[2023-08-14 04:24:41,220 INFO] Use GPU: 0 for training
[2023-08-14 04:24:55,374 INFO] unlabeled data number: 15813, labeled data number 1772
[2023-08-14 04:24:55,376 INFO] Create train and test data loaders
[2023-08-14 04:24:57,099 INFO] [!] data loader keys: dict_keys(['train_lb', 'train_ulb', 'eval', 'test'])
[2023-08-14 04:25:07,636 INFO] Create optimizer and scheduler
[2023-08-14 04:25:09,702 INFO] Number of Trainable Params: 94977684
[2023-08-14 04:25:10,145 INFO] Arguments: Namespace(save_dir='/liuzicheng/jwy/saved_models/usb_audio', save_name='pseudolabel_fsdnoisy_1773_0', resume=True, load_path='/liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth', overwrite=True, use_tensorboard=True, use_wandb=False, use_aim=False, epoch=100, num_train_iter=102400, num_warmup_iter=5120, num_eval_iter=2048, num_log_iter=256, num_labels=1773, batch_size=8, uratio=1, eval_batch_size=16, ema_m=0.0, ulb_loss_ratio=1.0, optim='AdamW', lr=0.0005, momentum=0.9, weight_decay=2e-05, layer_decay=0.75, net='hubert_base', net_from_name=False, use_pretrain=False, pretrain_path='', algorithm='pseudolabel', use_cat=False, amp=False, clip_grad=0, imb_algorithm=None, data_dir='/liuzicheng/data/data', dataset='fsdnoisy', num_classes=20, train_sampler='RandomSampler', num_workers=4, include_lb_to_ulb=True, lb_imb_ratio=1, ulb_imb_ratio=1, ulb_num_labels=None, img_size=32, crop_ratio=0.875, max_length=512, max_length_seconds=5.0, sample_rate=16000, world_size=1, rank=0, dist_url='tcp://127.0.0.1:23179', dist_backend='nccl', seed=0, gpu=0, multiprocessing_distributed=False, c='/liuzicheng/jwy/otherbaseline-main2/config/usb_audio/pseudolabel/pseudolabel_fsdnoisy_1773_0.yaml', p_cutoff=0.95, unsup_warm_up=0.4, clip=0.0, distributed=False, ulb_dest_len=15813, lb_dest_len=1772)
[2023-08-14 04:25:10,146 INFO] Resume load path /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth does not exist
[2023-08-14 04:25:10,146 INFO] Model training
[2023-08-14 04:27:45,649 INFO] 256 iteration USE_EMA: False, train/sup_loss: 2.9390, train/unsup_loss: 0.0000, train/total_loss: 2.9390, train/util_ratio: 0.0000, train/run_time: 0.4929, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-14 04:30:21,253 INFO] 512 iteration USE_EMA: False, train/sup_loss: 2.1013, train/unsup_loss: 0.0000, train/total_loss: 2.1013, train/util_ratio: 0.0000, train/run_time: 0.5646, lr: 0.0001, train/prefecth_time: 0.0028 
[2023-08-14 04:32:56,978 INFO] 768 iteration USE_EMA: False, train/sup_loss: 1.7566, train/unsup_loss: 0.0000, train/total_loss: 1.7566, train/util_ratio: 0.0000, train/run_time: 0.5507, lr: 0.0001, train/prefecth_time: 0.0024 
[2023-08-14 04:35:29,847 INFO] 1024 iteration USE_EMA: False, train/sup_loss: 1.3231, train/unsup_loss: 0.0000, train/total_loss: 1.3231, train/util_ratio: 0.0000, train/run_time: 0.5591, lr: 0.0001, train/prefecth_time: 0.0024 
[2023-08-14 04:38:04,098 INFO] 1280 iteration USE_EMA: False, train/sup_loss: 0.7730, train/unsup_loss: 0.0000, train/total_loss: 0.7730, train/util_ratio: 0.0000, train/run_time: 0.5512, lr: 0.0001, train/prefecth_time: 0.0033 
[2023-08-14 04:40:32,106 INFO] 1536 iteration USE_EMA: False, train/sup_loss: 0.4655, train/unsup_loss: 0.0000, train/total_loss: 0.4655, train/util_ratio: 0.0000, train/run_time: 0.5316, lr: 0.0001, train/prefecth_time: 0.0024 
[2023-08-14 04:43:05,488 INFO] 1792 iteration USE_EMA: False, train/sup_loss: 0.6496, train/unsup_loss: 0.0000, train/total_loss: 0.6496, train/util_ratio: 0.0000, train/run_time: 0.5771, lr: 0.0002, train/prefecth_time: 0.0037 
[2023-08-14 04:45:38,721 INFO] validating...
[2023-08-14 04:45:47,768 INFO] confusion matrix:
[[0.63461538 0.         0.         0.         0.         0.
  0.         0.01923077 0.01923077 0.         0.         0.
  0.15384615 0.         0.         0.07692308 0.         0.03846154
  0.         0.05769231]
 [0.         0.1        0.04285714 0.         0.         0.05714286
  0.01428571 0.         0.         0.         0.01428571 0.27142857
  0.04285714 0.         0.05714286 0.14285714 0.         0.25714286
  0.         0.        ]
 [0.         0.         0.39285714 0.         0.         0.14285714
  0.08928571 0.         0.         0.01785714 0.         0.03571429
  0.         0.08928571 0.         0.05357143 0.         0.17857143
  0.         0.        ]
 [0.         0.         0.         0.52112676 0.         0.
  0.         0.         0.04225352 0.04225352 0.         0.
  0.         0.16901408 0.09859155 0.         0.         0.12676056
  0.         0.        ]
 [0.01923077 0.         0.01923077 0.         0.71153846 0.
  0.01923077 0.         0.         0.         0.         0.
  0.         0.03846154 0.         0.         0.19230769 0.
  0.         0.        ]
 [0.0754717  0.         0.         0.         0.         0.81132075
  0.01886792 0.         0.         0.         0.         0.03773585
  0.         0.         0.         0.03773585 0.         0.01886792
  0.         0.        ]
 [0.10526316 0.         0.15789474 0.         0.         0.01754386
  0.50877193 0.         0.         0.         0.         0.
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  0.         0.        ]
 [0.08333333 0.         0.         0.         0.         0.
  0.         0.72222222 0.02777778 0.         0.         0.08333333
  0.02777778 0.         0.         0.         0.         0.
  0.         0.05555556]
 [0.05       0.         0.         0.         0.         0.
  0.         0.125      0.25       0.         0.2        0.
  0.25       0.         0.         0.         0.         0.125
  0.         0.        ]
 [0.         0.         0.18055556 0.         0.         0.02777778
  0.         0.         0.         0.16666667 0.         0.
  0.         0.48611111 0.01388889 0.01388889 0.         0.11111111
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         0.24489796 0.10204082 0.04081633 0.3877551  0.
  0.02040816 0.         0.         0.10204082 0.         0.10204082
  0.         0.        ]
 [0.         0.         0.14814815 0.         0.         0.01851852
  0.         0.         0.         0.         0.         0.68518519
  0.01851852 0.         0.         0.07407407 0.         0.05555556
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 [0.01851852 0.         0.         0.         0.         0.
  0.         0.05555556 0.25925926 0.03703704 0.05555556 0.03703704
  0.27777778 0.         0.09259259 0.09259259 0.         0.05555556
  0.         0.01851852]
 [0.         0.         0.14285714 0.02380952 0.04761905 0.
  0.         0.         0.         0.02380952 0.         0.
  0.         0.69047619 0.         0.         0.         0.07142857
  0.         0.        ]
 [0.03333333 0.03333333 0.         0.         0.         0.03333333
  0.03333333 0.         0.         0.         0.         0.
  0.         0.         0.66666667 0.         0.         0.1
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 [0.         0.         0.06666667 0.         0.         0.06666667
  0.06666667 0.         0.         0.         0.         0.06666667
  0.         0.         0.         0.56666667 0.03333333 0.1
  0.         0.03333333]
 [0.02631579 0.         0.26315789 0.         0.07894737 0.
  0.21052632 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.36842105 0.02631579
  0.02631579 0.        ]
 [0.03225806 0.         0.12903226 0.         0.         0.03225806
  0.         0.         0.         0.06451613 0.         0.06451613
  0.         0.         0.         0.19354839 0.         0.41935484
  0.03225806 0.03225806]
 [0.06666667 0.         0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.03333333
  0.         0.         0.03333333 0.         0.         0.43333333
  0.36666667 0.03333333]
 [0.03333333 0.         0.         0.         0.         0.
  0.         0.03333333 0.         0.         0.06666667 0.
  0.         0.         0.         0.03333333 0.         0.06666667
  0.         0.76666667]]
[2023-08-14 04:45:49,706 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 04:45:51,553 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/model_best.pth
[2023-08-14 04:45:51,553 INFO] 2048 iteration, USE_EMA: False, train/sup_loss: 0.7461, train/unsup_loss: 0.0038, train/total_loss: 0.7463, train/util_ratio: 0.3750, train/run_time: 0.6043, eval/loss: 2.2387, eval/top-1-acc: 0.4794, eval/balanced_acc: 0.5007, eval/precision: 0.5476, eval/recall: 0.5007, eval/F1: 0.4831, lr: 0.0002, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.4794, at 2048 iters
[2023-08-14 04:48:27,068 INFO] 2304 iteration USE_EMA: False, train/sup_loss: 0.5367, train/unsup_loss: 0.0000, train/total_loss: 0.5367, train/util_ratio: 0.0000, train/run_time: 0.5851, lr: 0.0002, train/prefecth_time: 0.0026 
[2023-08-14 04:50:59,281 INFO] 2560 iteration USE_EMA: False, train/sup_loss: 0.4664, train/unsup_loss: 0.0046, train/total_loss: 0.4667, train/util_ratio: 0.1250, train/run_time: 0.5658, lr: 0.0003, train/prefecth_time: 0.0025 
[2023-08-14 04:53:31,273 INFO] 2816 iteration USE_EMA: False, train/sup_loss: 0.6047, train/unsup_loss: 0.0042, train/total_loss: 0.6050, train/util_ratio: 0.3750, train/run_time: 0.5694, lr: 0.0003, train/prefecth_time: 0.0024 
[2023-08-14 04:56:01,973 INFO] 3072 iteration USE_EMA: False, train/sup_loss: 0.5398, train/unsup_loss: 0.0075, train/total_loss: 0.5403, train/util_ratio: 0.3750, train/run_time: 0.5948, lr: 0.0003, train/prefecth_time: 0.0024 
[2023-08-14 04:58:37,357 INFO] 3328 iteration USE_EMA: False, train/sup_loss: 0.0977, train/unsup_loss: 0.0003, train/total_loss: 0.0977, train/util_ratio: 0.1250, train/run_time: 0.5701, lr: 0.0003, train/prefecth_time: 0.0046 
[2023-08-14 05:01:08,597 INFO] 3584 iteration USE_EMA: False, train/sup_loss: 0.2143, train/unsup_loss: 0.0051, train/total_loss: 0.2148, train/util_ratio: 0.2500, train/run_time: 0.5582, lr: 0.0003, train/prefecth_time: 0.0046 
[2023-08-14 05:03:41,509 INFO] 3840 iteration USE_EMA: False, train/sup_loss: 0.0300, train/unsup_loss: 0.0000, train/total_loss: 0.0300, train/util_ratio: 0.0000, train/run_time: 0.5657, lr: 0.0004, train/prefecth_time: 0.0042 
[2023-08-14 05:06:14,739 INFO] validating...
[2023-08-14 05:06:23,627 INFO] confusion matrix:
[[0.34615385 0.01923077 0.09615385 0.         0.         0.
  0.01923077 0.         0.05769231 0.         0.         0.01923077
  0.05769231 0.         0.         0.09615385 0.03846154 0.01923077
  0.03846154 0.19230769]
 [0.         0.87142857 0.04285714 0.         0.         0.
  0.02857143 0.         0.         0.         0.         0.01428571
  0.         0.         0.         0.         0.         0.02857143
  0.         0.01428571]
 [0.         0.01785714 0.58928571 0.         0.         0.03571429
  0.01785714 0.         0.         0.05357143 0.01785714 0.05357143
  0.         0.08928571 0.         0.         0.01785714 0.10714286
  0.         0.        ]
 [0.         0.         0.01408451 0.43661972 0.         0.
  0.         0.         0.07042254 0.12676056 0.         0.01408451
  0.         0.25352113 0.01408451 0.         0.         0.07042254
  0.         0.        ]
 [0.         0.         0.07692308 0.         0.75       0.
  0.01923077 0.         0.         0.         0.         0.
  0.         0.03846154 0.         0.         0.11538462 0.
  0.         0.        ]
 [0.01886792 0.18867925 0.0754717  0.         0.         0.66037736
  0.01886792 0.         0.         0.         0.         0.01886792
  0.         0.         0.         0.01886792 0.         0.
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 [0.         0.07017544 0.35087719 0.         0.         0.
  0.45614035 0.         0.         0.         0.         0.05263158
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 [0.02777778 0.         0.05555556 0.         0.         0.
  0.         0.33333333 0.02777778 0.         0.33333333 0.08333333
  0.11111111 0.         0.         0.         0.         0.02777778
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 [0.         0.025      0.025      0.         0.025      0.
  0.         0.1        0.275      0.075      0.3        0.
  0.1        0.         0.         0.         0.         0.05
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 [0.         0.         0.05555556 0.         0.         0.
  0.         0.         0.         0.43055556 0.         0.
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 [0.         0.06122449 0.         0.         0.         0.
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  0.06122449 0.         0.         0.08163265 0.         0.02040816
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 [0.         0.11111111 0.16666667 0.         0.         0.
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  0.         0.         0.         0.03703704 0.         0.01851852
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 [0.         0.01851852 0.         0.         0.         0.
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  0.40740741 0.01851852 0.         0.09259259 0.         0.01851852
  0.01851852 0.03703704]
 [0.         0.         0.04761905 0.04761905 0.02380952 0.
  0.         0.         0.         0.16666667 0.         0.
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 [0.         0.13333333 0.         0.         0.         0.
  0.03333333 0.         0.         0.06666667 0.         0.
  0.         0.         0.6        0.         0.         0.16666667
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 [0.         0.1        0.06666667 0.         0.         0.03333333
  0.06666667 0.         0.06666667 0.03333333 0.03333333 0.1
  0.         0.         0.         0.33333333 0.03333333 0.13333333
  0.         0.        ]
 [0.         0.         0.42105263 0.         0.13157895 0.
  0.         0.         0.         0.05263158 0.         0.
  0.         0.02631579 0.         0.         0.31578947 0.02631579
  0.         0.02631579]
 [0.         0.16129032 0.19354839 0.         0.         0.
  0.         0.         0.         0.09677419 0.         0.03225806
  0.         0.         0.         0.         0.         0.41935484
  0.03225806 0.06451613]
 [0.         0.03333333 0.         0.         0.         0.03333333
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 [0.         0.03333333 0.03333333 0.         0.         0.
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  0.         0.         0.         0.         0.         0.1
  0.03333333 0.7       ]]
[2023-08-14 05:06:25,501 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 05:06:27,324 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/model_best.pth
[2023-08-14 05:06:27,326 INFO] 4096 iteration, USE_EMA: False, train/sup_loss: 0.8046, train/unsup_loss: 0.0028, train/total_loss: 0.8049, train/util_ratio: 0.1250, train/run_time: 0.5565, eval/loss: 2.3561, eval/top-1-acc: 0.5312, eval/balanced_acc: 0.5194, eval/precision: 0.6028, eval/recall: 0.5194, eval/F1: 0.5280, lr: 0.0004, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.5312, at 4096 iters
[2023-08-14 05:09:00,482 INFO] 4352 iteration USE_EMA: False, train/sup_loss: 0.1970, train/unsup_loss: 0.0059, train/total_loss: 0.1977, train/util_ratio: 0.2500, train/run_time: 0.5319, lr: 0.0004, train/prefecth_time: 0.0025 
[2023-08-14 05:11:32,923 INFO] 4608 iteration USE_EMA: False, train/sup_loss: 0.0683, train/unsup_loss: 0.0056, train/total_loss: 0.0689, train/util_ratio: 0.2500, train/run_time: 0.5576, lr: 0.0005, train/prefecth_time: 0.0063 
[2023-08-14 05:14:05,534 INFO] 4864 iteration USE_EMA: False, train/sup_loss: 0.6256, train/unsup_loss: 0.0012, train/total_loss: 0.6258, train/util_ratio: 0.1250, train/run_time: 0.5065, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 05:16:37,476 INFO] 5120 iteration USE_EMA: False, train/sup_loss: 0.6999, train/unsup_loss: 0.0001, train/total_loss: 0.6999, train/util_ratio: 0.1250, train/run_time: 0.5801, lr: 0.0005, train/prefecth_time: 0.0024 
[2023-08-14 05:19:14,495 INFO] 5376 iteration USE_EMA: False, train/sup_loss: 0.0745, train/unsup_loss: 0.0030, train/total_loss: 0.0749, train/util_ratio: 0.3750, train/run_time: 0.5630, lr: 0.0005, train/prefecth_time: 0.0024 
[2023-08-14 05:21:44,338 INFO] 5632 iteration USE_EMA: False, train/sup_loss: 2.5580, train/unsup_loss: 0.0072, train/total_loss: 2.5590, train/util_ratio: 0.5000, train/run_time: 0.5301, lr: 0.0005, train/prefecth_time: 0.0047 
[2023-08-14 05:24:16,000 INFO] 5888 iteration USE_EMA: False, train/sup_loss: 0.0689, train/unsup_loss: 0.0047, train/total_loss: 0.0696, train/util_ratio: 0.3750, train/run_time: 0.6072, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 05:26:47,498 INFO] validating...
[2023-08-14 05:26:56,419 INFO] confusion matrix:
[[0.86538462 0.         0.         0.         0.         0.
  0.         0.03846154 0.         0.         0.         0.
  0.01923077 0.         0.         0.03846154 0.         0.
  0.03846154 0.        ]
 [0.         0.51428571 0.01428571 0.         0.         0.05714286
  0.         0.         0.         0.         0.01428571 0.35714286
  0.         0.         0.01428571 0.         0.         0.01428571
  0.01428571 0.        ]
 [0.05357143 0.         0.5        0.         0.01785714 0.10714286
  0.03571429 0.         0.         0.         0.01785714 0.14285714
  0.         0.03571429 0.         0.         0.         0.07142857
  0.01785714 0.        ]
 [0.02816901 0.         0.11267606 0.33802817 0.         0.
  0.         0.01408451 0.11267606 0.         0.         0.15492958
  0.01408451 0.14084507 0.01408451 0.         0.         0.07042254
  0.         0.        ]
 [0.01923077 0.         0.19230769 0.         0.69230769 0.
  0.         0.         0.         0.         0.03846154 0.01923077
  0.         0.         0.         0.         0.03846154 0.
  0.         0.        ]
 [0.03773585 0.         0.01886792 0.         0.         0.86792453
  0.         0.         0.         0.         0.         0.03773585
  0.         0.         0.         0.         0.         0.
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 [0.22807018 0.05263158 0.19298246 0.         0.         0.05263158
  0.38596491 0.         0.         0.         0.         0.05263158
  0.         0.         0.         0.01754386 0.         0.01754386
  0.         0.        ]
 [0.13888889 0.         0.         0.         0.         0.02777778
  0.         0.41666667 0.         0.         0.33333333 0.05555556
  0.02777778 0.         0.         0.         0.         0.
  0.         0.        ]
 [0.075      0.05       0.         0.         0.025      0.
  0.         0.15       0.2        0.025      0.35       0.
  0.1        0.         0.         0.         0.         0.025
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 [0.         0.         0.23611111 0.         0.         0.01388889
  0.         0.         0.         0.33333333 0.         0.05555556
  0.01388889 0.27777778 0.02777778 0.         0.         0.04166667
  0.         0.        ]
 [0.         0.02040816 0.         0.         0.         0.02040816
  0.         0.02040816 0.04081633 0.04081633 0.75510204 0.
  0.04081633 0.         0.         0.04081633 0.         0.02040816
  0.         0.        ]
 [0.         0.         0.03703704 0.         0.         0.01851852
  0.         0.         0.         0.         0.         0.94444444
  0.         0.         0.         0.         0.         0.
  0.         0.        ]
 [0.05555556 0.01851852 0.         0.         0.         0.
  0.         0.01851852 0.18518519 0.01851852 0.18518519 0.
  0.35185185 0.         0.03703704 0.09259259 0.         0.
  0.03703704 0.        ]
 [0.02380952 0.         0.16666667 0.04761905 0.         0.0952381
  0.         0.         0.         0.04761905 0.         0.0952381
  0.         0.45238095 0.         0.         0.         0.07142857
  0.         0.        ]
 [0.03333333 0.06666667 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.03333333
  0.         0.         0.7        0.         0.         0.03333333
  0.13333333 0.        ]
 [0.03333333 0.         0.03333333 0.         0.         0.1
  0.1        0.         0.         0.03333333 0.         0.2
  0.         0.         0.         0.46666667 0.         0.03333333
  0.         0.        ]
 [0.05263158 0.         0.47368421 0.         0.15789474 0.02631579
  0.         0.         0.         0.02631579 0.         0.07894737
  0.02631579 0.02631579 0.         0.         0.10526316 0.
