Acasă Explorează Ajutor
Înregistrare Autentificare
rewriter
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triple-extraxtion
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triple-extraxti...
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bert_bilstm_crf_ner
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logs
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bert_bilstm_crf.log

bert_bilstm_crf.log 575 KB
Istoric Crud

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
  1. 2021-08-16 14:11:42,025 - INFO - main.py - <module> - 181 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    2. 

  2. 2021-08-16 14:11:42,026 - INFO - main.py - <module> - 183 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    3. 

  3. 2021-08-16 14:16:26,807 - INFO - main.py - <module> - 181 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    4. 

  4. 2021-08-16 14:16:26,808 - INFO - main.py - <module> - 183 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    5. 

  5. 2021-08-16 14:19:54,863 - INFO - main.py - <module> - 181 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    6. 

  6. 2021-08-16 14:19:55,070 - INFO - main.py - <module> - 183 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    7. 

  7. 2021-08-16 14:20:48,788 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    8. 

  8. 2021-08-16 14:20:51,144 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663
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    50. 

  50. 2021-08-16 14:22:40,678 - INFO - main.py - <module> - 181 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    51. 

  51. 2021-08-16 14:22:40,679 - INFO - main.py - <module> - 183 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    52. 

  52. 2021-08-16 14:23:31,729 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    53. 

  53. 2021-08-16 14:23:33,979 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663
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    143. 

  143. 2021-08-16 14:31:36,445 - INFO - main.py - <module> - 181 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    144. 

  144. 2021-08-16 14:31:36,446 - INFO - main.py - <module> - 183 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    145. 

  145. 2021-08-16 14:32:20,221 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    146. 

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    236. 

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    237. 

  237. 2021-08-16 14:51:22,450 - INFO - main.py - <module> - 185 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    238. 

  238. 2021-08-16 14:52:03,189 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    239. 

  239. 2021-08-16 14:52:05,335 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663
    240. 

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    241. 

  241. 2021-08-16 14:52:59,720 - INFO - main.py - <module> - 185 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    242. 

  242. 2021-08-16 14:53:35,871 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    243. 

  243. 2021-08-16 14:53:38,025 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663
    244. 

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    245. 

  245. 2021-08-16 14:55:35,277 - INFO - main.py - <module> - 187 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    246. 

  246. 2021-08-16 14:56:12,931 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    247. 

  247. 2021-08-16 14:56:15,118 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663
    248. 

  248. 2021-08-16 14:58:37,410 - INFO - main.py - <module> - 185 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    249. 

  249. 2021-08-16 14:58:37,411 - INFO - main.py - <module> - 187 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    250. 

  250. 2021-08-16 14:59:15,132 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
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  251. 2021-08-16 14:59:17,318 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663
    252. 

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    253. 

  253. 2021-08-16 15:01:13,265 - INFO - main.py - <module> - 183 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    254. 

  254. 2021-08-16 15:01:49,537 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    255. 

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    256. 

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    257. 

  257. 2021-08-16 15:11:12,162 - INFO - main.py - <module> - 185 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    258. 

  258. 2021-08-16 15:11:49,528 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    259. 

  259. 2021-08-16 15:11:51,880 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663
    260. 

  260. 2021-08-16 15:14:56,752 - INFO - main.py - <module> - 183 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    261. 

  261. 2021-08-16 15:14:56,753 - INFO - main.py - <module> - 185 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    262. 

  262. 2021-08-16 15:46:37,230 - INFO - main.py - <module> - 183 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    263. 

  263. 2021-08-16 15:46:37,231 - INFO - main.py - <module> - 185 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    264. 

  264. 2021-08-16 15:47:14,205 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    265. 

  265. 2021-08-16 15:47:16,362 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663
    266. 

  266. 2021-08-16 16:00:35,129 - INFO - main.py - <module> - 186 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    267. 

  267. 2021-08-16 16:00:35,130 - INFO - main.py - <module> - 188 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    268. 

  268. 2021-08-16 16:01:12,479 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
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  269. 2021-08-16 16:01:14,647 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663
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  270. 2021-08-16 16:01:36,610 - INFO - main.py - <module> - 186 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    271. 

  271. 2021-08-16 16:01:36,610 - INFO - main.py - <module> - 188 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    272. 

  272. 2021-08-16 16:02:13,763 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
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  273. 2021-08-16 16:02:15,927 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663
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  274. 2021-08-16 16:03:15,794 - INFO - main.py - <module> - 188 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    275. 

  275. 2021-08-16 16:03:15,795 - INFO - main.py - <module> - 190 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    276. 

  276. 2021-08-16 16:03:53,069 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
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  277. 2021-08-16 16:03:55,247 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663
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  278. 2021-08-16 16:04:25,287 - INFO - main.py - <module> - 188 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    279. 

  279. 2021-08-16 16:04:25,288 - INFO - main.py - <module> - 190 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    280. 

  280. 2021-08-16 16:05:02,701 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
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  281. 2021-08-16 16:05:04,888 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663
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  282. 2021-08-16 16:07:01,588 - INFO - main.py - <module> - 191 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    283. 

  283. 2021-08-16 16:07:01,592 - INFO - main.py - <module> - 193 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    284. 

  284. 2021-08-16 16:07:38,433 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
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  285. 2021-08-16 16:07:40,586 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663
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  286. 2021-08-16 16:19:24,864 - INFO - main.py - <module> - 193 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    287. 

  287. 2021-08-16 16:19:24,865 - INFO - main.py - <module> - 195 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    288. 

  288. 2021-08-16 16:20:02,878 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
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  289. 2021-08-16 16:20:05,099 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663
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  290. 2021-08-16 16:25:45,379 - INFO - main.py - <module> - 193 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    291. 

  291. 2021-08-16 16:25:45,380 - INFO - main.py - <module> - 195 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    292. 

  292. 2021-08-16 16:26:23,250 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
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  293. 2021-08-16 16:26:25,453 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663
    294. 

  294. 2021-08-17 13:10:50,899 - INFO - main.py - <module> - 193 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    295. 

  295. 2021-08-17 13:10:51,203 - INFO - main.py - <module> - 195 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    296. 

  296. 2021-08-17 13:11:54,297 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
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  297. 2021-08-17 13:11:56,606 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663
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  298. 2021-08-17 13:13:44,224 - INFO - main.py - <module> - 195 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    299. 

  299. 2021-08-17 13:13:44,225 - INFO - main.py - <module> - 197 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    300. 

  300. 2021-08-17 13:14:34,158 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    301. 

  301. 2021-08-17 13:14:36,401 - INFO - main.py - train - 55 - 【train】 epoch:0 0/53480 loss:97.5663
    302. 

  302. 2021-08-17 13:22:51,814 - INFO - main.py - <module> - 195 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    303. 

  303. 2021-08-17 13:22:51,815 - INFO - main.py - <module> - 197 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=10, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    304. 

  304. 2021-08-17 23:49:37,988 - INFO - main.py - <module> - 198 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    305. 

  305. 2021-08-17 23:49:38,422 - INFO - main.py - <module> - 200 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    306. 

  306. 2021-08-17 23:50:41,216 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
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  307. 2021-08-17 23:50:43,618 - INFO - main.py - train - 55 - 【train】 epoch:0 0/5348 loss:97.5663
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    682. 

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    687. 

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  834. 2021-08-18 00:08:58,176 - INFO - main.py - train - 55 - 【train】 epoch:0 527/5348 loss:7.1726
    835. 

  835. 2021-08-18 00:09:00,248 - INFO - main.py - train - 55 - 【train】 epoch:0 528/5348 loss:5.4575
    836. 

  836. 2021-08-18 00:09:02,320 - INFO - main.py - train - 55 - 【train】 epoch:0 529/5348 loss:5.5354
    837. 

  837. 2021-08-18 00:09:04,381 - INFO - main.py - train - 55 - 【train】 epoch:0 530/5348 loss:4.2315
    838. 

  838. 2021-08-18 00:09:06,444 - INFO - main.py - train - 55 - 【train】 epoch:0 531/5348 loss:6.9567
    839. 

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    840. 

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    841. 

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    842. 

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    843. 

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    844. 

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    845. 

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    846. 

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    847. 

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    848. 

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    849. 

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    850. 

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    851. 

  851. 2021-08-18 00:09:33,450 - INFO - main.py - train - 55 - 【train】 epoch:0 544/5348 loss:4.4052
    852. 

  852. 2021-08-18 00:09:35,551 - INFO - main.py - train - 55 - 【train】 epoch:0 545/5348 loss:6.9442
    853. 

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    854. 

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    855. 

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    856. 

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    857. 

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    858. 

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    859. 

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    860. 

  860. 2021-08-18 00:09:52,133 - INFO - main.py - train - 55 - 【train】 epoch:0 553/5348 loss:4.8675
    861. 

  861. 2021-08-18 00:09:54,211 - INFO - main.py - train - 55 - 【train】 epoch:0 554/5348 loss:5.4697
    862. 

  862. 2021-08-18 00:09:56,280 - INFO - main.py - train - 55 - 【train】 epoch:0 555/5348 loss:6.3782
    863. 

  863. 2021-08-18 00:09:58,341 - INFO - main.py - train - 55 - 【train】 epoch:0 556/5348 loss:4.0485
    864. 

  864. 2021-08-18 00:10:00,398 - INFO - main.py - train - 55 - 【train】 epoch:0 557/5348 loss:6.6539
    865. 

  865. 2021-08-18 00:10:02,461 - INFO - main.py - train - 55 - 【train】 epoch:0 558/5348 loss:4.8507
    866. 

  866. 2021-08-18 00:10:04,529 - INFO - main.py - train - 55 - 【train】 epoch:0 559/5348 loss:4.5518
    867. 

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    869. 

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    870. 

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    871. 

  871. 2021-08-18 00:10:14,869 - INFO - main.py - train - 55 - 【train】 epoch:0 564/5348 loss:8.1337
    872. 

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    873. 

  873. 2021-08-18 00:10:19,017 - INFO - main.py - train - 55 - 【train】 epoch:0 566/5348 loss:5.4999
    874. 

  874. 2021-08-18 00:10:21,092 - INFO - main.py - train - 55 - 【train】 epoch:0 567/5348 loss:5.2024
    875. 

  875. 2021-08-18 00:10:23,152 - INFO - main.py - train - 55 - 【train】 epoch:0 568/5348 loss:5.1217
    876. 

  876. 2021-08-18 00:10:25,229 - INFO - main.py - train - 55 - 【train】 epoch:0 569/5348 loss:3.7307
    877. 

  877. 2021-08-18 00:10:27,322 - INFO - main.py - train - 55 - 【train】 epoch:0 570/5348 loss:5.9996
    878. 

  878. 2021-08-18 00:10:29,405 - INFO - main.py - train - 55 - 【train】 epoch:0 571/5348 loss:5.0290
    879. 

  879. 2021-08-18 00:10:31,487 - INFO - main.py - train - 55 - 【train】 epoch:0 572/5348 loss:5.4983
    880. 

  880. 2021-08-18 00:10:33,571 - INFO - main.py - train - 55 - 【train】 epoch:0 573/5348 loss:6.6313
    881. 

  881. 2021-08-18 00:10:35,651 - INFO - main.py - train - 55 - 【train】 epoch:0 574/5348 loss:6.7276
    882. 

  882. 2021-08-18 00:10:37,734 - INFO - main.py - train - 55 - 【train】 epoch:0 575/5348 loss:4.6959
    883. 

  883. 2021-08-18 00:10:39,833 - INFO - main.py - train - 55 - 【train】 epoch:0 576/5348 loss:7.1437
    884. 

  884. 2021-08-18 00:10:41,926 - INFO - main.py - train - 55 - 【train】 epoch:0 577/5348 loss:6.3202
    885. 

  885. 2021-08-18 00:10:44,013 - INFO - main.py - train - 55 - 【train】 epoch:0 578/5348 loss:4.8063
    886. 

  886. 2021-08-18 00:10:46,086 - INFO - main.py - train - 55 - 【train】 epoch:0 579/5348 loss:4.2157
    887. 

  887. 2021-08-18 00:10:48,159 - INFO - main.py - train - 55 - 【train】 epoch:0 580/5348 loss:3.3156
    888. 

  888. 2021-08-18 00:10:50,221 - INFO - main.py - train - 55 - 【train】 epoch:0 581/5348 loss:3.8025
    889. 

  889. 2021-08-18 00:10:52,297 - INFO - main.py - train - 55 - 【train】 epoch:0 582/5348 loss:5.1600
    890. 

  890. 2021-08-18 00:10:54,375 - INFO - main.py - train - 55 - 【train】 epoch:0 583/5348 loss:4.5651
    891. 

  891. 2021-08-18 00:10:56,450 - INFO - main.py - train - 55 - 【train】 epoch:0 584/5348 loss:7.6597
    892. 

  892. 2021-08-18 00:10:58,525 - INFO - main.py - train - 55 - 【train】 epoch:0 585/5348 loss:3.8322
    893. 

  893. 2021-08-18 00:11:00,593 - INFO - main.py - train - 55 - 【train】 epoch:0 586/5348 loss:4.6671
    894. 

  894. 2021-08-18 00:11:02,653 - INFO - main.py - train - 55 - 【train】 epoch:0 587/5348 loss:6.4358
    895. 

  895. 2021-08-18 00:11:04,743 - INFO - main.py - train - 55 - 【train】 epoch:0 588/5348 loss:4.9699
    896. 

  896. 2021-08-18 00:11:06,807 - INFO - main.py - train - 55 - 【train】 epoch:0 589/5348 loss:7.4273
    897. 

  897. 2021-08-18 00:11:08,881 - INFO - main.py - train - 55 - 【train】 epoch:0 590/5348 loss:5.9338
    898. 

  898. 2021-08-18 00:11:10,957 - INFO - main.py - train - 55 - 【train】 epoch:0 591/5348 loss:6.1994
    899. 

  899. 2021-08-18 00:11:13,026 - INFO - main.py - train - 55 - 【train】 epoch:0 592/5348 loss:5.2130
    900. 

  900. 2021-08-18 00:11:15,103 - INFO - main.py - train - 55 - 【train】 epoch:0 593/5348 loss:5.0423
    901. 

  901. 2021-08-18 00:11:17,157 - INFO - main.py - train - 55 - 【train】 epoch:0 594/5348 loss:3.4365
    902. 

  902. 2021-08-18 00:11:19,218 - INFO - main.py - train - 55 - 【train】 epoch:0 595/5348 loss:4.1248
    903. 

  903. 2021-08-18 00:11:21,287 - INFO - main.py - train - 55 - 【train】 epoch:0 596/5348 loss:5.5704
    904. 

  904. 2021-08-18 00:11:23,367 - INFO - main.py - train - 55 - 【train】 epoch:0 597/5348 loss:5.9913
    905. 

  905. 2021-08-18 00:11:25,443 - INFO - main.py - train - 55 - 【train】 epoch:0 598/5348 loss:4.5212
    906. 

  906. 2021-08-18 00:11:27,493 - INFO - main.py - train - 55 - 【train】 epoch:0 599/5348 loss:5.4300
    907. 

  907. 2021-08-18 00:11:29,547 - INFO - main.py - train - 55 - 【train】 epoch:0 600/5348 loss:4.9523
    908. 

  908. 2021-08-18 00:11:31,621 - INFO - main.py - train - 55 - 【train】 epoch:0 601/5348 loss:5.7473
    909. 

  909. 2021-08-18 00:11:33,706 - INFO - main.py - train - 55 - 【train】 epoch:0 602/5348 loss:6.0879
    910. 

  910. 2021-08-18 00:11:35,790 - INFO - main.py - train - 55 - 【train】 epoch:0 603/5348 loss:3.3981
    911. 

  911. 2021-08-18 00:11:37,865 - INFO - main.py - train - 55 - 【train】 epoch:0 604/5348 loss:5.4636
    912. 

  912. 2021-08-18 00:11:39,959 - INFO - main.py - train - 55 - 【train】 epoch:0 605/5348 loss:3.8741
    913. 

  913. 2021-08-18 00:11:42,059 - INFO - main.py - train - 55 - 【train】 epoch:0 606/5348 loss:4.5311
    914. 

  914. 2021-08-18 00:11:44,143 - INFO - main.py - train - 55 - 【train】 epoch:0 607/5348 loss:5.5655
    915. 

  915. 2021-08-18 00:11:46,217 - INFO - main.py - train - 55 - 【train】 epoch:0 608/5348 loss:4.8762
    916. 

  916. 2021-08-18 00:11:48,316 - INFO - main.py - train - 55 - 【train】 epoch:0 609/5348 loss:5.3350
    917. 

  917. 2021-08-18 00:11:50,409 - INFO - main.py - train - 55 - 【train】 epoch:0 610/5348 loss:5.1922
    918. 

  918. 2021-08-18 00:11:52,478 - INFO - main.py - train - 55 - 【train】 epoch:0 611/5348 loss:4.0083
    919. 

  919. 2021-08-18 00:11:54,553 - INFO - main.py - train - 55 - 【train】 epoch:0 612/5348 loss:4.8674
    920. 

  920. 2021-08-18 00:11:56,616 - INFO - main.py - train - 55 - 【train】 epoch:0 613/5348 loss:4.3154
    921. 

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    922. 

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    923. 

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    924. 

  924. 2021-08-18 00:12:04,877 - INFO - main.py - train - 55 - 【train】 epoch:0 617/5348 loss:5.3207
    925. 

  925. 2021-08-18 00:12:06,951 - INFO - main.py - train - 55 - 【train】 epoch:0 618/5348 loss:3.6247
    926. 

  926. 2021-08-18 00:12:09,033 - INFO - main.py - train - 55 - 【train】 epoch:0 619/5348 loss:4.5556
    927. 

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    928. 

  928. 2021-08-18 00:12:13,169 - INFO - main.py - train - 55 - 【train】 epoch:0 621/5348 loss:5.3756
    929. 

  929. 2021-08-18 00:12:15,239 - INFO - main.py - train - 55 - 【train】 epoch:0 622/5348 loss:6.0217
    930. 

  930. 2021-08-18 00:12:17,308 - INFO - main.py - train - 55 - 【train】 epoch:0 623/5348 loss:5.3495
    931. 

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    932. 

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    933. 

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    934. 

  934. 2021-08-18 00:12:25,609 - INFO - main.py - train - 55 - 【train】 epoch:0 627/5348 loss:4.5008
    935. 

  935. 2021-08-18 00:12:27,700 - INFO - main.py - train - 55 - 【train】 epoch:0 628/5348 loss:7.4897
    936. 

  936. 2021-08-18 00:12:29,771 - INFO - main.py - train - 55 - 【train】 epoch:0 629/5348 loss:4.5568
    937. 

  937. 2021-08-18 00:12:31,841 - INFO - main.py - train - 55 - 【train】 epoch:0 630/5348 loss:5.6697
    938. 

  938. 2021-08-18 00:12:33,908 - INFO - main.py - train - 55 - 【train】 epoch:0 631/5348 loss:4.6713
    939. 

  939. 2021-08-18 00:12:35,988 - INFO - main.py - train - 55 - 【train】 epoch:0 632/5348 loss:5.8889
    940. 

  940. 2021-08-18 00:12:38,066 - INFO - main.py - train - 55 - 【train】 epoch:0 633/5348 loss:4.9740
    941. 

  941. 2021-08-18 00:12:40,161 - INFO - main.py - train - 55 - 【train】 epoch:0 634/5348 loss:5.1568
    942. 

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    943. 

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    944. 

  944. 2021-08-18 00:12:46,422 - INFO - main.py - train - 55 - 【train】 epoch:0 637/5348 loss:3.7737
    945. 

  945. 2021-08-18 00:12:48,502 - INFO - main.py - train - 55 - 【train】 epoch:0 638/5348 loss:2.9026
    946. 

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    947. 

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    948. 

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    949. 

  949. 2021-08-18 00:12:56,838 - INFO - main.py - train - 55 - 【train】 epoch:0 642/5348 loss:5.4709
    950. 

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    951. 

  951. 2021-08-18 00:13:01,025 - INFO - main.py - train - 55 - 【train】 epoch:0 644/5348 loss:5.0878
    952. 

  952. 2021-08-18 00:13:03,101 - INFO - main.py - train - 55 - 【train】 epoch:0 645/5348 loss:4.1811
    953. 

  953. 2021-08-18 00:13:05,170 - INFO - main.py - train - 55 - 【train】 epoch:0 646/5348 loss:4.9904
    954. 

  954. 2021-08-18 00:13:07,249 - INFO - main.py - train - 55 - 【train】 epoch:0 647/5348 loss:4.2166
    955. 

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    956. 

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    957. 

  957. 2021-08-18 00:13:13,459 - INFO - main.py - train - 55 - 【train】 epoch:0 650/5348 loss:4.9543
    958. 

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    959. 

  959. 2021-08-18 00:13:17,605 - INFO - main.py - train - 55 - 【train】 epoch:0 652/5348 loss:4.5998
    960. 

  960. 2021-08-18 00:13:19,674 - INFO - main.py - train - 55 - 【train】 epoch:0 653/5348 loss:6.6023
    961. 

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    962. 

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    963. 

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    964. 

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    965. 

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    966. 

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    967. 

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    969. 

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    971. 

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    972. 

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    973. 

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    974. 

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    978. 

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    979. 

  979. 2021-08-18 00:13:59,207 - INFO - main.py - train - 55 - 【train】 epoch:0 672/5348 loss:4.2022
    980. 

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    981. 

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    982. 

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    984. 

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    985. 

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    986. 

  986. 2021-08-18 00:14:13,749 - INFO - main.py - train - 55 - 【train】 epoch:0 679/5348 loss:5.9273
    987. 

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    988. 

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    989. 

  989. 2021-08-18 00:14:19,999 - INFO - main.py - train - 55 - 【train】 epoch:0 682/5348 loss:5.9517
    990. 

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    991. 

  991. 2021-08-18 00:14:24,195 - INFO - main.py - train - 55 - 【train】 epoch:0 684/5348 loss:3.1443
    992. 

  992. 2021-08-18 00:14:26,284 - INFO - main.py - train - 55 - 【train】 epoch:0 685/5348 loss:4.7860
    993. 

  993. 2021-08-18 00:14:28,368 - INFO - main.py - train - 55 - 【train】 epoch:0 686/5348 loss:5.1649
    994. 

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    996. 

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    997. 

  997. 2021-08-18 00:14:36,693 - INFO - main.py - train - 55 - 【train】 epoch:0 690/5348 loss:4.1112
    998. 

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    999. 

  999. 2021-08-18 00:14:40,842 - INFO - main.py - train - 55 - 【train】 epoch:0 692/5348 loss:5.5502
    1000. 

  1000. 2021-08-18 00:14:42,923 - INFO - main.py - train - 55 - 【train】 epoch:0 693/5348 loss:4.7332
    1001. 

  1001. 2021-08-18 00:14:44,993 - INFO - main.py - train - 55 - 【train】 epoch:0 694/5348 loss:4.5389
    1002. 

  1002. 2021-08-18 00:14:47,056 - INFO - main.py - train - 55 - 【train】 epoch:0 695/5348 loss:4.0376
    1003. 

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    1004. 

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    1005. 

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    1006. 

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    1024. 

  1024. 2021-08-18 00:15:32,637 - INFO - main.py - train - 55 - 【train】 epoch:0 717/5348 loss:2.9070
    1025. 

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    1044. 

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    1045. 

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    1049. 

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    1050. 

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    1051. 

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    1052. 

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    1053. 

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    1054. 

  1054. 2021-08-18 00:16:35,045 - INFO - main.py - train - 55 - 【train】 epoch:0 747/5348 loss:4.4753
    1055. 

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    1059. 

  1059. 2021-08-18 00:16:45,476 - INFO - main.py - train - 55 - 【train】 epoch:0 752/5348 loss:2.4612
    1060. 

  1060. 2021-08-18 00:16:47,561 - INFO - main.py - train - 55 - 【train】 epoch:0 753/5348 loss:3.9386
    1061. 

  1061. 2021-08-18 00:16:49,648 - INFO - main.py - train - 55 - 【train】 epoch:0 754/5348 loss:4.0107
    1062. 

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  1063. 2021-08-18 00:16:53,812 - INFO - main.py - train - 55 - 【train】 epoch:0 756/5348 loss:4.4207
    1064. 

  1064. 2021-08-18 00:16:55,884 - INFO - main.py - train - 55 - 【train】 epoch:0 757/5348 loss:4.5069
    1065. 

  1065. 2021-08-18 00:16:57,968 - INFO - main.py - train - 55 - 【train】 epoch:0 758/5348 loss:5.9823
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    1071. 

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    1073. 

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    1074. 

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    1075. 

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    1078. 

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    1079. 

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    1080. 

  1080. 2021-08-18 00:17:28,985 - INFO - main.py - train - 55 - 【train】 epoch:0 773/5348 loss:3.2800
    1081. 

  1081. 2021-08-18 00:17:31,056 - INFO - main.py - train - 55 - 【train】 epoch:0 774/5348 loss:3.8189
    1082. 

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    1083. 

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    1084. 

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    1085. 

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    1086. 

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    1088. 

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    1089. 

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    1090. 

  1090. 2021-08-18 00:17:49,842 - INFO - main.py - train - 55 - 【train】 epoch:0 783/5348 loss:3.9186
    1091. 

  1091. 2021-08-18 00:17:51,928 - INFO - main.py - train - 55 - 【train】 epoch:0 784/5348 loss:4.0431
    1092. 

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    1093. 

  1093. 2021-08-18 00:17:56,098 - INFO - main.py - train - 55 - 【train】 epoch:0 786/5348 loss:4.0695
    1094. 

  1094. 2021-08-18 00:17:58,182 - INFO - main.py - train - 55 - 【train】 epoch:0 787/5348 loss:3.0900
    1095. 

  1095. 2021-08-18 00:18:00,249 - INFO - main.py - train - 55 - 【train】 epoch:0 788/5348 loss:3.8685
    1096. 

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    1097. 

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    1098. 

  1098. 2021-08-18 00:18:06,442 - INFO - main.py - train - 55 - 【train】 epoch:0 791/5348 loss:2.5264
    1099. 

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    1100. 

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    1101. 

  1101. 2021-08-18 00:18:12,655 - INFO - main.py - train - 55 - 【train】 epoch:0 794/5348 loss:4.2024
    1102. 

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    1103. 

  1103. 2021-08-18 00:18:16,794 - INFO - main.py - train - 55 - 【train】 epoch:0 796/5348 loss:3.1826
    1104. 

  1104. 2021-08-18 00:18:18,873 - INFO - main.py - train - 55 - 【train】 epoch:0 797/5348 loss:4.8359
    1105. 

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    1106. 

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    1107. 

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    1111. 

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    1115. 

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    1117. 

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    1118. 

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    1119. 

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    1124. 

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    1125. 

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    1126. 

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    1127. 

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    1130. 

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    1132. 

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    1135. 

  1135. 2021-08-18 00:19:23,326 - INFO - main.py - train - 55 - 【train】 epoch:0 828/5348 loss:4.2978
    1136. 

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    1137. 

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    1138. 

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    1150. 

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    1179. 

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    1185. 

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    1190. 

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    1191. 

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    1195. 

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  1197. 2021-08-18 00:21:31,937 - INFO - main.py - train - 55 - 【train】 epoch:0 890/5348 loss:4.0676
    1198. 

  1198. 2021-08-18 00:21:34,008 - INFO - main.py - train - 55 - 【train】 epoch:0 891/5348 loss:3.7356
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    1200. 

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    1202. 

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  1206. 2021-08-18 00:21:50,682 - INFO - main.py - train - 55 - 【train】 epoch:0 899/5348 loss:4.8530
    1207. 

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    1208. 

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    1228. 

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    1229. 

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    1230. 

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    1234. 

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    1250. 

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    1283. 

  1283. 2021-08-18 00:24:29,941 - INFO - main.py - train - 55 - 【train】 epoch:0 976/5348 loss:4.8128
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  1334. 2021-08-18 00:26:15,920 - INFO - main.py - train - 55 - 【train】 epoch:0 1027/5348 loss:4.9946
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  1340. 2021-08-18 00:26:28,453 - INFO - main.py - train - 55 - 【train】 epoch:0 1033/5348 loss:5.3359
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  1341. 2021-08-18 00:26:30,546 - INFO - main.py - train - 55 - 【train】 epoch:0 1034/5348 loss:4.4309
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  1354. 2021-08-18 00:26:57,678 - INFO - main.py - train - 55 - 【train】 epoch:0 1047/5348 loss:2.5253
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  1358. 2021-08-18 00:27:05,978 - INFO - main.py - train - 55 - 【train】 epoch:0 1051/5348 loss:2.4457
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  1359. 2021-08-18 00:27:08,068 - INFO - main.py - train - 55 - 【train】 epoch:0 1052/5348 loss:5.6238
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    1380. 

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    1430. 

  1430. 2021-08-18 00:29:36,459 - INFO - main.py - train - 55 - 【train】 epoch:0 1123/5348 loss:2.4190
    1431. 

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    1432. 

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    1433. 

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    1434. 

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    1435. 

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    1437. 

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    1438. 

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    1439. 

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    1440. 

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    1441. 

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  1455. 2021-08-18 00:30:28,854 - INFO - main.py - train - 55 - 【train】 epoch:0 1148/5348 loss:3.4999
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  1459. 2021-08-18 00:30:37,275 - INFO - main.py - train - 55 - 【train】 epoch:0 1152/5348 loss:4.9427
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  1465. 2021-08-18 00:30:49,769 - INFO - main.py - train - 55 - 【train】 epoch:0 1158/5348 loss:4.2707
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  1466. 2021-08-18 00:30:51,886 - INFO - main.py - train - 55 - 【train】 epoch:0 1159/5348 loss:4.9037
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    1470. 

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  1471. 2021-08-18 00:31:02,309 - INFO - main.py - train - 55 - 【train】 epoch:0 1164/5348 loss:2.7819
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  1472. 2021-08-18 00:31:04,388 - INFO - main.py - train - 55 - 【train】 epoch:0 1165/5348 loss:4.7480
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  1473. 2021-08-18 00:31:06,471 - INFO - main.py - train - 55 - 【train】 epoch:0 1166/5348 loss:2.9045
    1474. 

  1474. 2021-08-18 00:31:08,542 - INFO - main.py - train - 55 - 【train】 epoch:0 1167/5348 loss:3.4493
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    1479. 

  1479. 2021-08-18 00:31:18,898 - INFO - main.py - train - 55 - 【train】 epoch:0 1172/5348 loss:3.3751
    1480. 

  1480. 2021-08-18 00:31:20,979 - INFO - main.py - train - 55 - 【train】 epoch:0 1173/5348 loss:2.8459
    1481. 

  1481. 2021-08-18 00:31:23,077 - INFO - main.py - train - 55 - 【train】 epoch:0 1174/5348 loss:4.3938
    1482. 

  1482. 2021-08-18 00:31:25,190 - INFO - main.py - train - 55 - 【train】 epoch:0 1175/5348 loss:3.6014
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  1483. 2021-08-18 00:31:27,276 - INFO - main.py - train - 55 - 【train】 epoch:0 1176/5348 loss:3.1817
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  1484. 2021-08-18 00:31:29,384 - INFO - main.py - train - 55 - 【train】 epoch:0 1177/5348 loss:3.2050
    1485. 

  1485. 2021-08-18 00:31:31,483 - INFO - main.py - train - 55 - 【train】 epoch:0 1178/5348 loss:4.6638
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  1486. 2021-08-18 00:31:33,569 - INFO - main.py - train - 55 - 【train】 epoch:0 1179/5348 loss:4.4587
    1487. 

  1487. 2021-08-18 00:31:35,666 - INFO - main.py - train - 55 - 【train】 epoch:0 1180/5348 loss:3.3479
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    1489. 

  1489. 2021-08-18 00:31:39,861 - INFO - main.py - train - 55 - 【train】 epoch:0 1182/5348 loss:2.2968
    1490. 

  1490. 2021-08-18 00:31:41,958 - INFO - main.py - train - 55 - 【train】 epoch:0 1183/5348 loss:2.0865
    1491. 

  1491. 2021-08-18 00:31:44,048 - INFO - main.py - train - 55 - 【train】 epoch:0 1184/5348 loss:4.5016
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  1492. 2021-08-18 00:31:46,145 - INFO - main.py - train - 55 - 【train】 epoch:0 1185/5348 loss:4.8281
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  1493. 2021-08-18 00:31:48,214 - INFO - main.py - train - 55 - 【train】 epoch:0 1186/5348 loss:2.6583
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  1494. 2021-08-18 00:31:50,291 - INFO - main.py - train - 55 - 【train】 epoch:0 1187/5348 loss:3.7994
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  1495. 2021-08-18 00:31:52,364 - INFO - main.py - train - 55 - 【train】 epoch:0 1188/5348 loss:2.6155
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  1496. 2021-08-18 00:31:54,452 - INFO - main.py - train - 55 - 【train】 epoch:0 1189/5348 loss:3.2402
    1497. 

  1497. 2021-08-18 00:31:56,524 - INFO - main.py - train - 55 - 【train】 epoch:0 1190/5348 loss:4.0819
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  1498. 2021-08-18 00:31:58,609 - INFO - main.py - train - 55 - 【train】 epoch:0 1191/5348 loss:4.2976
    1499. 

  1499. 2021-08-18 00:32:00,687 - INFO - main.py - train - 55 - 【train】 epoch:0 1192/5348 loss:3.9625
    1500. 

  1500. 2021-08-18 00:32:02,763 - INFO - main.py - train - 55 - 【train】 epoch:0 1193/5348 loss:3.2939
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  1501. 2021-08-18 00:32:04,829 - INFO - main.py - train - 55 - 【train】 epoch:0 1194/5348 loss:5.1189
    1502. 

  1502. 2021-08-18 00:32:06,929 - INFO - main.py - train - 55 - 【train】 epoch:0 1195/5348 loss:4.4736
    1503. 

  1503. 2021-08-18 00:32:09,004 - INFO - main.py - train - 55 - 【train】 epoch:0 1196/5348 loss:3.6776
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  1504. 2021-08-18 00:32:11,098 - INFO - main.py - train - 55 - 【train】 epoch:0 1197/5348 loss:5.4385
    1505. 

  1505. 2021-08-18 00:32:13,171 - INFO - main.py - train - 55 - 【train】 epoch:0 1198/5348 loss:4.1146
    1506. 

  1506. 2021-08-18 00:32:15,269 - INFO - main.py - train - 55 - 【train】 epoch:0 1199/5348 loss:4.3459
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    1508. 

  1508. 2021-08-18 00:32:19,430 - INFO - main.py - train - 55 - 【train】 epoch:0 1201/5348 loss:4.5260
    1509. 

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    1510. 

  1510. 2021-08-18 00:32:23,593 - INFO - main.py - train - 55 - 【train】 epoch:0 1203/5348 loss:4.4654
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  1512. 2021-08-18 00:32:27,771 - INFO - main.py - train - 55 - 【train】 epoch:0 1205/5348 loss:3.1270
    1513. 

  1513. 2021-08-18 00:32:29,882 - INFO - main.py - train - 55 - 【train】 epoch:0 1206/5348 loss:3.4632
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  1514. 2021-08-18 00:32:31,975 - INFO - main.py - train - 55 - 【train】 epoch:0 1207/5348 loss:3.5234
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  1515. 2021-08-18 00:32:34,076 - INFO - main.py - train - 55 - 【train】 epoch:0 1208/5348 loss:2.1894
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  1517. 2021-08-18 00:32:38,258 - INFO - main.py - train - 55 - 【train】 epoch:0 1210/5348 loss:2.9506
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    1520. 

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  1523. 2021-08-18 00:32:50,851 - INFO - main.py - train - 55 - 【train】 epoch:0 1216/5348 loss:6.0815
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    1525. 

