 30
2021-10-15 04:27:53,706 INFO [train.py:451] Epoch 11, batch 4180, batch avg loss 0.2059, total avg loss: 0.2179, batch size: 38
2021-10-15 04:27:58,655 INFO [train.py:451] Epoch 11, batch 4190, batch avg loss 0.2232, total avg loss: 0.2177, batch size: 35
2021-10-15 04:28:03,577 INFO [train.py:451] Epoch 11, batch 4200, batch avg loss 0.2114, total avg loss: 0.2173, batch size: 35
2021-10-15 04:28:08,305 INFO [train.py:451] Epoch 11, batch 4210, batch avg loss 0.1745, total avg loss: 0.2359, batch size: 28
2021-10-15 04:28:13,163 INFO [train.py:451] Epoch 11, batch 4220, batch avg loss 0.2008, total avg loss: 0.2267, batch size: 35
2021-10-15 04:28:17,966 INFO [train.py:451] Epoch 11, batch 4230, batch avg loss 0.2046, total avg loss: 0.2164, batch size: 42
2021-10-15 04:28:22,805 INFO [train.py:451] Epoch 11, batch 4240, batch avg loss 0.2160, total avg loss: 0.2138, batch size: 39
2021-10-15 04:28:27,672 INFO [train.py:451] Epoch 11, batch 4250, batch avg loss 0.1668, total avg loss: 0.2136, batch size: 31
2021-10-15 04:28:32,598 INFO [train.py:451] Epoch 11, batch 4260, batch avg loss 0.2005, total avg loss: 0.2138, batch size: 36
2021-10-15 04:28:37,434 INFO [train.py:451] Epoch 11, batch 4270, batch avg loss 0.1569, total avg loss: 0.2153, batch size: 35
2021-10-15 04:28:42,416 INFO [train.py:451] Epoch 11, batch 4280, batch avg loss 0.2006, total avg loss: 0.2134, batch size: 49
2021-10-15 04:28:47,236 INFO [train.py:451] Epoch 11, batch 4290, batch avg loss 0.1854, total avg loss: 0.2139, batch size: 31
2021-10-15 04:28:52,304 INFO [train.py:451] Epoch 11, batch 4300, batch avg loss 0.2359, total avg loss: 0.2139, batch size: 31
2021-10-15 04:28:57,172 INFO [train.py:451] Epoch 11, batch 4310, batch avg loss 0.2745, total avg loss: 0.2143, batch size: 72
2021-10-15 04:29:02,091 INFO [train.py:451] Epoch 11, batch 4320, batch avg loss 0.1957, total avg loss: 0.2142, batch size: 34
2021-10-15 04:29:07,118 INFO [train.py:451] Epoch 11, batch 4330, batch avg loss 0.1782, total avg loss: 0.2147, batch size: 29
2021-10-15 04:29:12,058 INFO [train.py:451] Epoch 11, batch 4340, batch avg loss 0.2092, total avg loss: 0.2151, batch size: 34
2021-10-15 04:29:17,053 INFO [train.py:451] Epoch 11, batch 4350, batch avg loss 0.2232, total avg loss: 0.2146, batch size: 31
2021-10-15 04:29:22,046 INFO [train.py:451] Epoch 11, batch 4360, batch avg loss 0.2104, total avg loss: 0.2145, batch size: 39
2021-10-15 04:29:26,916 INFO [train.py:451] Epoch 11, batch 4370, batch avg loss 0.2307, total avg loss: 0.2136, batch size: 45
2021-10-15 04:29:31,873 INFO [train.py:451] Epoch 11, batch 4380, batch avg loss 0.2064, total avg loss: 0.2151, batch size: 34
2021-10-15 04:29:36,758 INFO [train.py:451] Epoch 11, batch 4390, batch avg loss 0.2281, total avg loss: 0.2152, batch size: 30
2021-10-15 04:29:41,630 INFO [train.py:451] Epoch 11, batch 4400, batch avg loss 0.1733, total avg loss: 0.2155, batch size: 34
2021-10-15 04:29:46,629 INFO [train.py:451] Epoch 11, batch 4410, batch avg loss 0.3146, total avg loss: 0.2189, batch size: 123
2021-10-15 04:29:51,644 INFO [train.py:451] Epoch 11, batch 4420, batch avg loss 0.2353, total avg loss: 0.2194, batch size: 28
2021-10-15 04:29:56,638 INFO [train.py:451] Epoch 11, batch 4430, batch avg loss 0.1860, total avg loss: 0.2166, batch size: 30
2021-10-15 04:30:01,587 INFO [train.py:451] Epoch 11, batch 4440, batch avg loss 0.1956, total avg loss: 0.2177, batch size: 38
2021-10-15 04:30:06,464 INFO [train.py:451] Epoch 11, batch 4450, batch avg loss 0.2877, total avg loss: 0.2183, batch size: 45
2021-10-15 04:30:10,205 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "ac836145-c234-4f6b-9e34-b463f500bd12" will not be mixed in.
2021-10-15 04:30:11,423 INFO [train.py:451] Epoch 11, batch 4460, batch avg loss 0.1997, total avg loss: 0.2201, batch size: 33
2021-10-15 04:30:16,123 INFO [train.py:451] Epoch 11, batch 4470, batch avg loss 0.2501, total avg loss: 0.2225, batch size: 45
2021-10-15 04:30:20,900 INFO [train.py:451] Epoch 11, batch 4480, batch avg loss 0.2400, total avg loss: 0.2223, batch size: 35
2021-10-15 04:30:25,714 INFO [train.py:451] Epoch 11, batch 4490, batch avg loss 0.2240, total avg loss: 0.2247, batch size: 32
2021-10-15 04:30:30,551 INFO [train.py:451] Epoch 11, batch 4500, batch avg loss 0.2387, total avg loss: 0.2243, batch size: 42
2021-10-15 04:30:35,439 INFO [train.py:451] Epoch 11, batch 4510, batch avg loss 0.2619, total avg loss: 0.2241, batch size: 34
2021-10-15 04:30:40,300 INFO [train.py:451] Epoch 11, batch 4520, batch avg loss 0.2103, total avg loss: 0.2238, batch size: 37
2021-10-15 04:30:45,197 INFO [train.py:451] Epoch 11, batch 4530, batch avg loss 0.3082, total avg loss: 0.2235, batch size: 128
2021-10-15 04:30:49,972 INFO [train.py:451] Epoch 11, batch 4540, batch avg loss 0.2071, total avg loss: 0.2237, batch size: 34
2021-10-15 04:30:54,895 INFO [train.py:451] Epoch 11, batch 4550, batch avg loss 0.2154, total avg loss: 0.2231, batch size: 36
2021-10-15 04:30:59,847 INFO [train.py:451] Epoch 11, batch 4560, batch avg loss 0.2749, total avg loss: 0.2243, batch size: 42
2021-10-15 04:31:04,730 INFO [train.py:451] Epoch 11, batch 4570, batch avg loss 0.2300, total avg loss: 0.2241, batch size: 35
2021-10-15 04:31:09,781 INFO [train.py:451] Epoch 11, batch 4580, batch avg loss 0.1951, total avg loss: 0.2233, batch size: 34
2021-10-15 04:31:14,775 INFO [train.py:451] Epoch 11, batch 4590, batch avg loss 0.2218, total avg loss: 0.2226, batch size: 28
2021-10-15 04:31:19,730 INFO [train.py:451] Epoch 11, batch 4600, batch avg loss 0.1810, total avg loss: 0.2211, batch size: 41
2021-10-15 04:31:24,682 INFO [train.py:451] Epoch 11, batch 4610, batch avg loss 0.3036, total avg loss: 0.2269, batch size: 130
2021-10-15 04:31:29,610 INFO [train.py:451] Epoch 11, batch 4620, batch avg loss 0.2238, total avg loss: 0.2295, batch size: 38
2021-10-15 04:31:34,490 INFO [train.py:451] Epoch 11, batch 4630, batch avg loss 0.2963, total avg loss: 0.2282, batch size: 73
2021-10-15 04:31:39,568 INFO [train.py:451] Epoch 11, batch 4640, batch avg loss 0.2202, total avg loss: 0.2249, batch size: 35
2021-10-15 04:31:44,585 INFO [train.py:451] Epoch 11, batch 4650, batch avg loss 0.2192, total avg loss: 0.2251, batch size: 33
2021-10-15 04:31:49,516 INFO [train.py:451] Epoch 11, batch 4660, batch avg loss 0.2386, total avg loss: 0.2237, batch size: 36
2021-10-15 04:31:54,612 INFO [train.py:451] Epoch 11, batch 4670, batch avg loss 0.2337, total avg loss: 0.2205, batch size: 41
2021-10-15 04:31:59,508 INFO [train.py:451] Epoch 11, batch 4680, batch avg loss 0.2050, total avg loss: 0.2199, batch size: 41
2021-10-15 04:32:04,360 INFO [train.py:451] Epoch 11, batch 4690, batch avg loss 0.1796, total avg loss: 0.2183, batch size: 33
2021-10-15 04:32:09,205 INFO [train.py:451] Epoch 11, batch 4700, batch avg loss 0.2189, total avg loss: 0.2176, batch size: 38
2021-10-15 04:32:14,227 INFO [train.py:451] Epoch 11, batch 4710, batch avg loss 0.1647, total avg loss: 0.2155, batch size: 34
2021-10-15 04:32:19,090 INFO [train.py:451] Epoch 11, batch 4720, batch avg loss 0.2328, total avg loss: 0.2176, batch size: 36
2021-10-15 04:32:24,011 INFO [train.py:451] Epoch 11, batch 4730, batch avg loss 0.2373, total avg loss: 0.2171, batch size: 42
2021-10-15 04:32:28,873 INFO [train.py:451] Epoch 11, batch 4740, batch avg loss 0.3048, total avg loss: 0.2183, batch size: 128
2021-10-15 04:32:34,022 INFO [train.py:451] Epoch 11, batch 4750, batch avg loss 0.1953, total avg loss: 0.2166, batch size: 35
2021-10-15 04:32:38,743 INFO [train.py:451] Epoch 11, batch 4760, batch avg loss 0.1883, total avg loss: 0.2178, batch size: 30
2021-10-15 04:32:43,761 INFO [train.py:451] Epoch 11, batch 4770, batch avg loss 0.2175, total avg loss: 0.2173, batch size: 34
2021-10-15 04:32:48,604 INFO [train.py:451] Epoch 11, batch 4780, batch avg loss 0.2286, total avg loss: 0.2180, batch size: 56
2021-10-15 04:32:53,467 INFO [train.py:451] Epoch 11, batch 4790, batch avg loss 0.2659, total avg loss: 0.2185, batch size: 49
2021-10-15 04:32:58,300 INFO [train.py:451] Epoch 11, batch 4800, batch avg loss 0.2035, total avg loss: 0.2196, batch size: 36
2021-10-15 04:33:03,115 INFO [train.py:451] Epoch 11, batch 4810, batch avg loss 0.2102, total avg loss: 0.2209, batch size: 34
2021-10-15 04:33:07,874 INFO [train.py:451] Epoch 11, batch 4820, batch avg loss 0.2324, total avg loss: 0.2203, batch size: 45
2021-10-15 04:33:12,810 INFO [train.py:451] Epoch 11, batch 4830, batch avg loss 0.2511, total avg loss: 0.2136, batch size: 73
2021-10-15 04:33:17,797 INFO [train.py:451] Epoch 11, batch 4840, batch avg loss 0.2005, total avg loss: 0.2107, batch size: 36
2021-10-15 04:33:22,700 INFO [train.py:451] Epoch 11, batch 4850, batch avg loss 0.2245, total avg loss: 0.2114, batch size: 39
2021-10-15 04:33:27,624 INFO [train.py:451] Epoch 11, batch 4860, batch avg loss 0.2320, total avg loss: 0.2141, batch size: 38
2021-10-15 04:33:32,695 INFO [train.py:451] Epoch 11, batch 4870, batch avg loss 0.2281, total avg loss: 0.2141, batch size: 56
2021-10-15 04:33:37,794 INFO [train.py:451] Epoch 11, batch 4880, batch avg loss 0.2126, total avg loss: 0.2133, batch size: 35
2021-10-15 04:33:42,825 INFO [train.py:451] Epoch 11, batch 4890, batch avg loss 0.1500, total avg loss: 0.2139, batch size: 27
2021-10-15 04:33:47,548 INFO [train.py:451] Epoch 11, batch 4900, batch avg loss 0.2246, total avg loss: 0.2155, batch size: 35
2021-10-15 04:33:52,464 INFO [train.py:451] Epoch 11, batch 4910, batch avg loss 0.2346, total avg loss: 0.2169, batch size: 33
2021-10-15 04:33:57,313 INFO [train.py:451] Epoch 11, batch 4920, batch avg loss 0.2315, total avg loss: 0.2169, batch size: 42
2021-10-15 04:34:02,189 INFO [train.py:451] Epoch 11, batch 4930, batch avg loss 0.2738, total avg loss: 0.2162, batch size: 72
2021-10-15 04:34:07,018 INFO [train.py:451] Epoch 11, batch 4940, batch avg loss 0.2208, total avg loss: 0.2169, batch size: 33
2021-10-15 04:34:11,730 INFO [train.py:451] Epoch 11, batch 4950, batch avg loss 0.2052, total avg loss: 0.2186, batch size: 38
2021-10-15 04:34:16,625 INFO [train.py:451] Epoch 11, batch 4960, batch avg loss 0.2675, total avg loss: 0.2185, batch size: 45
2021-10-15 04:34:21,630 INFO [train.py:451] Epoch 11, batch 4970, batch avg loss 0.2357, total avg loss: 0.2186, batch size: 36
2021-10-15 04:34:26,557 INFO [train.py:451] Epoch 11, batch 4980, batch avg loss 0.2517, total avg loss: 0.2189, batch size: 72
2021-10-15 04:34:31,507 INFO [train.py:451] Epoch 11, batch 4990, batch avg loss 0.2017, total avg loss: 0.2188, batch size: 32
2021-10-15 04:34:36,517 INFO [train.py:451] Epoch 11, batch 5000, batch avg loss 0.2015, total avg loss: 0.2187, batch size: 28
2021-10-15 04:35:16,181 INFO [train.py:483] Epoch 11, valid loss 0.1616, best valid loss: 0.1610 best valid epoch: 11
2021-10-15 04:35:20,982 INFO [train.py:451] Epoch 11, batch 5010, batch avg loss 0.2338, total avg loss: 0.2361, batch size: 42
2021-10-15 04:35:25,899 INFO [train.py:451] Epoch 11, batch 5020, batch avg loss 0.2156, total avg loss: 0.2264, batch size: 28
2021-10-15 04:35:30,650 INFO [train.py:451] Epoch 11, batch 5030, batch avg loss 0.1885, total avg loss: 0.2256, batch size: 38
2021-10-15 04:35:35,513 INFO [train.py:451] Epoch 11, batch 5040, batch avg loss 0.2066, total avg loss: 0.2262, batch size: 30
2021-10-15 04:35:40,349 INFO [train.py:451] Epoch 11, batch 5050, batch avg loss 0.1991, total avg loss: 0.2252, batch size: 35
2021-10-15 04:35:45,239 INFO [train.py:451] Epoch 11, batch 5060, batch avg loss 0.1530, total avg loss: 0.2243, batch size: 29
2021-10-15 04:35:50,087 INFO [train.py:451] Epoch 11, batch 5070, batch avg loss 0.2111, total avg loss: 0.2241, batch size: 32
2021-10-15 04:35:54,879 INFO [train.py:451] Epoch 11, batch 5080, batch avg loss 0.2777, total avg loss: 0.2242, batch size: 73
2021-10-15 04:35:59,904 INFO [train.py:451] Epoch 11, batch 5090, batch avg loss 0.2132, total avg loss: 0.2227, batch size: 33
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2021-10-15 04:44:07,520 INFO [train.py:483] Epoch 11, valid loss 0.1607, best valid loss: 0.1607 best valid epoch: 11
2021-10-15 04:44:12,527 INFO [train.py:451] Epoch 11, batch 6010, batch avg loss 0.1674, total avg loss: 0.2041, batch size: 31
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2021-10-15 04:52:58,098 INFO [train.py:483] Epoch 11, valid loss 0.1621, best valid loss: 0.1607 best valid epoch: 11
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2021-10-15 05:01:49,456 INFO [train.py:483] Epoch 11, valid loss 0.1610, best valid loss: 0.1607 best valid epoch: 11
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2021-10-15 05:02:04,180 INFO [train.py:451] Epoch 11, batch 8030, batch avg loss 0.1838, total avg loss: 0.2241, batch size: 31
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2021-10-15 05:02:13,777 INFO [train.py:451] Epoch 11, batch 8050, batch avg loss 0.3141, total avg loss: 0.2224, batch size: 130
2021-10-15 05:02:18,777 INFO [train.py:451] Epoch 11, batch 8060, batch avg loss 0.2688, total avg loss: 0.2219, batch size: 34
2021-10-15 05:02:23,715 INFO [train.py:451] Epoch 11, batch 8070, batch avg loss 0.2348, total avg loss: 0.2206, batch size: 36
2021-10-15 05:02:28,755 INFO [train.py:451] Epoch 11, batch 8080, batch avg loss 0.2254, total avg loss: 0.2207, batch size: 35
2021-10-15 05:02:33,639 INFO [train.py:451] Epoch 11, batch 8090, batch avg loss 0.2215, total avg loss: 0.2216, batch size: 39
2021-10-15 05:02:38,681 INFO [train.py:451] Epoch 11, batch 8100, batch avg loss 0.1592, total avg loss: 0.2198, batch size: 28
2021-10-15 05:02:43,663 INFO [train.py:451] Epoch 11, batch 8110, batch avg loss 0.1991, total avg loss: 0.2182, batch size: 27
2021-10-15 05:02:48,599 INFO [train.py:451] Epoch 11, batch 8120, batch avg loss 0.2397, total avg loss: 0.2177, batch size: 57
2021-10-15 05:02:53,470 INFO [train.py:451] Epoch 11, batch 8130, batch avg loss 0.2069, total avg loss: 0.2178, batch size: 38
2021-10-15 05:02:58,487 INFO [train.py:451] Epoch 11, batch 8140, batch avg loss 0.2036, total avg loss: 0.2192, batch size: 35
2021-10-15 05:03:03,311 INFO [train.py:451] Epoch 11, batch 8150, batch avg loss 0.2261, total avg loss: 0.2184, batch size: 72
2021-10-15 05:03:08,325 INFO [train.py:451] Epoch 11, batch 8160, batch avg loss 0.2200, total avg loss: 0.2176, batch size: 34
2021-10-15 05:03:13,357 INFO [train.py:451] Epoch 11, batch 8170, batch avg loss 0.1441, total avg loss: 0.2160, batch size: 29
2021-10-15 05:03:18,314 INFO [train.py:451] Epoch 11, batch 8180, batch avg loss 0.1726, total avg loss: 0.2154, batch size: 30
2021-10-15 05:03:23,284 INFO [train.py:451] Epoch 11, batch 8190, batch avg loss 0.2104, total avg loss: 0.2149, batch size: 28
2021-10-15 05:03:28,193 INFO [train.py:451] Epoch 11, batch 8200, batch avg loss 0.2131, total avg loss: 0.2145, batch size: 37
2021-10-15 05:03:33,117 INFO [train.py:451] Epoch 11, batch 8210, batch avg loss 0.1890, total avg loss: 0.2130, batch size: 30
2021-10-15 05:03:37,994 INFO [train.py:451] Epoch 11, batch 8220, batch avg loss 0.2719, total avg loss: 0.2187, batch size: 39
2021-10-15 05:03:42,808 INFO [train.py:451] Epoch 11, batch 8230, batch avg loss 0.1679, total avg loss: 0.2155, batch size: 31
2021-10-15 05:03:47,779 INFO [train.py:451] Epoch 11, batch 8240, batch avg loss 0.1862, total avg loss: 0.2113, batch size: 30
2021-10-15 05:03:52,702 INFO [train.py:451] Epoch 11, batch 8250, batch avg loss 0.1765, total avg loss: 0.2101, batch size: 35
2021-10-15 05:03:57,700 INFO [train.py:451] Epoch 11, batch 8260, batch avg loss 0.2011, total avg loss: 0.2101, batch size: 34
2021-10-15 05:04:02,733 INFO [train.py:451] Epoch 11, batch 8270, batch avg loss 0.2034, total avg loss: 0.2125, batch size: 33
2021-10-15 05:04:07,649 INFO [train.py:451] Epoch 11, batch 8280, batch avg loss 0.2204, total avg loss: 0.2138, batch size: 30
2021-10-15 05:04:10,328 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "9499ec93-ea15-f582-ed56-9b84d6cf8cc6" will not be mixed in.
2021-10-15 05:04:12,639 INFO [train.py:451] Epoch 11, batch 8290, batch avg loss 0.2727, total avg loss: 0.2122, batch size: 72
2021-10-15 05:04:17,378 INFO [train.py:451] Epoch 11, batch 8300, batch avg loss 0.2132, total avg loss: 0.2143, batch size: 36
2021-10-15 05:04:22,374 INFO [train.py:451] Epoch 11, batch 8310, batch avg loss 0.2594, total avg loss: 0.2136, batch size: 45
2021-10-15 05:04:27,272 INFO [train.py:451] Epoch 11, batch 8320, batch avg loss 0.2349, total avg loss: 0.2139, batch size: 38
2021-10-15 05:04:32,107 INFO [train.py:451] Epoch 11, batch 8330, batch avg loss 0.2721, total avg loss: 0.2152, batch size: 35
2021-10-15 05:04:36,705 INFO [train.py:451] Epoch 11, batch 8340, batch avg loss 0.2483, total avg loss: 0.2174, batch size: 72
2021-10-15 05:04:41,703 INFO [train.py:451] Epoch 11, batch 8350, batch avg loss 0.2707, total avg loss: 0.2181, batch size: 36
2021-10-15 05:04:46,713 INFO [train.py:451] Epoch 11, batch 8360, batch avg loss 0.1904, total avg loss: 0.2173, batch size: 28
2021-10-15 05:04:51,582 INFO [train.py:451] Epoch 11, batch 8370, batch avg loss 0.1949, total avg loss: 0.2172, batch size: 31
2021-10-15 05:04:56,471 INFO [train.py:451] Epoch 11, batch 8380, batch avg loss 0.1870, total avg loss: 0.2162, batch size: 41
2021-10-15 05:05:01,440 INFO [train.py:451] Epoch 11, batch 8390, batch avg loss 0.2161, total avg loss: 0.2156, batch size: 34
2021-10-15 05:05:06,359 INFO [train.py:451] Epoch 11, batch 8400, batch avg loss 0.2375, total avg loss: 0.2162, batch size: 38
2021-10-15 05:05:11,307 INFO [train.py:451] Epoch 11, batch 8410, batch avg loss 0.1689, total avg loss: 0.2077, batch size: 32
2021-10-15 05:05:16,046 INFO [train.py:451] Epoch 11, batch 8420, batch avg loss 0.2431, total avg loss: 0.2199, batch size: 39
2021-10-15 05:05:21,229 INFO [train.py:451] Epoch 11, batch 8430, batch avg loss 0.2071, total avg loss: 0.2133, batch size: 36
2021-10-15 05:05:26,104 INFO [train.py:451] Epoch 11, batch 8440, batch avg loss 0.2149, total avg loss: 0.2166, batch size: 37
2021-10-15 05:05:31,111 INFO [train.py:451] Epoch 11, batch 8450, batch avg loss 0.1869, total avg loss: 0.2158, batch size: 29
2021-10-15 05:05:36,033 INFO [train.py:451] Epoch 11, batch 8460, batch avg loss 0.2269, total avg loss: 0.2151, batch size: 33
2021-10-15 05:05:41,035 INFO [train.py:451] Epoch 11, batch 8470, batch avg loss 0.1778, total avg loss: 0.2122, batch size: 30
2021-10-15 05:05:45,860 INFO [train.py:451] Epoch 11, batch 8480, batch avg loss 0.1874, total avg loss: 0.2143, batch size: 33
2021-10-15 05:05:50,708 INFO [train.py:451] Epoch 11, batch 8490, batch avg loss 0.2435, total avg loss: 0.2156, batch size: 49
2021-10-15 05:05:55,638 INFO [train.py:451] Epoch 11, batch 8500, batch avg loss 0.1894, total avg loss: 0.2148, batch size: 29
2021-10-15 05:06:00,604 INFO [train.py:451] Epoch 11, batch 8510, batch avg loss 0.1740, total avg loss: 0.2155, batch size: 30
2021-10-15 05:06:05,430 INFO [train.py:451] Epoch 11, batch 8520, batch avg loss 0.2269, total avg loss: 0.2170, batch size: 41
2021-10-15 05:06:10,474 INFO [train.py:451] Epoch 11, batch 8530, batch avg loss 0.1757, total avg loss: 0.2156, batch size: 28
2021-10-15 05:06:15,397 INFO [train.py:451] Epoch 11, batch 8540, batch avg loss 0.2125, total avg loss: 0.2150, batch size: 34
2021-10-15 05:06:20,334 INFO [train.py:451] Epoch 11, batch 8550, batch avg loss 0.2738, total avg loss: 0.2155, batch size: 73
2021-10-15 05:06:25,298 INFO [train.py:451] Epoch 11, batch 8560, batch avg loss 0.1980, total avg loss: 0.2153, batch size: 35
2021-10-15 05:06:30,239 INFO [train.py:451] Epoch 11, batch 8570, batch avg loss 0.1884, total avg loss: 0.2141, batch size: 29
2021-10-15 05:06:35,094 INFO [train.py:451] Epoch 11, batch 8580, batch avg loss 0.2257, total avg loss: 0.2143, batch size: 39
2021-10-15 05:06:40,042 INFO [train.py:451] Epoch 11, batch 8590, batch avg loss 0.2107, total avg loss: 0.2149, batch size: 42
2021-10-15 05:06:45,050 INFO [train.py:451] Epoch 11, batch 8600, batch avg loss 0.2125, total avg loss: 0.2151, batch size: 35
2021-10-15 05:06:49,887 INFO [train.py:451] Epoch 11, batch 8610, batch avg loss 0.2148, total avg loss: 0.2165, batch size: 41
2021-10-15 05:06:54,914 INFO [train.py:451] Epoch 11, batch 8620, batch avg loss 0.2170, total avg loss: 0.2135, batch size: 38
2021-10-15 05:06:59,781 INFO [train.py:451] Epoch 11, batch 8630, batch avg loss 0.2354, total avg loss: 0.2133, batch size: 57
2021-10-15 05:07:04,929 INFO [train.py:451] Epoch 11, batch 8640, batch avg loss 0.2383, total avg loss: 0.2123, batch size: 34
2021-10-15 05:07:09,712 INFO [train.py:451] Epoch 11, batch 8650, batch avg loss 0.2076, total avg loss: 0.2120, batch size: 36
2021-10-15 05:07:14,609 INFO [train.py:451] Epoch 11, batch 8660, batch avg loss 0.2231, total avg loss: 0.2114, batch size: 41
2021-10-15 05:07:19,526 INFO [train.py:451] Epoch 11, batch 8670, batch avg loss 0.2319, total avg loss: 0.2130, batch size: 35
2021-10-15 05:07:24,187 INFO [train.py:451] Epoch 11, batch 8680, batch avg loss 0.2499, total avg loss: 0.2165, batch size: 36
2021-10-15 05:07:29,056 INFO [train.py:451] Epoch 11, batch 8690, batch avg loss 0.1871, total avg loss: 0.2168, batch size: 38
2021-10-15 05:07:34,001 INFO [train.py:451] Epoch 11, batch 8700, batch avg loss 0.1960, total avg loss: 0.2155, batch size: 29
2021-10-15 05:07:38,957 INFO [train.py:451] Epoch 11, batch 8710, batch avg loss 0.1972, total avg loss: 0.2155, batch size: 33
2021-10-15 05:07:43,696 INFO [train.py:451] Epoch 11, batch 8720, batch avg loss 0.1614, total avg loss: 0.2157, batch size: 29
2021-10-15 05:07:48,513 INFO [train.py:451] Epoch 11, batch 8730, batch avg loss 0.1722, total avg loss: 0.2169, batch size: 32
2021-10-15 05:07:53,410 INFO [train.py:451] Epoch 11, batch 8740, batch avg loss 0.2555, total avg loss: 0.2172, batch size: 39
2021-10-15 05:07:58,327 INFO [train.py:451] Epoch 11, batch 8750, batch avg loss 0.2142, total avg loss: 0.2165, batch size: 41
2021-10-15 05:08:03,401 INFO [train.py:451] Epoch 11, batch 8760, batch avg loss 0.2115, total avg loss: 0.2160, batch size: 33
2021-10-15 05:08:08,282 INFO [train.py:451] Epoch 11, batch 8770, batch avg loss 0.1757, total avg loss: 0.2157, batch size: 42
2021-10-15 05:08:13,017 INFO [train.py:451] Epoch 11, batch 8780, batch avg loss 0.1902, total avg loss: 0.2155, batch size: 38
2021-10-15 05:08:17,889 INFO [train.py:451] Epoch 11, batch 8790, batch avg loss 0.2679, total avg loss: 0.2157, batch size: 73
2021-10-15 05:08:22,941 INFO [train.py:451] Epoch 11, batch 8800, batch avg loss 0.1657, total avg loss: 0.2151, batch size: 29
2021-10-15 05:08:27,703 INFO [train.py:451] Epoch 11, batch 8810, batch avg loss 0.2692, total avg loss: 0.2156, batch size: 37
2021-10-15 05:08:32,581 INFO [train.py:451] Epoch 11, batch 8820, batch avg loss 0.2318, total avg loss: 0.2158, batch size: 39
2021-10-15 05:08:37,520 INFO [train.py:451] Epoch 11, batch 8830, batch avg loss 0.2487, total avg loss: 0.2138, batch size: 34
2021-10-15 05:08:42,468 INFO [train.py:451] Epoch 11, batch 8840, batch avg loss 0.1823, total avg loss: 0.2127, batch size: 33
2021-10-15 05:08:47,436 INFO [train.py:451] Epoch 11, batch 8850, batch avg loss 0.2131, total avg loss: 0.2173, batch size: 32
2021-10-15 05:08:52,559 INFO [train.py:451] Epoch 11, batch 8860, batch avg loss 0.2546, total avg loss: 0.2153, batch size: 36
2021-10-15 05:08:57,598 INFO [train.py:451] Epoch 11, batch 8870, batch avg loss 0.2659, total avg loss: 0.2142, batch size: 33
2021-10-15 05:09:02,552 INFO [train.py:451] Epoch 11, batch 8880, batch avg loss 0.2852, total avg loss: 0.2164, batch size: 133
2021-10-15 05:09:07,557 INFO [train.py:451] Epoch 11, batch 8890, batch avg loss 0.2639, total avg loss: 0.2163, batch size: 57
2021-10-15 05:09:12,352 INFO [train.py:451] Epoch 11, batch 8900, batch avg loss 0.2133, total avg loss: 0.2165, batch size: 41
2021-10-15 05:09:17,399 INFO [train.py:451] Epoch 11, batch 8910, batch avg loss 0.2504, total avg loss: 0.2153, batch size: 38
2021-10-15 05:09:22,185 INFO [train.py:451] Epoch 11, batch 8920, batch avg loss 0.2048, total avg loss: 0.2151, batch size: 37
2021-10-15 05:09:27,062 INFO [train.py:451] Epoch 11, batch 8930, batch avg loss 0.2740, total avg loss: 0.2156, batch size: 72
2021-10-15 05:09:32,056 INFO [train.py:451] Epoch 11, batch 8940, batch avg loss 0.1717, total avg loss: 0.2153, batch size: 30
2021-10-15 05:09:37,179 INFO [train.py:451] Epoch 11, batch 8950, batch avg loss 0.2169, total avg loss: 0.2154, batch size: 31
2021-10-15 05:09:41,924 INFO [train.py:451] Epoch 11, batch 8960, batch avg loss 0.2123, total avg loss: 0.2162, batch size: 42
2021-10-15 05:09:46,913 INFO [train.py:451] Epoch 11, batch 8970, batch avg loss 0.1971, total avg loss: 0.2159, batch size: 38
2021-10-15 05:09:51,678 INFO [train.py:451] Epoch 11, batch 8980, batch avg loss 0.2315, total avg loss: 0.2162, batch size: 31
2021-10-15 05:09:56,486 INFO [train.py:451] Epoch 11, batch 8990, batch avg loss 0.2182, total avg loss: 0.2167, batch size: 38
2021-10-15 05:10:01,538 INFO [train.py:451] Epoch 11, batch 9000, batch avg loss 0.2006, total avg loss: 0.2159, batch size: 34
2021-10-15 05:10:38,953 INFO [train.py:483] Epoch 11, valid loss 0.1613, best valid loss: 0.1607 best valid epoch: 11
2021-10-15 05:10:43,931 INFO [train.py:451] Epoch 11, batch 9010, batch avg loss 0.2805, total avg loss: 0.2109, batch size: 34
2021-10-15 05:10:48,896 INFO [train.py:451] Epoch 11, batch 9020, batch avg loss 0.2028, total avg loss: 0.2228, batch size: 30
2021-10-15 05:10:53,952 INFO [train.py:451] Epoch 11, batch 9030, batch avg loss 0.1828, total avg loss: 0.2142, batch size: 29
2021-10-15 05:10:58,756 INFO [train.py:451] Epoch 11, batch 9040, batch avg loss 0.2139, total avg loss: 0.2169, batch size: 34
2021-10-15 05:11:03,748 INFO [train.py:451] Epoch 11, batch 9050, batch avg loss 0.2258, total avg loss: 0.2134, batch size: 37
2021-10-15 05:11:08,578 INFO [train.py:451] Epoch 11, batch 9060, batch avg loss 0.2325, total avg loss: 0.2103, batch size: 45
2021-10-15 05:11:13,616 INFO [train.py:451] Epoch 11, batch 9070, batch avg loss 0.2007, total avg loss: 0.2107, batch size: 36
2021-10-15 05:11:18,569 INFO [train.py:451] Epoch 11, batch 9080, batch avg loss 0.2038, total avg loss: 0.2091, batch size: 34
2021-10-15 05:11:23,281 INFO [train.py:451] Epoch 11, batch 9090, batch avg loss 0.2556, total avg loss: 0.2115, batch size: 49
2021-10-15 05:11:28,084 INFO [train.py:451] Epoch 11, batch 9100, batch avg loss 0.1887, total avg loss: 0.2126, batch size: 27
2021-10-15 05:11:33,071 INFO [train.py:451] Epoch 11, batch 9110, batch avg loss 0.1891, total avg loss: 0.2132, batch size: 32
2021-10-15 05:11:37,924 INFO [train.py:451] Epoch 11, batch 9120, batch avg loss 0.1849, total avg loss: 0.2138, batch size: 28
2021-10-15 05:11:42,865 INFO [train.py:451] Epoch 11, batch 9130, batch avg loss 0.1672, total avg loss: 0.2129, batch size: 31
2021-10-15 05:11:47,711 INFO [train.py:451] Epoch 11, batch 9140, batch avg loss 0.2044, total avg loss: 0.2129, batch size: 36
2021-10-15 05:11:52,672 INFO [train.py:451] Epoch 11, batch 9150, batch avg loss 0.2448, total avg loss: 0.2135, batch size: 39
2021-10-15 05:11:57,321 INFO [train.py:451] Epoch 11, batch 9160, batch avg loss 0.2147, total avg loss: 0.2145, batch size: 32
2021-10-15 05:12:02,207 INFO [train.py:451] Epoch 11, batch 9170, batch avg loss 0.2084, total avg loss: 0.2140, batch size: 38
2021-10-15 05:12:07,187 INFO [train.py:451] Epoch 11, batch 9180, batch avg loss 0.1926, total avg loss: 0.2138, batch size: 32
2021-10-15 05:12:12,192 INFO [train.py:451] Epoch 11, batch 9190, batch avg loss 0.1930, total avg loss: 0.2131, batch size: 37
2021-10-15 05:12:17,251 INFO [train.py:451] Epoch 11, batch 9200, batch avg loss 0.2358, total avg loss: 0.2127, batch size: 57
2021-10-15 05:12:22,318 INFO [train.py:451] Epoch 11, batch 9210, batch avg loss 0.2312, total avg loss: 0.2100, batch size: 34
2021-10-15 05:12:27,083 INFO [train.py:451] Epoch 11, batch 9220, batch avg loss 0.3355, total avg loss: 0.2284, batch size: 128
2021-10-15 05:12:31,862 INFO [train.py:451] Epoch 11, batch 9230, batch avg loss 0.1972, total avg loss: 0.2227, batch size: 37
2021-10-15 05:12:36,753 INFO [train.py:451] Epoch 11, batch 9240, batch avg loss 0.1972, total avg loss: 0.2175, batch size: 32
2021-10-15 05:12:41,546 INFO [train.py:451] Epoch 11, batch 9250, batch avg loss 0.2551, total avg loss: 0.2193, batch size: 34
2021-10-15 05:12:46,310 INFO [train.py:451] Epoch 11, batch 9260, batch avg loss 0.2071, total avg loss: 0.2192, batch size: 36
2021-10-15 05:12:51,240 INFO [train.py:451] Epoch 11, batch 9270, batch avg loss 0.1964, total avg loss: 0.2177, batch size: 33
2021-10-15 05:12:56,251 INFO [train.py:451] Epoch 11, batch 9280, batch avg loss 0.1797, total avg loss: 0.2161, batch size: 28
2021-10-15 05:13:01,142 INFO [train.py:451] Epoch 11, batch 9290, batch avg loss 0.1804, total avg loss: 0.2178, batch size: 38
2021-10-15 05:13:06,106 INFO [train.py:451] Epoch 11, batch 9300, batch avg loss 0.1859, total avg loss: 0.2170, batch size: 34
2021-10-15 05:13:11,056 INFO [train.py:451] Epoch 11, batch 9310, batch avg loss 0.2256, total avg loss: 0.2169, batch size: 39
2021-10-15 05:13:15,940 INFO [train.py:451] Epoch 11, batch 9320, batch avg loss 0.2202, total avg loss: 0.2168, batch size: 31
2021-10-15 05:13:20,652 INFO [train.py:451] Epoch 11, batch 9330, batch avg loss 0.2548, total avg loss: 0.2189, batch size: 35
2021-10-15 05:13:25,508 INFO [train.py:451] Epoch 11, batch 9340, batch avg loss 0.1876, total avg loss: 0.2198, batch size: 36
2021-10-15 05:13:30,397 INFO [train.py:451] Epoch 11, batch 9350, batch avg loss 0.2292, total avg loss: 0.2196, batch size: 37
2021-10-15 05:13:35,287 INFO [train.py:451] Epoch 11, batch 9360, batch avg loss 0.2452, total avg loss: 0.2199, batch size: 41
2021-10-15 05:13:40,415 INFO [train.py:451] Epoch 11, batch 9370, batch avg loss 0.2437, total avg loss: 0.2190, batch size: 34
2021-10-15 05:13:45,405 INFO [train.py:451] Epoch 11, batch 9380, batch avg loss 0.1996, total avg loss: 0.2190, batch size: 41