  0.02631579 0.        ]
 [0.         0.09677419 0.19354839 0.         0.         0.03225806
  0.         0.         0.         0.         0.         0.29032258
  0.         0.         0.         0.         0.         0.12903226
  0.25806452 0.        ]
 [0.03333333 0.03333333 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.13333333 0.         0.         0.
  0.76666667 0.        ]
 [0.13333333 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.06666667 0.
  0.         0.         0.         0.03333333 0.         0.03333333
  0.06666667 0.66666667]]
[2023-08-14 05:26:58,354 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 05:26:58,356 INFO] 6144 iteration, USE_EMA: False, train/sup_loss: 0.5803, train/unsup_loss: 0.0011, train/total_loss: 0.5805, train/util_ratio: 0.1250, train/run_time: 0.5376, eval/loss: 2.8752, eval/top-1-acc: 0.5238, eval/balanced_acc: 0.5226, eval/precision: 0.5861, eval/recall: 0.5226, eval/F1: 0.5092, lr: 0.0005, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.5312, at 4096 iters
[2023-08-14 05:29:32,806 INFO] 6400 iteration USE_EMA: False, train/sup_loss: 0.0464, train/unsup_loss: 0.0064, train/total_loss: 0.0474, train/util_ratio: 0.6250, train/run_time: 0.5856, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 05:32:01,873 INFO] 6656 iteration USE_EMA: False, train/sup_loss: 0.0420, train/unsup_loss: 0.0088, train/total_loss: 0.0435, train/util_ratio: 0.7500, train/run_time: 0.4891, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 05:34:21,524 INFO] 6912 iteration USE_EMA: False, train/sup_loss: 0.0346, train/unsup_loss: 0.0063, train/total_loss: 0.0357, train/util_ratio: 0.2500, train/run_time: 0.5050, lr: 0.0005, train/prefecth_time: 0.0046 
[2023-08-14 05:36:41,325 INFO] 7168 iteration USE_EMA: False, train/sup_loss: 1.3091, train/unsup_loss: 0.0068, train/total_loss: 1.3102, train/util_ratio: 0.6250, train/run_time: 0.5202, lr: 0.0005, train/prefecth_time: 0.0034 
[2023-08-14 05:39:04,341 INFO] 7424 iteration USE_EMA: False, train/sup_loss: 1.0655, train/unsup_loss: 0.0072, train/total_loss: 1.0669, train/util_ratio: 0.5000, train/run_time: 0.5084, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 05:41:25,317 INFO] 7680 iteration USE_EMA: False, train/sup_loss: 0.0135, train/unsup_loss: 0.0065, train/total_loss: 0.0148, train/util_ratio: 0.3750, train/run_time: 0.4757, lr: 0.0005, train/prefecth_time: 0.0046 
[2023-08-14 05:43:44,250 INFO] 7936 iteration USE_EMA: False, train/sup_loss: 0.0656, train/unsup_loss: 0.0025, train/total_loss: 0.0661, train/util_ratio: 0.1250, train/run_time: 0.5260, lr: 0.0005, train/prefecth_time: 0.0039 
[2023-08-14 05:46:05,884 INFO] validating...
[2023-08-14 05:46:14,315 INFO] confusion matrix:
[[0.80769231 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.03846154 0.
  0.03846154 0.11538462]
 [0.01428571 0.95714286 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.01428571 0.         0.         0.
  0.         0.01428571]
 [0.         0.07142857 0.44642857 0.         0.         0.08928571
  0.07142857 0.         0.         0.07142857 0.         0.07142857
  0.         0.07142857 0.01785714 0.01785714 0.01785714 0.
  0.05357143 0.        ]
 [0.         0.01408451 0.02816901 0.33802817 0.         0.
  0.         0.         0.         0.18309859 0.         0.05633803
  0.01408451 0.36619718 0.         0.         0.         0.
  0.         0.        ]
 [0.         0.         0.01923077 0.         0.53846154 0.
  0.01923077 0.         0.         0.11538462 0.         0.
  0.         0.         0.         0.         0.30769231 0.
  0.         0.        ]
 [0.01886792 0.03773585 0.0754717  0.         0.         0.73584906
  0.         0.         0.         0.         0.         0.03773585
  0.         0.05660377 0.         0.01886792 0.         0.
  0.01886792 0.        ]
 [0.05263158 0.0877193  0.05263158 0.         0.         0.01754386
  0.56140351 0.         0.         0.         0.         0.
  0.         0.01754386 0.01754386 0.03508772 0.03508772 0.01754386
  0.0877193  0.01754386]
 [0.08333333 0.08333333 0.         0.         0.         0.02777778
  0.         0.41666667 0.         0.16666667 0.08333333 0.02777778
  0.         0.         0.         0.         0.         0.
  0.         0.11111111]
 [0.225      0.075      0.025      0.         0.025      0.
  0.         0.025      0.075      0.25       0.1        0.075
  0.025      0.         0.         0.025      0.         0.
  0.         0.075     ]
 [0.         0.         0.05555556 0.         0.         0.
  0.         0.         0.         0.48611111 0.         0.01388889
  0.         0.44444444 0.         0.         0.         0.
  0.         0.        ]
 [0.04081633 0.08163265 0.         0.         0.02040816 0.
  0.         0.         0.04081633 0.26530612 0.42857143 0.02040816
  0.04081633 0.         0.02040816 0.         0.         0.
  0.         0.04081633]
 [0.         0.18518519 0.12962963 0.         0.         0.01851852
  0.         0.         0.         0.         0.         0.64814815
  0.01851852 0.         0.         0.         0.         0.
  0.         0.        ]
 [0.09259259 0.14814815 0.         0.         0.         0.
  0.         0.01851852 0.14814815 0.14814815 0.03703704 0.01851852
  0.2037037  0.         0.03703704 0.         0.         0.
  0.09259259 0.05555556]
 [0.         0.         0.07142857 0.02380952 0.         0.
  0.         0.         0.         0.23809524 0.         0.
  0.         0.66666667 0.         0.         0.         0.
  0.         0.        ]
 [0.         0.06666667 0.         0.         0.         0.
  0.         0.         0.         0.06666667 0.         0.
  0.         0.06666667 0.6        0.         0.         0.03333333
  0.13333333 0.03333333]
 [0.06666667 0.16666667 0.03333333 0.         0.         0.13333333
  0.03333333 0.         0.03333333 0.1        0.03333333 0.1
  0.         0.         0.03333333 0.13333333 0.03333333 0.
  0.1        0.        ]
 [0.02631579 0.02631579 0.23684211 0.         0.         0.
  0.02631579 0.         0.         0.07894737 0.         0.02631579
  0.         0.02631579 0.         0.         0.52631579 0.
  0.02631579 0.        ]
 [0.         0.12903226 0.06451613 0.         0.         0.
  0.         0.         0.         0.09677419 0.         0.19354839
  0.         0.         0.         0.         0.         0.22580645
  0.22580645 0.06451613]
 [0.         0.03333333 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.03333333
  0.         0.03333333 0.         0.         0.         0.
  0.83333333 0.03333333]
 [0.76666667 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.03333333
  0.03333333 0.13333333]]
[2023-08-14 05:46:16,275 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 05:46:16,276 INFO] 8192 iteration, USE_EMA: False, train/sup_loss: 0.0600, train/unsup_loss: 0.0042, train/total_loss: 0.0608, train/util_ratio: 0.2500, train/run_time: 0.4911, eval/loss: 2.7851, eval/top-1-acc: 0.5100, eval/balanced_acc: 0.4881, eval/precision: 0.5700, eval/recall: 0.4881, eval/F1: 0.4800, lr: 0.0005, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.5312, at 4096 iters
[2023-08-14 05:48:41,104 INFO] 8448 iteration USE_EMA: False, train/sup_loss: 0.0297, train/unsup_loss: 0.0021, train/total_loss: 0.0301, train/util_ratio: 0.3750, train/run_time: 0.5130, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 05:50:56,987 INFO] 8704 iteration USE_EMA: False, train/sup_loss: 0.0785, train/unsup_loss: 0.0007, train/total_loss: 0.0787, train/util_ratio: 0.5000, train/run_time: 0.5243, lr: 0.0005, train/prefecth_time: 0.0046 
[2023-08-14 05:53:17,461 INFO] 8960 iteration USE_EMA: False, train/sup_loss: 0.0661, train/unsup_loss: 0.0029, train/total_loss: 0.0667, train/util_ratio: 0.1250, train/run_time: 0.4875, lr: 0.0005, train/prefecth_time: 0.0032 
[2023-08-14 05:55:37,428 INFO] 9216 iteration USE_EMA: False, train/sup_loss: 0.4462, train/unsup_loss: 0.0107, train/total_loss: 0.4486, train/util_ratio: 0.7500, train/run_time: 0.4929, lr: 0.0005, train/prefecth_time: 0.0046 
[2023-08-14 05:58:02,877 INFO] 9472 iteration USE_EMA: False, train/sup_loss: 0.0128, train/unsup_loss: 0.0083, train/total_loss: 0.0147, train/util_ratio: 0.6250, train/run_time: 0.3366, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 06:00:22,132 INFO] 9728 iteration USE_EMA: False, train/sup_loss: 0.1998, train/unsup_loss: 0.0033, train/total_loss: 0.2006, train/util_ratio: 0.2500, train/run_time: 0.4469, lr: 0.0005, train/prefecth_time: 0.0039 
[2023-08-14 06:02:43,155 INFO] 9984 iteration USE_EMA: False, train/sup_loss: 0.1005, train/unsup_loss: 0.0030, train/total_loss: 0.1012, train/util_ratio: 0.6250, train/run_time: 0.5565, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 06:05:02,319 INFO] validating...
[2023-08-14 06:05:10,841 INFO] confusion matrix:
[[0.46153846 0.         0.         0.         0.         0.03846154
  0.01923077 0.         0.         0.         0.         0.01923077
  0.11538462 0.         0.         0.34615385 0.         0.
  0.         0.        ]
 [0.         0.62857143 0.         0.         0.         0.07142857
  0.11428571 0.         0.01428571 0.         0.01428571 0.01428571
  0.         0.         0.01428571 0.1        0.         0.
  0.02857143 0.        ]
 [0.         0.         0.16071429 0.01785714 0.         0.19642857
  0.16071429 0.         0.         0.125      0.         0.10714286
  0.         0.03571429 0.01785714 0.07142857 0.         0.07142857
  0.03571429 0.        ]
 [0.         0.         0.         0.35211268 0.         0.
  0.         0.         0.05633803 0.21126761 0.         0.07042254
  0.02816901 0.1971831  0.01408451 0.01408451 0.         0.04225352
  0.01408451 0.        ]
 [0.01923077 0.         0.01923077 0.         0.44230769 0.
  0.         0.         0.01923077 0.03846154 0.         0.01923077
  0.         0.         0.         0.28846154 0.15384615 0.
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.96226415
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.03773585 0.         0.
  0.         0.        ]
 [0.03508772 0.01754386 0.         0.         0.         0.03508772
  0.66666667 0.         0.         0.         0.         0.01754386
  0.03508772 0.         0.         0.19298246 0.         0.
  0.         0.        ]
 [0.         0.02777778 0.         0.         0.         0.
  0.         0.75       0.         0.         0.02777778 0.02777778
  0.08333333 0.         0.02777778 0.02777778 0.         0.02777778
  0.         0.        ]
 [0.025      0.         0.025      0.         0.         0.
  0.         0.05       0.425      0.025      0.025      0.025
  0.225      0.         0.         0.175      0.         0.
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.01388889
  0.         0.         0.         0.52777778 0.         0.01388889
  0.         0.375      0.01388889 0.         0.         0.05555556
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         0.04081633 0.10204082 0.02040816 0.65306122 0.
  0.         0.         0.         0.18367347 0.         0.
  0.         0.        ]
 [0.         0.03703704 0.03703704 0.         0.         0.03703704
  0.         0.         0.         0.         0.         0.7037037
  0.         0.         0.         0.16666667 0.         0.01851852
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         0.01851852 0.18518519 0.01851852 0.09259259 0.
  0.44444444 0.         0.         0.24074074 0.         0.
  0.         0.        ]
 [0.         0.         0.         0.04761905 0.         0.
  0.         0.         0.         0.26190476 0.         0.02380952
  0.         0.57142857 0.         0.07142857 0.         0.02380952
  0.         0.        ]
 [0.03333333 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.03333333
  0.         0.         0.76666667 0.         0.         0.06666667
  0.1        0.        ]
 [0.03333333 0.         0.         0.         0.         0.
  0.06666667 0.         0.         0.03333333 0.06666667 0.06666667
  0.03333333 0.         0.         0.7        0.         0.
  0.         0.        ]
 [0.02631579 0.         0.07894737 0.         0.07894737 0.05263158
  0.05263158 0.         0.         0.05263158 0.02631579 0.05263158
  0.02631579 0.02631579 0.02631579 0.02631579 0.44736842 0.02631579
  0.         0.        ]
 [0.         0.         0.03225806 0.         0.         0.06451613
  0.         0.         0.         0.03225806 0.03225806 0.09677419
  0.03225806 0.         0.         0.16129032 0.         0.29032258
  0.25806452 0.        ]
 [0.         0.         0.         0.         0.         0.1
  0.         0.         0.         0.         0.03333333 0.
  0.         0.         0.03333333 0.06666667 0.         0.
  0.76666667 0.        ]
 [0.06666667 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.03333333 0.
  0.1        0.         0.         0.06666667 0.         0.03333333
  0.03333333 0.66666667]]
[2023-08-14 06:05:12,728 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 06:05:14,502 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/model_best.pth
[2023-08-14 06:05:14,503 INFO] 10240 iteration, USE_EMA: False, train/sup_loss: 0.0308, train/unsup_loss: 0.0032, train/total_loss: 0.0316, train/util_ratio: 0.2500, train/run_time: 0.4992, eval/loss: 2.5229, eval/top-1-acc: 0.5565, eval/balanced_acc: 0.5694, eval/precision: 0.6295, eval/recall: 0.5694, eval/F1: 0.5664, lr: 0.0005, train/prefecth_time: 0.0045 BEST_EVAL_ACC: 0.5565, at 10240 iters
[2023-08-14 06:07:38,230 INFO] 10496 iteration USE_EMA: False, train/sup_loss: 0.0169, train/unsup_loss: 0.0053, train/total_loss: 0.0183, train/util_ratio: 0.1250, train/run_time: 0.4710, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 06:09:58,979 INFO] 10752 iteration USE_EMA: False, train/sup_loss: 0.0028, train/unsup_loss: 0.0043, train/total_loss: 0.0039, train/util_ratio: 0.6250, train/run_time: 0.5345, lr: 0.0005, train/prefecth_time: 0.0024 
[2023-08-14 06:12:20,325 INFO] 11008 iteration USE_EMA: False, train/sup_loss: 0.1611, train/unsup_loss: 0.0063, train/total_loss: 0.1628, train/util_ratio: 0.3750, train/run_time: 0.4735, lr: 0.0005, train/prefecth_time: 0.0070 
[2023-08-14 06:14:38,284 INFO] 11264 iteration USE_EMA: False, train/sup_loss: 0.1870, train/unsup_loss: 0.0060, train/total_loss: 0.1886, train/util_ratio: 0.5000, train/run_time: 0.5170, lr: 0.0005, train/prefecth_time: 0.0013 
[2023-08-14 06:17:03,487 INFO] 11520 iteration USE_EMA: False, train/sup_loss: 0.0097, train/unsup_loss: 0.0089, train/total_loss: 0.0122, train/util_ratio: 0.3750, train/run_time: 0.4567, lr: 0.0005, train/prefecth_time: 0.0046 
[2023-08-14 06:19:25,643 INFO] 11776 iteration USE_EMA: False, train/sup_loss: 0.1057, train/unsup_loss: 0.0055, train/total_loss: 0.1073, train/util_ratio: 0.2500, train/run_time: 0.4797, lr: 0.0005, train/prefecth_time: 0.0046 
[2023-08-14 06:21:42,970 INFO] 12032 iteration USE_EMA: False, train/sup_loss: 0.0564, train/unsup_loss: 0.0099, train/total_loss: 0.0593, train/util_ratio: 0.5000, train/run_time: 0.5033, lr: 0.0005, train/prefecth_time: 0.0041 
[2023-08-14 06:24:04,827 INFO] validating...
[2023-08-14 06:24:13,569 INFO] confusion matrix:
[[0.88461538 0.         0.         0.         0.         0.
  0.         0.01923077 0.01923077 0.         0.         0.
  0.01923077 0.         0.         0.01923077 0.         0.
  0.03846154 0.        ]
 [0.         0.95714286 0.         0.         0.01428571 0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.02857143 0.         0.         0.
  0.         0.        ]
 [0.         0.03571429 0.375      0.         0.01785714 0.08928571
  0.08928571 0.         0.         0.01785714 0.01785714 0.08928571
  0.         0.08928571 0.         0.07142857 0.01785714 0.07142857
  0.01785714 0.        ]
 [0.01408451 0.         0.02816901 0.38028169 0.         0.
  0.         0.         0.08450704 0.02816901 0.02816901 0.07042254
  0.02816901 0.25352113 0.05633803 0.         0.         0.01408451
  0.01408451 0.        ]
 [0.         0.         0.         0.         0.86538462 0.
  0.03846154 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.09615385 0.
  0.         0.        ]
 [0.0754717  0.01886792 0.01886792 0.         0.         0.77358491
  0.         0.01886792 0.         0.         0.         0.01886792
  0.         0.         0.         0.05660377 0.         0.
  0.01886792 0.        ]
 [0.05263158 0.07017544 0.07017544 0.         0.01754386 0.01754386
  0.61403509 0.03508772 0.         0.         0.         0.01754386
  0.01754386 0.         0.         0.07017544 0.         0.01754386
  0.         0.        ]
 [0.02777778 0.02777778 0.         0.         0.         0.
  0.         0.83333333 0.         0.         0.08333333 0.
  0.02777778 0.         0.         0.         0.         0.
  0.         0.        ]
 [0.025      0.025      0.025      0.         0.025      0.
  0.         0.225      0.325      0.         0.175      0.
  0.175      0.         0.         0.         0.         0.
  0.         0.        ]
 [0.         0.         0.125      0.         0.04166667 0.01388889
  0.         0.         0.         0.36111111 0.01388889 0.04166667
  0.01388889 0.375      0.         0.         0.         0.01388889
  0.         0.        ]
 [0.         0.08163265 0.         0.02040816 0.02040816 0.02040816
  0.         0.04081633 0.04081633 0.         0.73469388 0.
  0.02040816 0.         0.         0.02040816 0.         0.
  0.         0.        ]
 [0.         0.12962963 0.03703704 0.         0.01851852 0.
  0.         0.         0.         0.         0.         0.7962963
  0.         0.         0.         0.01851852 0.         0.
  0.         0.        ]
 [0.14814815 0.01851852 0.         0.         0.         0.
  0.         0.14814815 0.11111111 0.         0.11111111 0.03703704
  0.38888889 0.         0.         0.01851852 0.         0.
  0.01851852 0.        ]
 [0.         0.         0.02380952 0.02380952 0.         0.02380952
  0.02380952 0.         0.         0.07142857 0.04761905 0.04761905
  0.         0.71428571 0.         0.02380952 0.         0.
  0.         0.        ]
 [0.03333333 0.13333333 0.         0.         0.         0.
  0.         0.03333333 0.         0.         0.         0.
  0.         0.         0.66666667 0.         0.         0.06666667
  0.06666667 0.        ]
 [0.06666667 0.1        0.         0.         0.06666667 0.
  0.1        0.03333333 0.06666667 0.         0.         0.1
  0.03333333 0.         0.         0.36666667 0.         0.
  0.06666667 0.        ]
 [0.02631579 0.05263158 0.21052632 0.         0.15789474 0.
  0.02631579 0.         0.         0.         0.02631579 0.
  0.02631579 0.         0.         0.         0.42105263 0.
  0.02631579 0.02631579]
 [0.03225806 0.09677419 0.03225806 0.         0.09677419 0.
  0.03225806 0.03225806 0.         0.03225806 0.03225806 0.25806452
  0.03225806 0.         0.         0.03225806 0.         0.12903226
  0.16129032 0.        ]
 [0.03333333 0.06666667 0.         0.         0.         0.03333333
  0.         0.         0.03333333 0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.8        0.        ]
 [0.16666667 0.         0.         0.         0.         0.
  0.         0.03333333 0.         0.         0.06666667 0.