  1525. 2021-08-18 00:32:55,019 - INFO - main.py - train - 55 - 【train】 epoch:0 1218/5348 loss:3.7321
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  1529. 2021-08-18 00:33:03,320 - INFO - main.py - train - 55 - 【train】 epoch:0 1222/5348 loss:3.2506
    1530. 

  1530. 2021-08-18 00:33:05,409 - INFO - main.py - train - 55 - 【train】 epoch:0 1223/5348 loss:3.3988
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    1565. 

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  1577. 2021-08-18 00:34:43,400 - INFO - main.py - train - 55 - 【train】 epoch:0 1270/5348 loss:3.9335
    1578. 

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  1631. 2021-08-18 00:36:36,238 - INFO - main.py - train - 55 - 【train】 epoch:0 1324/5348 loss:4.5256
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  1635. 2021-08-18 00:36:44,624 - INFO - main.py - train - 55 - 【train】 epoch:0 1328/5348 loss:3.5952
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  1651. 2021-08-18 00:37:17,968 - INFO - main.py - train - 55 - 【train】 epoch:0 1344/5348 loss:4.6421
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  1654. 2021-08-18 00:37:24,264 - INFO - main.py - train - 55 - 【train】 epoch:0 1347/5348 loss:3.2479
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  1656. 2021-08-18 00:37:28,459 - INFO - main.py - train - 55 - 【train】 epoch:0 1349/5348 loss:2.7536
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  1659. 2021-08-18 00:37:34,786 - INFO - main.py - train - 55 - 【train】 epoch:0 1352/5348 loss:3.4329
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  1661. 2021-08-18 00:37:39,013 - INFO - main.py - train - 55 - 【train】 epoch:0 1354/5348 loss:5.2413
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    1666. 

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  1689. 2021-08-18 00:38:37,727 - INFO - main.py - train - 55 - 【train】 epoch:0 1382/5348 loss:3.7974
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  1724. 2021-08-18 00:39:51,223 - INFO - main.py - train - 55 - 【train】 epoch:0 1417/5348 loss:5.3020
    1725. 

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    1726. 

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    1727. 

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    1728. 

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    1729. 

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    1730. 

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    1740. 

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    1745. 

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    1747. 

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    1748. 

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    1749. 

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    1750. 

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    1751. 

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    1752. 

  1752. 2021-08-18 00:40:49,801 - INFO - main.py - train - 55 - 【train】 epoch:0 1445/5348 loss:5.3721
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  1753. 2021-08-18 00:40:51,905 - INFO - main.py - train - 55 - 【train】 epoch:0 1446/5348 loss:2.2108
    1754. 

  1754. 2021-08-18 00:40:54,000 - INFO - main.py - train - 55 - 【train】 epoch:0 1447/5348 loss:3.5145
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    1756. 

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    1757. 

  1757. 2021-08-18 00:41:00,275 - INFO - main.py - train - 55 - 【train】 epoch:0 1450/5348 loss:3.3737
    1758. 

  1758. 2021-08-18 00:41:02,372 - INFO - main.py - train - 55 - 【train】 epoch:0 1451/5348 loss:3.8277
    1759. 

  1759. 2021-08-18 00:41:04,486 - INFO - main.py - train - 55 - 【train】 epoch:0 1452/5348 loss:3.7860
    1760. 

  1760. 2021-08-18 00:41:06,566 - INFO - main.py - train - 55 - 【train】 epoch:0 1453/5348 loss:3.6130
    1761. 

  1761. 2021-08-18 00:41:08,645 - INFO - main.py - train - 55 - 【train】 epoch:0 1454/5348 loss:2.7373
    1762. 

  1762. 2021-08-18 00:41:10,724 - INFO - main.py - train - 55 - 【train】 epoch:0 1455/5348 loss:3.1201
    1763. 

  1763. 2021-08-18 00:41:12,818 - INFO - main.py - train - 55 - 【train】 epoch:0 1456/5348 loss:2.8437
    1764. 

  1764. 2021-08-18 00:41:14,930 - INFO - main.py - train - 55 - 【train】 epoch:0 1457/5348 loss:3.5059
    1765. 

  1765. 2021-08-18 00:41:17,035 - INFO - main.py - train - 55 - 【train】 epoch:0 1458/5348 loss:4.9027
    1766. 

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    1767. 

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    1768. 

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    1769. 

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    1770. 

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    1771. 

  1771. 2021-08-18 00:41:29,620 - INFO - main.py - train - 55 - 【train】 epoch:0 1464/5348 loss:3.1383
    1772. 

  1772. 2021-08-18 00:41:31,729 - INFO - main.py - train - 55 - 【train】 epoch:0 1465/5348 loss:3.2115
    1773. 

  1773. 2021-08-18 00:41:33,825 - INFO - main.py - train - 55 - 【train】 epoch:0 1466/5348 loss:6.0073
    1774. 

  1774. 2021-08-18 00:41:35,910 - INFO - main.py - train - 55 - 【train】 epoch:0 1467/5348 loss:2.6237
    1775. 

  1775. 2021-08-18 00:41:38,010 - INFO - main.py - train - 55 - 【train】 epoch:0 1468/5348 loss:3.9751
    1776. 

  1776. 2021-08-18 00:41:40,092 - INFO - main.py - train - 55 - 【train】 epoch:0 1469/5348 loss:4.2014
    1777. 

  1777. 2021-08-18 00:41:42,179 - INFO - main.py - train - 55 - 【train】 epoch:0 1470/5348 loss:4.3859
    1778. 

  1778. 2021-08-18 00:41:44,272 - INFO - main.py - train - 55 - 【train】 epoch:0 1471/5348 loss:3.6810
    1779. 

  1779. 2021-08-18 00:41:46,363 - INFO - main.py - train - 55 - 【train】 epoch:0 1472/5348 loss:3.6630
    1780. 

  1780. 2021-08-18 00:41:48,436 - INFO - main.py - train - 55 - 【train】 epoch:0 1473/5348 loss:4.0074
    1781. 

  1781. 2021-08-18 00:41:50,508 - INFO - main.py - train - 55 - 【train】 epoch:0 1474/5348 loss:1.7250
    1782. 

  1782. 2021-08-18 00:41:52,594 - INFO - main.py - train - 55 - 【train】 epoch:0 1475/5348 loss:3.8795
    1783. 

  1783. 2021-08-18 00:41:54,690 - INFO - main.py - train - 55 - 【train】 epoch:0 1476/5348 loss:3.9051
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  1784. 2021-08-18 00:41:56,752 - INFO - main.py - train - 55 - 【train】 epoch:0 1477/5348 loss:2.8842
    1785. 

  1785. 2021-08-18 00:41:58,836 - INFO - main.py - train - 55 - 【train】 epoch:0 1478/5348 loss:2.6884
    1786. 

  1786. 2021-08-18 00:42:00,905 - INFO - main.py - train - 55 - 【train】 epoch:0 1479/5348 loss:4.1130
    1787. 

  1787. 2021-08-18 00:42:02,980 - INFO - main.py - train - 55 - 【train】 epoch:0 1480/5348 loss:4.8910
    1788. 

  1788. 2021-08-18 00:42:05,063 - INFO - main.py - train - 55 - 【train】 epoch:0 1481/5348 loss:4.6551
    1789. 

  1789. 2021-08-18 00:42:07,163 - INFO - main.py - train - 55 - 【train】 epoch:0 1482/5348 loss:3.5316
    1790. 

  1790. 2021-08-18 00:42:09,250 - INFO - main.py - train - 55 - 【train】 epoch:0 1483/5348 loss:4.3216
    1791. 

  1791. 2021-08-18 00:42:11,328 - INFO - main.py - train - 55 - 【train】 epoch:0 1484/5348 loss:2.7100
    1792. 

  1792. 2021-08-18 00:42:13,405 - INFO - main.py - train - 55 - 【train】 epoch:0 1485/5348 loss:3.3688
    1793. 

  1793. 2021-08-18 00:42:15,493 - INFO - main.py - train - 55 - 【train】 epoch:0 1486/5348 loss:3.9312
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  1794. 2021-08-18 00:42:17,583 - INFO - main.py - train - 55 - 【train】 epoch:0 1487/5348 loss:4.6215
    1795. 

  1795. 2021-08-18 00:42:19,675 - INFO - main.py - train - 55 - 【train】 epoch:0 1488/5348 loss:3.1460
    1796. 

  1796. 2021-08-18 00:42:21,770 - INFO - main.py - train - 55 - 【train】 epoch:0 1489/5348 loss:5.3708
    1797. 

  1797. 2021-08-18 00:42:23,880 - INFO - main.py - train - 55 - 【train】 epoch:0 1490/5348 loss:2.5680
    1798. 

  1798. 2021-08-18 00:42:25,982 - INFO - main.py - train - 55 - 【train】 epoch:0 1491/5348 loss:3.3796
    1799. 

  1799. 2021-08-18 00:42:28,084 - INFO - main.py - train - 55 - 【train】 epoch:0 1492/5348 loss:3.8936
    1800. 

  1800. 2021-08-18 00:42:30,181 - INFO - main.py - train - 55 - 【train】 epoch:0 1493/5348 loss:3.5705
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  1801. 2021-08-18 00:42:32,313 - INFO - main.py - train - 55 - 【train】 epoch:0 1494/5348 loss:2.8419
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  1802. 2021-08-18 00:42:34,417 - INFO - main.py - train - 55 - 【train】 epoch:0 1495/5348 loss:3.8352
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  1803. 2021-08-18 00:42:36,520 - INFO - main.py - train - 55 - 【train】 epoch:0 1496/5348 loss:3.5158
    1804. 

  1804. 2021-08-18 00:42:38,627 - INFO - main.py - train - 55 - 【train】 epoch:0 1497/5348 loss:3.1025
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  1805. 2021-08-18 00:42:40,739 - INFO - main.py - train - 55 - 【train】 epoch:0 1498/5348 loss:3.8063
    1806. 

  1806. 2021-08-18 00:42:42,848 - INFO - main.py - train - 55 - 【train】 epoch:0 1499/5348 loss:4.1707
    1807. 

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    1808. 

  1808. 2021-08-18 00:42:47,004 - INFO - main.py - train - 55 - 【train】 epoch:0 1501/5348 loss:2.6808
    1809. 

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    1810. 

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    1814. 

  1814. 2021-08-18 00:42:59,513 - INFO - main.py - train - 55 - 【train】 epoch:0 1507/5348 loss:3.1653
    1815. 

  1815. 2021-08-18 00:43:01,593 - INFO - main.py - train - 55 - 【train】 epoch:0 1508/5348 loss:3.7455
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    1817. 

  1817. 2021-08-18 00:43:05,763 - INFO - main.py - train - 55 - 【train】 epoch:0 1510/5348 loss:3.7160
    1818. 

  1818. 2021-08-18 00:43:07,841 - INFO - main.py - train - 55 - 【train】 epoch:0 1511/5348 loss:3.6631
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    1820. 

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    1822. 

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  1823. 2021-08-18 00:43:18,219 - INFO - main.py - train - 55 - 【train】 epoch:0 1516/5348 loss:2.6060
    1824. 

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    1825. 

  1825. 2021-08-18 00:43:22,380 - INFO - main.py - train - 55 - 【train】 epoch:0 1518/5348 loss:2.6344
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  1826. 2021-08-18 00:43:24,463 - INFO - main.py - train - 55 - 【train】 epoch:0 1519/5348 loss:3.5427
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    1828. 

  1828. 2021-08-18 00:43:28,673 - INFO - main.py - train - 55 - 【train】 epoch:0 1521/5348 loss:3.2095
    1829. 

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    1830. 

  1830. 2021-08-18 00:43:32,882 - INFO - main.py - train - 55 - 【train】 epoch:0 1523/5348 loss:2.3443
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  1831. 2021-08-18 00:43:34,984 - INFO - main.py - train - 55 - 【train】 epoch:0 1524/5348 loss:3.3586
    1832. 

  1832. 2021-08-18 00:43:37,078 - INFO - main.py - train - 55 - 【train】 epoch:0 1525/5348 loss:3.6569
    1833. 

  1833. 2021-08-18 00:43:39,194 - INFO - main.py - train - 55 - 【train】 epoch:0 1526/5348 loss:4.0975
    1834. 

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    1835. 

  1835. 2021-08-18 00:43:43,406 - INFO - main.py - train - 55 - 【train】 epoch:0 1528/5348 loss:3.5301
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  1836. 2021-08-18 00:43:45,507 - INFO - main.py - train - 55 - 【train】 epoch:0 1529/5348 loss:3.4380
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  1837. 2021-08-18 00:43:47,596 - INFO - main.py - train - 55 - 【train】 epoch:0 1530/5348 loss:3.7634
    1838. 

  1838. 2021-08-18 00:43:49,706 - INFO - main.py - train - 55 - 【train】 epoch:0 1531/5348 loss:3.8389
    1839. 

  1839. 2021-08-18 00:43:51,802 - INFO - main.py - train - 55 - 【train】 epoch:0 1532/5348 loss:1.9460
    1840. 

  1840. 2021-08-18 00:43:53,889 - INFO - main.py - train - 55 - 【train】 epoch:0 1533/5348 loss:4.0559
    1841. 

  1841. 2021-08-18 00:43:55,975 - INFO - main.py - train - 55 - 【train】 epoch:0 1534/5348 loss:2.1656
    1842. 

  1842. 2021-08-18 00:43:58,068 - INFO - main.py - train - 55 - 【train】 epoch:0 1535/5348 loss:3.8886
    1843. 

  1843. 2021-08-18 00:44:00,167 - INFO - main.py - train - 55 - 【train】 epoch:0 1536/5348 loss:2.7574
    1844. 

  1844. 2021-08-18 00:44:02,263 - INFO - main.py - train - 55 - 【train】 epoch:0 1537/5348 loss:3.2845
    1845. 

  1845. 2021-08-18 00:44:04,360 - INFO - main.py - train - 55 - 【train】 epoch:0 1538/5348 loss:3.9379
    1846. 

  1846. 2021-08-18 00:44:06,450 - INFO - main.py - train - 55 - 【train】 epoch:0 1539/5348 loss:3.2123
    1847. 

  1847. 2021-08-18 00:44:08,534 - INFO - main.py - train - 55 - 【train】 epoch:0 1540/5348 loss:4.0499
    1848. 

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    1849. 

  1849. 2021-08-18 00:44:12,731 - INFO - main.py - train - 55 - 【train】 epoch:0 1542/5348 loss:4.2232
    1850. 

  1850. 2021-08-18 00:44:14,832 - INFO - main.py - train - 55 - 【train】 epoch:0 1543/5348 loss:3.8068
    1851. 

  1851. 2021-08-18 00:44:16,921 - INFO - main.py - train - 55 - 【train】 epoch:0 1544/5348 loss:3.2858
    1852. 

  1852. 2021-08-18 00:44:19,005 - INFO - main.py - train - 55 - 【train】 epoch:0 1545/5348 loss:2.9326
    1853. 

  1853. 2021-08-18 00:44:21,098 - INFO - main.py - train - 55 - 【train】 epoch:0 1546/5348 loss:4.7854
    1854. 

  1854. 2021-08-18 00:44:23,192 - INFO - main.py - train - 55 - 【train】 epoch:0 1547/5348 loss:3.5460
    1855. 

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    1856. 

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    1857. 

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    1858. 

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    1859. 

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    1860. 

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    1861. 

  1861. 2021-08-18 00:44:37,959 - INFO - main.py - train - 55 - 【train】 epoch:0 1554/5348 loss:5.5136
    1862. 

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    1864. 

  1864. 2021-08-18 00:44:44,280 - INFO - main.py - train - 55 - 【train】 epoch:0 1557/5348 loss:3.6969
    1865. 

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    1866. 

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    1867. 

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    1868. 

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    1869. 

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    1871. 

  1871. 2021-08-18 00:44:59,066 - INFO - main.py - train - 55 - 【train】 epoch:0 1564/5348 loss:4.0695
    1872. 

  1872. 2021-08-18 00:45:01,157 - INFO - main.py - train - 55 - 【train】 epoch:0 1565/5348 loss:4.9600
    1873. 

  1873. 2021-08-18 00:45:03,257 - INFO - main.py - train - 55 - 【train】 epoch:0 1566/5348 loss:3.6460
    1874. 

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    1875. 

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    1876. 

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    1877. 

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    1878. 

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    1879. 

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    1880. 

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    1881. 

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    1882. 

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    1883. 

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    1884. 

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    1886. 

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    1889. 

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    1890. 

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    1893. 

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  1896. 2021-08-18 00:45:51,795 - INFO - main.py - train - 55 - 【train】 epoch:0 1589/5348 loss:3.2160
    1897. 

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    1898. 

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    1908. 

  1908. 2021-08-18 00:46:16,953 - INFO - main.py - train - 55 - 【train】 epoch:0 1601/5348 loss:4.2876
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    1910. 

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    1911. 

  1911. 2021-08-18 00:46:23,214 - INFO - main.py - train - 55 - 【train】 epoch:0 1604/5348 loss:4.9998
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    1914. 

  1914. 2021-08-18 00:46:29,507 - INFO - main.py - train - 55 - 【train】 epoch:0 1607/5348 loss:2.6818
    1915. 

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    1916. 

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    1917. 

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    1918. 

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    1919. 

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    1920. 

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    1921. 

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    1923. 

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    1924. 

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    1925. 

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    1930. 

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    1931. 

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    1932. 

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    1933. 

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    1934. 

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    1935. 

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    1936. 

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    1937. 

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    1938. 

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    1939. 

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    1940. 

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    1941. 

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    1942. 

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    1945. 

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    1949. 

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    1955. 

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    1973. 

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    1980. 

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    1988. 

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    1991. 

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    1993. 

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    2001. 

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    2003. 

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    2015. 

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    2016. 

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    2018. 

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    2019. 

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    2020. 

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    2022. 

  2022. 2021-08-18 00:50:15,218 - INFO - main.py - train - 55 - 【train】 epoch:0 1715/5348 loss:4.5829
    2023. 

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    2024. 

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    2050. 

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    2060. 

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  2062. 2021-08-18 00:51:38,861 - INFO - main.py - train - 55 - 【train】 epoch:0 1755/5348 loss:3.7803
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    2069. 

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    2071. 

  2071. 2021-08-18 00:51:57,673 - INFO - main.py - train - 55 - 【train】 epoch:0 1764/5348 loss:2.6666
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  2072. 2021-08-18 00:51:59,767 - INFO - main.py - train - 55 - 【train】 epoch:0 1765/5348 loss:6.3816
    2073. 

  2073. 2021-08-18 00:52:01,874 - INFO - main.py - train - 55 - 【train】 epoch:0 1766/5348 loss:4.0855
    2074. 

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    2078. 

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    2080. 

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    2081. 

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    2083. 

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    2085. 

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    2086. 

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    2087. 

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    2088. 

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    2089. 

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    2090. 

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    2091. 

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    2092. 

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    2093. 

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  2094. 2021-08-18 00:52:45,813 - INFO - main.py - train - 55 - 【train】 epoch:0 1787/5348 loss:3.7888
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  2095. 2021-08-18 00:52:47,892 - INFO - main.py - train - 55 - 【train】 epoch:0 1788/5348 loss:3.7479
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  2096. 2021-08-18 00:52:49,974 - INFO - main.py - train - 55 - 【train】 epoch:0 1789/5348 loss:4.1814
    2097. 

  2097. 2021-08-18 00:52:52,058 - INFO - main.py - train - 55 - 【train】 epoch:0 1790/5348 loss:3.8411
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  2098. 2021-08-18 00:52:54,144 - INFO - main.py - train - 55 - 【train】 epoch:0 1791/5348 loss:2.9582
    2099. 

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    2100. 

  2100. 2021-08-18 00:52:58,330 - INFO - main.py - train - 55 - 【train】 epoch:0 1793/5348 loss:3.8983
    2101. 

  2101. 2021-08-18 00:53:00,411 - INFO - main.py - train - 55 - 【train】 epoch:0 1794/5348 loss:3.0579
    2102. 

  2102. 2021-08-18 00:53:02,511 - INFO - main.py - train - 55 - 【train】 epoch:0 1795/5348 loss:4.6279
    2103. 

  2103. 2021-08-18 00:53:04,596 - INFO - main.py - train - 55 - 【train】 epoch:0 1796/5348 loss:7.1120
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    2109. 

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    2110. 

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    2112. 

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    2114. 

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    2115. 

  2115. 2021-08-18 00:53:29,720 - INFO - main.py - train - 55 - 【train】 epoch:0 1808/5348 loss:2.9930
    2116. 

  2116. 2021-08-18 00:53:31,829 - INFO - main.py - train - 55 - 【train】 epoch:0 1809/5348 loss:4.4142
    2117. 

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    2118. 

  2118. 2021-08-18 00:53:36,066 - INFO - main.py - train - 55 - 【train】 epoch:0 1811/5348 loss:2.4179
    2119. 

  2119. 2021-08-18 00:53:38,165 - INFO - main.py - train - 55 - 【train】 epoch:0 1812/5348 loss:3.1535
    2120. 

  2120. 2021-08-18 00:53:40,294 - INFO - main.py - train - 55 - 【train】 epoch:0 1813/5348 loss:3.0781
    2121. 

  2121. 2021-08-18 00:53:42,406 - INFO - main.py - train - 55 - 【train】 epoch:0 1814/5348 loss:3.2240
    2122. 

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    2124. 

  2124. 2021-08-18 00:53:48,701 - INFO - main.py - train - 55 - 【train】 epoch:0 1817/5348 loss:3.8664
    2125. 

  2125. 2021-08-18 00:53:50,793 - INFO - main.py - train - 55 - 【train】 epoch:0 1818/5348 loss:3.5033
    2126. 

  2126. 2021-08-18 00:53:52,885 - INFO - main.py - train - 55 - 【train】 epoch:0 1819/5348 loss:3.9810
    2127. 

  2127. 2021-08-18 00:53:54,979 - INFO - main.py - train - 55 - 【train】 epoch:0 1820/5348 loss:3.9209
    2128. 

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  2129. 2021-08-18 00:53:59,165 - INFO - main.py - train - 55 - 【train】 epoch:0 1822/5348 loss:3.4966
    2130. 

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    2135. 

  2135. 2021-08-18 00:54:11,689 - INFO - main.py - train - 55 - 【train】 epoch:0 1828/5348 loss:2.9196
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    2137. 

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    2139. 

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    2145. 

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    2161. 

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    2162. 

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    2163. 

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    2164. 

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    2169. 

  2169. 2021-08-18 00:55:22,836 - INFO - main.py - train - 55 - 【train】 epoch:0 1862/5348 loss:2.1020
    2170. 

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    2171. 

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    2172. 

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    2173. 

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    2174. 

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    2175. 

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    2176. 

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    2180. 

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    2220. 

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    2230. 

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    2232. 

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    2233. 

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    2235. 

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    2236. 

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    2237. 

  2237. 2021-08-18 00:57:44,511 - INFO - main.py - train - 55 - 【train】 epoch:0 1930/5348 loss:4.0899
    2238. 

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    2239. 

  2239. 2021-08-18 00:57:48,683 - INFO - main.py - train - 55 - 【train】 epoch:0 1932/5348 loss:3.1336
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    2243. 

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    2244. 

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    2245. 

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    2246. 

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    2249. 

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    2250. 

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    2254. 

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    2259. 

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    2265. 

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  2319. 2021-08-18 01:00:35,257 - INFO - main.py - train - 55 - 【train】 epoch:0 2012/5348 loss:3.5569
    2320. 

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  2466. 2021-08-18 01:05:42,202 - INFO - main.py - train - 55 - 【train】 epoch:0 2159/5348 loss:5.1361
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  2613. 2021-08-18 01:10:48,926 - INFO - main.py - train - 55 - 【train】 epoch:0 2306/5348 loss:2.6494
    2614. 

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  2645. 2021-08-18 01:11:55,817 - INFO - main.py - train - 55 - 【train】 epoch:0 2338/5348 loss:2.6971
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  2658. 2021-08-18 01:12:22,877 - INFO - main.py - train - 55 - 【train】 epoch:0 2351/5348 loss:6.4522
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  2659. 2021-08-18 01:12:24,977 - INFO - main.py - train - 55 - 【train】 epoch:0 2352/5348 loss:3.8992
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  2660. 2021-08-18 01:12:27,058 - INFO - main.py - train - 55 - 【train】 epoch:0 2353/5348 loss:2.6094
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  2661. 2021-08-18 01:12:29,146 - INFO - main.py - train - 55 - 【train】 epoch:0 2354/5348 loss:3.8090
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  2662. 2021-08-18 01:12:31,238 - INFO - main.py - train - 55 - 【train】 epoch:0 2355/5348 loss:4.7395
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  2664. 2021-08-18 01:12:35,397 - INFO - main.py - train - 55 - 【train】 epoch:0 2357/5348 loss:3.9324
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  2665. 2021-08-18 01:12:37,506 - INFO - main.py - train - 55 - 【train】 epoch:0 2358/5348 loss:3.0238
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  2666. 2021-08-18 01:12:39,601 - INFO - main.py - train - 55 - 【train】 epoch:0 2359/5348 loss:2.7818
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  2667. 2021-08-18 01:12:41,726 - INFO - main.py - train - 55 - 【train】 epoch:0 2360/5348 loss:2.7969
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  2668. 2021-08-18 01:12:43,821 - INFO - main.py - train - 55 - 【train】 epoch:0 2361/5348 loss:3.0528
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  2669. 2021-08-18 01:12:45,929 - INFO - main.py - train - 55 - 【train】 epoch:0 2362/5348 loss:3.1484
    2670. 

  2670. 2021-08-18 01:12:48,043 - INFO - main.py - train - 55 - 【train】 epoch:0 2363/5348 loss:3.9850
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  2671. 2021-08-18 01:12:50,135 - INFO - main.py - train - 55 - 【train】 epoch:0 2364/5348 loss:5.5622
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  2679. 2021-08-18 01:13:06,872 - INFO - main.py - train - 55 - 【train】 epoch:0 2372/5348 loss:3.0423
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  2680. 2021-08-18 01:13:08,949 - INFO - main.py - train - 55 - 【train】 epoch:0 2373/5348 loss:2.8835
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  2681. 2021-08-18 01:13:11,036 - INFO - main.py - train - 55 - 【train】 epoch:0 2374/5348 loss:3.0378
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  2689. 2021-08-18 01:13:27,697 - INFO - main.py - train - 55 - 【train】 epoch:0 2382/5348 loss:3.4026
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  2690. 2021-08-18 01:13:29,782 - INFO - main.py - train - 55 - 【train】 epoch:0 2383/5348 loss:3.1944
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    2760. 

  2760. 2021-08-18 01:15:56,101 - INFO - main.py - train - 55 - 【train】 epoch:0 2453/5348 loss:2.9942
    2761. 

  2761. 2021-08-18 01:15:58,176 - INFO - main.py - train - 55 - 【train】 epoch:0 2454/5348 loss:1.8452
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  2762. 2021-08-18 01:16:00,265 - INFO - main.py - train - 55 - 【train】 epoch:0 2455/5348 loss:4.2449
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  2765. 2021-08-18 01:16:06,503 - INFO - main.py - train - 55 - 【train】 epoch:0 2458/5348 loss:2.3726
    2766. 

  2766. 2021-08-18 01:16:08,614 - INFO - main.py - train - 55 - 【train】 epoch:0 2459/5348 loss:2.6132
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  2793. 2021-08-18 01:17:04,937 - INFO - main.py - train - 55 - 【train】 epoch:0 2486/5348 loss:3.1314
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  2795. 2021-08-18 01:17:09,121 - INFO - main.py - train - 55 - 【train】 epoch:0 2488/5348 loss:3.7525
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  2796. 2021-08-18 01:17:11,194 - INFO - main.py - train - 55 - 【train】 epoch:0 2489/5348 loss:2.6174
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  2797. 2021-08-18 01:17:13,290 - INFO - main.py - train - 55 - 【train】 epoch:0 2490/5348 loss:3.9765
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  2798. 2021-08-18 01:17:15,380 - INFO - main.py - train - 55 - 【train】 epoch:0 2491/5348 loss:3.6221
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  2808. 2021-08-18 01:17:36,225 - INFO - main.py - train - 55 - 【train】 epoch:0 2501/5348 loss:3.5354
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  2811. 2021-08-18 01:17:42,535 - INFO - main.py - train - 55 - 【train】 epoch:0 2504/5348 loss:1.9480
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  2812. 2021-08-18 01:17:44,637 - INFO - main.py - train - 55 - 【train】 epoch:0 2505/5348 loss:2.3090
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  2815. 2021-08-18 01:17:50,954 - INFO - main.py - train - 55 - 【train】 epoch:0 2508/5348 loss:3.4445
    2816. 

  2816. 2021-08-18 01:17:53,054 - INFO - main.py - train - 55 - 【train】 epoch:0 2509/5348 loss:4.8707
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  2817. 2021-08-18 01:17:55,151 - INFO - main.py - train - 55 - 【train】 epoch:0 2510/5348 loss:4.3859
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  2825. 2021-08-18 01:18:11,759 - INFO - main.py - train - 55 - 【train】 epoch:0 2518/5348 loss:4.3884
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    2855. 

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  2907. 2021-08-18 01:21:03,250 - INFO - main.py - train - 55 - 【train】 epoch:0 2600/5348 loss:3.2773
    2908. 

  2908. 2021-08-18 01:21:05,328 - INFO - main.py - train - 55 - 【train】 epoch:0 2601/5348 loss:3.1988
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  2909. 2021-08-18 01:21:07,422 - INFO - main.py - train - 55 - 【train】 epoch:0 2602/5348 loss:3.5584
    2910. 

  2910. 2021-08-18 01:21:09,502 - INFO - main.py - train - 55 - 【train】 epoch:0 2603/5348 loss:4.0023
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  2911. 2021-08-18 01:21:11,596 - INFO - main.py - train - 55 - 【train】 epoch:0 2604/5348 loss:4.0671
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    2913. 

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    2914. 

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  2915. 2021-08-18 01:21:19,919 - INFO - main.py - train - 55 - 【train】 epoch:0 2608/5348 loss:3.3065
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  2916. 2021-08-18 01:21:21,989 - INFO - main.py - train - 55 - 【train】 epoch:0 2609/5348 loss:2.6999
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  2925. 2021-08-18 01:21:40,873 - INFO - main.py - train - 55 - 【train】 epoch:0 2618/5348 loss:3.5657
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  2927. 2021-08-18 01:21:45,089 - INFO - main.py - train - 55 - 【train】 epoch:0 2620/5348 loss:2.6825
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  2928. 2021-08-18 01:21:47,189 - INFO - main.py - train - 55 - 【train】 epoch:0 2621/5348 loss:2.4850
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  2929. 2021-08-18 01:21:49,273 - INFO - main.py - train - 55 - 【train】 epoch:0 2622/5348 loss:2.8976
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  2930. 2021-08-18 01:21:51,353 - INFO - main.py - train - 55 - 【train】 epoch:0 2623/5348 loss:3.4735
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  2931. 2021-08-18 01:21:53,444 - INFO - main.py - train - 55 - 【train】 epoch:0 2624/5348 loss:4.1105
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  2934. 2021-08-18 01:21:59,677 - INFO - main.py - train - 55 - 【train】 epoch:0 2627/5348 loss:2.8961
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  2935. 2021-08-18 01:22:01,768 - INFO - main.py - train - 55 - 【train】 epoch:0 2628/5348 loss:4.1758
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  2938. 2021-08-18 01:22:08,018 - INFO - main.py - train - 55 - 【train】 epoch:0 2631/5348 loss:3.0081
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  2939. 2021-08-18 01:22:10,102 - INFO - main.py - train - 55 - 【train】 epoch:0 2632/5348 loss:3.3348
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  2941. 2021-08-18 01:22:14,272 - INFO - main.py - train - 55 - 【train】 epoch:0 2634/5348 loss:3.8016
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  2942. 2021-08-18 01:22:16,343 - INFO - main.py - train - 55 - 【train】 epoch:0 2635/5348 loss:2.5687
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  2943. 2021-08-18 01:22:18,416 - INFO - main.py - train - 55 - 【train】 epoch:0 2636/5348 loss:3.2074
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  2944. 2021-08-18 01:22:20,488 - INFO - main.py - train - 55 - 【train】 epoch:0 2637/5348 loss:3.3710
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  2945. 2021-08-18 01:22:22,564 - INFO - main.py - train - 55 - 【train】 epoch:0 2638/5348 loss:1.9703
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  2946. 2021-08-18 01:22:24,646 - INFO - main.py - train - 55 - 【train】 epoch:0 2639/5348 loss:2.9035
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  2947. 2021-08-18 01:22:26,727 - INFO - main.py - train - 55 - 【train】 epoch:0 2640/5348 loss:3.0242
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  2948. 2021-08-18 01:22:28,794 - INFO - main.py - train - 55 - 【train】 epoch:0 2641/5348 loss:2.8742
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  2949. 2021-08-18 01:22:30,868 - INFO - main.py - train - 55 - 【train】 epoch:0 2642/5348 loss:3.4240
    2950. 

  2950. 2021-08-18 01:22:32,958 - INFO - main.py - train - 55 - 【train】 epoch:0 2643/5348 loss:4.1757
    2951. 

  2951. 2021-08-18 01:22:35,068 - INFO - main.py - train - 55 - 【train】 epoch:0 2644/5348 loss:2.0200
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  2952. 2021-08-18 01:22:37,177 - INFO - main.py - train - 55 - 【train】 epoch:0 2645/5348 loss:3.1830
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  2953. 2021-08-18 01:22:39,273 - INFO - main.py - train - 55 - 【train】 epoch:0 2646/5348 loss:3.4694
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  2954. 2021-08-18 01:22:41,370 - INFO - main.py - train - 55 - 【train】 epoch:0 2647/5348 loss:2.2181
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  2955. 2021-08-18 01:22:43,472 - INFO - main.py - train - 55 - 【train】 epoch:0 2648/5348 loss:2.6142
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  2956. 2021-08-18 01:22:45,598 - INFO - main.py - train - 55 - 【train】 epoch:0 2649/5348 loss:2.5434
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  2957. 2021-08-18 01:22:47,706 - INFO - main.py - train - 55 - 【train】 epoch:0 2650/5348 loss:3.9519
    2958. 

  2958. 2021-08-18 01:22:49,797 - INFO - main.py - train - 55 - 【train】 epoch:0 2651/5348 loss:1.9142
    2959. 

  2959. 2021-08-18 01:22:51,903 - INFO - main.py - train - 55 - 【train】 epoch:0 2652/5348 loss:3.4563
    2960. 