2021-10-15 05:13:50,211 INFO [train.py:451] Epoch 11, batch 9390, batch avg loss 0.2989, total avg loss: 0.2201, batch size: 131
2021-10-15 05:13:55,084 INFO [train.py:451] Epoch 11, batch 9400, batch avg loss 0.2327, total avg loss: 0.2201, batch size: 34
2021-10-15 05:14:00,122 INFO [train.py:451] Epoch 11, batch 9410, batch avg loss 0.2344, total avg loss: 0.2055, batch size: 45
2021-10-15 05:14:04,881 INFO [train.py:451] Epoch 11, batch 9420, batch avg loss 0.2646, total avg loss: 0.2157, batch size: 56
2021-10-15 05:14:09,671 INFO [train.py:451] Epoch 11, batch 9430, batch avg loss 0.2061, total avg loss: 0.2150, batch size: 38
2021-10-15 05:14:14,753 INFO [train.py:451] Epoch 11, batch 9440, batch avg loss 0.1508, total avg loss: 0.2105, batch size: 30
2021-10-15 05:14:19,706 INFO [train.py:451] Epoch 11, batch 9450, batch avg loss 0.2459, total avg loss: 0.2143, batch size: 45
2021-10-15 05:14:24,459 INFO [train.py:451] Epoch 11, batch 9460, batch avg loss 0.3096, total avg loss: 0.2162, batch size: 125
2021-10-15 05:14:29,204 INFO [train.py:451] Epoch 11, batch 9470, batch avg loss 0.1835, total avg loss: 0.2167, batch size: 31
2021-10-15 05:14:34,071 INFO [train.py:451] Epoch 11, batch 9480, batch avg loss 0.2459, total avg loss: 0.2178, batch size: 35
2021-10-15 05:14:39,025 INFO [train.py:451] Epoch 11, batch 9490, batch avg loss 0.1711, total avg loss: 0.2162, batch size: 35
2021-10-15 05:14:43,959 INFO [train.py:451] Epoch 11, batch 9500, batch avg loss 0.1707, total avg loss: 0.2170, batch size: 32
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2021-10-15 05:18:49,904 INFO [train.py:451] Epoch 11, batch 10000, batch avg loss 0.2439, total avg loss: 0.2172, batch size: 36
2021-10-15 05:19:29,383 INFO [train.py:483] Epoch 11, valid loss 0.1617, best valid loss: 0.1607 best valid epoch: 11
2021-10-15 05:19:34,230 INFO [train.py:451] Epoch 11, batch 10010, batch avg loss 0.2137, total avg loss: 0.2212, batch size: 33
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2021-10-15 05:28:19,182 INFO [train.py:483] Epoch 11, valid loss 0.1615, best valid loss: 0.1607 best valid epoch: 11
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2021-10-15 05:36:37,772 INFO [train.py:451] Epoch 11, batch 12000, batch avg loss 0.1614, total avg loss: 0.2185, batch size: 30
2021-10-15 05:37:15,543 INFO [train.py:483] Epoch 11, valid loss 0.1605, best valid loss: 0.1605 best valid epoch: 11
2021-10-15 05:37:20,305 INFO [train.py:451] Epoch 11, batch 12010, batch avg loss 0.3047, total avg loss: 0.2273, batch size: 128
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2021-10-15 05:40:57,148 INFO [train.py:451] Epoch 11, batch 12450, batch avg loss 0.2099, total avg loss: 0.2166, batch size: 38
2021-10-15 05:41:01,927 INFO [train.py:451] Epoch 11, batch 12460, batch avg loss 0.2210, total avg loss: 0.2221, batch size: 34
2021-10-15 05:41:06,925 INFO [train.py:451] Epoch 11, batch 12470, batch avg loss 0.2333, total avg loss: 0.2211, batch size: 57
2021-10-15 05:41:11,760 INFO [train.py:451] Epoch 11, batch 12480, batch avg loss 0.2081, total avg loss: 0.2218, batch size: 32
2021-10-15 05:41:16,604 INFO [train.py:451] Epoch 11, batch 12490, batch avg loss 0.2222, total avg loss: 0.2205, batch size: 39
2021-10-15 05:41:21,563 INFO [train.py:451] Epoch 11, batch 12500, batch avg loss 0.1847, total avg loss: 0.2191, batch size: 30
2021-10-15 05:41:26,541 INFO [train.py:451] Epoch 11, batch 12510, batch avg loss 0.2157, total avg loss: 0.2193, batch size: 37
2021-10-15 05:41:31,362 INFO [train.py:451] Epoch 11, batch 12520, batch avg loss 0.2431, total avg loss: 0.2183, batch size: 45
2021-10-15 05:41:36,219 INFO [train.py:451] Epoch 11, batch 12530, batch avg loss 0.2262, total avg loss: 0.2197, batch size: 49
2021-10-15 05:41:40,998 INFO [train.py:451] Epoch 11, batch 12540, batch avg loss 0.2257, total avg loss: 0.2198, batch size: 45
2021-10-15 05:41:45,999 INFO [train.py:451] Epoch 11, batch 12550, batch avg loss 0.2241, total avg loss: 0.2186, batch size: 38
2021-10-15 05:41:50,919 INFO [train.py:451] Epoch 11, batch 12560, batch avg loss 0.2000, total avg loss: 0.2182, batch size: 41
2021-10-15 05:41:55,789 INFO [train.py:451] Epoch 11, batch 12570, batch avg loss 0.2643, total avg loss: 0.2198, batch size: 39
2021-10-15 05:42:00,762 INFO [train.py:451] Epoch 11, batch 12580, batch avg loss 0.1850, total avg loss: 0.2195, batch size: 34
2021-10-15 05:42:05,665 INFO [train.py:451] Epoch 11, batch 12590, batch avg loss 0.1991, total avg loss: 0.2188, batch size: 34
2021-10-15 05:42:10,440 INFO [train.py:451] Epoch 11, batch 12600, batch avg loss 0.1675, total avg loss: 0.2189, batch size: 28
2021-10-15 05:42:15,362 INFO [train.py:451] Epoch 11, batch 12610, batch avg loss 0.1826, total avg loss: 0.2233, batch size: 30
2021-10-15 05:42:20,380 INFO [train.py:451] Epoch 11, batch 12620, batch avg loss 0.2067, total avg loss: 0.2191, batch size: 39
2021-10-15 05:42:25,330 INFO [train.py:451] Epoch 11, batch 12630, batch avg loss 0.3566, total avg loss: 0.2228, batch size: 127
2021-10-15 05:42:30,079 INFO [train.py:451] Epoch 11, batch 12640, batch avg loss 0.2161, total avg loss: 0.2251, batch size: 37
2021-10-15 05:42:34,938 INFO [train.py:451] Epoch 11, batch 12650, batch avg loss 0.2209, total avg loss: 0.2234, batch size: 33
2021-10-15 05:42:39,702 INFO [train.py:451] Epoch 11, batch 12660, batch avg loss 0.2145, total avg loss: 0.2241, batch size: 39
2021-10-15 05:42:44,580 INFO [train.py:451] Epoch 11, batch 12670, batch avg loss 0.1818, total avg loss: 0.2249, batch size: 33
2021-10-15 05:42:49,330 INFO [train.py:451] Epoch 11, batch 12680, batch avg loss 0.1813, total avg loss: 0.2256, batch size: 29
2021-10-15 05:42:54,263 INFO [train.py:451] Epoch 11, batch 12690, batch avg loss 0.3444, total avg loss: 0.2274, batch size: 126
2021-10-15 05:42:59,161 INFO [train.py:451] Epoch 11, batch 12700, batch avg loss 0.1718, total avg loss: 0.2266, batch size: 34
2021-10-15 05:43:04,072 INFO [train.py:451] Epoch 11, batch 12710, batch avg loss 0.1982, total avg loss: 0.2259, batch size: 38
2021-10-15 05:43:08,895 INFO [train.py:451] Epoch 11, batch 12720, batch avg loss 0.1917, total avg loss: 0.2250, batch size: 36
2021-10-15 05:43:13,755 INFO [train.py:451] Epoch 11, batch 12730, batch avg loss 0.2643, total avg loss: 0.2245, batch size: 38
2021-10-15 05:43:18,819 INFO [train.py:451] Epoch 11, batch 12740, batch avg loss 0.1906, total avg loss: 0.2226, batch size: 32
2021-10-15 05:43:23,755 INFO [train.py:451] Epoch 11, batch 12750, batch avg loss 0.2602, total avg loss: 0.2226, batch size: 56
2021-10-15 05:43:28,613 INFO [train.py:451] Epoch 11, batch 12760, batch avg loss 0.1855, total avg loss: 0.2226, batch size: 34
2021-10-15 05:43:33,487 INFO [train.py:451] Epoch 11, batch 12770, batch avg loss 0.1851, total avg loss: 0.2222, batch size: 29
2021-10-15 05:43:38,581 INFO [train.py:451] Epoch 11, batch 12780, batch avg loss 0.2289, total avg loss: 0.2218, batch size: 36
2021-10-15 05:43:43,541 INFO [train.py:451] Epoch 11, batch 12790, batch avg loss 0.1630, total avg loss: 0.2210, batch size: 29
2021-10-15 05:43:48,493 INFO [train.py:451] Epoch 11, batch 12800, batch avg loss 0.2159, total avg loss: 0.2215, batch size: 29
2021-10-15 05:43:53,350 INFO [train.py:451] Epoch 11, batch 12810, batch avg loss 0.2351, total avg loss: 0.2411, batch size: 31
2021-10-15 05:43:58,273 INFO [train.py:451] Epoch 11, batch 12820, batch avg loss 0.2086, total avg loss: 0.2299, batch size: 33
2021-10-15 05:44:03,283 INFO [train.py:451] Epoch 11, batch 12830, batch avg loss 0.1580, total avg loss: 0.2217, batch size: 27
2021-10-15 05:44:08,175 INFO [train.py:451] Epoch 11, batch 12840, batch avg loss 0.2076, total avg loss: 0.2221, batch size: 36
2021-10-15 05:44:13,202 INFO [train.py:451] Epoch 11, batch 12850, batch avg loss 0.2232, total avg loss: 0.2235, batch size: 39
2021-10-15 05:44:18,170 INFO [train.py:451] Epoch 11, batch 12860, batch avg loss 0.1941, total avg loss: 0.2226, batch size: 30
2021-10-15 05:44:23,091 INFO [train.py:451] Epoch 11, batch 12870, batch avg loss 0.1595, total avg loss: 0.2199, batch size: 30
2021-10-15 05:44:27,976 INFO [train.py:451] Epoch 11, batch 12880, batch avg loss 0.2251, total avg loss: 0.2216, batch size: 34
2021-10-15 05:44:32,943 INFO [train.py:451] Epoch 11, batch 12890, batch avg loss 0.2124, total avg loss: 0.2218, batch size: 30
2021-10-15 05:44:37,881 INFO [train.py:451] Epoch 11, batch 12900, batch avg loss 0.2362, total avg loss: 0.2216, batch size: 30
2021-10-15 05:44:43,433 INFO [train.py:451] Epoch 11, batch 12910, batch avg loss 0.2330, total avg loss: 0.2204, batch size: 37
2021-10-15 05:44:48,233 INFO [train.py:451] Epoch 11, batch 12920, batch avg loss 0.2806, total avg loss: 0.2210, batch size: 72
2021-10-15 05:44:53,203 INFO [train.py:451] Epoch 11, batch 12930, batch avg loss 0.2035, total avg loss: 0.2204, batch size: 35
2021-10-15 05:44:57,996 INFO [train.py:451] Epoch 11, batch 12940, batch avg loss 0.2104, total avg loss: 0.2210, batch size: 38
2021-10-15 05:45:02,950 INFO [train.py:451] Epoch 11, batch 12950, batch avg loss 0.2413, total avg loss: 0.2206, batch size: 41
2021-10-15 05:45:07,773 INFO [train.py:451] Epoch 11, batch 12960, batch avg loss 0.1810, total avg loss: 0.2208, batch size: 32
2021-10-15 05:45:12,591 INFO [train.py:451] Epoch 11, batch 12970, batch avg loss 0.2010, total avg loss: 0.2209, batch size: 41
2021-10-15 05:45:17,515 INFO [train.py:451] Epoch 11, batch 12980, batch avg loss 0.2197, total avg loss: 0.2199, batch size: 36
2021-10-15 05:45:22,409 INFO [train.py:451] Epoch 11, batch 12990, batch avg loss 0.1978, total avg loss: 0.2195, batch size: 29
2021-10-15 05:45:27,427 INFO [train.py:451] Epoch 11, batch 13000, batch avg loss 0.1955, total avg loss: 0.2190, batch size: 34
2021-10-15 05:46:07,651 INFO [train.py:483] Epoch 11, valid loss 0.1610, best valid loss: 0.1605 best valid epoch: 11
2021-10-15 05:46:12,669 INFO [train.py:451] Epoch 11, batch 13010, batch avg loss 0.2313, total avg loss: 0.1976, batch size: 35
2021-10-15 05:46:17,583 INFO [train.py:451] Epoch 11, batch 13020, batch avg loss 0.2399, total avg loss: 0.2046, batch size: 38
2021-10-15 05:46:22,527 INFO [train.py:451] Epoch 11, batch 13030, batch avg loss 0.2549, total avg loss: 0.2057, batch size: 39
2021-10-15 05:46:27,385 INFO [train.py:451] Epoch 11, batch 13040, batch avg loss 0.2330, total avg loss: 0.2100, batch size: 41
2021-10-15 05:46:32,227 INFO [train.py:451] Epoch 11, batch 13050, batch avg loss 0.1822, total avg loss: 0.2120, batch size: 30
2021-10-15 05:46:37,094 INFO [train.py:451] Epoch 11, batch 13060, batch avg loss 0.1919, total avg loss: 0.2119, batch size: 27
2021-10-15 05:46:41,826 INFO [train.py:451] Epoch 11, batch 13070, batch avg loss 0.2570, total avg loss: 0.2146, batch size: 57
2021-10-15 05:46:46,906 INFO [train.py:451] Epoch 11, batch 13080, batch avg loss 0.2354, total avg loss: 0.2147, batch size: 38
2021-10-15 05:46:51,696 INFO [train.py:451] Epoch 11, batch 13090, batch avg loss 0.3183, total avg loss: 0.2164, batch size: 128
2021-10-15 05:46:56,645 INFO [train.py:451] Epoch 11, batch 13100, batch avg loss 0.2168, total avg loss: 0.2169, batch size: 34
2021-10-15 05:47:01,455 INFO [train.py:451] Epoch 11, batch 13110, batch avg loss 0.2525, total avg loss: 0.2186, batch size: 45
2021-10-15 05:47:06,460 INFO [train.py:451] Epoch 11, batch 13120, batch avg loss 0.2266, total avg loss: 0.2189, batch size: 36
2021-10-15 05:47:11,335 INFO [train.py:451] Epoch 11, batch 13130, batch avg loss 0.1758, total avg loss: 0.2188, batch size: 28
2021-10-15 05:47:16,425 INFO [train.py:451] Epoch 11, batch 13140, batch avg loss 0.1630, total avg loss: 0.2184, batch size: 27
2021-10-15 05:47:21,443 INFO [train.py:451] Epoch 11, batch 13150, batch avg loss 0.2690, total avg loss: 0.2181, batch size: 39
2021-10-15 05:47:26,302 INFO [train.py:451] Epoch 11, batch 13160, batch avg loss 0.2355, total avg loss: 0.2178, batch size: 35
2021-10-15 05:47:31,266 INFO [train.py:451] Epoch 11, batch 13170, batch avg loss 0.2133, total avg loss: 0.2181, batch size: 31
2021-10-15 05:47:36,004 INFO [train.py:451] Epoch 11, batch 13180, batch avg loss 0.2380, total avg loss: 0.2182, batch size: 38
2021-10-15 05:47:40,749 INFO [train.py:451] Epoch 11, batch 13190, batch avg loss 0.2273, total avg loss: 0.2189, batch size: 34
2021-10-15 05:47:45,385 INFO [train.py:451] Epoch 11, batch 13200, batch avg loss 0.1962, total avg loss: 0.2199, batch size: 38
2021-10-15 05:47:50,333 INFO [train.py:451] Epoch 11, batch 13210, batch avg loss 0.2091, total avg loss: 0.2183, batch size: 36
2021-10-15 05:47:55,458 INFO [train.py:451] Epoch 11, batch 13220, batch avg loss 0.2511, total avg loss: 0.2190, batch size: 38
2021-10-15 05:48:00,413 INFO [train.py:451] Epoch 11, batch 13230, batch avg loss 0.1717, total avg loss: 0.2214, batch size: 32
2021-10-15 05:48:05,295 INFO [train.py:451] Epoch 11, batch 13240, batch avg loss 0.1983, total avg loss: 0.2241, batch size: 32
2021-10-15 05:48:10,315 INFO [train.py:451] Epoch 11, batch 13250, batch avg loss 0.1964, total avg loss: 0.2249, batch size: 33
2021-10-15 05:48:15,393 INFO [train.py:451] Epoch 11, batch 13260, batch avg loss 0.1539, total avg loss: 0.2210, batch size: 28
2021-10-15 05:48:17,632 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "ddc82030-8d59-285a-ec0e-42186e950e81" will not be mixed in.
2021-10-15 05:48:20,320 INFO [train.py:451] Epoch 11, batch 13270, batch avg loss 0.2380, total avg loss: 0.2200, batch size: 57
2021-10-15 05:48:25,292 INFO [train.py:451] Epoch 11, batch 13280, batch avg loss 0.1898, total avg loss: 0.2194, batch size: 37
2021-10-15 05:48:30,142 INFO [train.py:451] Epoch 11, batch 13290, batch avg loss 0.2001, total avg loss: 0.2211, batch size: 28
2021-10-15 05:48:35,028 INFO [train.py:451] Epoch 11, batch 13300, batch avg loss 0.1868, total avg loss: 0.2209, batch size: 29
2021-10-15 05:48:40,036 INFO [train.py:451] Epoch 11, batch 13310, batch avg loss 0.1972, total avg loss: 0.2197, batch size: 32
2021-10-15 05:48:45,127 INFO [train.py:451] Epoch 11, batch 13320, batch avg loss 0.1779, total avg loss: 0.2181, batch size: 29
2021-10-15 05:48:49,968 INFO [train.py:451] Epoch 11, batch 13330, batch avg loss 0.2500, total avg loss: 0.2184, batch size: 42
2021-10-15 05:48:54,963 INFO [train.py:451] Epoch 11, batch 13340, batch avg loss 0.2107, total avg loss: 0.2185, batch size: 33
2021-10-15 05:48:59,817 INFO [train.py:451] Epoch 11, batch 13350, batch avg loss 0.1309, total avg loss: 0.2177, batch size: 28
2021-10-15 05:49:04,733 INFO [train.py:451] Epoch 11, batch 13360, batch avg loss 0.1844, total avg loss: 0.2177, batch size: 35
2021-10-15 05:49:09,649 INFO [train.py:451] Epoch 11, batch 13370, batch avg loss 0.1725, total avg loss: 0.2182, batch size: 30
2021-10-15 05:49:14,596 INFO [train.py:451] Epoch 11, batch 13380, batch avg loss 0.2315, total avg loss: 0.2183, batch size: 49
2021-10-15 05:49:19,600 INFO [train.py:451] Epoch 11, batch 13390, batch avg loss 0.2113, total avg loss: 0.2182, batch size: 34
2021-10-15 05:49:24,537 INFO [train.py:451] Epoch 11, batch 13400, batch avg loss 0.2098, total avg loss: 0.2181, batch size: 31
2021-10-15 05:49:29,576 INFO [train.py:451] Epoch 11, batch 13410, batch avg loss 0.2480, total avg loss: 0.2069, batch size: 45
2021-10-15 05:49:34,574 INFO [train.py:451] Epoch 11, batch 13420, batch avg loss 0.2121, total avg loss: 0.2057, batch size: 38
2021-10-15 05:49:39,355 INFO [train.py:451] Epoch 11, batch 13430, batch avg loss 0.1597, total avg loss: 0.2135, batch size: 31
2021-10-15 05:49:44,262 INFO [train.py:451] Epoch 11, batch 13440, batch avg loss 0.2373, total avg loss: 0.2130, batch size: 39
2021-10-15 05:49:49,055 INFO [train.py:451] Epoch 11, batch 13450, batch avg loss 0.2077, total avg loss: 0.2162, batch size: 32
2021-10-15 05:49:53,898 INFO [train.py:451] Epoch 11, batch 13460, batch avg loss 0.2471, total avg loss: 0.2171, batch size: 74
2021-10-15 05:49:58,774 INFO [train.py:451] Epoch 11, batch 13470, batch avg loss 0.2075, total avg loss: 0.2182, batch size: 37
2021-10-15 05:50:03,464 INFO [train.py:451] Epoch 11, batch 13480, batch avg loss 0.2918, total avg loss: 0.2237, batch size: 131
2021-10-15 05:50:08,347 INFO [train.py:451] Epoch 11, batch 13490, batch avg loss 0.1746, total avg loss: 0.2213, batch size: 34
2021-10-15 05:50:13,220 INFO [train.py:451] Epoch 11, batch 13500, batch avg loss 0.2303, total avg loss: 0.2199, batch size: 49
2021-10-15 05:50:18,000 INFO [train.py:451] Epoch 11, batch 13510, batch avg loss 0.1826, total avg loss: 0.2205, batch size: 29
2021-10-15 05:50:22,907 INFO [train.py:451] Epoch 11, batch 13520, batch avg loss 0.1723, total avg loss: 0.2216, batch size: 29
2021-10-15 05:50:27,717 INFO [train.py:451] Epoch 11, batch 13530, batch avg loss 0.2063, total avg loss: 0.2215, batch size: 38
2021-10-15 05:50:32,614 INFO [train.py:451] Epoch 11, batch 13540, batch avg loss 0.2004, total avg loss: 0.2211, batch size: 37
2021-10-15 05:50:37,542 INFO [train.py:451] Epoch 11, batch 13550, batch avg loss 0.3029, total avg loss: 0.2198, batch size: 131
2021-10-15 05:50:42,594 INFO [train.py:451] Epoch 11, batch 13560, batch avg loss 0.2029, total avg loss: 0.2197, batch size: 31
2021-10-15 05:50:47,561 INFO [train.py:451] Epoch 11, batch 13570, batch avg loss 0.2910, total avg loss: 0.2194, batch size: 74
2021-10-15 05:50:52,519 INFO [train.py:451] Epoch 11, batch 13580, batch avg loss 0.2399, total avg loss: 0.2198, batch size: 45
2021-10-15 05:50:57,629 INFO [train.py:451] Epoch 11, batch 13590, batch avg loss 0.2040, total avg loss: 0.2184, batch size: 32
2021-10-15 05:51:02,380 INFO [train.py:451] Epoch 11, batch 13600, batch avg loss 0.2113, total avg loss: 0.2189, batch size: 45
2021-10-15 05:51:07,404 INFO [train.py:451] Epoch 11, batch 13610, batch avg loss 0.2104, total avg loss: 0.2226, batch size: 41
2021-10-15 05:51:12,360 INFO [train.py:451] Epoch 11, batch 13620, batch avg loss 0.2710, total avg loss: 0.2186, batch size: 42
2021-10-15 05:51:17,229 INFO [train.py:451] Epoch 11, batch 13630, batch avg loss 0.1969, total avg loss: 0.2225, batch size: 33
2021-10-15 05:51:22,257 INFO [train.py:451] Epoch 11, batch 13640, batch avg loss 0.1535, total avg loss: 0.2184, batch size: 31
2021-10-15 05:51:27,322 INFO [train.py:451] Epoch 11, batch 13650, batch avg loss 0.1643, total avg loss: 0.2137, batch size: 31
2021-10-15 05:51:32,250 INFO [train.py:451] Epoch 11, batch 13660, batch avg loss 0.1969, total avg loss: 0.2175, batch size: 31
2021-10-15 05:51:37,023 INFO [train.py:451] Epoch 11, batch 13670, batch avg loss 0.1960, total avg loss: 0.2185, batch size: 28
2021-10-15 05:51:42,054 INFO [train.py:451] Epoch 11, batch 13680, batch avg loss 0.1651, total avg loss: 0.2174, batch size: 28
2021-10-15 05:51:46,950 INFO [train.py:451] Epoch 11, batch 13690, batch avg loss 0.1832, total avg loss: 0.2173, batch size: 35
2021-10-15 05:51:51,744 INFO [train.py:451] Epoch 11, batch 13700, batch avg loss 0.1894, total avg loss: 0.2172, batch size: 35
2021-10-15 05:51:56,616 INFO [train.py:451] Epoch 11, batch 13710, batch avg loss 0.1667, total avg loss: 0.2173, batch size: 27
2021-10-15 05:52:01,437 INFO [train.py:451] Epoch 11, batch 13720, batch avg loss 0.2374, total avg loss: 0.2179, batch size: 34
2021-10-15 05:52:06,225 INFO [train.py:451] Epoch 11, batch 13730, batch avg loss 0.2038, total avg loss: 0.2175, batch size: 35
2021-10-15 05:52:11,072 INFO [train.py:451] Epoch 11, batch 13740, batch avg loss 0.2104, total avg loss: 0.2171, batch size: 42
2021-10-15 05:52:15,912 INFO [train.py:451] Epoch 11, batch 13750, batch avg loss 0.1869, total avg loss: 0.2177, batch size: 29
2021-10-15 05:52:20,780 INFO [train.py:451] Epoch 11, batch 13760, batch avg loss 0.2248, total avg loss: 0.2181, batch size: 57
2021-10-15 05:52:25,817 INFO [train.py:451] Epoch 11, batch 13770, batch avg loss 0.1876, total avg loss: 0.2170, batch size: 34
2021-10-15 05:52:30,675 INFO [train.py:451] Epoch 11, batch 13780, batch avg loss 0.2078, total avg loss: 0.2170, batch size: 32
2021-10-15 05:52:35,587 INFO [train.py:451] Epoch 11, batch 13790, batch avg loss 0.2259, total avg loss: 0.2172, batch size: 36
2021-10-15 05:52:40,382 INFO [train.py:451] Epoch 11, batch 13800, batch avg loss 0.1990, total avg loss: 0.2174, batch size: 29
2021-10-15 05:52:45,336 INFO [train.py:451] Epoch 11, batch 13810, batch avg loss 0.2075, total avg loss: 0.2168, batch size: 49
2021-10-15 05:52:50,261 INFO [train.py:451] Epoch 11, batch 13820, batch avg loss 0.2275, total avg loss: 0.2218, batch size: 36
2021-10-15 05:52:55,389 INFO [train.py:451] Epoch 11, batch 13830, batch avg loss 0.1947, total avg loss: 0.2157, batch size: 33
2021-10-15 05:53:00,483 INFO [train.py:451] Epoch 11, batch 13840, batch avg loss 0.1936, total avg loss: 0.2105, batch size: 34
2021-10-15 05:53:05,408 INFO [train.py:451] Epoch 11, batch 13850, batch avg loss 0.1958, total avg loss: 0.2107, batch size: 30
2021-10-15 05:53:10,357 INFO [train.py:451] Epoch 11, batch 13860, batch avg loss 0.2829, total avg loss: 0.2129, batch size: 73
2021-10-15 05:53:15,199 INFO [train.py:451] Epoch 11, batch 13870, batch avg loss 0.3216, total avg loss: 0.2124, batch size: 128
2021-10-15 05:53:20,517 INFO [train.py:451] Epoch 11, batch 13880, batch avg loss 0.1973, total avg loss: 0.2120, batch size: 27
2021-10-15 05:53:25,359 INFO [train.py:451] Epoch 11, batch 13890, batch avg loss 0.2134, total avg loss: 0.2135, batch size: 45
2021-10-15 05:53:30,172 INFO [train.py:451] Epoch 11, batch 13900, batch avg loss 0.1709, total avg loss: 0.2144, batch size: 29
2021-10-15 05:53:35,078 INFO [train.py:451] Epoch 11, batch 13910, batch avg loss 0.2197, total avg loss: 0.2157, batch size: 42
2021-10-15 05:53:40,070 INFO [train.py:451] Epoch 11, batch 13920, batch avg loss 0.1871, total avg loss: 0.2161, batch size: 29
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2021-10-15 05:54:59,261 INFO [train.py:483] Epoch 11, valid loss 0.1608, best valid loss: 0.1605 best valid epoch: 11
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2021-10-15 06:03:53,215 INFO [train.py:483] Epoch 11, valid loss 0.1609, best valid loss: 0.1605 best valid epoch: 11
2021-10-15 06:03:57,998 INFO [train.py:451] Epoch 11, batch 15010, batch avg loss 0.1976, total avg loss: 0.2294, batch size: 30
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2021-10-15 06:21:38,421 INFO [train.py:483] Epoch 11, valid loss 0.1609, best valid loss: 0.1605 best valid epoch: 11
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2021-10-15 06:30:34,136 INFO [train.py:483] Epoch 11, valid loss 0.1608, best valid loss: 0.1605 best valid epoch: 11
2021-10-15 06:30:38,972 INFO [train.py:451] Epoch 11, batch 18010, batch avg loss 0.2176, total avg loss: 0.2128, batch size: 32
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2021-10-15 06:39:43,855 INFO [train.py:483] Epoch 11, valid loss 0.1607, best valid loss: 0.1605 best valid epoch: 11
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2021-10-15 06:57:37,886 INFO [train.py:483] Epoch 11, valid loss 0.1606, best valid loss: 0.1605 best valid epoch: 11
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2021-10-15 06:59:11,604 INFO [checkpoint.py:62] Saving checkpoint to tdnn_lstm_ctc-v2/exp/epoch-11.pt
2021-10-15 06:59:12,449 INFO [train.py:564] epoch 12, lr: 2.5e-05
2021-10-15 06:59:16,854 INFO [train.py:451] Epoch 12, batch 0, batch avg loss 0.1924, total avg loss: 0.1924, batch size: 38
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2021-10-15 07:08:06,096 INFO [train.py:483] Epoch 12, valid loss 0.1607, best valid loss: 0.1605 best valid epoch: 11
2021-10-15 07:08:10,697 INFO [train.py:451] Epoch 12, batch 1010, batch avg loss 0.1880, total avg loss: 0.2211, batch size: 31
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2021-10-15 07:12:06,447 INFO [train.py:451] Epoch 12, batch 1490, batch avg loss 0.2284, total avg loss: 0.2174, batch size: 31
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2021-10-15 07:12:16,159 INFO [train.py:451] Epoch 12, batch 1510, batch avg loss 0.2294, total avg loss: 0.2155, batch size: 45
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2021-10-15 07:12:35,728 INFO [train.py:451] Epoch 12, batch 1550, batch avg loss 0.3229, total avg loss: 0.2170, batch size: 131
2021-10-15 07:12:40,569 INFO [train.py:451] Epoch 12, batch 1560, batch avg loss 0.1646, total avg loss: 0.2170, batch size: 29
2021-10-15 07:12:45,663 INFO [train.py:451] Epoch 12, batch 1570, batch avg loss 0.1980, total avg loss: 0.2157, batch size: 32
2021-10-15 07:12:50,523 INFO [train.py:451] Epoch 12, batch 1580, batch avg loss 0.3363, total avg loss: 0.2165, batch size: 72
2021-10-15 07:12:55,356 INFO [train.py:451] Epoch 12, batch 1590, batch avg loss 0.2686, total avg loss: 0.2164, batch size: 73
2021-10-15 07:13:00,463 INFO [train.py:451] Epoch 12, batch 1600, batch avg loss 0.1874, total avg loss: 0.2156, batch size: 34
2021-10-15 07:13:00,657 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "6e576274-7cc8-9cf8-5db8-ac937d5a026f" will not be mixed in.
2021-10-15 07:13:05,358 INFO [train.py:451] Epoch 12, batch 1610, batch avg loss 0.1875, total avg loss: 0.2202, batch size: 34
2021-10-15 07:13:10,443 INFO [train.py:451] Epoch 12, batch 1620, batch avg loss 0.2208, total avg loss: 0.2116, batch size: 33
2021-10-15 07:13:15,325 INFO [train.py:451] Epoch 12, batch 1630, batch avg loss 0.2612, total avg loss: 0.2133, batch size: 42
2021-10-15 07:13:20,250 INFO [train.py:451] Epoch 12, batch 1640, batch avg loss 0.2321, total avg loss: 0.2156, batch size: 32
2021-10-15 07:13:25,194 INFO [train.py:451] Epoch 12, batch 1650, batch avg loss 0.1870, total avg loss: 0.2147, batch size: 35
2021-10-15 07:13:30,171 INFO [train.py:451] Epoch 12, batch 1660, batch avg loss 0.1856, total avg loss: 0.2156, batch size: 34
2021-10-15 07:13:35,333 INFO [train.py:451] Epoch 12, batch 1670, batch avg loss 0.1723, total avg loss: 0.2158, batch size: 31
2021-10-15 07:13:40,118 INFO [train.py:451] Epoch 12, batch 1680, batch avg loss 0.1994, total avg loss: 0.2176, batch size: 34
2021-10-15 07:13:45,158 INFO [train.py:451] Epoch 12, batch 1690, batch avg loss 0.1804, total avg loss: 0.2155, batch size: 33
2021-10-15 07:13:50,160 INFO [train.py:451] Epoch 12, batch 1700, batch avg loss 0.1975, total avg loss: 0.2146, batch size: 38
2021-10-15 07:13:55,236 INFO [train.py:451] Epoch 12, batch 1710, batch avg loss 0.2228, total avg loss: 0.2147, batch size: 42
2021-10-15 07:14:00,428 INFO [train.py:451] Epoch 12, batch 1720, batch avg loss 0.2524, total avg loss: 0.2158, batch size: 40
2021-10-15 07:14:05,500 INFO [train.py:451] Epoch 12, batch 1730, batch avg loss 0.1611, total avg loss: 0.2143, batch size: 29
2021-10-15 07:14:10,706 INFO [train.py:451] Epoch 12, batch 1740, batch avg loss 0.1616, total avg loss: 0.2138, batch size: 29
2021-10-15 07:14:15,688 INFO [train.py:451] Epoch 12, batch 1750, batch avg loss 0.2745, total avg loss: 0.2140, batch size: 34
2021-10-15 07:14:20,905 INFO [train.py:451] Epoch 12, batch 1760, batch avg loss 0.2175, total avg loss: 0.2134, batch size: 34
2021-10-15 07:14:25,657 INFO [train.py:451] Epoch 12, batch 1770, batch avg loss 0.1922, total avg loss: 0.2140, batch size: 35
2021-10-15 07:14:30,593 INFO [train.py:451] Epoch 12, batch 1780, batch avg loss 0.1664, total avg loss: 0.2135, batch size: 33
2021-10-15 07:14:35,562 INFO [train.py:451] Epoch 12, batch 1790, batch avg loss 0.1970, total avg loss: 0.2134, batch size: 37
2021-10-15 07:14:40,535 INFO [train.py:451] Epoch 12, batch 1800, batch avg loss 0.3305, total avg loss: 0.2136, batch size: 135
2021-10-15 07:14:45,550 INFO [train.py:451] Epoch 12, batch 1810, batch avg loss 0.1955, total avg loss: 0.2153, batch size: 34
2021-10-15 07:14:50,486 INFO [train.py:451] Epoch 12, batch 1820, batch avg loss 0.2583, total avg loss: 0.2131, batch size: 45
2021-10-15 07:14:55,333 INFO [train.py:451] Epoch 12, batch 1830, batch avg loss 0.2042, total avg loss: 0.2125, batch size: 36
2021-10-15 07:15:00,196 INFO [train.py:451] Epoch 12, batch 1840, batch avg loss 0.2078, total avg loss: 0.2139, batch size: 35
2021-10-15 07:15:05,181 INFO [train.py:451] Epoch 12, batch 1850, batch avg loss 0.1769, total avg loss: 0.2165, batch size: 27
2021-10-15 07:15:10,293 INFO [train.py:451] Epoch 12, batch 1860, batch avg loss 0.1919, total avg loss: 0.2140, batch size: 31
2021-10-15 07:15:15,160 INFO [train.py:451] Epoch 12, batch 1870, batch avg loss 0.1704, total avg loss: 0.2165, batch size: 33
2021-10-15 07:15:19,910 INFO [train.py:451] Epoch 12, batch 1880, batch avg loss 0.2760, total avg loss: 0.2171, batch size: 42
2021-10-15 07:15:24,920 INFO [train.py:451] Epoch 12, batch 1890, batch avg loss 0.2109, total avg loss: 0.2173, batch size: 35
2021-10-15 07:15:29,778 INFO [train.py:451] Epoch 12, batch 1900, batch avg loss 0.2700, total avg loss: 0.2178, batch size: 73
2021-10-15 07:15:34,565 INFO [train.py:451] Epoch 12, batch 1910, batch avg loss 0.2304, total avg loss: 0.2183, batch size: 36
2021-10-15 07:15:39,478 INFO [train.py:451] Epoch 12, batch 1920, batch avg loss 0.2130, total avg loss: 0.2177, batch size: 37
2021-10-15 07:15:44,397 INFO [train.py:451] Epoch 12, batch 1930, batch avg loss 0.2186, total avg loss: 0.2164, batch size: 38
2021-10-15 07:15:49,316 INFO [train.py:451] Epoch 12, batch 1940, batch avg loss 0.1840, total avg loss: 0.2169, batch size: 28
2021-10-15 07:15:54,308 INFO [train.py:451] Epoch 12, batch 1950, batch avg loss 0.1809, total avg loss: 0.2162, batch size: 34
2021-10-15 07:15:59,197 INFO [train.py:451] Epoch 12, batch 1960, batch avg loss 0.1835, total avg loss: 0.2162, batch size: 32
2021-10-15 07:16:04,241 INFO [train.py:451] Epoch 12, batch 1970, batch avg loss 0.1830, total avg loss: 0.2156, batch size: 27
2021-10-15 07:16:08,911 INFO [train.py:451] Epoch 12, batch 1980, batch avg loss 0.1865, total avg loss: 0.2164, batch size: 33
2021-10-15 07:16:13,895 INFO [train.py:451] Epoch 12, batch 1990, batch avg loss 0.2545, total avg loss: 0.2170, batch size: 34
2021-10-15 07:16:16,995 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "f618cb6f-157e-21c7-3850-872256c0fa05" will not be mixed in.