  0.         0.         0.         0.         0.         0.03333333
  0.03333333 0.66666667]]
[2023-08-14 06:24:15,512 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 06:24:17,334 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/model_best.pth
[2023-08-14 06:24:17,335 INFO] 12288 iteration, USE_EMA: False, train/sup_loss: 0.1236, train/unsup_loss: 0.0015, train/total_loss: 0.1240, train/util_ratio: 0.5000, train/run_time: 0.5133, eval/loss: 2.2903, eval/top-1-acc: 0.6082, eval/balanced_acc: 0.6027, eval/precision: 0.6191, eval/recall: 0.6027, eval/F1: 0.5852, lr: 0.0005, train/prefecth_time: 0.0037 BEST_EVAL_ACC: 0.6082, at 12288 iters
[2023-08-14 06:26:42,122 INFO] 12544 iteration USE_EMA: False, train/sup_loss: 0.0803, train/unsup_loss: 0.0026, train/total_loss: 0.0811, train/util_ratio: 0.6250, train/run_time: 0.4893, lr: 0.0005, train/prefecth_time: 0.0015 
[2023-08-14 06:29:00,715 INFO] 12800 iteration USE_EMA: False, train/sup_loss: 0.0072, train/unsup_loss: 0.0040, train/total_loss: 0.0084, train/util_ratio: 0.2500, train/run_time: 0.5219, lr: 0.0005, train/prefecth_time: 0.0052 
[2023-08-14 06:31:21,371 INFO] 13056 iteration USE_EMA: False, train/sup_loss: 0.1554, train/unsup_loss: 0.0013, train/total_loss: 0.1558, train/util_ratio: 0.5000, train/run_time: 0.5220, lr: 0.0005, train/prefecth_time: 0.0046 
[2023-08-14 06:33:40,610 INFO] 13312 iteration USE_EMA: False, train/sup_loss: 0.0526, train/unsup_loss: 0.0013, train/total_loss: 0.0530, train/util_ratio: 0.2500, train/run_time: 0.5165, lr: 0.0005, train/prefecth_time: 0.0024 
[2023-08-14 06:36:04,229 INFO] 13568 iteration USE_EMA: False, train/sup_loss: 0.1102, train/unsup_loss: 0.0104, train/total_loss: 0.1137, train/util_ratio: 0.7500, train/run_time: 0.4765, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 06:38:21,631 INFO] 13824 iteration USE_EMA: False, train/sup_loss: 0.0798, train/unsup_loss: 0.0007, train/total_loss: 0.0800, train/util_ratio: 0.3750, train/run_time: 0.4819, lr: 0.0005, train/prefecth_time: 0.0040 
[2023-08-14 06:40:44,212 INFO] 14080 iteration USE_EMA: False, train/sup_loss: 0.0765, train/unsup_loss: 0.0091, train/total_loss: 0.0796, train/util_ratio: 0.6250, train/run_time: 0.4920, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 06:43:14,533 INFO] validating...
[2023-08-14 06:43:23,037 INFO] confusion matrix:
[[0.78846154 0.01923077 0.         0.         0.         0.
  0.         0.         0.03846154 0.         0.         0.
  0.         0.         0.         0.03846154 0.01923077 0.
  0.09615385 0.        ]
 [0.02857143 0.67142857 0.01428571 0.         0.01428571 0.
  0.         0.         0.         0.         0.         0.01428571
  0.         0.         0.         0.01428571 0.         0.
  0.24285714 0.        ]
 [0.03571429 0.03571429 0.39285714 0.         0.         0.16071429
  0.03571429 0.01785714 0.         0.01785714 0.01785714 0.01785714
  0.         0.05357143 0.         0.03571429 0.03571429 0.07142857
  0.07142857 0.        ]
 [0.         0.         0.01408451 0.3943662  0.         0.
  0.         0.02816901 0.05633803 0.16901408 0.01408451 0.
  0.         0.21126761 0.08450704 0.         0.         0.01408451
  0.01408451 0.        ]
 [0.         0.01923077 0.09615385 0.         0.63461538 0.
  0.         0.         0.         0.         0.         0.01923077
  0.         0.         0.         0.03846154 0.19230769 0.
  0.         0.        ]
 [0.05660377 0.         0.01886792 0.         0.         0.8490566
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.05660377 0.         0.
  0.01886792 0.        ]
 [0.07017544 0.         0.10526316 0.         0.         0.05263158
  0.35087719 0.         0.         0.         0.         0.
  0.01754386 0.         0.         0.19298246 0.10526316 0.
  0.10526316 0.        ]
 [0.08333333 0.08333333 0.         0.         0.         0.
  0.         0.72222222 0.08333333 0.         0.02777778 0.
  0.         0.         0.         0.         0.         0.
  0.         0.        ]
 [0.05       0.025      0.025      0.         0.025      0.
  0.         0.1        0.4        0.025      0.175      0.
  0.025      0.         0.         0.05       0.         0.025
  0.05       0.025     ]
 [0.01388889 0.         0.02777778 0.         0.04166667 0.01388889
  0.         0.         0.01388889 0.40277778 0.         0.
  0.         0.43055556 0.02777778 0.01388889 0.         0.01388889
  0.         0.        ]
 [0.         0.02040816 0.         0.         0.         0.02040816
  0.         0.2244898  0.12244898 0.02040816 0.3877551  0.
  0.         0.02040816 0.         0.06122449 0.         0.02040816
  0.10204082 0.        ]
 [0.         0.11111111 0.05555556 0.         0.         0.01851852
  0.         0.         0.         0.01851852 0.         0.72222222
  0.01851852 0.         0.         0.         0.         0.03703704
  0.01851852 0.        ]
 [0.05555556 0.01851852 0.         0.         0.         0.01851852
  0.         0.07407407 0.18518519 0.01851852 0.07407407 0.01851852
  0.16666667 0.         0.01851852 0.03703704 0.         0.03703704
  0.27777778 0.        ]
 [0.         0.         0.         0.04761905 0.         0.07142857
  0.02380952 0.         0.         0.21428571 0.         0.
  0.         0.57142857 0.02380952 0.02380952 0.         0.02380952
  0.         0.        ]
 [0.1        0.03333333 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.66666667 0.         0.         0.03333333
  0.16666667 0.        ]
 [0.06666667 0.         0.         0.         0.         0.03333333
  0.06666667 0.         0.         0.         0.         0.
  0.         0.         0.         0.6        0.06666667 0.03333333
  0.1        0.03333333]
 [0.02631579 0.05263158 0.02631579 0.         0.02631579 0.02631579
  0.         0.02631579 0.         0.         0.         0.05263158
  0.         0.02631579 0.         0.         0.73684211 0.
  0.         0.        ]
 [0.         0.         0.06451613 0.         0.         0.06451613
  0.03225806 0.         0.         0.03225806 0.03225806 0.12903226
  0.         0.         0.         0.         0.03225806 0.19354839
  0.38709677 0.03225806]
 [0.         0.         0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.96666667 0.        ]
 [0.03333333 0.         0.         0.         0.         0.
  0.         0.03333333 0.         0.         0.         0.
  0.         0.         0.         0.03333333 0.         0.03333333
  0.13333333 0.73333333]]
[2023-08-14 06:43:24,966 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 06:43:24,967 INFO] 14336 iteration, USE_EMA: False, train/sup_loss: 0.0107, train/unsup_loss: 0.0005, train/total_loss: 0.0109, train/util_ratio: 0.3750, train/run_time: 0.5454, eval/loss: 2.6954, eval/top-1-acc: 0.5502, eval/balanced_acc: 0.5676, eval/precision: 0.5929, eval/recall: 0.5676, eval/F1: 0.5428, lr: 0.0005, train/prefecth_time: 0.0031 BEST_EVAL_ACC: 0.6082, at 12288 iters
[2023-08-14 06:45:58,553 INFO] 14592 iteration USE_EMA: False, train/sup_loss: 0.0072, train/unsup_loss: 0.0046, train/total_loss: 0.0088, train/util_ratio: 0.6250, train/run_time: 0.5599, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 06:48:27,737 INFO] 14848 iteration USE_EMA: False, train/sup_loss: 0.0144, train/unsup_loss: 0.0036, train/total_loss: 0.0157, train/util_ratio: 0.7500, train/run_time: 0.5308, lr: 0.0005, train/prefecth_time: 0.0037 
[2023-08-14 06:50:57,260 INFO] 15104 iteration USE_EMA: False, train/sup_loss: 0.0047, train/unsup_loss: 0.0022, train/total_loss: 0.0055, train/util_ratio: 0.6250, train/run_time: 0.5287, lr: 0.0005, train/prefecth_time: 0.0067 
[2023-08-14 06:53:24,928 INFO] 15360 iteration USE_EMA: False, train/sup_loss: 0.0023, train/unsup_loss: 0.0025, train/total_loss: 0.0032, train/util_ratio: 0.6250, train/run_time: 0.5872, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 06:55:58,859 INFO] 15616 iteration USE_EMA: False, train/sup_loss: 0.4459, train/unsup_loss: 0.0083, train/total_loss: 0.4491, train/util_ratio: 0.5000, train/run_time: 0.5152, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 06:58:29,392 INFO] 15872 iteration USE_EMA: False, train/sup_loss: 0.2414, train/unsup_loss: 0.0013, train/total_loss: 0.2419, train/util_ratio: 0.3750, train/run_time: 0.5623, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 07:00:57,354 INFO] 16128 iteration USE_EMA: False, train/sup_loss: 0.0103, train/unsup_loss: 0.0086, train/total_loss: 0.0137, train/util_ratio: 0.6250, train/run_time: 0.6202, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 07:03:27,485 INFO] validating...
[2023-08-14 07:03:35,913 INFO] confusion matrix:
[[0.82692308 0.         0.         0.         0.         0.
  0.         0.01923077 0.         0.         0.         0.01923077
  0.         0.         0.01923077 0.05769231 0.01923077 0.
  0.03846154 0.        ]
 [0.         0.81428571 0.         0.         0.         0.01428571
  0.         0.         0.         0.         0.         0.07142857
  0.         0.         0.02857143 0.02857143 0.         0.01428571
  0.02857143 0.        ]
 [0.         0.         0.32142857 0.         0.         0.14285714
  0.03571429 0.         0.01785714 0.10714286 0.01785714 0.10714286
  0.03571429 0.08928571 0.         0.         0.01785714 0.07142857
  0.03571429 0.        ]
 [0.         0.         0.         0.26760563 0.         0.01408451
  0.         0.         0.21126761 0.12676056 0.09859155 0.02816901
  0.         0.21126761 0.04225352 0.         0.         0.
  0.         0.        ]
 [0.         0.         0.         0.         0.75       0.
  0.         0.         0.01923077 0.01923077 0.01923077 0.01923077
  0.         0.05769231 0.         0.01923077 0.09615385 0.
  0.         0.        ]
 [0.01886792 0.         0.01886792 0.         0.         0.9245283
  0.         0.         0.         0.         0.         0.01886792
  0.         0.         0.         0.         0.         0.
  0.01886792 0.        ]
 [0.03508772 0.01754386 0.         0.         0.01754386 0.05263158
  0.59649123 0.         0.         0.         0.01754386 0.05263158
  0.01754386 0.         0.         0.07017544 0.         0.03508772
  0.0877193  0.        ]
 [0.02777778 0.         0.         0.         0.         0.
  0.         0.75       0.02777778 0.         0.11111111 0.05555556
  0.         0.         0.02777778 0.         0.         0.
  0.         0.        ]
 [0.025      0.025      0.025      0.         0.         0.
  0.         0.075      0.375      0.05       0.3        0.
  0.1        0.         0.         0.         0.         0.
  0.         0.025     ]
 [0.         0.         0.02777778 0.         0.         0.01388889
  0.         0.         0.01388889 0.45833333 0.01388889 0.01388889
  0.         0.45833333 0.         0.         0.         0.
  0.         0.        ]
 [0.         0.02040816 0.         0.         0.         0.02040816
  0.         0.02040816 0.02040816 0.04081633 0.75510204 0.
  0.02040816 0.         0.         0.08163265 0.         0.
  0.02040816 0.        ]
 [0.         0.01851852 0.05555556 0.         0.         0.01851852
  0.         0.         0.         0.03703704 0.         0.83333333
  0.         0.03703704 0.         0.         0.         0.
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.01851852
  0.         0.01851852 0.31481481 0.01851852 0.18518519 0.03703704
  0.25925926 0.         0.05555556 0.05555556 0.         0.
  0.03703704 0.        ]
 [0.         0.         0.04761905 0.02380952 0.02380952 0.
  0.         0.         0.         0.14285714 0.         0.
  0.         0.73809524 0.         0.02380952 0.         0.
  0.         0.        ]
 [0.         0.03333333 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.76666667 0.         0.         0.06666667
  0.13333333 0.        ]
 [0.03333333 0.03333333 0.03333333 0.         0.         0.03333333
  0.1        0.         0.         0.03333333 0.06666667 0.06666667
  0.03333333 0.         0.         0.53333333 0.         0.
  0.03333333 0.        ]
 [0.         0.         0.05263158 0.         0.42105263 0.02631579
  0.         0.         0.02631579 0.         0.02631579 0.05263158
  0.         0.02631579 0.         0.         0.31578947 0.
  0.         0.05263158]
 [0.         0.06451613 0.03225806 0.         0.         0.
  0.         0.         0.         0.06451613 0.06451613 0.09677419
  0.03225806 0.         0.03225806 0.03225806 0.         0.32258065
  0.22580645 0.03225806]
 [0.         0.06666667 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.86666667 0.03333333]
 [0.06666667 0.         0.03333333 0.         0.         0.
  0.         0.03333333 0.         0.         0.03333333 0.
  0.03333333 0.         0.         0.         0.         0.03333333
  0.06666667 0.7       ]]
[2023-08-14 07:03:37,896 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 07:03:37,897 INFO] 16384 iteration, USE_EMA: False, train/sup_loss: 0.0292, train/unsup_loss: 0.0056, train/total_loss: 0.0314, train/util_ratio: 0.3750, train/run_time: 0.5426, eval/loss: 2.7248, eval/top-1-acc: 0.6008, eval/balanced_acc: 0.6088, eval/precision: 0.6281, eval/recall: 0.6088, eval/F1: 0.5886, lr: 0.0005, train/prefecth_time: 0.0045 BEST_EVAL_ACC: 0.6082, at 12288 iters
[2023-08-14 07:06:09,229 INFO] 16640 iteration USE_EMA: False, train/sup_loss: 0.0173, train/unsup_loss: 0.0066, train/total_loss: 0.0200, train/util_ratio: 0.3750, train/run_time: 0.5872, lr: 0.0005, train/prefecth_time: 0.0074 
[2023-08-14 07:08:40,881 INFO] 16896 iteration USE_EMA: False, train/sup_loss: 0.0912, train/unsup_loss: 0.0067, train/total_loss: 0.0939, train/util_ratio: 0.3750, train/run_time: 0.5334, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 07:11:11,949 INFO] 17152 iteration USE_EMA: False, train/sup_loss: 0.1342, train/unsup_loss: 0.0003, train/total_loss: 0.1343, train/util_ratio: 0.1250, train/run_time: 0.5006, lr: 0.0005, train/prefecth_time: 0.0046 
[2023-08-14 07:13:39,967 INFO] 17408 iteration USE_EMA: False, train/sup_loss: 0.1508, train/unsup_loss: 0.0067, train/total_loss: 0.1537, train/util_ratio: 0.6250, train/run_time: 0.5623, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 07:16:12,393 INFO] 17664 iteration USE_EMA: False, train/sup_loss: 0.0112, train/unsup_loss: 0.0063, train/total_loss: 0.0139, train/util_ratio: 0.1250, train/run_time: 0.5275, lr: 0.0005, train/prefecth_time: 0.0067 
[2023-08-14 07:18:41,023 INFO] 17920 iteration USE_EMA: False, train/sup_loss: 0.0354, train/unsup_loss: 0.0040, train/total_loss: 0.0372, train/util_ratio: 0.5000, train/run_time: 0.5436, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 07:21:07,415 INFO] 18176 iteration USE_EMA: False, train/sup_loss: 0.2400, train/unsup_loss: 0.0043, train/total_loss: 0.2419, train/util_ratio: 0.5000, train/run_time: 0.5565, lr: 0.0005, train/prefecth_time: 0.0047 
[2023-08-14 07:23:38,651 INFO] validating...
[2023-08-14 07:23:47,330 INFO] confusion matrix:
[[0.78846154 0.         0.         0.         0.         0.
  0.         0.         0.03846154 0.         0.         0.
  0.17307692 0.         0.         0.         0.         0.
  0.         0.        ]
 [0.02857143 0.72857143 0.01428571 0.         0.         0.01428571
  0.01428571 0.         0.         0.         0.         0.1
  0.01428571 0.         0.07142857 0.01428571 0.         0.
  0.         0.        ]
 [0.01785714 0.         0.32142857 0.         0.         0.10714286
  0.07142857 0.01785714 0.01785714 0.19642857 0.01785714 0.14285714
  0.         0.01785714 0.         0.01785714 0.01785714 0.01785714
  0.01785714 0.        ]
 [0.         0.         0.01408451 0.56338028 0.         0.
  0.         0.         0.02816901 0.07042254 0.16901408 0.04225352
  0.01408451 0.08450704 0.01408451 0.         0.         0.
  0.         0.        ]
 [0.         0.         0.01923077 0.         0.67307692 0.
  0.01923077 0.         0.         0.         0.         0.
  0.03846154 0.01923077 0.         0.01923077 0.19230769 0.
  0.         0.01923077]
 [0.03773585 0.         0.01886792 0.         0.         0.8490566
  0.         0.         0.         0.         0.         0.03773585
  0.01886792 0.         0.         0.01886792 0.         0.
  0.01886792 0.        ]
 [0.14035088 0.03508772 0.01754386 0.         0.         0.05263158
  0.57894737 0.         0.         0.         0.         0.
  0.03508772 0.         0.01754386 0.01754386 0.01754386 0.07017544
  0.01754386 0.        ]
 [0.08333333 0.02777778 0.         0.         0.         0.
  0.         0.80555556 0.         0.         0.         0.02777778
  0.05555556 0.         0.         0.         0.         0.
  0.         0.        ]
 [0.025      0.025      0.025      0.         0.         0.
  0.         0.325      0.125      0.         0.275      0.
  0.2        0.         0.         0.         0.         0.
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 [0.         0.         0.01388889 0.         0.01388889 0.01388889
  0.         0.         0.         0.56944444 0.01388889 0.
  0.         0.36111111 0.         0.         0.         0.01388889
  0.         0.        ]
 [0.         0.06122449 0.         0.         0.         0.02040816
  0.         0.08163265 0.12244898 0.         0.69387755 0.
  0.02040816 0.         0.         0.         0.         0.
  0.         0.        ]
 [0.         0.         0.05555556 0.         0.         0.
  0.         0.         0.         0.03703704 0.         0.7962963
  0.05555556 0.         0.         0.03703704 0.         0.01851852
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         0.07407407 0.07407407 0.         0.07407407 0.
  0.7037037  0.         0.03703704 0.01851852 0.         0.
  0.         0.01851852]
 [0.         0.         0.07142857 0.07142857 0.         0.07142857
  0.02380952 0.         0.         0.19047619 0.04761905 0.
  0.         0.52380952 0.         0.         0.         0.
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.8        0.         0.         0.1
  0.1        0.        ]
 [0.06666667 0.03333333 0.         0.         0.         0.
  0.06666667 0.         0.         0.         0.13333333 0.13333333
  0.1        0.         0.03333333 0.4        0.         0.
  0.03333333 0.        ]
 [0.05263158 0.         0.02631579 0.         0.02631579 0.
  0.         0.         0.         0.         0.         0.05263158
  0.07894737 0.02631579 0.02631579 0.         0.65789474 0.
  0.02631579 0.02631579]
 [0.         0.03225806 0.06451613 0.         0.         0.03225806
  0.         0.         0.         0.06451613 0.22580645 0.16129032
  0.06451613 0.         0.03225806 0.         0.         0.19354839
  0.12903226 0.        ]
 [0.1        0.03333333 0.         0.         0.         0.06666667
  0.         0.         0.         0.         0.         0.
  0.16666667 0.         0.         0.         0.         0.1
  0.53333333 0.        ]
 [0.06666667 0.         0.         0.         0.         0.
  0.         0.03333333 0.         0.         0.03333333 0.
  0.03333333 0.         0.         0.03333333 0.         0.