  2960. 2021-08-18 01:22:53,992 - INFO - main.py - train - 55 - 【train】 epoch:0 2653/5348 loss:2.7669
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  2961. 2021-08-18 01:22:56,071 - INFO - main.py - train - 55 - 【train】 epoch:0 2654/5348 loss:3.2844
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  2962. 2021-08-18 01:22:58,158 - INFO - main.py - train - 55 - 【train】 epoch:0 2655/5348 loss:2.6459
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  2963. 2021-08-18 01:23:00,236 - INFO - main.py - train - 55 - 【train】 epoch:0 2656/5348 loss:2.7969
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  2964. 2021-08-18 01:23:02,319 - INFO - main.py - train - 55 - 【train】 epoch:0 2657/5348 loss:4.5063
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  2965. 2021-08-18 01:23:04,388 - INFO - main.py - train - 55 - 【train】 epoch:0 2658/5348 loss:3.1903
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  2966. 2021-08-18 01:23:06,465 - INFO - main.py - train - 55 - 【train】 epoch:0 2659/5348 loss:2.5560
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  2967. 2021-08-18 01:23:08,551 - INFO - main.py - train - 55 - 【train】 epoch:0 2660/5348 loss:3.3907
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  2968. 2021-08-18 01:23:10,625 - INFO - main.py - train - 55 - 【train】 epoch:0 2661/5348 loss:2.9864
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  2969. 2021-08-18 01:23:12,703 - INFO - main.py - train - 55 - 【train】 epoch:0 2662/5348 loss:2.5483
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  2970. 2021-08-18 01:23:14,802 - INFO - main.py - train - 55 - 【train】 epoch:0 2663/5348 loss:3.5655
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  2971. 2021-08-18 01:23:16,876 - INFO - main.py - train - 55 - 【train】 epoch:0 2664/5348 loss:2.7571
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  2972. 2021-08-18 01:23:18,967 - INFO - main.py - train - 55 - 【train】 epoch:0 2665/5348 loss:2.4683
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  2973. 2021-08-18 01:23:21,040 - INFO - main.py - train - 55 - 【train】 epoch:0 2666/5348 loss:2.3482
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  2974. 2021-08-18 01:23:23,113 - INFO - main.py - train - 55 - 【train】 epoch:0 2667/5348 loss:2.8368
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  2975. 2021-08-18 01:23:25,201 - INFO - main.py - train - 55 - 【train】 epoch:0 2668/5348 loss:2.3741
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  2976. 2021-08-18 01:23:27,285 - INFO - main.py - train - 55 - 【train】 epoch:0 2669/5348 loss:2.2940
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  2977. 2021-08-18 01:23:29,358 - INFO - main.py - train - 55 - 【train】 epoch:0 2670/5348 loss:2.0138
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  2978. 2021-08-18 01:23:31,432 - INFO - main.py - train - 55 - 【train】 epoch:0 2671/5348 loss:2.9084
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  2979. 2021-08-18 01:23:33,502 - INFO - main.py - train - 55 - 【train】 epoch:0 2672/5348 loss:3.2529
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  2980. 2021-08-18 01:23:35,573 - INFO - main.py - train - 55 - 【train】 epoch:0 2673/5348 loss:2.4374
    2981. 

  2981. 2021-08-18 01:23:37,670 - INFO - main.py - train - 55 - 【train】 epoch:0 2674/5348 loss:4.2591
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  2982. 2021-08-18 01:23:39,852 - INFO - main.py - train - 55 - 【train】 epoch:0 2675/5348 loss:3.2285
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  2983. 2021-08-18 01:23:41,959 - INFO - main.py - train - 55 - 【train】 epoch:0 2676/5348 loss:3.7436
    2984. 

  2984. 2021-08-18 01:23:44,076 - INFO - main.py - train - 55 - 【train】 epoch:0 2677/5348 loss:5.0606
    2985. 

  2985. 2021-08-18 01:23:46,187 - INFO - main.py - train - 55 - 【train】 epoch:0 2678/5348 loss:2.9597
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  2986. 2021-08-18 01:23:48,300 - INFO - main.py - train - 55 - 【train】 epoch:0 2679/5348 loss:4.6440
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  2987. 2021-08-18 01:23:50,425 - INFO - main.py - train - 55 - 【train】 epoch:0 2680/5348 loss:3.4747
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  2988. 2021-08-18 01:23:52,514 - INFO - main.py - train - 55 - 【train】 epoch:0 2681/5348 loss:3.9578
    2989. 

  2989. 2021-08-18 01:23:54,633 - INFO - main.py - train - 55 - 【train】 epoch:0 2682/5348 loss:2.9708
    2990. 

  2990. 2021-08-18 01:23:56,749 - INFO - main.py - train - 55 - 【train】 epoch:0 2683/5348 loss:2.8264
    2991. 

  2991. 2021-08-18 01:23:58,856 - INFO - main.py - train - 55 - 【train】 epoch:0 2684/5348 loss:3.8430
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    2993. 

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  2994. 2021-08-18 01:24:05,157 - INFO - main.py - train - 55 - 【train】 epoch:0 2687/5348 loss:2.5237
    2995. 

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    2999. 

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    3000. 

  3000. 2021-08-18 01:24:17,680 - INFO - main.py - train - 55 - 【train】 epoch:0 2693/5348 loss:2.7268
    3001. 

  3001. 2021-08-18 01:24:19,763 - INFO - main.py - train - 55 - 【train】 epoch:0 2694/5348 loss:2.4240
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  3002. 2021-08-18 01:24:21,857 - INFO - main.py - train - 55 - 【train】 epoch:0 2695/5348 loss:3.5471
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    3004. 

  3004. 2021-08-18 01:24:26,017 - INFO - main.py - train - 55 - 【train】 epoch:0 2697/5348 loss:3.2917
    3005. 

  3005. 2021-08-18 01:24:28,098 - INFO - main.py - train - 55 - 【train】 epoch:0 2698/5348 loss:2.9807
    3006. 

  3006. 2021-08-18 01:24:30,189 - INFO - main.py - train - 55 - 【train】 epoch:0 2699/5348 loss:4.2848
    3007. 

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    3008. 

  3008. 2021-08-18 01:24:34,348 - INFO - main.py - train - 55 - 【train】 epoch:0 2701/5348 loss:2.0555
    3009. 

  3009. 2021-08-18 01:24:36,419 - INFO - main.py - train - 55 - 【train】 epoch:0 2702/5348 loss:3.6010
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  3010. 2021-08-18 01:24:38,491 - INFO - main.py - train - 55 - 【train】 epoch:0 2703/5348 loss:3.4221
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  3011. 2021-08-18 01:24:40,564 - INFO - main.py - train - 55 - 【train】 epoch:0 2704/5348 loss:3.2580
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  3012. 2021-08-18 01:24:42,632 - INFO - main.py - train - 55 - 【train】 epoch:0 2705/5348 loss:3.8763
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  3015. 2021-08-18 01:24:48,920 - INFO - main.py - train - 55 - 【train】 epoch:0 2708/5348 loss:4.0420
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  3016. 2021-08-18 01:24:51,018 - INFO - main.py - train - 55 - 【train】 epoch:0 2709/5348 loss:3.6418
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    3040. 

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    3041. 

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    3042. 

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    3044. 

  3044. 2021-08-18 01:25:49,633 - INFO - main.py - train - 55 - 【train】 epoch:0 2737/5348 loss:3.7181
    3045. 

  3045. 2021-08-18 01:25:51,742 - INFO - main.py - train - 55 - 【train】 epoch:0 2738/5348 loss:2.9710
    3046. 

  3046. 2021-08-18 01:25:53,835 - INFO - main.py - train - 55 - 【train】 epoch:0 2739/5348 loss:3.9560
    3047. 

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    3054. 

  3054. 2021-08-18 01:26:10,487 - INFO - main.py - train - 55 - 【train】 epoch:0 2747/5348 loss:3.0855
    3055. 

  3055. 2021-08-18 01:26:12,560 - INFO - main.py - train - 55 - 【train】 epoch:0 2748/5348 loss:2.9347
    3056. 

  3056. 2021-08-18 01:26:14,651 - INFO - main.py - train - 55 - 【train】 epoch:0 2749/5348 loss:2.8594
    3057. 

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    3058. 

  3058. 2021-08-18 01:26:18,819 - INFO - main.py - train - 55 - 【train】 epoch:0 2751/5348 loss:4.2343
    3059. 

  3059. 2021-08-18 01:26:20,906 - INFO - main.py - train - 55 - 【train】 epoch:0 2752/5348 loss:4.0640
    3060. 

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    3061. 

  3061. 2021-08-18 01:26:25,133 - INFO - main.py - train - 55 - 【train】 epoch:0 2754/5348 loss:4.4858
    3062. 

  3062. 2021-08-18 01:26:27,234 - INFO - main.py - train - 55 - 【train】 epoch:0 2755/5348 loss:2.7081
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    3064. 

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    3065. 

  3065. 2021-08-18 01:26:33,579 - INFO - main.py - train - 55 - 【train】 epoch:0 2758/5348 loss:2.8485
    3066. 

  3066. 2021-08-18 01:26:35,683 - INFO - main.py - train - 55 - 【train】 epoch:0 2759/5348 loss:3.2529
    3067. 

  3067. 2021-08-18 01:26:37,784 - INFO - main.py - train - 55 - 【train】 epoch:0 2760/5348 loss:3.1657
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  3068. 2021-08-18 01:26:39,889 - INFO - main.py - train - 55 - 【train】 epoch:0 2761/5348 loss:3.2701
    3069. 

  3069. 2021-08-18 01:26:42,011 - INFO - main.py - train - 55 - 【train】 epoch:0 2762/5348 loss:2.5407
    3070. 

  3070. 2021-08-18 01:26:44,128 - INFO - main.py - train - 55 - 【train】 epoch:0 2763/5348 loss:2.8762
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    3074. 

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  3075. 2021-08-18 01:26:54,618 - INFO - main.py - train - 55 - 【train】 epoch:0 2768/5348 loss:3.0986
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  3076. 2021-08-18 01:26:56,706 - INFO - main.py - train - 55 - 【train】 epoch:0 2769/5348 loss:2.9228
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  3077. 2021-08-18 01:26:58,807 - INFO - main.py - train - 55 - 【train】 epoch:0 2770/5348 loss:3.3581
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  3078. 2021-08-18 01:27:00,896 - INFO - main.py - train - 55 - 【train】 epoch:0 2771/5348 loss:3.1246
    3079. 

  3079. 2021-08-18 01:27:02,994 - INFO - main.py - train - 55 - 【train】 epoch:0 2772/5348 loss:3.9985
    3080. 

  3080. 2021-08-18 01:27:05,079 - INFO - main.py - train - 55 - 【train】 epoch:0 2773/5348 loss:2.9591
    3081. 

  3081. 2021-08-18 01:27:07,175 - INFO - main.py - train - 55 - 【train】 epoch:0 2774/5348 loss:3.2267
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  3082. 2021-08-18 01:27:09,277 - INFO - main.py - train - 55 - 【train】 epoch:0 2775/5348 loss:3.4266
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  3084. 2021-08-18 01:27:13,461 - INFO - main.py - train - 55 - 【train】 epoch:0 2777/5348 loss:3.1923
    3085. 

  3085. 2021-08-18 01:27:15,552 - INFO - main.py - train - 55 - 【train】 epoch:0 2778/5348 loss:4.4975
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  3086. 2021-08-18 01:27:17,639 - INFO - main.py - train - 55 - 【train】 epoch:0 2779/5348 loss:2.9212
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  3087. 2021-08-18 01:27:19,737 - INFO - main.py - train - 55 - 【train】 epoch:0 2780/5348 loss:2.9860
    3088. 

  3088. 2021-08-18 01:27:21,819 - INFO - main.py - train - 55 - 【train】 epoch:0 2781/5348 loss:3.6516
    3089. 

  3089. 2021-08-18 01:27:23,907 - INFO - main.py - train - 55 - 【train】 epoch:0 2782/5348 loss:3.1013
    3090. 

  3090. 2021-08-18 01:27:26,008 - INFO - main.py - train - 55 - 【train】 epoch:0 2783/5348 loss:2.7901
    3091. 

  3091. 2021-08-18 01:27:28,100 - INFO - main.py - train - 55 - 【train】 epoch:0 2784/5348 loss:2.7893
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  3092. 2021-08-18 01:27:30,228 - INFO - main.py - train - 55 - 【train】 epoch:0 2785/5348 loss:2.8615
    3093. 

  3093. 2021-08-18 01:27:32,351 - INFO - main.py - train - 55 - 【train】 epoch:0 2786/5348 loss:2.3464
    3094. 

  3094. 2021-08-18 01:27:34,461 - INFO - main.py - train - 55 - 【train】 epoch:0 2787/5348 loss:3.5667
    3095. 

  3095. 2021-08-18 01:27:36,568 - INFO - main.py - train - 55 - 【train】 epoch:0 2788/5348 loss:3.8081
    3096. 

  3096. 2021-08-18 01:27:38,681 - INFO - main.py - train - 55 - 【train】 epoch:0 2789/5348 loss:3.7577
    3097. 

  3097. 2021-08-18 01:27:40,789 - INFO - main.py - train - 55 - 【train】 epoch:0 2790/5348 loss:2.5879
    3098. 

  3098. 2021-08-18 01:27:42,892 - INFO - main.py - train - 55 - 【train】 epoch:0 2791/5348 loss:3.1519
    3099. 

  3099. 2021-08-18 01:27:45,022 - INFO - main.py - train - 55 - 【train】 epoch:0 2792/5348 loss:4.2802
    3100. 

  3100. 2021-08-18 01:27:47,141 - INFO - main.py - train - 55 - 【train】 epoch:0 2793/5348 loss:2.5560
    3101. 

  3101. 2021-08-18 01:27:49,240 - INFO - main.py - train - 55 - 【train】 epoch:0 2794/5348 loss:2.4117
    3102. 

  3102. 2021-08-18 01:27:51,370 - INFO - main.py - train - 55 - 【train】 epoch:0 2795/5348 loss:3.0642
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  3103. 2021-08-18 01:27:53,457 - INFO - main.py - train - 55 - 【train】 epoch:0 2796/5348 loss:2.5000
    3104. 

  3104. 2021-08-18 01:27:55,567 - INFO - main.py - train - 55 - 【train】 epoch:0 2797/5348 loss:2.0521
    3105. 

  3105. 2021-08-18 01:27:57,662 - INFO - main.py - train - 55 - 【train】 epoch:0 2798/5348 loss:2.6574
    3106. 

  3106. 2021-08-18 01:27:59,744 - INFO - main.py - train - 55 - 【train】 epoch:0 2799/5348 loss:3.1892
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  3107. 2021-08-18 01:28:01,835 - INFO - main.py - train - 55 - 【train】 epoch:0 2800/5348 loss:2.5782
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  3108. 2021-08-18 01:28:03,915 - INFO - main.py - train - 55 - 【train】 epoch:0 2801/5348 loss:2.9658
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  3109. 2021-08-18 01:28:06,012 - INFO - main.py - train - 55 - 【train】 epoch:0 2802/5348 loss:2.2925
    3110. 

  3110. 2021-08-18 01:28:08,117 - INFO - main.py - train - 55 - 【train】 epoch:0 2803/5348 loss:3.8525
    3111. 

  3111. 2021-08-18 01:28:10,210 - INFO - main.py - train - 55 - 【train】 epoch:0 2804/5348 loss:3.2470
    3112. 

  3112. 2021-08-18 01:28:12,289 - INFO - main.py - train - 55 - 【train】 epoch:0 2805/5348 loss:2.8730
    3113. 

  3113. 2021-08-18 01:28:14,368 - INFO - main.py - train - 55 - 【train】 epoch:0 2806/5348 loss:2.0271
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  3114. 2021-08-18 01:28:16,461 - INFO - main.py - train - 55 - 【train】 epoch:0 2807/5348 loss:3.3351
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  3115. 2021-08-18 01:28:18,539 - INFO - main.py - train - 55 - 【train】 epoch:0 2808/5348 loss:3.0969
    3116. 

  3116. 2021-08-18 01:28:20,611 - INFO - main.py - train - 55 - 【train】 epoch:0 2809/5348 loss:2.6688
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  3117. 2021-08-18 01:28:22,687 - INFO - main.py - train - 55 - 【train】 epoch:0 2810/5348 loss:2.6788
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  3118. 2021-08-18 01:28:24,785 - INFO - main.py - train - 55 - 【train】 epoch:0 2811/5348 loss:3.6741
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  3119. 2021-08-18 01:28:26,880 - INFO - main.py - train - 55 - 【train】 epoch:0 2812/5348 loss:3.4409
    3120. 

  3120. 2021-08-18 01:28:28,969 - INFO - main.py - train - 55 - 【train】 epoch:0 2813/5348 loss:2.6486
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  3121. 2021-08-18 01:28:31,069 - INFO - main.py - train - 55 - 【train】 epoch:0 2814/5348 loss:4.6891
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  3125. 2021-08-18 01:28:39,493 - INFO - main.py - train - 55 - 【train】 epoch:0 2818/5348 loss:4.9942
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  3129. 2021-08-18 01:28:47,900 - INFO - main.py - train - 55 - 【train】 epoch:0 2822/5348 loss:3.1986
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  3131. 2021-08-18 01:28:52,096 - INFO - main.py - train - 55 - 【train】 epoch:0 2824/5348 loss:2.4954
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  3134. 2021-08-18 01:28:58,423 - INFO - main.py - train - 55 - 【train】 epoch:0 2827/5348 loss:1.5094
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    3141. 

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    3144. 

  3144. 2021-08-18 01:29:19,237 - INFO - main.py - train - 55 - 【train】 epoch:0 2837/5348 loss:4.0717
    3145. 

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    3148. 

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    3149. 

  3149. 2021-08-18 01:29:29,641 - INFO - main.py - train - 55 - 【train】 epoch:0 2842/5348 loss:2.4779
    3150. 

  3150. 2021-08-18 01:29:31,732 - INFO - main.py - train - 55 - 【train】 epoch:0 2843/5348 loss:5.0548
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    3154. 

  3154. 2021-08-18 01:29:40,068 - INFO - main.py - train - 55 - 【train】 epoch:0 2847/5348 loss:2.3414
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  3155. 2021-08-18 01:29:42,157 - INFO - main.py - train - 55 - 【train】 epoch:0 2848/5348 loss:2.4472
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    3157. 

  3157. 2021-08-18 01:29:46,391 - INFO - main.py - train - 55 - 【train】 epoch:0 2850/5348 loss:3.6390
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  3159. 2021-08-18 01:29:50,598 - INFO - main.py - train - 55 - 【train】 epoch:0 2852/5348 loss:3.3496
    3160. 

  3160. 2021-08-18 01:29:52,691 - INFO - main.py - train - 55 - 【train】 epoch:0 2853/5348 loss:2.9592
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  3161. 2021-08-18 01:29:54,781 - INFO - main.py - train - 55 - 【train】 epoch:0 2854/5348 loss:4.2448
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  3162. 2021-08-18 01:29:56,889 - INFO - main.py - train - 55 - 【train】 epoch:0 2855/5348 loss:2.8324
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  3163. 2021-08-18 01:29:59,004 - INFO - main.py - train - 55 - 【train】 epoch:0 2856/5348 loss:2.4679
    3164. 

  3164. 2021-08-18 01:30:01,106 - INFO - main.py - train - 55 - 【train】 epoch:0 2857/5348 loss:3.1229
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  3165. 2021-08-18 01:30:03,195 - INFO - main.py - train - 55 - 【train】 epoch:0 2858/5348 loss:2.5129
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    3180. 

  3180. 2021-08-18 01:30:34,597 - INFO - main.py - train - 55 - 【train】 epoch:0 2873/5348 loss:3.2826
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  3181. 2021-08-18 01:30:36,691 - INFO - main.py - train - 55 - 【train】 epoch:0 2874/5348 loss:2.5843
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    3186. 

  3186. 2021-08-18 01:30:47,118 - INFO - main.py - train - 55 - 【train】 epoch:0 2879/5348 loss:3.5807
    3187. 

  3187. 2021-08-18 01:30:49,192 - INFO - main.py - train - 55 - 【train】 epoch:0 2880/5348 loss:2.6715
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    3189. 

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    3190. 

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    3191. 

  3191. 2021-08-18 01:30:57,489 - INFO - main.py - train - 55 - 【train】 epoch:0 2884/5348 loss:3.5883
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    3195. 

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    3199. 

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    3200. 

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    3201. 

  3201. 2021-08-18 01:31:18,298 - INFO - main.py - train - 55 - 【train】 epoch:0 2894/5348 loss:2.6502
    3202. 

  3202. 2021-08-18 01:31:20,396 - INFO - main.py - train - 55 - 【train】 epoch:0 2895/5348 loss:3.9007
    3203. 

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    3204. 

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    3205. 

  3205. 2021-08-18 01:31:26,705 - INFO - main.py - train - 55 - 【train】 epoch:0 2898/5348 loss:3.3386
    3206. 

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    3207. 

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    3210. 

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    3211. 

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    3213. 

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  3214. 2021-08-18 01:31:45,580 - INFO - main.py - train - 55 - 【train】 epoch:0 2907/5348 loss:3.0567
    3215. 

  3215. 2021-08-18 01:31:47,671 - INFO - main.py - train - 55 - 【train】 epoch:0 2908/5348 loss:3.1697
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  3216. 2021-08-18 01:31:49,750 - INFO - main.py - train - 55 - 【train】 epoch:0 2909/5348 loss:4.2908
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    3218. 

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    3225. 

  3225. 2021-08-18 01:32:08,476 - INFO - main.py - train - 55 - 【train】 epoch:0 2918/5348 loss:3.7346
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  3229. 2021-08-18 01:32:16,751 - INFO - main.py - train - 55 - 【train】 epoch:0 2922/5348 loss:3.7161
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  3234. 2021-08-18 01:32:27,200 - INFO - main.py - train - 55 - 【train】 epoch:0 2927/5348 loss:3.5665
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  3235. 2021-08-18 01:32:29,304 - INFO - main.py - train - 55 - 【train】 epoch:0 2928/5348 loss:2.6710
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  3236. 2021-08-18 01:32:31,403 - INFO - main.py - train - 55 - 【train】 epoch:0 2929/5348 loss:3.2820
    3237. 

  3237. 2021-08-18 01:32:33,497 - INFO - main.py - train - 55 - 【train】 epoch:0 2930/5348 loss:4.8292
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  3238. 2021-08-18 01:32:35,588 - INFO - main.py - train - 55 - 【train】 epoch:0 2931/5348 loss:2.4623
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  3239. 2021-08-18 01:32:37,685 - INFO - main.py - train - 55 - 【train】 epoch:0 2932/5348 loss:3.3147
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  3240. 2021-08-18 01:32:39,790 - INFO - main.py - train - 55 - 【train】 epoch:0 2933/5348 loss:2.8617
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  3241. 2021-08-18 01:32:41,875 - INFO - main.py - train - 55 - 【train】 epoch:0 2934/5348 loss:3.5870
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  3242. 2021-08-18 01:32:43,983 - INFO - main.py - train - 55 - 【train】 epoch:0 2935/5348 loss:3.1079
    3243. 

  3243. 2021-08-18 01:32:46,086 - INFO - main.py - train - 55 - 【train】 epoch:0 2936/5348 loss:4.3735
    3244. 

  3244. 2021-08-18 01:32:48,194 - INFO - main.py - train - 55 - 【train】 epoch:0 2937/5348 loss:2.3208
    3245. 

  3245. 2021-08-18 01:32:50,276 - INFO - main.py - train - 55 - 【train】 epoch:0 2938/5348 loss:3.1614
    3246. 

  3246. 2021-08-18 01:32:52,362 - INFO - main.py - train - 55 - 【train】 epoch:0 2939/5348 loss:2.9335
    3247. 

  3247. 2021-08-18 01:32:54,468 - INFO - main.py - train - 55 - 【train】 epoch:0 2940/5348 loss:2.9030
    3248. 

  3248. 2021-08-18 01:32:56,546 - INFO - main.py - train - 55 - 【train】 epoch:0 2941/5348 loss:3.8207
    3249. 

  3249. 2021-08-18 01:32:58,622 - INFO - main.py - train - 55 - 【train】 epoch:0 2942/5348 loss:2.9128
    3250. 

  3250. 2021-08-18 01:33:00,696 - INFO - main.py - train - 55 - 【train】 epoch:0 2943/5348 loss:2.8037
    3251. 

  3251. 2021-08-18 01:33:02,784 - INFO - main.py - train - 55 - 【train】 epoch:0 2944/5348 loss:3.6753
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  3252. 2021-08-18 01:33:04,878 - INFO - main.py - train - 55 - 【train】 epoch:0 2945/5348 loss:3.3553
    3253. 

  3253. 2021-08-18 01:33:06,964 - INFO - main.py - train - 55 - 【train】 epoch:0 2946/5348 loss:3.1918
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  3254. 2021-08-18 01:33:09,055 - INFO - main.py - train - 55 - 【train】 epoch:0 2947/5348 loss:3.4067
    3255. 

  3255. 2021-08-18 01:33:11,128 - INFO - main.py - train - 55 - 【train】 epoch:0 2948/5348 loss:2.1044
    3256. 

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  3258. 2021-08-18 01:33:17,371 - INFO - main.py - train - 55 - 【train】 epoch:0 2951/5348 loss:3.8290
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  3259. 2021-08-18 01:33:19,441 - INFO - main.py - train - 55 - 【train】 epoch:0 2952/5348 loss:2.8592
    3260. 

  3260. 2021-08-18 01:33:21,523 - INFO - main.py - train - 55 - 【train】 epoch:0 2953/5348 loss:3.1093
    3261. 

  3261. 2021-08-18 01:33:23,612 - INFO - main.py - train - 55 - 【train】 epoch:0 2954/5348 loss:3.4617
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  3262. 2021-08-18 01:33:25,691 - INFO - main.py - train - 55 - 【train】 epoch:0 2955/5348 loss:2.2341
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  3263. 2021-08-18 01:33:27,779 - INFO - main.py - train - 55 - 【train】 epoch:0 2956/5348 loss:3.8929
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  3264. 2021-08-18 01:33:29,869 - INFO - main.py - train - 55 - 【train】 epoch:0 2957/5348 loss:1.8267
    3265. 

  3265. 2021-08-18 01:33:31,975 - INFO - main.py - train - 55 - 【train】 epoch:0 2958/5348 loss:2.7550
    3266. 

  3266. 2021-08-18 01:33:34,054 - INFO - main.py - train - 55 - 【train】 epoch:0 2959/5348 loss:2.0258
    3267. 

  3267. 2021-08-18 01:33:36,170 - INFO - main.py - train - 55 - 【train】 epoch:0 2960/5348 loss:4.2575
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  3269. 2021-08-18 01:33:40,390 - INFO - main.py - train - 55 - 【train】 epoch:0 2962/5348 loss:3.0864
    3270. 

  3270. 2021-08-18 01:33:42,492 - INFO - main.py - train - 55 - 【train】 epoch:0 2963/5348 loss:3.7307
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  3271. 2021-08-18 01:33:44,600 - INFO - main.py - train - 55 - 【train】 epoch:0 2964/5348 loss:2.2177
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  3272. 2021-08-18 01:33:46,718 - INFO - main.py - train - 55 - 【train】 epoch:0 2965/5348 loss:2.6471
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  3273. 2021-08-18 01:33:48,847 - INFO - main.py - train - 55 - 【train】 epoch:0 2966/5348 loss:3.6331
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  3274. 2021-08-18 01:33:50,952 - INFO - main.py - train - 55 - 【train】 epoch:0 2967/5348 loss:1.9111
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  3275. 2021-08-18 01:33:53,070 - INFO - main.py - train - 55 - 【train】 epoch:0 2968/5348 loss:3.0379
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  3276. 2021-08-18 01:33:55,196 - INFO - main.py - train - 55 - 【train】 epoch:0 2969/5348 loss:3.5461
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  3277. 2021-08-18 01:33:57,307 - INFO - main.py - train - 55 - 【train】 epoch:0 2970/5348 loss:3.2305
    3278. 

  3278. 2021-08-18 01:33:59,408 - INFO - main.py - train - 55 - 【train】 epoch:0 2971/5348 loss:4.4445
    3279. 

  3279. 2021-08-18 01:34:01,505 - INFO - main.py - train - 55 - 【train】 epoch:0 2972/5348 loss:2.3046
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  3280. 2021-08-18 01:34:03,587 - INFO - main.py - train - 55 - 【train】 epoch:0 2973/5348 loss:3.0818
    3281. 

  3281. 2021-08-18 01:34:05,669 - INFO - main.py - train - 55 - 【train】 epoch:0 2974/5348 loss:3.0364
    3282. 

  3282. 2021-08-18 01:34:07,742 - INFO - main.py - train - 55 - 【train】 epoch:0 2975/5348 loss:2.6471
    3283. 

  3283. 2021-08-18 01:34:09,826 - INFO - main.py - train - 55 - 【train】 epoch:0 2976/5348 loss:2.7729
    3284. 

  3284. 2021-08-18 01:34:11,916 - INFO - main.py - train - 55 - 【train】 epoch:0 2977/5348 loss:2.7394
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  3285. 2021-08-18 01:34:14,004 - INFO - main.py - train - 55 - 【train】 epoch:0 2978/5348 loss:2.5244
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    3287. 

  3287. 2021-08-18 01:34:18,174 - INFO - main.py - train - 55 - 【train】 epoch:0 2980/5348 loss:2.7470
    3288. 

  3288. 2021-08-18 01:34:20,268 - INFO - main.py - train - 55 - 【train】 epoch:0 2981/5348 loss:3.3505
    3289. 

  3289. 2021-08-18 01:34:22,362 - INFO - main.py - train - 55 - 【train】 epoch:0 2982/5348 loss:4.5934
    3290. 

  3290. 2021-08-18 01:34:24,455 - INFO - main.py - train - 55 - 【train】 epoch:0 2983/5348 loss:4.0184
    3291. 

  3291. 2021-08-18 01:34:26,549 - INFO - main.py - train - 55 - 【train】 epoch:0 2984/5348 loss:2.9238
    3292. 

  3292. 2021-08-18 01:34:28,645 - INFO - main.py - train - 55 - 【train】 epoch:0 2985/5348 loss:2.7745
    3293. 

  3293. 2021-08-18 01:34:30,725 - INFO - main.py - train - 55 - 【train】 epoch:0 2986/5348 loss:3.7824
    3294. 

  3294. 2021-08-18 01:34:32,807 - INFO - main.py - train - 55 - 【train】 epoch:0 2987/5348 loss:2.5817
    3295. 

  3295. 2021-08-18 01:34:34,898 - INFO - main.py - train - 55 - 【train】 epoch:0 2988/5348 loss:3.4833
    3296. 

  3296. 2021-08-18 01:34:36,980 - INFO - main.py - train - 55 - 【train】 epoch:0 2989/5348 loss:2.7443
    3297. 

  3297. 2021-08-18 01:34:39,091 - INFO - main.py - train - 55 - 【train】 epoch:0 2990/5348 loss:3.6201
    3298. 

  3298. 2021-08-18 01:34:41,192 - INFO - main.py - train - 55 - 【train】 epoch:0 2991/5348 loss:2.3166
    3299. 

  3299. 2021-08-18 01:34:43,283 - INFO - main.py - train - 55 - 【train】 epoch:0 2992/5348 loss:2.2998
    3300. 

  3300. 2021-08-18 01:34:45,383 - INFO - main.py - train - 55 - 【train】 epoch:0 2993/5348 loss:3.0326
    3301. 

  3301. 2021-08-18 01:34:47,510 - INFO - main.py - train - 55 - 【train】 epoch:0 2994/5348 loss:3.1675
    3302. 

  3302. 2021-08-18 01:34:49,611 - INFO - main.py - train - 55 - 【train】 epoch:0 2995/5348 loss:3.0778
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  3303. 2021-08-18 01:34:51,724 - INFO - main.py - train - 55 - 【train】 epoch:0 2996/5348 loss:3.7477
    3304. 

  3304. 2021-08-18 01:34:53,824 - INFO - main.py - train - 55 - 【train】 epoch:0 2997/5348 loss:2.4055
    3305. 

  3305. 2021-08-18 01:34:55,939 - INFO - main.py - train - 55 - 【train】 epoch:0 2998/5348 loss:2.1585
    3306. 

  3306. 2021-08-18 01:34:58,060 - INFO - main.py - train - 55 - 【train】 epoch:0 2999/5348 loss:3.3244
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  3307. 2021-08-18 01:35:00,154 - INFO - main.py - train - 55 - 【train】 epoch:0 3000/5348 loss:5.3334
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  3308. 2021-08-18 01:35:02,254 - INFO - main.py - train - 55 - 【train】 epoch:0 3001/5348 loss:3.3955
    3309. 

  3309. 2021-08-18 01:35:04,357 - INFO - main.py - train - 55 - 【train】 epoch:0 3002/5348 loss:3.0958
    3310. 

  3310. 2021-08-18 01:35:06,440 - INFO - main.py - train - 55 - 【train】 epoch:0 3003/5348 loss:2.6819
    3311. 

  3311. 2021-08-18 01:35:08,533 - INFO - main.py - train - 55 - 【train】 epoch:0 3004/5348 loss:3.0319
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  3313. 2021-08-18 01:35:12,712 - INFO - main.py - train - 55 - 【train】 epoch:0 3006/5348 loss:3.3564
    3314. 

  3314. 2021-08-18 01:35:14,808 - INFO - main.py - train - 55 - 【train】 epoch:0 3007/5348 loss:3.0012
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  3315. 2021-08-18 01:35:16,890 - INFO - main.py - train - 55 - 【train】 epoch:0 3008/5348 loss:3.4488
    3316. 

  3316. 2021-08-18 01:35:18,976 - INFO - main.py - train - 55 - 【train】 epoch:0 3009/5348 loss:2.9898
    3317. 

  3317. 2021-08-18 01:35:21,058 - INFO - main.py - train - 55 - 【train】 epoch:0 3010/5348 loss:2.7889
    3318. 

  3318. 2021-08-18 01:35:23,144 - INFO - main.py - train - 55 - 【train】 epoch:0 3011/5348 loss:2.5417
    3319. 

  3319. 2021-08-18 01:35:25,232 - INFO - main.py - train - 55 - 【train】 epoch:0 3012/5348 loss:2.8966
    3320. 

  3320. 2021-08-18 01:35:27,318 - INFO - main.py - train - 55 - 【train】 epoch:0 3013/5348 loss:2.9307
    3321. 

  3321. 2021-08-18 01:35:29,411 - INFO - main.py - train - 55 - 【train】 epoch:0 3014/5348 loss:2.2384
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  3324. 2021-08-18 01:35:35,647 - INFO - main.py - train - 55 - 【train】 epoch:0 3017/5348 loss:2.4544
    3325. 

  3325. 2021-08-18 01:35:37,736 - INFO - main.py - train - 55 - 【train】 epoch:0 3018/5348 loss:3.3410
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    3327. 

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    3335. 

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    3338. 

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    3340. 

  3340. 2021-08-18 01:36:09,344 - INFO - main.py - train - 55 - 【train】 epoch:0 3033/5348 loss:3.7115
    3341. 

  3341. 2021-08-18 01:36:11,428 - INFO - main.py - train - 55 - 【train】 epoch:0 3034/5348 loss:4.2790
    3342. 

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    3343. 

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    3344. 

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    3345. 

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    3347. 

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    3348. 

  3348. 2021-08-18 01:36:26,140 - INFO - main.py - train - 55 - 【train】 epoch:0 3041/5348 loss:2.6323
    3349. 

  3349. 2021-08-18 01:36:28,258 - INFO - main.py - train - 55 - 【train】 epoch:0 3042/5348 loss:4.3453
    3350. 

  3350. 2021-08-18 01:36:30,364 - INFO - main.py - train - 55 - 【train】 epoch:0 3043/5348 loss:3.3098
    3351. 

  3351. 2021-08-18 01:36:32,468 - INFO - main.py - train - 55 - 【train】 epoch:0 3044/5348 loss:4.5176
    3352. 

  3352. 2021-08-18 01:36:34,556 - INFO - main.py - train - 55 - 【train】 epoch:0 3045/5348 loss:3.5351
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  3353. 2021-08-18 01:36:36,659 - INFO - main.py - train - 55 - 【train】 epoch:0 3046/5348 loss:2.8904
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  3355. 2021-08-18 01:36:40,851 - INFO - main.py - train - 55 - 【train】 epoch:0 3048/5348 loss:3.0392
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  3357. 2021-08-18 01:36:45,054 - INFO - main.py - train - 55 - 【train】 epoch:0 3050/5348 loss:4.2349
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  3358. 2021-08-18 01:36:47,136 - INFO - main.py - train - 55 - 【train】 epoch:0 3051/5348 loss:2.2523
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  3359. 2021-08-18 01:36:49,245 - INFO - main.py - train - 55 - 【train】 epoch:0 3052/5348 loss:3.1060
    3360. 