2021-10-15 07:16:18,818 INFO [train.py:451] Epoch 12, batch 2000, batch avg loss 0.2047, total avg loss: 0.2168, batch size: 38
2021-10-15 07:16:56,730 INFO [train.py:483] Epoch 12, valid loss 0.1606, best valid loss: 0.1605 best valid epoch: 11
2021-10-15 07:17:01,442 INFO [train.py:451] Epoch 12, batch 2010, batch avg loss 0.3226, total avg loss: 0.2216, batch size: 127
2021-10-15 07:17:06,409 INFO [train.py:451] Epoch 12, batch 2020, batch avg loss 0.2108, total avg loss: 0.2211, batch size: 32
2021-10-15 07:17:11,578 INFO [train.py:451] Epoch 12, batch 2030, batch avg loss 0.2381, total avg loss: 0.2196, batch size: 26
2021-10-15 07:17:16,337 INFO [train.py:451] Epoch 12, batch 2040, batch avg loss 0.2370, total avg loss: 0.2217, batch size: 38
2021-10-15 07:17:21,064 INFO [train.py:451] Epoch 12, batch 2050, batch avg loss 0.2640, total avg loss: 0.2236, batch size: 57
2021-10-15 07:17:26,145 INFO [train.py:451] Epoch 12, batch 2060, batch avg loss 0.2435, total avg loss: 0.2226, batch size: 34
2021-10-15 07:17:31,177 INFO [train.py:451] Epoch 12, batch 2070, batch avg loss 0.2168, total avg loss: 0.2212, batch size: 34
2021-10-15 07:17:36,155 INFO [train.py:451] Epoch 12, batch 2080, batch avg loss 0.2479, total avg loss: 0.2210, batch size: 31
2021-10-15 07:17:40,997 INFO [train.py:451] Epoch 12, batch 2090, batch avg loss 0.2114, total avg loss: 0.2221, batch size: 36
2021-10-15 07:17:45,964 INFO [train.py:451] Epoch 12, batch 2100, batch avg loss 0.1555, total avg loss: 0.2210, batch size: 32
2021-10-15 07:17:50,967 INFO [train.py:451] Epoch 12, batch 2110, batch avg loss 0.1748, total avg loss: 0.2205, batch size: 27
2021-10-15 07:17:55,918 INFO [train.py:451] Epoch 12, batch 2120, batch avg loss 0.2389, total avg loss: 0.2200, batch size: 38
2021-10-15 07:18:00,992 INFO [train.py:451] Epoch 12, batch 2130, batch avg loss 0.2291, total avg loss: 0.2203, batch size: 33
2021-10-15 07:18:05,897 INFO [train.py:451] Epoch 12, batch 2140, batch avg loss 0.1930, total avg loss: 0.2194, batch size: 41
2021-10-15 07:18:10,996 INFO [train.py:451] Epoch 12, batch 2150, batch avg loss 0.2455, total avg loss: 0.2182, batch size: 49
2021-10-15 07:18:16,002 INFO [train.py:451] Epoch 12, batch 2160, batch avg loss 0.1733, total avg loss: 0.2175, batch size: 30
2021-10-15 07:18:21,030 INFO [train.py:451] Epoch 12, batch 2170, batch avg loss 0.2406, total avg loss: 0.2169, batch size: 32
2021-10-15 07:18:26,057 INFO [train.py:451] Epoch 12, batch 2180, batch avg loss 0.2199, total avg loss: 0.2157, batch size: 41
2021-10-15 07:18:31,123 INFO [train.py:451] Epoch 12, batch 2190, batch avg loss 0.1949, total avg loss: 0.2153, batch size: 32
2021-10-15 07:18:35,940 INFO [train.py:451] Epoch 12, batch 2200, batch avg loss 0.2260, total avg loss: 0.2150, batch size: 45
2021-10-15 07:18:40,913 INFO [train.py:451] Epoch 12, batch 2210, batch avg loss 0.2463, total avg loss: 0.2078, batch size: 41
2021-10-15 07:18:45,820 INFO [train.py:451] Epoch 12, batch 2220, batch avg loss 0.1794, total avg loss: 0.2174, batch size: 29
2021-10-15 07:18:50,643 INFO [train.py:451] Epoch 12, batch 2230, batch avg loss 0.1996, total avg loss: 0.2170, batch size: 30
2021-10-15 07:18:55,590 INFO [train.py:451] Epoch 12, batch 2240, batch avg loss 0.2177, total avg loss: 0.2154, batch size: 35
2021-10-15 07:19:00,370 INFO [train.py:451] Epoch 12, batch 2250, batch avg loss 0.2608, total avg loss: 0.2191, batch size: 57
2021-10-15 07:19:05,245 INFO [train.py:451] Epoch 12, batch 2260, batch avg loss 0.2633, total avg loss: 0.2188, batch size: 49
2021-10-15 07:19:10,182 INFO [train.py:451] Epoch 12, batch 2270, batch avg loss 0.1739, total avg loss: 0.2151, batch size: 31
2021-10-15 07:19:15,046 INFO [train.py:451] Epoch 12, batch 2280, batch avg loss 0.2546, total avg loss: 0.2166, batch size: 34
2021-10-15 07:19:19,861 INFO [train.py:451] Epoch 12, batch 2290, batch avg loss 0.2362, total avg loss: 0.2159, batch size: 35
2021-10-15 07:19:24,747 INFO [train.py:451] Epoch 12, batch 2300, batch avg loss 0.2046, total avg loss: 0.2168, batch size: 33
2021-10-15 07:19:29,628 INFO [train.py:451] Epoch 12, batch 2310, batch avg loss 0.2643, total avg loss: 0.2172, batch size: 72
2021-10-15 07:19:34,543 INFO [train.py:451] Epoch 12, batch 2320, batch avg loss 0.1615, total avg loss: 0.2176, batch size: 31
2021-10-15 07:19:39,509 INFO [train.py:451] Epoch 12, batch 2330, batch avg loss 0.1964, total avg loss: 0.2166, batch size: 30
2021-10-15 07:19:44,668 INFO [train.py:451] Epoch 12, batch 2340, batch avg loss 0.2037, total avg loss: 0.2155, batch size: 31
2021-10-15 07:19:49,427 INFO [train.py:451] Epoch 12, batch 2350, batch avg loss 0.2112, total avg loss: 0.2156, batch size: 36
2021-10-15 07:19:54,320 INFO [train.py:451] Epoch 12, batch 2360, batch avg loss 0.2112, total avg loss: 0.2165, batch size: 30
2021-10-15 07:19:59,446 INFO [train.py:451] Epoch 12, batch 2370, batch avg loss 0.1530, total avg loss: 0.2156, batch size: 27
2021-10-15 07:20:04,510 INFO [train.py:451] Epoch 12, batch 2380, batch avg loss 0.2016, total avg loss: 0.2156, batch size: 34
2021-10-15 07:20:09,370 INFO [train.py:451] Epoch 12, batch 2390, batch avg loss 0.2076, total avg loss: 0.2154, batch size: 36
2021-10-15 07:20:14,595 INFO [train.py:451] Epoch 12, batch 2400, batch avg loss 0.1565, total avg loss: 0.2151, batch size: 33
2021-10-15 07:20:19,561 INFO [train.py:451] Epoch 12, batch 2410, batch avg loss 0.1823, total avg loss: 0.2162, batch size: 33
2021-10-15 07:20:24,613 INFO [train.py:451] Epoch 12, batch 2420, batch avg loss 0.3208, total avg loss: 0.2166, batch size: 132
2021-10-15 07:20:29,501 INFO [train.py:451] Epoch 12, batch 2430, batch avg loss 0.2445, total avg loss: 0.2193, batch size: 57
2021-10-15 07:20:34,265 INFO [train.py:451] Epoch 12, batch 2440, batch avg loss 0.2775, total avg loss: 0.2194, batch size: 72
2021-10-15 07:20:39,284 INFO [train.py:451] Epoch 12, batch 2450, batch avg loss 0.2028, total avg loss: 0.2170, batch size: 33
2021-10-15 07:20:44,192 INFO [train.py:451] Epoch 12, batch 2460, batch avg loss 0.2097, total avg loss: 0.2208, batch size: 35
2021-10-15 07:20:48,947 INFO [train.py:451] Epoch 12, batch 2470, batch avg loss 0.2459, total avg loss: 0.2222, batch size: 42
2021-10-15 07:20:54,006 INFO [train.py:451] Epoch 12, batch 2480, batch avg loss 0.2111, total avg loss: 0.2203, batch size: 33
2021-10-15 07:20:59,104 INFO [train.py:451] Epoch 12, batch 2490, batch avg loss 0.2551, total avg loss: 0.2200, batch size: 39
2021-10-15 07:21:04,173 INFO [train.py:451] Epoch 12, batch 2500, batch avg loss 0.2313, total avg loss: 0.2183, batch size: 33
2021-10-15 07:21:09,052 INFO [train.py:451] Epoch 12, batch 2510, batch avg loss 0.2777, total avg loss: 0.2206, batch size: 34
2021-10-15 07:21:14,100 INFO [train.py:451] Epoch 12, batch 2520, batch avg loss 0.2121, total avg loss: 0.2207, batch size: 31
2021-10-15 07:21:19,068 INFO [train.py:451] Epoch 12, batch 2530, batch avg loss 0.2019, total avg loss: 0.2192, batch size: 31
2021-10-15 07:21:24,002 INFO [train.py:451] Epoch 12, batch 2540, batch avg loss 0.2341, total avg loss: 0.2198, batch size: 30
2021-10-15 07:21:29,039 INFO [train.py:451] Epoch 12, batch 2550, batch avg loss 0.2213, total avg loss: 0.2193, batch size: 37
2021-10-15 07:21:33,953 INFO [train.py:451] Epoch 12, batch 2560, batch avg loss 0.2010, total avg loss: 0.2181, batch size: 38
2021-10-15 07:21:38,604 INFO [train.py:451] Epoch 12, batch 2570, batch avg loss 0.2277, total avg loss: 0.2196, batch size: 35
2021-10-15 07:21:43,659 INFO [train.py:451] Epoch 12, batch 2580, batch avg loss 0.1972, total avg loss: 0.2189, batch size: 35
2021-10-15 07:21:48,712 INFO [train.py:451] Epoch 12, batch 2590, batch avg loss 0.1852, total avg loss: 0.2181, batch size: 33
2021-10-15 07:21:53,633 INFO [train.py:451] Epoch 12, batch 2600, batch avg loss 0.2728, total avg loss: 0.2180, batch size: 36
2021-10-15 07:21:58,481 INFO [train.py:451] Epoch 12, batch 2610, batch avg loss 0.2290, total avg loss: 0.2395, batch size: 74
2021-10-15 07:22:03,469 INFO [train.py:451] Epoch 12, batch 2620, batch avg loss 0.2247, total avg loss: 0.2311, batch size: 35
2021-10-15 07:22:08,779 INFO [train.py:451] Epoch 12, batch 2630, batch avg loss 0.2040, total avg loss: 0.2186, batch size: 27
2021-10-15 07:22:13,614 INFO [train.py:451] Epoch 12, batch 2640, batch avg loss 0.2521, total avg loss: 0.2229, batch size: 45
2021-10-15 07:22:18,517 INFO [train.py:451] Epoch 12, batch 2650, batch avg loss 0.2157, total avg loss: 0.2225, batch size: 37
2021-10-15 07:22:23,676 INFO [train.py:451] Epoch 12, batch 2660, batch avg loss 0.2043, total avg loss: 0.2187, batch size: 35
2021-10-15 07:22:28,571 INFO [train.py:451] Epoch 12, batch 2670, batch avg loss 0.1967, total avg loss: 0.2170, batch size: 34
2021-10-15 07:22:33,515 INFO [train.py:451] Epoch 12, batch 2680, batch avg loss 0.2380, total avg loss: 0.2180, batch size: 33
2021-10-15 07:22:38,467 INFO [train.py:451] Epoch 12, batch 2690, batch avg loss 0.1937, total avg loss: 0.2182, batch size: 35
2021-10-15 07:22:43,336 INFO [train.py:451] Epoch 12, batch 2700, batch avg loss 0.2556, total avg loss: 0.2179, batch size: 57
2021-10-15 07:22:48,188 INFO [train.py:451] Epoch 12, batch 2710, batch avg loss 0.2607, total avg loss: 0.2193, batch size: 38
2021-10-15 07:22:53,027 INFO [train.py:451] Epoch 12, batch 2720, batch avg loss 0.2141, total avg loss: 0.2187, batch size: 38
2021-10-15 07:22:57,913 INFO [train.py:451] Epoch 12, batch 2730, batch avg loss 0.2421, total avg loss: 0.2183, batch size: 41
2021-10-15 07:23:02,983 INFO [train.py:451] Epoch 12, batch 2740, batch avg loss 0.1904, total avg loss: 0.2177, batch size: 33
2021-10-15 07:23:07,872 INFO [train.py:451] Epoch 12, batch 2750, batch avg loss 0.1894, total avg loss: 0.2172, batch size: 31
2021-10-15 07:23:12,567 INFO [train.py:451] Epoch 12, batch 2760, batch avg loss 0.2026, total avg loss: 0.2177, batch size: 32
2021-10-15 07:23:17,559 INFO [train.py:451] Epoch 12, batch 2770, batch avg loss 0.2417, total avg loss: 0.2179, batch size: 45
2021-10-15 07:23:22,397 INFO [train.py:451] Epoch 12, batch 2780, batch avg loss 0.1664, total avg loss: 0.2177, batch size: 28
2021-10-15 07:23:27,339 INFO [train.py:451] Epoch 12, batch 2790, batch avg loss 0.2224, total avg loss: 0.2178, batch size: 39
2021-10-15 07:23:32,462 INFO [train.py:451] Epoch 12, batch 2800, batch avg loss 0.1738, total avg loss: 0.2165, batch size: 28
2021-10-15 07:23:37,364 INFO [train.py:451] Epoch 12, batch 2810, batch avg loss 0.1874, total avg loss: 0.2210, batch size: 31
2021-10-15 07:23:42,330 INFO [train.py:451] Epoch 12, batch 2820, batch avg loss 0.2264, total avg loss: 0.2285, batch size: 36
2021-10-15 07:23:47,223 INFO [train.py:451] Epoch 12, batch 2830, batch avg loss 0.2304, total avg loss: 0.2234, batch size: 34
2021-10-15 07:23:51,932 INFO [train.py:451] Epoch 12, batch 2840, batch avg loss 0.2410, total avg loss: 0.2257, batch size: 39
2021-10-15 07:23:56,964 INFO [train.py:451] Epoch 12, batch 2850, batch avg loss 0.1907, total avg loss: 0.2232, batch size: 28
2021-10-15 07:24:01,934 INFO [train.py:451] Epoch 12, batch 2860, batch avg loss 0.2268, total avg loss: 0.2233, batch size: 34
2021-10-15 07:24:06,860 INFO [train.py:451] Epoch 12, batch 2870, batch avg loss 0.1618, total avg loss: 0.2236, batch size: 29
2021-10-15 07:24:11,800 INFO [train.py:451] Epoch 12, batch 2880, batch avg loss 0.2248, total avg loss: 0.2232, batch size: 36
2021-10-15 07:24:16,759 INFO [train.py:451] Epoch 12, batch 2890, batch avg loss 0.2332, total avg loss: 0.2229, batch size: 49
2021-10-15 07:24:21,852 INFO [train.py:451] Epoch 12, batch 2900, batch avg loss 0.2239, total avg loss: 0.2235, batch size: 39
2021-10-15 07:24:26,764 INFO [train.py:451] Epoch 12, batch 2910, batch avg loss 0.2947, total avg loss: 0.2230, batch size: 38
2021-10-15 07:24:31,466 INFO [train.py:451] Epoch 12, batch 2920, batch avg loss 0.1752, total avg loss: 0.2224, batch size: 28
2021-10-15 07:24:36,283 INFO [train.py:451] Epoch 12, batch 2930, batch avg loss 0.2236, total avg loss: 0.2229, batch size: 42
2021-10-15 07:24:41,076 INFO [train.py:451] Epoch 12, batch 2940, batch avg loss 0.2442, total avg loss: 0.2219, batch size: 30
2021-10-15 07:24:46,236 INFO [train.py:451] Epoch 12, batch 2950, batch avg loss 0.1873, total avg loss: 0.2211, batch size: 33
2021-10-15 07:24:51,081 INFO [train.py:451] Epoch 12, batch 2960, batch avg loss 0.2355, total avg loss: 0.2205, batch size: 57
2021-10-15 07:24:55,961 INFO [train.py:451] Epoch 12, batch 2970, batch avg loss 0.1988, total avg loss: 0.2199, batch size: 32
2021-10-15 07:25:00,995 INFO [train.py:451] Epoch 12, batch 2980, batch avg loss 0.1710, total avg loss: 0.2192, batch size: 28
2021-10-15 07:25:05,802 INFO [train.py:451] Epoch 12, batch 2990, batch avg loss 0.2062, total avg loss: 0.2192, batch size: 30
2021-10-15 07:25:10,598 INFO [train.py:451] Epoch 12, batch 3000, batch avg loss 0.1651, total avg loss: 0.2198, batch size: 31
2021-10-15 07:25:50,183 INFO [train.py:483] Epoch 12, valid loss 0.1604, best valid loss: 0.1604 best valid epoch: 12
2021-10-15 07:25:55,085 INFO [train.py:451] Epoch 12, batch 3010, batch avg loss 0.1921, total avg loss: 0.2211, batch size: 30
2021-10-15 07:25:59,961 INFO [train.py:451] Epoch 12, batch 3020, batch avg loss 0.3245, total avg loss: 0.2160, batch size: 124
2021-10-15 07:26:04,945 INFO [train.py:451] Epoch 12, batch 3030, batch avg loss 0.2231, total avg loss: 0.2133, batch size: 29
2021-10-15 07:26:09,897 INFO [train.py:451] Epoch 12, batch 3040, batch avg loss 0.2108, total avg loss: 0.2124, batch size: 45
2021-10-15 07:26:14,825 INFO [train.py:451] Epoch 12, batch 3050, batch avg loss 0.2282, total avg loss: 0.2133, batch size: 38
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2021-10-15 07:27:28,604 INFO [train.py:451] Epoch 12, batch 3200, batch avg loss 0.2066, total avg loss: 0.2119, batch size: 36
2021-10-15 07:27:33,753 INFO [train.py:451] Epoch 12, batch 3210, batch avg loss 0.1726, total avg loss: 0.2002, batch size: 33
2021-10-15 07:27:38,710 INFO [train.py:451] Epoch 12, batch 3220, batch avg loss 0.2450, total avg loss: 0.2100, batch size: 36
2021-10-15 07:27:43,764 INFO [train.py:451] Epoch 12, batch 3230, batch avg loss 0.2291, total avg loss: 0.2094, batch size: 38
2021-10-15 07:27:48,523 INFO [train.py:451] Epoch 12, batch 3240, batch avg loss 0.1684, total avg loss: 0.2109, batch size: 30
2021-10-15 07:27:53,228 INFO [train.py:451] Epoch 12, batch 3250, batch avg loss 0.2394, total avg loss: 0.2131, batch size: 57
2021-10-15 07:27:58,139 INFO [train.py:451] Epoch 12, batch 3260, batch avg loss 0.2396, total avg loss: 0.2141, batch size: 49
2021-10-15 07:28:03,236 INFO [train.py:451] Epoch 12, batch 3270, batch avg loss 0.2192, total avg loss: 0.2128, batch size: 36
2021-10-15 07:28:08,105 INFO [train.py:451] Epoch 12, batch 3280, batch avg loss 0.2858, total avg loss: 0.2147, batch size: 33
2021-10-15 07:28:13,151 INFO [train.py:451] Epoch 12, batch 3290, batch avg loss 0.1878, total avg loss: 0.2140, batch size: 33
2021-10-15 07:28:17,978 INFO [train.py:451] Epoch 12, batch 3300, batch avg loss 0.2221, total avg loss: 0.2143, batch size: 38
2021-10-15 07:28:22,849 INFO [train.py:451] Epoch 12, batch 3310, batch avg loss 0.2157, total avg loss: 0.2134, batch size: 31
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2021-10-15 07:28:37,776 INFO [train.py:451] Epoch 12, batch 3340, batch avg loss 0.2203, total avg loss: 0.2138, batch size: 42
2021-10-15 07:28:42,841 INFO [train.py:451] Epoch 12, batch 3350, batch avg loss 0.2095, total avg loss: 0.2140, batch size: 33
2021-10-15 07:28:47,624 INFO [train.py:451] Epoch 12, batch 3360, batch avg loss 0.2460, total avg loss: 0.2150, batch size: 38
2021-10-15 07:28:52,517 INFO [train.py:451] Epoch 12, batch 3370, batch avg loss 0.2354, total avg loss: 0.2142, batch size: 37
2021-10-15 07:28:57,529 INFO [train.py:451] Epoch 12, batch 3380, batch avg loss 0.2279, total avg loss: 0.2140, batch size: 34
2021-10-15 07:29:02,441 INFO [train.py:451] Epoch 12, batch 3390, batch avg loss 0.2586, total avg loss: 0.2139, batch size: 38
2021-10-15 07:29:07,327 INFO [train.py:451] Epoch 12, batch 3400, batch avg loss 0.1590, total avg loss: 0.2132, batch size: 31
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2021-10-15 07:29:17,669 INFO [train.py:451] Epoch 12, batch 3420, batch avg loss 0.2075, total avg loss: 0.1959, batch size: 27
2021-10-15 07:29:22,679 INFO [train.py:451] Epoch 12, batch 3430, batch avg loss 0.1566, total avg loss: 0.1969, batch size: 30
2021-10-15 07:29:27,695 INFO [train.py:451] Epoch 12, batch 3440, batch avg loss 0.2177, total avg loss: 0.2007, batch size: 30
2021-10-15 07:29:32,757 INFO [train.py:451] Epoch 12, batch 3450, batch avg loss 0.1866, total avg loss: 0.2044, batch size: 27
2021-10-15 07:29:37,807 INFO [train.py:451] Epoch 12, batch 3460, batch avg loss 0.1915, total avg loss: 0.2066, batch size: 29
2021-10-15 07:29:42,645 INFO [train.py:451] Epoch 12, batch 3470, batch avg loss 0.1926, total avg loss: 0.2093, batch size: 31
2021-10-15 07:29:47,704 INFO [train.py:451] Epoch 12, batch 3480, batch avg loss 0.2076, total avg loss: 0.2104, batch size: 32
2021-10-15 07:29:52,669 INFO [train.py:451] Epoch 12, batch 3490, batch avg loss 0.2038, total avg loss: 0.2116, batch size: 27
2021-10-15 07:29:57,481 INFO [train.py:451] Epoch 12, batch 3500, batch avg loss 0.2621, total avg loss: 0.2138, batch size: 42
2021-10-15 07:30:02,412 INFO [train.py:451] Epoch 12, batch 3510, batch avg loss 0.1970, total avg loss: 0.2125, batch size: 35
2021-10-15 07:30:07,522 INFO [train.py:451] Epoch 12, batch 3520, batch avg loss 0.1765, total avg loss: 0.2111, batch size: 27
2021-10-15 07:30:12,375 INFO [train.py:451] Epoch 12, batch 3530, batch avg loss 0.2271, total avg loss: 0.2115, batch size: 35
2021-10-15 07:30:17,288 INFO [train.py:451] Epoch 12, batch 3540, batch avg loss 0.2272, total avg loss: 0.2108, batch size: 35
2021-10-15 07:30:22,144 INFO [train.py:451] Epoch 12, batch 3550, batch avg loss 0.1784, total avg loss: 0.2125, batch size: 32
2021-10-15 07:30:27,111 INFO [train.py:451] Epoch 12, batch 3560, batch avg loss 0.2568, total avg loss: 0.2134, batch size: 31
2021-10-15 07:30:32,165 INFO [train.py:451] Epoch 12, batch 3570, batch avg loss 0.1791, total avg loss: 0.2133, batch size: 28
2021-10-15 07:30:37,156 INFO [train.py:451] Epoch 12, batch 3580, batch avg loss 0.1440, total avg loss: 0.2131, batch size: 29
2021-10-15 07:30:42,111 INFO [train.py:451] Epoch 12, batch 3590, batch avg loss 0.2015, total avg loss: 0.2129, batch size: 34
2021-10-15 07:30:47,122 INFO [train.py:451] Epoch 12, batch 3600, batch avg loss 0.1737, total avg loss: 0.2126, batch size: 35
2021-10-15 07:30:52,083 INFO [train.py:451] Epoch 12, batch 3610, batch avg loss 0.1989, total avg loss: 0.2017, batch size: 37
2021-10-15 07:30:56,811 INFO [train.py:451] Epoch 12, batch 3620, batch avg loss 0.2803, total avg loss: 0.2106, batch size: 72
2021-10-15 07:31:01,728 INFO [train.py:451] Epoch 12, batch 3630, batch avg loss 0.1799, total avg loss: 0.2138, batch size: 31
2021-10-15 07:31:06,533 INFO [train.py:451] Epoch 12, batch 3640, batch avg loss 0.1815, total avg loss: 0.2144, batch size: 29
2021-10-15 07:31:11,443 INFO [train.py:451] Epoch 12, batch 3650, batch avg loss 0.1953, total avg loss: 0.2167, batch size: 28
2021-10-15 07:31:16,258 INFO [train.py:451] Epoch 12, batch 3660, batch avg loss 0.1871, total avg loss: 0.2185, batch size: 30
2021-10-15 07:31:21,419 INFO [train.py:451] Epoch 12, batch 3670, batch avg loss 0.1843, total avg loss: 0.2172, batch size: 27
2021-10-15 07:31:26,255 INFO [train.py:451] Epoch 12, batch 3680, batch avg loss 0.2244, total avg loss: 0.2158, batch size: 45
2021-10-15 07:31:31,284 INFO [train.py:451] Epoch 12, batch 3690, batch avg loss 0.1975, total avg loss: 0.2143, batch size: 38
2021-10-15 07:31:36,279 INFO [train.py:451] Epoch 12, batch 3700, batch avg loss 0.1942, total avg loss: 0.2142, batch size: 29
2021-10-15 07:31:41,199 INFO [train.py:451] Epoch 12, batch 3710, batch avg loss 0.1414, total avg loss: 0.2133, batch size: 31
2021-10-15 07:31:45,959 INFO [train.py:451] Epoch 12, batch 3720, batch avg loss 0.2175, total avg loss: 0.2145, batch size: 49
2021-10-15 07:31:50,651 INFO [train.py:451] Epoch 12, batch 3730, batch avg loss 0.1645, total avg loss: 0.2134, batch size: 31
2021-10-15 07:31:55,592 INFO [train.py:451] Epoch 12, batch 3740, batch avg loss 0.1751, total avg loss: 0.2125, batch size: 29
2021-10-15 07:32:00,568 INFO [train.py:451] Epoch 12, batch 3750, batch avg loss 0.1961, total avg loss: 0.2128, batch size: 33
2021-10-15 07:32:05,655 INFO [train.py:451] Epoch 12, batch 3760, batch avg loss 0.2158, total avg loss: 0.2112, batch size: 32
2021-10-15 07:32:10,271 INFO [train.py:451] Epoch 12, batch 3770, batch avg loss 0.1922, total avg loss: 0.2134, batch size: 31
2021-10-15 07:32:15,146 INFO [train.py:451] Epoch 12, batch 3780, batch avg loss 0.1645, total avg loss: 0.2135, batch size: 33
2021-10-15 07:32:20,320 INFO [train.py:451] Epoch 12, batch 3790, batch avg loss 0.2225, total avg loss: 0.2132, batch size: 45
2021-10-15 07:32:25,369 INFO [train.py:451] Epoch 12, batch 3800, batch avg loss 0.1946, total avg loss: 0.2133, batch size: 40
2021-10-15 07:32:30,551 INFO [train.py:451] Epoch 12, batch 3810, batch avg loss 0.1801, total avg loss: 0.2117, batch size: 32
2021-10-15 07:32:35,622 INFO [train.py:451] Epoch 12, batch 3820, batch avg loss 0.1938, total avg loss: 0.2097, batch size: 31
2021-10-15 07:32:40,520 INFO [train.py:451] Epoch 12, batch 3830, batch avg loss 0.2483, total avg loss: 0.2134, batch size: 45
2021-10-15 07:32:45,426 INFO [train.py:451] Epoch 12, batch 3840, batch avg loss 0.2334, total avg loss: 0.2116, batch size: 56
2021-10-15 07:32:50,233 INFO [train.py:451] Epoch 12, batch 3850, batch avg loss 0.1758, total avg loss: 0.2114, batch size: 30
2021-10-15 07:32:55,166 INFO [train.py:451] Epoch 12, batch 3860, batch avg loss 0.1879, total avg loss: 0.2127, batch size: 33
2021-10-15 07:33:00,121 INFO [train.py:451] Epoch 12, batch 3870, batch avg loss 0.2449, total avg loss: 0.2114, batch size: 35
2021-10-15 07:33:05,162 INFO [train.py:451] Epoch 12, batch 3880, batch avg loss 0.2547, total avg loss: 0.2090, batch size: 39
2021-10-15 07:33:09,802 INFO [train.py:451] Epoch 12, batch 3890, batch avg loss 0.1902, total avg loss: 0.2126, batch size: 33
2021-10-15 07:33:14,649 INFO [train.py:451] Epoch 12, batch 3900, batch avg loss 0.2369, total avg loss: 0.2134, batch size: 42
2021-10-15 07:33:19,544 INFO [train.py:451] Epoch 12, batch 3910, batch avg loss 0.2209, total avg loss: 0.2135, batch size: 35
2021-10-15 07:33:24,461 INFO [train.py:451] Epoch 12, batch 3920, batch avg loss 0.2156, total avg loss: 0.2133, batch size: 33
2021-10-15 07:33:29,281 INFO [train.py:451] Epoch 12, batch 3930, batch avg loss 0.1911, total avg loss: 0.2139, batch size: 31
2021-10-15 07:33:34,272 INFO [train.py:451] Epoch 12, batch 3940, batch avg loss 0.2529, total avg loss: 0.2151, batch size: 36
2021-10-15 07:33:39,224 INFO [train.py:451] Epoch 12, batch 3950, batch avg loss 0.1860, total avg loss: 0.2139, batch size: 30
2021-10-15 07:33:44,208 INFO [train.py:451] Epoch 12, batch 3960, batch avg loss 0.1920, total avg loss: 0.2140, batch size: 32
2021-10-15 07:33:48,834 INFO [train.py:451] Epoch 12, batch 3970, batch avg loss 0.1944, total avg loss: 0.2147, batch size: 29
2021-10-15 07:33:53,708 INFO [train.py:451] Epoch 12, batch 3980, batch avg loss 0.1771, total avg loss: 0.2153, batch size: 30
2021-10-15 07:33:58,590 INFO [train.py:451] Epoch 12, batch 3990, batch avg loss 0.2007, total avg loss: 0.2156, batch size: 37
2021-10-15 07:34:03,442 INFO [train.py:451] Epoch 12, batch 4000, batch avg loss 0.1926, total avg loss: 0.2152, batch size: 31
2021-10-15 07:34:43,115 INFO [train.py:483] Epoch 12, valid loss 0.1603, best valid loss: 0.1603 best valid epoch: 12
2021-10-15 07:34:47,819 INFO [train.py:451] Epoch 12, batch 4010, batch avg loss 0.1505, total avg loss: 0.2235, batch size: 29
2021-10-15 07:34:52,951 INFO [train.py:451] Epoch 12, batch 4020, batch avg loss 0.1476, total avg loss: 0.2095, batch size: 29
2021-10-15 07:34:57,802 INFO [train.py:451] Epoch 12, batch 4030, batch avg loss 0.2463, total avg loss: 0.2186, batch size: 38
2021-10-15 07:35:02,795 INFO [train.py:451] Epoch 12, batch 4040, batch avg loss 0.2293, total avg loss: 0.2152, batch size: 42
2021-10-15 07:35:07,669 INFO [train.py:451] Epoch 12, batch 4050, batch avg loss 0.1835, total avg loss: 0.2149, batch size: 36
2021-10-15 07:35:12,640 INFO [train.py:451] Epoch 12, batch 4060, batch avg loss 0.1908, total avg loss: 0.2155, batch size: 32
2021-10-15 07:35:17,492 INFO [train.py:451] Epoch 12, batch 4070, batch avg loss 0.2240, total avg loss: 0.2148, batch size: 42
2021-10-15 07:35:22,430 INFO [train.py:451] Epoch 12, batch 4080, batch avg loss 0.1961, total avg loss: 0.2134, batch size: 42
2021-10-15 07:35:27,578 INFO [train.py:451] Epoch 12, batch 4090, batch avg loss 0.1813, total avg loss: 0.2134, batch size: 29
2021-10-15 07:35:32,422 INFO [train.py:451] Epoch 12, batch 4100, batch avg loss 0.2287, total avg loss: 0.2154, batch size: 49
2021-10-15 07:35:37,452 INFO [train.py:451] Epoch 12, batch 4110, batch avg loss 0.1941, total avg loss: 0.2166, batch size: 32
2021-10-15 07:35:42,363 INFO [train.py:451] Epoch 12, batch 4120, batch avg loss 0.2271, total avg loss: 0.2177, batch size: 28
2021-10-15 07:35:47,530 INFO [train.py:451] Epoch 12, batch 4130, batch avg loss 0.1852, total avg loss: 0.2175, batch size: 33
2021-10-15 07:35:52,404 INFO [train.py:451] Epoch 12, batch 4140, batch avg loss 0.2145, total avg loss: 0.2177, batch size: 35
2021-10-15 07:35:57,202 INFO [train.py:451] Epoch 12, batch 4150, batch avg loss 0.2929, total avg loss: 0.2179, batch size: 73
2021-10-15 07:36:02,175 INFO [train.py:451] Epoch 12, batch 4160, batch avg loss 0.1655, total avg loss: 0.2173, batch size: 30
2021-10-15 07:36:07,088 INFO [train.py:451] Epoch 12, batch 4170, batch avg loss 0.2451, total avg loss: 0.2162, batch size: 56
2021-10-15 07:36:11,838 INFO [train.py:451] Epoch 12, batch 4180, batch avg loss 0.2285, total avg loss: 0.2170, batch size: 33
2021-10-15 07:36:16,763 INFO [train.py:451] Epoch 12, batch 4190, batch avg loss 0.2013, total avg loss: 0.2167, batch size: 32
2021-10-15 07:36:21,577 INFO [train.py:451] Epoch 12, batch 4200, batch avg loss 0.1574, total avg loss: 0.2158, batch size: 33
2021-10-15 07:36:26,356 INFO [train.py:451] Epoch 12, batch 4210, batch avg loss 0.2194, total avg loss: 0.2250, batch size: 45
2021-10-15 07:36:31,201 INFO [train.py:451] Epoch 12, batch 4220, batch avg loss 0.2399, total avg loss: 0.2257, batch size: 38
2021-10-15 07:36:36,062 INFO [train.py:451] Epoch 12, batch 4230, batch avg loss 0.2011, total avg loss: 0.2273, batch size: 33
2021-10-15 07:36:40,999 INFO [train.py:451] Epoch 12, batch 4240, batch avg loss 0.2016, total avg loss: 0.2266, batch size: 36
2021-10-15 07:36:45,986 INFO [train.py:451] Epoch 12, batch 4250, batch avg loss 0.1993, total avg loss: 0.2252, batch size: 38
2021-10-15 07:36:50,916 INFO [train.py:451] Epoch 12, batch 4260, batch avg loss 0.1897, total avg loss: 0.2229, batch size: 30
2021-10-15 07:36:55,855 INFO [train.py:451] Epoch 12, batch 4270, batch avg loss 0.1839, total avg loss: 0.2215, batch size: 28
2021-10-15 07:37:00,823 INFO [train.py:451] Epoch 12, batch 4280, batch avg loss 0.2258, total avg loss: 0.2197, batch size: 36
2021-10-15 07:37:05,790 INFO [train.py:451] Epoch 12, batch 4290, batch avg loss 0.2229, total avg loss: 0.2195, batch size: 35
2021-10-15 07:37:10,743 INFO [train.py:451] Epoch 12, batch 4300, batch avg loss 0.1833, total avg loss: 0.2188, batch size: 38
2021-10-15 07:37:15,684 INFO [train.py:451] Epoch 12, batch 4310, batch avg loss 0.3013, total avg loss: 0.2189, batch size: 72
2021-10-15 07:37:20,576 INFO [train.py:451] Epoch 12, batch 4320, batch avg loss 0.2266, total avg loss: 0.2187, batch size: 49
2021-10-15 07:37:25,445 INFO [train.py:451] Epoch 12, batch 4330, batch avg loss 0.1732, total avg loss: 0.2181, batch size: 29
2021-10-15 07:37:30,444 INFO [train.py:451] Epoch 12, batch 4340, batch avg loss 0.2539, total avg loss: 0.2185, batch size: 35
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2021-10-15 07:37:40,354 INFO [train.py:451] Epoch 12, batch 4360, batch avg loss 0.1784, total avg loss: 0.2175, batch size: 32
2021-10-15 07:37:45,003 INFO [train.py:451] Epoch 12, batch 4370, batch avg loss 0.2536, total avg loss: 0.2189, batch size: 34
2021-10-15 07:37:49,834 INFO [train.py:451] Epoch 12, batch 4380, batch avg loss 0.1892, total avg loss: 0.2197, batch size: 39
2021-10-15 07:37:54,743 INFO [train.py:451] Epoch 12, batch 4390, batch avg loss 0.1646, total avg loss: 0.2198, batch size: 33
2021-10-15 07:37:59,567 INFO [train.py:451] Epoch 12, batch 4400, batch avg loss 0.2245, total avg loss: 0.2201, batch size: 33
2021-10-15 07:38:04,419 INFO [train.py:451] Epoch 12, batch 4410, batch avg loss 0.2627, total avg loss: 0.2093, batch size: 38
2021-10-15 07:38:09,389 INFO [train.py:451] Epoch 12, batch 4420, batch avg loss 0.2312, total avg loss: 0.2143, batch size: 38
2021-10-15 07:38:14,394 INFO [train.py:451] Epoch 12, batch 4430, batch avg loss 0.1960, total avg loss: 0.2124, batch size: 33
2021-10-15 07:38:19,225 INFO [train.py:451] Epoch 12, batch 4440, batch avg loss 0.2009, total avg loss: 0.2107, batch size: 56
2021-10-15 07:38:24,146 INFO [train.py:451] Epoch 12, batch 4450, batch avg loss 0.2535, total avg loss: 0.2117, batch size: 45
2021-10-15 07:38:29,079 INFO [train.py:451] Epoch 12, batch 4460, batch avg loss 0.2400, total avg loss: 0.2113, batch size: 36
2021-10-15 07:38:33,994 INFO [train.py:451] Epoch 12, batch 4470, batch avg loss 0.2557, total avg loss: 0.2118, batch size: 56
2021-10-15 07:38:38,870 INFO [train.py:451] Epoch 12, batch 4480, batch avg loss 0.2338, total avg loss: 0.2123, batch size: 34
2021-10-15 07:38:43,825 INFO [train.py:451] Epoch 12, batch 4490, batch avg loss 0.1837, total avg loss: 0.2120, batch size: 32
2021-10-15 07:38:48,715 INFO [train.py:451] Epoch 12, batch 4500, batch avg loss 0.1795, total avg loss: 0.2129, batch size: 29
2021-10-15 07:38:53,626 INFO [train.py:451] Epoch 12, batch 4510, batch avg loss 0.1850, total avg loss: 0.2129, batch size: 34
2021-10-15 07:38:58,613 INFO [train.py:451] Epoch 12, batch 4520, batch avg loss 0.2551, total avg loss: 0.2122, batch size: 38
2021-10-15 07:39:03,577 INFO [train.py:451] Epoch 12, batch 4530, batch avg loss 0.2493, total avg loss: 0.2121, batch size: 41
2021-10-15 07:39:08,563 INFO [train.py:451] Epoch 12, batch 4540, batch avg loss 0.2233, total avg loss: 0.2129, batch size: 40
2021-10-15 07:39:13,459 INFO [train.py:451] Epoch 12, batch 4550, batch avg loss 0.2475, total avg loss: 0.2133, batch size: 42
2021-10-15 07:39:18,446 INFO [train.py:451] Epoch 12, batch 4560, batch avg loss 0.2821, total avg loss: 0.2147, batch size: 39
2021-10-15 07:39:23,514 INFO [train.py:451] Epoch 12, batch 4570, batch avg loss 0.1914, total avg loss: 0.2149, batch size: 31
2021-10-15 07:39:28,311 INFO [train.py:451] Epoch 12, batch 4580, batch avg loss 0.2820, total avg loss: 0.2160, batch size: 57
2021-10-15 07:39:33,228 INFO [train.py:451] Epoch 12, batch 4590, batch avg loss 0.2163, total avg loss: 0.2161, batch size: 41
2021-10-15 07:39:38,091 INFO [train.py:451] Epoch 12, batch 4600, batch avg loss 0.2802, total avg loss: 0.2171, batch size: 38
2021-10-15 07:39:43,075 INFO [train.py:451] Epoch 12, batch 4610, batch avg loss 0.2446, total avg loss: 0.2165, batch size: 34
2021-10-15 07:39:47,827 INFO [train.py:451] Epoch 12, batch 4620, batch avg loss 0.3186, total avg loss: 0.2321, batch size: 131
2021-10-15 07:39:52,788 INFO [train.py:451] Epoch 12, batch 4630, batch avg loss 0.1863, total avg loss: 0.2262, batch size: 27
2021-10-15 07:39:57,703 INFO [train.py:451] Epoch 12, batch 4640, batch avg loss 0.1843, total avg loss: 0.2225, batch size: 30
2021-10-15 07:40:02,704 INFO [train.py:451] Epoch 12, batch 4650, batch avg loss 0.1947, total avg loss: 0.2211, batch size: 29
2021-10-15 07:40:07,593 INFO [train.py:451] Epoch 12, batch 4660, batch avg loss 0.2067, total avg loss: 0.2186, batch size: 36
2021-10-15 07:40:12,499 INFO [train.py:451] Epoch 12, batch 4670, batch avg loss 0.1993, total avg loss: 0.2191, batch size: 35
2021-10-15 07:40:17,413 INFO [train.py:451] Epoch 12, batch 4680, batch avg loss 0.2160, total avg loss: 0.2191, batch size: 30
2021-10-15 07:40:22,469 INFO [train.py:451] Epoch 12, batch 4690, batch avg loss 0.1617, total avg loss: 0.2178, batch size: 33
2021-10-15 07:40:27,438 INFO [train.py:451] Epoch 12, batch 4700, batch avg loss 0.1968, total avg loss: 0.2162, batch size: 45
2021-10-15 07:40:32,341 INFO [train.py:451] Epoch 12, batch 4710, batch avg loss 0.2133, total avg loss: 0.2185, batch size: 33
2021-10-15 07:40:37,268 INFO [train.py:451] Epoch 12, batch 4720, batch avg loss 0.2190, total avg loss: 0.2187, batch size: 36
2021-10-15 07:40:42,261 INFO [train.py:451] Epoch 12, batch 4730, batch avg loss 0.2211, total avg loss: 0.2181, batch size: 35
2021-10-15 07:40:47,114 INFO [train.py:451] Epoch 12, batch 4740, batch avg loss 0.2492, total avg loss: 0.2179, batch size: 36
2021-10-15 07:40:51,999 INFO [train.py:451] Epoch 12, batch 4750, batch avg loss 0.1887, total avg loss: 0.2175, batch size: 32
2021-10-15 07:40:56,822 INFO [train.py:451] Epoch 12, batch 4760, batch avg loss 0.2632, total avg loss: 0.2176, batch size: 49
2021-10-15 07:41:01,928 INFO [train.py:451] Epoch 12, batch 4770, batch avg loss 0.2443, total avg loss: 0.2168, batch size: 42
2021-10-15 07:41:06,889 INFO [train.py:451] Epoch 12, batch 4780, batch avg loss 0.1946, total avg loss: 0.2161, batch size: 28
2021-10-15 07:41:11,757 INFO [train.py:451] Epoch 12, batch 4790, batch avg loss 0.2240, total avg loss: 0.2173, batch size: 41
2021-10-15 07:41:16,805 INFO [train.py:451] Epoch 12, batch 4800, batch avg loss 0.1998, total avg loss: 0.2171, batch size: 29
2021-10-15 07:41:21,785 INFO [train.py:451] Epoch 12, batch 4810, batch avg loss 0.2274, total avg loss: 0.2137, batch size: 39
2021-10-15 07:41:26,902 INFO [train.py:451] Epoch 12, batch 4820, batch avg loss 0.1913, total avg loss: 0.2189, batch size: 30
2021-10-15 07:41:31,936 INFO [train.py:451] Epoch 12, batch 4830, batch avg loss 0.1503, total avg loss: 0.2159, batch size: 30