  0.         0.8       ]]
[2023-08-14 07:23:49,159 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 07:23:51,030 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/model_best.pth
[2023-08-14 07:23:51,031 INFO] 18432 iteration, USE_EMA: False, train/sup_loss: 1.7525, train/unsup_loss: 0.0039, train/total_loss: 1.7543, train/util_ratio: 0.5000, train/run_time: 0.5596, eval/loss: 2.7347, eval/top-1-acc: 0.6146, eval/balanced_acc: 0.6053, eval/precision: 0.6155, eval/recall: 0.6053, eval/F1: 0.5957, lr: 0.0005, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.6146, at 18432 iters
[2023-08-14 07:26:25,798 INFO] 18688 iteration USE_EMA: False, train/sup_loss: 0.0828, train/unsup_loss: 0.0024, train/total_loss: 0.0839, train/util_ratio: 0.6250, train/run_time: 0.5292, lr: 0.0005, train/prefecth_time: 0.0039 
[2023-08-14 07:28:54,676 INFO] 18944 iteration USE_EMA: False, train/sup_loss: 0.3880, train/unsup_loss: 0.0021, train/total_loss: 0.3890, train/util_ratio: 0.2500, train/run_time: 0.5066, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 07:31:26,672 INFO] 19200 iteration USE_EMA: False, train/sup_loss: 0.0261, train/unsup_loss: 0.0004, train/total_loss: 0.0262, train/util_ratio: 0.3750, train/run_time: 0.5684, lr: 0.0005, train/prefecth_time: 0.0046 
[2023-08-14 07:33:56,133 INFO] 19456 iteration USE_EMA: False, train/sup_loss: 0.6379, train/unsup_loss: 0.0039, train/total_loss: 0.6398, train/util_ratio: 0.5000, train/run_time: 0.5496, lr: 0.0005, train/prefecth_time: 0.0024 
[2023-08-14 07:36:28,044 INFO] 19712 iteration USE_EMA: False, train/sup_loss: 0.0012, train/unsup_loss: 0.0009, train/total_loss: 0.0016, train/util_ratio: 0.3750, train/run_time: 0.5757, lr: 0.0005, train/prefecth_time: 0.0031 
[2023-08-14 07:38:58,378 INFO] 19968 iteration USE_EMA: False, train/sup_loss: 0.0017, train/unsup_loss: 0.0030, train/total_loss: 0.0032, train/util_ratio: 0.6250, train/run_time: 0.5976, lr: 0.0005, train/prefecth_time: 0.0024 
[2023-08-14 07:41:28,313 INFO] 20224 iteration USE_EMA: False, train/sup_loss: 0.0010, train/unsup_loss: 0.0024, train/total_loss: 0.0022, train/util_ratio: 0.3750, train/run_time: 0.5297, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 07:43:55,711 INFO] validating...
[2023-08-14 07:44:04,399 INFO] confusion matrix:
[[0.98076923 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.01923077 0.        ]
 [0.05714286 0.71428571 0.         0.         0.         0.
  0.01428571 0.01428571 0.         0.         0.         0.17142857
  0.         0.         0.         0.02857143 0.         0.
  0.         0.        ]
 [0.         0.01785714 0.375      0.         0.         0.05357143
  0.07142857 0.         0.         0.07142857 0.01785714 0.17857143
  0.         0.05357143 0.         0.01785714 0.01785714 0.10714286
  0.01785714 0.        ]
 [0.         0.         0.01408451 0.33802817 0.         0.
  0.         0.         0.01408451 0.01408451 0.08450704 0.05633803
  0.05633803 0.29577465 0.05633803 0.         0.         0.07042254
  0.         0.        ]
 [0.         0.01923077 0.01923077 0.         0.55769231 0.
  0.01923077 0.         0.         0.01923077 0.         0.
  0.         0.         0.         0.         0.34615385 0.
  0.         0.01923077]
 [0.01886792 0.         0.01886792 0.         0.         0.88679245
  0.         0.         0.         0.         0.01886792 0.03773585
  0.         0.         0.         0.         0.         0.
  0.01886792 0.        ]
 [0.10526316 0.         0.         0.         0.         0.01754386
  0.78947368 0.         0.         0.         0.         0.01754386
  0.         0.         0.         0.01754386 0.01754386 0.03508772
  0.         0.        ]
 [0.05555556 0.         0.02777778 0.         0.         0.
  0.         0.5        0.         0.         0.22222222 0.11111111
  0.02777778 0.         0.         0.         0.         0.05555556
  0.         0.        ]
 [0.05       0.025      0.         0.         0.         0.
  0.         0.075      0.15       0.         0.3        0.
  0.1        0.         0.025      0.075      0.         0.175
  0.         0.025     ]
 [0.         0.         0.125      0.         0.         0.01388889
  0.         0.         0.         0.45833333 0.01388889 0.
  0.         0.34722222 0.         0.         0.         0.04166667
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.02040816
  0.         0.         0.02040816 0.04081633 0.79591837 0.02040816
  0.         0.         0.         0.04081633 0.         0.06122449
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.01851852 0.         0.94444444
  0.         0.         0.         0.01851852 0.         0.01851852
  0.         0.        ]
 [0.03703704 0.         0.         0.         0.         0.
  0.01851852 0.07407407 0.01851852 0.         0.24074074 0.05555556
  0.38888889 0.         0.05555556 0.03703704 0.         0.05555556
  0.01851852 0.        ]
 [0.         0.         0.04761905 0.02380952 0.         0.04761905
  0.02380952 0.         0.         0.07142857 0.         0.
  0.         0.69047619 0.         0.04761905 0.         0.04761905
  0.         0.        ]
 [0.03333333 0.03333333 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.8        0.         0.         0.06666667
  0.06666667 0.        ]
 [0.03333333 0.         0.         0.         0.         0.
  0.2        0.         0.         0.         0.06666667 0.23333333
  0.         0.         0.         0.4        0.03333333 0.
  0.03333333 0.        ]
 [0.10526316 0.02631579 0.07894737 0.         0.         0.02631579
  0.         0.         0.         0.         0.         0.02631579
  0.         0.         0.         0.         0.71052632 0.
  0.         0.02631579]
 [0.         0.03225806 0.03225806 0.         0.         0.
  0.         0.         0.         0.03225806 0.09677419 0.12903226
  0.         0.         0.         0.         0.         0.48387097
  0.19354839 0.        ]
 [0.1        0.1        0.         0.         0.         0.06666667
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.03333333
  0.66666667 0.03333333]
 [0.2        0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.03333333
  0.         0.76666667]]
[2023-08-14 07:44:06,341 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 07:44:08,060 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/model_best.pth
[2023-08-14 07:44:08,061 INFO] 20480 iteration, USE_EMA: False, train/sup_loss: 0.0103, train/unsup_loss: 0.0055, train/total_loss: 0.0131, train/util_ratio: 0.3750, train/run_time: 0.5774, eval/loss: 3.2992, eval/top-1-acc: 0.6177, eval/balanced_acc: 0.6199, eval/precision: 0.6573, eval/recall: 0.6199, eval/F1: 0.6023, lr: 0.0005, train/prefecth_time: 0.0047 BEST_EVAL_ACC: 0.6177, at 20480 iters
[2023-08-14 07:46:42,522 INFO] 20736 iteration USE_EMA: False, train/sup_loss: 0.0221, train/unsup_loss: 0.0032, train/total_loss: 0.0237, train/util_ratio: 0.7500, train/run_time: 0.5837, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 07:49:12,625 INFO] 20992 iteration USE_EMA: False, train/sup_loss: 0.2260, train/unsup_loss: 0.0032, train/total_loss: 0.2276, train/util_ratio: 0.5000, train/run_time: 0.6189, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 07:51:40,102 INFO] 21248 iteration USE_EMA: False, train/sup_loss: 0.1973, train/unsup_loss: 0.0001, train/total_loss: 0.1974, train/util_ratio: 0.3750, train/run_time: 0.5897, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 07:54:10,408 INFO] 21504 iteration USE_EMA: False, train/sup_loss: 0.1439, train/unsup_loss: 0.0106, train/total_loss: 0.1495, train/util_ratio: 0.7500, train/run_time: 0.5748, lr: 0.0005, train/prefecth_time: 0.0024 
[2023-08-14 07:56:44,285 INFO] 21760 iteration USE_EMA: False, train/sup_loss: 0.0187, train/unsup_loss: 0.0050, train/total_loss: 0.0214, train/util_ratio: 0.3750, train/run_time: 0.5237, lr: 0.0005, train/prefecth_time: 0.0034 
[2023-08-14 07:59:15,943 INFO] 22016 iteration USE_EMA: False, train/sup_loss: 2.3099, train/unsup_loss: 0.0078, train/total_loss: 2.3141, train/util_ratio: 0.7500, train/run_time: 0.5831, lr: 0.0005, train/prefecth_time: 0.0024 
[2023-08-14 08:01:46,268 INFO] 22272 iteration USE_EMA: False, train/sup_loss: 0.1030, train/unsup_loss: 0.0136, train/total_loss: 0.1104, train/util_ratio: 0.6250, train/run_time: 0.5576, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 08:04:14,035 INFO] validating...
[2023-08-14 08:04:22,731 INFO] confusion matrix:
[[0.80769231 0.05769231 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.03846154 0.         0.         0.01923077 0.         0.
  0.03846154 0.03846154]
 [0.         0.94285714 0.         0.         0.         0.
  0.         0.         0.         0.         0.01428571 0.
  0.         0.         0.         0.         0.         0.01428571
  0.         0.02857143]
 [0.         0.03571429 0.25       0.         0.         0.08928571
  0.05357143 0.         0.         0.07142857 0.01785714 0.07142857
  0.         0.05357143 0.05357143 0.         0.01785714 0.28571429
  0.         0.        ]
 [0.         0.         0.         0.33802817 0.         0.
  0.         0.         0.01408451 0.14084507 0.04225352 0.1971831
  0.         0.25352113 0.         0.         0.         0.01408451
  0.         0.        ]
 [0.         0.         0.01923077 0.         0.88461538 0.
  0.         0.         0.         0.         0.01923077 0.
  0.         0.01923077 0.         0.         0.05769231 0.
  0.         0.        ]
 [0.01886792 0.03773585 0.         0.         0.         0.8490566
  0.         0.         0.         0.         0.         0.01886792
  0.         0.         0.         0.01886792 0.         0.05660377
  0.         0.        ]
 [0.03508772 0.05263158 0.03508772 0.         0.01754386 0.03508772
  0.50877193 0.         0.         0.         0.         0.
  0.         0.         0.         0.01754386 0.03508772 0.24561404
  0.         0.01754386]
 [0.05555556 0.05555556 0.         0.         0.         0.
  0.         0.36111111 0.         0.         0.44444444 0.05555556
  0.         0.         0.         0.         0.         0.02777778
  0.         0.        ]
 [0.025      0.05       0.         0.         0.025      0.
  0.         0.05       0.075      0.05       0.425      0.
  0.075      0.         0.         0.         0.         0.2
  0.         0.025     ]
 [0.         0.         0.02777778 0.         0.         0.01388889
  0.         0.         0.         0.41666667 0.01388889 0.01388889
  0.01388889 0.375      0.02777778 0.         0.         0.09722222
  0.         0.        ]
 [0.         0.04081633 0.         0.         0.02040816 0.02040816
  0.         0.         0.         0.06122449 0.75510204 0.02040816
  0.         0.         0.         0.         0.02040816 0.06122449
  0.         0.        ]
 [0.01851852 0.44444444 0.09259259 0.         0.         0.03703704
  0.         0.         0.         0.         0.01851852 0.22222222
  0.         0.         0.         0.         0.         0.14814815
  0.01851852 0.        ]
 [0.         0.14814815 0.         0.         0.         0.
  0.         0.05555556 0.09259259 0.01851852 0.2962963  0.
  0.2037037  0.         0.01851852 0.03703704 0.         0.11111111
  0.01851852 0.        ]
 [0.         0.         0.02380952 0.02380952 0.02380952 0.
  0.         0.         0.         0.07142857 0.         0.
  0.         0.78571429 0.02380952 0.         0.         0.04761905
  0.         0.        ]
 [0.         0.06666667 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.76666667 0.         0.         0.1
  0.06666667 0.        ]
 [0.         0.13333333 0.         0.         0.03333333 0.06666667
  0.06666667 0.         0.03333333 0.06666667 0.03333333 0.13333333
  0.         0.         0.         0.2        0.         0.16666667
  0.03333333 0.03333333]
 [0.05263158 0.02631579 0.10526316 0.         0.28947368 0.02631579
  0.         0.         0.         0.         0.         0.02631579
  0.         0.         0.         0.         0.34210526 0.10526316
  0.02631579 0.        ]
 [0.         0.09677419 0.         0.         0.03225806 0.06451613
  0.         0.         0.         0.03225806 0.         0.
  0.         0.         0.03225806 0.         0.         0.58064516
  0.12903226 0.03225806]
 [0.         0.06666667 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.03333333 0.         0.         0.         0.13333333
  0.7        0.03333333]
 [0.03333333 0.03333333 0.03333333 0.         0.         0.
  0.         0.         0.         0.         0.03333333 0.
  0.         0.         0.         0.         0.         0.03333333
  0.03333333 0.8       ]]
[2023-08-14 08:04:24,771 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 08:04:24,772 INFO] 22528 iteration, USE_EMA: False, train/sup_loss: 0.5122, train/unsup_loss: 0.0036, train/total_loss: 0.5142, train/util_ratio: 0.5000, train/run_time: 0.5044, eval/loss: 3.1953, eval/top-1-acc: 0.5385, eval/balanced_acc: 0.5395, eval/precision: 0.5902, eval/recall: 0.5395, eval/F1: 0.5187, lr: 0.0005, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.6177, at 20480 iters
[2023-08-14 08:07:00,096 INFO] 22784 iteration USE_EMA: False, train/sup_loss: 0.0098, train/unsup_loss: 0.0115, train/total_loss: 0.0161, train/util_ratio: 0.7500, train/run_time: 0.5199, lr: 0.0005, train/prefecth_time: 0.0061 
[2023-08-14 08:09:30,338 INFO] 23040 iteration USE_EMA: False, train/sup_loss: 0.0009, train/unsup_loss: 0.0050, train/total_loss: 0.0036, train/util_ratio: 0.5000, train/run_time: 0.5192, lr: 0.0005, train/prefecth_time: 0.0046 
[2023-08-14 08:11:58,967 INFO] 23296 iteration USE_EMA: False, train/sup_loss: 0.0046, train/unsup_loss: 0.0018, train/total_loss: 0.0056, train/util_ratio: 0.6250, train/run_time: 0.5661, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 08:14:27,617 INFO] 23552 iteration USE_EMA: False, train/sup_loss: 0.0133, train/unsup_loss: 0.0080, train/total_loss: 0.0179, train/util_ratio: 0.5000, train/run_time: 0.5460, lr: 0.0005, train/prefecth_time: 0.0024 
[2023-08-14 08:17:01,081 INFO] 23808 iteration USE_EMA: False, train/sup_loss: 0.0017, train/unsup_loss: 0.0125, train/total_loss: 0.0089, train/util_ratio: 0.8750, train/run_time: 0.5261, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 08:19:27,509 INFO] 24064 iteration USE_EMA: False, train/sup_loss: 0.0212, train/unsup_loss: 0.0055, train/total_loss: 0.0244, train/util_ratio: 0.5000, train/run_time: 0.5747, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 08:21:56,527 INFO] 24320 iteration USE_EMA: False, train/sup_loss: 0.0552, train/unsup_loss: 0.0083, train/total_loss: 0.0601, train/util_ratio: 0.6250, train/run_time: 0.5345, lr: 0.0005, train/prefecth_time: 0.0046 
[2023-08-14 08:24:26,614 INFO] validating...
[2023-08-14 08:24:35,336 INFO] confusion matrix:
[[0.84615385 0.         0.         0.         0.         0.
  0.         0.         0.03846154 0.         0.         0.
  0.05769231 0.         0.01923077 0.         0.         0.
  0.03846154 0.        ]
 [0.01428571 0.85714286 0.         0.         0.         0.04285714
  0.         0.         0.         0.         0.         0.
  0.01428571 0.         0.05714286 0.01428571 0.         0.
  0.         0.        ]
 [0.         0.05357143 0.21428571 0.         0.         0.19642857
  0.05357143 0.         0.         0.125      0.01785714 0.03571429
  0.         0.05357143 0.01785714 0.03571429 0.01785714 0.125
  0.03571429 0.01785714]
 [0.         0.         0.         0.38028169 0.         0.01408451
  0.         0.         0.04225352 0.02816901 0.05633803 0.04225352
  0.01408451 0.35211268 0.05633803 0.         0.         0.01408451
  0.         0.        ]
 [0.         0.         0.01923077 0.         0.63461538 0.
  0.01923077 0.         0.01923077 0.01923077 0.         0.
  0.         0.         0.         0.01923077 0.23076923 0.01923077
  0.         0.01923077]
 [0.03773585 0.         0.         0.         0.         0.96226415
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.        ]
 [0.05263158 0.05263158 0.         0.         0.         0.07017544
  0.63157895 0.         0.01754386 0.         0.         0.
  0.         0.         0.05263158 0.05263158 0.         0.03508772
  0.01754386 0.01754386]
 [0.08333333 0.02777778 0.         0.         0.         0.02777778
  0.         0.47222222 0.27777778 0.         0.         0.02777778
  0.05555556 0.         0.02777778 0.         0.         0.
  0.         0.        ]
 [0.025      0.025      0.         0.         0.025      0.
  0.         0.05       0.35       0.         0.175      0.
  0.275      0.         0.         0.075      0.         0.
  0.         0.        ]
 [0.         0.01388889 0.02777778 0.         0.         0.01388889
  0.         0.         0.         0.48611111 0.04166667 0.
  0.01388889 0.31944444 0.04166667 0.         0.         0.04166667
  0.         0.        ]
 [0.         0.04081633 0.         0.02040816 0.         0.04081633
  0.         0.02040816 0.18367347 0.04081633 0.57142857 0.
  0.04081633 0.         0.         0.02040816 0.         0.02040816
  0.         0.        ]
 [0.         0.07407407 0.01851852 0.         0.         0.05555556
  0.         0.         0.         0.01851852 0.         0.7037037
  0.09259259 0.         0.         0.03703704 0.         0.
  0.         0.        ]
 [0.03703704 0.01851852 0.         0.         0.         0.
  0.         0.01851852 0.14814815 0.         0.12962963 0.
  0.61111111 0.         0.01851852 0.         0.         0.
  0.         0.01851852]
 [0.         0.         0.04761905 0.02380952 0.         0.04761905
  0.02380952 0.         0.         0.0952381  0.02380952 0.02380952
  0.         0.69047619 0.         0.         0.         0.02380952
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.03333333 0.         0.9        0.         0.         0.
  0.06666667 0.        ]
 [0.         0.06666667 0.         0.         0.         0.03333333
  0.03333333 0.         0.         0.03333333 0.06666667 0.03333333
  0.03333333 0.         0.1        0.53333333 0.         0.03333333
  0.         0.03333333]
 [0.02631579 0.         0.10526316 0.         0.10526316 0.02631579
  0.         0.         0.02631579 0.         0.02631579 0.05263158
  0.02631579 0.         0.05263158 0.         0.5        0.
  0.         0.05263158]
 [0.         0.         0.         0.         0.         0.12903226
  0.         0.         0.         0.06451613 0.03225806 0.06451613
  0.03225806 0.         0.09677419 0.06451613 0.         0.38709677
  0.12903226 0.        ]
 [0.03333333 0.03333333 0.         0.         0.         0.06666667
  0.         0.         0.         0.         0.03333333 0.
  0.         0.         0.2        0.         0.         0.03333333
  0.56666667 0.03333333]
 [0.03333333 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.1        0.
  0.03333333 0.         0.         0.         0.         0.03333333
  0.03333333 0.76666667]]
[2023-08-14 08:24:37,278 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 08:24:37,279 INFO] 24576 iteration, USE_EMA: False, train/sup_loss: 0.0247, train/unsup_loss: 0.0065, train/total_loss: 0.0285, train/util_ratio: 0.6250, train/run_time: 0.5598, eval/loss: 2.7309, eval/top-1-acc: 0.6030, eval/balanced_acc: 0.6033, eval/precision: 0.6210, eval/recall: 0.6033, eval/F1: 0.5886, lr: 0.0005, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.6177, at 20480 iters
[2023-08-14 08:27:11,320 INFO] 24832 iteration USE_EMA: False, train/sup_loss: 0.0060, train/unsup_loss: 0.0018, train/total_loss: 0.0071, train/util_ratio: 0.3750, train/run_time: 0.4846, lr: 0.0005, train/prefecth_time: 0.0059 
[2023-08-14 08:29:41,443 INFO] 25088 iteration USE_EMA: False, train/sup_loss: 0.0005, train/unsup_loss: 0.0042, train/total_loss: 0.0031, train/util_ratio: 0.6250, train/run_time: 0.5558, lr: 0.0005, train/prefecth_time: 0.0034 
[2023-08-14 08:32:11,311 INFO] 25344 iteration USE_EMA: False, train/sup_loss: 0.0047, train/unsup_loss: 0.0029, train/total_loss: 0.0064, train/util_ratio: 0.5000, train/run_time: 0.4924, lr: 0.0005, train/prefecth_time: 0.0063 
[2023-08-14 08:34:41,041 INFO] 25600 iteration USE_EMA: False, train/sup_loss: 0.7588, train/unsup_loss: 0.0040, train/total_loss: 0.7613, train/util_ratio: 0.2500, train/run_time: 0.5619, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 08:37:14,441 INFO] 25856 iteration USE_EMA: False, train/sup_loss: 0.0561, train/unsup_loss: 0.0095, train/total_loss: 0.0620, train/util_ratio: 0.8750, train/run_time: 0.5626, lr: 0.0005, train/prefecth_time: 0.0024 
[2023-08-14 08:39:44,628 INFO] 26112 iteration USE_EMA: False, train/sup_loss: 0.0407, train/unsup_loss: 0.0025, train/total_loss: 0.0423, train/util_ratio: 0.3750, train/run_time: 0.5181, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 08:42:12,002 INFO] 26368 iteration USE_EMA: False, train/sup_loss: 0.0059, train/unsup_loss: 0.0045, train/total_loss: 0.0088, train/util_ratio: 0.1250, train/run_time: 0.5177, lr: 0.0005, train/prefecth_time: 0.0024 
[2023-08-14 08:44:41,771 INFO] validating...