  3360. 2021-08-18 01:36:51,349 - INFO - main.py - train - 55 - 【train】 epoch:0 3053/5348 loss:3.2512
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  3361. 2021-08-18 01:36:53,438 - INFO - main.py - train - 55 - 【train】 epoch:0 3054/5348 loss:2.4158
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  3364. 2021-08-18 01:36:59,710 - INFO - main.py - train - 55 - 【train】 epoch:0 3057/5348 loss:4.3182
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  3365. 2021-08-18 01:37:01,827 - INFO - main.py - train - 55 - 【train】 epoch:0 3058/5348 loss:2.1246
    3366. 

  3366. 2021-08-18 01:37:03,916 - INFO - main.py - train - 55 - 【train】 epoch:0 3059/5348 loss:4.0344
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  3367. 2021-08-18 01:37:06,003 - INFO - main.py - train - 55 - 【train】 epoch:0 3060/5348 loss:1.4477
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  3370. 2021-08-18 01:37:12,291 - INFO - main.py - train - 55 - 【train】 epoch:0 3063/5348 loss:3.0488
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  3371. 2021-08-18 01:37:14,372 - INFO - main.py - train - 55 - 【train】 epoch:0 3064/5348 loss:2.7694
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  3372. 2021-08-18 01:37:16,455 - INFO - main.py - train - 55 - 【train】 epoch:0 3065/5348 loss:1.9339
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  3373. 2021-08-18 01:37:18,531 - INFO - main.py - train - 55 - 【train】 epoch:0 3066/5348 loss:2.4849
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  3374. 2021-08-18 01:37:20,611 - INFO - main.py - train - 55 - 【train】 epoch:0 3067/5348 loss:3.4415
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  3375. 2021-08-18 01:37:22,701 - INFO - main.py - train - 55 - 【train】 epoch:0 3068/5348 loss:2.0510
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  3376. 2021-08-18 01:37:24,807 - INFO - main.py - train - 55 - 【train】 epoch:0 3069/5348 loss:3.0993
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  3377. 2021-08-18 01:37:26,904 - INFO - main.py - train - 55 - 【train】 epoch:0 3070/5348 loss:2.9643
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  3378. 2021-08-18 01:37:29,028 - INFO - main.py - train - 55 - 【train】 epoch:0 3071/5348 loss:4.2594
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  3379. 2021-08-18 01:37:31,131 - INFO - main.py - train - 55 - 【train】 epoch:0 3072/5348 loss:4.2429
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  3380. 2021-08-18 01:37:33,234 - INFO - main.py - train - 55 - 【train】 epoch:0 3073/5348 loss:2.4437
    3381. 

  3381. 2021-08-18 01:37:35,333 - INFO - main.py - train - 55 - 【train】 epoch:0 3074/5348 loss:3.3103
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  3382. 2021-08-18 01:37:37,441 - INFO - main.py - train - 55 - 【train】 epoch:0 3075/5348 loss:3.0133
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  3383. 2021-08-18 01:37:39,556 - INFO - main.py - train - 55 - 【train】 epoch:0 3076/5348 loss:3.4809
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  3384. 2021-08-18 01:37:41,664 - INFO - main.py - train - 55 - 【train】 epoch:0 3077/5348 loss:3.1593
    3385. 

  3385. 2021-08-18 01:37:43,778 - INFO - main.py - train - 55 - 【train】 epoch:0 3078/5348 loss:3.1662
    3386. 

  3386. 2021-08-18 01:37:45,883 - INFO - main.py - train - 55 - 【train】 epoch:0 3079/5348 loss:1.8380
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  3387. 2021-08-18 01:37:47,983 - INFO - main.py - train - 55 - 【train】 epoch:0 3080/5348 loss:3.4095
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  3388. 2021-08-18 01:37:50,063 - INFO - main.py - train - 55 - 【train】 epoch:0 3081/5348 loss:2.3828
    3389. 

  3389. 2021-08-18 01:37:52,156 - INFO - main.py - train - 55 - 【train】 epoch:0 3082/5348 loss:2.8694
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  3390. 2021-08-18 01:37:54,262 - INFO - main.py - train - 55 - 【train】 epoch:0 3083/5348 loss:2.7258
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  3391. 2021-08-18 01:37:56,365 - INFO - main.py - train - 55 - 【train】 epoch:0 3084/5348 loss:3.9152
    3392. 

  3392. 2021-08-18 01:37:58,463 - INFO - main.py - train - 55 - 【train】 epoch:0 3085/5348 loss:3.1253
    3393. 

  3393. 2021-08-18 01:38:00,552 - INFO - main.py - train - 55 - 【train】 epoch:0 3086/5348 loss:4.2888
    3394. 

  3394. 2021-08-18 01:38:02,640 - INFO - main.py - train - 55 - 【train】 epoch:0 3087/5348 loss:3.4026
    3395. 

  3395. 2021-08-18 01:38:04,736 - INFO - main.py - train - 55 - 【train】 epoch:0 3088/5348 loss:2.5512
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  3396. 2021-08-18 01:38:06,828 - INFO - main.py - train - 55 - 【train】 epoch:0 3089/5348 loss:3.3232
    3397. 

  3397. 2021-08-18 01:38:08,915 - INFO - main.py - train - 55 - 【train】 epoch:0 3090/5348 loss:2.8203
    3398. 

  3398. 2021-08-18 01:38:11,005 - INFO - main.py - train - 55 - 【train】 epoch:0 3091/5348 loss:2.1887
    3399. 

  3399. 2021-08-18 01:38:13,088 - INFO - main.py - train - 55 - 【train】 epoch:0 3092/5348 loss:2.2312
    3400. 

  3400. 2021-08-18 01:38:15,178 - INFO - main.py - train - 55 - 【train】 epoch:0 3093/5348 loss:3.6328
    3401. 

  3401. 2021-08-18 01:38:17,270 - INFO - main.py - train - 55 - 【train】 epoch:0 3094/5348 loss:3.0745
    3402. 

  3402. 2021-08-18 01:38:19,354 - INFO - main.py - train - 55 - 【train】 epoch:0 3095/5348 loss:3.3413
    3403. 

  3403. 2021-08-18 01:38:21,435 - INFO - main.py - train - 55 - 【train】 epoch:0 3096/5348 loss:3.1781
    3404. 

  3404. 2021-08-18 01:38:23,518 - INFO - main.py - train - 55 - 【train】 epoch:0 3097/5348 loss:3.2459
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  3405. 2021-08-18 01:38:25,629 - INFO - main.py - train - 55 - 【train】 epoch:0 3098/5348 loss:3.1605
    3406. 

  3406. 2021-08-18 01:38:27,726 - INFO - main.py - train - 55 - 【train】 epoch:0 3099/5348 loss:3.4528
    3407. 

  3407. 2021-08-18 01:38:29,823 - INFO - main.py - train - 55 - 【train】 epoch:0 3100/5348 loss:1.9661
    3408. 

  3408. 2021-08-18 01:38:31,938 - INFO - main.py - train - 55 - 【train】 epoch:0 3101/5348 loss:3.1697
    3409. 

  3409. 2021-08-18 01:38:34,044 - INFO - main.py - train - 55 - 【train】 epoch:0 3102/5348 loss:2.6502
    3410. 

  3410. 2021-08-18 01:38:36,132 - INFO - main.py - train - 55 - 【train】 epoch:0 3103/5348 loss:2.4091
    3411. 

  3411. 2021-08-18 01:38:38,241 - INFO - main.py - train - 55 - 【train】 epoch:0 3104/5348 loss:3.9951
    3412. 

  3412. 2021-08-18 01:38:40,359 - INFO - main.py - train - 55 - 【train】 epoch:0 3105/5348 loss:3.6502
    3413. 

  3413. 2021-08-18 01:38:42,461 - INFO - main.py - train - 55 - 【train】 epoch:0 3106/5348 loss:1.8755
    3414. 

  3414. 2021-08-18 01:38:44,573 - INFO - main.py - train - 55 - 【train】 epoch:0 3107/5348 loss:3.9225
    3415. 

  3415. 2021-08-18 01:38:46,673 - INFO - main.py - train - 55 - 【train】 epoch:0 3108/5348 loss:3.1667
    3416. 

  3416. 2021-08-18 01:38:48,784 - INFO - main.py - train - 55 - 【train】 epoch:0 3109/5348 loss:3.8190
    3417. 

  3417. 2021-08-18 01:38:50,895 - INFO - main.py - train - 55 - 【train】 epoch:0 3110/5348 loss:2.3687
    3418. 

  3418. 2021-08-18 01:38:53,008 - INFO - main.py - train - 55 - 【train】 epoch:0 3111/5348 loss:2.9357
    3419. 

  3419. 2021-08-18 01:38:55,107 - INFO - main.py - train - 55 - 【train】 epoch:0 3112/5348 loss:3.7716
    3420. 

  3420. 2021-08-18 01:38:57,207 - INFO - main.py - train - 55 - 【train】 epoch:0 3113/5348 loss:2.6599
    3421. 

  3421. 2021-08-18 01:38:59,299 - INFO - main.py - train - 55 - 【train】 epoch:0 3114/5348 loss:2.2910
    3422. 

  3422. 2021-08-18 01:39:01,389 - INFO - main.py - train - 55 - 【train】 epoch:0 3115/5348 loss:3.3799
    3423. 

  3423. 2021-08-18 01:39:03,489 - INFO - main.py - train - 55 - 【train】 epoch:0 3116/5348 loss:2.6706
    3424. 

  3424. 2021-08-18 01:39:05,588 - INFO - main.py - train - 55 - 【train】 epoch:0 3117/5348 loss:3.2034
    3425. 

  3425. 2021-08-18 01:39:07,681 - INFO - main.py - train - 55 - 【train】 epoch:0 3118/5348 loss:1.9686
    3426. 

  3426. 2021-08-18 01:39:09,759 - INFO - main.py - train - 55 - 【train】 epoch:0 3119/5348 loss:3.9314
    3427. 

  3427. 2021-08-18 01:39:11,848 - INFO - main.py - train - 55 - 【train】 epoch:0 3120/5348 loss:3.8663
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  3428. 2021-08-18 01:39:13,936 - INFO - main.py - train - 55 - 【train】 epoch:0 3121/5348 loss:3.2558
    3429. 

  3429. 2021-08-18 01:39:16,042 - INFO - main.py - train - 55 - 【train】 epoch:0 3122/5348 loss:2.4187
    3430. 

  3430. 2021-08-18 01:39:18,134 - INFO - main.py - train - 55 - 【train】 epoch:0 3123/5348 loss:2.2171
    3431. 

  3431. 2021-08-18 01:39:20,232 - INFO - main.py - train - 55 - 【train】 epoch:0 3124/5348 loss:2.1587
    3432. 

  3432. 2021-08-18 01:39:22,330 - INFO - main.py - train - 55 - 【train】 epoch:0 3125/5348 loss:2.2429
    3433. 

  3433. 2021-08-18 01:39:24,419 - INFO - main.py - train - 55 - 【train】 epoch:0 3126/5348 loss:2.2288
    3434. 

  3434. 2021-08-18 01:39:26,523 - INFO - main.py - train - 55 - 【train】 epoch:0 3127/5348 loss:2.9153
    3435. 

  3435. 2021-08-18 01:39:28,603 - INFO - main.py - train - 55 - 【train】 epoch:0 3128/5348 loss:3.0208
    3436. 

  3436. 2021-08-18 01:39:30,685 - INFO - main.py - train - 55 - 【train】 epoch:0 3129/5348 loss:3.3869
    3437. 

  3437. 2021-08-18 01:39:32,781 - INFO - main.py - train - 55 - 【train】 epoch:0 3130/5348 loss:6.2232
    3438. 

  3438. 2021-08-18 01:39:34,873 - INFO - main.py - train - 55 - 【train】 epoch:0 3131/5348 loss:2.9837
    3439. 

  3439. 2021-08-18 01:39:36,986 - INFO - main.py - train - 55 - 【train】 epoch:0 3132/5348 loss:4.2416
    3440. 

  3440. 2021-08-18 01:39:39,086 - INFO - main.py - train - 55 - 【train】 epoch:0 3133/5348 loss:2.4540
    3441. 

  3441. 2021-08-18 01:39:41,192 - INFO - main.py - train - 55 - 【train】 epoch:0 3134/5348 loss:2.4307
    3442. 

  3442. 2021-08-18 01:39:43,296 - INFO - main.py - train - 55 - 【train】 epoch:0 3135/5348 loss:2.3965
    3443. 

  3443. 2021-08-18 01:39:45,399 - INFO - main.py - train - 55 - 【train】 epoch:0 3136/5348 loss:2.9226
    3444. 

  3444. 2021-08-18 01:39:47,516 - INFO - main.py - train - 55 - 【train】 epoch:0 3137/5348 loss:5.6319
    3445. 

  3445. 2021-08-18 01:39:49,638 - INFO - main.py - train - 55 - 【train】 epoch:0 3138/5348 loss:3.2180
    3446. 

  3446. 2021-08-18 01:39:51,757 - INFO - main.py - train - 55 - 【train】 epoch:0 3139/5348 loss:4.0131
    3447. 

  3447. 2021-08-18 01:39:53,873 - INFO - main.py - train - 55 - 【train】 epoch:0 3140/5348 loss:2.7440
    3448. 

  3448. 2021-08-18 01:39:55,978 - INFO - main.py - train - 55 - 【train】 epoch:0 3141/5348 loss:2.5516
    3449. 

  3449. 2021-08-18 01:39:58,085 - INFO - main.py - train - 55 - 【train】 epoch:0 3142/5348 loss:3.9798
    3450. 

  3450. 2021-08-18 01:40:00,155 - INFO - main.py - train - 55 - 【train】 epoch:0 3143/5348 loss:2.4879
    3451. 

  3451. 2021-08-18 01:40:02,250 - INFO - main.py - train - 55 - 【train】 epoch:0 3144/5348 loss:3.7592
    3452. 

  3452. 2021-08-18 01:40:04,342 - INFO - main.py - train - 55 - 【train】 epoch:0 3145/5348 loss:2.5205
    3453. 

  3453. 2021-08-18 01:40:06,419 - INFO - main.py - train - 55 - 【train】 epoch:0 3146/5348 loss:3.7231
    3454. 

  3454. 2021-08-18 01:40:08,507 - INFO - main.py - train - 55 - 【train】 epoch:0 3147/5348 loss:3.0978
    3455. 

  3455. 2021-08-18 01:40:10,596 - INFO - main.py - train - 55 - 【train】 epoch:0 3148/5348 loss:2.8943
    3456. 

  3456. 2021-08-18 01:40:12,684 - INFO - main.py - train - 55 - 【train】 epoch:0 3149/5348 loss:3.9902
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  3457. 2021-08-18 01:40:14,766 - INFO - main.py - train - 55 - 【train】 epoch:0 3150/5348 loss:3.0867
    3458. 

  3458. 2021-08-18 01:40:16,841 - INFO - main.py - train - 55 - 【train】 epoch:0 3151/5348 loss:2.1822
    3459. 

  3459. 2021-08-18 01:40:18,919 - INFO - main.py - train - 55 - 【train】 epoch:0 3152/5348 loss:1.7974
    3460. 

  3460. 2021-08-18 01:40:21,003 - INFO - main.py - train - 55 - 【train】 epoch:0 3153/5348 loss:3.2565
    3461. 

  3461. 2021-08-18 01:40:23,086 - INFO - main.py - train - 55 - 【train】 epoch:0 3154/5348 loss:3.3847
    3462. 

  3462. 2021-08-18 01:40:25,186 - INFO - main.py - train - 55 - 【train】 epoch:0 3155/5348 loss:2.1604
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  3463. 2021-08-18 01:40:27,285 - INFO - main.py - train - 55 - 【train】 epoch:0 3156/5348 loss:2.9861
    3464. 

  3464. 2021-08-18 01:40:29,376 - INFO - main.py - train - 55 - 【train】 epoch:0 3157/5348 loss:2.2530
    3465. 

  3465. 2021-08-18 01:40:31,457 - INFO - main.py - train - 55 - 【train】 epoch:0 3158/5348 loss:4.4567
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    3468. 

  3468. 2021-08-18 01:40:37,729 - INFO - main.py - train - 55 - 【train】 epoch:0 3161/5348 loss:3.0001
    3469. 

  3469. 2021-08-18 01:40:39,818 - INFO - main.py - train - 55 - 【train】 epoch:0 3162/5348 loss:2.9651
    3470. 

  3470. 2021-08-18 01:40:41,916 - INFO - main.py - train - 55 - 【train】 epoch:0 3163/5348 loss:3.2124
    3471. 

  3471. 2021-08-18 01:40:44,025 - INFO - main.py - train - 55 - 【train】 epoch:0 3164/5348 loss:3.2347
    3472. 

  3472. 2021-08-18 01:40:46,132 - INFO - main.py - train - 55 - 【train】 epoch:0 3165/5348 loss:4.5109
    3473. 

  3473. 2021-08-18 01:40:48,230 - INFO - main.py - train - 55 - 【train】 epoch:0 3166/5348 loss:3.2932
    3474. 

  3474. 2021-08-18 01:40:50,343 - INFO - main.py - train - 55 - 【train】 epoch:0 3167/5348 loss:3.6868
    3475. 

  3475. 2021-08-18 01:40:52,443 - INFO - main.py - train - 55 - 【train】 epoch:0 3168/5348 loss:3.6390
    3476. 

  3476. 2021-08-18 01:40:54,554 - INFO - main.py - train - 55 - 【train】 epoch:0 3169/5348 loss:2.3831
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  3477. 2021-08-18 01:40:56,666 - INFO - main.py - train - 55 - 【train】 epoch:0 3170/5348 loss:4.3479
    3478. 

  3478. 2021-08-18 01:40:58,763 - INFO - main.py - train - 55 - 【train】 epoch:0 3171/5348 loss:1.8272
    3479. 

  3479. 2021-08-18 01:41:00,854 - INFO - main.py - train - 55 - 【train】 epoch:0 3172/5348 loss:2.7974
    3480. 

  3480. 2021-08-18 01:41:02,971 - INFO - main.py - train - 55 - 【train】 epoch:0 3173/5348 loss:2.3061
    3481. 

  3481. 2021-08-18 01:41:05,089 - INFO - main.py - train - 55 - 【train】 epoch:0 3174/5348 loss:4.7198
    3482. 

  3482. 2021-08-18 01:41:07,170 - INFO - main.py - train - 55 - 【train】 epoch:0 3175/5348 loss:2.8729
    3483. 

  3483. 2021-08-18 01:41:09,260 - INFO - main.py - train - 55 - 【train】 epoch:0 3176/5348 loss:3.1152
    3484. 

  3484. 2021-08-18 01:41:11,337 - INFO - main.py - train - 55 - 【train】 epoch:0 3177/5348 loss:2.3562
    3485. 

  3485. 2021-08-18 01:41:13,414 - INFO - main.py - train - 55 - 【train】 epoch:0 3178/5348 loss:2.9211
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    3487. 

  3487. 2021-08-18 01:41:17,611 - INFO - main.py - train - 55 - 【train】 epoch:0 3180/5348 loss:3.6901
    3488. 

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    3489. 

  3489. 2021-08-18 01:41:21,856 - INFO - main.py - train - 55 - 【train】 epoch:0 3182/5348 loss:3.5983
    3490. 

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    3491. 

  3491. 2021-08-18 01:41:26,053 - INFO - main.py - train - 55 - 【train】 epoch:0 3184/5348 loss:5.6219
    3492. 

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    3493. 

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    3494. 

  3494. 2021-08-18 01:41:32,363 - INFO - main.py - train - 55 - 【train】 epoch:0 3187/5348 loss:1.9595
    3495. 

  3495. 2021-08-18 01:41:34,467 - INFO - main.py - train - 55 - 【train】 epoch:0 3188/5348 loss:2.6251
    3496. 

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    3497. 

  3497. 2021-08-18 01:41:38,672 - INFO - main.py - train - 55 - 【train】 epoch:0 3190/5348 loss:2.8298
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  3498. 2021-08-18 01:41:40,762 - INFO - main.py - train - 55 - 【train】 epoch:0 3191/5348 loss:4.1427
    3499. 

  3499. 2021-08-18 01:41:42,831 - INFO - main.py - train - 55 - 【train】 epoch:0 3192/5348 loss:2.8628
    3500. 

  3500. 2021-08-18 01:41:44,922 - INFO - main.py - train - 55 - 【train】 epoch:0 3193/5348 loss:2.8207
    3501. 

  3501. 2021-08-18 01:41:46,996 - INFO - main.py - train - 55 - 【train】 epoch:0 3194/5348 loss:2.3842
    3502. 

  3502. 2021-08-18 01:41:49,067 - INFO - main.py - train - 55 - 【train】 epoch:0 3195/5348 loss:3.2564
    3503. 

  3503. 2021-08-18 01:41:51,152 - INFO - main.py - train - 55 - 【train】 epoch:0 3196/5348 loss:2.7980
    3504. 

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    3505. 

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    3511. 

  3511. 2021-08-18 01:42:07,807 - INFO - main.py - train - 55 - 【train】 epoch:0 3204/5348 loss:3.9448
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  3524. 2021-08-18 01:42:34,976 - INFO - main.py - train - 55 - 【train】 epoch:0 3217/5348 loss:3.4188
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  3525. 2021-08-18 01:42:37,072 - INFO - main.py - train - 55 - 【train】 epoch:0 3218/5348 loss:2.2979
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  3528. 2021-08-18 01:42:43,365 - INFO - main.py - train - 55 - 【train】 epoch:0 3221/5348 loss:4.5085
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  3529. 2021-08-18 01:42:45,436 - INFO - main.py - train - 55 - 【train】 epoch:0 3222/5348 loss:2.6799
    3530. 

  3530. 2021-08-18 01:42:47,511 - INFO - main.py - train - 55 - 【train】 epoch:0 3223/5348 loss:2.4529
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  3531. 2021-08-18 01:42:49,591 - INFO - main.py - train - 55 - 【train】 epoch:0 3224/5348 loss:2.2881
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  3532. 2021-08-18 01:42:51,666 - INFO - main.py - train - 55 - 【train】 epoch:0 3225/5348 loss:3.5878
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  3533. 2021-08-18 01:42:53,756 - INFO - main.py - train - 55 - 【train】 epoch:0 3226/5348 loss:3.4132
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    3535. 

  3535. 2021-08-18 01:42:57,914 - INFO - main.py - train - 55 - 【train】 epoch:0 3228/5348 loss:5.1491
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  3536. 2021-08-18 01:42:59,994 - INFO - main.py - train - 55 - 【train】 epoch:0 3229/5348 loss:3.6429
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  3537. 2021-08-18 01:43:02,074 - INFO - main.py - train - 55 - 【train】 epoch:0 3230/5348 loss:3.3308
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  3539. 2021-08-18 01:43:06,232 - INFO - main.py - train - 55 - 【train】 epoch:0 3232/5348 loss:2.0549
    3540. 

  3540. 2021-08-18 01:43:08,307 - INFO - main.py - train - 55 - 【train】 epoch:0 3233/5348 loss:2.4900
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  3541. 2021-08-18 01:43:10,391 - INFO - main.py - train - 55 - 【train】 epoch:0 3234/5348 loss:3.5303
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  3542. 2021-08-18 01:43:12,460 - INFO - main.py - train - 55 - 【train】 epoch:0 3235/5348 loss:2.1826
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  3543. 2021-08-18 01:43:14,542 - INFO - main.py - train - 55 - 【train】 epoch:0 3236/5348 loss:3.0718
    3544. 

  3544. 2021-08-18 01:43:16,622 - INFO - main.py - train - 55 - 【train】 epoch:0 3237/5348 loss:3.6935
    3545. 

  3545. 2021-08-18 01:43:18,691 - INFO - main.py - train - 55 - 【train】 epoch:0 3238/5348 loss:3.2801
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  3546. 2021-08-18 01:43:20,761 - INFO - main.py - train - 55 - 【train】 epoch:0 3239/5348 loss:2.4675
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  3547. 2021-08-18 01:43:22,833 - INFO - main.py - train - 55 - 【train】 epoch:0 3240/5348 loss:4.2831
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  3548. 2021-08-18 01:43:24,918 - INFO - main.py - train - 55 - 【train】 epoch:0 3241/5348 loss:3.2510
    3549. 

  3549. 2021-08-18 01:43:27,000 - INFO - main.py - train - 55 - 【train】 epoch:0 3242/5348 loss:2.1666
    3550. 

  3550. 2021-08-18 01:43:29,107 - INFO - main.py - train - 55 - 【train】 epoch:0 3243/5348 loss:2.9122
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  3551. 2021-08-18 01:43:31,202 - INFO - main.py - train - 55 - 【train】 epoch:0 3244/5348 loss:2.7169
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  3552. 2021-08-18 01:43:33,298 - INFO - main.py - train - 55 - 【train】 epoch:0 3245/5348 loss:4.3536
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  3553. 2021-08-18 01:43:35,383 - INFO - main.py - train - 55 - 【train】 epoch:0 3246/5348 loss:3.0518
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  3554. 2021-08-18 01:43:37,480 - INFO - main.py - train - 55 - 【train】 epoch:0 3247/5348 loss:3.3353
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  3555. 2021-08-18 01:43:39,583 - INFO - main.py - train - 55 - 【train】 epoch:0 3248/5348 loss:3.2220
    3556. 

  3556. 2021-08-18 01:43:41,681 - INFO - main.py - train - 55 - 【train】 epoch:0 3249/5348 loss:2.2128
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  3557. 2021-08-18 01:43:43,781 - INFO - main.py - train - 55 - 【train】 epoch:0 3250/5348 loss:3.4154
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  3558. 2021-08-18 01:43:45,882 - INFO - main.py - train - 55 - 【train】 epoch:0 3251/5348 loss:1.9086
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  3559. 2021-08-18 01:43:47,992 - INFO - main.py - train - 55 - 【train】 epoch:0 3252/5348 loss:3.0352
    3560. 

  3560. 2021-08-18 01:43:50,093 - INFO - main.py - train - 55 - 【train】 epoch:0 3253/5348 loss:3.6684
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  3561. 2021-08-18 01:43:52,163 - INFO - main.py - train - 55 - 【train】 epoch:0 3254/5348 loss:3.3869
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  3562. 2021-08-18 01:43:54,242 - INFO - main.py - train - 55 - 【train】 epoch:0 3255/5348 loss:1.9168
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  3563. 2021-08-18 01:43:56,324 - INFO - main.py - train - 55 - 【train】 epoch:0 3256/5348 loss:3.0254
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  3564. 2021-08-18 01:43:58,403 - INFO - main.py - train - 55 - 【train】 epoch:0 3257/5348 loss:2.4067
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  3565. 2021-08-18 01:44:00,485 - INFO - main.py - train - 55 - 【train】 epoch:0 3258/5348 loss:3.2815
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  3566. 2021-08-18 01:44:02,559 - INFO - main.py - train - 55 - 【train】 epoch:0 3259/5348 loss:1.4827
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  3567. 2021-08-18 01:44:04,656 - INFO - main.py - train - 55 - 【train】 epoch:0 3260/5348 loss:4.1399
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  3570. 2021-08-18 01:44:10,910 - INFO - main.py - train - 55 - 【train】 epoch:0 3263/5348 loss:3.0949
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  3571. 2021-08-18 01:44:12,999 - INFO - main.py - train - 55 - 【train】 epoch:0 3264/5348 loss:4.3204
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  3574. 2021-08-18 01:44:19,274 - INFO - main.py - train - 55 - 【train】 epoch:0 3267/5348 loss:4.6408
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  3575. 2021-08-18 01:44:21,355 - INFO - main.py - train - 55 - 【train】 epoch:0 3268/5348 loss:1.5715
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  3579. 2021-08-18 01:44:29,689 - INFO - main.py - train - 55 - 【train】 epoch:0 3272/5348 loss:4.2639
    3580. 

  3580. 2021-08-18 01:44:31,755 - INFO - main.py - train - 55 - 【train】 epoch:0 3273/5348 loss:2.4760
    3581. 

  3581. 2021-08-18 01:44:33,866 - INFO - main.py - train - 55 - 【train】 epoch:0 3274/5348 loss:2.0694
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  3582. 2021-08-18 01:44:35,990 - INFO - main.py - train - 55 - 【train】 epoch:0 3275/5348 loss:2.0898
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  3583. 2021-08-18 01:44:38,084 - INFO - main.py - train - 55 - 【train】 epoch:0 3276/5348 loss:3.1049
    3584. 

  3584. 2021-08-18 01:44:40,188 - INFO - main.py - train - 55 - 【train】 epoch:0 3277/5348 loss:1.8607
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  3585. 2021-08-18 01:44:42,295 - INFO - main.py - train - 55 - 【train】 epoch:0 3278/5348 loss:3.2413
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  3587. 2021-08-18 01:44:46,519 - INFO - main.py - train - 55 - 【train】 epoch:0 3280/5348 loss:2.7357
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  3588. 2021-08-18 01:44:48,617 - INFO - main.py - train - 55 - 【train】 epoch:0 3281/5348 loss:2.3829
    3589. 

  3589. 2021-08-18 01:44:50,725 - INFO - main.py - train - 55 - 【train】 epoch:0 3282/5348 loss:3.7063
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  3590. 2021-08-18 01:44:52,842 - INFO - main.py - train - 55 - 【train】 epoch:0 3283/5348 loss:3.8253
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  3591. 2021-08-18 01:44:54,946 - INFO - main.py - train - 55 - 【train】 epoch:0 3284/5348 loss:2.1822
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  3592. 2021-08-18 01:44:57,034 - INFO - main.py - train - 55 - 【train】 epoch:0 3285/5348 loss:3.0349
    3593. 

  3593. 2021-08-18 01:44:59,120 - INFO - main.py - train - 55 - 【train】 epoch:0 3286/5348 loss:3.8290
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  3594. 2021-08-18 01:45:01,190 - INFO - main.py - train - 55 - 【train】 epoch:0 3287/5348 loss:2.3411
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  3595. 2021-08-18 01:45:03,273 - INFO - main.py - train - 55 - 【train】 epoch:0 3288/5348 loss:1.4377
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  3596. 2021-08-18 01:45:05,362 - INFO - main.py - train - 55 - 【train】 epoch:0 3289/5348 loss:2.3661
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  3597. 2021-08-18 01:45:07,437 - INFO - main.py - train - 55 - 【train】 epoch:0 3290/5348 loss:2.3655
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  3598. 2021-08-18 01:45:09,515 - INFO - main.py - train - 55 - 【train】 epoch:0 3291/5348 loss:3.4570
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  3599. 2021-08-18 01:45:11,633 - INFO - main.py - train - 55 - 【train】 epoch:0 3292/5348 loss:2.9909
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  3600. 2021-08-18 01:45:13,721 - INFO - main.py - train - 55 - 【train】 epoch:0 3293/5348 loss:4.4907
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  3601. 2021-08-18 01:45:15,815 - INFO - main.py - train - 55 - 【train】 epoch:0 3294/5348 loss:3.9641
    3602. 

  3602. 2021-08-18 01:45:17,896 - INFO - main.py - train - 55 - 【train】 epoch:0 3295/5348 loss:1.9425
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    3642. 

  3642. 2021-08-18 01:46:41,591 - INFO - main.py - train - 55 - 【train】 epoch:0 3335/5348 loss:4.7197
    3643. 

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    3645. 

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    3650. 

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  3679. 2021-08-18 01:47:59,293 - INFO - main.py - train - 55 - 【train】 epoch:0 3372/5348 loss:3.5809
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  3680. 2021-08-18 01:48:01,387 - INFO - main.py - train - 55 - 【train】 epoch:0 3373/5348 loss:2.1228
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  3694. 2021-08-18 01:48:30,747 - INFO - main.py - train - 55 - 【train】 epoch:0 3387/5348 loss:4.1546
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  3696. 2021-08-18 01:48:34,970 - INFO - main.py - train - 55 - 【train】 epoch:0 3389/5348 loss:2.6484
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  3698. 2021-08-18 01:48:39,190 - INFO - main.py - train - 55 - 【train】 epoch:0 3391/5348 loss:3.9720
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  3699. 2021-08-18 01:48:41,307 - INFO - main.py - train - 55 - 【train】 epoch:0 3392/5348 loss:3.0323
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  3700. 2021-08-18 01:48:43,436 - INFO - main.py - train - 55 - 【train】 epoch:0 3393/5348 loss:2.2235
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  3701. 2021-08-18 01:48:45,542 - INFO - main.py - train - 55 - 【train】 epoch:0 3394/5348 loss:3.5857
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  3704. 2021-08-18 01:48:51,833 - INFO - main.py - train - 55 - 【train】 epoch:0 3397/5348 loss:2.5205
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  3706. 2021-08-18 01:48:56,020 - INFO - main.py - train - 55 - 【train】 epoch:0 3399/5348 loss:3.0173
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  3789. 2021-08-18 01:51:50,037 - INFO - main.py - train - 55 - 【train】 epoch:0 3482/5348 loss:2.7940
    3790. 

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    3791. 

  3791. 2021-08-18 01:51:54,239 - INFO - main.py - train - 55 - 【train】 epoch:0 3484/5348 loss:3.6277
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  3792. 2021-08-18 01:51:56,336 - INFO - main.py - train - 55 - 【train】 epoch:0 3485/5348 loss:1.7614
    3793. 

  3793. 2021-08-18 01:51:58,424 - INFO - main.py - train - 55 - 【train】 epoch:0 3486/5348 loss:1.8013
    3794. 

  3794. 2021-08-18 01:52:00,525 - INFO - main.py - train - 55 - 【train】 epoch:0 3487/5348 loss:3.6070
    3795. 

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    3796. 

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    3797. 

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    3799. 

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    3801. 

  3801. 2021-08-18 01:52:15,172 - INFO - main.py - train - 55 - 【train】 epoch:0 3494/5348 loss:3.1504
    3802. 

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    3805. 

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    3807. 

  3807. 2021-08-18 01:52:27,775 - INFO - main.py - train - 55 - 【train】 epoch:0 3500/5348 loss:2.4972
    3808. 

  3808. 2021-08-18 01:52:29,865 - INFO - main.py - train - 55 - 【train】 epoch:0 3501/5348 loss:1.8400
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  3811. 2021-08-18 01:52:36,174 - INFO - main.py - train - 55 - 【train】 epoch:0 3504/5348 loss:1.4242
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  3815. 2021-08-18 01:52:44,569 - INFO - main.py - train - 55 - 【train】 epoch:0 3508/5348 loss:2.6555
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  3816. 2021-08-18 01:52:46,657 - INFO - main.py - train - 55 - 【train】 epoch:0 3509/5348 loss:2.1097
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  3817. 2021-08-18 01:52:48,761 - INFO - main.py - train - 55 - 【train】 epoch:0 3510/5348 loss:2.9197
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  3818. 2021-08-18 01:52:50,869 - INFO - main.py - train - 55 - 【train】 epoch:0 3511/5348 loss:5.1715
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  3824. 2021-08-18 01:53:03,461 - INFO - main.py - train - 55 - 【train】 epoch:0 3517/5348 loss:2.7599
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  3825. 2021-08-18 01:53:05,544 - INFO - main.py - train - 55 - 【train】 epoch:0 3518/5348 loss:4.0208
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  3826. 2021-08-18 01:53:07,637 - INFO - main.py - train - 55 - 【train】 epoch:0 3519/5348 loss:3.5184
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  3827. 2021-08-18 01:53:09,738 - INFO - main.py - train - 55 - 【train】 epoch:0 3520/5348 loss:2.6534
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  3829. 2021-08-18 01:53:13,925 - INFO - main.py - train - 55 - 【train】 epoch:0 3522/5348 loss:3.9946
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  3831. 2021-08-18 01:53:18,107 - INFO - main.py - train - 55 - 【train】 epoch:0 3524/5348 loss:2.7443
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  3838. 2021-08-18 01:53:32,854 - INFO - main.py - train - 55 - 【train】 epoch:0 3531/5348 loss:2.8187
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  3839. 2021-08-18 01:53:34,966 - INFO - main.py - train - 55 - 【train】 epoch:0 3532/5348 loss:3.2930
    3840. 