2021-10-15 07:41:36,656 INFO [train.py:451] Epoch 12, batch 4840, batch avg loss 0.2361, total avg loss: 0.2253, batch size: 36
2021-10-15 07:41:41,520 INFO [train.py:451] Epoch 12, batch 4850, batch avg loss 0.2014, total avg loss: 0.2240, batch size: 33
2021-10-15 07:41:46,517 INFO [train.py:451] Epoch 12, batch 4860, batch avg loss 0.1755, total avg loss: 0.2217, batch size: 30
2021-10-15 07:41:51,317 INFO [train.py:451] Epoch 12, batch 4870, batch avg loss 0.2284, total avg loss: 0.2213, batch size: 34
2021-10-15 07:41:56,159 INFO [train.py:451] Epoch 12, batch 4880, batch avg loss 0.1804, total avg loss: 0.2205, batch size: 31
2021-10-15 07:42:01,176 INFO [train.py:451] Epoch 12, batch 4890, batch avg loss 0.1751, total avg loss: 0.2192, batch size: 29
2021-10-15 07:42:06,183 INFO [train.py:451] Epoch 12, batch 4900, batch avg loss 0.2107, total avg loss: 0.2163, batch size: 31
2021-10-15 07:42:11,126 INFO [train.py:451] Epoch 12, batch 4910, batch avg loss 0.2421, total avg loss: 0.2162, batch size: 36
2021-10-15 07:42:15,973 INFO [train.py:451] Epoch 12, batch 4920, batch avg loss 0.2060, total avg loss: 0.2169, batch size: 38
2021-10-15 07:42:20,929 INFO [train.py:451] Epoch 12, batch 4930, batch avg loss 0.1957, total avg loss: 0.2148, batch size: 29
2021-10-15 07:42:25,768 INFO [train.py:451] Epoch 12, batch 4940, batch avg loss 0.2526, total avg loss: 0.2159, batch size: 36
2021-10-15 07:42:30,628 INFO [train.py:451] Epoch 12, batch 4950, batch avg loss 0.2792, total avg loss: 0.2157, batch size: 38
2021-10-15 07:42:35,604 INFO [train.py:451] Epoch 12, batch 4960, batch avg loss 0.1700, total avg loss: 0.2156, batch size: 29
2021-10-15 07:42:40,490 INFO [train.py:451] Epoch 12, batch 4970, batch avg loss 0.2479, total avg loss: 0.2151, batch size: 57
2021-10-15 07:42:45,376 INFO [train.py:451] Epoch 12, batch 4980, batch avg loss 0.1879, total avg loss: 0.2162, batch size: 30
2021-10-15 07:42:50,210 INFO [train.py:451] Epoch 12, batch 4990, batch avg loss 0.2257, total avg loss: 0.2176, batch size: 49
2021-10-15 07:42:54,885 INFO [train.py:451] Epoch 12, batch 5000, batch avg loss 0.2068, total avg loss: 0.2184, batch size: 42
2021-10-15 07:43:34,553 INFO [train.py:483] Epoch 12, valid loss 0.1598, best valid loss: 0.1598 best valid epoch: 12
2021-10-15 07:43:39,532 INFO [train.py:451] Epoch 12, batch 5010, batch avg loss 0.2627, total avg loss: 0.2171, batch size: 39
2021-10-15 07:43:44,658 INFO [train.py:451] Epoch 12, batch 5020, batch avg loss 0.1815, total avg loss: 0.2111, batch size: 32
2021-10-15 07:43:49,560 INFO [train.py:451] Epoch 12, batch 5030, batch avg loss 0.2775, total avg loss: 0.2123, batch size: 41
2021-10-15 07:43:54,582 INFO [train.py:451] Epoch 12, batch 5040, batch avg loss 0.1658, total avg loss: 0.2075, batch size: 28
2021-10-15 07:43:59,435 INFO [train.py:451] Epoch 12, batch 5050, batch avg loss 0.2335, total avg loss: 0.2065, batch size: 42
2021-10-15 07:44:04,338 INFO [train.py:451] Epoch 12, batch 5060, batch avg loss 0.2017, total avg loss: 0.2103, batch size: 36
2021-10-15 07:44:09,172 INFO [train.py:451] Epoch 12, batch 5070, batch avg loss 0.2011, total avg loss: 0.2111, batch size: 35
2021-10-15 07:44:14,019 INFO [train.py:451] Epoch 12, batch 5080, batch avg loss 0.1852, total avg loss: 0.2121, batch size: 30
2021-10-15 07:44:18,922 INFO [train.py:451] Epoch 12, batch 5090, batch avg loss 0.1979, total avg loss: 0.2122, batch size: 33
2021-10-15 07:44:23,813 INFO [train.py:451] Epoch 12, batch 5100, batch avg loss 0.2010, total avg loss: 0.2133, batch size: 35
2021-10-15 07:44:28,660 INFO [train.py:451] Epoch 12, batch 5110, batch avg loss 0.1876, total avg loss: 0.2144, batch size: 32
2021-10-15 07:44:33,789 INFO [train.py:451] Epoch 12, batch 5120, batch avg loss 0.1773, total avg loss: 0.2137, batch size: 27
2021-10-15 07:44:38,853 INFO [train.py:451] Epoch 12, batch 5130, batch avg loss 0.1600, total avg loss: 0.2127, batch size: 30
2021-10-15 07:44:43,790 INFO [train.py:451] Epoch 12, batch 5140, batch avg loss 0.1888, total avg loss: 0.2120, batch size: 42
2021-10-15 07:44:48,771 INFO [train.py:451] Epoch 12, batch 5150, batch avg loss 0.2150, total avg loss: 0.2113, batch size: 38
2021-10-15 07:44:53,677 INFO [train.py:451] Epoch 12, batch 5160, batch avg loss 0.1933, total avg loss: 0.2119, batch size: 30
2021-10-15 07:44:58,660 INFO [train.py:451] Epoch 12, batch 5170, batch avg loss 0.1969, total avg loss: 0.2109, batch size: 31
2021-10-15 07:45:03,512 INFO [train.py:451] Epoch 12, batch 5180, batch avg loss 0.2427, total avg loss: 0.2115, batch size: 42
2021-10-15 07:45:08,468 INFO [train.py:451] Epoch 12, batch 5190, batch avg loss 0.1685, total avg loss: 0.2106, batch size: 28
2021-10-15 07:45:13,413 INFO [train.py:451] Epoch 12, batch 5200, batch avg loss 0.2070, total avg loss: 0.2107, batch size: 31
2021-10-15 07:45:18,263 INFO [train.py:451] Epoch 12, batch 5210, batch avg loss 0.1559, total avg loss: 0.2298, batch size: 29
2021-10-15 07:45:23,047 INFO [train.py:451] Epoch 12, batch 5220, batch avg loss 0.2367, total avg loss: 0.2312, batch size: 33
2021-10-15 07:45:28,071 INFO [train.py:451] Epoch 12, batch 5230, batch avg loss 0.2619, total avg loss: 0.2225, batch size: 34
2021-10-15 07:45:33,039 INFO [train.py:451] Epoch 12, batch 5240, batch avg loss 0.1974, total avg loss: 0.2213, batch size: 32
2021-10-15 07:45:37,869 INFO [train.py:451] Epoch 12, batch 5250, batch avg loss 0.2047, total avg loss: 0.2212, batch size: 34
2021-10-15 07:45:42,696 INFO [train.py:451] Epoch 12, batch 5260, batch avg loss 0.2291, total avg loss: 0.2209, batch size: 39
2021-10-15 07:45:47,633 INFO [train.py:451] Epoch 12, batch 5270, batch avg loss 0.2006, total avg loss: 0.2187, batch size: 36
2021-10-15 07:45:52,625 INFO [train.py:451] Epoch 12, batch 5280, batch avg loss 0.1648, total avg loss: 0.2180, batch size: 28
2021-10-15 07:45:55,807 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "659a6b59-44f1-812d-815f-9a10649b5777" will not be mixed in.
2021-10-15 07:45:57,455 INFO [train.py:451] Epoch 12, batch 5290, batch avg loss 0.2162, total avg loss: 0.2181, batch size: 33
2021-10-15 07:46:02,431 INFO [train.py:451] Epoch 12, batch 5300, batch avg loss 0.2318, total avg loss: 0.2185, batch size: 45
2021-10-15 07:46:07,474 INFO [train.py:451] Epoch 12, batch 5310, batch avg loss 0.1994, total avg loss: 0.2164, batch size: 39
2021-10-15 07:46:12,536 INFO [train.py:451] Epoch 12, batch 5320, batch avg loss 0.1552, total avg loss: 0.2142, batch size: 29
2021-10-15 07:46:17,401 INFO [train.py:451] Epoch 12, batch 5330, batch avg loss 0.2031, total avg loss: 0.2149, batch size: 29
2021-10-15 07:46:22,179 INFO [train.py:451] Epoch 12, batch 5340, batch avg loss 0.2270, total avg loss: 0.2157, batch size: 41
2021-10-15 07:46:26,994 INFO [train.py:451] Epoch 12, batch 5350, batch avg loss 0.1770, total avg loss: 0.2162, batch size: 34
2021-10-15 07:46:31,810 INFO [train.py:451] Epoch 12, batch 5360, batch avg loss 0.1646, total avg loss: 0.2165, batch size: 28
2021-10-15 07:46:36,628 INFO [train.py:451] Epoch 12, batch 5370, batch avg loss 0.2559, total avg loss: 0.2174, batch size: 72
2021-10-15 07:46:41,681 INFO [train.py:451] Epoch 12, batch 5380, batch avg loss 0.2390, total avg loss: 0.2175, batch size: 38
2021-10-15 07:46:46,415 INFO [train.py:451] Epoch 12, batch 5390, batch avg loss 0.2401, total avg loss: 0.2189, batch size: 57
2021-10-15 07:46:51,618 INFO [train.py:451] Epoch 12, batch 5400, batch avg loss 0.1913, total avg loss: 0.2184, batch size: 28
2021-10-15 07:46:56,480 INFO [train.py:451] Epoch 12, batch 5410, batch avg loss 0.1647, total avg loss: 0.2073, batch size: 31
2021-10-15 07:47:01,362 INFO [train.py:451] Epoch 12, batch 5420, batch avg loss 0.2253, total avg loss: 0.2110, batch size: 32
2021-10-15 07:47:06,285 INFO [train.py:451] Epoch 12, batch 5430, batch avg loss 0.1703, total avg loss: 0.2118, batch size: 34
2021-10-15 07:47:11,158 INFO [train.py:451] Epoch 12, batch 5440, batch avg loss 0.1895, total avg loss: 0.2125, batch size: 30
2021-10-15 07:47:16,021 INFO [train.py:451] Epoch 12, batch 5450, batch avg loss 0.2024, total avg loss: 0.2101, batch size: 34
2021-10-15 07:47:20,938 INFO [train.py:451] Epoch 12, batch 5460, batch avg loss 0.2128, total avg loss: 0.2095, batch size: 31
2021-10-15 07:47:25,872 INFO [train.py:451] Epoch 12, batch 5470, batch avg loss 0.2161, total avg loss: 0.2107, batch size: 31
2021-10-15 07:47:30,718 INFO [train.py:451] Epoch 12, batch 5480, batch avg loss 0.2473, total avg loss: 0.2129, batch size: 72
2021-10-15 07:47:35,911 INFO [train.py:451] Epoch 12, batch 5490, batch avg loss 0.2321, total avg loss: 0.2098, batch size: 34
2021-10-15 07:47:40,985 INFO [train.py:451] Epoch 12, batch 5500, batch avg loss 0.2029, total avg loss: 0.2089, batch size: 37
2021-10-15 07:47:45,980 INFO [train.py:451] Epoch 12, batch 5510, batch avg loss 0.1981, total avg loss: 0.2092, batch size: 36
2021-10-15 07:47:50,951 INFO [train.py:451] Epoch 12, batch 5520, batch avg loss 0.2725, total avg loss: 0.2103, batch size: 39
2021-10-15 07:47:55,716 INFO [train.py:451] Epoch 12, batch 5530, batch avg loss 0.1915, total avg loss: 0.2112, batch size: 33
2021-10-15 07:48:00,343 INFO [train.py:451] Epoch 12, batch 5540, batch avg loss 0.1726, total avg loss: 0.2132, batch size: 30
2021-10-15 07:48:05,179 INFO [train.py:451] Epoch 12, batch 5550, batch avg loss 0.3262, total avg loss: 0.2141, batch size: 126
2021-10-15 07:48:10,058 INFO [train.py:451] Epoch 12, batch 5560, batch avg loss 0.2359, total avg loss: 0.2134, batch size: 37
2021-10-15 07:48:14,958 INFO [train.py:451] Epoch 12, batch 5570, batch avg loss 0.2391, total avg loss: 0.2131, batch size: 38
2021-10-15 07:48:19,919 INFO [train.py:451] Epoch 12, batch 5580, batch avg loss 0.2036, total avg loss: 0.2122, batch size: 34
2021-10-15 07:48:24,679 INFO [train.py:451] Epoch 12, batch 5590, batch avg loss 0.2252, total avg loss: 0.2129, batch size: 36
2021-10-15 07:48:29,546 INFO [train.py:451] Epoch 12, batch 5600, batch avg loss 0.1758, total avg loss: 0.2131, batch size: 34
2021-10-15 07:48:34,253 INFO [train.py:451] Epoch 12, batch 5610, batch avg loss 0.1602, total avg loss: 0.2241, batch size: 30
2021-10-15 07:48:39,058 INFO [train.py:451] Epoch 12, batch 5620, batch avg loss 0.1886, total avg loss: 0.2160, batch size: 30
2021-10-15 07:48:43,732 INFO [train.py:451] Epoch 12, batch 5630, batch avg loss 0.2760, total avg loss: 0.2204, batch size: 37
2021-10-15 07:48:48,518 INFO [train.py:451] Epoch 12, batch 5640, batch avg loss 0.2037, total avg loss: 0.2218, batch size: 31
2021-10-15 07:48:53,518 INFO [train.py:451] Epoch 12, batch 5650, batch avg loss 0.2105, total avg loss: 0.2243, batch size: 34
2021-10-15 07:48:58,404 INFO [train.py:451] Epoch 12, batch 5660, batch avg loss 0.2002, total avg loss: 0.2238, batch size: 35
2021-10-15 07:49:03,333 INFO [train.py:451] Epoch 12, batch 5670, batch avg loss 0.1732, total avg loss: 0.2234, batch size: 30
2021-10-15 07:49:08,222 INFO [train.py:451] Epoch 12, batch 5680, batch avg loss 0.2270, total avg loss: 0.2222, batch size: 35
2021-10-15 07:49:13,107 INFO [train.py:451] Epoch 12, batch 5690, batch avg loss 0.1810, total avg loss: 0.2241, batch size: 27
2021-10-15 07:49:18,067 INFO [train.py:451] Epoch 12, batch 5700, batch avg loss 0.1908, total avg loss: 0.2224, batch size: 36
2021-10-15 07:49:23,033 INFO [train.py:451] Epoch 12, batch 5710, batch avg loss 0.1753, total avg loss: 0.2211, batch size: 31
2021-10-15 07:49:27,868 INFO [train.py:451] Epoch 12, batch 5720, batch avg loss 0.2015, total avg loss: 0.2202, batch size: 31
2021-10-15 07:49:32,817 INFO [train.py:451] Epoch 12, batch 5730, batch avg loss 0.1813, total avg loss: 0.2184, batch size: 36
2021-10-15 07:49:37,708 INFO [train.py:451] Epoch 12, batch 5740, batch avg loss 0.1778, total avg loss: 0.2173, batch size: 31
2021-10-15 07:49:42,440 INFO [train.py:451] Epoch 12, batch 5750, batch avg loss 0.1787, total avg loss: 0.2179, batch size: 37
2021-10-15 07:49:47,336 INFO [train.py:451] Epoch 12, batch 5760, batch avg loss 0.2328, total avg loss: 0.2178, batch size: 38
2021-10-15 07:49:52,395 INFO [train.py:451] Epoch 12, batch 5770, batch avg loss 0.2761, total avg loss: 0.2182, batch size: 34
2021-10-15 07:49:57,419 INFO [train.py:451] Epoch 12, batch 5780, batch avg loss 0.2010, total avg loss: 0.2165, batch size: 27
2021-10-15 07:50:02,358 INFO [train.py:451] Epoch 12, batch 5790, batch avg loss 0.1961, total avg loss: 0.2159, batch size: 41
2021-10-15 07:50:07,166 INFO [train.py:451] Epoch 12, batch 5800, batch avg loss 0.2075, total avg loss: 0.2167, batch size: 32
2021-10-15 07:50:12,197 INFO [train.py:451] Epoch 12, batch 5810, batch avg loss 0.2252, total avg loss: 0.2129, batch size: 33
2021-10-15 07:50:17,240 INFO [train.py:451] Epoch 12, batch 5820, batch avg loss 0.2289, total avg loss: 0.2128, batch size: 49
2021-10-15 07:50:22,294 INFO [train.py:451] Epoch 12, batch 5830, batch avg loss 0.2099, total avg loss: 0.2078, batch size: 35
2021-10-15 07:50:27,210 INFO [train.py:451] Epoch 12, batch 5840, batch avg loss 0.2455, total avg loss: 0.2099, batch size: 49
2021-10-15 07:50:32,126 INFO [train.py:451] Epoch 12, batch 5850, batch avg loss 0.2175, total avg loss: 0.2120, batch size: 38
2021-10-15 07:50:37,269 INFO [train.py:451] Epoch 12, batch 5860, batch avg loss 0.2190, total avg loss: 0.2127, batch size: 37
2021-10-15 07:50:42,059 INFO [train.py:451] Epoch 12, batch 5870, batch avg loss 0.2425, total avg loss: 0.2173, batch size: 36
2021-10-15 07:50:47,047 INFO [train.py:451] Epoch 12, batch 5880, batch avg loss 0.1549, total avg loss: 0.2142, batch size: 32
2021-10-15 07:50:51,983 INFO [train.py:451] Epoch 12, batch 5890, batch avg loss 0.2549, total avg loss: 0.2138, batch size: 34
2021-10-15 07:50:56,737 INFO [train.py:451] Epoch 12, batch 5900, batch avg loss 0.2524, total avg loss: 0.2151, batch size: 41
2021-10-15 07:51:01,607 INFO [train.py:451] Epoch 12, batch 5910, batch avg loss 0.2538, total avg loss: 0.2149, batch size: 72
2021-10-15 07:51:06,537 INFO [train.py:451] Epoch 12, batch 5920, batch avg loss 0.1988, total avg loss: 0.2160, batch size: 27
2021-10-15 07:51:11,574 INFO [train.py:451] Epoch 12, batch 5930, batch avg loss 0.2359, total avg loss: 0.2151, batch size: 33
2021-10-15 07:51:16,437 INFO [train.py:451] Epoch 12, batch 5940, batch avg loss 0.2345, total avg loss: 0.2151, batch size: 48
2021-10-15 07:51:21,331 INFO [train.py:451] Epoch 12, batch 5950, batch avg loss 0.1994, total avg loss: 0.2147, batch size: 45
2021-10-15 07:51:26,286 INFO [train.py:451] Epoch 12, batch 5960, batch avg loss 0.1638, total avg loss: 0.2147, batch size: 30
2021-10-15 07:51:31,493 INFO [train.py:451] Epoch 12, batch 5970, batch avg loss 0.2420, total avg loss: 0.2148, batch size: 35
2021-10-15 07:51:36,354 INFO [train.py:451] Epoch 12, batch 5980, batch avg loss 0.2122, total avg loss: 0.2152, batch size: 31
2021-10-15 07:51:41,338 INFO [train.py:451] Epoch 12, batch 5990, batch avg loss 0.2176, total avg loss: 0.2145, batch size: 36
2021-10-15 07:51:46,585 INFO [train.py:451] Epoch 12, batch 6000, batch avg loss 0.2095, total avg loss: 0.2147, batch size: 36
2021-10-15 07:52:24,730 INFO [train.py:483] Epoch 12, valid loss 0.1601, best valid loss: 0.1598 best valid epoch: 12
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2021-10-15 07:59:07,231 INFO [train.py:451] Epoch 12, batch 6800, batch avg loss 0.2059, total avg loss: 0.2116, batch size: 33
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2021-10-15 08:01:24,219 INFO [train.py:483] Epoch 12, valid loss 0.1606, best valid loss: 0.1598 best valid epoch: 12
2021-10-15 08:01:29,095 INFO [train.py:451] Epoch 12, batch 7010, batch avg loss 0.2643, total avg loss: 0.2171, batch size: 73
2021-10-15 08:01:34,026 INFO [train.py:451] Epoch 12, batch 7020, batch avg loss 0.1993, total avg loss: 0.2063, batch size: 32
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2021-10-15 08:04:39,689 INFO [train.py:451] Epoch 12, batch 7400, batch avg loss 0.1928, total avg loss: 0.2136, batch size: 33
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2021-10-15 08:10:11,221 INFO [train.py:483] Epoch 12, valid loss 0.1609, best valid loss: 0.1598 best valid epoch: 12
2021-10-15 08:10:16,151 INFO [train.py:451] Epoch 12, batch 8010, batch avg loss 0.1818, total avg loss: 0.2227, batch size: 36
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2021-10-15 08:19:00,052 INFO [train.py:483] Epoch 12, valid loss 0.1602, best valid loss: 0.1598 best valid epoch: 12
2021-10-15 08:19:05,078 INFO [train.py:451] Epoch 12, batch 9010, batch avg loss 0.2092, total avg loss: 0.2231, batch size: 34
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2021-10-15 08:21:03,021 INFO [train.py:451] Epoch 12, batch 9250, batch avg loss 0.2180, total avg loss: 0.2187, batch size: 35
2021-10-15 08:21:07,901 INFO [train.py:451] Epoch 12, batch 9260, batch avg loss 0.2221, total avg loss: 0.2200, batch size: 32
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2021-10-15 08:22:51,038 INFO [train.py:451] Epoch 12, batch 9470, batch avg loss 0.2178, total avg loss: 0.2115, batch size: 32
2021-10-15 08:22:55,923 INFO [train.py:451] Epoch 12, batch 9480, batch avg loss 0.2252, total avg loss: 0.2112, batch size: 33
2021-10-15 08:23:00,865 INFO [train.py:451] Epoch 12, batch 9490, batch avg loss 0.2288, total avg loss: 0.2134, batch size: 36
2021-10-15 08:23:05,804 INFO [train.py:451] Epoch 12, batch 9500, batch avg loss 0.2350, total avg loss: 0.2133, batch size: 49
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2021-10-15 08:23:15,335 INFO [train.py:451] Epoch 12, batch 9520, batch avg loss 0.1517, total avg loss: 0.2141, batch size: 29
2021-10-15 08:23:20,345 INFO [train.py:451] Epoch 12, batch 9530, batch avg loss 0.2596, total avg loss: 0.2130, batch size: 49
2021-10-15 08:23:25,142 INFO [train.py:451] Epoch 12, batch 9540, batch avg loss 0.2309, total avg loss: 0.2137, batch size: 36
2021-10-15 08:23:30,108 INFO [train.py:451] Epoch 12, batch 9550, batch avg loss 0.1705, total avg loss: 0.2123, batch size: 29
2021-10-15 08:23:34,852 INFO [train.py:451] Epoch 12, batch 9560, batch avg loss 0.2588, total avg loss: 0.2135, batch size: 38
2021-10-15 08:23:39,718 INFO [train.py:451] Epoch 12, batch 9570, batch avg loss 0.2120, total avg loss: 0.2141, batch size: 49
2021-10-15 08:23:44,546 INFO [train.py:451] Epoch 12, batch 9580, batch avg loss 0.1692, total avg loss: 0.2144, batch size: 31
2021-10-15 08:23:49,442 INFO [train.py:451] Epoch 12, batch 9590, batch avg loss 0.1843, total avg loss: 0.2143, batch size: 30
2021-10-15 08:23:54,208 INFO [train.py:451] Epoch 12, batch 9600, batch avg loss 0.2242, total avg loss: 0.2144, batch size: 36
2021-10-15 08:23:59,163 INFO [train.py:451] Epoch 12, batch 9610, batch avg loss 0.2285, total avg loss: 0.2178, batch size: 45
2021-10-15 08:24:04,014 INFO [train.py:451] Epoch 12, batch 9620, batch avg loss 0.2113, total avg loss: 0.2253, batch size: 31
2021-10-15 08:24:08,854 INFO [train.py:451] Epoch 12, batch 9630, batch avg loss 0.2440, total avg loss: 0.2211, batch size: 41
2021-10-15 08:24:13,666 INFO [train.py:451] Epoch 12, batch 9640, batch avg loss 0.1995, total avg loss: 0.2213, batch size: 32
2021-10-15 08:24:18,476 INFO [train.py:451] Epoch 12, batch 9650, batch avg loss 0.3318, total avg loss: 0.2215, batch size: 131
2021-10-15 08:24:23,244 INFO [train.py:451] Epoch 12, batch 9660, batch avg loss 0.3463, total avg loss: 0.2229, batch size: 132
2021-10-15 08:24:28,238 INFO [train.py:451] Epoch 12, batch 9670, batch avg loss 0.2056, total avg loss: 0.2204, batch size: 41
2021-10-15 08:24:33,121 INFO [train.py:451] Epoch 12, batch 9680, batch avg loss 0.1997, total avg loss: 0.2195, batch size: 32
2021-10-15 08:24:37,955 INFO [train.py:451] Epoch 12, batch 9690, batch avg loss 0.2306, total avg loss: 0.2192, batch size: 39
2021-10-15 08:24:42,890 INFO [train.py:451] Epoch 12, batch 9700, batch avg loss 0.2415, total avg loss: 0.2197, batch size: 45
2021-10-15 08:24:47,896 INFO [train.py:451] Epoch 12, batch 9710, batch avg loss 0.1960, total avg loss: 0.2175, batch size: 37
2021-10-15 08:24:52,864 INFO [train.py:451] Epoch 12, batch 9720, batch avg loss 0.1959, total avg loss: 0.2166, batch size: 31
2021-10-15 08:24:57,642 INFO [train.py:451] Epoch 12, batch 9730, batch avg loss 0.2204, total avg loss: 0.2166, batch size: 33
2021-10-15 08:25:02,607 INFO [train.py:451] Epoch 12, batch 9740, batch avg loss 0.2190, total avg loss: 0.2151, batch size: 38
2021-10-15 08:25:07,398 INFO [train.py:451] Epoch 12, batch 9750, batch avg loss 0.2383, total avg loss: 0.2154, batch size: 42
2021-10-15 08:25:12,349 INFO [train.py:451] Epoch 12, batch 9760, batch avg loss 0.2493, total avg loss: 0.2160, batch size: 32
2021-10-15 08:25:17,183 INFO [train.py:451] Epoch 12, batch 9770, batch avg loss 0.2163, total avg loss: 0.2159, batch size: 38
2021-10-15 08:25:22,136 INFO [train.py:451] Epoch 12, batch 9780, batch avg loss 0.2387, total avg loss: 0.2166, batch size: 35
2021-10-15 08:25:27,085 INFO [train.py:451] Epoch 12, batch 9790, batch avg loss 0.2760, total avg loss: 0.2173, batch size: 41
2021-10-15 08:25:32,127 INFO [train.py:451] Epoch 12, batch 9800, batch avg loss 0.1912, total avg loss: 0.2163, batch size: 33
2021-10-15 08:25:37,113 INFO [train.py:451] Epoch 12, batch 9810, batch avg loss 0.2229, total avg loss: 0.2060, batch size: 41
2021-10-15 08:25:42,079 INFO [train.py:451] Epoch 12, batch 9820, batch avg loss 0.2144, total avg loss: 0.2089, batch size: 45
2021-10-15 08:25:46,930 INFO [train.py:451] Epoch 12, batch 9830, batch avg loss 0.2323, total avg loss: 0.2167, batch size: 36
2021-10-15 08:25:51,773 INFO [train.py:451] Epoch 12, batch 9840, batch avg loss 0.1766, total avg loss: 0.2181, batch size: 31
2021-10-15 08:25:56,660 INFO [train.py:451] Epoch 12, batch 9850, batch avg loss 0.1996, total avg loss: 0.2158, batch size: 34
2021-10-15 08:26:01,338 INFO [train.py:451] Epoch 12, batch 9860, batch avg loss 0.2176, total avg loss: 0.2173, batch size: 32
2021-10-15 08:26:06,241 INFO [train.py:451] Epoch 12, batch 9870, batch avg loss 0.2137, total avg loss: 0.2151, batch size: 33
2021-10-15 08:26:11,177 INFO [train.py:451] Epoch 12, batch 9880, batch avg loss 0.2153, total avg loss: 0.2143, batch size: 45
2021-10-15 08:26:16,232 INFO [train.py:451] Epoch 12, batch 9890, batch avg loss 0.2115, total avg loss: 0.2141, batch size: 38
2021-10-15 08:26:21,172 INFO [train.py:451] Epoch 12, batch 9900, batch avg loss 0.2151, total avg loss: 0.2136, batch size: 29
2021-10-15 08:26:26,021 INFO [train.py:451] Epoch 12, batch 9910, batch avg loss 0.1579, total avg loss: 0.2130, batch size: 29
2021-10-15 08:26:30,915 INFO [train.py:451] Epoch 12, batch 9920, batch avg loss 0.1818, total avg loss: 0.2130, batch size: 31
2021-10-15 08:26:35,732 INFO [train.py:451] Epoch 12, batch 9930, batch avg loss 0.2454, total avg loss: 0.2127, batch size: 45
2021-10-15 08:26:40,537 INFO [train.py:451] Epoch 12, batch 9940, batch avg loss 0.2114, total avg loss: 0.2135, batch size: 40
2021-10-15 08:26:45,284 INFO [train.py:451] Epoch 12, batch 9950, batch avg loss 0.2264, total avg loss: 0.2146, batch size: 49
2021-10-15 08:26:50,540 INFO [train.py:451] Epoch 12, batch 9960, batch avg loss 0.2874, total avg loss: 0.2138, batch size: 127
2021-10-15 08:26:55,456 INFO [train.py:451] Epoch 12, batch 9970, batch avg loss 0.2100, total avg loss: 0.2140, batch size: 33
2021-10-15 08:27:00,351 INFO [train.py:451] Epoch 12, batch 9980, batch avg loss 0.2603, total avg loss: 0.2145, batch size: 35
2021-10-15 08:27:05,239 INFO [train.py:451] Epoch 12, batch 9990, batch avg loss 0.2592, total avg loss: 0.2151, batch size: 37
2021-10-15 08:27:10,087 INFO [train.py:451] Epoch 12, batch 10000, batch avg loss 0.1972, total avg loss: 0.2151, batch size: 41
2021-10-15 08:27:49,511 INFO [train.py:483] Epoch 12, valid loss 0.1606, best valid loss: 0.1598 best valid epoch: 12
2021-10-15 08:27:54,592 INFO [train.py:451] Epoch 12, batch 10010, batch avg loss 0.1941, total avg loss: 0.2022, batch size: 33
2021-10-15 08:27:59,456 INFO [train.py:451] Epoch 12, batch 10020, batch avg loss 0.2268, total avg loss: 0.2083, batch size: 34
2021-10-15 08:28:04,430 INFO [train.py:451] Epoch 12, batch 10030, batch avg loss 0.1637, total avg loss: 0.2084, batch size: 28
2021-10-15 08:28:09,326 INFO [train.py:451] Epoch 12, batch 10040, batch avg loss 0.1904, total avg loss: 0.2109, batch size: 31
2021-10-15 08:28:14,079 INFO [train.py:451] Epoch 12, batch 10050, batch avg loss 0.3027, total avg loss: 0.2152, batch size: 131
2021-10-15 08:28:18,947 INFO [train.py:451] Epoch 12, batch 10060, batch avg loss 0.1763, total avg loss: 0.2137, batch size: 28
2021-10-15 08:28:23,850 INFO [train.py:451] Epoch 12, batch 10070, batch avg loss 0.2373, total avg loss: 0.2102, batch size: 49
2021-10-15 08:28:28,699 INFO [train.py:451] Epoch 12, batch 10080, batch avg loss 0.2229, total avg loss: 0.2081, batch size: 34
2021-10-15 08:28:33,713 INFO [train.py:451] Epoch 12, batch 10090, batch avg loss 0.1717, total avg loss: 0.2074, batch size: 30
2021-10-15 08:28:38,684 INFO [train.py:451] Epoch 12, batch 10100, batch avg loss 0.2163, total avg loss: 0.2080, batch size: 36
2021-10-15 08:28:43,543 INFO [train.py:451] Epoch 12, batch 10110, batch avg loss 0.1769, total avg loss: 0.2085, batch size: 33
2021-10-15 08:28:48,467 INFO [train.py:451] Epoch 12, batch 10120, batch avg loss 0.2184, total avg loss: 0.2093, batch size: 36
2021-10-15 08:28:53,281 INFO [train.py:451] Epoch 12, batch 10130, batch avg loss 0.1761, total avg loss: 0.2100, batch size: 32
2021-10-15 08:28:58,264 INFO [train.py:451] Epoch 12, batch 10140, batch avg loss 0.3005, total avg loss: 0.2096, batch size: 129
2021-10-15 08:29:03,100 INFO [train.py:451] Epoch 12, batch 10150, batch avg loss 0.1755, total avg loss: 0.2110, batch size: 37
2021-10-15 08:29:07,996 INFO [train.py:451] Epoch 12, batch 10160, batch avg loss 0.2492, total avg loss: 0.2103, batch size: 49
2021-10-15 08:29:12,826 INFO [train.py:451] Epoch 12, batch 10170, batch avg loss 0.2086, total avg loss: 0.2104, batch size: 32
2021-10-15 08:29:17,875 INFO [train.py:451] Epoch 12, batch 10180, batch avg loss 0.1730, total avg loss: 0.2099, batch size: 29
2021-10-15 08:29:22,807 INFO [train.py:451] Epoch 12, batch 10190, batch avg loss 0.1672, total avg loss: 0.2098, batch size: 30
2021-10-15 08:29:27,569 INFO [train.py:451] Epoch 12, batch 10200, batch avg loss 0.2195, total avg loss: 0.2113, batch size: 36
2021-10-15 08:29:32,374 INFO [train.py:451] Epoch 12, batch 10210, batch avg loss 0.3290, total avg loss: 0.2214, batch size: 130
2021-10-15 08:29:37,259 INFO [train.py:451] Epoch 12, batch 10220, batch avg loss 0.2238, total avg loss: 0.2170, batch size: 41
2021-10-15 08:29:42,215 INFO [train.py:451] Epoch 12, batch 10230, batch avg loss 0.1936, total avg loss: 0.2119, batch size: 28
2021-10-15 08:29:47,329 INFO [train.py:451] Epoch 12, batch 10240, batch avg loss 0.2076, total avg loss: 0.2096, batch size: 27
2021-10-15 08:29:52,300 INFO [train.py:451] Epoch 12, batch 10250, batch avg loss 0.2422, total avg loss: 0.2107, batch size: 45
2021-10-15 08:29:57,358 INFO [train.py:451] Epoch 12, batch 10260, batch avg loss 0.2268, total avg loss: 0.2124, batch size: 34
2021-10-15 08:30:02,275 INFO [train.py:451] Epoch 12, batch 10270, batch avg loss 0.2043, total avg loss: 0.2113, batch size: 32
2021-10-15 08:30:07,315 INFO [train.py:451] Epoch 12, batch 10280, batch avg loss 0.1931, total avg loss: 0.2099, batch size: 33
2021-10-15 08:30:12,176 INFO [train.py:451] Epoch 12, batch 10290, batch avg loss 0.2203, total avg loss: 0.2112, batch size: 49
2021-10-15 08:30:17,037 INFO [train.py:451] Epoch 12, batch 10300, batch avg loss 0.1946, total avg loss: 0.2114, batch size: 31
2021-10-15 08:30:22,142 INFO [train.py:451] Epoch 12, batch 10310, batch avg loss 0.2209, total avg loss: 0.2105, batch size: 35
2021-10-15 08:30:27,000 INFO [train.py:451] Epoch 12, batch 10320, batch avg loss 0.1942, total avg loss: 0.2127, batch size: 35
2021-10-15 08:30:31,936 INFO [train.py:451] Epoch 12, batch 10330, batch avg loss 0.1946, total avg loss: 0.2133, batch size: 35
2021-10-15 08:30:36,714 INFO [train.py:451] Epoch 12, batch 10340, batch avg loss 0.2256, total avg loss: 0.2143, batch size: 49
2021-10-15 08:30:41,553 INFO [train.py:451] Epoch 12, batch 10350, batch avg loss 0.2478, total avg loss: 0.2145, batch size: 36
2021-10-15 08:30:46,400 INFO [train.py:451] Epoch 12, batch 10360, batch avg loss 0.1704, total avg loss: 0.2145, batch size: 29
2021-10-15 08:30:51,209 INFO [train.py:451] Epoch 12, batch 10370, batch avg loss 0.1761, total avg loss: 0.2141, batch size: 39
2021-10-15 08:30:56,590 INFO [train.py:451] Epoch 12, batch 10380, batch avg loss 0.2315, total avg loss: 0.2138, batch size: 34
2021-10-15 08:31:01,366 INFO [train.py:451] Epoch 12, batch 10390, batch avg loss 0.2227, total avg loss: 0.2139, batch size: 38
2021-10-15 08:31:06,325 INFO [train.py:451] Epoch 12, batch 10400, batch avg loss 0.2439, total avg loss: 0.2140, batch size: 31
2021-10-15 08:31:11,215 INFO [train.py:451] Epoch 12, batch 10410, batch avg loss 0.1943, total avg loss: 0.2065, batch size: 28
2021-10-15 08:31:16,146 INFO [train.py:451] Epoch 12, batch 10420, batch avg loss 0.2115, total avg loss: 0.2181, batch size: 27
2021-10-15 08:31:20,925 INFO [train.py:451] Epoch 12, batch 10430, batch avg loss 0.2086, total avg loss: 0.2241, batch size: 36
2021-10-15 08:31:25,724 INFO [train.py:451] Epoch 12, batch 10440, batch avg loss 0.1886, total avg loss: 0.2237, batch size: 29
2021-10-15 08:31:30,753 INFO [train.py:451] Epoch 12, batch 10450, batch avg loss 0.1880, total avg loss: 0.2207, batch size: 28
2021-10-15 08:31:35,650 INFO [train.py:451] Epoch 12, batch 10460, batch avg loss 0.2152, total avg loss: 0.2181, batch size: 38
2021-10-15 08:31:40,518 INFO [train.py:451] Epoch 12, batch 10470, batch avg loss 0.3620, total avg loss: 0.2203, batch size: 128
2021-10-15 08:31:45,341 INFO [train.py:451] Epoch 12, batch 10480, batch avg loss 0.3094, total avg loss: 0.2204, batch size: 124
2021-10-15 08:31:50,030 INFO [train.py:451] Epoch 12, batch 10490, batch avg loss 0.3347, total avg loss: 0.2229, batch size: 129
2021-10-15 08:31:54,894 INFO [train.py:451] Epoch 12, batch 10500, batch avg loss 0.1744, total avg loss: 0.2218, batch size: 34
2021-10-15 08:31:59,633 INFO [train.py:451] Epoch 12, batch 10510, batch avg loss 0.2261, total avg loss: 0.2209, batch size: 49
2021-10-15 08:32:04,528 INFO [train.py:451] Epoch 12, batch 10520, batch avg loss 0.1995, total avg loss: 0.2201, batch size: 32
2021-10-15 08:32:09,507 INFO [train.py:451] Epoch 12, batch 10530, batch avg loss 0.2014, total avg loss: 0.2189, batch size: 37
2021-10-15 08:32:14,422 INFO [train.py:451] Epoch 12, batch 10540, batch avg loss 0.2138, total avg loss: 0.2194, batch size: 34
2021-10-15 08:32:19,414 INFO [train.py:451] Epoch 12, batch 10550, batch avg loss 0.2216, total avg loss: 0.2183, batch size: 38
2021-10-15 08:32:24,254 INFO [train.py:451] Epoch 12, batch 10560, batch avg loss 0.2279, total avg loss: 0.2194, batch size: 38
2021-10-15 08:32:29,286 INFO [train.py:451] Epoch 12, batch 10570, batch avg loss 0.2666, total avg loss: 0.2193, batch size: 37
2021-10-15 08:32:34,290 INFO [train.py:451] Epoch 12, batch 10580, batch avg loss 0.1855, total avg loss: 0.2187, batch size: 30
2021-10-15 08:32:39,355 INFO [train.py:451] Epoch 12, batch 10590, batch avg loss 0.2317, total avg loss: 0.2185, batch size: 42
2021-10-15 08:32:44,511 INFO [train.py:451] Epoch 12, batch 10600, batch avg loss 0.2311, total avg loss: 0.2186, batch size: 38
2021-10-15 08:32:49,522 INFO [train.py:451] Epoch 12, batch 10610, batch avg loss 0.1715, total avg loss: 0.1933, batch size: 36
2021-10-15 08:32:54,533 INFO [train.py:451] Epoch 12, batch 10620, batch avg loss 0.1946, total avg loss: 0.1970, batch size: 34
2021-10-15 08:32:59,297 INFO [train.py:451] Epoch 12, batch 10630, batch avg loss 0.2378, total avg loss: 0.2011, batch size: 57
2021-10-15 08:33:04,179 INFO [train.py:451] Epoch 12, batch 10640, batch avg loss 0.2194, total avg loss: 0.2047, batch size: 39
2021-10-15 08:33:09,059 INFO [train.py:451] Epoch 12, batch 10650, batch avg loss 0.2208, total avg loss: 0.2094, batch size: 49
2021-10-15 08:33:13,743 INFO [train.py:451] Epoch 12, batch 10660, batch avg loss 0.1846, total avg loss: 0.2119, batch size: 35
2021-10-15 08:33:18,796 INFO [train.py:451] Epoch 12, batch 10670, batch avg loss 0.1912, total avg loss: 0.2102, batch size: 36
2021-10-15 08:33:23,662 INFO [train.py:451] Epoch 12, batch 10680, batch avg loss 0.2657, total avg loss: 0.2121, batch size: 39
2021-10-15 08:33:28,421 INFO [train.py:451] Epoch 12, batch 10690, batch avg loss 0.2220, total avg loss: 0.2136, batch size: 38
2021-10-15 08:33:33,323 INFO [train.py:451] Epoch 12, batch 10700, batch avg loss 0.2021, total avg loss: 0.2132, batch size: 34
2021-10-15 08:33:38,291 INFO [train.py:451] Epoch 12, batch 10710, batch avg loss 0.2736, total avg loss: 0.2127, batch size: 39
2021-10-15 08:33:43,169 INFO [train.py:451] Epoch 12, batch 10720, batch avg loss 0.2349, total avg loss: 0.2133, batch size: 36
2021-10-15 08:33:48,313 INFO [train.py:451] Epoch 12, batch 10730, batch avg loss 0.2327, total avg loss: 0.2127, batch size: 34
2021-10-15 08:33:53,211 INFO [train.py:451] Epoch 12, batch 10740, batch avg loss 0.2185, total avg loss: 0.2139, batch size: 30
2021-10-15 08:33:58,135 INFO [train.py:451] Epoch 12, batch 10750, batch avg loss 0.1866, total avg loss: 0.2137, batch size: 29
2021-10-15 08:34:02,941 INFO [train.py:451] Epoch 12, batch 10760, batch avg loss 0.2702, total avg loss: 0.2147, batch size: 74
2021-10-15 08:34:07,702 INFO [train.py:451] Epoch 12, batch 10770, batch avg loss 0.1969, total avg loss: 0.2140, batch size: 32
2021-10-15 08:34:12,600 INFO [train.py:451] Epoch 12, batch 10780, batch avg loss 0.2164, total avg loss: 0.2135, batch size: 38
2021-10-15 08:34:17,390 INFO [train.py:451] Epoch 12, batch 10790, batch avg loss 0.1851, total avg loss: 0.2143, batch size: 34
2021-10-15 08:34:22,218 INFO [train.py:451] Epoch 12, batch 10800, batch avg loss 0.1742, total avg loss: 0.2144, batch size: 31
2021-10-15 08:34:26,266 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "473a1cd5-9b6c-ed66-bcf9-a5cb2be7315a" will not be mixed in.