[2023-08-14 08:44:50,520 INFO] confusion matrix:
[[0.86538462 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.05769231 0.         0.         0.01923077 0.01923077 0.
  0.01923077 0.01923077]
 [0.02857143 0.81428571 0.         0.         0.         0.
  0.         0.         0.         0.         0.01428571 0.08571429
  0.02857143 0.         0.         0.         0.         0.
  0.01428571 0.01428571]
 [0.         0.         0.26785714 0.01785714 0.         0.125
  0.03571429 0.         0.         0.07142857 0.10714286 0.16071429
  0.         0.05357143 0.01785714 0.01785714 0.05357143 0.03571429
  0.01785714 0.01785714]
 [0.         0.         0.         0.49295775 0.         0.
  0.         0.         0.01408451 0.05633803 0.11267606 0.04225352
  0.         0.25352113 0.         0.         0.         0.
  0.02816901 0.        ]
 [0.         0.         0.01923077 0.         0.65384615 0.
  0.         0.         0.         0.         0.         0.
  0.         0.01923077 0.         0.         0.30769231 0.
  0.         0.        ]
 [0.01886792 0.01886792 0.01886792 0.         0.         0.83018868
  0.         0.         0.         0.         0.         0.03773585
  0.         0.         0.         0.05660377 0.         0.
  0.01886792 0.        ]
 [0.07017544 0.         0.03508772 0.         0.         0.03508772
  0.42105263 0.         0.         0.         0.14035088 0.07017544
  0.         0.03508772 0.         0.05263158 0.05263158 0.03508772
  0.05263158 0.        ]
 [0.11111111 0.02777778 0.         0.         0.         0.
  0.         0.47222222 0.         0.         0.30555556 0.08333333
  0.         0.         0.         0.         0.         0.
  0.         0.        ]
 [0.025      0.025      0.         0.         0.         0.
  0.         0.05       0.15       0.05       0.5        0.
  0.15       0.         0.         0.         0.         0.025
  0.         0.025     ]
 [0.         0.         0.01388889 0.         0.01388889 0.01388889
  0.         0.         0.         0.52777778 0.02777778 0.
  0.         0.38888889 0.         0.         0.         0.01388889
  0.         0.        ]
 [0.         0.04081633 0.         0.         0.         0.02040816
  0.         0.02040816 0.04081633 0.04081633 0.81632653 0.
  0.         0.         0.         0.         0.         0.
  0.02040816 0.        ]
 [0.         0.         0.01851852 0.         0.         0.
  0.         0.         0.         0.         0.05555556 0.88888889
  0.01851852 0.         0.         0.         0.01851852 0.
  0.         0.        ]
 [0.01851852 0.         0.         0.         0.         0.
  0.         0.03703704 0.09259259 0.07407407 0.24074074 0.01851852
  0.48148148 0.         0.         0.01851852 0.         0.
  0.01851852 0.        ]
 [0.         0.         0.         0.0952381  0.         0.
  0.02380952 0.         0.         0.0952381  0.02380952 0.04761905
  0.         0.69047619 0.         0.         0.         0.02380952
  0.         0.        ]
 [0.         0.03333333 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.03333333
  0.         0.         0.76666667 0.         0.         0.03333333
  0.13333333 0.        ]
 [0.03333333 0.         0.         0.         0.03333333 0.
  0.03333333 0.         0.         0.         0.1        0.23333333
  0.06666667 0.         0.         0.46666667 0.03333333 0.
  0.         0.        ]
 [0.02631579 0.02631579 0.         0.         0.10526316 0.
  0.         0.         0.         0.         0.02631579 0.02631579
  0.         0.05263158 0.         0.         0.71052632 0.
  0.02631579 0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.12903226 0.12903226 0.16129032
  0.03225806 0.         0.         0.         0.03225806 0.32258065
  0.16129032 0.03225806]
 [0.06666667 0.03333333 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.03333333 0.         0.         0.
  0.83333333 0.        ]
 [0.06666667 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.06666667 0.
  0.         0.         0.         0.         0.         0.
  0.03333333 0.83333333]]
[2023-08-14 08:44:52,618 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 08:44:52,620 INFO] 26624 iteration, USE_EMA: False, train/sup_loss: 0.6490, train/unsup_loss: 0.0026, train/total_loss: 0.6507, train/util_ratio: 0.5000, train/run_time: 0.5653, eval/loss: 3.1349, eval/top-1-acc: 0.6146, eval/balanced_acc: 0.6153, eval/precision: 0.6633, eval/recall: 0.6153, eval/F1: 0.6055, lr: 0.0005, train/prefecth_time: 0.0046 BEST_EVAL_ACC: 0.6177, at 20480 iters
[2023-08-14 08:47:26,516 INFO] 26880 iteration USE_EMA: False, train/sup_loss: 0.0048, train/unsup_loss: 0.0076, train/total_loss: 0.0098, train/util_ratio: 0.5000, train/run_time: 0.2808, lr: 0.0005, train/prefecth_time: 0.0039 
[2023-08-14 08:49:54,042 INFO] 27136 iteration USE_EMA: False, train/sup_loss: 0.0034, train/unsup_loss: 0.0075, train/total_loss: 0.0083, train/util_ratio: 0.7500, train/run_time: 0.5661, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 08:52:24,718 INFO] 27392 iteration USE_EMA: False, train/sup_loss: 0.0059, train/unsup_loss: 0.0066, train/total_loss: 0.0102, train/util_ratio: 0.8750, train/run_time: 0.5675, lr: 0.0005, train/prefecth_time: 0.0046 
[2023-08-14 08:54:53,163 INFO] 27648 iteration USE_EMA: False, train/sup_loss: 0.0078, train/unsup_loss: 0.0062, train/total_loss: 0.0120, train/util_ratio: 0.6250, train/run_time: 0.5631, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 08:57:24,501 INFO] 27904 iteration USE_EMA: False, train/sup_loss: 0.5161, train/unsup_loss: 0.0003, train/total_loss: 0.5163, train/util_ratio: 0.2500, train/run_time: 0.5196, lr: 0.0005, train/prefecth_time: 0.0044 
[2023-08-14 08:59:53,453 INFO] 28160 iteration USE_EMA: False, train/sup_loss: 0.0029, train/unsup_loss: 0.0109, train/total_loss: 0.0105, train/util_ratio: 0.8750, train/run_time: 0.5098, lr: 0.0005, train/prefecth_time: 0.0024 
[2023-08-14 09:02:21,099 INFO] 28416 iteration USE_EMA: False, train/sup_loss: 0.0007, train/unsup_loss: 0.0028, train/total_loss: 0.0027, train/util_ratio: 0.5000, train/run_time: 0.5515, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 09:04:50,700 INFO] validating...
[2023-08-14 09:04:59,429 INFO] confusion matrix:
[[0.94230769 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.01923077 0.01923077 0.         0.
  0.01923077 0.        ]
 [0.         0.92857143 0.         0.         0.         0.01428571
  0.         0.         0.         0.         0.01428571 0.02857143
  0.         0.         0.01428571 0.         0.         0.
  0.         0.        ]
 [0.         0.07142857 0.33928571 0.         0.         0.10714286
  0.03571429 0.         0.         0.08928571 0.01785714 0.17857143
  0.         0.07142857 0.         0.         0.01785714 0.05357143
  0.         0.01785714]
 [0.01408451 0.         0.01408451 0.63380282 0.         0.
  0.         0.01408451 0.02816901 0.05633803 0.02816901 0.04225352
  0.         0.12676056 0.01408451 0.         0.         0.02816901
  0.         0.        ]
 [0.         0.01923077 0.         0.         0.75       0.
  0.         0.01923077 0.         0.         0.         0.
  0.01923077 0.01923077 0.         0.         0.15384615 0.
  0.         0.01923077]
 [0.03773585 0.         0.01886792 0.         0.         0.8490566
  0.         0.         0.         0.         0.01886792 0.03773585
  0.         0.         0.01886792 0.         0.         0.
  0.01886792 0.        ]
 [0.05263158 0.03508772 0.         0.         0.         0.05263158
  0.59649123 0.05263158 0.         0.         0.         0.03508772
  0.         0.         0.         0.05263158 0.07017544 0.01754386
  0.01754386 0.01754386]
 [0.         0.02777778 0.         0.         0.         0.
  0.         0.88888889 0.         0.         0.         0.02777778
  0.         0.         0.         0.         0.         0.02777778
  0.         0.02777778]
 [0.05       0.1        0.         0.         0.         0.
  0.         0.225      0.15       0.         0.375      0.025
  0.025      0.         0.         0.         0.         0.025
  0.         0.025     ]
 [0.         0.         0.01388889 0.         0.         0.01388889
  0.         0.         0.         0.48611111 0.01388889 0.04166667
  0.         0.375      0.         0.02777778 0.         0.02777778
  0.         0.        ]
 [0.         0.04081633 0.         0.02040816 0.         0.02040816
  0.         0.04081633 0.04081633 0.02040816 0.73469388 0.
  0.         0.         0.02040816 0.02040816 0.         0.02040816
  0.02040816 0.        ]
 [0.         0.03703704 0.         0.         0.         0.
  0.         0.         0.         0.01851852 0.         0.92592593
  0.         0.01851852 0.         0.         0.         0.
  0.         0.        ]
 [0.09259259 0.11111111 0.         0.         0.         0.
  0.         0.12962963 0.12962963 0.         0.16666667 0.05555556
  0.18518519 0.         0.05555556 0.01851852 0.         0.01851852
  0.01851852 0.01851852]
 [0.         0.         0.         0.19047619 0.         0.
  0.02380952 0.         0.         0.04761905 0.         0.
  0.         0.66666667 0.         0.         0.         0.07142857
  0.         0.        ]
 [0.         0.06666667 0.         0.         0.         0.
  0.         0.         0.         0.03333333 0.         0.
  0.         0.         0.76666667 0.         0.         0.03333333
  0.1        0.        ]
 [0.06666667 0.13333333 0.         0.         0.         0.
  0.03333333 0.03333333 0.         0.         0.03333333 0.23333333
  0.03333333 0.         0.         0.36666667 0.         0.03333333
  0.03333333 0.        ]
 [0.07894737 0.02631579 0.10526316 0.         0.07894737 0.02631579
  0.         0.02631579 0.         0.05263158 0.05263158 0.
  0.         0.         0.         0.         0.52631579 0.
  0.         0.02631579]
 [0.03225806 0.09677419 0.         0.         0.         0.
  0.         0.         0.         0.         0.09677419 0.12903226
  0.         0.         0.06451613 0.         0.         0.35483871
  0.19354839 0.03225806]
 [0.06666667 0.1        0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.06666667 0.         0.         0.
  0.7        0.03333333]
 [0.06666667 0.         0.         0.         0.         0.
  0.         0.03333333 0.         0.         0.06666667 0.
  0.03333333 0.         0.         0.         0.         0.
  0.         0.8       ]]
[2023-08-14 09:05:01,236 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 09:05:02,963 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/model_best.pth
[2023-08-14 09:05:02,964 INFO] 28672 iteration, USE_EMA: False, train/sup_loss: 0.0135, train/unsup_loss: 0.0020, train/total_loss: 0.0149, train/util_ratio: 0.7500, train/run_time: 0.5460, eval/loss: 2.8575, eval/top-1-acc: 0.6367, eval/balanced_acc: 0.6296, eval/precision: 0.6393, eval/recall: 0.6296, eval/F1: 0.6061, lr: 0.0005, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.6367, at 28672 iters
[2023-08-14 09:07:36,886 INFO] 28928 iteration USE_EMA: False, train/sup_loss: 0.0012, train/unsup_loss: 0.0064, train/total_loss: 0.0057, train/util_ratio: 0.5000, train/run_time: 0.5447, lr: 0.0005, train/prefecth_time: 0.0067 
[2023-08-14 09:10:06,348 INFO] 29184 iteration USE_EMA: False, train/sup_loss: 0.0030, train/unsup_loss: 0.0055, train/total_loss: 0.0069, train/util_ratio: 0.8750, train/run_time: 0.5467, lr: 0.0005, train/prefecth_time: 0.0024 
[2023-08-14 09:12:35,292 INFO] 29440 iteration USE_EMA: False, train/sup_loss: 0.0032, train/unsup_loss: 0.0028, train/total_loss: 0.0052, train/util_ratio: 0.6250, train/run_time: 0.5724, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 09:15:04,528 INFO] 29696 iteration USE_EMA: False, train/sup_loss: 0.0041, train/unsup_loss: 0.0016, train/total_loss: 0.0053, train/util_ratio: 0.7500, train/run_time: 0.6231, lr: 0.0005, train/prefecth_time: 0.0024 
[2023-08-14 09:17:34,930 INFO] 29952 iteration USE_EMA: False, train/sup_loss: 1.0087, train/unsup_loss: 0.0022, train/total_loss: 1.0103, train/util_ratio: 0.5000, train/run_time: 0.4982, lr: 0.0005, train/prefecth_time: 0.0095 
[2023-08-14 09:20:05,164 INFO] 30208 iteration USE_EMA: False, train/sup_loss: 0.0685, train/unsup_loss: 0.0041, train/total_loss: 0.0715, train/util_ratio: 0.8750, train/run_time: 0.5060, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 09:22:34,836 INFO] 30464 iteration USE_EMA: False, train/sup_loss: 0.0014, train/unsup_loss: 0.0021, train/total_loss: 0.0030, train/util_ratio: 0.6250, train/run_time: 0.5472, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 09:25:02,761 INFO] validating...
[2023-08-14 09:25:11,484 INFO] confusion matrix:
[[0.86538462 0.         0.01923077 0.         0.         0.
  0.         0.         0.03846154 0.01923077 0.         0.
  0.01923077 0.         0.         0.         0.         0.
  0.01923077 0.01923077]
 [0.02857143 0.94285714 0.01428571 0.         0.         0.01428571
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.        ]
 [0.         0.08928571 0.46428571 0.         0.         0.10714286
  0.03571429 0.         0.01785714 0.07142857 0.01785714 0.05357143
  0.         0.05357143 0.         0.         0.01785714 0.07142857
  0.         0.        ]
 [0.         0.         0.01408451 0.46478873 0.         0.
  0.         0.         0.14084507 0.04225352 0.04225352 0.04225352
  0.         0.23943662 0.         0.         0.         0.01408451
  0.         0.        ]
 [0.         0.03846154 0.01923077 0.         0.55769231 0.
  0.         0.01923077 0.         0.         0.         0.03846154
  0.01923077 0.         0.         0.         0.30769231 0.
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 [0.05660377 0.01886792 0.         0.         0.         0.86792453
  0.         0.         0.         0.         0.01886792 0.01886792
  0.         0.         0.         0.         0.         0.
  0.01886792 0.        ]
 [0.05263158 0.05263158 0.01754386 0.         0.         0.05263158
  0.70175439 0.         0.         0.         0.         0.
  0.01754386 0.         0.01754386 0.         0.03508772 0.03508772
  0.01754386 0.        ]
 [0.05555556 0.05555556 0.02777778 0.         0.         0.
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  0.02777778 0.         0.         0.         0.         0.02777778
  0.         0.        ]
 [0.         0.075      0.         0.         0.         0.
  0.         0.05       0.375      0.05       0.125      0.025
  0.25       0.         0.         0.025      0.         0.025
  0.         0.        ]
 [0.         0.         0.04166667 0.         0.         0.01388889
  0.         0.         0.04166667 0.375      0.         0.05555556
  0.01388889 0.43055556 0.         0.         0.         0.02777778
  0.         0.        ]
 [0.         0.04081633 0.         0.         0.02040816 0.02040816
  0.         0.         0.08163265 0.04081633 0.63265306 0.02040816
  0.08163265 0.         0.         0.04081633 0.         0.
  0.02040816 0.        ]
 [0.         0.07407407 0.01851852 0.         0.         0.
  0.         0.         0.         0.01851852 0.         0.85185185
  0.01851852 0.         0.         0.         0.         0.01851852
  0.         0.        ]
 [0.03703704 0.03703704 0.         0.         0.         0.
  0.         0.11111111 0.16666667 0.01851852 0.07407407 0.07407407
  0.42592593 0.         0.         0.03703704 0.         0.
  0.01851852 0.        ]
 [0.         0.         0.02380952 0.07142857 0.         0.02380952
  0.02380952 0.         0.         0.07142857 0.         0.
  0.         0.76190476 0.         0.         0.         0.02380952
  0.         0.        ]
 [0.         0.13333333 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.73333333 0.         0.         0.06666667
  0.06666667 0.        ]
 [0.06666667 0.13333333 0.         0.         0.         0.
  0.1        0.         0.06666667 0.         0.03333333 0.13333333
  0.1        0.         0.         0.3        0.         0.03333333
  0.03333333 0.        ]
 [0.         0.02631579 0.10526316 0.         0.05263158 0.05263158
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.76315789 0.
  0.         0.        ]
 [0.         0.09677419 0.09677419 0.         0.         0.
  0.         0.         0.         0.03225806 0.06451613 0.12903226
  0.03225806 0.         0.03225806 0.         0.         0.32258065
  0.19354839 0.        ]
 [0.         0.1        0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.06666667 0.         0.         0.         0.         0.03333333
  0.76666667 0.        ]
 [0.06666667 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.06666667 0.
  0.06666667 0.         0.         0.         0.         0.
  0.06666667 0.73333333]]
[2023-08-14 09:25:13,361 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 09:25:13,362 INFO] 30720 iteration, USE_EMA: False, train/sup_loss: 0.0077, train/unsup_loss: 0.0068, train/total_loss: 0.0128, train/util_ratio: 0.5000, train/run_time: 0.5257, eval/loss: 2.5630, eval/top-1-acc: 0.6262, eval/balanced_acc: 0.6217, eval/precision: 0.6527, eval/recall: 0.6217, eval/F1: 0.6178, lr: 0.0005, train/prefecth_time: 0.0050 BEST_EVAL_ACC: 0.6367, at 28672 iters
[2023-08-14 09:27:46,677 INFO] 30976 iteration USE_EMA: False, train/sup_loss: 0.0004, train/unsup_loss: 0.0008, train/total_loss: 0.0010, train/util_ratio: 0.8750, train/run_time: 0.5374, lr: 0.0005, train/prefecth_time: 0.0109 
[2023-08-14 09:30:16,445 INFO] 31232 iteration USE_EMA: False, train/sup_loss: 0.0272, train/unsup_loss: 0.0030, train/total_loss: 0.0294, train/util_ratio: 0.6250, train/run_time: 0.5408, lr: 0.0005, train/prefecth_time: 0.0024 
[2023-08-14 09:32:42,860 INFO] 31488 iteration USE_EMA: False, train/sup_loss: 0.1896, train/unsup_loss: 0.0085, train/total_loss: 0.1961, train/util_ratio: 0.7500, train/run_time: 0.5835, lr: 0.0005, train/prefecth_time: 0.0017 
[2023-08-14 09:35:12,841 INFO] 31744 iteration USE_EMA: False, train/sup_loss: 0.0041, train/unsup_loss: 0.0039, train/total_loss: 0.0071, train/util_ratio: 0.6250, train/run_time: 0.5775, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 09:37:45,225 INFO] 32000 iteration USE_EMA: False, train/sup_loss: 0.0015, train/unsup_loss: 0.0091, train/total_loss: 0.0086, train/util_ratio: 0.5000, train/run_time: 0.5532, lr: 0.0005, train/prefecth_time: 0.0040 
[2023-08-14 09:40:13,462 INFO] 32256 iteration USE_EMA: False, train/sup_loss: 0.0044, train/unsup_loss: 0.0030, train/total_loss: 0.0067, train/util_ratio: 0.6250, train/run_time: 0.5717, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 09:42:44,725 INFO] 32512 iteration USE_EMA: False, train/sup_loss: 0.2339, train/unsup_loss: 0.0014, train/total_loss: 0.2350, train/util_ratio: 0.7500, train/run_time: 0.5940, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 09:45:13,378 INFO] validating...
[2023-08-14 09:45:22,208 INFO] confusion matrix:
[[0.76923077 0.01923077 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.01923077
  0.03846154 0.15384615]
 [0.         0.87142857 0.         0.         0.         0.
  0.         0.01428571 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.08571429
  0.01428571 0.01428571]
 [0.         0.01785714 0.33928571 0.         0.         0.125
  0.03571429 0.         0.         0.07142857 0.01785714 0.01785714
  0.         0.08928571 0.         0.         0.         0.28571429
  0.         0.        ]
 [0.         0.         0.01408451 0.36619718 0.         0.
  0.         0.         0.04225352 0.05633803 0.08450704 0.01408451
  0.         0.38028169 0.         0.         0.         0.01408451
  0.01408451 0.01408451]
 [0.         0.01923077 0.09615385 0.         0.75       0.
  0.         0.         0.         0.         0.         0.
  0.         0.01923077 0.         0.         0.09615385 0.
  0.         0.01923077]
 [0.         0.01886792 0.05660377 0.         0.         0.88679245
  0.         0.         0.         0.         0.01886792 0.