  3840. 2021-08-18 01:53:37,067 - INFO - main.py - train - 55 - 【train】 epoch:0 3533/5348 loss:2.7634
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  3841. 2021-08-18 01:53:39,169 - INFO - main.py - train - 55 - 【train】 epoch:0 3534/5348 loss:2.8793
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  3842. 2021-08-18 01:53:41,264 - INFO - main.py - train - 55 - 【train】 epoch:0 3535/5348 loss:2.9750
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  3843. 2021-08-18 01:53:43,367 - INFO - main.py - train - 55 - 【train】 epoch:0 3536/5348 loss:3.5457
    3844. 

  3844. 2021-08-18 01:53:45,457 - INFO - main.py - train - 55 - 【train】 epoch:0 3537/5348 loss:3.5064
    3845. 

  3845. 2021-08-18 01:53:47,520 - INFO - main.py - train - 55 - 【train】 epoch:0 3538/5348 loss:4.3415
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  3846. 2021-08-18 01:53:49,599 - INFO - main.py - train - 55 - 【train】 epoch:0 3539/5348 loss:2.4339
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  3847. 2021-08-18 01:53:51,688 - INFO - main.py - train - 55 - 【train】 epoch:0 3540/5348 loss:2.4071
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  3849. 2021-08-18 01:53:55,887 - INFO - main.py - train - 55 - 【train】 epoch:0 3542/5348 loss:2.3713
    3850. 

  3850. 2021-08-18 01:53:57,962 - INFO - main.py - train - 55 - 【train】 epoch:0 3543/5348 loss:2.1414
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  3851. 2021-08-18 01:54:00,042 - INFO - main.py - train - 55 - 【train】 epoch:0 3544/5348 loss:2.5720
    3852. 

  3852. 2021-08-18 01:54:02,116 - INFO - main.py - train - 55 - 【train】 epoch:0 3545/5348 loss:2.5115
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  3853. 2021-08-18 01:54:04,188 - INFO - main.py - train - 55 - 【train】 epoch:0 3546/5348 loss:3.3222
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  3854. 2021-08-18 01:54:06,267 - INFO - main.py - train - 55 - 【train】 epoch:0 3547/5348 loss:1.8425
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  3855. 2021-08-18 01:54:08,343 - INFO - main.py - train - 55 - 【train】 epoch:0 3548/5348 loss:2.0481
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  3856. 2021-08-18 01:54:10,412 - INFO - main.py - train - 55 - 【train】 epoch:0 3549/5348 loss:3.2993
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  3857. 2021-08-18 01:54:12,485 - INFO - main.py - train - 55 - 【train】 epoch:0 3550/5348 loss:2.8190
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  3858. 2021-08-18 01:54:14,566 - INFO - main.py - train - 55 - 【train】 epoch:0 3551/5348 loss:4.0985
    3859. 

  3859. 2021-08-18 01:54:16,631 - INFO - main.py - train - 55 - 【train】 epoch:0 3552/5348 loss:2.7294
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  3860. 2021-08-18 01:54:18,703 - INFO - main.py - train - 55 - 【train】 epoch:0 3553/5348 loss:2.7430
    3861. 

  3861. 2021-08-18 01:54:20,777 - INFO - main.py - train - 55 - 【train】 epoch:0 3554/5348 loss:2.8037
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  3862. 2021-08-18 01:54:22,829 - INFO - main.py - train - 55 - 【train】 epoch:0 3555/5348 loss:2.8612
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  3863. 2021-08-18 01:54:24,898 - INFO - main.py - train - 55 - 【train】 epoch:0 3556/5348 loss:2.3264
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  3864. 2021-08-18 01:54:26,999 - INFO - main.py - train - 55 - 【train】 epoch:0 3557/5348 loss:2.9781
    3865. 

  3865. 2021-08-18 01:54:29,101 - INFO - main.py - train - 55 - 【train】 epoch:0 3558/5348 loss:3.7665
    3866. 

  3866. 2021-08-18 01:54:31,199 - INFO - main.py - train - 55 - 【train】 epoch:0 3559/5348 loss:2.3690
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  3867. 2021-08-18 01:54:33,304 - INFO - main.py - train - 55 - 【train】 epoch:0 3560/5348 loss:2.5679
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  3869. 2021-08-18 01:54:37,505 - INFO - main.py - train - 55 - 【train】 epoch:0 3562/5348 loss:2.4495
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  3870. 2021-08-18 01:54:39,598 - INFO - main.py - train - 55 - 【train】 epoch:0 3563/5348 loss:3.4807
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  3871. 2021-08-18 01:54:41,700 - INFO - main.py - train - 55 - 【train】 epoch:0 3564/5348 loss:2.7333
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    3879. 

  3879. 2021-08-18 01:54:58,407 - INFO - main.py - train - 55 - 【train】 epoch:0 3572/5348 loss:2.5189
    3880. 

  3880. 2021-08-18 01:55:00,478 - INFO - main.py - train - 55 - 【train】 epoch:0 3573/5348 loss:2.6272
    3881. 

  3881. 2021-08-18 01:55:02,549 - INFO - main.py - train - 55 - 【train】 epoch:0 3574/5348 loss:2.6153
    3882. 

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    3883. 

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    3885. 

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  3886. 2021-08-18 01:55:12,937 - INFO - main.py - train - 55 - 【train】 epoch:0 3579/5348 loss:2.4107
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  3889. 2021-08-18 01:55:19,140 - INFO - main.py - train - 55 - 【train】 epoch:0 3582/5348 loss:2.1361
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  3895. 2021-08-18 01:55:31,604 - INFO - main.py - train - 55 - 【train】 epoch:0 3588/5348 loss:3.8387
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  3896. 2021-08-18 01:55:33,710 - INFO - main.py - train - 55 - 【train】 epoch:0 3589/5348 loss:3.7256
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  3897. 2021-08-18 01:55:35,829 - INFO - main.py - train - 55 - 【train】 epoch:0 3590/5348 loss:2.4243
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    3900. 

  3900. 2021-08-18 01:55:42,175 - INFO - main.py - train - 55 - 【train】 epoch:0 3593/5348 loss:3.4488
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  3913. 2021-08-18 01:56:09,536 - INFO - main.py - train - 55 - 【train】 epoch:0 3606/5348 loss:4.2022
    3914. 

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    3936. 

  3936. 2021-08-18 01:56:57,585 - INFO - main.py - train - 55 - 【train】 epoch:0 3629/5348 loss:2.6103
    3937. 

  3937. 2021-08-18 01:56:59,679 - INFO - main.py - train - 55 - 【train】 epoch:0 3630/5348 loss:2.4780
    3938. 

  3938. 2021-08-18 01:57:01,776 - INFO - main.py - train - 55 - 【train】 epoch:0 3631/5348 loss:3.5321
    3939. 

  3939. 2021-08-18 01:57:03,860 - INFO - main.py - train - 55 - 【train】 epoch:0 3632/5348 loss:3.2223
    3940. 

  3940. 2021-08-18 01:57:05,933 - INFO - main.py - train - 55 - 【train】 epoch:0 3633/5348 loss:2.7667
    3941. 

  3941. 2021-08-18 01:57:08,022 - INFO - main.py - train - 55 - 【train】 epoch:0 3634/5348 loss:3.1478
    3942. 

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    3943. 

  3943. 2021-08-18 01:57:12,207 - INFO - main.py - train - 55 - 【train】 epoch:0 3636/5348 loss:2.2648
    3944. 

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    3945. 

  3945. 2021-08-18 01:57:16,397 - INFO - main.py - train - 55 - 【train】 epoch:0 3638/5348 loss:2.4810
    3946. 

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    3947. 

  3947. 2021-08-18 01:57:20,577 - INFO - main.py - train - 55 - 【train】 epoch:0 3640/5348 loss:4.5591
    3948. 

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    3949. 

  3949. 2021-08-18 01:57:24,746 - INFO - main.py - train - 55 - 【train】 epoch:0 3642/5348 loss:2.0695
    3950. 

  3950. 2021-08-18 01:57:26,839 - INFO - main.py - train - 55 - 【train】 epoch:0 3643/5348 loss:2.8457
    3951. 

  3951. 2021-08-18 01:57:28,918 - INFO - main.py - train - 55 - 【train】 epoch:0 3644/5348 loss:4.2265
    3952. 

  3952. 2021-08-18 01:57:31,009 - INFO - main.py - train - 55 - 【train】 epoch:0 3645/5348 loss:1.8458
    3953. 

  3953. 2021-08-18 01:57:33,096 - INFO - main.py - train - 55 - 【train】 epoch:0 3646/5348 loss:3.0708
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  3954. 2021-08-18 01:57:35,189 - INFO - main.py - train - 55 - 【train】 epoch:0 3647/5348 loss:2.7311
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  3958. 2021-08-18 01:57:43,547 - INFO - main.py - train - 55 - 【train】 epoch:0 3651/5348 loss:3.0198
    3959. 

  3959. 2021-08-18 01:57:45,655 - INFO - main.py - train - 55 - 【train】 epoch:0 3652/5348 loss:3.7358
    3960. 

  3960. 2021-08-18 01:57:47,763 - INFO - main.py - train - 55 - 【train】 epoch:0 3653/5348 loss:2.7309
    3961. 

  3961. 2021-08-18 01:57:49,881 - INFO - main.py - train - 55 - 【train】 epoch:0 3654/5348 loss:3.8185
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  3962. 2021-08-18 01:57:51,993 - INFO - main.py - train - 55 - 【train】 epoch:0 3655/5348 loss:2.4465
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  3963. 2021-08-18 01:57:54,104 - INFO - main.py - train - 55 - 【train】 epoch:0 3656/5348 loss:1.8669
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  3964. 2021-08-18 01:57:56,194 - INFO - main.py - train - 55 - 【train】 epoch:0 3657/5348 loss:2.5944
    3965. 

  3965. 2021-08-18 01:57:58,307 - INFO - main.py - train - 55 - 【train】 epoch:0 3658/5348 loss:2.6722
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  3966. 2021-08-18 01:58:00,426 - INFO - main.py - train - 55 - 【train】 epoch:0 3659/5348 loss:2.5351
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  3968. 2021-08-18 01:58:04,651 - INFO - main.py - train - 55 - 【train】 epoch:0 3661/5348 loss:2.5036
    3969. 

  3969. 2021-08-18 01:58:06,758 - INFO - main.py - train - 55 - 【train】 epoch:0 3662/5348 loss:3.4301
    3970. 

  3970. 2021-08-18 01:58:08,862 - INFO - main.py - train - 55 - 【train】 epoch:0 3663/5348 loss:2.0354
    3971. 

  3971. 2021-08-18 01:58:10,956 - INFO - main.py - train - 55 - 【train】 epoch:0 3664/5348 loss:2.4497
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  3972. 2021-08-18 01:58:13,055 - INFO - main.py - train - 55 - 【train】 epoch:0 3665/5348 loss:2.6699
    3973. 

  3973. 2021-08-18 01:58:15,142 - INFO - main.py - train - 55 - 【train】 epoch:0 3666/5348 loss:3.9903
    3974. 

  3974. 2021-08-18 01:58:17,244 - INFO - main.py - train - 55 - 【train】 epoch:0 3667/5348 loss:4.0634
    3975. 

  3975. 2021-08-18 01:58:19,376 - INFO - main.py - train - 55 - 【train】 epoch:0 3668/5348 loss:3.6935
    3976. 

  3976. 2021-08-18 01:58:21,481 - INFO - main.py - train - 55 - 【train】 epoch:0 3669/5348 loss:2.9789
    3977. 

  3977. 2021-08-18 01:58:23,582 - INFO - main.py - train - 55 - 【train】 epoch:0 3670/5348 loss:1.9137
    3978. 

  3978. 2021-08-18 01:58:25,689 - INFO - main.py - train - 55 - 【train】 epoch:0 3671/5348 loss:4.6059
    3979. 

  3979. 2021-08-18 01:58:27,786 - INFO - main.py - train - 55 - 【train】 epoch:0 3672/5348 loss:2.7668
    3980. 

  3980. 2021-08-18 01:58:29,884 - INFO - main.py - train - 55 - 【train】 epoch:0 3673/5348 loss:2.6656
    3981. 

  3981. 2021-08-18 01:58:31,985 - INFO - main.py - train - 55 - 【train】 epoch:0 3674/5348 loss:2.9991
    3982. 

  3982. 2021-08-18 01:58:34,096 - INFO - main.py - train - 55 - 【train】 epoch:0 3675/5348 loss:2.7155
    3983. 

  3983. 2021-08-18 01:58:36,207 - INFO - main.py - train - 55 - 【train】 epoch:0 3676/5348 loss:2.9737
    3984. 

  3984. 2021-08-18 01:58:38,305 - INFO - main.py - train - 55 - 【train】 epoch:0 3677/5348 loss:2.7076
    3985. 

  3985. 2021-08-18 01:58:40,430 - INFO - main.py - train - 55 - 【train】 epoch:0 3678/5348 loss:2.7064
    3986. 

  3986. 2021-08-18 01:58:42,512 - INFO - main.py - train - 55 - 【train】 epoch:0 3679/5348 loss:1.9271
    3987. 

  3987. 2021-08-18 01:58:44,602 - INFO - main.py - train - 55 - 【train】 epoch:0 3680/5348 loss:3.9206
    3988. 

  3988. 2021-08-18 01:58:46,696 - INFO - main.py - train - 55 - 【train】 epoch:0 3681/5348 loss:4.9232
    3989. 

  3989. 2021-08-18 01:58:48,799 - INFO - main.py - train - 55 - 【train】 epoch:0 3682/5348 loss:2.8331
    3990. 

  3990. 2021-08-18 01:58:50,887 - INFO - main.py - train - 55 - 【train】 epoch:0 3683/5348 loss:2.2910
    3991. 

  3991. 2021-08-18 01:58:52,979 - INFO - main.py - train - 55 - 【train】 epoch:0 3684/5348 loss:3.7511
    3992. 

  3992. 2021-08-18 01:58:55,073 - INFO - main.py - train - 55 - 【train】 epoch:0 3685/5348 loss:4.3802
    3993. 

  3993. 2021-08-18 01:58:57,140 - INFO - main.py - train - 55 - 【train】 epoch:0 3686/5348 loss:3.2897
    3994. 

  3994. 2021-08-18 01:58:59,221 - INFO - main.py - train - 55 - 【train】 epoch:0 3687/5348 loss:3.5874
    3995. 

  3995. 2021-08-18 01:59:01,304 - INFO - main.py - train - 55 - 【train】 epoch:0 3688/5348 loss:3.4964
    3996. 

  3996. 2021-08-18 01:59:03,391 - INFO - main.py - train - 55 - 【train】 epoch:0 3689/5348 loss:2.5455
    3997. 

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  3998. 2021-08-18 01:59:07,563 - INFO - main.py - train - 55 - 【train】 epoch:0 3691/5348 loss:3.0432
    3999. 

  3999. 2021-08-18 01:59:09,653 - INFO - main.py - train - 55 - 【train】 epoch:0 3692/5348 loss:3.4655
    4000. 

  4000. 2021-08-18 01:59:11,735 - INFO - main.py - train - 55 - 【train】 epoch:0 3693/5348 loss:3.1538
    4001. 

  4001. 2021-08-18 01:59:13,813 - INFO - main.py - train - 55 - 【train】 epoch:0 3694/5348 loss:2.3730
    4002. 

  4002. 2021-08-18 01:59:15,887 - INFO - main.py - train - 55 - 【train】 epoch:0 3695/5348 loss:2.9117
    4003. 

  4003. 2021-08-18 01:59:17,979 - INFO - main.py - train - 55 - 【train】 epoch:0 3696/5348 loss:2.8477
    4004. 

  4004. 2021-08-18 01:59:20,041 - INFO - main.py - train - 55 - 【train】 epoch:0 3697/5348 loss:1.7663
    4005. 

  4005. 2021-08-18 01:59:22,134 - INFO - main.py - train - 55 - 【train】 epoch:0 3698/5348 loss:2.4918
    4006. 

  4006. 2021-08-18 01:59:24,247 - INFO - main.py - train - 55 - 【train】 epoch:0 3699/5348 loss:2.1819
    4007. 

  4007. 2021-08-18 01:59:26,361 - INFO - main.py - train - 55 - 【train】 epoch:0 3700/5348 loss:3.9735
    4008. 

  4008. 2021-08-18 01:59:28,468 - INFO - main.py - train - 55 - 【train】 epoch:0 3701/5348 loss:2.6237
    4009. 

  4009. 2021-08-18 01:59:30,570 - INFO - main.py - train - 55 - 【train】 epoch:0 3702/5348 loss:3.4321
    4010. 

  4010. 2021-08-18 01:59:32,670 - INFO - main.py - train - 55 - 【train】 epoch:0 3703/5348 loss:3.3541
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  4011. 2021-08-18 01:59:34,778 - INFO - main.py - train - 55 - 【train】 epoch:0 3704/5348 loss:1.6722
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    4013. 

  4013. 2021-08-18 01:59:39,008 - INFO - main.py - train - 55 - 【train】 epoch:0 3706/5348 loss:5.1313
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  4014. 2021-08-18 01:59:41,113 - INFO - main.py - train - 55 - 【train】 epoch:0 3707/5348 loss:3.8344
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  4015. 2021-08-18 01:59:43,208 - INFO - main.py - train - 55 - 【train】 epoch:0 3708/5348 loss:2.3449
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  4016. 2021-08-18 01:59:45,316 - INFO - main.py - train - 55 - 【train】 epoch:0 3709/5348 loss:2.5064
    4017. 

  4017. 2021-08-18 01:59:47,424 - INFO - main.py - train - 55 - 【train】 epoch:0 3710/5348 loss:2.4810
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  4018. 2021-08-18 01:59:49,528 - INFO - main.py - train - 55 - 【train】 epoch:0 3711/5348 loss:2.5929
    4019. 

  4019. 2021-08-18 01:59:51,642 - INFO - main.py - train - 55 - 【train】 epoch:0 3712/5348 loss:2.3908
    4020. 

  4020. 2021-08-18 01:59:53,715 - INFO - main.py - train - 55 - 【train】 epoch:0 3713/5348 loss:2.1359
    4021. 

  4021. 2021-08-18 01:59:55,821 - INFO - main.py - train - 55 - 【train】 epoch:0 3714/5348 loss:2.6215
    4022. 

  4022. 2021-08-18 01:59:57,912 - INFO - main.py - train - 55 - 【train】 epoch:0 3715/5348 loss:1.7360
    4023. 

  4023. 2021-08-18 02:00:00,006 - INFO - main.py - train - 55 - 【train】 epoch:0 3716/5348 loss:3.0288
    4024. 

  4024. 2021-08-18 02:00:02,080 - INFO - main.py - train - 55 - 【train】 epoch:0 3717/5348 loss:3.5058
    4025. 

  4025. 2021-08-18 02:00:04,154 - INFO - main.py - train - 55 - 【train】 epoch:0 3718/5348 loss:2.2343
    4026. 

  4026. 2021-08-18 02:00:06,229 - INFO - main.py - train - 55 - 【train】 epoch:0 3719/5348 loss:3.0831
    4027. 

  4027. 2021-08-18 02:00:08,313 - INFO - main.py - train - 55 - 【train】 epoch:0 3720/5348 loss:3.0235
    4028. 

  4028. 2021-08-18 02:00:10,386 - INFO - main.py - train - 55 - 【train】 epoch:0 3721/5348 loss:2.9129
    4029. 

  4029. 2021-08-18 02:00:12,463 - INFO - main.py - train - 55 - 【train】 epoch:0 3722/5348 loss:4.1928
    4030. 

  4030. 2021-08-18 02:00:14,546 - INFO - main.py - train - 55 - 【train】 epoch:0 3723/5348 loss:2.3217
    4031. 

  4031. 2021-08-18 02:00:16,624 - INFO - main.py - train - 55 - 【train】 epoch:0 3724/5348 loss:4.7807
    4032. 

  4032. 2021-08-18 02:00:18,715 - INFO - main.py - train - 55 - 【train】 epoch:0 3725/5348 loss:2.8125
    4033. 

  4033. 2021-08-18 02:00:20,798 - INFO - main.py - train - 55 - 【train】 epoch:0 3726/5348 loss:3.4060
    4034. 

  4034. 2021-08-18 02:00:22,888 - INFO - main.py - train - 55 - 【train】 epoch:0 3727/5348 loss:2.0773
    4035. 

  4035. 2021-08-18 02:00:24,967 - INFO - main.py - train - 55 - 【train】 epoch:0 3728/5348 loss:2.8894
    4036. 

  4036. 2021-08-18 02:00:27,030 - INFO - main.py - train - 55 - 【train】 epoch:0 3729/5348 loss:3.3013
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  4037. 2021-08-18 02:00:29,116 - INFO - main.py - train - 55 - 【train】 epoch:0 3730/5348 loss:3.4927
    4038. 

  4038. 2021-08-18 02:00:31,220 - INFO - main.py - train - 55 - 【train】 epoch:0 3731/5348 loss:2.6982
    4039. 

  4039. 2021-08-18 02:00:33,325 - INFO - main.py - train - 55 - 【train】 epoch:0 3732/5348 loss:3.9668
    4040. 

  4040. 2021-08-18 02:00:35,433 - INFO - main.py - train - 55 - 【train】 epoch:0 3733/5348 loss:4.1019
    4041. 

  4041. 2021-08-18 02:00:37,527 - INFO - main.py - train - 55 - 【train】 epoch:0 3734/5348 loss:3.9723
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  4042. 2021-08-18 02:00:39,628 - INFO - main.py - train - 55 - 【train】 epoch:0 3735/5348 loss:3.0759
    4043. 

  4043. 2021-08-18 02:00:41,727 - INFO - main.py - train - 55 - 【train】 epoch:0 3736/5348 loss:3.1510
    4044. 

  4044. 2021-08-18 02:00:43,820 - INFO - main.py - train - 55 - 【train】 epoch:0 3737/5348 loss:3.0635
    4045. 

  4045. 2021-08-18 02:00:45,903 - INFO - main.py - train - 55 - 【train】 epoch:0 3738/5348 loss:2.0564
    4046. 

  4046. 2021-08-18 02:00:48,001 - INFO - main.py - train - 55 - 【train】 epoch:0 3739/5348 loss:3.3334
    4047. 

  4047. 2021-08-18 02:00:50,094 - INFO - main.py - train - 55 - 【train】 epoch:0 3740/5348 loss:2.6002
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  4048. 2021-08-18 02:00:52,204 - INFO - main.py - train - 55 - 【train】 epoch:0 3741/5348 loss:3.4274
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  4049. 2021-08-18 02:00:54,298 - INFO - main.py - train - 55 - 【train】 epoch:0 3742/5348 loss:2.5543
    4050. 

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    4051. 

  4051. 2021-08-18 02:00:58,481 - INFO - main.py - train - 55 - 【train】 epoch:0 3744/5348 loss:2.8560
    4052. 

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    4055. 

  4055. 2021-08-18 02:01:06,825 - INFO - main.py - train - 55 - 【train】 epoch:0 3748/5348 loss:2.2644
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  4056. 2021-08-18 02:01:08,910 - INFO - main.py - train - 55 - 【train】 epoch:0 3749/5348 loss:3.1079
    4057. 

  4057. 2021-08-18 02:01:11,002 - INFO - main.py - train - 55 - 【train】 epoch:0 3750/5348 loss:2.4159
    4058. 

  4058. 2021-08-18 02:01:13,093 - INFO - main.py - train - 55 - 【train】 epoch:0 3751/5348 loss:3.8559
    4059. 

  4059. 2021-08-18 02:01:15,172 - INFO - main.py - train - 55 - 【train】 epoch:0 3752/5348 loss:2.8323
    4060. 

  4060. 2021-08-18 02:01:17,273 - INFO - main.py - train - 55 - 【train】 epoch:0 3753/5348 loss:4.1202
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  4061. 2021-08-18 02:01:19,356 - INFO - main.py - train - 55 - 【train】 epoch:0 3754/5348 loss:3.0350
    4062. 

  4062. 2021-08-18 02:01:21,427 - INFO - main.py - train - 55 - 【train】 epoch:0 3755/5348 loss:1.8043
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    4064. 

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    4065. 

  4065. 2021-08-18 02:01:27,690 - INFO - main.py - train - 55 - 【train】 epoch:0 3758/5348 loss:2.4617
    4066. 

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    4069. 

  4069. 2021-08-18 02:01:36,056 - INFO - main.py - train - 55 - 【train】 epoch:0 3762/5348 loss:1.9737
    4070. 

  4070. 2021-08-18 02:01:38,192 - INFO - main.py - train - 55 - 【train】 epoch:0 3763/5348 loss:3.3421
    4071. 

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    4072. 

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    4073. 

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    4074. 

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    4075. 

  4075. 2021-08-18 02:01:48,712 - INFO - main.py - train - 55 - 【train】 epoch:0 3768/5348 loss:1.9700
    4076. 

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    4077. 

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    4078. 

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    4079. 

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    4080. 

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    4081. 

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    4083. 

  4083. 2021-08-18 02:02:05,585 - INFO - main.py - train - 55 - 【train】 epoch:0 3776/5348 loss:2.6317
    4084. 

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    4085. 

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    4086. 

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    4087. 

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    4089. 

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    4090. 

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    4093. 

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    4095. 

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    4096. 

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    4098. 

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  4125. 2021-08-18 02:03:34,006 - INFO - main.py - train - 55 - 【train】 epoch:0 3818/5348 loss:2.7420
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  4130. 2021-08-18 02:03:44,521 - INFO - main.py - train - 55 - 【train】 epoch:0 3823/5348 loss:4.4319
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  4134. 2021-08-18 02:03:52,903 - INFO - main.py - train - 55 - 【train】 epoch:0 3827/5348 loss:2.2458
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  4135. 2021-08-18 02:03:55,001 - INFO - main.py - train - 55 - 【train】 epoch:0 3828/5348 loss:3.4047
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  4136. 2021-08-18 02:03:57,114 - INFO - main.py - train - 55 - 【train】 epoch:0 3829/5348 loss:2.8849
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  4137. 2021-08-18 02:03:59,213 - INFO - main.py - train - 55 - 【train】 epoch:0 3830/5348 loss:2.8282
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  4138. 2021-08-18 02:04:01,305 - INFO - main.py - train - 55 - 【train】 epoch:0 3831/5348 loss:3.4064
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  4139. 2021-08-18 02:04:03,398 - INFO - main.py - train - 55 - 【train】 epoch:0 3832/5348 loss:4.6645
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  4140. 2021-08-18 02:04:05,490 - INFO - main.py - train - 55 - 【train】 epoch:0 3833/5348 loss:2.5357
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  4141. 2021-08-18 02:04:07,580 - INFO - main.py - train - 55 - 【train】 epoch:0 3834/5348 loss:2.4520
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  4143. 2021-08-18 02:04:11,765 - INFO - main.py - train - 55 - 【train】 epoch:0 3836/5348 loss:2.4806
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  4147. 2021-08-18 02:04:20,180 - INFO - main.py - train - 55 - 【train】 epoch:0 3840/5348 loss:2.8673
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  4149. 2021-08-18 02:04:24,420 - INFO - main.py - train - 55 - 【train】 epoch:0 3842/5348 loss:1.5262
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  4150. 2021-08-18 02:04:26,534 - INFO - main.py - train - 55 - 【train】 epoch:0 3843/5348 loss:3.8699
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  4151. 2021-08-18 02:04:28,626 - INFO - main.py - train - 55 - 【train】 epoch:0 3844/5348 loss:3.1687
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  4156. 2021-08-18 02:04:39,213 - INFO - main.py - train - 55 - 【train】 epoch:0 3849/5348 loss:3.3212
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  4157. 2021-08-18 02:04:41,336 - INFO - main.py - train - 55 - 【train】 epoch:0 3850/5348 loss:2.2770
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  4159. 2021-08-18 02:04:45,563 - INFO - main.py - train - 55 - 【train】 epoch:0 3852/5348 loss:4.6801
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  4160. 2021-08-18 02:04:47,660 - INFO - main.py - train - 55 - 【train】 epoch:0 3853/5348 loss:2.2520
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  4161. 2021-08-18 02:04:49,752 - INFO - main.py - train - 55 - 【train】 epoch:0 3854/5348 loss:2.4230
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  4162. 2021-08-18 02:04:51,853 - INFO - main.py - train - 55 - 【train】 epoch:0 3855/5348 loss:3.0738
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    4180. 

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  4190. 2021-08-18 02:05:50,779 - INFO - main.py - train - 55 - 【train】 epoch:0 3883/5348 loss:3.3349
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    4210. 

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    4213. 

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    4215. 

  4215. 2021-08-18 02:06:43,182 - INFO - main.py - train - 55 - 【train】 epoch:0 3908/5348 loss:1.6981
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    4218. 

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    4219. 

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    4220. 

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    4221. 

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    4230. 

  4230. 2021-08-18 02:07:14,724 - INFO - main.py - train - 55 - 【train】 epoch:0 3923/5348 loss:2.6203
    4231. 

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    4232. 

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    4233. 

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    4234. 

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    4235. 

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    4237. 

  4237. 2021-08-18 02:07:29,351 - INFO - main.py - train - 55 - 【train】 epoch:0 3930/5348 loss:2.7241
    4238. 

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    4239. 

  4239. 2021-08-18 02:07:33,521 - INFO - main.py - train - 55 - 【train】 epoch:0 3932/5348 loss:3.5174
    4240. 

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    4241. 

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    4242. 

  4242. 2021-08-18 02:07:39,814 - INFO - main.py - train - 55 - 【train】 epoch:0 3935/5348 loss:2.0965
    4243. 

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    4245. 

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    4246. 

  4246. 2021-08-18 02:07:48,237 - INFO - main.py - train - 55 - 【train】 epoch:0 3939/5348 loss:3.2567
    4247. 

  4247. 2021-08-18 02:07:50,347 - INFO - main.py - train - 55 - 【train】 epoch:0 3940/5348 loss:2.7471
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  4248. 2021-08-18 02:07:52,464 - INFO - main.py - train - 55 - 【train】 epoch:0 3941/5348 loss:1.9023
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  4249. 2021-08-18 02:07:54,570 - INFO - main.py - train - 55 - 【train】 epoch:0 3942/5348 loss:4.4030
    4250. 

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  4252. 2021-08-18 02:08:00,871 - INFO - main.py - train - 55 - 【train】 epoch:0 3945/5348 loss:4.8407
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  4254. 2021-08-18 02:08:05,062 - INFO - main.py - train - 55 - 【train】 epoch:0 3947/5348 loss:4.1079
    4255. 

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  4259. 2021-08-18 02:08:15,519 - INFO - main.py - train - 55 - 【train】 epoch:0 3952/5348 loss:2.9202
    4260. 

  4260. 2021-08-18 02:08:17,612 - INFO - main.py - train - 55 - 【train】 epoch:0 3953/5348 loss:3.9684
    4261. 

  4261. 2021-08-18 02:08:19,712 - INFO - main.py - train - 55 - 【train】 epoch:0 3954/5348 loss:2.0253
    4262. 

  4262. 2021-08-18 02:08:21,815 - INFO - main.py - train - 55 - 【train】 epoch:0 3955/5348 loss:3.1439
    4263. 

  4263. 2021-08-18 02:08:23,912 - INFO - main.py - train - 55 - 【train】 epoch:0 3956/5348 loss:1.9858
    4264. 

  4264. 2021-08-18 02:08:26,012 - INFO - main.py - train - 55 - 【train】 epoch:0 3957/5348 loss:2.6106
    4265. 

  4265. 2021-08-18 02:08:28,126 - INFO - main.py - train - 55 - 【train】 epoch:0 3958/5348 loss:3.6129
    4266. 

  4266. 2021-08-18 02:08:30,231 - INFO - main.py - train - 55 - 【train】 epoch:0 3959/5348 loss:2.6753
    4267. 

  4267. 2021-08-18 02:08:32,340 - INFO - main.py - train - 55 - 【train】 epoch:0 3960/5348 loss:2.1796
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  4269. 2021-08-18 02:08:36,543 - INFO - main.py - train - 55 - 【train】 epoch:0 3962/5348 loss:2.3896
    4270. 

  4270. 2021-08-18 02:08:38,636 - INFO - main.py - train - 55 - 【train】 epoch:0 3963/5348 loss:3.3222
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  4271. 2021-08-18 02:08:40,723 - INFO - main.py - train - 55 - 【train】 epoch:0 3964/5348 loss:3.2715
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  4272. 2021-08-18 02:08:42,815 - INFO - main.py - train - 55 - 【train】 epoch:0 3965/5348 loss:4.6315
    4273. 

  4273. 2021-08-18 02:08:44,923 - INFO - main.py - train - 55 - 【train】 epoch:0 3966/5348 loss:2.8486
    4274. 

  4274. 2021-08-18 02:08:46,987 - INFO - main.py - train - 55 - 【train】 epoch:0 3967/5348 loss:1.6214
    4275. 

  4275. 2021-08-18 02:08:49,086 - INFO - main.py - train - 55 - 【train】 epoch:0 3968/5348 loss:2.7925
    4276. 

  4276. 2021-08-18 02:08:51,166 - INFO - main.py - train - 55 - 【train】 epoch:0 3969/5348 loss:1.8922
    4277. 

  4277. 2021-08-18 02:08:53,250 - INFO - main.py - train - 55 - 【train】 epoch:0 3970/5348 loss:2.6220
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  4278. 2021-08-18 02:08:55,345 - INFO - main.py - train - 55 - 【train】 epoch:0 3971/5348 loss:3.7349
    4279. 

  4279. 2021-08-18 02:08:57,423 - INFO - main.py - train - 55 - 【train】 epoch:0 3972/5348 loss:2.3537
    4280. 

  4280. 2021-08-18 02:08:59,524 - INFO - main.py - train - 55 - 【train】 epoch:0 3973/5348 loss:2.3371
    4281. 

  4281. 2021-08-18 02:09:01,610 - INFO - main.py - train - 55 - 【train】 epoch:0 3974/5348 loss:2.4245
    4282. 

  4282. 2021-08-18 02:09:03,704 - INFO - main.py - train - 55 - 【train】 epoch:0 3975/5348 loss:2.2044
    4283. 

  4283. 2021-08-18 02:09:05,799 - INFO - main.py - train - 55 - 【train】 epoch:0 3976/5348 loss:3.0485
    4284. 

  4284. 2021-08-18 02:09:07,880 - INFO - main.py - train - 55 - 【train】 epoch:0 3977/5348 loss:2.1548
    4285. 

  4285. 2021-08-18 02:09:09,963 - INFO - main.py - train - 55 - 【train】 epoch:0 3978/5348 loss:1.5482
    4286. 

  4286. 2021-08-18 02:09:12,063 - INFO - main.py - train - 55 - 【train】 epoch:0 3979/5348 loss:3.0504
    4287. 

  4287. 2021-08-18 02:09:14,143 - INFO - main.py - train - 55 - 【train】 epoch:0 3980/5348 loss:3.1598
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  4288. 2021-08-18 02:09:16,216 - INFO - main.py - train - 55 - 【train】 epoch:0 3981/5348 loss:5.3563
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  4289. 2021-08-18 02:09:18,300 - INFO - main.py - train - 55 - 【train】 epoch:0 3982/5348 loss:3.3187
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  4290. 2021-08-18 02:09:20,400 - INFO - main.py - train - 55 - 【train】 epoch:0 3983/5348 loss:2.6295
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  4291. 2021-08-18 02:09:22,520 - INFO - main.py - train - 55 - 【train】 epoch:0 3984/5348 loss:2.4477
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  4292. 2021-08-18 02:09:24,619 - INFO - main.py - train - 55 - 【train】 epoch:0 3985/5348 loss:3.1676
    4293. 