2021-10-15 08:34:26,995 INFO [train.py:451] Epoch 12, batch 10810, batch avg loss 0.2654, total avg loss: 0.2254, batch size: 35
2021-10-15 08:34:31,764 INFO [train.py:451] Epoch 12, batch 10820, batch avg loss 0.1845, total avg loss: 0.2166, batch size: 30
2021-10-15 08:34:36,862 INFO [train.py:451] Epoch 12, batch 10830, batch avg loss 0.1673, total avg loss: 0.2070, batch size: 30
2021-10-15 08:34:41,866 INFO [train.py:451] Epoch 12, batch 10840, batch avg loss 0.2562, total avg loss: 0.2099, batch size: 34
2021-10-15 08:34:46,894 INFO [train.py:451] Epoch 12, batch 10850, batch avg loss 0.2403, total avg loss: 0.2129, batch size: 37
2021-10-15 08:34:51,920 INFO [train.py:451] Epoch 12, batch 10860, batch avg loss 0.2790, total avg loss: 0.2128, batch size: 56
2021-10-15 08:34:56,884 INFO [train.py:451] Epoch 12, batch 10870, batch avg loss 0.2112, total avg loss: 0.2111, batch size: 45
2021-10-15 08:35:01,914 INFO [train.py:451] Epoch 12, batch 10880, batch avg loss 0.1973, total avg loss: 0.2118, batch size: 34
2021-10-15 08:35:06,540 INFO [train.py:451] Epoch 12, batch 10890, batch avg loss 0.2098, total avg loss: 0.2164, batch size: 42
2021-10-15 08:35:11,489 INFO [train.py:451] Epoch 12, batch 10900, batch avg loss 0.2035, total avg loss: 0.2159, batch size: 33
2021-10-15 08:35:16,403 INFO [train.py:451] Epoch 12, batch 10910, batch avg loss 0.2671, total avg loss: 0.2168, batch size: 41
2021-10-15 08:35:21,351 INFO [train.py:451] Epoch 12, batch 10920, batch avg loss 0.1901, total avg loss: 0.2174, batch size: 35
2021-10-15 08:35:26,205 INFO [train.py:451] Epoch 12, batch 10930, batch avg loss 0.2418, total avg loss: 0.2180, batch size: 34
2021-10-15 08:35:31,149 INFO [train.py:451] Epoch 12, batch 10940, batch avg loss 0.2319, total avg loss: 0.2182, batch size: 42
2021-10-15 08:35:36,177 INFO [train.py:451] Epoch 12, batch 10950, batch avg loss 0.2054, total avg loss: 0.2172, batch size: 34
2021-10-15 08:35:41,270 INFO [train.py:451] Epoch 12, batch 10960, batch avg loss 0.1509, total avg loss: 0.2150, batch size: 30
2021-10-15 08:35:46,029 INFO [train.py:451] Epoch 12, batch 10970, batch avg loss 0.1776, total avg loss: 0.2150, batch size: 31
2021-10-15 08:35:50,918 INFO [train.py:451] Epoch 12, batch 10980, batch avg loss 0.2119, total avg loss: 0.2151, batch size: 27
2021-10-15 08:35:56,109 INFO [train.py:451] Epoch 12, batch 10990, batch avg loss 0.2010, total avg loss: 0.2144, batch size: 39
2021-10-15 08:36:01,185 INFO [train.py:451] Epoch 12, batch 11000, batch avg loss 0.1819, total avg loss: 0.2138, batch size: 32
2021-10-15 08:36:40,741 INFO [train.py:483] Epoch 12, valid loss 0.1604, best valid loss: 0.1598 best valid epoch: 12
2021-10-15 08:36:45,752 INFO [train.py:451] Epoch 12, batch 11010, batch avg loss 0.2677, total avg loss: 0.2141, batch size: 49
2021-10-15 08:36:50,625 INFO [train.py:451] Epoch 12, batch 11020, batch avg loss 0.3596, total avg loss: 0.2200, batch size: 129
2021-10-15 08:36:55,415 INFO [train.py:451] Epoch 12, batch 11030, batch avg loss 0.2702, total avg loss: 0.2227, batch size: 72
2021-10-15 08:37:00,448 INFO [train.py:451] Epoch 12, batch 11040, batch avg loss 0.1899, total avg loss: 0.2189, batch size: 31
2021-10-15 08:37:05,069 INFO [train.py:451] Epoch 12, batch 11050, batch avg loss 0.1687, total avg loss: 0.2211, batch size: 28
2021-10-15 08:37:10,069 INFO [train.py:451] Epoch 12, batch 11060, batch avg loss 0.2165, total avg loss: 0.2177, batch size: 36
2021-10-15 08:37:15,107 INFO [train.py:451] Epoch 12, batch 11070, batch avg loss 0.1830, total avg loss: 0.2148, batch size: 29
2021-10-15 08:37:19,993 INFO [train.py:451] Epoch 12, batch 11080, batch avg loss 0.2528, total avg loss: 0.2139, batch size: 49
2021-10-15 08:37:24,925 INFO [train.py:451] Epoch 12, batch 11090, batch avg loss 0.2161, total avg loss: 0.2129, batch size: 38
2021-10-15 08:37:29,854 INFO [train.py:451] Epoch 12, batch 11100, batch avg loss 0.2319, total avg loss: 0.2138, batch size: 41
2021-10-15 08:37:34,676 INFO [train.py:451] Epoch 12, batch 11110, batch avg loss 0.2106, total avg loss: 0.2151, batch size: 30
2021-10-15 08:37:39,542 INFO [train.py:451] Epoch 12, batch 11120, batch avg loss 0.2129, total avg loss: 0.2148, batch size: 45
2021-10-15 08:37:44,318 INFO [train.py:451] Epoch 12, batch 11130, batch avg loss 0.1794, total avg loss: 0.2142, batch size: 32
2021-10-15 08:37:49,382 INFO [train.py:451] Epoch 12, batch 11140, batch avg loss 0.2610, total avg loss: 0.2133, batch size: 72
2021-10-15 08:37:54,109 INFO [train.py:451] Epoch 12, batch 11150, batch avg loss 0.2556, total avg loss: 0.2142, batch size: 57
2021-10-15 08:37:58,982 INFO [train.py:451] Epoch 12, batch 11160, batch avg loss 0.3475, total avg loss: 0.2162, batch size: 135
2021-10-15 08:38:03,990 INFO [train.py:451] Epoch 12, batch 11170, batch avg loss 0.2165, total avg loss: 0.2165, batch size: 34
2021-10-15 08:38:09,054 INFO [train.py:451] Epoch 12, batch 11180, batch avg loss 0.2027, total avg loss: 0.2149, batch size: 35
2021-10-15 08:38:13,731 INFO [train.py:451] Epoch 12, batch 11190, batch avg loss 0.2535, total avg loss: 0.2155, batch size: 45
2021-10-15 08:38:18,689 INFO [train.py:451] Epoch 12, batch 11200, batch avg loss 0.2383, total avg loss: 0.2149, batch size: 49
2021-10-15 08:38:23,708 INFO [train.py:451] Epoch 12, batch 11210, batch avg loss 0.2168, total avg loss: 0.2096, batch size: 31
2021-10-15 08:38:28,523 INFO [train.py:451] Epoch 12, batch 11220, batch avg loss 0.1981, total avg loss: 0.2176, batch size: 35
2021-10-15 08:38:33,468 INFO [train.py:451] Epoch 12, batch 11230, batch avg loss 0.2009, total avg loss: 0.2180, batch size: 28
2021-10-15 08:38:38,356 INFO [train.py:451] Epoch 12, batch 11240, batch avg loss 0.2250, total avg loss: 0.2184, batch size: 39
2021-10-15 08:38:43,266 INFO [train.py:451] Epoch 12, batch 11250, batch avg loss 0.1867, total avg loss: 0.2166, batch size: 29
2021-10-15 08:38:48,184 INFO [train.py:451] Epoch 12, batch 11260, batch avg loss 0.2192, total avg loss: 0.2176, batch size: 35
2021-10-15 08:38:53,079 INFO [train.py:451] Epoch 12, batch 11270, batch avg loss 0.3506, total avg loss: 0.2196, batch size: 129
2021-10-15 08:38:58,151 INFO [train.py:451] Epoch 12, batch 11280, batch avg loss 0.1672, total avg loss: 0.2176, batch size: 32
2021-10-15 08:39:03,148 INFO [train.py:451] Epoch 12, batch 11290, batch avg loss 0.1497, total avg loss: 0.2154, batch size: 28
2021-10-15 08:39:07,981 INFO [train.py:451] Epoch 12, batch 11300, batch avg loss 0.1864, total avg loss: 0.2146, batch size: 36
2021-10-15 08:39:12,955 INFO [train.py:451] Epoch 12, batch 11310, batch avg loss 0.1559, total avg loss: 0.2146, batch size: 32
2021-10-15 08:39:18,044 INFO [train.py:451] Epoch 12, batch 11320, batch avg loss 0.2151, total avg loss: 0.2142, batch size: 33
2021-10-15 08:39:23,168 INFO [train.py:451] Epoch 12, batch 11330, batch avg loss 0.1907, total avg loss: 0.2132, batch size: 28
2021-10-15 08:39:28,010 INFO [train.py:451] Epoch 12, batch 11340, batch avg loss 0.1841, total avg loss: 0.2136, batch size: 30
2021-10-15 08:39:32,863 INFO [train.py:451] Epoch 12, batch 11350, batch avg loss 0.2144, total avg loss: 0.2134, batch size: 34
2021-10-15 08:39:37,880 INFO [train.py:451] Epoch 12, batch 11360, batch avg loss 0.1919, total avg loss: 0.2139, batch size: 33
2021-10-15 08:39:42,727 INFO [train.py:451] Epoch 12, batch 11370, batch avg loss 0.1761, total avg loss: 0.2145, batch size: 33
2021-10-15 08:39:47,705 INFO [train.py:451] Epoch 12, batch 11380, batch avg loss 0.1762, total avg loss: 0.2146, batch size: 33
2021-10-15 08:39:52,716 INFO [train.py:451] Epoch 12, batch 11390, batch avg loss 0.2319, total avg loss: 0.2142, batch size: 38
2021-10-15 08:39:57,438 INFO [train.py:451] Epoch 12, batch 11400, batch avg loss 0.2578, total avg loss: 0.2152, batch size: 73
2021-10-15 08:40:02,324 INFO [train.py:451] Epoch 12, batch 11410, batch avg loss 0.2493, total avg loss: 0.2192, batch size: 73
2021-10-15 08:40:07,241 INFO [train.py:451] Epoch 12, batch 11420, batch avg loss 0.1807, total avg loss: 0.2217, batch size: 39
2021-10-15 08:40:12,189 INFO [train.py:451] Epoch 12, batch 11430, batch avg loss 0.1598, total avg loss: 0.2209, batch size: 34
2021-10-15 08:40:16,968 INFO [train.py:451] Epoch 12, batch 11440, batch avg loss 0.1810, total avg loss: 0.2236, batch size: 27
2021-10-15 08:40:21,924 INFO [train.py:451] Epoch 12, batch 11450, batch avg loss 0.2193, total avg loss: 0.2223, batch size: 36
2021-10-15 08:40:26,707 INFO [train.py:451] Epoch 12, batch 11460, batch avg loss 0.2127, total avg loss: 0.2230, batch size: 42
2021-10-15 08:40:31,721 INFO [train.py:451] Epoch 12, batch 11470, batch avg loss 0.2126, total avg loss: 0.2216, batch size: 38
2021-10-15 08:40:36,793 INFO [train.py:451] Epoch 12, batch 11480, batch avg loss 0.2254, total avg loss: 0.2192, batch size: 38
2021-10-15 08:40:41,711 INFO [train.py:451] Epoch 12, batch 11490, batch avg loss 0.1887, total avg loss: 0.2192, batch size: 31
2021-10-15 08:40:46,525 INFO [train.py:451] Epoch 12, batch 11500, batch avg loss 0.3072, total avg loss: 0.2204, batch size: 128
2021-10-15 08:40:51,341 INFO [train.py:451] Epoch 12, batch 11510, batch avg loss 0.2073, total avg loss: 0.2201, batch size: 42
2021-10-15 08:40:56,163 INFO [train.py:451] Epoch 12, batch 11520, batch avg loss 0.2176, total avg loss: 0.2203, batch size: 33
2021-10-15 08:41:01,103 INFO [train.py:451] Epoch 12, batch 11530, batch avg loss 0.2681, total avg loss: 0.2207, batch size: 49
2021-10-15 08:41:06,158 INFO [train.py:451] Epoch 12, batch 11540, batch avg loss 0.2232, total avg loss: 0.2192, batch size: 39
2021-10-15 08:41:11,125 INFO [train.py:451] Epoch 12, batch 11550, batch avg loss 0.2097, total avg loss: 0.2184, batch size: 45
2021-10-15 08:41:16,082 INFO [train.py:451] Epoch 12, batch 11560, batch avg loss 0.2436, total avg loss: 0.2176, batch size: 39
2021-10-15 08:41:20,979 INFO [train.py:451] Epoch 12, batch 11570, batch avg loss 0.2545, total avg loss: 0.2173, batch size: 35
2021-10-15 08:41:25,848 INFO [train.py:451] Epoch 12, batch 11580, batch avg loss 0.2487, total avg loss: 0.2161, batch size: 34
2021-10-15 08:41:30,766 INFO [train.py:451] Epoch 12, batch 11590, batch avg loss 0.2077, total avg loss: 0.2157, batch size: 35
2021-10-15 08:41:35,841 INFO [train.py:451] Epoch 12, batch 11600, batch avg loss 0.2185, total avg loss: 0.2150, batch size: 35
2021-10-15 08:41:40,550 INFO [train.py:451] Epoch 12, batch 11610, batch avg loss 0.2560, total avg loss: 0.2431, batch size: 73
2021-10-15 08:41:45,402 INFO [train.py:451] Epoch 12, batch 11620, batch avg loss 0.1990, total avg loss: 0.2258, batch size: 33
2021-10-15 08:41:50,265 INFO [train.py:451] Epoch 12, batch 11630, batch avg loss 0.2052, total avg loss: 0.2224, batch size: 29
2021-10-15 08:41:55,142 INFO [train.py:451] Epoch 12, batch 11640, batch avg loss 0.2849, total avg loss: 0.2227, batch size: 57
2021-10-15 08:42:00,010 INFO [train.py:451] Epoch 12, batch 11650, batch avg loss 0.1994, total avg loss: 0.2233, batch size: 33
2021-10-15 08:42:04,957 INFO [train.py:451] Epoch 12, batch 11660, batch avg loss 0.2391, total avg loss: 0.2205, batch size: 37
2021-10-15 08:42:09,816 INFO [train.py:451] Epoch 12, batch 11670, batch avg loss 0.2154, total avg loss: 0.2209, batch size: 45
2021-10-15 08:42:15,031 INFO [train.py:451] Epoch 12, batch 11680, batch avg loss 0.1908, total avg loss: 0.2173, batch size: 26
2021-10-15 08:42:19,996 INFO [train.py:451] Epoch 12, batch 11690, batch avg loss 0.2071, total avg loss: 0.2162, batch size: 31
2021-10-15 08:42:25,235 INFO [train.py:451] Epoch 12, batch 11700, batch avg loss 0.2508, total avg loss: 0.2165, batch size: 35
2021-10-15 08:42:30,185 INFO [train.py:451] Epoch 12, batch 11710, batch avg loss 0.2967, total avg loss: 0.2169, batch size: 130
2021-10-15 08:42:35,150 INFO [train.py:451] Epoch 12, batch 11720, batch avg loss 0.2108, total avg loss: 0.2169, batch size: 38
2021-10-15 08:42:40,002 INFO [train.py:451] Epoch 12, batch 11730, batch avg loss 0.1679, total avg loss: 0.2176, batch size: 30
2021-10-15 08:42:44,960 INFO [train.py:451] Epoch 12, batch 11740, batch avg loss 0.2657, total avg loss: 0.2172, batch size: 41
2021-10-15 08:42:49,834 INFO [train.py:451] Epoch 12, batch 11750, batch avg loss 0.1848, total avg loss: 0.2169, batch size: 34
2021-10-15 08:42:54,829 INFO [train.py:451] Epoch 12, batch 11760, batch avg loss 0.1971, total avg loss: 0.2160, batch size: 37
2021-10-15 08:42:59,893 INFO [train.py:451] Epoch 12, batch 11770, batch avg loss 0.2108, total avg loss: 0.2147, batch size: 49
2021-10-15 08:43:04,967 INFO [train.py:451] Epoch 12, batch 11780, batch avg loss 0.2003, total avg loss: 0.2144, batch size: 34
2021-10-15 08:43:09,849 INFO [train.py:451] Epoch 12, batch 11790, batch avg loss 0.2405, total avg loss: 0.2148, batch size: 39
2021-10-15 08:43:14,680 INFO [train.py:451] Epoch 12, batch 11800, batch avg loss 0.2543, total avg loss: 0.2150, batch size: 72
2021-10-15 08:43:19,800 INFO [train.py:451] Epoch 12, batch 11810, batch avg loss 0.1580, total avg loss: 0.2047, batch size: 29
2021-10-15 08:43:24,767 INFO [train.py:451] Epoch 12, batch 11820, batch avg loss 0.2182, total avg loss: 0.2096, batch size: 45
2021-10-15 08:43:29,736 INFO [train.py:451] Epoch 12, batch 11830, batch avg loss 0.2473, total avg loss: 0.2133, batch size: 35
2021-10-15 08:43:34,783 INFO [train.py:451] Epoch 12, batch 11840, batch avg loss 0.2062, total avg loss: 0.2153, batch size: 33
2021-10-15 08:43:39,698 INFO [train.py:451] Epoch 12, batch 11850, batch avg loss 0.2270, total avg loss: 0.2188, batch size: 32
2021-10-15 08:43:44,604 INFO [train.py:451] Epoch 12, batch 11860, batch avg loss 0.3436, total avg loss: 0.2198, batch size: 130
2021-10-15 08:43:49,787 INFO [train.py:451] Epoch 12, batch 11870, batch avg loss 0.2518, total avg loss: 0.2181, batch size: 35
2021-10-15 08:43:54,795 INFO [train.py:451] Epoch 12, batch 11880, batch avg loss 0.1710, total avg loss: 0.2190, batch size: 34
2021-10-15 08:43:59,795 INFO [train.py:451] Epoch 12, batch 11890, batch avg loss 0.3116, total avg loss: 0.2203, batch size: 129
2021-10-15 08:44:04,693 INFO [train.py:451] Epoch 12, batch 11900, batch avg loss 0.2319, total avg loss: 0.2209, batch size: 36
2021-10-15 08:44:09,841 INFO [train.py:451] Epoch 12, batch 11910, batch avg loss 0.2250, total avg loss: 0.2192, batch size: 33
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2021-10-15 08:44:44,199 INFO [train.py:451] Epoch 12, batch 11980, batch avg loss 0.2169, total avg loss: 0.2169, batch size: 32
2021-10-15 08:44:49,340 INFO [train.py:451] Epoch 12, batch 11990, batch avg loss 0.1722, total avg loss: 0.2161, batch size: 27
2021-10-15 08:44:54,254 INFO [train.py:451] Epoch 12, batch 12000, batch avg loss 0.2541, total avg loss: 0.2162, batch size: 45
2021-10-15 08:45:33,479 INFO [train.py:483] Epoch 12, valid loss 0.1602, best valid loss: 0.1598 best valid epoch: 12
2021-10-15 08:45:38,361 INFO [train.py:451] Epoch 12, batch 12010, batch avg loss 0.2149, total avg loss: 0.2251, batch size: 32
2021-10-15 08:45:43,295 INFO [train.py:451] Epoch 12, batch 12020, batch avg loss 0.2024, total avg loss: 0.2181, batch size: 38
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2021-10-15 08:46:03,055 INFO [train.py:451] Epoch 12, batch 12060, batch avg loss 0.1848, total avg loss: 0.2177, batch size: 28
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2021-10-15 08:46:12,603 INFO [train.py:451] Epoch 12, batch 12080, batch avg loss 0.2333, total avg loss: 0.2188, batch size: 42
2021-10-15 08:46:17,589 INFO [train.py:451] Epoch 12, batch 12090, batch avg loss 0.1809, total avg loss: 0.2169, batch size: 29
2021-10-15 08:46:22,541 INFO [train.py:451] Epoch 12, batch 12100, batch avg loss 0.2133, total avg loss: 0.2152, batch size: 38
2021-10-15 08:46:27,394 INFO [train.py:451] Epoch 12, batch 12110, batch avg loss 0.2180, total avg loss: 0.2151, batch size: 34
2021-10-15 08:46:32,294 INFO [train.py:451] Epoch 12, batch 12120, batch avg loss 0.2267, total avg loss: 0.2148, batch size: 34
2021-10-15 08:46:37,242 INFO [train.py:451] Epoch 12, batch 12130, batch avg loss 0.2655, total avg loss: 0.2153, batch size: 35
2021-10-15 08:46:42,359 INFO [train.py:451] Epoch 12, batch 12140, batch avg loss 0.2285, total avg loss: 0.2148, batch size: 34
2021-10-15 08:46:47,390 INFO [train.py:451] Epoch 12, batch 12150, batch avg loss 0.2014, total avg loss: 0.2150, batch size: 34
2021-10-15 08:46:52,379 INFO [train.py:451] Epoch 12, batch 12160, batch avg loss 0.1823, total avg loss: 0.2157, batch size: 27
2021-10-15 08:46:57,421 INFO [train.py:451] Epoch 12, batch 12170, batch avg loss 0.1564, total avg loss: 0.2148, batch size: 31
2021-10-15 08:47:02,527 INFO [train.py:451] Epoch 12, batch 12180, batch avg loss 0.1897, total avg loss: 0.2141, batch size: 36
2021-10-15 08:47:07,629 INFO [train.py:451] Epoch 12, batch 12190, batch avg loss 0.1920, total avg loss: 0.2135, batch size: 36
2021-10-15 08:47:12,605 INFO [train.py:451] Epoch 12, batch 12200, batch avg loss 0.2130, total avg loss: 0.2144, batch size: 30
2021-10-15 08:47:17,603 INFO [train.py:451] Epoch 12, batch 12210, batch avg loss 0.2494, total avg loss: 0.2162, batch size: 38
2021-10-15 08:47:22,503 INFO [train.py:451] Epoch 12, batch 12220, batch avg loss 0.2497, total avg loss: 0.2206, batch size: 73
2021-10-15 08:47:27,319 INFO [train.py:451] Epoch 12, batch 12230, batch avg loss 0.1880, total avg loss: 0.2279, batch size: 32
2021-10-15 08:47:32,237 INFO [train.py:451] Epoch 12, batch 12240, batch avg loss 0.3224, total avg loss: 0.2310, batch size: 129
2021-10-15 08:47:37,069 INFO [train.py:451] Epoch 12, batch 12250, batch avg loss 0.1993, total avg loss: 0.2270, batch size: 36
2021-10-15 08:47:41,994 INFO [train.py:451] Epoch 12, batch 12260, batch avg loss 0.2502, total avg loss: 0.2258, batch size: 36
2021-10-15 08:47:46,978 INFO [train.py:451] Epoch 12, batch 12270, batch avg loss 0.1970, total avg loss: 0.2239, batch size: 29
2021-10-15 08:47:51,871 INFO [train.py:451] Epoch 12, batch 12280, batch avg loss 0.2791, total avg loss: 0.2221, batch size: 72
2021-10-15 08:47:56,696 INFO [train.py:451] Epoch 12, batch 12290, batch avg loss 0.2052, total avg loss: 0.2214, batch size: 27
2021-10-15 08:48:01,492 INFO [train.py:451] Epoch 12, batch 12300, batch avg loss 0.2257, total avg loss: 0.2228, batch size: 27
2021-10-15 08:48:06,577 INFO [train.py:451] Epoch 12, batch 12310, batch avg loss 0.1920, total avg loss: 0.2204, batch size: 36
2021-10-15 08:48:11,756 INFO [train.py:451] Epoch 12, batch 12320, batch avg loss 0.1673, total avg loss: 0.2194, batch size: 30
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2021-10-15 08:48:21,504 INFO [train.py:451] Epoch 12, batch 12340, batch avg loss 0.1976, total avg loss: 0.2207, batch size: 29
2021-10-15 08:48:26,473 INFO [train.py:451] Epoch 12, batch 12350, batch avg loss 0.2380, total avg loss: 0.2197, batch size: 35
2021-10-15 08:48:31,361 INFO [train.py:451] Epoch 12, batch 12360, batch avg loss 0.2321, total avg loss: 0.2191, batch size: 57
2021-10-15 08:48:35,938 INFO [train.py:451] Epoch 12, batch 12370, batch avg loss 0.2320, total avg loss: 0.2202, batch size: 41
2021-10-15 08:48:40,685 INFO [train.py:451] Epoch 12, batch 12380, batch avg loss 0.2514, total avg loss: 0.2206, batch size: 49
2021-10-15 08:48:45,621 INFO [train.py:451] Epoch 12, batch 12390, batch avg loss 0.1624, total avg loss: 0.2196, batch size: 33
2021-10-15 08:48:50,605 INFO [train.py:451] Epoch 12, batch 12400, batch avg loss 0.2022, total avg loss: 0.2189, batch size: 31
2021-10-15 08:48:55,451 INFO [train.py:451] Epoch 12, batch 12410, batch avg loss 0.2069, total avg loss: 0.2219, batch size: 34
2021-10-15 08:49:00,436 INFO [train.py:451] Epoch 12, batch 12420, batch avg loss 0.1600, total avg loss: 0.2195, batch size: 27
2021-10-15 08:49:05,396 INFO [train.py:451] Epoch 12, batch 12430, batch avg loss 0.1710, total avg loss: 0.2166, batch size: 31
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2021-10-15 08:49:15,296 INFO [train.py:451] Epoch 12, batch 12450, batch avg loss 0.1943, total avg loss: 0.2187, batch size: 34
2021-10-15 08:49:20,074 INFO [train.py:451] Epoch 12, batch 12460, batch avg loss 0.3786, total avg loss: 0.2233, batch size: 135
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2021-10-15 08:49:30,116 INFO [train.py:451] Epoch 12, batch 12480, batch avg loss 0.1503, total avg loss: 0.2186, batch size: 32
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2021-10-15 08:49:40,043 INFO [train.py:451] Epoch 12, batch 12500, batch avg loss 0.1972, total avg loss: 0.2181, batch size: 32
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2021-10-15 08:49:49,886 INFO [train.py:451] Epoch 12, batch 12520, batch avg loss 0.2266, total avg loss: 0.2174, batch size: 35
2021-10-15 08:49:54,843 INFO [train.py:451] Epoch 12, batch 12530, batch avg loss 0.2773, total avg loss: 0.2170, batch size: 35
2021-10-15 08:49:59,791 INFO [train.py:451] Epoch 12, batch 12540, batch avg loss 0.1813, total avg loss: 0.2164, batch size: 31
2021-10-15 08:50:04,677 INFO [train.py:451] Epoch 12, batch 12550, batch avg loss 0.2090, total avg loss: 0.2159, batch size: 37
2021-10-15 08:50:09,613 INFO [train.py:451] Epoch 12, batch 12560, batch avg loss 0.1898, total avg loss: 0.2152, batch size: 33
2021-10-15 08:50:14,424 INFO [train.py:451] Epoch 12, batch 12570, batch avg loss 0.2629, total avg loss: 0.2160, batch size: 49
2021-10-15 08:50:19,249 INFO [train.py:451] Epoch 12, batch 12580, batch avg loss 0.2008, total avg loss: 0.2163, batch size: 31
2021-10-15 08:50:24,169 INFO [train.py:451] Epoch 12, batch 12590, batch avg loss 0.3422, total avg loss: 0.2163, batch size: 126
2021-10-15 08:50:28,833 INFO [train.py:451] Epoch 12, batch 12600, batch avg loss 0.2754, total avg loss: 0.2178, batch size: 74
2021-10-15 08:50:33,928 INFO [train.py:451] Epoch 12, batch 12610, batch avg loss 0.1687, total avg loss: 0.1969, batch size: 34
2021-10-15 08:50:38,959 INFO [train.py:451] Epoch 12, batch 12620, batch avg loss 0.2175, total avg loss: 0.2150, batch size: 56
2021-10-15 08:50:44,148 INFO [train.py:451] Epoch 12, batch 12630, batch avg loss 0.1939, total avg loss: 0.2115, batch size: 34
2021-10-15 08:50:49,143 INFO [train.py:451] Epoch 12, batch 12640, batch avg loss 0.1801, total avg loss: 0.2115, batch size: 27
2021-10-15 08:50:53,954 INFO [train.py:451] Epoch 12, batch 12650, batch avg loss 0.2082, total avg loss: 0.2098, batch size: 41
2021-10-15 08:50:58,878 INFO [train.py:451] Epoch 12, batch 12660, batch avg loss 0.2453, total avg loss: 0.2108, batch size: 41
2021-10-15 08:51:03,728 INFO [train.py:451] Epoch 12, batch 12670, batch avg loss 0.2138, total avg loss: 0.2102, batch size: 32
2021-10-15 08:51:08,671 INFO [train.py:451] Epoch 12, batch 12680, batch avg loss 0.2263, total avg loss: 0.2095, batch size: 49
2021-10-15 08:51:13,490 INFO [train.py:451] Epoch 12, batch 12690, batch avg loss 0.2491, total avg loss: 0.2112, batch size: 74
2021-10-15 08:51:18,493 INFO [train.py:451] Epoch 12, batch 12700, batch avg loss 0.2101, total avg loss: 0.2126, batch size: 29
2021-10-15 08:51:23,477 INFO [train.py:451] Epoch 12, batch 12710, batch avg loss 0.2327, total avg loss: 0.2120, batch size: 36
2021-10-15 08:51:28,618 INFO [train.py:451] Epoch 12, batch 12720, batch avg loss 0.2112, total avg loss: 0.2121, batch size: 27
2021-10-15 08:51:33,727 INFO [train.py:451] Epoch 12, batch 12730, batch avg loss 0.2353, total avg loss: 0.2123, batch size: 35
2021-10-15 08:51:38,740 INFO [train.py:451] Epoch 12, batch 12740, batch avg loss 0.2063, total avg loss: 0.2120, batch size: 30
2021-10-15 08:51:43,630 INFO [train.py:451] Epoch 12, batch 12750, batch avg loss 0.2088, total avg loss: 0.2118, batch size: 32
2021-10-15 08:51:48,393 INFO [train.py:451] Epoch 12, batch 12760, batch avg loss 0.2520, total avg loss: 0.2126, batch size: 37
2021-10-15 08:51:53,253 INFO [train.py:451] Epoch 12, batch 12770, batch avg loss 0.1776, total avg loss: 0.2116, batch size: 32
2021-10-15 08:51:58,104 INFO [train.py:451] Epoch 12, batch 12780, batch avg loss 0.2197, total avg loss: 0.2129, batch size: 36