  0.         0.         0.         0.         0.         0.01886792
  0.         0.        ]
 [0.03508772 0.01754386 0.07017544 0.         0.         0.05263158
  0.59649123 0.         0.         0.         0.         0.
  0.         0.         0.         0.01754386 0.         0.19298246
  0.01754386 0.        ]
 [0.         0.02777778 0.02777778 0.         0.         0.02777778
  0.         0.61111111 0.         0.         0.22222222 0.
  0.         0.         0.02777778 0.         0.         0.05555556
  0.         0.        ]
 [0.025      0.         0.         0.         0.         0.
  0.         0.075      0.15       0.         0.5        0.
  0.05       0.         0.         0.         0.         0.125
  0.         0.075     ]
 [0.         0.         0.09722222 0.         0.         0.01388889
  0.         0.         0.01388889 0.44444444 0.01388889 0.
  0.         0.33333333 0.         0.         0.         0.08333333
  0.         0.        ]
 [0.         0.04081633 0.         0.         0.         0.02040816
  0.         0.         0.06122449 0.02040816 0.81632653 0.
  0.         0.         0.         0.         0.         0.02040816
  0.02040816 0.        ]
 [0.         0.11111111 0.12962963 0.         0.         0.01851852
  0.         0.         0.         0.01851852 0.01851852 0.55555556
  0.         0.         0.         0.01851852 0.         0.11111111
  0.01851852 0.        ]
 [0.07407407 0.03703704 0.         0.         0.         0.
  0.         0.03703704 0.25925926 0.         0.24074074 0.01851852
  0.18518519 0.         0.01851852 0.         0.         0.03703704
  0.09259259 0.        ]
 [0.         0.         0.04761905 0.04761905 0.         0.02380952
  0.         0.         0.         0.02380952 0.02380952 0.
  0.         0.76190476 0.         0.         0.         0.07142857
  0.         0.        ]
 [0.         0.06666667 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.73333333 0.         0.         0.16666667
  0.03333333 0.        ]
 [0.03333333 0.1        0.         0.         0.         0.1
  0.16666667 0.03333333 0.06666667 0.03333333 0.03333333 0.
  0.         0.         0.         0.1        0.         0.23333333
  0.03333333 0.06666667]
 [0.         0.         0.31578947 0.         0.10526316 0.02631579
  0.02631579 0.02631579 0.         0.02631579 0.         0.
  0.02631579 0.         0.02631579 0.         0.36842105 0.05263158
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.06451613 0.03225806
  0.         0.         0.06451613 0.         0.         0.61290323
  0.19354839 0.03225806]
 [0.         0.03333333 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.06666667 0.         0.         0.06666667
  0.8        0.        ]
 [0.1        0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.06666667 0.
  0.         0.         0.         0.         0.         0.03333333
  0.03333333 0.76666667]]
[2023-08-14 09:45:24,199 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 09:45:24,200 INFO] 32768 iteration, USE_EMA: False, train/sup_loss: 0.0016, train/unsup_loss: 0.0004, train/total_loss: 0.0019, train/util_ratio: 0.7500, train/run_time: 0.5470, eval/loss: 4.1759, eval/top-1-acc: 0.5734, eval/balanced_acc: 0.5743, eval/precision: 0.6326, eval/recall: 0.5743, eval/F1: 0.5561, lr: 0.0005, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.6367, at 28672 iters
[2023-08-14 09:47:40,642 INFO] 33024 iteration USE_EMA: False, train/sup_loss: 0.0080, train/unsup_loss: 0.0043, train/total_loss: 0.0115, train/util_ratio: 0.3750, train/run_time: 0.5357, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 09:50:10,813 INFO] 33280 iteration USE_EMA: False, train/sup_loss: 0.0017, train/unsup_loss: 0.0005, train/total_loss: 0.0021, train/util_ratio: 0.6250, train/run_time: 0.6019, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 09:52:39,987 INFO] 33536 iteration USE_EMA: False, train/sup_loss: 0.0217, train/unsup_loss: 0.0055, train/total_loss: 0.0262, train/util_ratio: 0.8750, train/run_time: 0.5826, lr: 0.0005, train/prefecth_time: 0.0025 
[2023-08-14 09:55:08,677 INFO] 33792 iteration USE_EMA: False, train/sup_loss: 0.0027, train/unsup_loss: 0.0059, train/total_loss: 0.0076, train/util_ratio: 0.7500, train/run_time: 0.5185, lr: 0.0005, train/prefecth_time: 0.0029 
[2023-08-14 09:57:42,807 INFO] 34048 iteration USE_EMA: False, train/sup_loss: 0.0053, train/unsup_loss: 0.0059, train/total_loss: 0.0102, train/util_ratio: 0.2500, train/run_time: 0.4853, lr: 0.0005, train/prefecth_time: 0.0063 
[2023-08-14 10:00:13,122 INFO] 34304 iteration USE_EMA: False, train/sup_loss: 0.0155, train/unsup_loss: 0.0054, train/total_loss: 0.0200, train/util_ratio: 0.8750, train/run_time: 0.5687, lr: 0.0005, train/prefecth_time: 0.0067 
[2023-08-14 10:02:41,339 INFO] 34560 iteration USE_EMA: False, train/sup_loss: 0.2048, train/unsup_loss: 0.0009, train/total_loss: 0.2056, train/util_ratio: 0.7500, train/run_time: 0.5598, lr: 0.0005, train/prefecth_time: 0.0046 
[2023-08-14 10:05:12,338 INFO] validating...
[2023-08-14 10:05:21,075 INFO] confusion matrix:
[[0.86538462 0.03846154 0.         0.         0.         0.
  0.         0.03846154 0.         0.         0.         0.
  0.01923077 0.         0.         0.         0.         0.
  0.03846154 0.        ]
 [0.01428571 0.94285714 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.02857143
  0.         0.         0.         0.         0.         0.01428571
  0.         0.        ]
 [0.         0.05357143 0.39285714 0.         0.         0.125
  0.01785714 0.         0.         0.08928571 0.01785714 0.10714286
  0.         0.05357143 0.         0.         0.         0.10714286
  0.01785714 0.01785714]
 [0.         0.         0.         0.52112676 0.         0.01408451
  0.         0.01408451 0.04225352 0.09859155 0.04225352 0.05633803
  0.02816901 0.14084507 0.01408451 0.         0.         0.01408451
  0.01408451 0.        ]
 [0.         0.01923077 0.03846154 0.         0.73076923 0.
  0.         0.         0.         0.         0.01923077 0.
  0.         0.         0.         0.         0.17307692 0.
  0.         0.01923077]
 [0.03773585 0.01886792 0.         0.         0.         0.81132075
  0.         0.         0.         0.         0.         0.03773585
  0.         0.01886792 0.         0.01886792 0.         0.01886792
  0.03773585 0.        ]
 [0.03508772 0.03508772 0.05263158 0.         0.         0.01754386
  0.61403509 0.         0.         0.         0.         0.07017544
  0.01754386 0.         0.         0.05263158 0.03508772 0.05263158
  0.01754386 0.        ]
 [0.02777778 0.02777778 0.02777778 0.         0.         0.02777778
  0.         0.66666667 0.02777778 0.         0.13888889 0.05555556
  0.         0.         0.         0.         0.         0.
  0.         0.        ]
 [0.05       0.025      0.         0.         0.         0.
  0.         0.125      0.175      0.         0.425      0.025
  0.125      0.         0.         0.         0.         0.025
  0.025      0.        ]
 [0.         0.         0.02777778 0.         0.         0.01388889
  0.         0.         0.         0.52777778 0.         0.
  0.01388889 0.375      0.         0.         0.         0.04166667
  0.         0.        ]
 [0.         0.04081633 0.         0.         0.         0.02040816
  0.         0.06122449 0.08163265 0.02040816 0.69387755 0.02040816
  0.04081633 0.         0.         0.         0.         0.
  0.02040816 0.        ]
 [0.         0.01851852 0.03703704 0.         0.         0.
  0.         0.         0.         0.03703704 0.         0.90740741
  0.         0.         0.         0.         0.         0.
  0.         0.        ]
 [0.03703704 0.05555556 0.         0.         0.         0.
  0.         0.14814815 0.05555556 0.01851852 0.16666667 0.03703704
  0.44444444 0.         0.         0.         0.         0.01851852
  0.01851852 0.        ]
 [0.         0.         0.02380952 0.0952381  0.         0.02380952
  0.         0.         0.         0.14285714 0.         0.
  0.         0.71428571 0.         0.         0.         0.
  0.         0.        ]
 [0.         0.1        0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.03333333 0.         0.7        0.         0.         0.06666667
  0.1        0.        ]
 [0.         0.13333333 0.         0.         0.         0.
  0.03333333 0.         0.         0.06666667 0.06666667 0.3
  0.03333333 0.         0.         0.26666667 0.         0.03333333
  0.06666667 0.        ]
 [0.         0.02631579 0.23684211 0.         0.05263158 0.02631579
  0.         0.         0.         0.02631579 0.02631579 0.02631579
  0.         0.         0.         0.         0.52631579 0.
  0.05263158 0.        ]
 [0.         0.06451613 0.03225806 0.         0.         0.
  0.         0.03225806 0.         0.06451613 0.06451613 0.09677419
  0.         0.         0.         0.         0.         0.41935484
  0.19354839 0.03225806]
 [0.         0.1        0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.03333333 0.         0.         0.03333333
  0.76666667 0.03333333]
 [0.13333333 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.06666667 0.
  0.         0.         0.         0.         0.         0.03333333
  0.06666667 0.7       ]]
[2023-08-14 10:05:22,913 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 10:05:22,914 INFO] 34816 iteration, USE_EMA: False, train/sup_loss: 0.0048, train/unsup_loss: 0.0096, train/total_loss: 0.0129, train/util_ratio: 0.8750, train/run_time: 0.5773, eval/loss: 3.1255, eval/top-1-acc: 0.6315, eval/balanced_acc: 0.6193, eval/precision: 0.6501, eval/recall: 0.6193, eval/F1: 0.6123, lr: 0.0005, train/prefecth_time: 0.0053 BEST_EVAL_ACC: 0.6367, at 28672 iters
[2023-08-14 10:07:56,081 INFO] 35072 iteration USE_EMA: False, train/sup_loss: 0.0837, train/unsup_loss: 0.0022, train/total_loss: 0.0856, train/util_ratio: 0.7500, train/run_time: 0.6164, lr: 0.0005, train/prefecth_time: 0.0046 
[2023-08-14 10:10:25,615 INFO] 35328 iteration USE_EMA: False, train/sup_loss: 0.0006, train/unsup_loss: 0.0004, train/total_loss: 0.0009, train/util_ratio: 0.7500, train/run_time: 0.5757, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 10:12:56,254 INFO] 35584 iteration USE_EMA: False, train/sup_loss: 0.0107, train/unsup_loss: 0.0009, train/total_loss: 0.0115, train/util_ratio: 0.5000, train/run_time: 0.5549, lr: 0.0005, train/prefecth_time: 0.0040 
[2023-08-14 10:15:21,655 INFO] 35840 iteration USE_EMA: False, train/sup_loss: 0.0020, train/unsup_loss: 0.0038, train/total_loss: 0.0053, train/util_ratio: 0.7500, train/run_time: 0.5194, lr: 0.0005, train/prefecth_time: 0.0038 
[2023-08-14 10:17:58,349 INFO] 36096 iteration USE_EMA: False, train/sup_loss: 0.0021, train/unsup_loss: 0.0060, train/total_loss: 0.0074, train/util_ratio: 0.5000, train/run_time: 0.5313, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 10:20:28,817 INFO] 36352 iteration USE_EMA: False, train/sup_loss: 0.0045, train/unsup_loss: 0.0079, train/total_loss: 0.0115, train/util_ratio: 0.6250, train/run_time: 0.5861, lr: 0.0005, train/prefecth_time: 0.0045 
[2023-08-14 10:22:56,023 INFO] 36608 iteration USE_EMA: False, train/sup_loss: 0.6480, train/unsup_loss: 0.0006, train/total_loss: 0.6486, train/util_ratio: 0.3750, train/run_time: 0.5462, lr: 0.0005, train/prefecth_time: 0.0046 
[2023-08-14 10:25:26,839 INFO] validating...
[2023-08-14 10:25:35,600 INFO] confusion matrix:
[[0.86538462 0.01923077 0.         0.         0.         0.
  0.         0.01923077 0.         0.         0.         0.
  0.01923077 0.         0.01923077 0.         0.         0.
  0.03846154 0.01923077]
 [0.         0.94285714 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.01428571
  0.         0.         0.01428571 0.         0.         0.01428571
  0.01428571 0.        ]
 [0.         0.03571429 0.39285714 0.         0.         0.07142857
  0.03571429 0.         0.         0.10714286 0.01785714 0.07142857
  0.         0.03571429 0.         0.         0.01785714 0.21428571
  0.         0.        ]
 [0.         0.         0.05633803 0.30985915 0.         0.
  0.         0.04225352 0.02816901 0.02816901 0.04225352 0.05633803
  0.         0.23943662 0.         0.         0.         0.05633803
  0.         0.14084507]
 [0.         0.         0.01923077 0.         0.86538462 0.
  0.         0.         0.         0.         0.01923077 0.
  0.         0.         0.         0.         0.07692308 0.01923077
  0.         0.        ]
 [0.01886792 0.01886792 0.03773585 0.         0.         0.81132075
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.01886792 0.         0.09433962
  0.         0.        ]
 [0.         0.03508772 0.05263158 0.         0.         0.01754386
  0.56140351 0.         0.         0.         0.         0.
  0.01754386 0.         0.         0.03508772 0.03508772 0.24561404
  0.         0.        ]
 [0.05555556 0.02777778 0.         0.         0.         0.
  0.         0.72222222 0.         0.         0.11111111 0.
  0.         0.         0.         0.         0.         0.08333333
  0.         0.        ]
 [0.05       0.025      0.         0.         0.025      0.
  0.         0.225      0.1        0.         0.35       0.025
  0.075      0.         0.         0.         0.         0.075
  0.         0.05      ]
 [0.         0.         0.06944444 0.         0.01388889 0.01388889
  0.         0.         0.         0.41666667 0.         0.02777778
  0.         0.36111111 0.         0.02777778 0.         0.06944444
  0.         0.        ]
 [0.         0.04081633 0.02040816 0.02040816 0.         0.02040816
  0.         0.06122449 0.04081633 0.         0.75510204 0.02040816
  0.         0.         0.         0.         0.         0.
  0.02040816 0.        ]
 [0.         0.03703704 0.07407407 0.         0.01851852 0.
  0.         0.         0.         0.         0.         0.81481481
  0.         0.         0.         0.         0.         0.05555556
  0.         0.        ]
 [0.01851852 0.         0.         0.         0.         0.
  0.         0.24074074 0.07407407 0.         0.2037037  0.01851852
  0.25925926 0.         0.01851852 0.01851852 0.         0.09259259
  0.01851852 0.03703704]
 [0.         0.02380952 0.04761905 0.02380952 0.         0.02380952
  0.02380952 0.         0.         0.07142857 0.         0.
  0.         0.66666667 0.         0.         0.         0.11904762
  0.         0.        ]
 [0.         0.1        0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.7        0.         0.         0.13333333
  0.06666667 0.        ]
 [0.03333333 0.13333333 0.03333333 0.         0.         0.
  0.03333333 0.         0.         0.         0.03333333 0.13333333
  0.         0.         0.         0.3        0.         0.23333333
  0.03333333 0.03333333]
 [0.         0.02631579 0.18421053 0.         0.18421053 0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.42105263 0.13157895
  0.02631579 0.02631579]
 [0.         0.06451613 0.03225806 0.         0.         0.
  0.         0.         0.         0.         0.         0.03225806
  0.         0.         0.         0.         0.         0.70967742
  0.12903226 0.03225806]
 [0.         0.         0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.16666667
  0.76666667 0.03333333]
 [0.06666667 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.06666667 0.
  0.         0.         0.         0.         0.         0.03333333
  0.06666667 0.76666667]]
[2023-08-14 10:25:37,679 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 10:25:37,680 INFO] 36864 iteration, USE_EMA: False, train/sup_loss: 0.0009, train/unsup_loss: 0.0046, train/total_loss: 0.0050, train/util_ratio: 0.8750, train/run_time: 0.5695, eval/loss: 3.8779, eval/top-1-acc: 0.6040, eval/balanced_acc: 0.6074, eval/precision: 0.6415, eval/recall: 0.6074, eval/F1: 0.5858, lr: 0.0005, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.6367, at 28672 iters
[2023-08-14 10:28:12,308 INFO] 37120 iteration USE_EMA: False, train/sup_loss: 0.0474, train/unsup_loss: 0.0027, train/total_loss: 0.0499, train/util_ratio: 0.5000, train/run_time: 0.5529, lr: 0.0004, train/prefecth_time: 0.0057 
[2023-08-14 10:30:40,799 INFO] 37376 iteration USE_EMA: False, train/sup_loss: 0.2388, train/unsup_loss: 0.0052, train/total_loss: 0.2435, train/util_ratio: 0.3750, train/run_time: 0.4640, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 10:33:10,766 INFO] 37632 iteration USE_EMA: False, train/sup_loss: 0.5996, train/unsup_loss: 0.0111, train/total_loss: 0.6099, train/util_ratio: 0.6250, train/run_time: 0.5958, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 10:35:39,928 INFO] 37888 iteration USE_EMA: False, train/sup_loss: 0.0027, train/unsup_loss: 0.0006, train/total_loss: 0.0032, train/util_ratio: 0.7500, train/run_time: 0.5553, lr: 0.0004, train/prefecth_time: 0.0025 
[2023-08-14 10:38:12,414 INFO] 38144 iteration USE_EMA: False, train/sup_loss: 0.0004, train/unsup_loss: 0.0036, train/total_loss: 0.0038, train/util_ratio: 0.6250, train/run_time: 0.5638, lr: 0.0004, train/prefecth_time: 0.0043 
[2023-08-14 10:40:42,422 INFO] 38400 iteration USE_EMA: False, train/sup_loss: 0.0001, train/unsup_loss: 0.0051, train/total_loss: 0.0048, train/util_ratio: 0.6250, train/run_time: 0.5236, lr: 0.0004, train/prefecth_time: 0.0025 
[2023-08-14 10:43:12,451 INFO] 38656 iteration USE_EMA: False, train/sup_loss: 0.0008, train/unsup_loss: 0.0025, train/total_loss: 0.0031, train/util_ratio: 0.6250, train/run_time: 0.5570, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 10:45:40,891 INFO] validating...
[2023-08-14 10:45:49,672 INFO] confusion matrix:
[[0.80769231 0.01923077 0.         0.         0.         0.
  0.         0.         0.01923077 0.         0.         0.
  0.01923077 0.         0.         0.         0.01923077 0.
  0.03846154 0.07692308]
 [0.         0.92857143 0.         0.         0.         0.
  0.         0.         0.         0.         0.01428571 0.
  0.01428571 0.         0.         0.         0.         0.
  0.01428571 0.02857143]
 [0.01785714 0.125      0.21428571 0.         0.         0.07142857
  0.01785714 0.03571429 0.         0.10714286 0.05357143 0.125
  0.         0.05357143 0.         0.         0.03571429 0.10714286
  0.01785714 0.01785714]
 [0.01408451 0.         0.         0.43661972 0.         0.
  0.         0.04225352 0.08450704 0.02816901 0.02816901 0.
  0.         0.15492958 0.         0.         0.         0.04225352
  0.         0.16901408]
 [0.         0.01923077 0.         0.         0.63461538 0.
  0.         0.         0.         0.01923077 0.         0.
  0.         0.         0.         0.         0.30769231 0.
  0.         0.01923077]
 [0.13207547 0.03773585 0.01886792 0.         0.         0.67924528
  0.         0.         0.         0.         0.         0.
  0.01886792 0.         0.         0.05660377 0.         0.
  0.01886792 0.03773585]
 [0.         0.21052632 0.         0.         0.         0.03508772
  0.42105263 0.01754386 0.         0.         0.01754386 0.01754386
  0.         0.         0.         0.07017544 0.14035088 0.01754386
  0.         0.05263158]
 [0.08333333 0.02777778 0.         0.         0.         0.
  0.         0.66666667 0.02777778 0.         0.08333333 0.02777778
  0.05555556 0.         0.         0.         0.         0.02777778
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         0.05       0.225      0.         0.4        0.
  0.225      0.         0.         0.         0.         0.
  0.         0.1       ]
 [0.         0.         0.01388889 0.01388889 0.         0.
  0.         0.         0.01388889 0.44444444 0.02777778 0.
  0.02777778 0.41666667 0.         0.         0.         0.04166667
  0.         0.        ]
 [0.         0.02040816 0.         0.02040816 0.         0.02040816
  0.         0.         0.04081633 0.02040816 0.7755102  0.
  0.06122449 0.         0.         0.         0.         0.
  0.02040816 0.02040816]
 [0.         0.05555556 0.01851852 0.         0.         0.01851852
  0.         0.         0.         0.         0.         0.81481481
  0.09259259 0.         0.         0.         0.         0.
  0.         0.        ]
 [0.03703704 0.01851852 0.         0.         0.         0.
  0.         0.07407407 0.16666667 0.         0.14814815 0.