  4293. 2021-08-18 02:09:26,720 - INFO - main.py - train - 55 - 【train】 epoch:0 3986/5348 loss:4.8163
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  4294. 2021-08-18 02:09:28,825 - INFO - main.py - train - 55 - 【train】 epoch:0 3987/5348 loss:4.5154
    4295. 

  4295. 2021-08-18 02:09:30,925 - INFO - main.py - train - 55 - 【train】 epoch:0 3988/5348 loss:2.6769
    4296. 

  4296. 2021-08-18 02:09:33,014 - INFO - main.py - train - 55 - 【train】 epoch:0 3989/5348 loss:3.4539
    4297. 

  4297. 2021-08-18 02:09:35,116 - INFO - main.py - train - 55 - 【train】 epoch:0 3990/5348 loss:3.8097
    4298. 

  4298. 2021-08-18 02:09:37,216 - INFO - main.py - train - 55 - 【train】 epoch:0 3991/5348 loss:2.1909
    4299. 

  4299. 2021-08-18 02:09:39,312 - INFO - main.py - train - 55 - 【train】 epoch:0 3992/5348 loss:2.0560
    4300. 

  4300. 2021-08-18 02:09:41,422 - INFO - main.py - train - 55 - 【train】 epoch:0 3993/5348 loss:2.3588
    4301. 

  4301. 2021-08-18 02:09:43,510 - INFO - main.py - train - 55 - 【train】 epoch:0 3994/5348 loss:2.5359
    4302. 

  4302. 2021-08-18 02:09:45,593 - INFO - main.py - train - 55 - 【train】 epoch:0 3995/5348 loss:3.2723
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  4303. 2021-08-18 02:09:47,679 - INFO - main.py - train - 55 - 【train】 epoch:0 3996/5348 loss:2.9416
    4304. 

  4304. 2021-08-18 02:09:49,748 - INFO - main.py - train - 55 - 【train】 epoch:0 3997/5348 loss:3.4797
    4305. 

  4305. 2021-08-18 02:09:51,837 - INFO - main.py - train - 55 - 【train】 epoch:0 3998/5348 loss:2.4333
    4306. 

  4306. 2021-08-18 02:09:53,931 - INFO - main.py - train - 55 - 【train】 epoch:0 3999/5348 loss:2.4280
    4307. 

  4307. 2021-08-18 02:09:56,016 - INFO - main.py - train - 55 - 【train】 epoch:0 4000/5348 loss:2.6944
    4308. 

  4308. 2021-08-18 02:09:58,096 - INFO - main.py - train - 55 - 【train】 epoch:0 4001/5348 loss:3.1920
    4309. 

  4309. 2021-08-18 02:10:00,166 - INFO - main.py - train - 55 - 【train】 epoch:0 4002/5348 loss:3.3372
    4310. 

  4310. 2021-08-18 02:10:02,249 - INFO - main.py - train - 55 - 【train】 epoch:0 4003/5348 loss:1.9378
    4311. 

  4311. 2021-08-18 02:10:04,348 - INFO - main.py - train - 55 - 【train】 epoch:0 4004/5348 loss:5.7298
    4312. 

  4312. 2021-08-18 02:10:06,430 - INFO - main.py - train - 55 - 【train】 epoch:0 4005/5348 loss:3.6932
    4313. 

  4313. 2021-08-18 02:10:08,504 - INFO - main.py - train - 55 - 【train】 epoch:0 4006/5348 loss:3.1218
    4314. 

  4314. 2021-08-18 02:10:10,600 - INFO - main.py - train - 55 - 【train】 epoch:0 4007/5348 loss:3.3808
    4315. 

  4315. 2021-08-18 02:10:12,666 - INFO - main.py - train - 55 - 【train】 epoch:0 4008/5348 loss:2.7811
    4316. 

  4316. 2021-08-18 02:10:14,749 - INFO - main.py - train - 55 - 【train】 epoch:0 4009/5348 loss:1.8331
    4317. 

  4317. 2021-08-18 02:10:16,836 - INFO - main.py - train - 55 - 【train】 epoch:0 4010/5348 loss:1.6334
    4318. 

  4318. 2021-08-18 02:10:18,927 - INFO - main.py - train - 55 - 【train】 epoch:0 4011/5348 loss:2.5517
    4319. 

  4319. 2021-08-18 02:10:21,004 - INFO - main.py - train - 55 - 【train】 epoch:0 4012/5348 loss:2.7945
    4320. 

  4320. 2021-08-18 02:10:23,088 - INFO - main.py - train - 55 - 【train】 epoch:0 4013/5348 loss:2.4205
    4321. 

  4321. 2021-08-18 02:10:25,180 - INFO - main.py - train - 55 - 【train】 epoch:0 4014/5348 loss:2.9224
    4322. 

  4322. 2021-08-18 02:10:27,271 - INFO - main.py - train - 55 - 【train】 epoch:0 4015/5348 loss:2.7804
    4323. 

  4323. 2021-08-18 02:10:29,376 - INFO - main.py - train - 55 - 【train】 epoch:0 4016/5348 loss:4.0179
    4324. 

  4324. 2021-08-18 02:10:31,473 - INFO - main.py - train - 55 - 【train】 epoch:0 4017/5348 loss:2.8624
    4325. 

  4325. 2021-08-18 02:10:33,568 - INFO - main.py - train - 55 - 【train】 epoch:0 4018/5348 loss:2.7629
    4326. 

  4326. 2021-08-18 02:10:35,656 - INFO - main.py - train - 55 - 【train】 epoch:0 4019/5348 loss:2.9521
    4327. 

  4327. 2021-08-18 02:10:37,749 - INFO - main.py - train - 55 - 【train】 epoch:0 4020/5348 loss:2.4676
    4328. 

  4328. 2021-08-18 02:10:39,838 - INFO - main.py - train - 55 - 【train】 epoch:0 4021/5348 loss:2.2323
    4329. 

  4329. 2021-08-18 02:10:41,923 - INFO - main.py - train - 55 - 【train】 epoch:0 4022/5348 loss:2.6117
    4330. 

  4330. 2021-08-18 02:10:44,040 - INFO - main.py - train - 55 - 【train】 epoch:0 4023/5348 loss:3.6281
    4331. 

  4331. 2021-08-18 02:10:46,143 - INFO - main.py - train - 55 - 【train】 epoch:0 4024/5348 loss:2.2196
    4332. 

  4332. 2021-08-18 02:10:48,233 - INFO - main.py - train - 55 - 【train】 epoch:0 4025/5348 loss:3.5680
    4333. 

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    4335. 

  4335. 2021-08-18 02:10:54,487 - INFO - main.py - train - 55 - 【train】 epoch:0 4028/5348 loss:3.4661
    4336. 

  4336. 2021-08-18 02:10:56,576 - INFO - main.py - train - 55 - 【train】 epoch:0 4029/5348 loss:4.2508
    4337. 

  4337. 2021-08-18 02:10:58,674 - INFO - main.py - train - 55 - 【train】 epoch:0 4030/5348 loss:3.8105
    4338. 

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    4339. 

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    4340. 

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    4341. 

  4341. 2021-08-18 02:11:07,020 - INFO - main.py - train - 55 - 【train】 epoch:0 4034/5348 loss:2.6827
    4342. 

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    4343. 

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    4345. 

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    4346. 

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    4347. 

  4347. 2021-08-18 02:11:19,477 - INFO - main.py - train - 55 - 【train】 epoch:0 4040/5348 loss:4.1650
    4348. 

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    4349. 

  4349. 2021-08-18 02:11:23,676 - INFO - main.py - train - 55 - 【train】 epoch:0 4042/5348 loss:3.8337
    4350. 

  4350. 2021-08-18 02:11:25,763 - INFO - main.py - train - 55 - 【train】 epoch:0 4043/5348 loss:3.3622
    4351. 

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    4352. 

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    4353. 

  4353. 2021-08-18 02:11:32,044 - INFO - main.py - train - 55 - 【train】 epoch:0 4046/5348 loss:3.4265
    4354. 

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    4355. 

  4355. 2021-08-18 02:11:36,263 - INFO - main.py - train - 55 - 【train】 epoch:0 4048/5348 loss:2.5454
    4356. 

  4356. 2021-08-18 02:11:38,348 - INFO - main.py - train - 55 - 【train】 epoch:0 4049/5348 loss:2.8546
    4357. 

  4357. 2021-08-18 02:11:40,456 - INFO - main.py - train - 55 - 【train】 epoch:0 4050/5348 loss:3.1255
    4358. 

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    4359. 

  4359. 2021-08-18 02:11:44,658 - INFO - main.py - train - 55 - 【train】 epoch:0 4052/5348 loss:2.6286
    4360. 

  4360. 2021-08-18 02:11:46,775 - INFO - main.py - train - 55 - 【train】 epoch:0 4053/5348 loss:2.5744
    4361. 

  4361. 2021-08-18 02:11:48,878 - INFO - main.py - train - 55 - 【train】 epoch:0 4054/5348 loss:2.4623
    4362. 

  4362. 2021-08-18 02:11:50,987 - INFO - main.py - train - 55 - 【train】 epoch:0 4055/5348 loss:3.5888
    4363. 

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    4364. 

  4364. 2021-08-18 02:11:55,185 - INFO - main.py - train - 55 - 【train】 epoch:0 4057/5348 loss:2.3498
    4365. 

  4365. 2021-08-18 02:11:57,270 - INFO - main.py - train - 55 - 【train】 epoch:0 4058/5348 loss:2.5271
    4366. 

  4366. 2021-08-18 02:11:59,363 - INFO - main.py - train - 55 - 【train】 epoch:0 4059/5348 loss:4.0555
    4367. 

  4367. 2021-08-18 02:12:01,438 - INFO - main.py - train - 55 - 【train】 epoch:0 4060/5348 loss:2.4089
    4368. 

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    4369. 

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    4370. 

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    4371. 

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    4373. 

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    4375. 

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    4376. 

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    4377. 

  4377. 2021-08-18 02:12:22,271 - INFO - main.py - train - 55 - 【train】 epoch:0 4070/5348 loss:2.8513
    4378. 

  4378. 2021-08-18 02:12:24,370 - INFO - main.py - train - 55 - 【train】 epoch:0 4071/5348 loss:3.0623
    4379. 

  4379. 2021-08-18 02:12:26,454 - INFO - main.py - train - 55 - 【train】 epoch:0 4072/5348 loss:2.0247
    4380. 

  4380. 2021-08-18 02:12:28,542 - INFO - main.py - train - 55 - 【train】 epoch:0 4073/5348 loss:2.6996
    4381. 

  4381. 2021-08-18 02:12:30,632 - INFO - main.py - train - 55 - 【train】 epoch:0 4074/5348 loss:3.2107
    4382. 

  4382. 2021-08-18 02:12:32,721 - INFO - main.py - train - 55 - 【train】 epoch:0 4075/5348 loss:2.8432
    4383. 

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    4384. 

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    4385. 

  4385. 2021-08-18 02:12:39,019 - INFO - main.py - train - 55 - 【train】 epoch:0 4078/5348 loss:2.5263
    4386. 

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    4388. 

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    4389. 

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    4390. 

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    4391. 

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    4399. 

  4399. 2021-08-18 02:13:08,463 - INFO - main.py - train - 55 - 【train】 epoch:0 4092/5348 loss:3.0209
    4400. 

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  4401. 2021-08-18 02:13:12,638 - INFO - main.py - train - 55 - 【train】 epoch:0 4094/5348 loss:2.7078
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  4405. 2021-08-18 02:13:20,998 - INFO - main.py - train - 55 - 【train】 epoch:0 4098/5348 loss:1.8757
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  4412. 2021-08-18 02:13:35,617 - INFO - main.py - train - 55 - 【train】 epoch:0 4105/5348 loss:2.5398
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  4413. 2021-08-18 02:13:37,690 - INFO - main.py - train - 55 - 【train】 epoch:0 4106/5348 loss:2.4958
    4414. 

  4414. 2021-08-18 02:13:39,785 - INFO - main.py - train - 55 - 【train】 epoch:0 4107/5348 loss:2.6226
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    4416. 

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  4417. 2021-08-18 02:13:46,080 - INFO - main.py - train - 55 - 【train】 epoch:0 4110/5348 loss:2.6157
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  4424. 2021-08-18 02:14:00,866 - INFO - main.py - train - 55 - 【train】 epoch:0 4117/5348 loss:3.0195
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  4425. 2021-08-18 02:14:02,973 - INFO - main.py - train - 55 - 【train】 epoch:0 4118/5348 loss:4.7165
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  4426. 2021-08-18 02:14:05,076 - INFO - main.py - train - 55 - 【train】 epoch:0 4119/5348 loss:2.3907
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  4428. 2021-08-18 02:14:09,258 - INFO - main.py - train - 55 - 【train】 epoch:0 4121/5348 loss:4.6895
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  4429. 2021-08-18 02:14:11,350 - INFO - main.py - train - 55 - 【train】 epoch:0 4122/5348 loss:2.7484
    4430. 

  4430. 2021-08-18 02:14:13,413 - INFO - main.py - train - 55 - 【train】 epoch:0 4123/5348 loss:2.6424
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  4431. 2021-08-18 02:14:15,500 - INFO - main.py - train - 55 - 【train】 epoch:0 4124/5348 loss:4.0740
    4432. 

  4432. 2021-08-18 02:14:17,611 - INFO - main.py - train - 55 - 【train】 epoch:0 4125/5348 loss:3.0772
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  4433. 2021-08-18 02:14:19,716 - INFO - main.py - train - 55 - 【train】 epoch:0 4126/5348 loss:3.5654
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  4434. 2021-08-18 02:14:21,832 - INFO - main.py - train - 55 - 【train】 epoch:0 4127/5348 loss:2.7850
    4435. 

  4435. 2021-08-18 02:14:23,937 - INFO - main.py - train - 55 - 【train】 epoch:0 4128/5348 loss:1.8730
    4436. 

  4436. 2021-08-18 02:14:26,042 - INFO - main.py - train - 55 - 【train】 epoch:0 4129/5348 loss:3.9644
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  4437. 2021-08-18 02:14:28,154 - INFO - main.py - train - 55 - 【train】 epoch:0 4130/5348 loss:4.0477
    4438. 

  4438. 2021-08-18 02:14:30,268 - INFO - main.py - train - 55 - 【train】 epoch:0 4131/5348 loss:2.5793
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  4439. 2021-08-18 02:14:32,371 - INFO - main.py - train - 55 - 【train】 epoch:0 4132/5348 loss:2.5713
    4440. 

  4440. 2021-08-18 02:14:34,477 - INFO - main.py - train - 55 - 【train】 epoch:0 4133/5348 loss:2.6310
    4441. 

  4441. 2021-08-18 02:14:36,575 - INFO - main.py - train - 55 - 【train】 epoch:0 4134/5348 loss:3.4632
    4442. 

  4442. 2021-08-18 02:14:38,706 - INFO - main.py - train - 55 - 【train】 epoch:0 4135/5348 loss:2.4775
    4443. 

  4443. 2021-08-18 02:14:40,792 - INFO - main.py - train - 55 - 【train】 epoch:0 4136/5348 loss:2.7383
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  4444. 2021-08-18 02:14:42,866 - INFO - main.py - train - 55 - 【train】 epoch:0 4137/5348 loss:2.6189
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  4445. 2021-08-18 02:14:44,944 - INFO - main.py - train - 55 - 【train】 epoch:0 4138/5348 loss:2.8826
    4446. 

  4446. 2021-08-18 02:14:47,029 - INFO - main.py - train - 55 - 【train】 epoch:0 4139/5348 loss:2.4311
    4447. 

  4447. 2021-08-18 02:14:49,110 - INFO - main.py - train - 55 - 【train】 epoch:0 4140/5348 loss:2.8982
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  4448. 2021-08-18 02:14:51,190 - INFO - main.py - train - 55 - 【train】 epoch:0 4141/5348 loss:2.4627
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  4449. 2021-08-18 02:14:53,275 - INFO - main.py - train - 55 - 【train】 epoch:0 4142/5348 loss:2.7881
    4450. 

  4450. 2021-08-18 02:14:55,370 - INFO - main.py - train - 55 - 【train】 epoch:0 4143/5348 loss:3.2280
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  4451. 2021-08-18 02:14:57,454 - INFO - main.py - train - 55 - 【train】 epoch:0 4144/5348 loss:3.1930
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  4452. 2021-08-18 02:14:59,543 - INFO - main.py - train - 55 - 【train】 epoch:0 4145/5348 loss:2.2413
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  4453. 2021-08-18 02:15:01,638 - INFO - main.py - train - 55 - 【train】 epoch:0 4146/5348 loss:3.2301
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  4454. 2021-08-18 02:15:03,731 - INFO - main.py - train - 55 - 【train】 epoch:0 4147/5348 loss:2.3535
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  4455. 2021-08-18 02:15:05,819 - INFO - main.py - train - 55 - 【train】 epoch:0 4148/5348 loss:3.7697
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  4456. 2021-08-18 02:15:07,897 - INFO - main.py - train - 55 - 【train】 epoch:0 4149/5348 loss:3.0828
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  4458. 2021-08-18 02:15:12,063 - INFO - main.py - train - 55 - 【train】 epoch:0 4151/5348 loss:1.3863
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  4459. 2021-08-18 02:15:14,158 - INFO - main.py - train - 55 - 【train】 epoch:0 4152/5348 loss:4.1213
    4460. 

  4460. 2021-08-18 02:15:16,254 - INFO - main.py - train - 55 - 【train】 epoch:0 4153/5348 loss:3.2905
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  4461. 2021-08-18 02:15:18,339 - INFO - main.py - train - 55 - 【train】 epoch:0 4154/5348 loss:2.5301
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  4462. 2021-08-18 02:15:20,425 - INFO - main.py - train - 55 - 【train】 epoch:0 4155/5348 loss:3.5528
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  4463. 2021-08-18 02:15:22,544 - INFO - main.py - train - 55 - 【train】 epoch:0 4156/5348 loss:2.8701
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  4464. 2021-08-18 02:15:24,651 - INFO - main.py - train - 55 - 【train】 epoch:0 4157/5348 loss:2.1319
    4465. 

  4465. 2021-08-18 02:15:26,760 - INFO - main.py - train - 55 - 【train】 epoch:0 4158/5348 loss:4.4712
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  4466. 2021-08-18 02:15:28,872 - INFO - main.py - train - 55 - 【train】 epoch:0 4159/5348 loss:2.6552
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  4467. 2021-08-18 02:15:30,980 - INFO - main.py - train - 55 - 【train】 epoch:0 4160/5348 loss:2.8722
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  4469. 2021-08-18 02:15:35,189 - INFO - main.py - train - 55 - 【train】 epoch:0 4162/5348 loss:2.2303
    4470. 

  4470. 2021-08-18 02:15:37,294 - INFO - main.py - train - 55 - 【train】 epoch:0 4163/5348 loss:2.7659
    4471. 

  4471. 2021-08-18 02:15:39,400 - INFO - main.py - train - 55 - 【train】 epoch:0 4164/5348 loss:3.1991
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  4472. 2021-08-18 02:15:41,503 - INFO - main.py - train - 55 - 【train】 epoch:0 4165/5348 loss:2.2550
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  4474. 2021-08-18 02:15:45,706 - INFO - main.py - train - 55 - 【train】 epoch:0 4167/5348 loss:3.0242
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  4475. 2021-08-18 02:15:47,797 - INFO - main.py - train - 55 - 【train】 epoch:0 4168/5348 loss:3.4022
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  4476. 2021-08-18 02:15:49,901 - INFO - main.py - train - 55 - 【train】 epoch:0 4169/5348 loss:2.3674
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  4477. 2021-08-18 02:15:51,990 - INFO - main.py - train - 55 - 【train】 epoch:0 4170/5348 loss:2.6175
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  4478. 2021-08-18 02:15:54,091 - INFO - main.py - train - 55 - 【train】 epoch:0 4171/5348 loss:2.3522
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  4479. 2021-08-18 02:15:56,188 - INFO - main.py - train - 55 - 【train】 epoch:0 4172/5348 loss:3.0263
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  4480. 2021-08-18 02:15:58,275 - INFO - main.py - train - 55 - 【train】 epoch:0 4173/5348 loss:2.9435
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  4481. 2021-08-18 02:16:00,387 - INFO - main.py - train - 55 - 【train】 epoch:0 4174/5348 loss:3.3143
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  4484. 2021-08-18 02:16:06,672 - INFO - main.py - train - 55 - 【train】 epoch:0 4177/5348 loss:3.2643
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  4496. 2021-08-18 02:16:31,713 - INFO - main.py - train - 55 - 【train】 epoch:0 4189/5348 loss:4.0582
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    4499. 

  4499. 2021-08-18 02:16:38,019 - INFO - main.py - train - 55 - 【train】 epoch:0 4192/5348 loss:3.2500
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  4500. 2021-08-18 02:16:40,125 - INFO - main.py - train - 55 - 【train】 epoch:0 4193/5348 loss:2.5496
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    4502. 

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    4503. 

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    4504. 

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    4505. 

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    4510. 

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    4520. 

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    4524. 

  4524. 2021-08-18 02:17:30,254 - INFO - main.py - train - 55 - 【train】 epoch:0 4217/5348 loss:2.4051
    4525. 

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    4526. 

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  4574. 2021-08-18 02:19:14,846 - INFO - main.py - train - 55 - 【train】 epoch:0 4267/5348 loss:2.0656
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  4575. 2021-08-18 02:19:16,938 - INFO - main.py - train - 55 - 【train】 epoch:0 4268/5348 loss:2.0080
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  4578. 2021-08-18 02:19:23,245 - INFO - main.py - train - 55 - 【train】 epoch:0 4271/5348 loss:3.5271
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  4579. 2021-08-18 02:19:25,346 - INFO - main.py - train - 55 - 【train】 epoch:0 4272/5348 loss:3.9792
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  4580. 2021-08-18 02:19:27,433 - INFO - main.py - train - 55 - 【train】 epoch:0 4273/5348 loss:2.8470
    4581. 

  4581. 2021-08-18 02:19:29,544 - INFO - main.py - train - 55 - 【train】 epoch:0 4274/5348 loss:1.8246
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  4582. 2021-08-18 02:19:31,641 - INFO - main.py - train - 55 - 【train】 epoch:0 4275/5348 loss:3.1978
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  4583. 2021-08-18 02:19:33,738 - INFO - main.py - train - 55 - 【train】 epoch:0 4276/5348 loss:2.8352
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  4584. 2021-08-18 02:19:35,834 - INFO - main.py - train - 55 - 【train】 epoch:0 4277/5348 loss:1.9965
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  4585. 2021-08-18 02:19:37,908 - INFO - main.py - train - 55 - 【train】 epoch:0 4278/5348 loss:2.0605
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  4586. 2021-08-18 02:19:39,985 - INFO - main.py - train - 55 - 【train】 epoch:0 4279/5348 loss:3.2299
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  4587. 2021-08-18 02:19:42,069 - INFO - main.py - train - 55 - 【train】 epoch:0 4280/5348 loss:2.2083
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  4588. 2021-08-18 02:19:44,162 - INFO - main.py - train - 55 - 【train】 epoch:0 4281/5348 loss:4.1873
    4589. 

  4589. 2021-08-18 02:19:46,262 - INFO - main.py - train - 55 - 【train】 epoch:0 4282/5348 loss:4.2032
    4590. 

  4590. 2021-08-18 02:19:48,342 - INFO - main.py - train - 55 - 【train】 epoch:0 4283/5348 loss:2.1743
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  4591. 2021-08-18 02:19:50,434 - INFO - main.py - train - 55 - 【train】 epoch:0 4284/5348 loss:2.2032
    4592. 

  4592. 2021-08-18 02:19:52,530 - INFO - main.py - train - 55 - 【train】 epoch:0 4285/5348 loss:3.0363
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  4593. 2021-08-18 02:19:54,616 - INFO - main.py - train - 55 - 【train】 epoch:0 4286/5348 loss:3.3835
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  4594. 2021-08-18 02:19:56,694 - INFO - main.py - train - 55 - 【train】 epoch:0 4287/5348 loss:2.5960
    4595. 

  4595. 2021-08-18 02:19:58,775 - INFO - main.py - train - 55 - 【train】 epoch:0 4288/5348 loss:2.4828
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  4596. 2021-08-18 02:20:00,869 - INFO - main.py - train - 55 - 【train】 epoch:0 4289/5348 loss:3.0068
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  4597. 2021-08-18 02:20:02,958 - INFO - main.py - train - 55 - 【train】 epoch:0 4290/5348 loss:2.3580
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  4598. 2021-08-18 02:20:05,042 - INFO - main.py - train - 55 - 【train】 epoch:0 4291/5348 loss:2.4026
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  4599. 2021-08-18 02:20:07,129 - INFO - main.py - train - 55 - 【train】 epoch:0 4292/5348 loss:3.3369
    4600. 

  4600. 2021-08-18 02:20:09,204 - INFO - main.py - train - 55 - 【train】 epoch:0 4293/5348 loss:3.5540
    4601. 

  4601. 2021-08-18 02:20:11,270 - INFO - main.py - train - 55 - 【train】 epoch:0 4294/5348 loss:2.7961
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  4602. 2021-08-18 02:20:13,354 - INFO - main.py - train - 55 - 【train】 epoch:0 4295/5348 loss:2.7294
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  4604. 2021-08-18 02:20:17,522 - INFO - main.py - train - 55 - 【train】 epoch:0 4297/5348 loss:2.8824
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    4614. 

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  4617. 2021-08-18 02:20:44,859 - INFO - main.py - train - 55 - 【train】 epoch:0 4310/5348 loss:4.4431
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  4618. 2021-08-18 02:20:46,949 - INFO - main.py - train - 55 - 【train】 epoch:0 4311/5348 loss:3.7123
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  4619. 2021-08-18 02:20:49,033 - INFO - main.py - train - 55 - 【train】 epoch:0 4312/5348 loss:2.5070
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  4620. 2021-08-18 02:20:51,125 - INFO - main.py - train - 55 - 【train】 epoch:0 4313/5348 loss:3.4466
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  4621. 2021-08-18 02:20:53,224 - INFO - main.py - train - 55 - 【train】 epoch:0 4314/5348 loss:3.8116
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  4624. 2021-08-18 02:20:59,506 - INFO - main.py - train - 55 - 【train】 epoch:0 4317/5348 loss:1.8442
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  4625. 2021-08-18 02:21:01,591 - INFO - main.py - train - 55 - 【train】 epoch:0 4318/5348 loss:2.4282
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  4626. 2021-08-18 02:21:03,693 - INFO - main.py - train - 55 - 【train】 epoch:0 4319/5348 loss:3.3091
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  4627. 2021-08-18 02:21:05,775 - INFO - main.py - train - 55 - 【train】 epoch:0 4320/5348 loss:4.0916
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  4628. 2021-08-18 02:21:07,867 - INFO - main.py - train - 55 - 【train】 epoch:0 4321/5348 loss:1.8632
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  4629. 2021-08-18 02:21:09,953 - INFO - main.py - train - 55 - 【train】 epoch:0 4322/5348 loss:3.1329
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  4630. 2021-08-18 02:21:12,034 - INFO - main.py - train - 55 - 【train】 epoch:0 4323/5348 loss:2.4248
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  4631. 2021-08-18 02:21:14,120 - INFO - main.py - train - 55 - 【train】 epoch:0 4324/5348 loss:2.2019
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  4632. 2021-08-18 02:21:16,210 - INFO - main.py - train - 55 - 【train】 epoch:0 4325/5348 loss:3.0317
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  4642. 2021-08-18 02:21:37,201 - INFO - main.py - train - 55 - 【train】 epoch:0 4335/5348 loss:2.1653
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    4650. 

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    4660. 

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    4661. 

  4661. 2021-08-18 02:22:17,006 - INFO - main.py - train - 55 - 【train】 epoch:0 4354/5348 loss:2.2140
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    4664. 

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    4670. 

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    4671. 

  4671. 2021-08-18 02:22:38,020 - INFO - main.py - train - 55 - 【train】 epoch:0 4364/5348 loss:3.5560
    4672. 

  4672. 2021-08-18 02:22:40,114 - INFO - main.py - train - 55 - 【train】 epoch:0 4365/5348 loss:2.9733
    4673. 

  4673. 2021-08-18 02:22:42,231 - INFO - main.py - train - 55 - 【train】 epoch:0 4366/5348 loss:2.9731
    4674. 

  4674. 2021-08-18 02:22:44,340 - INFO - main.py - train - 55 - 【train】 epoch:0 4367/5348 loss:3.9008
    4675. 

  4675. 2021-08-18 02:22:46,455 - INFO - main.py - train - 55 - 【train】 epoch:0 4368/5348 loss:3.5029
    4676. 

  4676. 2021-08-18 02:22:48,565 - INFO - main.py - train - 55 - 【train】 epoch:0 4369/5348 loss:2.7382
    4677. 

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    4678. 

  4678. 2021-08-18 02:22:52,785 - INFO - main.py - train - 55 - 【train】 epoch:0 4371/5348 loss:2.4135
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  4679. 2021-08-18 02:22:54,904 - INFO - main.py - train - 55 - 【train】 epoch:0 4372/5348 loss:3.5484
    4680. 

  4680. 2021-08-18 02:22:56,984 - INFO - main.py - train - 55 - 【train】 epoch:0 4373/5348 loss:1.7134
    4681. 

  4681. 2021-08-18 02:22:59,074 - INFO - main.py - train - 55 - 【train】 epoch:0 4374/5348 loss:2.9447
    4682. 

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    4683. 

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    4684. 

  4684. 2021-08-18 02:23:05,353 - INFO - main.py - train - 55 - 【train】 epoch:0 4377/5348 loss:4.0696
    4685. 

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    4686. 

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    4687. 

  4687. 2021-08-18 02:23:11,615 - INFO - main.py - train - 55 - 【train】 epoch:0 4380/5348 loss:3.8192
    4688. 

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  4689. 2021-08-18 02:23:15,773 - INFO - main.py - train - 55 - 【train】 epoch:0 4382/5348 loss:1.6959
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    4699. 

  4699. 2021-08-18 02:23:36,706 - INFO - main.py - train - 55 - 【train】 epoch:0 4392/5348 loss:2.3320
    4700. 

  4700. 2021-08-18 02:23:38,831 - INFO - main.py - train - 55 - 【train】 epoch:0 4393/5348 loss:3.6464
    4701. 

  4701. 2021-08-18 02:23:40,947 - INFO - main.py - train - 55 - 【train】 epoch:0 4394/5348 loss:2.6494
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  4702. 2021-08-18 02:23:43,061 - INFO - main.py - train - 55 - 【train】 epoch:0 4395/5348 loss:2.2723
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  4704. 2021-08-18 02:23:47,301 - INFO - main.py - train - 55 - 【train】 epoch:0 4397/5348 loss:4.7555
    4705. 

  4705. 2021-08-18 02:23:49,413 - INFO - main.py - train - 55 - 【train】 epoch:0 4398/5348 loss:2.6760
    4706. 

  4706. 2021-08-18 02:23:51,518 - INFO - main.py - train - 55 - 【train】 epoch:0 4399/5348 loss:2.0999
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    4708. 

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    4710. 

  4710. 2021-08-18 02:23:59,960 - INFO - main.py - train - 55 - 【train】 epoch:0 4403/5348 loss:3.7013
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  4711. 2021-08-18 02:24:02,068 - INFO - main.py - train - 55 - 【train】 epoch:0 4404/5348 loss:2.7638
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  4712. 2021-08-18 02:24:04,174 - INFO - main.py - train - 55 - 【train】 epoch:0 4405/5348 loss:2.8501
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  4713. 2021-08-18 02:24:06,253 - INFO - main.py - train - 55 - 【train】 epoch:0 4406/5348 loss:2.4023
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  4714. 2021-08-18 02:24:08,342 - INFO - main.py - train - 55 - 【train】 epoch:0 4407/5348 loss:4.1042
    4715. 

  4715. 2021-08-18 02:24:10,417 - INFO - main.py - train - 55 - 【train】 epoch:0 4408/5348 loss:3.6724
    4716. 

  4716. 2021-08-18 02:24:12,523 - INFO - main.py - train - 55 - 【train】 epoch:0 4409/5348 loss:3.4924
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  4717. 2021-08-18 02:24:14,622 - INFO - main.py - train - 55 - 【train】 epoch:0 4410/5348 loss:2.2789
    4718. 

  4718. 2021-08-18 02:24:16,715 - INFO - main.py - train - 55 - 【train】 epoch:0 4411/5348 loss:3.0258
    4719. 

  4719. 2021-08-18 02:24:18,808 - INFO - main.py - train - 55 - 【train】 epoch:0 4412/5348 loss:3.9742
    4720. 

  4720. 2021-08-18 02:24:20,889 - INFO - main.py - train - 55 - 【train】 epoch:0 4413/5348 loss:2.9881
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  4721. 2021-08-18 02:24:22,993 - INFO - main.py - train - 55 - 【train】 epoch:0 4414/5348 loss:2.6079
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  4723. 2021-08-18 02:24:27,174 - INFO - main.py - train - 55 - 【train】 epoch:0 4416/5348 loss:2.3915
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  4724. 2021-08-18 02:24:29,260 - INFO - main.py - train - 55 - 【train】 epoch:0 4417/5348 loss:3.2469
    4725. 

  4725. 2021-08-18 02:24:31,353 - INFO - main.py - train - 55 - 【train】 epoch:0 4418/5348 loss:2.9682
    4726. 

  4726. 2021-08-18 02:24:33,443 - INFO - main.py - train - 55 - 【train】 epoch:0 4419/5348 loss:3.3183
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  4727. 2021-08-18 02:24:35,520 - INFO - main.py - train - 55 - 【train】 epoch:0 4420/5348 loss:2.2804
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  4728. 2021-08-18 02:24:37,612 - INFO - main.py - train - 55 - 【train】 epoch:0 4421/5348 loss:2.0583
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  4729. 2021-08-18 02:24:39,703 - INFO - main.py - train - 55 - 【train】 epoch:0 4422/5348 loss:2.9429
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  4730. 2021-08-18 02:24:41,788 - INFO - main.py - train - 55 - 【train】 epoch:0 4423/5348 loss:1.6771
    4731. 

  4731. 2021-08-18 02:24:43,910 - INFO - main.py - train - 55 - 【train】 epoch:0 4424/5348 loss:3.5682
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  4732. 2021-08-18 02:24:46,019 - INFO - main.py - train - 55 - 【train】 epoch:0 4425/5348 loss:3.8860
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  4733. 2021-08-18 02:24:48,129 - INFO - main.py - train - 55 - 【train】 epoch:0 4426/5348 loss:2.2295
    4734. 

  4734. 2021-08-18 02:24:50,230 - INFO - main.py - train - 55 - 【train】 epoch:0 4427/5348 loss:3.0344
    4735. 

  4735. 2021-08-18 02:24:52,357 - INFO - main.py - train - 55 - 【train】 epoch:0 4428/5348 loss:2.5478
    4736. 

  4736. 2021-08-18 02:24:54,484 - INFO - main.py - train - 55 - 【train】 epoch:0 4429/5348 loss:2.9109
    4737. 

  4737. 2021-08-18 02:24:56,603 - INFO - main.py - train - 55 - 【train】 epoch:0 4430/5348 loss:3.6487
    4738. 

  4738. 2021-08-18 02:24:58,709 - INFO - main.py - train - 55 - 【train】 epoch:0 4431/5348 loss:2.4032
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  4739. 2021-08-18 02:25:00,823 - INFO - main.py - train - 55 - 【train】 epoch:0 4432/5348 loss:4.4095
    4740. 