2021-10-15 08:52:03,173 INFO [train.py:451] Epoch 12, batch 12790, batch avg loss 0.1908, total avg loss: 0.2127, batch size: 30
2021-10-15 08:52:08,094 INFO [train.py:451] Epoch 12, batch 12800, batch avg loss 0.1715, total avg loss: 0.2129, batch size: 31
2021-10-15 08:52:13,007 INFO [train.py:451] Epoch 12, batch 12810, batch avg loss 0.2061, total avg loss: 0.2280, batch size: 38
2021-10-15 08:52:17,934 INFO [train.py:451] Epoch 12, batch 12820, batch avg loss 0.2134, total avg loss: 0.2260, batch size: 39
2021-10-15 08:52:22,797 INFO [train.py:451] Epoch 12, batch 12830, batch avg loss 0.1835, total avg loss: 0.2253, batch size: 32
2021-10-15 08:52:27,584 INFO [train.py:451] Epoch 12, batch 12840, batch avg loss 0.2291, total avg loss: 0.2285, batch size: 35
2021-10-15 08:52:32,547 INFO [train.py:451] Epoch 12, batch 12850, batch avg loss 0.1585, total avg loss: 0.2238, batch size: 31
2021-10-15 08:52:37,684 INFO [train.py:451] Epoch 12, batch 12860, batch avg loss 0.2368, total avg loss: 0.2226, batch size: 37
2021-10-15 08:52:42,820 INFO [train.py:451] Epoch 12, batch 12870, batch avg loss 0.2006, total avg loss: 0.2212, batch size: 34
2021-10-15 08:52:47,519 INFO [train.py:451] Epoch 12, batch 12880, batch avg loss 0.2009, total avg loss: 0.2215, batch size: 42
2021-10-15 08:52:52,543 INFO [train.py:451] Epoch 12, batch 12890, batch avg loss 0.2461, total avg loss: 0.2206, batch size: 34
2021-10-15 08:52:57,298 INFO [train.py:451] Epoch 12, batch 12900, batch avg loss 0.3172, total avg loss: 0.2213, batch size: 129
2021-10-15 08:53:02,086 INFO [train.py:451] Epoch 12, batch 12910, batch avg loss 0.1947, total avg loss: 0.2201, batch size: 32
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2021-10-15 08:53:11,874 INFO [train.py:451] Epoch 12, batch 12930, batch avg loss 0.2296, total avg loss: 0.2196, batch size: 37
2021-10-15 08:53:17,035 INFO [train.py:451] Epoch 12, batch 12940, batch avg loss 0.2846, total avg loss: 0.2189, batch size: 34
2021-10-15 08:53:21,961 INFO [train.py:451] Epoch 12, batch 12950, batch avg loss 0.1736, total avg loss: 0.2187, batch size: 30
2021-10-15 08:53:26,852 INFO [train.py:451] Epoch 12, batch 12960, batch avg loss 0.3213, total avg loss: 0.2188, batch size: 129
2021-10-15 08:53:31,846 INFO [train.py:451] Epoch 12, batch 12970, batch avg loss 0.1905, total avg loss: 0.2185, batch size: 42
2021-10-15 08:53:36,846 INFO [train.py:451] Epoch 12, batch 12980, batch avg loss 0.2085, total avg loss: 0.2190, batch size: 37
2021-10-15 08:53:41,863 INFO [train.py:451] Epoch 12, batch 12990, batch avg loss 0.2283, total avg loss: 0.2186, batch size: 34
2021-10-15 08:53:46,766 INFO [train.py:451] Epoch 12, batch 13000, batch avg loss 0.2271, total avg loss: 0.2186, batch size: 36
2021-10-15 08:54:27,597 INFO [train.py:483] Epoch 12, valid loss 0.1602, best valid loss: 0.1598 best valid epoch: 12
2021-10-15 08:54:32,683 INFO [train.py:451] Epoch 12, batch 13010, batch avg loss 0.2223, total avg loss: 0.2228, batch size: 28
2021-10-15 08:54:37,701 INFO [train.py:451] Epoch 12, batch 13020, batch avg loss 0.2276, total avg loss: 0.2164, batch size: 39
2021-10-15 08:54:42,799 INFO [train.py:451] Epoch 12, batch 13030, batch avg loss 0.1803, total avg loss: 0.2137, batch size: 32
2021-10-15 08:54:47,606 INFO [train.py:451] Epoch 12, batch 13040, batch avg loss 0.2454, total avg loss: 0.2148, batch size: 42
2021-10-15 08:54:52,536 INFO [train.py:451] Epoch 12, batch 13050, batch avg loss 0.2291, total avg loss: 0.2145, batch size: 36
2021-10-15 08:54:57,683 INFO [train.py:451] Epoch 12, batch 13060, batch avg loss 0.1933, total avg loss: 0.2146, batch size: 35
2021-10-15 08:55:02,533 INFO [train.py:451] Epoch 12, batch 13070, batch avg loss 0.2736, total avg loss: 0.2152, batch size: 57
2021-10-15 08:55:07,551 INFO [train.py:451] Epoch 12, batch 13080, batch avg loss 0.2106, total avg loss: 0.2144, batch size: 33
2021-10-15 08:55:12,530 INFO [train.py:451] Epoch 12, batch 13090, batch avg loss 0.3427, total avg loss: 0.2138, batch size: 124
2021-10-15 08:55:17,533 INFO [train.py:451] Epoch 12, batch 13100, batch avg loss 0.1721, total avg loss: 0.2124, batch size: 29
2021-10-15 08:55:22,528 INFO [train.py:451] Epoch 12, batch 13110, batch avg loss 0.2165, total avg loss: 0.2120, batch size: 33
2021-10-15 08:55:27,461 INFO [train.py:451] Epoch 12, batch 13120, batch avg loss 0.1464, total avg loss: 0.2116, batch size: 28
2021-10-15 08:55:32,317 INFO [train.py:451] Epoch 12, batch 13130, batch avg loss 0.2183, total avg loss: 0.2125, batch size: 41
2021-10-15 08:55:37,066 INFO [train.py:451] Epoch 12, batch 13140, batch avg loss 0.2840, total avg loss: 0.2139, batch size: 73
2021-10-15 08:55:42,046 INFO [train.py:451] Epoch 12, batch 13150, batch avg loss 0.2275, total avg loss: 0.2147, batch size: 32
2021-10-15 08:55:46,868 INFO [train.py:451] Epoch 12, batch 13160, batch avg loss 0.1735, total avg loss: 0.2146, batch size: 32
2021-10-15 08:55:51,850 INFO [train.py:451] Epoch 12, batch 13170, batch avg loss 0.2281, total avg loss: 0.2139, batch size: 38
2021-10-15 08:55:56,840 INFO [train.py:451] Epoch 12, batch 13180, batch avg loss 0.2639, total avg loss: 0.2141, batch size: 36
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2021-10-15 08:56:16,401 INFO [train.py:451] Epoch 12, batch 13220, batch avg loss 0.2068, total avg loss: 0.2220, batch size: 36
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2021-10-15 08:56:31,132 INFO [train.py:451] Epoch 12, batch 13250, batch avg loss 0.2099, total avg loss: 0.2120, batch size: 34
2021-10-15 08:56:36,054 INFO [train.py:451] Epoch 12, batch 13260, batch avg loss 0.1616, total avg loss: 0.2141, batch size: 29
2021-10-15 08:56:40,710 INFO [train.py:451] Epoch 12, batch 13270, batch avg loss 0.2220, total avg loss: 0.2152, batch size: 56
2021-10-15 08:56:45,641 INFO [train.py:451] Epoch 12, batch 13280, batch avg loss 0.2459, total avg loss: 0.2143, batch size: 37
2021-10-15 08:56:50,605 INFO [train.py:451] Epoch 12, batch 13290, batch avg loss 0.2485, total avg loss: 0.2130, batch size: 33
2021-10-15 08:56:55,548 INFO [train.py:451] Epoch 12, batch 13300, batch avg loss 0.2334, total avg loss: 0.2115, batch size: 42
2021-10-15 08:57:00,630 INFO [train.py:451] Epoch 12, batch 13310, batch avg loss 0.1715, total avg loss: 0.2112, batch size: 29
2021-10-15 08:57:05,533 INFO [train.py:451] Epoch 12, batch 13320, batch avg loss 0.2556, total avg loss: 0.2117, batch size: 71
2021-10-15 08:57:10,549 INFO [train.py:451] Epoch 12, batch 13330, batch avg loss 0.2464, total avg loss: 0.2125, batch size: 38
2021-10-15 08:57:15,639 INFO [train.py:451] Epoch 12, batch 13340, batch avg loss 0.2020, total avg loss: 0.2127, batch size: 37
2021-10-15 08:57:20,503 INFO [train.py:451] Epoch 12, batch 13350, batch avg loss 0.1931, total avg loss: 0.2133, batch size: 31
2021-10-15 08:57:25,495 INFO [train.py:451] Epoch 12, batch 13360, batch avg loss 0.2145, total avg loss: 0.2135, batch size: 42
2021-10-15 08:57:30,588 INFO [train.py:451] Epoch 12, batch 13370, batch avg loss 0.1958, total avg loss: 0.2144, batch size: 33
2021-10-15 08:57:35,417 INFO [train.py:451] Epoch 12, batch 13380, batch avg loss 0.2842, total avg loss: 0.2155, batch size: 73
2021-10-15 08:57:40,521 INFO [train.py:451] Epoch 12, batch 13390, batch avg loss 0.2364, total avg loss: 0.2150, batch size: 36
2021-10-15 08:57:45,665 INFO [train.py:451] Epoch 12, batch 13400, batch avg loss 0.2044, total avg loss: 0.2147, batch size: 34
2021-10-15 08:57:50,484 INFO [train.py:451] Epoch 12, batch 13410, batch avg loss 0.2125, total avg loss: 0.2251, batch size: 31
2021-10-15 08:57:55,386 INFO [train.py:451] Epoch 12, batch 13420, batch avg loss 0.1748, total avg loss: 0.2253, batch size: 29
2021-10-15 08:58:00,113 INFO [train.py:451] Epoch 12, batch 13430, batch avg loss 0.2098, total avg loss: 0.2321, batch size: 31
2021-10-15 08:58:04,899 INFO [train.py:451] Epoch 12, batch 13440, batch avg loss 0.2098, total avg loss: 0.2284, batch size: 34
2021-10-15 08:58:10,001 INFO [train.py:451] Epoch 12, batch 13450, batch avg loss 0.1907, total avg loss: 0.2249, batch size: 29
2021-10-15 08:58:15,066 INFO [train.py:451] Epoch 12, batch 13460, batch avg loss 0.1745, total avg loss: 0.2207, batch size: 29
2021-10-15 08:58:19,843 INFO [train.py:451] Epoch 12, batch 13470, batch avg loss 0.2800, total avg loss: 0.2248, batch size: 36
2021-10-15 08:58:24,752 INFO [train.py:451] Epoch 12, batch 13480, batch avg loss 0.2086, total avg loss: 0.2235, batch size: 31
2021-10-15 08:58:29,594 INFO [train.py:451] Epoch 12, batch 13490, batch avg loss 0.1813, total avg loss: 0.2219, batch size: 29
2021-10-15 08:58:34,513 INFO [train.py:451] Epoch 12, batch 13500, batch avg loss 0.1970, total avg loss: 0.2203, batch size: 34
2021-10-15 08:58:39,305 INFO [train.py:451] Epoch 12, batch 13510, batch avg loss 0.1966, total avg loss: 0.2194, batch size: 35
2021-10-15 08:58:44,278 INFO [train.py:451] Epoch 12, batch 13520, batch avg loss 0.1530, total avg loss: 0.2174, batch size: 30
2021-10-15 08:58:49,072 INFO [train.py:451] Epoch 12, batch 13530, batch avg loss 0.1916, total avg loss: 0.2179, batch size: 30
2021-10-15 08:58:54,133 INFO [train.py:451] Epoch 12, batch 13540, batch avg loss 0.2291, total avg loss: 0.2170, batch size: 34
2021-10-15 08:58:59,198 INFO [train.py:451] Epoch 12, batch 13550, batch avg loss 0.1873, total avg loss: 0.2168, batch size: 37
2021-10-15 08:59:03,966 INFO [train.py:451] Epoch 12, batch 13560, batch avg loss 0.2286, total avg loss: 0.2171, batch size: 49
2021-10-15 08:59:08,837 INFO [train.py:451] Epoch 12, batch 13570, batch avg loss 0.2452, total avg loss: 0.2175, batch size: 38
2021-10-15 08:59:13,735 INFO [train.py:451] Epoch 12, batch 13580, batch avg loss 0.2046, total avg loss: 0.2174, batch size: 35
2021-10-15 08:59:18,569 INFO [train.py:451] Epoch 12, batch 13590, batch avg loss 0.2053, total avg loss: 0.2168, batch size: 31
2021-10-15 08:59:23,463 INFO [train.py:451] Epoch 12, batch 13600, batch avg loss 0.3349, total avg loss: 0.2172, batch size: 123
2021-10-15 08:59:28,455 INFO [train.py:451] Epoch 12, batch 13610, batch avg loss 0.1794, total avg loss: 0.2091, batch size: 30
2021-10-15 08:59:33,672 INFO [train.py:451] Epoch 12, batch 13620, batch avg loss 0.1694, total avg loss: 0.1997, batch size: 29
2021-10-15 08:59:38,624 INFO [train.py:451] Epoch 12, batch 13630, batch avg loss 0.2647, total avg loss: 0.2106, batch size: 72
2021-10-15 08:59:43,425 INFO [train.py:451] Epoch 12, batch 13640, batch avg loss 0.2285, total avg loss: 0.2169, batch size: 45
2021-10-15 08:59:48,209 INFO [train.py:451] Epoch 12, batch 13650, batch avg loss 0.2045, total avg loss: 0.2166, batch size: 36
2021-10-15 08:59:53,221 INFO [train.py:451] Epoch 12, batch 13660, batch avg loss 0.2301, total avg loss: 0.2156, batch size: 41
2021-10-15 08:59:58,129 INFO [train.py:451] Epoch 12, batch 13670, batch avg loss 0.2625, total avg loss: 0.2157, batch size: 34
2021-10-15 09:00:03,060 INFO [train.py:451] Epoch 12, batch 13680, batch avg loss 0.2089, total avg loss: 0.2145, batch size: 34
2021-10-15 09:00:07,951 INFO [train.py:451] Epoch 12, batch 13690, batch avg loss 0.1841, total avg loss: 0.2142, batch size: 27
2021-10-15 09:00:12,737 INFO [train.py:451] Epoch 12, batch 13700, batch avg loss 0.1928, total avg loss: 0.2141, batch size: 45
2021-10-15 09:00:17,733 INFO [train.py:451] Epoch 12, batch 13710, batch avg loss 0.2251, total avg loss: 0.2142, batch size: 57
2021-10-15 09:00:22,728 INFO [train.py:451] Epoch 12, batch 13720, batch avg loss 0.1698, total avg loss: 0.2138, batch size: 29
2021-10-15 09:00:27,664 INFO [train.py:451] Epoch 12, batch 13730, batch avg loss 0.2002, total avg loss: 0.2136, batch size: 31
2021-10-15 09:00:32,635 INFO [train.py:451] Epoch 12, batch 13740, batch avg loss 0.2239, total avg loss: 0.2128, batch size: 32
2021-10-15 09:00:37,544 INFO [train.py:451] Epoch 12, batch 13750, batch avg loss 0.1881, total avg loss: 0.2116, batch size: 32
2021-10-15 09:00:42,612 INFO [train.py:451] Epoch 12, batch 13760, batch avg loss 0.2351, total avg loss: 0.2121, batch size: 33
2021-10-15 09:00:47,343 INFO [train.py:451] Epoch 12, batch 13770, batch avg loss 0.2352, total avg loss: 0.2120, batch size: 49
2021-10-15 09:00:52,363 INFO [train.py:451] Epoch 12, batch 13780, batch avg loss 0.1909, total avg loss: 0.2118, batch size: 32
2021-10-15 09:00:57,310 INFO [train.py:451] Epoch 12, batch 13790, batch avg loss 0.2049, total avg loss: 0.2114, batch size: 35
2021-10-15 09:01:02,222 INFO [train.py:451] Epoch 12, batch 13800, batch avg loss 0.1802, total avg loss: 0.2109, batch size: 34
2021-10-15 09:01:07,031 INFO [train.py:451] Epoch 12, batch 13810, batch avg loss 0.2408, total avg loss: 0.2132, batch size: 35
2021-10-15 09:01:11,944 INFO [train.py:451] Epoch 12, batch 13820, batch avg loss 0.1946, total avg loss: 0.2126, batch size: 31
2021-10-15 09:01:16,878 INFO [train.py:451] Epoch 12, batch 13830, batch avg loss 0.2231, total avg loss: 0.2123, batch size: 41
2021-10-15 09:01:21,619 INFO [train.py:451] Epoch 12, batch 13840, batch avg loss 0.1822, total avg loss: 0.2129, batch size: 29
2021-10-15 09:01:26,574 INFO [train.py:451] Epoch 12, batch 13850, batch avg loss 0.2273, total avg loss: 0.2127, batch size: 34
2021-10-15 09:01:31,334 INFO [train.py:451] Epoch 12, batch 13860, batch avg loss 0.2416, total avg loss: 0.2149, batch size: 57
2021-10-15 09:01:36,178 INFO [train.py:451] Epoch 12, batch 13870, batch avg loss 0.1814, total avg loss: 0.2174, batch size: 29
2021-10-15 09:01:41,104 INFO [train.py:451] Epoch 12, batch 13880, batch avg loss 0.2430, total avg loss: 0.2190, batch size: 31
2021-10-15 09:01:45,985 INFO [train.py:451] Epoch 12, batch 13890, batch avg loss 0.2129, total avg loss: 0.2179, batch size: 34
2021-10-15 09:01:50,925 INFO [train.py:451] Epoch 12, batch 13900, batch avg loss 0.2146, total avg loss: 0.2175, batch size: 32
2021-10-15 09:01:56,018 INFO [train.py:451] Epoch 12, batch 13910, batch avg loss 0.2141, total avg loss: 0.2172, batch size: 32
2021-10-15 09:02:00,839 INFO [train.py:451] Epoch 12, batch 13920, batch avg loss 0.2572, total avg loss: 0.2176, batch size: 38
2021-10-15 09:02:05,672 INFO [train.py:451] Epoch 12, batch 13930, batch avg loss 0.2090, total avg loss: 0.2180, batch size: 42
2021-10-15 09:02:10,405 INFO [train.py:451] Epoch 12, batch 13940, batch avg loss 0.2251, total avg loss: 0.2203, batch size: 39
2021-10-15 09:02:15,314 INFO [train.py:451] Epoch 12, batch 13950, batch avg loss 0.2121, total avg loss: 0.2201, batch size: 32
2021-10-15 09:02:20,287 INFO [train.py:451] Epoch 12, batch 13960, batch avg loss 0.1973, total avg loss: 0.2204, batch size: 32
2021-10-15 09:02:25,228 INFO [train.py:451] Epoch 12, batch 13970, batch avg loss 0.1926, total avg loss: 0.2192, batch size: 34
2021-10-15 09:02:30,215 INFO [train.py:451] Epoch 12, batch 13980, batch avg loss 0.2274, total avg loss: 0.2186, batch size: 34
2021-10-15 09:02:35,228 INFO [train.py:451] Epoch 12, batch 13990, batch avg loss 0.1586, total avg loss: 0.2176, batch size: 28
2021-10-15 09:02:40,098 INFO [train.py:451] Epoch 12, batch 14000, batch avg loss 0.1878, total avg loss: 0.2179, batch size: 36
2021-10-15 09:03:20,338 INFO [train.py:483] Epoch 12, valid loss 0.1602, best valid loss: 0.1598 best valid epoch: 12
2021-10-15 09:03:25,330 INFO [train.py:451] Epoch 12, batch 14010, batch avg loss 0.2059, total avg loss: 0.2061, batch size: 34
2021-10-15 09:03:30,321 INFO [train.py:451] Epoch 12, batch 14020, batch avg loss 0.1922, total avg loss: 0.2092, batch size: 32
2021-10-15 09:03:35,086 INFO [train.py:451] Epoch 12, batch 14030, batch avg loss 0.1746, total avg loss: 0.2072, batch size: 30
2021-10-15 09:03:40,207 INFO [train.py:451] Epoch 12, batch 14040, batch avg loss 0.2320, total avg loss: 0.2095, batch size: 34
2021-10-15 09:03:45,152 INFO [train.py:451] Epoch 12, batch 14050, batch avg loss 0.2054, total avg loss: 0.2081, batch size: 30
2021-10-15 09:03:49,908 INFO [train.py:451] Epoch 12, batch 14060, batch avg loss 0.2841, total avg loss: 0.2152, batch size: 73
2021-10-15 09:03:54,809 INFO [train.py:451] Epoch 12, batch 14070, batch avg loss 0.1576, total avg loss: 0.2136, batch size: 28
2021-10-15 09:03:59,796 INFO [train.py:451] Epoch 12, batch 14080, batch avg loss 0.2197, total avg loss: 0.2133, batch size: 32
2021-10-15 09:04:04,662 INFO [train.py:451] Epoch 12, batch 14090, batch avg loss 0.2047, total avg loss: 0.2132, batch size: 34
2021-10-15 09:04:09,675 INFO [train.py:451] Epoch 12, batch 14100, batch avg loss 0.2101, total avg loss: 0.2132, batch size: 36
2021-10-15 09:04:14,630 INFO [train.py:451] Epoch 12, batch 14110, batch avg loss 0.2317, total avg loss: 0.2133, batch size: 33
2021-10-15 09:04:19,586 INFO [train.py:451] Epoch 12, batch 14120, batch avg loss 0.2399, total avg loss: 0.2133, batch size: 41
2021-10-15 09:04:24,522 INFO [train.py:451] Epoch 12, batch 14130, batch avg loss 0.2286, total avg loss: 0.2138, batch size: 39
2021-10-15 09:04:29,468 INFO [train.py:451] Epoch 12, batch 14140, batch avg loss 0.2184, total avg loss: 0.2127, batch size: 39
2021-10-15 09:04:34,409 INFO [train.py:451] Epoch 12, batch 14150, batch avg loss 0.2446, total avg loss: 0.2129, batch size: 57
2021-10-15 09:04:39,256 INFO [train.py:451] Epoch 12, batch 14160, batch avg loss 0.2289, total avg loss: 0.2135, batch size: 49
2021-10-15 09:04:44,063 INFO [train.py:451] Epoch 12, batch 14170, batch avg loss 0.2735, total avg loss: 0.2159, batch size: 57
2021-10-15 09:04:48,777 INFO [train.py:451] Epoch 12, batch 14180, batch avg loss 0.3009, total avg loss: 0.2165, batch size: 71
2021-10-15 09:04:53,566 INFO [train.py:451] Epoch 12, batch 14190, batch avg loss 0.1989, total avg loss: 0.2170, batch size: 35
2021-10-15 09:04:58,449 INFO [train.py:451] Epoch 12, batch 14200, batch avg loss 0.2248, total avg loss: 0.2173, batch size: 33
2021-10-15 09:05:03,376 INFO [train.py:451] Epoch 12, batch 14210, batch avg loss 0.2265, total avg loss: 0.2111, batch size: 71
2021-10-15 09:05:08,391 INFO [train.py:451] Epoch 12, batch 14220, batch avg loss 0.2019, total avg loss: 0.2086, batch size: 30
2021-10-15 09:05:13,565 INFO [train.py:451] Epoch 12, batch 14230, batch avg loss 0.2015, total avg loss: 0.2057, batch size: 36
2021-10-15 09:05:18,436 INFO [train.py:451] Epoch 12, batch 14240, batch avg loss 0.1622, total avg loss: 0.2116, batch size: 27
2021-10-15 09:05:23,244 INFO [train.py:451] Epoch 12, batch 14250, batch avg loss 0.2450, total avg loss: 0.2135, batch size: 56
2021-10-15 09:05:28,195 INFO [train.py:451] Epoch 12, batch 14260, batch avg loss 0.1671, total avg loss: 0.2105, batch size: 33
2021-10-15 09:05:33,013 INFO [train.py:451] Epoch 12, batch 14270, batch avg loss 0.1956, total avg loss: 0.2100, batch size: 33
2021-10-15 09:05:37,944 INFO [train.py:451] Epoch 12, batch 14280, batch avg loss 0.1846, total avg loss: 0.2098, batch size: 34
2021-10-15 09:05:42,942 INFO [train.py:451] Epoch 12, batch 14290, batch avg loss 0.2258, total avg loss: 0.2124, batch size: 34
2021-10-15 09:05:47,829 INFO [train.py:451] Epoch 12, batch 14300, batch avg loss 0.2431, total avg loss: 0.2139, batch size: 57
2021-10-15 09:05:52,912 INFO [train.py:451] Epoch 12, batch 14310, batch avg loss 0.2648, total avg loss: 0.2133, batch size: 56
2021-10-15 09:05:57,753 INFO [train.py:451] Epoch 12, batch 14320, batch avg loss 0.2267, total avg loss: 0.2138, batch size: 41
2021-10-15 09:06:02,760 INFO [train.py:451] Epoch 12, batch 14330, batch avg loss 0.2177, total avg loss: 0.2140, batch size: 42
2021-10-15 09:06:14,706 INFO [train.py:451] Epoch 12, batch 14340, batch avg loss 0.2322, total avg loss: 0.2163, batch size: 45
2021-10-15 09:06:19,682 INFO [train.py:451] Epoch 12, batch 14350, batch avg loss 0.1869, total avg loss: 0.2147, batch size: 34
2021-10-15 09:06:24,459 INFO [train.py:451] Epoch 12, batch 14360, batch avg loss 0.2522, total avg loss: 0.2150, batch size: 56
2021-10-15 09:06:29,378 INFO [train.py:451] Epoch 12, batch 14370, batch avg loss 0.2153, total avg loss: 0.2147, batch size: 34
2021-10-15 09:06:34,098 INFO [train.py:451] Epoch 12, batch 14380, batch avg loss 0.1914, total avg loss: 0.2161, batch size: 30
2021-10-15 09:06:38,975 INFO [train.py:451] Epoch 12, batch 14390, batch avg loss 0.1995, total avg loss: 0.2162, batch size: 29
2021-10-15 09:06:43,752 INFO [train.py:451] Epoch 12, batch 14400, batch avg loss 0.2477, total avg loss: 0.2160, batch size: 74
2021-10-15 09:06:48,628 INFO [train.py:451] Epoch 12, batch 14410, batch avg loss 0.1971, total avg loss: 0.2074, batch size: 30
2021-10-15 09:06:53,453 INFO [train.py:451] Epoch 12, batch 14420, batch avg loss 0.1649, total avg loss: 0.2015, batch size: 38
2021-10-15 09:06:58,204 INFO [train.py:451] Epoch 12, batch 14430, batch avg loss 0.2683, total avg loss: 0.2056, batch size: 73
2021-10-15 09:07:03,362 INFO [train.py:451] Epoch 12, batch 14440, batch avg loss 0.1971, total avg loss: 0.2078, batch size: 36
2021-10-15 09:07:08,134 INFO [train.py:451] Epoch 12, batch 14450, batch avg loss 0.1984, total avg loss: 0.2089, batch size: 37
2021-10-15 09:07:12,938 INFO [train.py:451] Epoch 12, batch 14460, batch avg loss 0.2249, total avg loss: 0.2102, batch size: 39
2021-10-15 09:07:18,150 INFO [train.py:451] Epoch 12, batch 14470, batch avg loss 0.1658, total avg loss: 0.2092, batch size: 30
2021-10-15 09:07:23,175 INFO [train.py:451] Epoch 12, batch 14480, batch avg loss 0.2309, total avg loss: 0.2114, batch size: 29
2021-10-15 09:07:28,140 INFO [train.py:451] Epoch 12, batch 14490, batch avg loss 0.2032, total avg loss: 0.2113, batch size: 31
2021-10-15 09:07:33,140 INFO [train.py:451] Epoch 12, batch 14500, batch avg loss 0.1985, total avg loss: 0.2114, batch size: 38
2021-10-15 09:07:38,175 INFO [train.py:451] Epoch 12, batch 14510, batch avg loss 0.2146, total avg loss: 0.2117, batch size: 34
2021-10-15 09:07:43,216 INFO [train.py:451] Epoch 12, batch 14520, batch avg loss 0.2129, total avg loss: 0.2122, batch size: 35
2021-10-15 09:07:48,222 INFO [train.py:451] Epoch 12, batch 14530, batch avg loss 0.2214, total avg loss: 0.2124, batch size: 33
2021-10-15 09:07:53,069 INFO [train.py:451] Epoch 12, batch 14540, batch avg loss 0.1973, total avg loss: 0.2118, batch size: 42
2021-10-15 09:07:58,093 INFO [train.py:451] Epoch 12, batch 14550, batch avg loss 0.2326, total avg loss: 0.2119, batch size: 49
2021-10-15 09:08:02,865 INFO [train.py:451] Epoch 12, batch 14560, batch avg loss 0.2459, total avg loss: 0.2129, batch size: 36
2021-10-15 09:08:07,686 INFO [train.py:451] Epoch 12, batch 14570, batch avg loss 0.2866, total avg loss: 0.2132, batch size: 73
2021-10-15 09:08:12,671 INFO [train.py:451] Epoch 12, batch 14580, batch avg loss 0.2481, total avg loss: 0.2129, batch size: 45
2021-10-15 09:08:17,620 INFO [train.py:451] Epoch 12, batch 14590, batch avg loss 0.1746, total avg loss: 0.2132, batch size: 33
2021-10-15 09:08:22,476 INFO [train.py:451] Epoch 12, batch 14600, batch avg loss 0.1574, total avg loss: 0.2137, batch size: 28
2021-10-15 09:08:27,637 INFO [train.py:451] Epoch 12, batch 14610, batch avg loss 0.2185, total avg loss: 0.2073, batch size: 35
2021-10-15 09:08:32,515 INFO [train.py:451] Epoch 12, batch 14620, batch avg loss 0.2387, total avg loss: 0.2134, batch size: 45
2021-10-15 09:08:37,494 INFO [train.py:451] Epoch 12, batch 14630, batch avg loss 0.2310, total avg loss: 0.2111, batch size: 56
2021-10-15 09:08:42,444 INFO [train.py:451] Epoch 12, batch 14640, batch avg loss 0.1956, total avg loss: 0.2107, batch size: 38
2021-10-15 09:08:47,196 INFO [train.py:451] Epoch 12, batch 14650, batch avg loss 0.2253, total avg loss: 0.2130, batch size: 45
2021-10-15 09:08:52,135 INFO [train.py:451] Epoch 12, batch 14660, batch avg loss 0.2507, total avg loss: 0.2133, batch size: 72
2021-10-15 09:08:57,066 INFO [train.py:451] Epoch 12, batch 14670, batch avg loss 0.1861, total avg loss: 0.2111, batch size: 31
2021-10-15 09:09:02,034 INFO [train.py:451] Epoch 12, batch 14680, batch avg loss 0.2531, total avg loss: 0.2128, batch size: 42
2021-10-15 09:09:03,628 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "7e605ff6-b194-4359-3c1d-7ce9d38ced29" will not be mixed in.