  0.51851852 0.         0.         0.         0.         0.
  0.03703704 0.        ]
 [0.02380952 0.         0.02380952 0.02380952 0.         0.
  0.         0.         0.02380952 0.07142857 0.         0.
  0.         0.78571429 0.         0.         0.02380952 0.02380952
  0.         0.        ]
 [0.         0.1        0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.7        0.         0.06666667 0.03333333
  0.1        0.        ]
 [0.03333333 0.13333333 0.         0.         0.         0.
  0.         0.         0.03333333 0.         0.         0.1
  0.06666667 0.         0.         0.36666667 0.13333333 0.03333333
  0.03333333 0.06666667]
 [0.         0.05263158 0.         0.         0.         0.
  0.         0.         0.         0.         0.02631579 0.
  0.         0.         0.         0.         0.89473684 0.
  0.         0.02631579]
 [0.         0.09677419 0.         0.         0.         0.
  0.         0.         0.         0.06451613 0.09677419 0.
  0.06451613 0.         0.         0.         0.         0.38709677
  0.22580645 0.06451613]
 [0.         0.1        0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.03333333 0.
  0.73333333 0.13333333]
 [0.03333333 0.         0.         0.         0.         0.
  0.         0.03333333 0.         0.         0.03333333 0.
  0.03333333 0.         0.         0.         0.         0.
  0.         0.86666667]]
[2023-08-14 10:45:51,680 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 10:45:51,681 INFO] 38912 iteration, USE_EMA: False, train/sup_loss: 0.0022, train/unsup_loss: 0.0010, train/total_loss: 0.0031, train/util_ratio: 0.2500, train/run_time: 0.5167, eval/loss: 3.6142, eval/top-1-acc: 0.6093, eval/balanced_acc: 0.6151, eval/precision: 0.6504, eval/recall: 0.6151, eval/F1: 0.5949, lr: 0.0004, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.6367, at 28672 iters
[2023-08-14 10:48:26,335 INFO] 39168 iteration USE_EMA: False, train/sup_loss: 1.1048, train/unsup_loss: 0.0066, train/total_loss: 1.1111, train/util_ratio: 1.0000, train/run_time: 0.5655, lr: 0.0004, train/prefecth_time: 0.0024 
[2023-08-14 10:50:55,265 INFO] 39424 iteration USE_EMA: False, train/sup_loss: 0.0107, train/unsup_loss: 0.0038, train/total_loss: 0.0143, train/util_ratio: 0.7500, train/run_time: 0.5336, lr: 0.0004, train/prefecth_time: 0.0050 
[2023-08-14 10:53:23,503 INFO] 39680 iteration USE_EMA: False, train/sup_loss: 0.0042, train/unsup_loss: 0.0018, train/total_loss: 0.0059, train/util_ratio: 1.0000, train/run_time: 0.5942, lr: 0.0004, train/prefecth_time: 0.0038 
[2023-08-14 10:55:54,379 INFO] 39936 iteration USE_EMA: False, train/sup_loss: 0.0007, train/unsup_loss: 0.0001, train/total_loss: 0.0008, train/util_ratio: 0.2500, train/run_time: 0.5815, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 10:58:28,685 INFO] 40192 iteration USE_EMA: False, train/sup_loss: 0.1247, train/unsup_loss: 0.0014, train/total_loss: 0.1261, train/util_ratio: 0.2500, train/run_time: 0.5604, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 11:00:56,049 INFO] 40448 iteration USE_EMA: False, train/sup_loss: 0.0013, train/unsup_loss: 0.0026, train/total_loss: 0.0039, train/util_ratio: 0.7500, train/run_time: 0.5662, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 11:03:28,074 INFO] 40704 iteration USE_EMA: False, train/sup_loss: 0.0001, train/unsup_loss: 0.0009, train/total_loss: 0.0009, train/util_ratio: 1.0000, train/run_time: 0.5902, lr: 0.0004, train/prefecth_time: 0.0054 
[2023-08-14 11:05:57,176 INFO] validating...
[2023-08-14 11:06:04,831 INFO] confusion matrix:
[[0.92307692 0.01923077 0.         0.         0.         0.01923077
  0.         0.         0.         0.         0.         0.
  0.01923077 0.         0.         0.         0.         0.
  0.01923077 0.        ]
 [0.01428571 0.94285714 0.         0.         0.         0.01428571
  0.         0.         0.         0.         0.01428571 0.
  0.         0.01428571 0.         0.         0.         0.
  0.         0.        ]
 [0.         0.08928571 0.33928571 0.         0.         0.125
  0.03571429 0.         0.         0.08928571 0.03571429 0.10714286
  0.01785714 0.07142857 0.         0.         0.01785714 0.03571429
  0.03571429 0.        ]
 [0.02816901 0.         0.01408451 0.47887324 0.         0.
  0.         0.01408451 0.         0.04225352 0.02816901 0.
  0.         0.3943662  0.         0.         0.         0.
  0.         0.        ]
 [0.         0.01923077 0.07692308 0.         0.63461538 0.
  0.         0.         0.         0.         0.         0.01923077
  0.         0.         0.         0.         0.25       0.
  0.         0.        ]
 [0.05660377 0.         0.01886792 0.         0.         0.9245283
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.        ]
 [0.03508772 0.0877193  0.03508772 0.         0.         0.05263158
  0.49122807 0.         0.         0.         0.01754386 0.0877193
  0.         0.         0.         0.0877193  0.05263158 0.03508772
  0.01754386 0.        ]
 [0.11111111 0.02777778 0.         0.         0.         0.
  0.         0.61111111 0.02777778 0.         0.13888889 0.05555556
  0.02777778 0.         0.         0.         0.         0.
  0.         0.        ]
 [0.025      0.05       0.         0.         0.         0.
  0.         0.075      0.125      0.         0.475      0.05
  0.125      0.         0.         0.025      0.         0.025
  0.         0.025     ]
 [0.         0.01388889 0.02777778 0.         0.         0.01388889
  0.         0.         0.         0.375      0.         0.01388889
  0.         0.51388889 0.         0.02777778 0.         0.01388889
  0.         0.        ]
 [0.         0.02040816 0.         0.         0.         0.02040816
  0.         0.         0.04081633 0.02040816 0.85714286 0.02040816
  0.         0.         0.         0.         0.         0.
  0.02040816 0.        ]
 [0.         0.03703704 0.05555556 0.         0.         0.
  0.         0.         0.         0.         0.         0.88888889
  0.         0.01851852 0.         0.         0.         0.
  0.         0.        ]
 [0.03703704 0.09259259 0.         0.         0.         0.01851852
  0.         0.11111111 0.09259259 0.         0.25925926 0.07407407
  0.27777778 0.         0.         0.         0.         0.
  0.03703704 0.        ]
 [0.         0.         0.02380952 0.02380952 0.         0.02380952
  0.         0.         0.         0.02380952 0.         0.02380952
  0.         0.88095238 0.         0.         0.         0.
  0.         0.        ]
 [0.03333333 0.1        0.         0.         0.         0.
  0.         0.         0.         0.03333333 0.         0.03333333
  0.         0.         0.7        0.         0.         0.
  0.1        0.        ]
 [0.03333333 0.2        0.         0.         0.         0.
  0.         0.         0.         0.03333333 0.06666667 0.2
  0.03333333 0.         0.         0.36666667 0.03333333 0.
  0.03333333 0.        ]
 [0.         0.07894737 0.13157895 0.         0.05263158 0.05263158
  0.         0.         0.         0.         0.02631579 0.05263158
  0.         0.         0.         0.         0.60526316 0.
  0.         0.        ]
 [0.         0.12903226 0.03225806 0.         0.         0.12903226
  0.         0.         0.         0.06451613 0.06451613 0.16129032
  0.         0.         0.         0.         0.         0.25806452
  0.16129032 0.        ]
 [0.03333333 0.03333333 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.03333333 0.         0.         0.
  0.86666667 0.        ]
 [0.03333333 0.         0.         0.         0.         0.
  0.         0.03333333 0.         0.         0.06666667 0.03333333
  0.03333333 0.         0.         0.         0.         0.
  0.03333333 0.76666667]]
[2023-08-14 11:06:06,757 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 11:06:06,758 INFO] 40960 iteration, USE_EMA: False, train/sup_loss: 0.7898, train/unsup_loss: 0.0067, train/total_loss: 0.7965, train/util_ratio: 0.6250, train/run_time: 0.5268, eval/loss: 4.4822, eval/top-1-acc: 0.6177, eval/balanced_acc: 0.6157, eval/precision: 0.6620, eval/recall: 0.6157, eval/F1: 0.5997, lr: 0.0004, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.6367, at 28672 iters
[2023-08-14 11:08:39,139 INFO] 41216 iteration USE_EMA: False, train/sup_loss: 0.0037, train/unsup_loss: 0.0005, train/total_loss: 0.0042, train/util_ratio: 0.6250, train/run_time: 0.5400, lr: 0.0004, train/prefecth_time: 0.0024 
[2023-08-14 11:11:07,175 INFO] 41472 iteration USE_EMA: False, train/sup_loss: 0.0713, train/unsup_loss: 0.0014, train/total_loss: 0.0727, train/util_ratio: 0.5000, train/run_time: 0.5761, lr: 0.0004, train/prefecth_time: 0.0046 
[2023-08-14 11:13:34,459 INFO] 41728 iteration USE_EMA: False, train/sup_loss: 0.0621, train/unsup_loss: 0.0112, train/total_loss: 0.0733, train/util_ratio: 0.8750, train/run_time: 0.6067, lr: 0.0004, train/prefecth_time: 0.0052 
[2023-08-14 11:16:02,738 INFO] 41984 iteration USE_EMA: False, train/sup_loss: 0.0036, train/unsup_loss: 0.0010, train/total_loss: 0.0045, train/util_ratio: 0.7500, train/run_time: 0.5174, lr: 0.0004, train/prefecth_time: 0.0046 
[2023-08-14 11:18:36,355 INFO] 42240 iteration USE_EMA: False, train/sup_loss: 0.0004, train/unsup_loss: 0.0130, train/total_loss: 0.0134, train/util_ratio: 1.0000, train/run_time: 0.5880, lr: 0.0004, train/prefecth_time: 0.0024 
[2023-08-14 11:21:05,286 INFO] 42496 iteration USE_EMA: False, train/sup_loss: 0.0075, train/unsup_loss: 0.0025, train/total_loss: 0.0099, train/util_ratio: 0.3750, train/run_time: 0.5232, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 11:23:34,217 INFO] 42752 iteration USE_EMA: False, train/sup_loss: 0.2900, train/unsup_loss: 0.0008, train/total_loss: 0.2908, train/util_ratio: 0.5000, train/run_time: 0.5321, lr: 0.0004, train/prefecth_time: 0.0035 
[2023-08-14 11:26:03,079 INFO] validating...
[2023-08-14 11:26:11,881 INFO] confusion matrix:
[[0.78846154 0.03846154 0.         0.         0.         0.
  0.         0.07692308 0.         0.         0.         0.05769231
  0.         0.         0.         0.01923077 0.         0.01923077
  0.         0.        ]
 [0.         0.91428571 0.01428571 0.         0.         0.01428571
  0.         0.         0.         0.         0.         0.01428571
  0.01428571 0.01428571 0.         0.         0.         0.01428571
  0.         0.        ]
 [0.         0.03571429 0.25       0.         0.         0.14285714
  0.01785714 0.         0.         0.08928571 0.03571429 0.17857143
  0.03571429 0.08928571 0.         0.         0.01785714 0.10714286
  0.         0.        ]
 [0.         0.         0.         0.4084507  0.         0.
  0.         0.01408451 0.         0.05633803 0.05633803 0.01408451
  0.01408451 0.42253521 0.         0.         0.         0.01408451
  0.         0.        ]
 [0.         0.01923077 0.03846154 0.         0.82692308 0.
  0.01923077 0.         0.         0.01923077 0.         0.
  0.         0.         0.         0.         0.07692308 0.
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 [0.01886792 0.01886792 0.         0.         0.         0.90566038
  0.         0.         0.         0.         0.         0.05660377
  0.         0.         0.         0.         0.         0.
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 [0.0877193  0.01754386 0.12280702 0.         0.         0.05263158
  0.40350877 0.         0.         0.         0.         0.0877193
  0.01754386 0.         0.         0.05263158 0.03508772 0.10526316
  0.01754386 0.        ]
 [0.02777778 0.02777778 0.         0.         0.         0.
  0.         0.88888889 0.         0.         0.         0.05555556
  0.         0.         0.         0.         0.         0.
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 [0.         0.025      0.         0.         0.025      0.
  0.         0.275      0.1        0.         0.4        0.
  0.125      0.         0.         0.025      0.         0.025
  0.         0.        ]
 [0.         0.         0.04166667 0.         0.         0.01388889
  0.         0.         0.01388889 0.52777778 0.         0.
  0.         0.34722222 0.         0.01388889 0.         0.04166667
  0.         0.        ]
 [0.         0.04081633 0.02040816 0.         0.         0.02040816
  0.         0.04081633 0.04081633 0.04081633 0.73469388 0.02040816
  0.         0.         0.         0.         0.         0.04081633
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 [0.         0.07407407 0.03703704 0.         0.         0.
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  0.         0.         0.         0.         0.         0.01851852
  0.         0.        ]
 [0.         0.03703704 0.         0.         0.         0.
  0.         0.16666667 0.05555556 0.         0.22222222 0.03703704
  0.42592593 0.03703704 0.         0.01851852 0.         0.
  0.         0.        ]
 [0.         0.         0.04761905 0.07142857 0.         0.
  0.         0.         0.         0.0952381  0.         0.
  0.         0.76190476 0.         0.02380952 0.         0.
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 [0.         0.06666667 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.7        0.         0.         0.2
  0.03333333 0.        ]
 [0.03333333 0.1        0.03333333 0.         0.         0.
  0.03333333 0.03333333 0.         0.         0.06666667 0.16666667
  0.         0.         0.         0.43333333 0.03333333 0.03333333
  0.03333333 0.        ]
 [0.         0.15789474 0.07894737 0.         0.10526316 0.02631579
  0.         0.         0.         0.         0.02631579 0.02631579
  0.         0.         0.         0.         0.57894737 0.
  0.         0.        ]
 [0.         0.19354839 0.03225806 0.         0.         0.06451613
  0.         0.         0.         0.06451613 0.         0.
  0.06451613 0.         0.         0.         0.         0.51612903
  0.03225806 0.03225806]
 [0.         0.06666667 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.06666667 0.         0.         0.16666667
  0.66666667 0.        ]
 [0.03333333 0.         0.03333333 0.         0.         0.
  0.         0.03333333 0.         0.         0.03333333 0.
  0.03333333 0.         0.         0.         0.         0.03333333
  0.         0.8       ]]
[2023-08-14 11:26:13,807 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 11:26:13,807 INFO] 43008 iteration, USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0003, train/total_loss: 0.0006, train/util_ratio: 0.6250, train/run_time: 0.5355, eval/loss: 4.0475, eval/top-1-acc: 0.6209, eval/balanced_acc: 0.6232, eval/precision: 0.6613, eval/recall: 0.6232, eval/F1: 0.6118, lr: 0.0004, train/prefecth_time: 0.0045 BEST_EVAL_ACC: 0.6367, at 28672 iters
[2023-08-14 11:28:44,178 INFO] 43264 iteration USE_EMA: False, train/sup_loss: 0.0041, train/unsup_loss: 0.0193, train/total_loss: 0.0234, train/util_ratio: 0.7500, train/run_time: 0.5585, lr: 0.0004, train/prefecth_time: 0.0046 
[2023-08-14 11:31:14,311 INFO] 43520 iteration USE_EMA: False, train/sup_loss: 0.0013, train/unsup_loss: 0.0087, train/total_loss: 0.0100, train/util_ratio: 1.0000, train/run_time: 0.5208, lr: 0.0004, train/prefecth_time: 0.0024 
[2023-08-14 11:33:44,789 INFO] 43776 iteration USE_EMA: False, train/sup_loss: 0.0021, train/unsup_loss: 0.0013, train/total_loss: 0.0034, train/util_ratio: 0.7500, train/run_time: 0.5402, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 11:36:12,639 INFO] 44032 iteration USE_EMA: False, train/sup_loss: 0.0425, train/unsup_loss: 0.0095, train/total_loss: 0.0521, train/util_ratio: 0.6250, train/run_time: 0.4566, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 11:38:47,417 INFO] 44288 iteration USE_EMA: False, train/sup_loss: 1.0634, train/unsup_loss: 0.0033, train/total_loss: 1.0667, train/util_ratio: 0.7500, train/run_time: 0.5313, lr: 0.0004, train/prefecth_time: 0.0024 
[2023-08-14 11:41:16,860 INFO] 44544 iteration USE_EMA: False, train/sup_loss: 0.0018, train/unsup_loss: 0.0019, train/total_loss: 0.0037, train/util_ratio: 0.5000, train/run_time: 0.5524, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 11:43:43,495 INFO] 44800 iteration USE_EMA: False, train/sup_loss: 0.0035, train/unsup_loss: 0.0000, train/total_loss: 0.0035, train/util_ratio: 0.5000, train/run_time: 0.5430, lr: 0.0004, train/prefecth_time: 0.0046 
[2023-08-14 11:46:12,090 INFO] validating...
[2023-08-14 11:46:20,836 INFO] confusion matrix:
[[0.78846154 0.01923077 0.         0.         0.         0.01923077
  0.         0.03846154 0.         0.01923077 0.         0.
  0.01923077 0.         0.01923077 0.         0.         0.05769231
  0.01923077 0.        ]
 [0.         0.82857143 0.         0.         0.         0.01428571
  0.01428571 0.         0.         0.         0.01428571 0.08571429
  0.         0.01428571 0.         0.01428571 0.         0.
  0.01428571 0.        ]
 [0.         0.01785714 0.30357143 0.         0.         0.16071429
  0.07142857 0.         0.         0.125      0.03571429 0.08928571
  0.         0.03571429 0.01785714 0.         0.         0.10714286
  0.03571429 0.        ]
 [0.         0.         0.         0.4084507  0.         0.
  0.         0.         0.05633803 0.12676056 0.01408451 0.07042254
  0.         0.30985915 0.         0.         0.         0.01408451
  0.         0.        ]
 [0.         0.         0.05769231 0.         0.63461538 0.01923077
  0.         0.         0.         0.         0.         0.07692308
  0.         0.         0.         0.         0.21153846 0.
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 [0.         0.         0.         0.         0.         0.94339623
  0.         0.         0.         0.03773585 0.         0.01886792
  0.         0.         0.         0.         0.         0.
  0.         0.        ]
 [0.07017544 0.         0.01754386 0.         0.         0.05263158
  0.73684211 0.         0.         0.         0.01754386 0.01754386
  0.         0.         0.01754386 0.         0.         0.03508772
  0.03508772 0.        ]
 [0.         0.02777778 0.         0.         0.         0.
  0.         0.80555556 0.         0.         0.05555556 0.02777778
  0.         0.02777778 0.02777778 0.         0.         0.02777778
  0.         0.        ]
 [0.075      0.         0.         0.         0.         0.025
  0.         0.2        0.1        0.         0.425      0.05
  0.1        0.         0.         0.         0.         0.025
  0.         0.        ]
 [0.         0.         0.01388889 0.         0.         0.01388889
  0.         0.         0.01388889 0.58333333 0.         0.01388889
  0.         0.31944444 0.         0.         0.         0.04166667
  0.         0.        ]
 [0.         0.         0.         0.02040816 0.         0.02040816
  0.         0.02040816 0.04081633 0.04081633 0.79591837 0.02040816
  0.         0.         0.         0.         0.         0.02040816
  0.02040816 0.        ]
 [0.         0.05555556 0.01851852 0.         0.         0.01851852
  0.         0.         0.         0.03703704 0.         0.85185185
  0.         0.         0.         0.         0.         0.01851852
  0.         0.        ]
 [0.03703704 0.01851852 0.         0.         0.         0.
  0.         0.07407407 0.14814815 0.11111111 0.16666667 0.12962963
  0.24074074 0.         0.01851852 0.03703704 0.         0.01851852
  0.         0.        ]
 [0.         0.         0.02380952 0.04761905 0.         0.02380952
  0.         0.         0.         0.11904762 0.         0.
  0.         0.76190476 0.         0.         0.         0.02380952
  0.         0.        ]
 [0.         0.06666667 0.         0.         0.         0.
  0.         0.         0.         0.03333333 0.         0.
  0.         0.         0.73333333 0.         0.         0.03333333
  0.13333333 0.        ]
 [0.         0.         0.03333333 0.         0.         0.06666667
  0.13333333 0.         0.         0.06666667 0.06666667 0.1
  0.03333333 0.         0.03333333 0.3        0.         0.1
  0.06666667 0.        ]
 [0.         0.02631579 0.13157895 0.         0.05263158 0.05263158
  0.02631579 0.         0.         0.         0.05263158 0.02631579
  0.         0.02631579 0.02631579 0.         0.55263158 0.