  4740. 2021-08-18 02:25:02,929 - INFO - main.py - train - 55 - 【train】 epoch:0 4433/5348 loss:1.7857
    4741. 

  4741. 2021-08-18 02:25:05,033 - INFO - main.py - train - 55 - 【train】 epoch:0 4434/5348 loss:3.1080
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  4742. 2021-08-18 02:25:07,140 - INFO - main.py - train - 55 - 【train】 epoch:0 4435/5348 loss:2.3219
    4743. 

  4743. 2021-08-18 02:25:09,230 - INFO - main.py - train - 55 - 【train】 epoch:0 4436/5348 loss:3.5934
    4744. 

  4744. 2021-08-18 02:25:11,309 - INFO - main.py - train - 55 - 【train】 epoch:0 4437/5348 loss:2.8939
    4745. 

  4745. 2021-08-18 02:25:13,407 - INFO - main.py - train - 55 - 【train】 epoch:0 4438/5348 loss:5.0672
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  4746. 2021-08-18 02:25:15,496 - INFO - main.py - train - 55 - 【train】 epoch:0 4439/5348 loss:3.1668
    4747. 

  4747. 2021-08-18 02:25:17,595 - INFO - main.py - train - 55 - 【train】 epoch:0 4440/5348 loss:2.8764
    4748. 

  4748. 2021-08-18 02:25:19,712 - INFO - main.py - train - 55 - 【train】 epoch:0 4441/5348 loss:2.0785
    4749. 

  4749. 2021-08-18 02:25:21,829 - INFO - main.py - train - 55 - 【train】 epoch:0 4442/5348 loss:3.1152
    4750. 

  4750. 2021-08-18 02:25:23,936 - INFO - main.py - train - 55 - 【train】 epoch:0 4443/5348 loss:3.5534
    4751. 

  4751. 2021-08-18 02:25:26,046 - INFO - main.py - train - 55 - 【train】 epoch:0 4444/5348 loss:4.5930
    4752. 

  4752. 2021-08-18 02:25:28,128 - INFO - main.py - train - 55 - 【train】 epoch:0 4445/5348 loss:1.5819
    4753. 

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    4754. 

  4754. 2021-08-18 02:25:32,332 - INFO - main.py - train - 55 - 【train】 epoch:0 4447/5348 loss:2.8024
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  4755. 2021-08-18 02:25:34,447 - INFO - main.py - train - 55 - 【train】 epoch:0 4448/5348 loss:2.7892
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  4756. 2021-08-18 02:25:36,545 - INFO - main.py - train - 55 - 【train】 epoch:0 4449/5348 loss:2.7679
    4757. 

  4757. 2021-08-18 02:25:38,652 - INFO - main.py - train - 55 - 【train】 epoch:0 4450/5348 loss:3.0176
    4758. 

  4758. 2021-08-18 02:25:40,744 - INFO - main.py - train - 55 - 【train】 epoch:0 4451/5348 loss:3.6429
    4759. 

  4759. 2021-08-18 02:25:42,839 - INFO - main.py - train - 55 - 【train】 epoch:0 4452/5348 loss:1.7007
    4760. 

  4760. 2021-08-18 02:25:44,918 - INFO - main.py - train - 55 - 【train】 epoch:0 4453/5348 loss:1.7971
    4761. 

  4761. 2021-08-18 02:25:46,995 - INFO - main.py - train - 55 - 【train】 epoch:0 4454/5348 loss:3.1484
    4762. 

  4762. 2021-08-18 02:25:49,082 - INFO - main.py - train - 55 - 【train】 epoch:0 4455/5348 loss:3.1733
    4763. 

  4763. 2021-08-18 02:25:51,170 - INFO - main.py - train - 55 - 【train】 epoch:0 4456/5348 loss:2.5914
    4764. 

  4764. 2021-08-18 02:25:53,264 - INFO - main.py - train - 55 - 【train】 epoch:0 4457/5348 loss:2.4814
    4765. 

  4765. 2021-08-18 02:25:55,349 - INFO - main.py - train - 55 - 【train】 epoch:0 4458/5348 loss:2.3680
    4766. 

  4766. 2021-08-18 02:25:57,429 - INFO - main.py - train - 55 - 【train】 epoch:0 4459/5348 loss:2.8385
    4767. 

  4767. 2021-08-18 02:25:59,509 - INFO - main.py - train - 55 - 【train】 epoch:0 4460/5348 loss:2.8643
    4768. 

  4768. 2021-08-18 02:26:01,590 - INFO - main.py - train - 55 - 【train】 epoch:0 4461/5348 loss:2.2445
    4769. 

  4769. 2021-08-18 02:26:03,658 - INFO - main.py - train - 55 - 【train】 epoch:0 4462/5348 loss:2.8539
    4770. 

  4770. 2021-08-18 02:26:05,747 - INFO - main.py - train - 55 - 【train】 epoch:0 4463/5348 loss:2.2617
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    4780. 

  4780. 2021-08-18 02:26:26,608 - INFO - main.py - train - 55 - 【train】 epoch:0 4473/5348 loss:2.5581
    4781. 

  4781. 2021-08-18 02:26:28,708 - INFO - main.py - train - 55 - 【train】 epoch:0 4474/5348 loss:2.7448
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  4782. 2021-08-18 02:26:30,818 - INFO - main.py - train - 55 - 【train】 epoch:0 4475/5348 loss:4.3502
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    4784. 

  4784. 2021-08-18 02:26:35,036 - INFO - main.py - train - 55 - 【train】 epoch:0 4477/5348 loss:5.1876
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  4785. 2021-08-18 02:26:37,127 - INFO - main.py - train - 55 - 【train】 epoch:0 4478/5348 loss:3.4266
    4786. 

  4786. 2021-08-18 02:26:39,209 - INFO - main.py - train - 55 - 【train】 epoch:0 4479/5348 loss:3.2114
    4787. 

  4787. 2021-08-18 02:26:41,328 - INFO - main.py - train - 55 - 【train】 epoch:0 4480/5348 loss:3.0429
    4788. 

  4788. 2021-08-18 02:26:43,438 - INFO - main.py - train - 55 - 【train】 epoch:0 4481/5348 loss:3.6575
    4789. 

  4789. 2021-08-18 02:26:45,527 - INFO - main.py - train - 55 - 【train】 epoch:0 4482/5348 loss:2.2039
    4790. 

  4790. 2021-08-18 02:26:47,618 - INFO - main.py - train - 55 - 【train】 epoch:0 4483/5348 loss:2.2181
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  4791. 2021-08-18 02:26:49,697 - INFO - main.py - train - 55 - 【train】 epoch:0 4484/5348 loss:2.5569
    4792. 

  4792. 2021-08-18 02:26:51,768 - INFO - main.py - train - 55 - 【train】 epoch:0 4485/5348 loss:2.8060
    4793. 

  4793. 2021-08-18 02:26:53,848 - INFO - main.py - train - 55 - 【train】 epoch:0 4486/5348 loss:2.1467
    4794. 

  4794. 2021-08-18 02:26:55,928 - INFO - main.py - train - 55 - 【train】 epoch:0 4487/5348 loss:3.8915
    4795. 

  4795. 2021-08-18 02:26:58,025 - INFO - main.py - train - 55 - 【train】 epoch:0 4488/5348 loss:4.0119
    4796. 

  4796. 2021-08-18 02:27:00,103 - INFO - main.py - train - 55 - 【train】 epoch:0 4489/5348 loss:4.6211
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  4797. 2021-08-18 02:27:02,186 - INFO - main.py - train - 55 - 【train】 epoch:0 4490/5348 loss:3.2546
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  4798. 2021-08-18 02:27:04,281 - INFO - main.py - train - 55 - 【train】 epoch:0 4491/5348 loss:4.1037
    4799. 

  4799. 2021-08-18 02:27:06,358 - INFO - main.py - train - 55 - 【train】 epoch:0 4492/5348 loss:2.9419
    4800. 

  4800. 2021-08-18 02:27:08,463 - INFO - main.py - train - 55 - 【train】 epoch:0 4493/5348 loss:4.5290
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  4801. 2021-08-18 02:27:10,531 - INFO - main.py - train - 55 - 【train】 epoch:0 4494/5348 loss:2.7532
    4802. 

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    4803. 

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    4804. 

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    4805. 

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    4806. 

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    4807. 

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    4808. 

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    4809. 

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    4810. 

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  4811. 2021-08-18 02:27:31,365 - INFO - main.py - train - 55 - 【train】 epoch:0 4504/5348 loss:2.7377
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    4815. 

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    4816. 

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    4817. 

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    4818. 

  4818. 2021-08-18 02:27:46,066 - INFO - main.py - train - 55 - 【train】 epoch:0 4511/5348 loss:3.5255
    4819. 

  4819. 2021-08-18 02:27:48,162 - INFO - main.py - train - 55 - 【train】 epoch:0 4512/5348 loss:2.8502
    4820. 

  4820. 2021-08-18 02:27:50,253 - INFO - main.py - train - 55 - 【train】 epoch:0 4513/5348 loss:2.1609
    4821. 

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  4823. 2021-08-18 02:27:56,496 - INFO - main.py - train - 55 - 【train】 epoch:0 4516/5348 loss:2.6018
    4824. 

  4824. 2021-08-18 02:27:58,570 - INFO - main.py - train - 55 - 【train】 epoch:0 4517/5348 loss:3.8102
    4825. 

  4825. 2021-08-18 02:28:00,644 - INFO - main.py - train - 55 - 【train】 epoch:0 4518/5348 loss:2.9334
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  4826. 2021-08-18 02:28:02,719 - INFO - main.py - train - 55 - 【train】 epoch:0 4519/5348 loss:2.4557
    4827. 

  4827. 2021-08-18 02:28:04,789 - INFO - main.py - train - 55 - 【train】 epoch:0 4520/5348 loss:2.3664
    4828. 

  4828. 2021-08-18 02:28:06,886 - INFO - main.py - train - 55 - 【train】 epoch:0 4521/5348 loss:1.7666
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  4829. 2021-08-18 02:28:08,964 - INFO - main.py - train - 55 - 【train】 epoch:0 4522/5348 loss:2.1009
    4830. 

  4830. 2021-08-18 02:28:11,050 - INFO - main.py - train - 55 - 【train】 epoch:0 4523/5348 loss:2.9901
    4831. 

  4831. 2021-08-18 02:28:13,145 - INFO - main.py - train - 55 - 【train】 epoch:0 4524/5348 loss:2.8679
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  4832. 2021-08-18 02:28:15,224 - INFO - main.py - train - 55 - 【train】 epoch:0 4525/5348 loss:2.9125
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  4833. 2021-08-18 02:28:17,302 - INFO - main.py - train - 55 - 【train】 epoch:0 4526/5348 loss:2.4427
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  4834. 2021-08-18 02:28:19,382 - INFO - main.py - train - 55 - 【train】 epoch:0 4527/5348 loss:3.2142
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  4835. 2021-08-18 02:28:21,461 - INFO - main.py - train - 55 - 【train】 epoch:0 4528/5348 loss:2.0283
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  4836. 2021-08-18 02:28:23,531 - INFO - main.py - train - 55 - 【train】 epoch:0 4529/5348 loss:2.9288
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  4838. 2021-08-18 02:28:27,673 - INFO - main.py - train - 55 - 【train】 epoch:0 4531/5348 loss:2.2248
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  4840. 2021-08-18 02:28:31,834 - INFO - main.py - train - 55 - 【train】 epoch:0 4533/5348 loss:3.0771
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  4841. 2021-08-18 02:28:33,905 - INFO - main.py - train - 55 - 【train】 epoch:0 4534/5348 loss:3.2153
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  4842. 2021-08-18 02:28:36,004 - INFO - main.py - train - 55 - 【train】 epoch:0 4535/5348 loss:3.6360
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  4843. 2021-08-18 02:28:38,106 - INFO - main.py - train - 55 - 【train】 epoch:0 4536/5348 loss:3.2262
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  4844. 2021-08-18 02:28:40,193 - INFO - main.py - train - 55 - 【train】 epoch:0 4537/5348 loss:2.0512
    4845. 

  4845. 2021-08-18 02:28:42,285 - INFO - main.py - train - 55 - 【train】 epoch:0 4538/5348 loss:3.9537
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  4846. 2021-08-18 02:28:44,389 - INFO - main.py - train - 55 - 【train】 epoch:0 4539/5348 loss:4.9305
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  4847. 2021-08-18 02:28:46,479 - INFO - main.py - train - 55 - 【train】 epoch:0 4540/5348 loss:2.8505
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  4848. 2021-08-18 02:28:48,591 - INFO - main.py - train - 55 - 【train】 epoch:0 4541/5348 loss:3.6447
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  4849. 2021-08-18 02:28:50,693 - INFO - main.py - train - 55 - 【train】 epoch:0 4542/5348 loss:2.0076
    4850. 

  4850. 2021-08-18 02:28:52,793 - INFO - main.py - train - 55 - 【train】 epoch:0 4543/5348 loss:3.2018
    4851. 

  4851. 2021-08-18 02:28:54,893 - INFO - main.py - train - 55 - 【train】 epoch:0 4544/5348 loss:3.3446
    4852. 

  4852. 2021-08-18 02:28:56,985 - INFO - main.py - train - 55 - 【train】 epoch:0 4545/5348 loss:2.7401
    4853. 

  4853. 2021-08-18 02:28:59,073 - INFO - main.py - train - 55 - 【train】 epoch:0 4546/5348 loss:2.8858
    4854. 

  4854. 2021-08-18 02:29:01,144 - INFO - main.py - train - 55 - 【train】 epoch:0 4547/5348 loss:2.0951
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  4855. 2021-08-18 02:29:03,226 - INFO - main.py - train - 55 - 【train】 epoch:0 4548/5348 loss:2.9806
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  4856. 2021-08-18 02:29:05,305 - INFO - main.py - train - 55 - 【train】 epoch:0 4549/5348 loss:3.5541
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  4857. 2021-08-18 02:29:07,371 - INFO - main.py - train - 55 - 【train】 epoch:0 4550/5348 loss:2.7481
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  4858. 2021-08-18 02:29:09,448 - INFO - main.py - train - 55 - 【train】 epoch:0 4551/5348 loss:4.4407
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  4859. 2021-08-18 02:29:11,541 - INFO - main.py - train - 55 - 【train】 epoch:0 4552/5348 loss:2.3690
    4860. 

  4860. 2021-08-18 02:29:13,627 - INFO - main.py - train - 55 - 【train】 epoch:0 4553/5348 loss:3.3678
    4861. 

  4861. 2021-08-18 02:29:15,705 - INFO - main.py - train - 55 - 【train】 epoch:0 4554/5348 loss:1.8500
    4862. 

  4862. 2021-08-18 02:29:17,785 - INFO - main.py - train - 55 - 【train】 epoch:0 4555/5348 loss:3.1242
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  4863. 2021-08-18 02:29:19,861 - INFO - main.py - train - 55 - 【train】 epoch:0 4556/5348 loss:2.1501
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  4864. 2021-08-18 02:29:21,942 - INFO - main.py - train - 55 - 【train】 epoch:0 4557/5348 loss:2.9904
    4865. 

  4865. 2021-08-18 02:29:24,022 - INFO - main.py - train - 55 - 【train】 epoch:0 4558/5348 loss:3.7470
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  4866. 2021-08-18 02:29:26,104 - INFO - main.py - train - 55 - 【train】 epoch:0 4559/5348 loss:2.2215
    4867. 

  4867. 2021-08-18 02:29:28,196 - INFO - main.py - train - 55 - 【train】 epoch:0 4560/5348 loss:3.2285
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  4868. 2021-08-18 02:29:30,287 - INFO - main.py - train - 55 - 【train】 epoch:0 4561/5348 loss:3.2852
    4869. 

  4869. 2021-08-18 02:29:32,353 - INFO - main.py - train - 55 - 【train】 epoch:0 4562/5348 loss:3.8024
    4870. 

  4870. 2021-08-18 02:29:34,429 - INFO - main.py - train - 55 - 【train】 epoch:0 4563/5348 loss:2.1815
    4871. 

  4871. 2021-08-18 02:29:36,513 - INFO - main.py - train - 55 - 【train】 epoch:0 4564/5348 loss:2.8638
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  4872. 2021-08-18 02:29:38,591 - INFO - main.py - train - 55 - 【train】 epoch:0 4565/5348 loss:2.4674
    4873. 

  4873. 2021-08-18 02:29:40,674 - INFO - main.py - train - 55 - 【train】 epoch:0 4566/5348 loss:2.2511
    4874. 

  4874. 2021-08-18 02:29:42,773 - INFO - main.py - train - 55 - 【train】 epoch:0 4567/5348 loss:3.0095
    4875. 

  4875. 2021-08-18 02:29:44,884 - INFO - main.py - train - 55 - 【train】 epoch:0 4568/5348 loss:2.4082
    4876. 

  4876. 2021-08-18 02:29:46,968 - INFO - main.py - train - 55 - 【train】 epoch:0 4569/5348 loss:4.2501
    4877. 

  4877. 2021-08-18 02:29:49,063 - INFO - main.py - train - 55 - 【train】 epoch:0 4570/5348 loss:3.3354
    4878. 

  4878. 2021-08-18 02:29:51,153 - INFO - main.py - train - 55 - 【train】 epoch:0 4571/5348 loss:2.6353
    4879. 

  4879. 2021-08-18 02:29:53,246 - INFO - main.py - train - 55 - 【train】 epoch:0 4572/5348 loss:2.7123
    4880. 

  4880. 2021-08-18 02:29:55,333 - INFO - main.py - train - 55 - 【train】 epoch:0 4573/5348 loss:2.7476
    4881. 

  4881. 2021-08-18 02:29:57,424 - INFO - main.py - train - 55 - 【train】 epoch:0 4574/5348 loss:1.8140
    4882. 

  4882. 2021-08-18 02:29:59,520 - INFO - main.py - train - 55 - 【train】 epoch:0 4575/5348 loss:2.6069
    4883. 

  4883. 2021-08-18 02:30:01,619 - INFO - main.py - train - 55 - 【train】 epoch:0 4576/5348 loss:2.8195
    4884. 

  4884. 2021-08-18 02:30:03,719 - INFO - main.py - train - 55 - 【train】 epoch:0 4577/5348 loss:2.5161
    4885. 

  4885. 2021-08-18 02:30:05,812 - INFO - main.py - train - 55 - 【train】 epoch:0 4578/5348 loss:1.9791
    4886. 

  4886. 2021-08-18 02:30:07,874 - INFO - main.py - train - 55 - 【train】 epoch:0 4579/5348 loss:2.8970
    4887. 

  4887. 2021-08-18 02:30:09,944 - INFO - main.py - train - 55 - 【train】 epoch:0 4580/5348 loss:3.3230
    4888. 

  4888. 2021-08-18 02:30:12,022 - INFO - main.py - train - 55 - 【train】 epoch:0 4581/5348 loss:2.9189
    4889. 

  4889. 2021-08-18 02:30:14,116 - INFO - main.py - train - 55 - 【train】 epoch:0 4582/5348 loss:3.2504
    4890. 

  4890. 2021-08-18 02:30:16,199 - INFO - main.py - train - 55 - 【train】 epoch:0 4583/5348 loss:2.8336
    4891. 

  4891. 2021-08-18 02:30:18,303 - INFO - main.py - train - 55 - 【train】 epoch:0 4584/5348 loss:4.2664
    4892. 

  4892. 2021-08-18 02:30:20,395 - INFO - main.py - train - 55 - 【train】 epoch:0 4585/5348 loss:2.5371
    4893. 

  4893. 2021-08-18 02:30:22,470 - INFO - main.py - train - 55 - 【train】 epoch:0 4586/5348 loss:2.9553
    4894. 

  4894. 2021-08-18 02:30:24,562 - INFO - main.py - train - 55 - 【train】 epoch:0 4587/5348 loss:3.3842
    4895. 

  4895. 2021-08-18 02:30:26,656 - INFO - main.py - train - 55 - 【train】 epoch:0 4588/5348 loss:3.4792
    4896. 

  4896. 2021-08-18 02:30:28,755 - INFO - main.py - train - 55 - 【train】 epoch:0 4589/5348 loss:2.6944
    4897. 

  4897. 2021-08-18 02:30:30,848 - INFO - main.py - train - 55 - 【train】 epoch:0 4590/5348 loss:2.5457
    4898. 

  4898. 2021-08-18 02:30:32,949 - INFO - main.py - train - 55 - 【train】 epoch:0 4591/5348 loss:2.3879
    4899. 

  4899. 2021-08-18 02:30:35,055 - INFO - main.py - train - 55 - 【train】 epoch:0 4592/5348 loss:3.6665
    4900. 

  4900. 2021-08-18 02:30:37,154 - INFO - main.py - train - 55 - 【train】 epoch:0 4593/5348 loss:2.4273
    4901. 

  4901. 2021-08-18 02:30:39,254 - INFO - main.py - train - 55 - 【train】 epoch:0 4594/5348 loss:3.8989
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  4902. 2021-08-18 02:30:41,345 - INFO - main.py - train - 55 - 【train】 epoch:0 4595/5348 loss:2.7170
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  4903. 2021-08-18 02:30:43,427 - INFO - main.py - train - 55 - 【train】 epoch:0 4596/5348 loss:3.5225
    4904. 

  4904. 2021-08-18 02:30:45,515 - INFO - main.py - train - 55 - 【train】 epoch:0 4597/5348 loss:3.4758
    4905. 

  4905. 2021-08-18 02:30:47,596 - INFO - main.py - train - 55 - 【train】 epoch:0 4598/5348 loss:2.7249
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  4906. 2021-08-18 02:30:49,663 - INFO - main.py - train - 55 - 【train】 epoch:0 4599/5348 loss:2.9752
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  4907. 2021-08-18 02:30:51,739 - INFO - main.py - train - 55 - 【train】 epoch:0 4600/5348 loss:1.3316
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  4908. 2021-08-18 02:30:53,823 - INFO - main.py - train - 55 - 【train】 epoch:0 4601/5348 loss:4.4811
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  4909. 2021-08-18 02:30:55,920 - INFO - main.py - train - 55 - 【train】 epoch:0 4602/5348 loss:3.0484
    4910. 

  4910. 2021-08-18 02:30:57,983 - INFO - main.py - train - 55 - 【train】 epoch:0 4603/5348 loss:3.1711
    4911. 

  4911. 2021-08-18 02:31:00,059 - INFO - main.py - train - 55 - 【train】 epoch:0 4604/5348 loss:2.9554
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  4912. 2021-08-18 02:31:02,133 - INFO - main.py - train - 55 - 【train】 epoch:0 4605/5348 loss:1.7108
    4913. 

  4913. 2021-08-18 02:31:04,220 - INFO - main.py - train - 55 - 【train】 epoch:0 4606/5348 loss:3.4197
    4914. 

  4914. 2021-08-18 02:31:06,321 - INFO - main.py - train - 55 - 【train】 epoch:0 4607/5348 loss:3.8780
    4915. 

  4915. 2021-08-18 02:31:08,403 - INFO - main.py - train - 55 - 【train】 epoch:0 4608/5348 loss:2.9632
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  4916. 2021-08-18 02:31:10,478 - INFO - main.py - train - 55 - 【train】 epoch:0 4609/5348 loss:2.8648
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  4917. 2021-08-18 02:31:12,575 - INFO - main.py - train - 55 - 【train】 epoch:0 4610/5348 loss:3.2126
    4918. 

  4918. 2021-08-18 02:31:14,644 - INFO - main.py - train - 55 - 【train】 epoch:0 4611/5348 loss:1.7218
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    4920. 

  4920. 2021-08-18 02:31:18,816 - INFO - main.py - train - 55 - 【train】 epoch:0 4613/5348 loss:2.7582
    4921. 

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  4924. 2021-08-18 02:31:27,235 - INFO - main.py - train - 55 - 【train】 epoch:0 4617/5348 loss:3.1588
    4925. 

  4925. 2021-08-18 02:31:29,336 - INFO - main.py - train - 55 - 【train】 epoch:0 4618/5348 loss:4.1594
    4926. 

  4926. 2021-08-18 02:31:31,438 - INFO - main.py - train - 55 - 【train】 epoch:0 4619/5348 loss:3.1103
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    4930. 

  4930. 2021-08-18 02:31:39,838 - INFO - main.py - train - 55 - 【train】 epoch:0 4623/5348 loss:1.9131
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  4931. 2021-08-18 02:31:41,937 - INFO - main.py - train - 55 - 【train】 epoch:0 4624/5348 loss:2.2338
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    4937. 

  4937. 2021-08-18 02:31:54,467 - INFO - main.py - train - 55 - 【train】 epoch:0 4630/5348 loss:1.7735
    4938. 

  4938. 2021-08-18 02:31:56,560 - INFO - main.py - train - 55 - 【train】 epoch:0 4631/5348 loss:2.7915
    4939. 

  4939. 2021-08-18 02:31:58,666 - INFO - main.py - train - 55 - 【train】 epoch:0 4632/5348 loss:3.1347
    4940. 

  4940. 2021-08-18 02:32:00,754 - INFO - main.py - train - 55 - 【train】 epoch:0 4633/5348 loss:3.9952
    4941. 

  4941. 2021-08-18 02:32:02,839 - INFO - main.py - train - 55 - 【train】 epoch:0 4634/5348 loss:2.2514
    4942. 

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    4943. 

  4943. 2021-08-18 02:32:07,025 - INFO - main.py - train - 55 - 【train】 epoch:0 4636/5348 loss:3.0841
    4944. 

  4944. 2021-08-18 02:32:09,114 - INFO - main.py - train - 55 - 【train】 epoch:0 4637/5348 loss:1.9496
    4945. 

  4945. 2021-08-18 02:32:11,215 - INFO - main.py - train - 55 - 【train】 epoch:0 4638/5348 loss:2.5116
    4946. 

  4946. 2021-08-18 02:32:13,309 - INFO - main.py - train - 55 - 【train】 epoch:0 4639/5348 loss:4.2847
    4947. 

  4947. 2021-08-18 02:32:15,406 - INFO - main.py - train - 55 - 【train】 epoch:0 4640/5348 loss:2.8079
    4948. 

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    4949. 

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    4950. 

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    4951. 

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    4952. 

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    4953. 

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    4954. 

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    4955. 

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    4956. 

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    4957. 

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    4958. 

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    4959. 

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    4960. 

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    4961. 

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    4963. 

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    4964. 

  4964. 2021-08-18 02:32:51,030 - INFO - main.py - train - 55 - 【train】 epoch:0 4657/5348 loss:3.1328
    4965. 

  4965. 2021-08-18 02:32:53,115 - INFO - main.py - train - 55 - 【train】 epoch:0 4658/5348 loss:2.2905
    4966. 

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    4967. 

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    4991. 

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    5000. 

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  5002. 2021-08-18 02:34:10,577 - INFO - main.py - train - 55 - 【train】 epoch:0 4695/5348 loss:2.6070
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    5004. 

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  5054. 2021-08-18 02:35:59,535 - INFO - main.py - train - 55 - 【train】 epoch:0 4747/5348 loss:3.2898
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  5081. 2021-08-18 02:36:56,185 - INFO - main.py - train - 55 - 【train】 epoch:0 4774/5348 loss:2.8057
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    5089. 

  5089. 2021-08-18 02:37:12,946 - INFO - main.py - train - 55 - 【train】 epoch:0 4782/5348 loss:4.4773
    5090. 

  5090. 2021-08-18 02:37:15,034 - INFO - main.py - train - 55 - 【train】 epoch:0 4783/5348 loss:1.2538
    5091. 

  5091. 2021-08-18 02:37:17,145 - INFO - main.py - train - 55 - 【train】 epoch:0 4784/5348 loss:4.8061
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  5112. 2021-08-18 02:38:01,371 - INFO - main.py - train - 55 - 【train】 epoch:0 4805/5348 loss:3.1692
    5113. 

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    5115. 

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    5116. 

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    5119. 

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    5120. 

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    5143. 

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  5147. 2021-08-18 02:39:14,660 - INFO - main.py - train - 55 - 【train】 epoch:0 4840/5348 loss:2.9533
    5148. 

  5148. 2021-08-18 02:39:16,737 - INFO - main.py - train - 55 - 【train】 epoch:0 4841/5348 loss:2.7717
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  5149. 2021-08-18 02:39:18,822 - INFO - main.py - train - 55 - 【train】 epoch:0 4842/5348 loss:4.7333
    5150. 

  5150. 2021-08-18 02:39:20,911 - INFO - main.py - train - 55 - 【train】 epoch:0 4843/5348 loss:3.1972
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  5153. 2021-08-18 02:39:27,170 - INFO - main.py - train - 55 - 【train】 epoch:0 4846/5348 loss:3.5138
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  5156. 2021-08-18 02:39:33,406 - INFO - main.py - train - 55 - 【train】 epoch:0 4849/5348 loss:1.8250
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  5157. 2021-08-18 02:39:35,506 - INFO - main.py - train - 55 - 【train】 epoch:0 4850/5348 loss:2.7618
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  5159. 2021-08-18 02:39:39,690 - INFO - main.py - train - 55 - 【train】 epoch:0 4852/5348 loss:2.4198
    5160. 

  5160. 2021-08-18 02:39:41,775 - INFO - main.py - train - 55 - 【train】 epoch:0 4853/5348 loss:2.5307
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  5162. 2021-08-18 02:39:45,962 - INFO - main.py - train - 55 - 【train】 epoch:0 4855/5348 loss:2.4707
    5163. 

  5163. 2021-08-18 02:39:48,064 - INFO - main.py - train - 55 - 【train】 epoch:0 4856/5348 loss:3.1672
    5164. 

  5164. 2021-08-18 02:39:50,164 - INFO - main.py - train - 55 - 【train】 epoch:0 4857/5348 loss:1.9495
    5165. 

  5165. 2021-08-18 02:39:52,265 - INFO - main.py - train - 55 - 【train】 epoch:0 4858/5348 loss:3.9133
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  5166. 2021-08-18 02:39:54,369 - INFO - main.py - train - 55 - 【train】 epoch:0 4859/5348 loss:2.7562
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  5167. 2021-08-18 02:39:56,469 - INFO - main.py - train - 55 - 【train】 epoch:0 4860/5348 loss:3.3800
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  5168. 2021-08-18 02:39:58,578 - INFO - main.py - train - 55 - 【train】 epoch:0 4861/5348 loss:2.3362
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  5169. 2021-08-18 02:40:00,659 - INFO - main.py - train - 55 - 【train】 epoch:0 4862/5348 loss:2.6106
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  5170. 2021-08-18 02:40:02,750 - INFO - main.py - train - 55 - 【train】 epoch:0 4863/5348 loss:2.7662
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  5171. 2021-08-18 02:40:04,840 - INFO - main.py - train - 55 - 【train】 epoch:0 4864/5348 loss:2.2419
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  5172. 2021-08-18 02:40:06,923 - INFO - main.py - train - 55 - 【train】 epoch:0 4865/5348 loss:4.6221
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  5173. 2021-08-18 02:40:09,013 - INFO - main.py - train - 55 - 【train】 epoch:0 4866/5348 loss:2.3783
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  5174. 2021-08-18 02:40:11,089 - INFO - main.py - train - 55 - 【train】 epoch:0 4867/5348 loss:3.2386
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  5175. 2021-08-18 02:40:13,178 - INFO - main.py - train - 55 - 【train】 epoch:0 4868/5348 loss:4.0942
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  5177. 2021-08-18 02:40:17,354 - INFO - main.py - train - 55 - 【train】 epoch:0 4870/5348 loss:4.5072
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  5178. 2021-08-18 02:40:19,432 - INFO - main.py - train - 55 - 【train】 epoch:0 4871/5348 loss:2.7987
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  5179. 2021-08-18 02:40:21,493 - INFO - main.py - train - 55 - 【train】 epoch:0 4872/5348 loss:3.3415
    5180. 

  5180. 2021-08-18 02:40:23,569 - INFO - main.py - train - 55 - 【train】 epoch:0 4873/5348 loss:2.5007
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  5181. 2021-08-18 02:40:25,645 - INFO - main.py - train - 55 - 【train】 epoch:0 4874/5348 loss:2.2429
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  5183. 2021-08-18 02:40:29,809 - INFO - main.py - train - 55 - 【train】 epoch:0 4876/5348 loss:4.1866
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  5184. 2021-08-18 02:40:31,877 - INFO - main.py - train - 55 - 【train】 epoch:0 4877/5348 loss:2.3388
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  5185. 2021-08-18 02:40:33,959 - INFO - main.py - train - 55 - 【train】 epoch:0 4878/5348 loss:2.5185
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  5186. 2021-08-18 02:40:36,028 - INFO - main.py - train - 55 - 【train】 epoch:0 4879/5348 loss:1.7590
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  5187. 2021-08-18 02:40:38,120 - INFO - main.py - train - 55 - 【train】 epoch:0 4880/5348 loss:3.2655
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  5188. 2021-08-18 02:40:40,197 - INFO - main.py - train - 55 - 【train】 epoch:0 4881/5348 loss:3.0439
    5189. 

  5189. 2021-08-18 02:40:42,297 - INFO - main.py - train - 55 - 【train】 epoch:0 4882/5348 loss:3.3944
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  5190. 2021-08-18 02:40:44,391 - INFO - main.py - train - 55 - 【train】 epoch:0 4883/5348 loss:1.8702
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  5191. 2021-08-18 02:40:46,491 - INFO - main.py - train - 55 - 【train】 epoch:0 4884/5348 loss:4.0255
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  5193. 2021-08-18 02:40:50,668 - INFO - main.py - train - 55 - 【train】 epoch:0 4886/5348 loss:3.0706
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  5195. 2021-08-18 02:40:54,837 - INFO - main.py - train - 55 - 【train】 epoch:0 4888/5348 loss:1.6131
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  5197. 2021-08-18 02:40:59,045 - INFO - main.py - train - 55 - 【train】 epoch:0 4890/5348 loss:2.0412
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  5199. 2021-08-18 02:41:03,227 - INFO - main.py - train - 55 - 【train】 epoch:0 4892/5348 loss:3.8659
    5200. 

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  5203. 2021-08-18 02:41:11,609 - INFO - main.py - train - 55 - 【train】 epoch:0 4896/5348 loss:2.5720
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  5204. 2021-08-18 02:41:13,691 - INFO - main.py - train - 55 - 【train】 epoch:0 4897/5348 loss:3.5439
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  5205. 2021-08-18 02:41:15,780 - INFO - main.py - train - 55 - 【train】 epoch:0 4898/5348 loss:1.8050
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  5206. 2021-08-18 02:41:17,880 - INFO - main.py - train - 55 - 【train】 epoch:0 4899/5348 loss:4.4362
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  5210. 2021-08-18 02:41:26,253 - INFO - main.py - train - 55 - 【train】 epoch:0 4903/5348 loss:2.7948
    5211. 

  5211. 2021-08-18 02:41:28,336 - INFO - main.py - train - 55 - 【train】 epoch:0 4904/5348 loss:1.9310
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  5215. 2021-08-18 02:41:36,703 - INFO - main.py - train - 55 - 【train】 epoch:0 4908/5348 loss:2.9744
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  5217. 2021-08-18 02:41:40,863 - INFO - main.py - train - 55 - 【train】 epoch:0 4910/5348 loss:2.4344
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  5219. 2021-08-18 02:41:45,020 - INFO - main.py - train - 55 - 【train】 epoch:0 4912/5348 loss:2.6027
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  5224. 2021-08-18 02:41:55,408 - INFO - main.py - train - 55 - 【train】 epoch:0 4917/5348 loss:4.0305
    5225. 

  5225. 2021-08-18 02:41:57,478 - INFO - main.py - train - 55 - 【train】 epoch:0 4918/5348 loss:2.3516
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  5259. 2021-08-18 02:43:08,370 - INFO - main.py - train - 55 - 【train】 epoch:0 4952/5348 loss:3.6499
    5260. 