2021-10-15 09:09:06,905 INFO [train.py:451] Epoch 12, batch 14690, batch avg loss 0.2142, total avg loss: 0.2127, batch size: 33
2021-10-15 09:09:12,053 INFO [train.py:451] Epoch 12, batch 14700, batch avg loss 0.1803, total avg loss: 0.2135, batch size: 36
2021-10-15 09:09:16,780 INFO [train.py:451] Epoch 12, batch 14710, batch avg loss 0.2100, total avg loss: 0.2136, batch size: 49
2021-10-15 09:09:21,808 INFO [train.py:451] Epoch 12, batch 14720, batch avg loss 0.2337, total avg loss: 0.2142, batch size: 38
2021-10-15 09:09:26,581 INFO [train.py:451] Epoch 12, batch 14730, batch avg loss 0.2432, total avg loss: 0.2140, batch size: 37
2021-10-15 09:09:31,498 INFO [train.py:451] Epoch 12, batch 14740, batch avg loss 0.2629, total avg loss: 0.2158, batch size: 71
2021-10-15 09:09:36,678 INFO [train.py:451] Epoch 12, batch 14750, batch avg loss 0.1996, total avg loss: 0.2151, batch size: 32
2021-10-15 09:09:41,794 INFO [train.py:451] Epoch 12, batch 14760, batch avg loss 0.2184, total avg loss: 0.2147, batch size: 41
2021-10-15 09:09:46,572 INFO [train.py:451] Epoch 12, batch 14770, batch avg loss 0.1676, total avg loss: 0.2148, batch size: 30
2021-10-15 09:09:51,535 INFO [train.py:451] Epoch 12, batch 14780, batch avg loss 0.3187, total avg loss: 0.2155, batch size: 133
2021-10-15 09:09:56,340 INFO [train.py:451] Epoch 12, batch 14790, batch avg loss 0.2044, total avg loss: 0.2156, batch size: 38
2021-10-15 09:10:01,188 INFO [train.py:451] Epoch 12, batch 14800, batch avg loss 0.3331, total avg loss: 0.2169, batch size: 131
2021-10-15 09:10:06,081 INFO [train.py:451] Epoch 12, batch 14810, batch avg loss 0.3065, total avg loss: 0.2216, batch size: 124
2021-10-15 09:10:11,034 INFO [train.py:451] Epoch 12, batch 14820, batch avg loss 0.1604, total avg loss: 0.2100, batch size: 29
2021-10-15 09:10:16,002 INFO [train.py:451] Epoch 12, batch 14830, batch avg loss 0.2308, total avg loss: 0.2082, batch size: 38
2021-10-15 09:10:20,858 INFO [train.py:451] Epoch 12, batch 14840, batch avg loss 0.2080, total avg loss: 0.2117, batch size: 36
2021-10-15 09:10:25,674 INFO [train.py:451] Epoch 12, batch 14850, batch avg loss 0.2055, total avg loss: 0.2140, batch size: 32
2021-10-15 09:10:30,656 INFO [train.py:451] Epoch 12, batch 14860, batch avg loss 0.2251, total avg loss: 0.2111, batch size: 34
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2021-10-15 09:12:19,219 INFO [train.py:483] Epoch 12, valid loss 0.1597, best valid loss: 0.1597 best valid epoch: 12
2021-10-15 09:12:23,985 INFO [train.py:451] Epoch 12, batch 15010, batch avg loss 0.2234, total avg loss: 0.2291, batch size: 30
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2021-10-15 09:18:58,966 INFO [train.py:451] Epoch 12, batch 15810, batch avg loss 0.1819, total avg loss: 0.2005, batch size: 28
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2021-10-15 09:19:43,558 INFO [train.py:451] Epoch 12, batch 15900, batch avg loss 0.2252, total avg loss: 0.2100, batch size: 34
2021-10-15 09:19:48,540 INFO [train.py:451] Epoch 12, batch 15910, batch avg loss 0.2101, total avg loss: 0.2105, batch size: 36
2021-10-15 09:19:53,564 INFO [train.py:451] Epoch 12, batch 15920, batch avg loss 0.3052, total avg loss: 0.2110, batch size: 129
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2021-10-15 09:20:03,695 INFO [train.py:451] Epoch 12, batch 15940, batch avg loss 0.2229, total avg loss: 0.2109, batch size: 42
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2021-10-15 09:20:13,701 INFO [train.py:451] Epoch 12, batch 15960, batch avg loss 0.2193, total avg loss: 0.2112, batch size: 38
2021-10-15 09:20:18,668 INFO [train.py:451] Epoch 12, batch 15970, batch avg loss 0.1985, total avg loss: 0.2110, batch size: 30
2021-10-15 09:20:23,640 INFO [train.py:451] Epoch 12, batch 15980, batch avg loss 0.1511, total avg loss: 0.2103, batch size: 29
2021-10-15 09:20:28,450 INFO [train.py:451] Epoch 12, batch 15990, batch avg loss 0.1732, total avg loss: 0.2110, batch size: 28
2021-10-15 09:20:33,330 INFO [train.py:451] Epoch 12, batch 16000, batch avg loss 0.2145, total avg loss: 0.2109, batch size: 29
2021-10-15 09:21:11,060 INFO [train.py:483] Epoch 12, valid loss 0.1600, best valid loss: 0.1597 best valid epoch: 12
2021-10-15 09:21:16,129 INFO [train.py:451] Epoch 12, batch 16010, batch avg loss 0.1895, total avg loss: 0.2142, batch size: 31
2021-10-15 09:21:20,968 INFO [train.py:451] Epoch 12, batch 16020, batch avg loss 0.2189, total avg loss: 0.2193, batch size: 45
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2021-10-15 09:21:55,186 INFO [train.py:451] Epoch 12, batch 16090, batch avg loss 0.2072, total avg loss: 0.2169, batch size: 49
2021-10-15 09:22:00,039 INFO [train.py:451] Epoch 12, batch 16100, batch avg loss 0.2382, total avg loss: 0.2170, batch size: 39
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2021-10-15 09:22:09,952 INFO [train.py:451] Epoch 12, batch 16120, batch avg loss 0.2557, total avg loss: 0.2166, batch size: 42
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2021-10-15 09:22:29,220 INFO [train.py:451] Epoch 12, batch 16160, batch avg loss 0.1828, total avg loss: 0.2186, batch size: 30
2021-10-15 09:22:34,191 INFO [train.py:451] Epoch 12, batch 16170, batch avg loss 0.1711, total avg loss: 0.2179, batch size: 33
2021-10-15 09:22:39,079 INFO [train.py:451] Epoch 12, batch 16180, batch avg loss 0.2639, total avg loss: 0.2179, batch size: 73
2021-10-15 09:22:44,094 INFO [train.py:451] Epoch 12, batch 16190, batch avg loss 0.2255, total avg loss: 0.2178, batch size: 32
2021-10-15 09:22:48,878 INFO [train.py:451] Epoch 12, batch 16200, batch avg loss 0.2670, total avg loss: 0.2190, batch size: 73
2021-10-15 09:22:53,731 INFO [train.py:451] Epoch 12, batch 16210, batch avg loss 0.2358, total avg loss: 0.2101, batch size: 30
2021-10-15 09:22:58,650 INFO [train.py:451] Epoch 12, batch 16220, batch avg loss 0.1860, total avg loss: 0.2105, batch size: 34
2021-10-15 09:23:03,450 INFO [train.py:451] Epoch 12, batch 16230, batch avg loss 0.1941, total avg loss: 0.2197, batch size: 31
2021-10-15 09:23:08,267 INFO [train.py:451] Epoch 12, batch 16240, batch avg loss 0.2415, total avg loss: 0.2191, batch size: 73
2021-10-15 09:23:13,356 INFO [train.py:451] Epoch 12, batch 16250, batch avg loss 0.1919, total avg loss: 0.2165, batch size: 32
2021-10-15 09:23:18,474 INFO [train.py:451] Epoch 12, batch 16260, batch avg loss 0.2809, total avg loss: 0.2164, batch size: 49
2021-10-15 09:23:23,536 INFO [train.py:451] Epoch 12, batch 16270, batch avg loss 0.2040, total avg loss: 0.2143, batch size: 32
2021-10-15 09:23:28,451 INFO [train.py:451] Epoch 12, batch 16280, batch avg loss 0.2316, total avg loss: 0.2161, batch size: 39
2021-10-15 09:23:33,128 INFO [train.py:451] Epoch 12, batch 16290, batch avg loss 0.1594, total avg loss: 0.2173, batch size: 30
2021-10-15 09:23:38,233 INFO [train.py:451] Epoch 12, batch 16300, batch avg loss 0.2043, total avg loss: 0.2159, batch size: 40
2021-10-15 09:23:43,355 INFO [train.py:451] Epoch 12, batch 16310, batch avg loss 0.2088, total avg loss: 0.2137, batch size: 37
2021-10-15 09:23:48,124 INFO [train.py:451] Epoch 12, batch 16320, batch avg loss 0.1954, total avg loss: 0.2154, batch size: 36
2021-10-15 09:23:52,873 INFO [train.py:451] Epoch 12, batch 16330, batch avg loss 0.2628, total avg loss: 0.2160, batch size: 72
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2021-10-15 09:24:02,314 INFO [train.py:451] Epoch 12, batch 16350, batch avg loss 0.1917, total avg loss: 0.2166, batch size: 29
2021-10-15 09:24:07,205 INFO [train.py:451] Epoch 12, batch 16360, batch avg loss 0.2420, total avg loss: 0.2168, batch size: 39
2021-10-15 09:24:12,175 INFO [train.py:451] Epoch 12, batch 16370, batch avg loss 0.1769, total avg loss: 0.2165, batch size: 30
2021-10-15 09:24:16,914 INFO [train.py:451] Epoch 12, batch 16380, batch avg loss 0.2285, total avg loss: 0.2170, batch size: 57
2021-10-15 09:24:21,784 INFO [train.py:451] Epoch 12, batch 16390, batch avg loss 0.2327, total avg loss: 0.2164, batch size: 39
2021-10-15 09:24:26,616 INFO [train.py:451] Epoch 12, batch 16400, batch avg loss 0.1972, total avg loss: 0.2169, batch size: 33
2021-10-15 09:24:27,843 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "06b27abb-eb95-a5e9-0615-84c1e8d2e4f4" will not be mixed in.
2021-10-15 09:24:31,661 INFO [train.py:451] Epoch 12, batch 16410, batch avg loss 0.1860, total avg loss: 0.2030, batch size: 29
2021-10-15 09:24:36,658 INFO [train.py:451] Epoch 12, batch 16420, batch avg loss 0.1818, total avg loss: 0.2085, batch size: 30
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2021-10-15 09:24:56,789 INFO [train.py:451] Epoch 12, batch 16460, batch avg loss 0.2146, total avg loss: 0.2077, batch size: 36
2021-10-15 09:25:01,839 INFO [train.py:451] Epoch 12, batch 16470, batch avg loss 0.2147, total avg loss: 0.2082, batch size: 36
2021-10-15 09:25:06,975 INFO [train.py:451] Epoch 12, batch 16480, batch avg loss 0.1703, total avg loss: 0.2070, batch size: 31
2021-10-15 09:25:11,967 INFO [train.py:451] Epoch 12, batch 16490, batch avg loss 0.2227, total avg loss: 0.2082, batch size: 38
2021-10-15 09:25:16,992 INFO [train.py:451] Epoch 12, batch 16500, batch avg loss 0.1829, total avg loss: 0.2093, batch size: 33
2021-10-15 09:25:21,899 INFO [train.py:451] Epoch 12, batch 16510, batch avg loss 0.1926, total avg loss: 0.2092, batch size: 30
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2021-10-15 09:25:31,829 INFO [train.py:451] Epoch 12, batch 16530, batch avg loss 0.2625, total avg loss: 0.2097, batch size: 38
2021-10-15 09:25:36,760 INFO [train.py:451] Epoch 12, batch 16540, batch avg loss 0.1906, total avg loss: 0.2110, batch size: 30
2021-10-15 09:25:41,740 INFO [train.py:451] Epoch 12, batch 16550, batch avg loss 0.2215, total avg loss: 0.2114, batch size: 36
2021-10-15 09:25:46,665 INFO [train.py:451] Epoch 12, batch 16560, batch avg loss 0.2496, total avg loss: 0.2119, batch size: 34
2021-10-15 09:25:51,581 INFO [train.py:451] Epoch 12, batch 16570, batch avg loss 0.2127, total avg loss: 0.2120, batch size: 39
2021-10-15 09:25:56,470 INFO [train.py:451] Epoch 12, batch 16580, batch avg loss 0.2434, total avg loss: 0.2124, batch size: 35
2021-10-15 09:26:01,426 INFO [train.py:451] Epoch 12, batch 16590, batch avg loss 0.2010, total avg loss: 0.2126, batch size: 36
2021-10-15 09:26:06,333 INFO [train.py:451] Epoch 12, batch 16600, batch avg loss 0.2012, total avg loss: 0.2131, batch size: 29
2021-10-15 09:26:11,410 INFO [train.py:451] Epoch 12, batch 16610, batch avg loss 0.1827, total avg loss: 0.2065, batch size: 30
2021-10-15 09:26:16,299 INFO [train.py:451] Epoch 12, batch 16620, batch avg loss 0.2177, total avg loss: 0.2117, batch size: 36
2021-10-15 09:26:21,322 INFO [train.py:451] Epoch 12, batch 16630, batch avg loss 0.2372, total avg loss: 0.2072, batch size: 39
2021-10-15 09:26:26,240 INFO [train.py:451] Epoch 12, batch 16640, batch avg loss 0.1857, total avg loss: 0.2113, batch size: 33
2021-10-15 09:26:31,222 INFO [train.py:451] Epoch 12, batch 16650, batch avg loss 0.2461, total avg loss: 0.2164, batch size: 35
2021-10-15 09:26:36,473 INFO [train.py:451] Epoch 12, batch 16660, batch avg loss 0.1759, total avg loss: 0.2162, batch size: 31
2021-10-15 09:26:41,533 INFO [train.py:451] Epoch 12, batch 16670, batch avg loss 0.1582, total avg loss: 0.2163, batch size: 27
2021-10-15 09:26:46,522 INFO [train.py:451] Epoch 12, batch 16680, batch avg loss 0.1754, total avg loss: 0.2160, batch size: 31
2021-10-15 09:26:51,468 INFO [train.py:451] Epoch 12, batch 16690, batch avg loss 0.2016, total avg loss: 0.2161, batch size: 32
2021-10-15 09:26:56,341 INFO [train.py:451] Epoch 12, batch 16700, batch avg loss 0.2446, total avg loss: 0.2172, batch size: 38
2021-10-15 09:27:01,326 INFO [train.py:451] Epoch 12, batch 16710, batch avg loss 0.2289, total avg loss: 0.2177, batch size: 38
2021-10-15 09:27:06,283 INFO [train.py:451] Epoch 12, batch 16720, batch avg loss 0.2495, total avg loss: 0.2195, batch size: 57
2021-10-15 09:27:11,361 INFO [train.py:451] Epoch 12, batch 16730, batch avg loss 0.1898, total avg loss: 0.2189, batch size: 38
2021-10-15 09:27:16,513 INFO [train.py:451] Epoch 12, batch 16740, batch avg loss 0.2574, total avg loss: 0.2182, batch size: 32
2021-10-15 09:27:21,283 INFO [train.py:451] Epoch 12, batch 16750, batch avg loss 0.3151, total avg loss: 0.2187, batch size: 117
2021-10-15 09:27:26,101 INFO [train.py:451] Epoch 12, batch 16760, batch avg loss 0.3406, total avg loss: 0.2191, batch size: 128
2021-10-15 09:27:30,905 INFO [train.py:451] Epoch 12, batch 16770, batch avg loss 0.2069, total avg loss: 0.2204, batch size: 33
2021-10-15 09:27:35,856 INFO [train.py:451] Epoch 12, batch 16780, batch avg loss 0.1885, total avg loss: 0.2203, batch size: 29
2021-10-15 09:27:40,816 INFO [train.py:451] Epoch 12, batch 16790, batch avg loss 0.2031, total avg loss: 0.2198, batch size: 33
2021-10-15 09:27:45,775 INFO [train.py:451] Epoch 12, batch 16800, batch avg loss 0.3206, total avg loss: 0.2200, batch size: 132
2021-10-15 09:27:50,709 INFO [train.py:451] Epoch 12, batch 16810, batch avg loss 0.1742, total avg loss: 0.2091, batch size: 29
2021-10-15 09:27:55,701 INFO [train.py:451] Epoch 12, batch 16820, batch avg loss 0.1542, total avg loss: 0.2093, batch size: 29
2021-10-15 09:28:00,651 INFO [train.py:451] Epoch 12, batch 16830, batch avg loss 0.2230, total avg loss: 0.2054, batch size: 45
2021-10-15 09:28:05,487 INFO [train.py:451] Epoch 12, batch 16840, batch avg loss 0.1974, total avg loss: 0.2061, batch size: 36
2021-10-15 09:28:10,157 INFO [train.py:451] Epoch 12, batch 16850, batch avg loss 0.1985, total avg loss: 0.2089, batch size: 38
2021-10-15 09:28:15,099 INFO [train.py:451] Epoch 12, batch 16860, batch avg loss 0.2072, total avg loss: 0.2091, batch size: 33
2021-10-15 09:28:19,981 INFO [train.py:451] Epoch 12, batch 16870, batch avg loss 0.2185, total avg loss: 0.2112, batch size: 41
2021-10-15 09:28:24,768 INFO [train.py:451] Epoch 12, batch 16880, batch avg loss 0.3031, total avg loss: 0.2133, batch size: 72
2021-10-15 09:28:29,672 INFO [train.py:451] Epoch 12, batch 16890, batch avg loss 0.1905, total avg loss: 0.2131, batch size: 32
2021-10-15 09:28:34,562 INFO [train.py:451] Epoch 12, batch 16900, batch avg loss 0.3176, total avg loss: 0.2136, batch size: 128
2021-10-15 09:28:39,499 INFO [train.py:451] Epoch 12, batch 16910, batch avg loss 0.3357, total avg loss: 0.2142, batch size: 130
2021-10-15 09:28:44,513 INFO [train.py:451] Epoch 12, batch 16920, batch avg loss 0.1792, total avg loss: 0.2132, batch size: 29
2021-10-15 09:28:49,427 INFO [train.py:451] Epoch 12, batch 16930, batch avg loss 0.2019, total avg loss: 0.2138, batch size: 37
2021-10-15 09:28:54,335 INFO [train.py:451] Epoch 12, batch 16940, batch avg loss 0.2477, total avg loss: 0.2138, batch size: 42
2021-10-15 09:28:59,310 INFO [train.py:451] Epoch 12, batch 16950, batch avg loss 0.2058, total avg loss: 0.2129, batch size: 35
2021-10-15 09:29:04,248 INFO [train.py:451] Epoch 12, batch 16960, batch avg loss 0.1892, total avg loss: 0.2113, batch size: 32
2021-10-15 09:29:09,095 INFO [train.py:451] Epoch 12, batch 16970, batch avg loss 0.2751, total avg loss: 0.2121, batch size: 129
2021-10-15 09:29:13,987 INFO [train.py:451] Epoch 12, batch 16980, batch avg loss 0.1831, total avg loss: 0.2126, batch size: 32
2021-10-15 09:29:18,972 INFO [train.py:451] Epoch 12, batch 16990, batch avg loss 0.1947, total avg loss: 0.2128, batch size: 34
2021-10-15 09:29:23,814 INFO [train.py:451] Epoch 12, batch 17000, batch avg loss 0.2409, total avg loss: 0.2139, batch size: 40
2021-10-15 09:30:01,626 INFO [train.py:483] Epoch 12, valid loss 0.1594, best valid loss: 0.1594 best valid epoch: 12
2021-10-15 09:30:06,613 INFO [train.py:451] Epoch 12, batch 17010, batch avg loss 0.3309, total avg loss: 0.2177, batch size: 130
2021-10-15 09:30:11,524 INFO [train.py:451] Epoch 12, batch 17020, batch avg loss 0.2264, total avg loss: 0.2193, batch size: 35
2021-10-15 09:30:16,560 INFO [train.py:451] Epoch 12, batch 17030, batch avg loss 0.1906, total avg loss: 0.2101, batch size: 34
2021-10-15 09:30:21,518 INFO [train.py:451] Epoch 12, batch 17040, batch avg loss 0.1583, total avg loss: 0.2076, batch size: 29
2021-10-15 09:30:26,520 INFO [train.py:451] Epoch 12, batch 17050, batch avg loss 0.1526, total avg loss: 0.2080, batch size: 28
2021-10-15 09:30:31,505 INFO [train.py:451] Epoch 12, batch 17060, batch avg loss 0.1674, total avg loss: 0.2077, batch size: 31
2021-10-15 09:30:36,776 INFO [train.py:451] Epoch 12, batch 17070, batch avg loss 0.1714, total avg loss: 0.2054, batch size: 30
2021-10-15 09:30:41,825 INFO [train.py:451] Epoch 12, batch 17080, batch avg loss 0.1947, total avg loss: 0.2070, batch size: 30
2021-10-15 09:30:46,677 INFO [train.py:451] Epoch 12, batch 17090, batch avg loss 0.1970, total avg loss: 0.2083, batch size: 29
2021-10-15 09:30:51,786 INFO [train.py:451] Epoch 12, batch 17100, batch avg loss 0.1565, total avg loss: 0.2078, batch size: 29
2021-10-15 09:30:56,917 INFO [train.py:451] Epoch 12, batch 17110, batch avg loss 0.2273, total avg loss: 0.2085, batch size: 36
2021-10-15 09:31:01,942 INFO [train.py:451] Epoch 12, batch 17120, batch avg loss 0.3283, total avg loss: 0.2103, batch size: 129
2021-10-15 09:31:06,728 INFO [train.py:451] Epoch 12, batch 17130, batch avg loss 0.2258, total avg loss: 0.2104, batch size: 42
2021-10-15 09:31:11,712 INFO [train.py:451] Epoch 12, batch 17140, batch avg loss 0.2069, total avg loss: 0.2107, batch size: 34
2021-10-15 09:31:16,672 INFO [train.py:451] Epoch 12, batch 17150, batch avg loss 0.2313, total avg loss: 0.2101, batch size: 49
2021-10-15 09:31:21,548 INFO [train.py:451] Epoch 12, batch 17160, batch avg loss 0.2565, total avg loss: 0.2091, batch size: 57
2021-10-15 09:31:26,787 INFO [train.py:451] Epoch 12, batch 17170, batch avg loss 0.1890, total avg loss: 0.2085, batch size: 31
2021-10-15 09:31:31,725 INFO [train.py:451] Epoch 12, batch 17180, batch avg loss 0.1804, total avg loss: 0.2087, batch size: 29
2021-10-15 09:31:36,578 INFO [train.py:451] Epoch 12, batch 17190, batch avg loss 0.2140, total avg loss: 0.2090, batch size: 49
2021-10-15 09:31:41,399 INFO [train.py:451] Epoch 12, batch 17200, batch avg loss 0.2110, total avg loss: 0.2097, batch size: 33
2021-10-15 09:31:46,389 INFO [train.py:451] Epoch 12, batch 17210, batch avg loss 0.2047, total avg loss: 0.2022, batch size: 32
2021-10-15 09:31:51,472 INFO [train.py:451] Epoch 12, batch 17220, batch avg loss 0.2520, total avg loss: 0.2052, batch size: 45
2021-10-15 09:31:56,244 INFO [train.py:451] Epoch 12, batch 17230, batch avg loss 0.1903, total avg loss: 0.2110, batch size: 38
2021-10-15 09:32:01,100 INFO [train.py:451] Epoch 12, batch 17240, batch avg loss 0.2133, total avg loss: 0.2120, batch size: 41
2021-10-15 09:32:05,796 INFO [train.py:451] Epoch 12, batch 17250, batch avg loss 0.3081, total avg loss: 0.2195, batch size: 127
2021-10-15 09:32:10,897 INFO [train.py:451] Epoch 12, batch 17260, batch avg loss 0.2000, total avg loss: 0.2172, batch size: 35
2021-10-15 09:32:15,690 INFO [train.py:451] Epoch 12, batch 17270, batch avg loss 0.1922, total avg loss: 0.2194, batch size: 31
2021-10-15 09:32:20,600 INFO [train.py:451] Epoch 12, batch 17280, batch avg loss 0.2040, total avg loss: 0.2184, batch size: 36
2021-10-15 09:32:25,356 INFO [train.py:451] Epoch 12, batch 17290, batch avg loss 0.2541, total avg loss: 0.2199, batch size: 39
2021-10-15 09:32:30,369 INFO [train.py:451] Epoch 12, batch 17300, batch avg loss 0.1889, total avg loss: 0.2196, batch size: 34
2021-10-15 09:32:35,265 INFO [train.py:451] Epoch 12, batch 17310, batch avg loss 0.1828, total avg loss: 0.2191, batch size: 30
2021-10-15 09:32:40,216 INFO [train.py:451] Epoch 12, batch 17320, batch avg loss 0.2643, total avg loss: 0.2191, batch size: 38
2021-10-15 09:32:45,089 INFO [train.py:451] Epoch 12, batch 17330, batch avg loss 0.2316, total avg loss: 0.2213, batch size: 31
2021-10-15 09:32:49,944 INFO [train.py:451] Epoch 12, batch 17340, batch avg loss 0.1782, total avg loss: 0.2197, batch size: 30
2021-10-15 09:32:54,911 INFO [train.py:451] Epoch 12, batch 17350, batch avg loss 0.2561, total avg loss: 0.2194, batch size: 42
2021-10-15 09:32:59,705 INFO [train.py:451] Epoch 12, batch 17360, batch avg loss 0.1813, total avg loss: 0.2184, batch size: 32
2021-10-15 09:33:04,647 INFO [train.py:451] Epoch 12, batch 17370, batch avg loss 0.2251, total avg loss: 0.2182, batch size: 45
2021-10-15 09:33:09,520 INFO [train.py:451] Epoch 12, batch 17380, batch avg loss 0.1656, total avg loss: 0.2179, batch size: 30
2021-10-15 09:33:14,367 INFO [train.py:451] Epoch 12, batch 17390, batch avg loss 0.1777, total avg loss: 0.2171, batch size: 28
2021-10-15 09:33:19,433 INFO [train.py:451] Epoch 12, batch 17400, batch avg loss 0.2534, total avg loss: 0.2172, batch size: 49
2021-10-15 09:33:24,419 INFO [train.py:451] Epoch 12, batch 17410, batch avg loss 0.2053, total avg loss: 0.2056, batch size: 31
2021-10-15 09:33:29,251 INFO [train.py:451] Epoch 12, batch 17420, batch avg loss 0.2246, total avg loss: 0.2093, batch size: 35
2021-10-15 09:33:34,268 INFO [train.py:451] Epoch 12, batch 17430, batch avg loss 0.2197, total avg loss: 0.2143, batch size: 34
2021-10-15 09:33:39,284 INFO [train.py:451] Epoch 12, batch 17440, batch avg loss 0.2038, total avg loss: 0.2140, batch size: 39
2021-10-15 09:33:44,132 INFO [train.py:451] Epoch 12, batch 17450, batch avg loss 0.2495, total avg loss: 0.2137, batch size: 35
2021-10-15 09:33:49,201 INFO [train.py:451] Epoch 12, batch 17460, batch avg loss 0.1896, total avg loss: 0.2115, batch size: 29
2021-10-15 09:33:54,077 INFO [train.py:451] Epoch 12, batch 17470, batch avg loss 0.2381, total avg loss: 0.2125, batch size: 39
2021-10-15 09:33:59,152 INFO [train.py:451] Epoch 12, batch 17480, batch avg loss 0.2045, total avg loss: 0.2110, batch size: 30
2021-10-15 09:34:03,969 INFO [train.py:451] Epoch 12, batch 17490, batch avg loss 0.1995, total avg loss: 0.2127, batch size: 31
2021-10-15 09:34:08,916 INFO [train.py:451] Epoch 12, batch 17500, batch avg loss 0.2508, total avg loss: 0.2118, batch size: 45
2021-10-15 09:34:13,655 INFO [train.py:451] Epoch 12, batch 17510, batch avg loss 0.2060, total avg loss: 0.2116, batch size: 36
2021-10-15 09:34:18,553 INFO [train.py:451] Epoch 12, batch 17520, batch avg loss 0.1627, total avg loss: 0.2130, batch size: 31
2021-10-15 09:34:23,681 INFO [train.py:451] Epoch 12, batch 17530, batch avg loss 0.2334, total avg loss: 0.2133, batch size: 36
2021-10-15 09:34:28,663 INFO [train.py:451] Epoch 12, batch 17540, batch avg loss 0.2263, total avg loss: 0.2127, batch size: 34
2021-10-15 09:34:33,695 INFO [train.py:451] Epoch 12, batch 17550, batch avg loss 0.1934, total avg loss: 0.2125, batch size: 39
2021-10-15 09:34:38,641 INFO [train.py:451] Epoch 12, batch 17560, batch avg loss 0.2237, total avg loss: 0.2122, batch size: 33
2021-10-15 09:34:43,705 INFO [train.py:451] Epoch 12, batch 17570, batch avg loss 0.2144, total avg loss: 0.2119, batch size: 29
2021-10-15 09:34:48,736 INFO [train.py:451] Epoch 12, batch 17580, batch avg loss 0.1997, total avg loss: 0.2116, batch size: 29
2021-10-15 09:34:53,684 INFO [train.py:451] Epoch 12, batch 17590, batch avg loss 0.1740, total avg loss: 0.2121, batch size: 28
2021-10-15 09:34:58,707 INFO [train.py:451] Epoch 12, batch 17600, batch avg loss 0.1894, total avg loss: 0.2117, batch size: 31
2021-10-15 09:35:03,588 INFO [train.py:451] Epoch 12, batch 17610, batch avg loss 0.2418, total avg loss: 0.2231, batch size: 38
2021-10-15 09:35:08,478 INFO [train.py:451] Epoch 12, batch 17620, batch avg loss 0.2251, total avg loss: 0.2105, batch size: 36
2021-10-15 09:35:13,368 INFO [train.py:451] Epoch 12, batch 17630, batch avg loss 0.2755, total avg loss: 0.2160, batch size: 72
2021-10-15 09:35:18,232 INFO [train.py:451] Epoch 12, batch 17640, batch avg loss 0.1683, total avg loss: 0.2162, batch size: 27
2021-10-15 09:35:23,252 INFO [train.py:451] Epoch 12, batch 17650, batch avg loss 0.2733, total avg loss: 0.2180, batch size: 41
2021-10-15 09:35:28,121 INFO [train.py:451] Epoch 12, batch 17660, batch avg loss 0.2100, total avg loss: 0.2185, batch size: 49
2021-10-15 09:35:32,971 INFO [train.py:451] Epoch 12, batch 17670, batch avg loss 0.1877, total avg loss: 0.2203, batch size: 31
2021-10-15 09:35:38,043 INFO [train.py:451] Epoch 12, batch 17680, batch avg loss 0.2030, total avg loss: 0.2196, batch size: 37
2021-10-15 09:35:43,110 INFO [train.py:451] Epoch 12, batch 17690, batch avg loss 0.2051, total avg loss: 0.2173, batch size: 27
2021-10-15 09:35:48,265 INFO [train.py:451] Epoch 12, batch 17700, batch avg loss 0.1958, total avg loss: 0.2148, batch size: 30
2021-10-15 09:35:53,212 INFO [train.py:451] Epoch 12, batch 17710, batch avg loss 0.1886, total avg loss: 0.2145, batch size: 35
2021-10-15 09:35:57,930 INFO [train.py:451] Epoch 12, batch 17720, batch avg loss 0.2231, total avg loss: 0.2176, batch size: 36
2021-10-15 09:36:02,695 INFO [train.py:451] Epoch 12, batch 17730, batch avg loss 0.2160, total avg loss: 0.2176, batch size: 38
2021-10-15 09:36:07,659 INFO [train.py:451] Epoch 12, batch 17740, batch avg loss 0.2125, total avg loss: 0.2171, batch size: 37
2021-10-15 09:36:12,560 INFO [train.py:451] Epoch 12, batch 17750, batch avg loss 0.2065, total avg loss: 0.2173, batch size: 34
2021-10-15 09:36:17,539 INFO [train.py:451] Epoch 12, batch 17760, batch avg loss 0.2179, total avg loss: 0.2167, batch size: 31
2021-10-15 09:36:22,454 INFO [train.py:451] Epoch 12, batch 17770, batch avg loss 0.1613, total avg loss: 0.2168, batch size: 30
2021-10-15 09:36:27,513 INFO [train.py:451] Epoch 12, batch 17780, batch avg loss 0.2055, total avg loss: 0.2158, batch size: 35
2021-10-15 09:36:32,487 INFO [train.py:451] Epoch 12, batch 17790, batch avg loss 0.1826, total avg loss: 0.2152, batch size: 33
2021-10-15 09:36:37,625 INFO [train.py:451] Epoch 12, batch 17800, batch avg loss 0.2586, total avg loss: 0.2150, batch size: 33
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2021-10-15 09:38:57,370 INFO [train.py:483] Epoch 12, valid loss 0.1602, best valid loss: 0.1594 best valid epoch: 12
2021-10-15 09:39:02,271 INFO [train.py:451] Epoch 12, batch 18010, batch avg loss 0.2472, total avg loss: 0.2225, batch size: 33
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2021-10-15 09:47:48,155 INFO [train.py:483] Epoch 12, valid loss 0.1601, best valid loss: 0.1594 best valid epoch: 12
2021-10-15 09:47:53,060 INFO [train.py:451] Epoch 12, batch 19010, batch avg loss 0.1887, total avg loss: 0.2232, batch size: 37
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2021-10-15 09:56:40,913 INFO [train.py:483] Epoch 12, valid loss 0.1601, best valid loss: 0.1594 best valid epoch: 12
2021-10-15 09:56:45,799 INFO [train.py:451] Epoch 12, batch 20010, batch avg loss 0.2680, total avg loss: 0.2220, batch size: 34
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2021-10-15 10:02:51,512 INFO [train.py:451] Epoch 12, batch 20750, batch avg loss 0.1622, total avg loss: 0.2136, batch size: 27
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2021-10-15 10:05:34,159 INFO [train.py:483] Epoch 12, valid loss 0.1600, best valid loss: 0.1594 best valid epoch: 12
2021-10-15 10:05:38,976 INFO [train.py:451] Epoch 12, batch 21010, batch avg loss 0.2429, total avg loss: 0.2151, batch size: 41
2021-10-15 10:05:43,729 INFO [train.py:451] Epoch 12, batch 21020, batch avg loss 0.1982, total avg loss: 0.2182, batch size: 31
2021-10-15 10:05:48,572 INFO [train.py:451] Epoch 12, batch 21030, batch avg loss 0.2090, total avg loss: 0.2191, batch size: 38
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2021-10-15 10:06:03,061 INFO [train.py:451] Epoch 12, batch 21060, batch avg loss 0.2020, total avg loss: 0.2220, batch size: 38
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2021-10-15 10:06:52,754 INFO [train.py:451] Epoch 12, batch 21160, batch avg loss 0.2027, total avg loss: 0.2162, batch size: 41
2021-10-15 10:06:57,522 INFO [train.py:451] Epoch 12, batch 21170, batch avg loss 0.2463, total avg loss: 0.2162, batch size: 45
2021-10-15 10:07:02,383 INFO [train.py:451] Epoch 12, batch 21180, batch avg loss 0.2194, total avg loss: 0.2154, batch size: 41
2021-10-15 10:07:07,437 INFO [checkpoint.py:62] Saving checkpoint to tdnn_lstm_ctc-v2/exp/epoch-12.pt
2021-10-15 10:07:08,273 INFO [train.py:564] epoch 13, lr: 2.5e-05
2021-10-15 10:07:12,592 INFO [train.py:451] Epoch 13, batch 0, batch avg loss 0.2073, total avg loss: 0.2073, batch size: 38
2021-10-15 10:07:17,574 INFO [train.py:451] Epoch 13, batch 10, batch avg loss 0.1393, total avg loss: 0.1979, batch size: 28
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2021-10-15 10:07:34,446 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "ca9128fb-c094-8932-52cb-8f692182b518" will not be mixed in.
2021-10-15 10:07:37,076 INFO [train.py:451] Epoch 13, batch 50, batch avg loss 0.2838, total avg loss: 0.2096, batch size: 45
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2021-10-15 10:12:16,785 INFO [train.py:451] Epoch 13, batch 620, batch avg loss 0.1723, total avg loss: 0.2144, batch size: 31
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2021-10-15 10:15:23,040 INFO [train.py:451] Epoch 13, batch 1000, batch avg loss 0.2282, total avg loss: 0.2147, batch size: 41
2021-10-15 10:16:02,371 INFO [train.py:483] Epoch 13, valid loss 0.1599, best valid loss: 0.1594 best valid epoch: 12
2021-10-15 10:16:07,281 INFO [train.py:451] Epoch 13, batch 1010, batch avg loss 0.1785, total avg loss: 0.2224, batch size: 34
2021-10-15 10:16:12,363 INFO [train.py:451] Epoch 13, batch 1020, batch avg loss 0.1845, total avg loss: 0.2183, batch size: 29
2021-10-15 10:16:17,053 INFO [train.py:451] Epoch 13, batch 1030, batch avg loss 0.2115, total avg loss: 0.2217, batch size: 49
2021-10-15 10:16:21,895 INFO [train.py:451] Epoch 13, batch 1040, batch avg loss 0.1677, total avg loss: 0.2153, batch size: 29
2021-10-15 10:16:26,820 INFO [train.py:451] Epoch 13, batch 1050, batch avg loss 0.1859, total avg loss: 0.2138, batch size: 32
2021-10-15 10:16:31,700 INFO [train.py:451] Epoch 13, batch 1060, batch avg loss 0.1945, total avg loss: 0.2139, batch size: 35
2021-10-15 10:16:36,513 INFO [train.py:451] Epoch 13, batch 1070, batch avg loss 0.1504, total avg loss: 0.2160, batch size: 27
2021-10-15 10:16:41,538 INFO [train.py:451] Epoch 13, batch 1080, batch avg loss 0.2317, total avg loss: 0.2147, batch size: 31
2021-10-15 10:16:46,376 INFO [train.py:451] Epoch 13, batch 1090, batch avg loss 0.2178, total avg loss: 0.2140, batch size: 49
2021-10-15 10:16:51,435 INFO [train.py:451] Epoch 13, batch 1100, batch avg loss 0.2434, total avg loss: 0.2130, batch size: 35
2021-10-15 10:16:56,395 INFO [train.py:451] Epoch 13, batch 1110, batch avg loss 0.2153, total avg loss: 0.2132, batch size: 35
2021-10-15 10:17:01,384 INFO [train.py:451] Epoch 13, batch 1120, batch avg loss 0.2324, total avg loss: 0.2142, batch size: 36
2021-10-15 10:17:06,193 INFO [train.py:451] Epoch 13, batch 1130, batch avg loss 0.2307, total avg loss: 0.2151, batch size: 35
2021-10-15 10:17:11,071 INFO [train.py:451] Epoch 13, batch 1140, batch avg loss 0.2394, total avg loss: 0.2152, batch size: 49
2021-10-15 10:17:15,977 INFO [train.py:451] Epoch 13, batch 1150, batch avg loss 0.2085, total avg loss: 0.2155, batch size: 45
2021-10-15 10:17:17,231 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "da56d072-c8b5-1c70-c1ed-c39a81f1b055" will not be mixed in.
2021-10-15 10:17:20,809 INFO [train.py:451] Epoch 13, batch 1160, batch avg loss 0.2510, total avg loss: 0.2161, batch size: 49
2021-10-15 10:17:25,763 INFO [train.py:451] Epoch 13, batch 1170, batch avg loss 0.2587, total avg loss: 0.2154, batch size: 49
2021-10-15 10:17:30,706 INFO [train.py:451] Epoch 13, batch 1180, batch avg loss 0.2331, total avg loss: 0.2163, batch size: 35
2021-10-15 10:17:35,519 INFO [train.py:451] Epoch 13, batch 1190, batch avg loss 0.2798, total avg loss: 0.2178, batch size: 57
2021-10-15 10:17:40,184 INFO [train.py:451] Epoch 13, batch 1200, batch avg loss 0.3075, total avg loss: 0.2182, batch size: 128
2021-10-15 10:17:45,169 INFO [train.py:451] Epoch 13, batch 1210, batch avg loss 0.1653, total avg loss: 0.2124, batch size: 29
2021-10-15 10:17:50,022 INFO [train.py:451] Epoch 13, batch 1220, batch avg loss 0.1995, total avg loss: 0.2192, batch size: 37
2021-10-15 10:17:54,831 INFO [train.py:451] Epoch 13, batch 1230, batch avg loss 0.2705, total avg loss: 0.2273, batch size: 45
2021-10-15 10:17:59,837 INFO [train.py:451] Epoch 13, batch 1240, batch avg loss 0.1740, total avg loss: 0.2221, batch size: 29
2021-10-15 10:18:04,740 INFO [train.py:451] Epoch 13, batch 1250, batch avg loss 0.2588, total avg loss: 0.2218, batch size: 35
2021-10-15 10:18:09,461 INFO [train.py:451] Epoch 13, batch 1260, batch avg loss 0.2167, total avg loss: 0.2242, batch size: 38
2021-10-15 10:18:14,397 INFO [train.py:451] Epoch 13, batch 1270, batch avg loss 0.2326, total avg loss: 0.2211, batch size: 41
2021-10-15 10:18:19,226 INFO [train.py:451] Epoch 13, batch 1280, batch avg loss 0.1850, total avg loss: 0.2185, batch size: 35
2021-10-15 10:18:24,143 INFO [train.py:451] Epoch 13, batch 1290, batch avg loss 0.2274, total avg loss: 0.2186, batch size: 35
2021-10-15 10:18:28,978 INFO [train.py:451] Epoch 13, batch 1300, batch avg loss 0.2378, total avg loss: 0.2182, batch size: 38
2021-10-15 10:18:33,956 INFO [train.py:451] Epoch 13, batch 1310, batch avg loss 0.2168, total avg loss: 0.2179, batch size: 35
2021-10-15 10:18:38,895 INFO [train.py:451] Epoch 13, batch 1320, batch avg loss 0.2183, total avg loss: 0.2167, batch size: 33
2021-10-15 10:18:43,864 INFO [train.py:451] Epoch 13, batch 1330, batch avg loss 0.1643, total avg loss: 0.2156, batch size: 31
2021-10-15 10:18:48,863 INFO [train.py:451] Epoch 13, batch 1340, batch avg loss 0.1817, total avg loss: 0.2146, batch size: 34
2021-10-15 10:18:53,771 INFO [train.py:451] Epoch 13, batch 1350, batch avg loss 0.2354, total avg loss: 0.2157, batch size: 39
2021-10-15 10:18:58,485 INFO [train.py:451] Epoch 13, batch 1360, batch avg loss 0.2331, total avg loss: 0.2165, batch size: 57
2021-10-15 10:19:03,205 INFO [train.py:451] Epoch 13, batch 1370, batch avg loss 0.1831, total avg loss: 0.2166, batch size: 29
2021-10-15 10:19:08,110 INFO [train.py:451] Epoch 13, batch 1380, batch avg loss 0.1517, total avg loss: 0.2157, batch size: 30
2021-10-15 10:19:13,009 INFO [train.py:451] Epoch 13, batch 1390, batch avg loss 0.1692, total avg loss: 0.2157, batch size: 31
2021-10-15 10:19:17,803 INFO [train.py:451] Epoch 13, batch 1400, batch avg loss 0.2459, total avg loss: 0.2160, batch size: 39
2021-10-15 10:19:22,703 INFO [train.py:451] Epoch 13, batch 1410, batch avg loss 0.2575, total avg loss: 0.2105, batch size: 38
2021-10-15 10:19:27,579 INFO [train.py:451] Epoch 13, batch 1420, batch avg loss 0.2644, total avg loss: 0.2113, batch size: 42
2021-10-15 10:19:32,613 INFO [train.py:451] Epoch 13, batch 1430, batch avg loss 0.1941, total avg loss: 0.2116, batch size: 31
2021-10-15 10:19:44,859 INFO [train.py:451] Epoch 13, batch 1440, batch avg loss 0.1709, total avg loss: 0.2145, batch size: 29
2021-10-15 10:19:49,935 INFO [train.py:451] Epoch 13, batch 1450, batch avg loss 0.2036, total avg loss: 0.2117, batch size: 33
2021-10-15 10:19:54,979 INFO [train.py:451] Epoch 13, batch 1460, batch avg loss 0.1819, total avg loss: 0.2110, batch size: 28
2021-10-15 10:19:59,885 INFO [train.py:451] Epoch 13, batch 1470, batch avg loss 0.2135, total avg loss: 0.2095, batch size: 34
2021-10-15 10:20:04,822 INFO [train.py:451] Epoch 13, batch 1480, batch avg loss 0.2248, total avg loss: 0.2101, batch size: 31
2021-10-15 10:20:09,839 INFO [train.py:451] Epoch 13, batch 1490, batch avg loss 0.1737, total avg loss: 0.2090, batch size: 28
2021-10-15 10:20:14,850 INFO [train.py:451] Epoch 13, batch 1500, batch avg loss 0.2443, total avg loss: 0.2077, batch size: 49
2021-10-15 10:20:19,778 INFO [train.py:451] Epoch 13, batch 1510, batch avg loss 0.2034, total avg loss: 0.2088, batch size: 35
2021-10-15 10:20:24,802 INFO [train.py:451] Epoch 13, batch 1520, batch avg loss 0.2621, total avg loss: 0.2105, batch size: 42
2021-10-15 10:20:29,700 INFO [train.py:451] Epoch 13, batch 1530, batch avg loss 0.2355, total avg loss: 0.2105, batch size: 37
2021-10-15 10:20:34,783 INFO [train.py:451] Epoch 13, batch 1540, batch avg loss 0.2168, total avg loss: 0.2120, batch size: 41
2021-10-15 10:20:40,003 INFO [train.py:451] Epoch 13, batch 1550, batch avg loss 0.1578, total avg loss: 0.2103, batch size: 29
2021-10-15 10:20:44,775 INFO [train.py:451] Epoch 13, batch 1560, batch avg loss 0.2021, total avg loss: 0.2113, batch size: 34
2021-10-15 10:20:49,741 INFO [train.py:451] Epoch 13, batch 1570, batch avg loss 0.2802, total avg loss: 0.2119, batch size: 72
2021-10-15 10:20:54,524 INFO [train.py:451] Epoch 13, batch 1580, batch avg loss 0.2405, total avg loss: 0.2122, batch size: 39
2021-10-15 10:20:59,473 INFO [train.py:451] Epoch 13, batch 1590, batch avg loss 0.2294, total avg loss: 0.2128, batch size: 38
2021-10-15 10:21:04,469 INFO [train.py:451] Epoch 13, batch 1600, batch avg loss 0.2463, total avg loss: 0.2135, batch size: 39
2021-10-15 10:21:09,477 INFO [train.py:451] Epoch 13, batch 1610, batch avg loss 0.2007, total avg loss: 0.2198, batch size: 36
2021-10-15 10:21:14,395 INFO [train.py:451] Epoch 13, batch 1620, batch avg loss 0.1893, total avg loss: 0.2300, batch size: 30
2021-10-15 10:21:19,348 INFO [train.py:451] Epoch 13, batch 1630, batch avg loss 0.2320, total avg loss: 0.2300, batch size: 49
2021-10-15 10:21:24,092 INFO [train.py:451] Epoch 13, batch 1640, batch avg loss 0.2032, total avg loss: 0.2284, batch size: 36
2021-10-15 10:21:29,042 INFO [train.py:451] Epoch 13, batch 1650, batch avg loss 0.1948, total avg loss: 0.2265, batch size: 35
2021-10-15 10:21:33,808 INFO [train.py:451] Epoch 13, batch 1660, batch avg loss 0.2724, total avg loss: 0.2250, batch size: 73
2021-10-15 10:21:38,494 INFO [train.py:451] Epoch 13, batch 1670, batch avg loss 0.2640, total avg loss: 0.2278, batch size: 71
2021-10-15 10:21:43,506 INFO [train.py:451] Epoch 13, batch 1680, batch avg loss 0.1608, total avg loss: 0.2251, batch size: 28
2021-10-15 10:21:48,398 INFO [train.py:451] Epoch 13, batch 1690, batch avg loss 0.1615, total avg loss: 0.2237, batch size: 28
2021-10-15 10:21:53,208 INFO [train.py:451] Epoch 13, batch 1700, batch avg loss 0.2411, total avg loss: 0.2230, batch size: 36
2021-10-15 10:21:58,171 INFO [train.py:451] Epoch 13, batch 1710, batch avg loss 0.2273, total avg loss: 0.2227, batch size: 28
2021-10-15 10:22:03,232 INFO [train.py:451] Epoch 13, batch 1720, batch avg loss 0.2139, total avg loss: 0.2207, batch size: 35
2021-10-15 10:22:08,066 INFO [train.py:451] Epoch 13, batch 1730, batch avg loss 0.2516, total avg loss: 0.2199, batch size: 72
2021-10-15 10:22:12,978 INFO [train.py:451] Epoch 13, batch 1740, batch avg loss 0.2050, total avg loss: 0.2199, batch size: 34
2021-10-15 10:22:17,762 INFO [train.py:451] Epoch 13, batch 1750, batch avg loss 0.2228, total avg loss: 0.2225, batch size: 33
2021-10-15 10:22:22,771 INFO [train.py:451] Epoch 13, batch 1760, batch avg loss 0.1927, total avg loss: 0.2223, batch size: 29
2021-10-15 10:22:27,534 INFO [train.py:451] Epoch 13, batch 1770, batch avg loss 0.2164, total avg loss: 0.2235, batch size: 32
2021-10-15 10:22:32,515 INFO [train.py:451] Epoch 13, batch 1780, batch avg loss 0.2127, total avg loss: 0.2237, batch size: 36
2021-10-15 10:22:37,381 INFO [train.py:451] Epoch 13, batch 1790, batch avg loss 0.2102, total avg loss: 0.2229, batch size: 36
2021-10-15 10:22:42,096 INFO [train.py:451] Epoch 13, batch 1800, batch avg loss 0.1966, total avg loss: 0.2229, batch size: 32
2021-10-15 10:22:47,038 INFO [train.py:451] Epoch 13, batch 1810, batch avg loss 0.2398, total avg loss: 0.2249, batch size: 38
2021-10-15 10:22:52,082 INFO [train.py:451] Epoch 13, batch 1820, batch avg loss 0.2237, total avg loss: 0.2287, batch size: 38
2021-10-15 10:22:56,907 INFO [train.py:451] Epoch 13, batch 1830, batch avg loss 0.2145, total avg loss: 0.2305, batch size: 32
2021-10-15 10:23:01,791 INFO [train.py:451] Epoch 13, batch 1840, batch avg loss 0.1830, total avg loss: 0.2302, batch size: 36
2021-10-15 10:23:06,702 INFO [train.py:451] Epoch 13, batch 1850, batch avg loss 0.2251, total avg loss: 0.2258, batch size: 39
2021-10-15 10:23:11,553 INFO [train.py:451] Epoch 13, batch 1860, batch avg loss 0.2043, total avg loss: 0.2262, batch size: 30
2021-10-15 10:23:16,548 INFO [train.py:451] Epoch 13, batch 1870, batch avg loss 0.2583, total avg loss: 0.2253, batch size: 72
2021-10-15 10:23:21,553 INFO [train.py:451] Epoch 13, batch 1880, batch avg loss 0.1738, total avg loss: 0.2232, batch size: 31
2021-10-15 10:23:26,590 INFO [train.py:451] Epoch 13, batch 1890, batch avg loss 0.1592, total avg loss: 0.2217, batch size: 28
2021-10-15 10:23:31,620 INFO [train.py:451] Epoch 13, batch 1900, batch avg loss 0.2268, total avg loss: 0.2200, batch size: 28
2021-10-15 10:23:36,996 INFO [train.py:451] Epoch 13, batch 1910, batch avg loss 0.1769, total avg loss: 0.2182, batch size: 27
2021-10-15 10:23:42,090 INFO [train.py:451] Epoch 13, batch 1920, batch avg loss 0.1721, total avg loss: 0.2174, batch size: 29
2021-10-15 10:23:46,791 INFO [train.py:451] Epoch 13, batch 1930, batch avg loss 0.2283, total avg loss: 0.2187, batch size: 56
2021-10-15 10:23:51,831 INFO [train.py:451] Epoch 13, batch 1940, batch avg loss 0.1591, total avg loss: 0.2180, batch size: 28
2021-10-15 10:23:56,558 INFO [train.py:451] Epoch 13, batch 1950, batch avg loss 0.3412, total avg loss: 0.2199, batch size: 126
2021-10-15 10:24:01,736 INFO [train.py:451] Epoch 13, batch 1960, batch avg loss 0.2739, total avg loss: 0.2188, batch size: 38
2021-10-15 10:24:06,901 INFO [train.py:451] Epoch 13, batch 1970, batch avg loss 0.1849, total avg loss: 0.2179, batch size: 30
2021-10-15 10:24:11,774 INFO [train.py:451] Epoch 13, batch 1980, batch avg loss 0.3005, total avg loss: 0.2177, batch size: 134
2021-10-15 10:24:16,658 INFO [train.py:451] Epoch 13, batch 1990, batch avg loss 0.1754, total avg loss: 0.2179, batch size: 33
2021-10-15 10:24:21,686 INFO [train.py:451] Epoch 13, batch 2000, batch avg loss 0.2076, total avg loss: 0.2175, batch size: 41
2021-10-15 10:24:59,525 INFO [train.py:483] Epoch 13, valid loss 0.1600, best valid loss: 0.1594 best valid epoch: 12
2021-10-15 10:25:04,452 INFO [train.py:451] Epoch 13, batch 2010, batch avg loss 0.2141, total avg loss: 0.2027, batch size: 38
2021-10-15 10:25:09,311 INFO [train.py:451] Epoch 13, batch 2020, batch avg loss 0.2243, total avg loss: 0.2109, batch size: 57
2021-10-15 10:25:14,305 INFO [train.py:451] Epoch 13, batch 2030, batch avg loss 0.1748, total avg loss: 0.2109, batch size: 29
2021-10-15 10:25:19,223 INFO [train.py:451] Epoch 13, batch 2040, batch avg loss 0.2339, total avg loss: 0.2136, batch size: 39
2021-10-15 10:25:24,073 INFO [train.py:451] Epoch 13, batch 2050, batch avg loss 0.2609, total avg loss: 0.2167, batch size: 74
2021-10-15 10:25:28,818 INFO [train.py:451] Epoch 13, batch 2060, batch avg loss 0.2465, total avg loss: 0.2196, batch size: 49
2021-10-15 10:25:33,639 INFO [train.py:451] Epoch 13, batch 2070, batch avg loss 0.1744, total avg loss: 0.2151, batch size: 30
2021-10-15 10:25:38,596 INFO [train.py:451] Epoch 13, batch 2080, batch avg loss 0.2584, total avg loss: 0.2158, batch size: 36
2021-10-15 10:25:43,609 INFO [train.py:451] Epoch 13, batch 2090, batch avg loss 0.2274, total avg loss: 0.2165, batch size: 31
2021-10-15 10:25:48,666 INFO [train.py:451] Epoch 13, batch 2100, batch avg loss 0.2225, total avg loss: 0.2162, batch size: 38
2021-10-15 10:25:53,505 INFO [train.py:451] Epoch 13, batch 2110, batch avg loss 0.2400, total avg loss: 0.2173, batch size: 45
2021-10-15 10:25:58,466 INFO [train.py:451] Epoch 13, batch 2120, batch avg loss 0.2158, total avg loss: 0.2158, batch size: 33
2021-10-15 10:26:03,249 INFO [train.py:451] Epoch 13, batch 2130, batch avg loss 0.2327, total avg loss: 0.2170, batch size: 33
2021-10-15 10:26:08,196 INFO [train.py:451] Epoch 13, batch 2140, batch avg loss 0.1751, total avg loss: 0.2164, batch size: 30
2021-10-15 10:26:12,881 INFO [train.py:451] Epoch 13, batch 2150, batch avg loss 0.1617, total avg loss: 0.2171, batch size: 29
2021-10-15 10:26:17,939 INFO [train.py:451] Epoch 13, batch 2160, batch avg loss 0.1471, total avg loss: 0.2160, batch size: 29
2021-10-15 10:26:23,051 INFO [train.py:451] Epoch 13, batch 2170, batch avg loss 0.2089, total avg loss: 0.2143, batch size: 35
2021-10-15 10:26:27,979 INFO [train.py:451] Epoch 13, batch 2180, batch avg loss 0.1785, total avg loss: 0.2145, batch size: 30
2021-10-15 10:26:32,946 INFO [train.py:451] Epoch 13, batch 2190, batch avg loss 0.2217, total avg loss: 0.2145, batch size: 36
2021-10-15 10:26:38,003 INFO [train.py:451] Epoch 13, batch 2200, batch avg loss 0.2352, total avg loss: 0.2146, batch size: 38
2021-10-15 10:26:42,999 INFO [train.py:451] Epoch 13, batch 2210, batch avg loss 0.1847, total avg loss: 0.2102, batch size: 32
2021-10-15 10:26:47,970 INFO [train.py:451] Epoch 13, batch 2220, batch avg loss 0.1871, total avg loss: 0.2080, batch size: 29
2021-10-15 10:26:53,025 INFO [train.py:451] Epoch 13, batch 2230, batch avg loss 0.2208, total avg loss: 0.2114, batch size: 35
2021-10-15 10:26:58,098 INFO [train.py:451] Epoch 13, batch 2240, batch avg loss 0.2815, total avg loss: 0.2136, batch size: 74
2021-10-15 10:27:03,123 INFO [train.py:451] Epoch 13, batch 2250, batch avg loss 0.1840, total avg loss: 0.2169, batch size: 27
2021-10-15 10:27:08,081 INFO [train.py:451] Epoch 13, batch 2260, batch avg loss 0.1650, total avg loss: 0.2173, batch size: 28
2021-10-15 10:27:13,249 INFO [train.py:451] Epoch 13, batch 2270, batch avg loss 0.1658, total avg loss: 0.2150, batch size: 30
2021-10-15 10:27:18,243 INFO [train.py:451] Epoch 13, batch 2280, batch avg loss 0.1888, total avg loss: 0.2143, batch size: 27
2021-10-15 10:27:23,175 INFO [train.py:451] Epoch 13, batch 2290, batch avg loss 0.2269, total avg loss: 0.2146, batch size: 30
2021-10-15 10:27:28,175 INFO [train.py:451] Epoch 13, batch 2300, batch avg loss 0.2071, total avg loss: 0.2145, batch size: 37
2021-10-15 10:27:33,122 INFO [train.py:451] Epoch 13, batch 2310, batch avg loss 0.2254, total avg loss: 0.2145, batch size: 35
2021-10-15 10:27:38,189 INFO [train.py:451] Epoch 13, batch 2320, batch avg loss 0.1742, total avg loss: 0.2143, batch size: 33
2021-10-15 10:27:42,803 INFO [train.py:451] Epoch 13, batch 2330, batch avg loss 0.2783, total avg loss: 0.2168, batch size: 73
2021-10-15 10:27:47,735 INFO [train.py:451] Epoch 13, batch 2340, batch avg loss 0.1661, total avg loss: 0.2168, batch size: 31
2021-10-15 10:27:52,588 INFO [train.py:451] Epoch 13, batch 2350, batch avg loss 0.3158, total avg loss: 0.2169, batch size: 129
2021-10-15 10:27:57,308 INFO [train.py:451] Epoch 13, batch 2360, batch avg loss 0.1981, total avg loss: 0.2163, batch size: 38
2021-10-15 10:28:02,243 INFO [train.py:451] Epoch 13, batch 2370, batch avg loss 0.1735, total avg loss: 0.2154, batch size: 31
2021-10-15 10:28:07,221 INFO [train.py:451] Epoch 13, batch 2380, batch avg loss 0.2140, total avg loss: 0.2143, batch size: 35
2021-10-15 10:28:12,254 INFO [train.py:451] Epoch 13, batch 2390, batch avg loss 0.2217, total avg loss: 0.2142, batch size: 30
2021-10-15 10:28:16,930 INFO [train.py:451] Epoch 13, batch 2400, batch avg loss 0.2963, total avg loss: 0.2149, batch size: 125
2021-10-15 10:28:21,847 INFO [train.py:451] Epoch 13, batch 2410, batch avg loss 0.1815, total avg loss: 0.2244, batch size: 27
2021-10-15 10:28:26,878 INFO [train.py:451] Epoch 13, batch 2420, batch avg loss 0.1935, total avg loss: 0.2176, batch size: 34
2021-10-15 10:28:31,873 INFO [train.py:451] Epoch 13, batch 2430, batch avg loss 0.1835, total avg loss: 0.2093, batch size: 33
2021-10-15 10:28:36,752 INFO [train.py:451] Epoch 13, batch 2440, batch avg loss 0.2118, total avg loss: 0.2092, batch size: 41
2021-10-15 10:28:41,623 INFO [train.py:451] Epoch 13, batch 2450, batch avg loss 0.2052, total avg loss: 0.2110, batch size: 36
2021-10-15 10:28:46,535 INFO [train.py:451] Epoch 13, batch 2460, batch avg loss 0.1925, total avg loss: 0.2098, batch size: 33
2021-10-15 10:28:51,558 INFO [train.py:451] Epoch 13, batch 2470, batch avg loss 0.2233, total avg loss: 0.2095, batch size: 34
2021-10-15 10:28:56,456 INFO [train.py:451] Epoch 13, batch 2480, batch avg loss 0.2451, total avg loss: 0.2101, batch size: 45
2021-10-15 10:29:01,498 INFO [train.py:451] Epoch 13, batch 2490, batch avg loss 0.1810, total avg loss: 0.2092, batch size: 29
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2021-10-15 10:33:52,636 INFO [train.py:483] Epoch 13, valid loss 0.1598, best valid loss: 0.1594 best valid epoch: 12
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2021-10-15 10:42:44,405 INFO [train.py:483] Epoch 13, valid loss 0.1594, best valid loss: 0.1594 best valid epoch: 13
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2021-10-15 10:51:37,571 INFO [train.py:483] Epoch 13, valid loss 0.1599, best valid loss: 0.1594 best valid epoch: 13
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2021-10-15 10:55:19,414 INFO [train.py:451] Epoch 13, batch 5450, batch avg loss 0.1687, total avg loss: 0.2134, batch size: 31
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2021-10-15 10:55:29,025 INFO [train.py:451] Epoch 13, batch 5470, batch avg loss 0.2538, total avg loss: 0.2120, batch size: 56
2021-10-15 10:55:34,102 INFO [train.py:451] Epoch 13, batch 5480, batch avg loss 0.1736, total avg loss: 0.2115, batch size: 33
2021-10-15 10:55:39,139 INFO [train.py:451] Epoch 13, batch 5490, batch avg loss 0.1934, total avg loss: 0.2090, batch size: 30
2021-10-15 10:55:44,056 INFO [train.py:451] Epoch 13, batch 5500, batch avg loss 0.1643, total avg loss: 0.2086, batch size: 31
2021-10-15 10:55:48,828 INFO [train.py:451] Epoch 13, batch 5510, batch avg loss 0.1993, total avg loss: 0.2097, batch size: 39
2021-10-15 10:55:53,744 INFO [train.py:451] Epoch 13, batch 5520, batch avg loss 0.2000, total avg loss: 0.2103, batch size: 31
2021-10-15 10:55:58,668 INFO [train.py:451] Epoch 13, batch 5530, batch avg loss 0.2076, total avg loss: 0.2109, batch size: 37
2021-10-15 10:56:03,483 INFO [train.py:451] Epoch 13, batch 5540, batch avg loss 0.2851, total avg loss: 0.2126, batch size: 38
2021-10-15 10:56:08,619 INFO [train.py:451] Epoch 13, batch 5550, batch avg loss 0.1887, total avg loss: 0.2128, batch size: 31
2021-10-15 10:56:13,442 INFO [train.py:451] Epoch 13, batch 5560, batch avg loss 0.2329, total avg loss: 0.2146, batch size: 42
2021-10-15 10:56:18,447 INFO [train.py:451] Epoch 13, batch 5570, batch avg loss 0.1894, total avg loss: 0.2139, batch size: 34
2021-10-15 10:56:23,355 INFO [train.py:451] Epoch 13, batch 5580, batch avg loss 0.1651, total avg loss: 0.2137, batch size: 30
2021-10-15 10:56:28,324 INFO [train.py:451] Epoch 13, batch 5590, batch avg loss 0.1830, total avg loss: 0.2133, batch size: 27
2021-10-15 10:56:33,124 INFO [train.py:451] Epoch 13, batch 5600, batch avg loss 0.1678, total avg loss: 0.2138, batch size: 32
2021-10-15 10:56:37,965 INFO [train.py:451] Epoch 13, batch 5610, batch avg loss 0.2460, total avg loss: 0.1982, batch size: 49
2021-10-15 10:56:42,813 INFO [train.py:451] Epoch 13, batch 5620, batch avg loss 0.2331, total avg loss: 0.2017, batch size: 35
2021-10-15 10:56:47,608 INFO [train.py:451] Epoch 13, batch 5630, batch avg loss 0.1938, total avg loss: 0.2081, batch size: 35
2021-10-15 10:56:52,571 INFO [train.py:451] Epoch 13, batch 5640, batch avg loss 0.3075, total avg loss: 0.2100, batch size: 130
2021-10-15 10:56:57,599 INFO [train.py:451] Epoch 13, batch 5650, batch avg loss 0.2636, total avg loss: 0.2087, batch size: 72
2021-10-15 10:57:02,674 INFO [train.py:451] Epoch 13, batch 5660, batch avg loss 0.1800, total avg loss: 0.2073, batch size: 33
2021-10-15 10:57:07,698 INFO [train.py:451] Epoch 13, batch 5670, batch avg loss 0.1889, total avg loss: 0.2062, batch size: 29
2021-10-15 10:57:12,445 INFO [train.py:451] Epoch 13, batch 5680, batch avg loss 0.1917, total avg loss: 0.2078, batch size: 32
2021-10-15 10:57:17,250 INFO [train.py:451] Epoch 13, batch 5690, batch avg loss 0.1823, total avg loss: 0.2107, batch size: 31
2021-10-15 10:57:22,231 INFO [train.py:451] Epoch 13, batch 5700, batch avg loss 0.2435, total avg loss: 0.2110, batch size: 35
2021-10-15 10:57:27,425 INFO [train.py:451] Epoch 13, batch 5710, batch avg loss 0.1991, total avg loss: 0.2101, batch size: 30
2021-10-15 10:57:32,225 INFO [train.py:451] Epoch 13, batch 5720, batch avg loss 0.2349, total avg loss: 0.2114, batch size: 37
2021-10-15 10:57:37,323 INFO [train.py:451] Epoch 13, batch 5730, batch avg loss 0.1918, total avg loss: 0.2108, batch size: 33
2021-10-15 10:57:42,147 INFO [train.py:451] Epoch 13, batch 5740, batch avg loss 0.3623, total avg loss: 0.2137, batch size: 127
2021-10-15 10:57:47,096 INFO [train.py:451] Epoch 13, batch 5750, batch avg loss 0.2849, total avg loss: 0.2139, batch size: 130
2021-10-15 10:57:52,022 INFO [train.py:451] Epoch 13, batch 5760, batch avg loss 0.2386, total avg loss: 0.2134, batch size: 44
2021-10-15 10:57:57,071 INFO [train.py:451] Epoch 13, batch 5770, batch avg loss 0.2034, total avg loss: 0.2119, batch size: 32
2021-10-15 10:58:01,937 INFO [train.py:451] Epoch 13, batch 5780, batch avg loss 0.1506, total avg loss: 0.2119, batch size: 29
2021-10-15 10:58:06,869 INFO [train.py:451] Epoch 13, batch 5790, batch avg loss 0.2539, total avg loss: 0.2131, batch size: 37
2021-10-15 10:58:12,070 INFO [train.py:451] Epoch 13, batch 5800, batch avg loss 0.2199, total avg loss: 0.2126, batch size: 27
2021-10-15 10:58:16,925 INFO [train.py:451] Epoch 13, batch 5810, batch avg loss 0.3260, total avg loss: 0.2135, batch size: 126
2021-10-15 10:58:21,946 INFO [train.py:451] Epoch 13, batch 5820, batch avg loss 0.1980, total avg loss: 0.2075, batch size: 34
2021-10-15 10:58:26,871 INFO [train.py:451] Epoch 13, batch 5830, batch avg loss 0.3550, total avg loss: 0.2120, batch size: 130
2021-10-15 10:58:28,984 WARNING [cut.py:1694] To perform mix, energy must be non-zero and non-negative (got 0.0).  MonoCut with id "06d79832-b2f1-0998-7ae2-cabce29ec3bb" will not be mixed in.
2021-10-15 10:58:31,798 INFO [train.py:451] Epoch 13, batch 5840, batch avg loss 0.2378, total avg loss: 0.2141, batch size: 42
2021-10-15 10:58:36,770 INFO [train.py:451] Epoch 13, batch 5850, batch avg loss 0.3034, total avg loss: 0.2152, batch size: 124
2021-10-15 10:58:41,906 INFO [train.py:451] Epoch 13, batch 5860, batch avg loss 0.2086, total avg loss: 0.2141, batch size: 36
2021-10-15 10:58:46,914 INFO [train.py:451] Epoch 13, batch 5870, batch avg loss 0.2308, total avg loss: 0.2113, batch size: 49
2021-10-15 10:58:51,895 INFO [train.py:451] Epoch 13, batch 5880, batch avg loss 0.2269, total avg loss: 0.2113, batch size: 34
2021-10-15 10:58:56,785 INFO [train.py:451] Epoch 13, batch 5890, batch avg loss 0.2191, total avg loss: 0.2113, batch size: 57
2021-10-15 10:59:01,731 INFO [train.py:451] Epoch 13, batch 5900, batch avg loss 0.2307, total avg loss: 0.2117, batch size: 36
2021-10-15 10:59:06,704 INFO [train.py:451] Epoch 13, batch 5910, batch avg loss 0.2164, total avg loss: 0.2117, batch size: 32
2021-10-15 10:59:11,605 INFO [train.py:451] Epoch 13, batch 5920, batch avg loss 0.2086, total avg loss: 0.2121, batch size: 36
2021-10-15 10:59:16,582 INFO [train.py:451] Epoch 13, batch 5930, batch avg loss 0.2193, total avg loss: 0.2121, batch size: 57
2021-10-15 10:59:21,667 INFO [train.py:451] Epoch 13, batch 5940, batch avg loss 0.1911, total avg loss: 0.2108, batch size: 29
2021-10-15 10:59:26,517 INFO [train.py:451] Epoch 13, batch 5950, batch avg loss 0.2256, total avg loss: 0.2127, batch size: 30
2021-10-15 10:59:31,426 INFO [train.py:451] Epoch 13, batch 5960, batch avg loss 0.2996, total avg loss: 0.2138, batch size: 133
2021-10-15 10:59:36,215 INFO [train.py:451] Epoch 13, batch 5970, batch avg loss 0.1563, total avg loss: 0.2140, batch size: 30
2021-10-15 10:59:41,107 INFO [train.py:451] Epoch 13, batch 5980, batch avg loss 0.2157, total avg loss: 0.2134, batch size: 34
2021-10-15 10:59:45,903 INFO [train.py:451] Epoch 13, batch 5990, batch avg loss 0.3253, total avg loss: 0.2147, batch size: 128
2021-10-15 10:59:50,751 INFO [train.py:451] Epoch 13, batch 6000, batch avg loss 0.1743, total avg loss: 0.2154, batch size: 31
2021-10-15 11:00:32,173 INFO [train.py:483] Epoch 13, valid loss 0.1598, best valid loss: 0.1594 best valid epoch: 13
2021-10-15 11:00:37,120 INFO [train.py:451] Epoch 13, batch 6010, batch avg loss 0.2043, total avg loss: 0.2148, batch size: 39
2021-10-15 11:00:42,052 INFO [train.py:451] Epoch 13, batch 6020, batch avg loss 0.1713, total avg loss: 0.2112, batch size: 32
2021-10-15 11:00:46,981 INFO [train.py:451] Epoch 13, batch 6030, batch avg loss 0.2098, total avg loss: 0.2137, batch size: 34
2021-10-15 11:00:51,855 INFO [train.py:451] Epoch 13, batch 6040, batch avg loss 0.2395, total avg loss: 0.2130, batch size: 72
2021-10-15 11:00:56,715 INFO [train.py:451] Epoch 13, batch 6050, batch avg loss 0.2305, total avg loss: 0.2148, batch size: 49
2021-10-15 11:01:01,611 INFO [train.py:451] Epoch 13, batch 6060, batch avg loss 0.1992, total avg loss: 0.2130, batch size: 39
2021-10-15 11:01:06,532 INFO [train.py:451] Epoch 13, batch 6070, batch avg loss 0.2146, total avg loss: 0.2143, batch size: 36
2021-10-15 11:01:11,369 INFO [train.py:451] Epoch 13, batch 6080, batch avg loss 0.1697, total avg loss: 0.2129, batch size: 27
2021-10-15 11:01:16,275 INFO [train.py:451] Epoch 13, batch 6090, batch avg loss 0.1570, total avg loss: 0.2127, batch size: 32
2021-10-15 11:01:20,990 INFO [train.py:451] Epoch 13, batch 6100, batch avg loss 0.2430, total avg loss: 0.2149, batch size: 57
2021-10-15 11:01:25,904 INFO [train.py:451] Epoch 13, batch 6110, batch avg loss 0.1912, total avg loss: 0.2146, batch size: 29
2021-10-15 11:01:30,818 INFO [train.py:451] Epoch 13, batch 6120, batch avg loss 0.2248, total avg loss: 0.2139, batch size: 56
2021-10-15 11:01:35,742 INFO [train.py:451] Epoch 13, batch 6130, batch avg loss 0.1897, total avg loss: 0.2143, batch size: 31
2021-10-15 11:01:40,570 INFO [train.py:451] Epoch 13, batch 6140, batch avg loss 0.2656, total avg loss: 0.2142, batch size: 73
2021-10-15 11:01:45,554 INFO [train.py:451] Epoch 13, batch 6150, batch avg loss 0.1962, total avg loss: 0.2133, batch size: 33
2021-10-15 11:01:50,356 INFO [train.py:451] Epoch 13, batch 6160, batch avg loss 0.2214, total avg loss: 0.2138, batch size: 41
2021-10-15 11:01:55,197 INFO [train.py:451] Epoch 13, batch 6170, batch avg loss 0.1958, total avg loss: 0.2135, batch size: 37
2021-10-15 11:02:00,010 INFO [train.py:451] Epoch 13, batch 6180, batch avg loss 0.2099, total avg loss: 0.2137, batch size: 39
2021-10-15 11:02:04,775 INFO [train.py:451] Epoch 13, batch 6190, batch avg loss 0.1928, total avg loss: 0.2137, batch size: 34
2021-10-15 11:02:09,507 INFO [train.py:451] Epoch 13, batch 6200, batch avg loss 0.1537, total avg loss: 0.2138, batch size: 29
2021-10-15 11:02:14,553 INFO [train.py:451] Epoch 13, batch 6210, batch avg loss 0.2086, total avg loss: 0.2115, batch size: 32
2021-10-15 11:02:19,544 INFO [train.py:451] Epoch 13, batch 6220, batch avg loss 0.2717, total avg loss: 0.2104, batch size: 34
2021-10-15 11:02:24,470 INFO [train.py:451] Epoch 13, batch 6230, batch avg loss 0.1930, total avg loss: 0.2116, batch size: 34
2021-10-15 11:02:29,567 INFO [train.py:451] Epoch 13, batch 6240, batch avg loss 0.1686, total avg loss: 0.2106, batch size: 32
2021-10-15 11:02:34,638 INFO [train.py:451] Epoch 13, batch 6250, batch avg loss 0.2003, total avg loss: 0.2088, batch size: 34
2021-10-15 11:02:39,539 INFO [train.py:451] Epoch 13, batch 6260, batch avg loss 0.2764, total avg loss: 0.2107, batch size: 35
2021-10-15 11:02:44,413 INFO [train.py:451] Epoch 13, batch 6270, batch avg loss 0.2300, total avg loss: 0.2119, batch size: 38
2021-10-15 11:02:49,373 INFO [train.py:451] Epoch 13, batch 6280, batch avg loss 0.2011, total avg loss: 0.2118, batch size: 35
2021-10-15 11:02:54,263 INFO [train.py:451] Epoch 13, batch 6290, batch avg loss 0.1935, total avg loss: 0.2124, batch size: 30
2021-10-15 11:02:59,135 INFO [train.py:451] Epoch 13, batch 6300, batch avg loss 0.2143, total avg loss: 0.2114, batch size: 45
2021-10-15 11:03:03,995 INFO [train.py:451] Epoch 13, batch 6310, batch avg loss 0.2112, total avg loss: 0.2131, batch size: 56
2021-10-15 11:03:08,645 INFO [train.py:451] Epoch 13, batch 6320, batch avg loss 0.2079, total avg loss: 0.2163, batch size: 27
2021-10-15 11:03:13,387 INFO [train.py:451] Epoch 13, batch 6330, batch avg loss 0.2556, total avg loss: 0.2170, batch size: 56
2021-10-15 11:03:18,360 INFO [train.py:451] Epoch 13, batch 6340, batch avg loss 0.2212, total avg loss: 0.2166, batch size: 27
2021-10-15 11:03:23,278 INFO [train.py:451] Epoch 13, batch 6350, batch avg loss 0.2054, total avg loss: 0.2155, batch size: 29
2021-10-15 11:03:28,325 INFO [train.py:451] Epoch 13, batch 6360, batch avg loss 0.2172, total avg loss: 0.2149, batch size: 42
2021-10-15 11:03:33,176 INFO [train.py:451] Epoch 13, batch 6370, batch avg loss 0.1758, total avg loss: 0.2145, batch size: 31
2021-10-15 11:03:38,086 INFO [train.py:451] Epoch 13, batch 6380, batch avg loss 0.2499, total avg loss: 0.2144, batch size: 42
2021-10-15 11:03:42,946 INFO [train.py:451] Epoch 13, batch 6390, batch avg loss 0.2174, total avg loss: 0.2145, batch size: 34
2021-10-15 11:03:47,826 INFO [train.py:451] Epoch 13, batch 6400, batch avg loss 0.2249, total avg loss: 0.2143, batch size: 49
2021-10-15 11:03:52,909 INFO [train.py:451] Epoch 13, batch 6410, batch avg loss 0.1803, total avg loss: 0.2206, batch size: 35
2021-10-15 11:03:57,930 INFO [train.py:451] Epoch 13, batch 6420, batch avg loss 0.1925, total avg loss: 0.2137, batch size: 36
2021-10-15 11:04:02,930 INFO [train.py:451] Epoch 13, batch 6430, batch avg loss 0.2051, total avg loss: 0.2096, batch size: 33
2021-10-15 11:04:07,879 INFO [train.py:451] Epoch 13, batch 6440, batch avg loss 0.2689, total avg loss: 0.2109, batch size: 38
2021-10-15 11:04:12,735 INFO [train.py:451] Epoch 13, batch 6450, batch avg loss 0.1970, total avg loss: 0.2100, batch size: 36
2021-10-15 11:04:17,527 INFO [train.py:451] Epoch 13, batch 6460, batch avg loss 0.2577, total avg loss: 0.2128, batch size: 38
2021-10-15 11:04:22,287 INFO [train.py:451] Epoch 13, batch 6470, batch avg loss 0.2711, total avg loss: 0.2143, batch size: 45
2021-10-15 11:04:26,948 INFO [train.py:451] Epoch 13, batch 6480, batch avg loss 0.2031, total avg loss: 0.2159, batch size: 38
2021-10-15 11:04:31,835 INFO [train.py:451] Epoch 13, batch 6490, batch avg loss 0.2400, total avg loss: 0.2153, batch size: 36
2021-10-15 11:04:36,975 INFO [train.py:451] Epoch 13, batch 6500, batch avg loss 0.2186, total avg loss: 0.2151, batch size: 31
2021-10-15 11:04:41,797 INFO [train.py:451] Epoch 13, batch 6510, batch avg loss 0.1807, total avg loss: 0.2138, batch size: 33
2021-10-15 11:04:46,522 INFO [train.py:451] Epoch 13, batch 6520, batch avg loss 0.2038, total avg loss: 0.2145, batch size: 36
2021-10-15 11:04:51,533 INFO [train.py:451] Epoch 13, batch 6530, batch avg loss 0.2057, total avg loss: 0.2128, batch size: 57
2021-10-15 11:04:56,508 INFO [train.py:451] Epoch 13, batch 6540, batch avg loss 0.2491, total avg loss: 0.2126, batch size: 37
2021-10-15 11:05:01,483 INFO [train.py:451] Epoch 13, batch 6550, batch avg loss 0.2224, total avg loss: 0.2132, batch size: 34
2021-10-15 11:05:06,456 INFO [train.py:451] Epoch 13, batch 6560, batch avg loss 0.2275, total avg loss: 0.2145, batch size: 36
2021-10-15 11:05:11,273 INFO [train.py:451] Epoch 13, batch 6570, batch avg loss 0.2597, total avg loss: 0.2152, batch size: 49
2021-10-15 11:05:16,199 INFO [train.py:451] Epoch 13, batch 6580, batch avg loss 0.2096, total avg loss: 0.2157, batch size: 42
2021-10-15 11:05:21,022 INFO [train.py:451] Epoch 13, batch 6590, batch avg loss 0.2011, total avg loss: 0.2162, batch size: 30
2021-10-15 11:05:25,905 INFO [train.py:451] Epoch 13, batch 6600, batch avg loss 0.2222, total avg loss: 0.2166, batch size: 42
2021-10-15 11:05:30,766 INFO [train.py:451] Epoch 13, batch 6610, batch avg loss 0.1960, total avg loss: 0.2194, batch size: 35
2021-10-15 11:05:35,592 INFO [train.py:451] Epoch 13, batch 6620, batch avg loss 0.2074, total avg loss: 0.2161, batch size: 38