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    5261. 

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    5262. 

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    5263. 

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    5264. 

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    5265. 

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    5266. 

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    5267. 

  5267. 2021-08-18 02:43:25,020 - INFO - main.py - train - 55 - 【train】 epoch:0 4960/5348 loss:1.7335
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    5269. 

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    5270. 

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    5271. 

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    5281. 

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  5289. 2021-08-18 02:44:11,087 - INFO - main.py - train - 55 - 【train】 epoch:0 4982/5348 loss:3.5726
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    5295. 

  5295. 2021-08-18 02:44:23,558 - INFO - main.py - train - 55 - 【train】 epoch:0 4988/5348 loss:2.5425
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  5296. 2021-08-18 02:44:25,639 - INFO - main.py - train - 55 - 【train】 epoch:0 4989/5348 loss:1.7682
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  5297. 2021-08-18 02:44:27,727 - INFO - main.py - train - 55 - 【train】 epoch:0 4990/5348 loss:2.0773
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    5300. 

  5300. 2021-08-18 02:44:33,999 - INFO - main.py - train - 55 - 【train】 epoch:0 4993/5348 loss:3.5887
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  5301. 2021-08-18 02:44:36,090 - INFO - main.py - train - 55 - 【train】 epoch:0 4994/5348 loss:2.1994
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  5302. 2021-08-18 02:44:38,200 - INFO - main.py - train - 55 - 【train】 epoch:0 4995/5348 loss:2.6617
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  5303. 2021-08-18 02:44:40,305 - INFO - main.py - train - 55 - 【train】 epoch:0 4996/5348 loss:3.1199
    5304. 

  5304. 2021-08-18 02:44:42,399 - INFO - main.py - train - 55 - 【train】 epoch:0 4997/5348 loss:2.6321
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  5305. 2021-08-18 02:44:44,500 - INFO - main.py - train - 55 - 【train】 epoch:0 4998/5348 loss:3.1515
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  5306. 2021-08-18 02:44:46,597 - INFO - main.py - train - 55 - 【train】 epoch:0 4999/5348 loss:3.0167
    5307. 

  5307. 2021-08-18 02:44:48,692 - INFO - main.py - train - 55 - 【train】 epoch:0 5000/5348 loss:3.3680
    5308. 

  5308. 2021-08-18 02:44:50,795 - INFO - main.py - train - 55 - 【train】 epoch:0 5001/5348 loss:3.4535
    5309. 

  5309. 2021-08-18 02:44:52,888 - INFO - main.py - train - 55 - 【train】 epoch:0 5002/5348 loss:3.0891
    5310. 

  5310. 2021-08-18 02:44:54,983 - INFO - main.py - train - 55 - 【train】 epoch:0 5003/5348 loss:3.1758
    5311. 

  5311. 2021-08-18 02:44:57,068 - INFO - main.py - train - 55 - 【train】 epoch:0 5004/5348 loss:2.2988
    5312. 

  5312. 2021-08-18 02:44:59,160 - INFO - main.py - train - 55 - 【train】 epoch:0 5005/5348 loss:2.8152
    5313. 

  5313. 2021-08-18 02:45:01,237 - INFO - main.py - train - 55 - 【train】 epoch:0 5006/5348 loss:3.7042
    5314. 

  5314. 2021-08-18 02:45:03,329 - INFO - main.py - train - 55 - 【train】 epoch:0 5007/5348 loss:4.2093
    5315. 

  5315. 2021-08-18 02:45:05,423 - INFO - main.py - train - 55 - 【train】 epoch:0 5008/5348 loss:3.1924
    5316. 

  5316. 2021-08-18 02:45:07,519 - INFO - main.py - train - 55 - 【train】 epoch:0 5009/5348 loss:3.6268
    5317. 

  5317. 2021-08-18 02:45:09,603 - INFO - main.py - train - 55 - 【train】 epoch:0 5010/5348 loss:3.2565
    5318. 

  5318. 2021-08-18 02:45:11,709 - INFO - main.py - train - 55 - 【train】 epoch:0 5011/5348 loss:3.3407
    5319. 

  5319. 2021-08-18 02:45:13,815 - INFO - main.py - train - 55 - 【train】 epoch:0 5012/5348 loss:2.4938
    5320. 

  5320. 2021-08-18 02:45:15,892 - INFO - main.py - train - 55 - 【train】 epoch:0 5013/5348 loss:2.6589
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  5321. 2021-08-18 02:45:17,981 - INFO - main.py - train - 55 - 【train】 epoch:0 5014/5348 loss:3.1193
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  5322. 2021-08-18 02:45:20,077 - INFO - main.py - train - 55 - 【train】 epoch:0 5015/5348 loss:3.7353
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  5323. 2021-08-18 02:45:22,171 - INFO - main.py - train - 55 - 【train】 epoch:0 5016/5348 loss:3.8842
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  5324. 2021-08-18 02:45:24,270 - INFO - main.py - train - 55 - 【train】 epoch:0 5017/5348 loss:2.5315
    5325. 

  5325. 2021-08-18 02:45:26,365 - INFO - main.py - train - 55 - 【train】 epoch:0 5018/5348 loss:2.2076
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  5326. 2021-08-18 02:45:28,451 - INFO - main.py - train - 55 - 【train】 epoch:0 5019/5348 loss:1.8435
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  5327. 2021-08-18 02:45:30,542 - INFO - main.py - train - 55 - 【train】 epoch:0 5020/5348 loss:2.7811
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  5328. 2021-08-18 02:45:32,642 - INFO - main.py - train - 55 - 【train】 epoch:0 5021/5348 loss:3.3768
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  5329. 2021-08-18 02:45:34,741 - INFO - main.py - train - 55 - 【train】 epoch:0 5022/5348 loss:3.7580
    5330. 

  5330. 2021-08-18 02:45:36,840 - INFO - main.py - train - 55 - 【train】 epoch:0 5023/5348 loss:1.9102
    5331. 

  5331. 2021-08-18 02:45:38,955 - INFO - main.py - train - 55 - 【train】 epoch:0 5024/5348 loss:2.7714
    5332. 

  5332. 2021-08-18 02:45:41,056 - INFO - main.py - train - 55 - 【train】 epoch:0 5025/5348 loss:2.7036
    5333. 

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  5335. 2021-08-18 02:45:47,360 - INFO - main.py - train - 55 - 【train】 epoch:0 5028/5348 loss:4.2872
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    5340. 

  5340. 2021-08-18 02:45:57,891 - INFO - main.py - train - 55 - 【train】 epoch:0 5033/5348 loss:2.7361
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    5345. 

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    5348. 

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    5349. 

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    5350. 

  5350. 2021-08-18 02:46:18,803 - INFO - main.py - train - 55 - 【train】 epoch:0 5043/5348 loss:2.7330
    5351. 

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    5353. 

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    5356. 

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    5359. 

  5359. 2021-08-18 02:46:37,590 - INFO - main.py - train - 55 - 【train】 epoch:0 5052/5348 loss:2.5384
    5360. 

  5360. 2021-08-18 02:46:39,686 - INFO - main.py - train - 55 - 【train】 epoch:0 5053/5348 loss:4.2773
    5361. 

  5361. 2021-08-18 02:46:41,788 - INFO - main.py - train - 55 - 【train】 epoch:0 5054/5348 loss:1.9915
    5362. 

  5362. 2021-08-18 02:46:43,881 - INFO - main.py - train - 55 - 【train】 epoch:0 5055/5348 loss:2.7957
    5363. 

  5363. 2021-08-18 02:46:45,990 - INFO - main.py - train - 55 - 【train】 epoch:0 5056/5348 loss:4.1788
    5364. 

  5364. 2021-08-18 02:46:48,099 - INFO - main.py - train - 55 - 【train】 epoch:0 5057/5348 loss:2.7719
    5365. 

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    5373. 

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    5380. 

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  5381. 2021-08-18 02:47:23,832 - INFO - main.py - train - 55 - 【train】 epoch:0 5074/5348 loss:4.2813
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    5383. 

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    5395. 

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    5399. 

  5399. 2021-08-18 02:48:01,562 - INFO - main.py - train - 55 - 【train】 epoch:0 5092/5348 loss:3.2029
    5400. 

  5400. 2021-08-18 02:48:03,658 - INFO - main.py - train - 55 - 【train】 epoch:0 5093/5348 loss:3.0102
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    5406. 

  5406. 2021-08-18 02:48:16,240 - INFO - main.py - train - 55 - 【train】 epoch:0 5099/5348 loss:4.0210
    5407. 

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    5419. 

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  5429. 2021-08-18 02:49:04,441 - INFO - main.py - train - 55 - 【train】 epoch:0 5122/5348 loss:3.7360
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  5430. 2021-08-18 02:49:06,531 - INFO - main.py - train - 55 - 【train】 epoch:0 5123/5348 loss:2.2240
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  5434. 2021-08-18 02:49:14,894 - INFO - main.py - train - 55 - 【train】 epoch:0 5127/5348 loss:2.4405
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  5436. 2021-08-18 02:49:19,040 - INFO - main.py - train - 55 - 【train】 epoch:0 5129/5348 loss:2.1733
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  5437. 2021-08-18 02:49:21,125 - INFO - main.py - train - 55 - 【train】 epoch:0 5130/5348 loss:2.9719
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  5438. 2021-08-18 02:49:23,213 - INFO - main.py - train - 55 - 【train】 epoch:0 5131/5348 loss:2.0126
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  5439. 2021-08-18 02:49:25,304 - INFO - main.py - train - 55 - 【train】 epoch:0 5132/5348 loss:4.4047
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  5440. 2021-08-18 02:49:27,384 - INFO - main.py - train - 55 - 【train】 epoch:0 5133/5348 loss:2.6502
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  5441. 2021-08-18 02:49:29,457 - INFO - main.py - train - 55 - 【train】 epoch:0 5134/5348 loss:2.2378
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  5442. 2021-08-18 02:49:31,555 - INFO - main.py - train - 55 - 【train】 epoch:0 5135/5348 loss:3.1448
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  5443. 2021-08-18 02:49:33,665 - INFO - main.py - train - 55 - 【train】 epoch:0 5136/5348 loss:2.9792
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  5445. 2021-08-18 02:49:37,885 - INFO - main.py - train - 55 - 【train】 epoch:0 5138/5348 loss:3.4435
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  5446. 2021-08-18 02:49:39,955 - INFO - main.py - train - 55 - 【train】 epoch:0 5139/5348 loss:2.9908
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  5450. 2021-08-18 02:49:48,351 - INFO - main.py - train - 55 - 【train】 epoch:0 5143/5348 loss:2.6091
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  5457. 2021-08-18 02:50:02,924 - INFO - main.py - train - 55 - 【train】 epoch:0 5150/5348 loss:2.6411
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  5459. 2021-08-18 02:50:07,089 - INFO - main.py - train - 55 - 【train】 epoch:0 5152/5348 loss:4.2489
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  5460. 2021-08-18 02:50:09,184 - INFO - main.py - train - 55 - 【train】 epoch:0 5153/5348 loss:3.9173
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  5461. 2021-08-18 02:50:11,270 - INFO - main.py - train - 55 - 【train】 epoch:0 5154/5348 loss:3.1715
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  5462. 2021-08-18 02:50:13,373 - INFO - main.py - train - 55 - 【train】 epoch:0 5155/5348 loss:3.1027
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  5463. 2021-08-18 02:50:15,458 - INFO - main.py - train - 55 - 【train】 epoch:0 5156/5348 loss:2.7575
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  5464. 2021-08-18 02:50:17,540 - INFO - main.py - train - 55 - 【train】 epoch:0 5157/5348 loss:4.0650
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  5465. 2021-08-18 02:50:19,633 - INFO - main.py - train - 55 - 【train】 epoch:0 5158/5348 loss:3.7642
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  5466. 2021-08-18 02:50:21,712 - INFO - main.py - train - 55 - 【train】 epoch:0 5159/5348 loss:3.9296
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  5467. 2021-08-18 02:50:23,791 - INFO - main.py - train - 55 - 【train】 epoch:0 5160/5348 loss:2.5421
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  5468. 2021-08-18 02:50:25,870 - INFO - main.py - train - 55 - 【train】 epoch:0 5161/5348 loss:2.4539
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  5469. 2021-08-18 02:50:27,955 - INFO - main.py - train - 55 - 【train】 epoch:0 5162/5348 loss:3.8371
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  5470. 2021-08-18 02:50:30,033 - INFO - main.py - train - 55 - 【train】 epoch:0 5163/5348 loss:2.2468
    5471. 

  5471. 2021-08-18 02:50:32,098 - INFO - main.py - train - 55 - 【train】 epoch:0 5164/5348 loss:3.0828
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  5472. 2021-08-18 02:50:34,177 - INFO - main.py - train - 55 - 【train】 epoch:0 5165/5348 loss:3.1544
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  5473. 2021-08-18 02:50:36,284 - INFO - main.py - train - 55 - 【train】 epoch:0 5166/5348 loss:2.8759
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  5474. 2021-08-18 02:50:38,381 - INFO - main.py - train - 55 - 【train】 epoch:0 5167/5348 loss:2.4993
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  5475. 2021-08-18 02:50:40,489 - INFO - main.py - train - 55 - 【train】 epoch:0 5168/5348 loss:2.6796
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  5476. 2021-08-18 02:50:42,593 - INFO - main.py - train - 55 - 【train】 epoch:0 5169/5348 loss:2.3607
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  5477. 2021-08-18 02:50:44,687 - INFO - main.py - train - 55 - 【train】 epoch:0 5170/5348 loss:2.2319
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  5478. 2021-08-18 02:50:46,787 - INFO - main.py - train - 55 - 【train】 epoch:0 5171/5348 loss:1.5117
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  5479. 2021-08-18 02:50:48,888 - INFO - main.py - train - 55 - 【train】 epoch:0 5172/5348 loss:3.2141
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  5480. 2021-08-18 02:50:50,991 - INFO - main.py - train - 55 - 【train】 epoch:0 5173/5348 loss:3.5075
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  5481. 2021-08-18 02:50:53,082 - INFO - main.py - train - 55 - 【train】 epoch:0 5174/5348 loss:3.0000
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  5482. 2021-08-18 02:50:55,191 - INFO - main.py - train - 55 - 【train】 epoch:0 5175/5348 loss:3.8795
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  5483. 2021-08-18 02:50:57,279 - INFO - main.py - train - 55 - 【train】 epoch:0 5176/5348 loss:2.0039
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  5484. 2021-08-18 02:50:59,369 - INFO - main.py - train - 55 - 【train】 epoch:0 5177/5348 loss:2.0009
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  5485. 2021-08-18 02:51:01,444 - INFO - main.py - train - 55 - 【train】 epoch:0 5178/5348 loss:3.0145
    5486. 

  5486. 2021-08-18 02:51:03,520 - INFO - main.py - train - 55 - 【train】 epoch:0 5179/5348 loss:3.6592
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  5487. 2021-08-18 02:51:05,591 - INFO - main.py - train - 55 - 【train】 epoch:0 5180/5348 loss:2.7930
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  5488. 2021-08-18 02:51:07,669 - INFO - main.py - train - 55 - 【train】 epoch:0 5181/5348 loss:4.5988
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  5489. 2021-08-18 02:51:09,747 - INFO - main.py - train - 55 - 【train】 epoch:0 5182/5348 loss:3.6745
    5490. 

  5490. 2021-08-18 02:51:11,827 - INFO - main.py - train - 55 - 【train】 epoch:0 5183/5348 loss:1.8430
    5491. 

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    5492. 

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    5494. 

  5494. 2021-08-18 02:51:20,198 - INFO - main.py - train - 55 - 【train】 epoch:0 5187/5348 loss:3.2778
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  5495. 2021-08-18 02:51:22,273 - INFO - main.py - train - 55 - 【train】 epoch:0 5188/5348 loss:2.8204
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  5496. 2021-08-18 02:51:24,361 - INFO - main.py - train - 55 - 【train】 epoch:0 5189/5348 loss:2.8790
    5497. 

  5497. 2021-08-18 02:51:26,457 - INFO - main.py - train - 55 - 【train】 epoch:0 5190/5348 loss:3.1559
    5498. 

  5498. 2021-08-18 02:51:28,540 - INFO - main.py - train - 55 - 【train】 epoch:0 5191/5348 loss:2.8974
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  5499. 2021-08-18 02:51:30,628 - INFO - main.py - train - 55 - 【train】 epoch:0 5192/5348 loss:2.3994
    5500. 

  5500. 2021-08-18 02:51:32,709 - INFO - main.py - train - 55 - 【train】 epoch:0 5193/5348 loss:3.1384
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  5501. 2021-08-18 02:51:34,785 - INFO - main.py - train - 55 - 【train】 epoch:0 5194/5348 loss:2.3348
    5502. 

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  5503. 2021-08-18 02:51:38,937 - INFO - main.py - train - 55 - 【train】 epoch:0 5196/5348 loss:2.3521
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  5504. 2021-08-18 02:51:41,022 - INFO - main.py - train - 55 - 【train】 epoch:0 5197/5348 loss:2.5787
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  5505. 2021-08-18 02:51:43,122 - INFO - main.py - train - 55 - 【train】 epoch:0 5198/5348 loss:2.9396
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  5512. 2021-08-18 02:51:57,858 - INFO - main.py - train - 55 - 【train】 epoch:0 5205/5348 loss:1.8974
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  5530. 2021-08-18 02:52:35,697 - INFO - main.py - train - 55 - 【train】 epoch:0 5223/5348 loss:4.1967
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  5531. 2021-08-18 02:52:37,779 - INFO - main.py - train - 55 - 【train】 epoch:0 5224/5348 loss:1.5532
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  5553. 2021-08-18 02:53:23,621 - INFO - main.py - train - 55 - 【train】 epoch:0 5246/5348 loss:1.9427
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  5599. 2021-08-18 02:54:59,683 - INFO - main.py - train - 55 - 【train】 epoch:0 5292/5348 loss:2.1316
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  5600. 2021-08-18 02:55:01,762 - INFO - main.py - train - 55 - 【train】 epoch:0 5293/5348 loss:3.5741
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  5601. 2021-08-18 02:55:03,857 - INFO - main.py - train - 55 - 【train】 epoch:0 5294/5348 loss:2.1481
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  5602. 2021-08-18 02:55:05,931 - INFO - main.py - train - 55 - 【train】 epoch:0 5295/5348 loss:2.9687
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  5603. 2021-08-18 02:55:07,994 - INFO - main.py - train - 55 - 【train】 epoch:0 5296/5348 loss:2.2426
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  5617. 2021-08-18 02:55:37,123 - INFO - main.py - train - 55 - 【train】 epoch:0 5310/5348 loss:2.9642
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  5629. 2021-08-18 02:56:02,229 - INFO - main.py - train - 55 - 【train】 epoch:0 5322/5348 loss:1.8735
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  5630. 2021-08-18 02:56:04,307 - INFO - main.py - train - 55 - 【train】 epoch:0 5323/5348 loss:3.4912
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  5631. 2021-08-18 02:56:06,385 - INFO - main.py - train - 55 - 【train】 epoch:0 5324/5348 loss:2.6600
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  5641. 2021-08-18 02:56:27,194 - INFO - main.py - train - 55 - 【train】 epoch:0 5334/5348 loss:3.2444
    5642. 

  5642. 2021-08-18 02:56:29,258 - INFO - main.py - train - 55 - 【train】 epoch:0 5335/5348 loss:1.7769
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  5643. 2021-08-18 02:56:31,336 - INFO - main.py - train - 55 - 【train】 epoch:0 5336/5348 loss:2.0439
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  5644. 2021-08-18 02:56:33,412 - INFO - main.py - train - 55 - 【train】 epoch:0 5337/5348 loss:2.0487
    5645. 

  5645. 2021-08-18 02:56:35,495 - INFO - main.py - train - 55 - 【train】 epoch:0 5338/5348 loss:2.4738
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  5646. 2021-08-18 02:56:37,556 - INFO - main.py - train - 55 - 【train】 epoch:0 5339/5348 loss:2.2595
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  5647. 2021-08-18 02:56:39,652 - INFO - main.py - train - 55 - 【train】 epoch:0 5340/5348 loss:3.3357
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  5648. 2021-08-18 02:56:41,746 - INFO - main.py - train - 55 - 【train】 epoch:0 5341/5348 loss:2.8148
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  5649. 2021-08-18 02:56:43,843 - INFO - main.py - train - 55 - 【train】 epoch:0 5342/5348 loss:2.4879
    5650. 

  5650. 2021-08-18 02:56:45,948 - INFO - main.py - train - 55 - 【train】 epoch:0 5343/5348 loss:2.6592
    5651. 

  5651. 2021-08-18 02:56:48,045 - INFO - main.py - train - 55 - 【train】 epoch:0 5344/5348 loss:3.1773
    5652. 

  5652. 2021-08-18 02:56:50,124 - INFO - main.py - train - 55 - 【train】 epoch:0 5345/5348 loss:2.4169
    5653. 

  5653. 2021-08-18 02:56:52,219 - INFO - main.py - train - 55 - 【train】 epoch:0 5346/5348 loss:3.5758
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  5654. 2021-08-18 02:56:54,270 - INFO - main.py - train - 55 - 【train】 epoch:0 5347/5348 loss:5.5199
    5655. 

  5655. 2021-08-18 02:56:54,520 - INFO - trainUtils.py - save_model - 70 - Saving model checkpoint to ./checkpoints/bert_bilstm_crf
    5656. 

  5656. 2021-08-18 11:19:55,422 - INFO - main.py - <module> - 209 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    5657. 

  5657. 2021-08-18 11:19:55,861 - INFO - main.py - <module> - 211 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    5658. 

  5658. 2021-08-18 11:20:56,432 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5659. 

  5659. 2021-08-18 11:21:03,329 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5660. 

  5660. 2021-08-18 11:21:08,816 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5661. 

  5661. 2021-08-18 11:22:33,666 - INFO - main.py - <module> - 209 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    5662. 

  5662. 2021-08-18 11:22:33,667 - INFO - main.py - <module> - 211 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    5663. 

  5663. 2021-08-18 11:23:16,795 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5664. 

  5664. 2021-08-18 11:23:18,294 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5665. 

  5665. 2021-08-18 11:23:30,091 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5666. 

  5666. 2021-08-18 11:40:13,156 - INFO - main.py - <module> - 209 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    5667. 

  5667. 2021-08-18 11:40:13,158 - INFO - main.py - <module> - 211 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    5668. 

  5668. 2021-08-18 11:40:56,515 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5669. 

  5669. 2021-08-18 11:40:58,007 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5670. 

  5670. 2021-08-18 11:41:09,745 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5671. 

  5671. 2021-08-18 12:02:09,646 - INFO - main.py - <module> - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    5672. 

  5672. 2021-08-18 12:02:09,651 - INFO - main.py - <module> - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    5673. 

  5673. 2021-08-18 12:02:50,804 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5674. 

  5674. 2021-08-18 12:02:52,299 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5675. 

  5675. 2021-08-18 12:03:03,592 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5676. 

  5676. 2021-08-18 12:03:51,780 - INFO - main.py - <module> - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    5677. 

  5677. 2021-08-18 12:03:51,781 - INFO - main.py - <module> - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    5678. 

  5678. 2021-08-18 12:04:31,452 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5679. 

  5679. 2021-08-18 12:04:32,941 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5680. 

  5680. 2021-08-18 12:04:44,208 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5681. 

  5681. 2021-08-18 12:08:05,903 - INFO - main.py - <module> - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    5682. 

  5682. 2021-08-18 12:08:05,908 - INFO - main.py - <module> - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    5683. 

  5683. 2021-08-18 12:08:43,557 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5684. 

  5684. 2021-08-18 12:08:45,181 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5685. 

  5685. 2021-08-18 12:08:51,819 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5686. 

  5686. 2021-08-18 12:17:05,417 - INFO - main.py - test - 144 -               precision    recall  f1-score   support
    5687. 

  5687. 

  5688.       object       0.76      0.89      0.82     38656
    5689. 

  5689.      subject       0.76      0.85      0.80     25103
    5690. 

  5690. 

  5691.    micro avg       0.76      0.88      0.81     63759
    5692. 

  5692.    macro avg       0.76      0.87      0.81     63759
    5693. 

  5693. weighted avg       0.76      0.88      0.81     63759
    5694. 

  5694. 

  5695. 2021-08-18 12:35:52,912 - INFO - main.py - <module> - 208 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    5696. 

  5696. 2021-08-18 12:35:52,915 - INFO - main.py - <module> - 210 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    5697. 

  5697. 2021-08-18 12:36:32,932 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5698. 

  5698. 2021-08-18 12:36:32,936 - INFO - main.py - <module> - 236 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s
    5699. 

  5699. 2021-08-18 12:36:34,408 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5700. 

  5700. 2021-08-18 12:36:42,078 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5701. 

  5701. 2021-08-18 12:40:09,187 - INFO - main.py - <module> - 208 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    5702. 

  5702. 2021-08-18 12:40:09,191 - INFO - main.py - <module> - 210 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    5703. 

  5703. 2021-08-18 12:40:46,152 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5704. 

  5704. 2021-08-18 12:40:46,156 - INFO - main.py - <module> - 236 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s
    5705. 

  5705. 2021-08-18 12:40:47,636 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5706. 

  5706. 2021-08-18 12:40:53,854 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5707. 

  5707. 2021-08-18 12:41:57,908 - INFO - main.py - <module> - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    5708. 

  5708. 2021-08-18 12:41:57,909 - INFO - main.py - <module> - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    5709. 

  5709. 2021-08-18 12:42:34,894 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5710. 

  5710. 2021-08-18 12:42:34,898 - INFO - main.py - <module> - 239 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s
    5711. 

  5711. 2021-08-18 12:42:36,383 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5712. 

  5712. 2021-08-18 12:42:43,950 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5713. 

  5713. 2021-08-18 12:45:56,680 - INFO - main.py - <module> - 212 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    5714. 

  5714. 2021-08-18 12:45:56,683 - INFO - main.py - <module> - 214 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    5715. 

  5715. 2021-08-18 12:46:33,166 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5716. 

  5716. 2021-08-18 12:46:33,170 - INFO - main.py - <module> - 240 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s
    5717. 

  5717. 2021-08-18 12:46:34,650 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5718. 

  5718. 2021-08-18 12:46:37,771 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5719. 

  5719. 2021-08-18 12:49:58,061 - INFO - main.py - <module> - 210 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    5720. 

  5720. 2021-08-18 12:49:58,062 - INFO - main.py - <module> - 212 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    5721. 

  5721. 2021-08-18 12:50:34,756 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5722. 

  5722. 2021-08-18 12:50:34,760 - INFO - main.py - <module> - 238 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s
    5723. 

  5723. 2021-08-18 12:50:36,258 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5724. 

  5724. 2021-08-18 12:50:44,329 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5725. 

  5725. 2021-08-18 12:50:44,452 - INFO - main.py - predict - 170 - [('身》', 2, 'B'), ('》是', 3, 'I'), ('ut', 6, 'B'), ('tsider演唱', 7, 'I'), ('辑M', 26, 'B'), ('Maestro》。', 27, 'I'), ('身的', 42, 'B'), ('的感', 43, 'I'), ('身即', 48, 'B'), ('即是', 49, 'I')]
    5726. 

  5726. 2021-08-18 12:50:44,454 - INFO - main.py - <module> - 241 - 歌曲《墨写你的美》是由歌手冷漠演唱的一首歌曲
    5727. 

  5727. 2021-08-18 12:50:45,853 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5728. 

  5728. 2021-08-18 12:50:46,692 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5729. 

  5729. 2021-08-18 12:50:46,774 - INFO - main.py - predict - 170 - [('写你', 4, 'B'), ('你的美》是', 5, 'I'), ('漠演', 14, 'B'), ('演唱', 15, 'I')]
    5730. 

  5730. 2021-08-18 12:52:15,992 - INFO - main.py - <module> - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    5731. 

  5731. 2021-08-18 12:52:15,993 - INFO - main.py - <module> - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    5732. 

  5732. 2021-08-18 12:52:54,909 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5733. 

  5733. 2021-08-18 12:52:54,913 - INFO - main.py - <module> - 239 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s
    5734. 

  5734. 2021-08-18 12:52:56,424 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5735. 

  5735. 2021-08-18 12:52:57,563 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5736. 

  5736. 2021-08-18 12:52:57,671 - INFO - main.py - predict - 171 - [('身》', 2, 'B'), ('》是', 3, 'I'), ('ut', 6, 'B'), ('tsider演唱', 7, 'I'), ('辑M', 26, 'B'), ('Maestro》。', 27, 'I'), ('身的', 42, 'B'), ('的感', 43, 'I'), ('身即', 48, 'B'), ('即是', 49, 'I')]
    5737. 

  5737. 2021-08-18 12:52:57,673 - INFO - main.py - <module> - 242 - 歌曲《墨写你的美》是由歌手冷漠演唱的一首歌曲
    5738. 

  5738. 2021-08-18 12:52:59,084 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5739. 

  5739. 2021-08-18 12:53:00,075 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5740. 

  5740. 2021-08-18 12:53:00,158 - INFO - main.py - predict - 171 - [('写你', 4, 'B'), ('你的美》是', 5, 'I'), ('漠演', 14, 'B'), ('演唱', 15, 'I')]
    5741. 

  5741. 2021-08-18 12:54:04,343 - INFO - main.py - <module> - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    5742. 

  5742. 2021-08-18 12:54:04,344 - INFO - main.py - <module> - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    5743. 

  5743. 2021-08-18 12:54:47,898 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5744. 

  5744. 2021-08-18 12:54:47,902 - INFO - main.py - <module> - 239 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s
    5745. 

  5745. 2021-08-18 12:54:49,415 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5746. 

  5746. 2021-08-18 12:54:50,514 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5747. 

  5747. 2021-08-18 12:54:50,620 - INFO - main.py - predict - 171 - [('身》是', 2, 'subject'), ('utsider演唱', 6, 'object'), ('辑Maestro》。', 26, 'object'), ('身的感', 42, 'subject'), ('身即是', 48, 'subject')]
    5748. 

  5748. 2021-08-18 12:54:50,623 - INFO - main.py - <module> - 242 - 歌曲《墨写你的美》是由歌手冷漠演唱的一首歌曲
    5749. 

  5749. 2021-08-18 12:54:51,974 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5750. 

  5750. 2021-08-18 12:54:52,762 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5751. 

  5751. 2021-08-18 12:54:52,843 - INFO - main.py - predict - 171 - [('写你的美》是', 4, 'subject'), ('漠演唱', 14, 'object')]
    5752. 

  5752. 2021-08-18 12:55:46,286 - INFO - main.py - <module> - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    5753. 

  5753. 2021-08-18 12:55:46,287 - INFO - main.py - <module> - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    5754. 

  5754. 2021-08-18 12:56:29,495 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5755. 

  5755. 2021-08-18 12:56:29,499 - INFO - main.py - <module> - 239 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s
    5756. 

  5756. 2021-08-18 12:56:30,996 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5757. 

  5757. 2021-08-18 12:56:32,076 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5758. 

  5758. 2021-08-18 12:56:32,208 - INFO - main.py - predict - 171 - [('单身》', 1, 'subject'), ('Outsider演', 5, 'object'), ('2辑Maestro》', 25, 'object'), ('单身的', 41, 'subject'), ('单身即', 47, 'subject')]
    5759. 

  5759. 2021-08-18 12:56:32,210 - INFO - main.py - <module> - 242 - 歌曲《墨写你的美》是由歌手冷漠演唱的一首歌曲
    5760. 

  5760. 2021-08-18 12:56:33,766 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5761. 

  5761. 2021-08-18 12:56:34,764 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5762. 

  5762. 2021-08-18 12:56:34,850 - INFO - main.py - predict - 171 - [('墨写你的美》', 3, 'subject'), ('冷漠演', 13, 'object')]
    5763. 

  5763. 2021-08-18 12:58:01,458 - INFO - main.py - <module> - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    5764. 

  5764. 2021-08-18 12:58:01,459 - INFO - main.py - <module> - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    5765. 

  5765. 2021-08-18 12:58:37,558 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5766. 

  5766. 2021-08-18 12:58:37,562 - INFO - main.py - <module> - 239 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s
    5767. 

  5767. 2021-08-18 12:58:39,044 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5768. 

  5768. 2021-08-18 12:58:40,212 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5769. 

  5769. 2021-08-18 12:58:40,318 - INFO - main.py - predict - 171 - [('单身', 1, 'subject'), ('Outsider', 5, 'object'), ('2辑Maestro', 25, 'object'), ('单身', 41, 'subject'), ('单身', 47, 'subject')]
    5770. 

  5770. 2021-08-18 12:58:40,321 - INFO - main.py - <module> - 242 - 歌曲《墨写你的美》是由歌手冷漠演唱的一首歌曲
    5771. 

  5771. 2021-08-18 12:58:41,656 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5772. 

  5772. 2021-08-18 12:58:42,454 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['0']
    5773. 

  5773. 2021-08-18 12:58:42,536 - INFO - main.py - predict - 171 - [('墨写你的美', 3, 'subject'), ('冷漠', 13, 'object')]
    5774. 

  5774. 2021-08-18 13:20:10,327 - INFO - main.py - <module> - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    5775. 

  5775. 2021-08-18 13:20:10,560 - INFO - main.py - <module> - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='0', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    5776. 

  5776. 2021-08-18 13:22:28,005 - INFO - main.py - <module> - 211 - {0: 'O', 1: 'B-object', 2: 'I-object', 3: 'B-subject', 4: 'I-subject'}
    5777. 

  5777. 2021-08-18 13:22:28,006 - INFO - main.py - <module> - 213 - Namespace(adam_epsilon=1e-08, bert_dir='../model_hub/chinese-roberta-wwm-ext/', crf_lr=0.03, data_dir='../data/', dropout=0.3, dropout_prob=0.3, eval_batch_size=32, gpu_ids='-1', log_dir='./logs/', lr=3e-05, lstm_hidden=128, max_grad_norm=1.0, max_seq_len=300, num_layers=1, num_tags=5, other_lr=0.0003, output_dir='./checkpoints/', seed=123, swa_start=3, train_batch_size=32, train_epochs=1, use_crf='True', use_lstm='True', warmup_proportion=0.1, weight_decay=0.01)
    5778. 

  5778. 2021-08-18 13:23:07,063 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['-1']
    5779. 

  5779. 2021-08-18 13:23:07,068 - INFO - main.py - <module> - 239 - 《单身》是Outsider演唱的歌曲，收录于专辑《2辑Maestro》。描写一个人单身的感觉，单身即是痛苦的也是幸福的，在于人们如何去看待s
    5780. 

  5780. 2021-08-18 13:23:09,576 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5781. 

  5781. 2021-08-18 13:23:15,200 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['-1']
    5782. 

  5782. 2021-08-18 13:23:17,002 - INFO - main.py - predict - 171 - [('单身', 1, 'subject'), ('Outsider', 5, 'object'), ('2辑Maestro', 25, 'object'), ('单身', 41, 'subject'), ('单身', 47, 'subject')]
    5783. 

  5783. 2021-08-18 13:23:17,022 - INFO - main.py - <module> - 242 - 歌曲《墨写你的美》是由歌手冷漠演唱的一首歌曲
    5784. 

  5784. 2021-08-18 13:23:18,589 - INFO - trainUtils.py - load_model_and_parallel - 96 - Load ckpt from ./checkpoints/bert_bilstm_crf/model.pt
    5785. 

  5785. 2021-08-18 13:23:19,506 - INFO - trainUtils.py - load_model_and_parallel - 106 - Use single gpu in: ['-1']
    5786. 

  5786. 2021-08-18 13:23:20,846 - INFO - main.py - predict - 171 - [('墨写你的美', 3, 'subject'), ('冷漠', 13, 'object')]
    5787.