  0.         0.02631579]
 [0.         0.06451613 0.06451613 0.         0.         0.06451613
  0.         0.         0.03225806 0.06451613 0.03225806 0.06451613
  0.         0.         0.03225806 0.         0.         0.35483871
  0.19354839 0.03225806]
 [0.03333333 0.03333333 0.         0.         0.         0.06666667
  0.         0.         0.         0.         0.         0.
  0.         0.         0.06666667 0.         0.         0.
  0.8        0.        ]
 [0.06666667 0.         0.         0.         0.         0.
  0.         0.03333333 0.         0.03333333 0.06666667 0.03333333
  0.         0.         0.         0.         0.         0.03333333
  0.03333333 0.7       ]]
[2023-08-14 11:46:22,918 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 11:46:22,919 INFO] 45056 iteration, USE_EMA: False, train/sup_loss: 0.0035, train/unsup_loss: 0.0043, train/total_loss: 0.0077, train/util_ratio: 1.0000, train/run_time: 0.5335, eval/loss: 4.1963, eval/top-1-acc: 0.6156, eval/balanced_acc: 0.6112, eval/precision: 0.6361, eval/recall: 0.6112, eval/F1: 0.5939, lr: 0.0004, train/prefecth_time: 0.0045 BEST_EVAL_ACC: 0.6367, at 28672 iters
[2023-08-14 11:48:57,390 INFO] 45312 iteration USE_EMA: False, train/sup_loss: 0.0017, train/unsup_loss: 0.0061, train/total_loss: 0.0078, train/util_ratio: 0.8750, train/run_time: 0.5850, lr: 0.0004, train/prefecth_time: 0.0038 
[2023-08-14 11:51:26,263 INFO] 45568 iteration USE_EMA: False, train/sup_loss: 0.0058, train/unsup_loss: 0.0044, train/total_loss: 0.0102, train/util_ratio: 0.7500, train/run_time: 0.5764, lr: 0.0004, train/prefecth_time: 0.0046 
[2023-08-14 11:53:56,863 INFO] 45824 iteration USE_EMA: False, train/sup_loss: 0.0022, train/unsup_loss: 0.0038, train/total_loss: 0.0060, train/util_ratio: 0.8750, train/run_time: 0.5337, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 11:56:27,492 INFO] 46080 iteration USE_EMA: False, train/sup_loss: 0.0017, train/unsup_loss: 0.0038, train/total_loss: 0.0056, train/util_ratio: 0.7500, train/run_time: 0.5441, lr: 0.0004, train/prefecth_time: 0.0029 
[2023-08-14 11:58:58,805 INFO] 46336 iteration USE_EMA: False, train/sup_loss: 0.9010, train/unsup_loss: 0.0032, train/total_loss: 0.9043, train/util_ratio: 0.7500, train/run_time: 0.5457, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 12:01:27,672 INFO] 46592 iteration USE_EMA: False, train/sup_loss: 0.0649, train/unsup_loss: 0.0047, train/total_loss: 0.0696, train/util_ratio: 0.2500, train/run_time: 0.5333, lr: 0.0004, train/prefecth_time: 0.0034 
[2023-08-14 12:03:57,803 INFO] 46848 iteration USE_EMA: False, train/sup_loss: 0.1713, train/unsup_loss: 0.0072, train/total_loss: 0.1785, train/util_ratio: 0.7500, train/run_time: 0.4974, lr: 0.0004, train/prefecth_time: 0.0025 
[2023-08-14 12:06:25,607 INFO] validating...
[2023-08-14 12:06:34,459 INFO] confusion matrix:
[[0.94230769 0.01923077 0.         0.         0.         0.
  0.         0.01923077 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.01923077 0.        ]
 [0.04285714 0.84285714 0.01428571 0.         0.         0.04285714
  0.         0.         0.         0.         0.01428571 0.02857143
  0.         0.         0.01428571 0.         0.         0.
  0.         0.        ]
 [0.01785714 0.05357143 0.42857143 0.01785714 0.         0.10714286
  0.03571429 0.         0.         0.03571429 0.03571429 0.10714286
  0.07142857 0.03571429 0.         0.         0.         0.05357143
  0.         0.        ]
 [0.         0.         0.01408451 0.71830986 0.         0.
  0.         0.         0.         0.         0.08450704 0.01408451
  0.02816901 0.12676056 0.01408451 0.         0.         0.
  0.         0.        ]
 [0.         0.01923077 0.09615385 0.         0.73076923 0.
  0.01923077 0.         0.         0.         0.01923077 0.01923077
  0.01923077 0.         0.         0.         0.07692308 0.
  0.         0.        ]
 [0.05660377 0.01886792 0.         0.         0.         0.9245283
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.        ]
 [0.07017544 0.03508772 0.10526316 0.         0.         0.01754386
  0.66666667 0.         0.         0.         0.01754386 0.01754386
  0.         0.         0.         0.01754386 0.         0.03508772
  0.01754386 0.        ]
 [0.05555556 0.02777778 0.02777778 0.         0.         0.
  0.         0.75       0.         0.         0.08333333 0.02777778
  0.02777778 0.         0.         0.         0.         0.
  0.         0.        ]
 [0.05       0.05       0.         0.         0.         0.
  0.         0.2        0.125      0.         0.375      0.
  0.175      0.         0.         0.         0.         0.025
  0.         0.        ]
 [0.         0.         0.125      0.         0.         0.02777778
  0.         0.         0.         0.36111111 0.01388889 0.
  0.06944444 0.33333333 0.01388889 0.02777778 0.         0.02777778
  0.         0.        ]
 [0.         0.02040816 0.         0.         0.         0.02040816
  0.         0.02040816 0.02040816 0.         0.85714286 0.
  0.04081633 0.         0.         0.         0.         0.02040816
  0.         0.        ]
 [0.         0.09259259 0.05555556 0.         0.         0.01851852
  0.         0.         0.         0.         0.         0.81481481
  0.         0.         0.         0.         0.         0.01851852
  0.         0.        ]
 [0.11111111 0.01851852 0.         0.         0.         0.01851852
  0.01851852 0.12962963 0.03703704 0.         0.16666667 0.01851852
  0.46296296 0.         0.         0.         0.         0.01851852
  0.         0.        ]
 [0.         0.         0.07142857 0.0952381  0.         0.04761905
  0.         0.         0.         0.         0.         0.02380952
  0.02380952 0.69047619 0.         0.         0.         0.04761905
  0.         0.        ]
 [0.03333333 0.06666667 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.73333333 0.         0.         0.1
  0.06666667 0.        ]
 [0.1        0.         0.03333333 0.         0.03333333 0.
  0.1        0.         0.         0.         0.03333333 0.16666667
  0.1        0.         0.         0.3        0.         0.06666667
  0.06666667 0.        ]
 [0.02631579 0.02631579 0.34210526 0.         0.10526316 0.
  0.         0.         0.         0.         0.02631579 0.02631579
  0.         0.         0.02631579 0.         0.42105263 0.
  0.         0.        ]
 [0.         0.06451613 0.06451613 0.         0.         0.03225806
  0.         0.         0.         0.         0.06451613 0.09677419
  0.09677419 0.         0.         0.         0.         0.48387097
  0.09677419 0.        ]
 [0.06666667 0.03333333 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.03333333 0.         0.         0.1
  0.73333333 0.        ]
 [0.13333333 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.06666667 0.
  0.         0.         0.         0.         0.         0.03333333
  0.06666667 0.7       ]]
[2023-08-14 12:06:36,447 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 12:06:38,328 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/model_best.pth
[2023-08-14 12:06:38,329 INFO] 47104 iteration, USE_EMA: False, train/sup_loss: 0.0062, train/unsup_loss: 0.0083, train/total_loss: 0.0145, train/util_ratio: 1.0000, train/run_time: 0.5987, eval/loss: 2.9436, eval/top-1-acc: 0.6452, eval/balanced_acc: 0.6344, eval/precision: 0.6841, eval/recall: 0.6344, eval/F1: 0.6262, lr: 0.0004, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.6452, at 47104 iters
[2023-08-14 12:09:11,683 INFO] 47360 iteration USE_EMA: False, train/sup_loss: 0.0024, train/unsup_loss: 0.0036, train/total_loss: 0.0060, train/util_ratio: 0.7500, train/run_time: 0.6182, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 12:11:39,484 INFO] 47616 iteration USE_EMA: False, train/sup_loss: 0.0007, train/unsup_loss: 0.0041, train/total_loss: 0.0048, train/util_ratio: 0.8750, train/run_time: 0.3025, lr: 0.0004, train/prefecth_time: 0.0036 
[2023-08-14 12:14:08,441 INFO] 47872 iteration USE_EMA: False, train/sup_loss: 1.0942, train/unsup_loss: 0.0088, train/total_loss: 1.1030, train/util_ratio: 1.0000, train/run_time: 0.5515, lr: 0.0004, train/prefecth_time: 0.0053 
[2023-08-14 12:16:39,369 INFO] 48128 iteration USE_EMA: False, train/sup_loss: 0.0404, train/unsup_loss: 0.0048, train/total_loss: 0.0451, train/util_ratio: 0.6250, train/run_time: 0.5565, lr: 0.0004, train/prefecth_time: 0.0046 
[2023-08-14 12:19:11,671 INFO] 48384 iteration USE_EMA: False, train/sup_loss: 0.0015, train/unsup_loss: 0.0016, train/total_loss: 0.0031, train/util_ratio: 0.8750, train/run_time: 0.5451, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 12:21:40,254 INFO] 48640 iteration USE_EMA: False, train/sup_loss: 0.0081, train/unsup_loss: 0.0080, train/total_loss: 0.0161, train/util_ratio: 0.7500, train/run_time: 0.5302, lr: 0.0004, train/prefecth_time: 0.0034 
[2023-08-14 12:24:09,716 INFO] 48896 iteration USE_EMA: False, train/sup_loss: 0.0178, train/unsup_loss: 0.0011, train/total_loss: 0.0189, train/util_ratio: 1.0000, train/run_time: 0.5475, lr: 0.0004, train/prefecth_time: 0.0025 
[2023-08-14 12:26:36,482 INFO] validating...
[2023-08-14 12:26:45,366 INFO] confusion matrix:
[[0.76923077 0.         0.01923077 0.         0.         0.
  0.         0.01923077 0.03846154 0.         0.05769231 0.
  0.03846154 0.         0.         0.03846154 0.         0.
  0.01923077 0.        ]
 [0.02857143 0.72857143 0.         0.         0.         0.01428571
  0.04285714 0.01428571 0.         0.01428571 0.05714286 0.
  0.04285714 0.         0.         0.02857143 0.         0.01428571
  0.01428571 0.        ]
 [0.01785714 0.         0.35714286 0.         0.         0.125
  0.07142857 0.         0.         0.07142857 0.07142857 0.03571429
  0.         0.07142857 0.03571429 0.03571429 0.01785714 0.07142857
  0.01785714 0.        ]
 [0.         0.         0.01408451 0.3943662  0.         0.
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 [0.         0.         0.         0.         0.82692308 0.
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 [0.01886792 0.         0.         0.         0.         0.9245283
  0.         0.         0.         0.01886792 0.         0.
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 [0.10526316 0.         0.01754386 0.         0.         0.03508772
  0.64912281 0.         0.         0.         0.01754386 0.
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  0.03508772 0.        ]
 [0.02777778 0.         0.02777778 0.         0.         0.
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 [0.025      0.         0.025      0.         0.025      0.
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  0.15       0.         0.         0.         0.         0.
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 [0.         0.         0.04166667 0.         0.         0.01388889
  0.         0.01388889 0.         0.5        0.01388889 0.02777778
  0.         0.31944444 0.01388889 0.02777778 0.         0.02777778
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.02040816
  0.         0.         0.02040816 0.02040816 0.91836735 0.
  0.         0.         0.         0.         0.         0.02040816
  0.         0.        ]
 [0.         0.01851852 0.11111111 0.         0.         0.03703704
  0.         0.01851852 0.01851852 0.         0.01851852 0.68518519
  0.07407407 0.01851852 0.         0.         0.         0.
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 [0.03703704 0.         0.         0.         0.         0.
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 [0.         0.         0.04761905 0.04761905 0.         0.02380952
  0.02380952 0.         0.         0.04761905 0.04761905 0.
  0.         0.73809524 0.02380952 0.         0.         0.
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 [0.13333333 0.03333333 0.         0.         0.         0.
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  0.         0.         0.73333333 0.         0.         0.03333333
  0.06666667 0.        ]
 [0.03333333 0.         0.         0.         0.         0.
  0.16666667 0.         0.         0.         0.13333333 0.03333333
  0.1        0.         0.         0.5        0.         0.
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 [0.02631579 0.         0.18421053 0.         0.10526316 0.02631579
  0.05263158 0.         0.02631579 0.         0.05263158 0.
  0.         0.         0.         0.         0.5        0.
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 [0.         0.03225806 0.03225806 0.         0.         0.03225806
  0.03225806 0.         0.03225806 0.         0.16129032 0.06451613
  0.09677419 0.         0.03225806 0.03225806 0.         0.25806452
  0.16129032 0.03225806]
 [0.03333333 0.03333333 0.         0.         0.         0.03333333
  0.         0.         0.03333333 0.         0.03333333 0.
  0.         0.         0.03333333 0.06666667 0.         0.03333333
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 [0.06666667 0.         0.03333333 0.         0.         0.
  0.         0.03333333 0.         0.         0.03333333 0.
  0.         0.         0.         0.03333333 0.         0.
  0.03333333 0.76666667]]
[2023-08-14 12:26:47,497 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 12:26:47,499 INFO] 49152 iteration, USE_EMA: False, train/sup_loss: 0.0303, train/unsup_loss: 0.0060, train/total_loss: 0.0362, train/util_ratio: 0.7500, train/run_time: 0.5356, eval/loss: 3.0689, eval/top-1-acc: 0.5998, eval/balanced_acc: 0.6000, eval/precision: 0.6162, eval/recall: 0.6000, eval/F1: 0.5870, lr: 0.0004, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.6452, at 47104 iters
[2023-08-14 12:29:21,537 INFO] 49408 iteration USE_EMA: False, train/sup_loss: 0.1963, train/unsup_loss: 0.0054, train/total_loss: 0.2017, train/util_ratio: 0.7500, train/run_time: 0.5868, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 12:31:50,194 INFO] 49664 iteration USE_EMA: False, train/sup_loss: 1.8554, train/unsup_loss: 0.0061, train/total_loss: 1.8615, train/util_ratio: 0.5000, train/run_time: 0.4483, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 12:34:18,431 INFO] 49920 iteration USE_EMA: False, train/sup_loss: 0.0183, train/unsup_loss: 0.0040, train/total_loss: 0.0223, train/util_ratio: 0.8750, train/run_time: 0.5289, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 12:36:48,406 INFO] 50176 iteration USE_EMA: False, train/sup_loss: 0.0626, train/unsup_loss: 0.0145, train/total_loss: 0.0772, train/util_ratio: 1.0000, train/run_time: 0.5164, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 12:39:22,466 INFO] 50432 iteration USE_EMA: False, train/sup_loss: 0.0329, train/unsup_loss: 0.0072, train/total_loss: 0.0402, train/util_ratio: 0.8750, train/run_time: 0.6050, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 12:41:48,379 INFO] 50688 iteration USE_EMA: False, train/sup_loss: 0.0053, train/unsup_loss: 0.0011, train/total_loss: 0.0065, train/util_ratio: 0.7500, train/run_time: 0.5466, lr: 0.0004, train/prefecth_time: 0.0046 
[2023-08-14 12:44:17,476 INFO] 50944 iteration USE_EMA: False, train/sup_loss: 0.0008, train/unsup_loss: 0.0041, train/total_loss: 0.0049, train/util_ratio: 0.7500, train/run_time: 0.5405, lr: 0.0004, train/prefecth_time: 0.0025 
[2023-08-14 12:46:46,444 INFO] validating...
[2023-08-14 12:46:55,292 INFO] confusion matrix:
[[0.94230769 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.01923077 0.         0.
  0.03846154 0.        ]
 [0.01428571 0.84285714 0.01428571 0.         0.         0.02857143
  0.         0.         0.         0.         0.         0.02857143
  0.         0.01428571 0.02857143 0.         0.         0.
  0.02857143 0.        ]
 [0.         0.         0.39285714 0.         0.01785714 0.16071429
  0.03571429 0.         0.         0.10714286 0.01785714 0.10714286
  0.         0.03571429 0.01785714 0.         0.         0.08928571
  0.01785714 0.        ]
 [0.         0.         0.01408451 0.33802817 0.         0.
  0.         0.01408451 0.09859155 0.11267606 0.02816901 0.05633803
  0.04225352 0.26760563 0.01408451 0.         0.         0.01408451
  0.         0.        ]
 [0.         0.01923077 0.05769231 0.         0.65384615 0.
  0.01923077 0.         0.         0.05769231 0.         0.
  0.         0.         0.         0.01923077 0.17307692 0.
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 [0.         0.         0.         0.         0.         0.96226415
  0.         0.         0.         0.         0.         0.01886792
  0.01886792 0.         0.         0.         0.         0.
  0.         0.        ]
 [0.01754386 0.         0.05263158 0.         0.         0.05263158
  0.70175439 0.         0.         0.         0.         0.
  0.01754386 0.         0.03508772 0.03508772 0.         0.
  0.0877193  0.        ]
 [0.05555556 0.         0.         0.         0.         0.
  0.         0.63888889 0.02777778 0.         0.08333333 0.13888889
  0.05555556 0.         0.         0.         0.         0.
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 [0.025      0.05       0.025      0.         0.025      0.
  0.         0.125      0.2        0.025      0.225      0.05
  0.2        0.         0.         0.         0.         0.025
  0.         0.025     ]
 [0.         0.         0.06944444 0.         0.         0.04166667
  0.         0.         0.         0.61111111 0.         0.
  0.         0.25       0.         0.         0.         0.02777778
  0.         0.        ]
 [0.         0.02040816 0.         0.         0.         0.02040816
  0.         0.02040816 0.04081633 0.04081633 0.79591837 0.
  0.         0.         0.02040816 0.02040816 0.         0.
  0.02040816 0.        ]
 [0.         0.09259259 0.09259259 0.         0.         0.
  0.         0.01851852 0.         0.01851852 0.         0.75925926
  0.         0.01851852 0.         0.         0.         0.
  0.         0.        ]
 [0.05555556 0.         0.         0.         0.         0.
  0.01851852 0.05555556 0.11111111 0.         0.09259259 0.
  0.55555556 0.         0.07407407 0.01851852 0.         0.01851852
  0.         0.        ]
 [0.         0.         0.02380952 0.02380952 0.         0.
  0.02380952 0.         0.         0.0952381  0.         0.
  0.         0.80952381 0.         0.         0.         0.02380952
  0.         0.        ]
 [0.03333333 0.06666667 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.73333333 0.         0.         0.13333333
  0.03333333 0.        ]
 [0.06666667 0.06666667 0.03333333 0.         0.         0.06666667
  0.03333333 0.         0.03333333 0.         0.03333333 0.
  0.         0.         0.1        0.43333333 0.         0.1
  0.03333333 0.        ]
 [0.         0.02631579 0.13157895 0.         0.07894737 0.02631579
  0.10526316 0.         0.         0.         0.         0.
  0.         0.         0.02631579 0.         0.60526316 0.
  0.         0.        ]
 [0.         0.03225806 0.03225806 0.         0.         0.09677419
  0.         0.03225806 0.         0.03225806 0.03225806 0.03225806
  0.         0.         0.06451613 0.         0.         0.4516129
  0.19354839 0.        ]
 [0.         0.03333333 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.06666667 0.         0.         0.16666667
  0.7        0.        ]
 [0.06666667 0.         0.         0.         0.         0.
  0.         0.03333333 0.         0.         0.06666667 0.
  0.03333333 0.         0.         0.         0.         0.03333333
  0.         0.76666667]]
[2023-08-14 12:46:57,224 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 12:46:59,238 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/model_best.pth
[2023-08-14 12:46:59,239 INFO] 51200 iteration, USE_EMA: False, train/sup_loss: 0.0012, train/unsup_loss: 0.0056, train/total_loss: 0.0067, train/util_ratio: 0.7500, train/run_time: 0.5360, eval/loss: 2.9071, eval/top-1-acc: 0.6484, eval/balanced_acc: 0.6447, eval/precision: 0.6547, eval/recall: 0.6447, eval/F1: 0.6316, lr: 0.0004, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.6484, at 51200 iters
[2023-08-14 12:49:32,785 INFO] 51456 iteration USE_EMA: False, train/sup_loss: 0.2542, train/unsup_loss: 0.0026, train/total_loss: 0.2568, train/util_ratio: 0.5000, train/run_time: 0.5681, lr: 0.0004, train/prefecth_time: 0.0034 
[2023-08-14 12:52:03,226 INFO] 51712 iteration USE_EMA: False, train/sup_loss: 0.0010, train/unsup_loss: 0.0061, train/total_loss: 0.0071, train/util_ratio: 0.6250, train/run_time: 0.5615, lr: 0.0004, train/prefecth_time: 0.0024 
[2023-08-14 12:54:31,792 INFO] 51968 iteration USE_EMA: False, train/sup_loss: 0.0009, train/unsup_loss: 0.0022, train/total_loss: 0.0031, train/util_ratio: 0.8750, train/run_time: 0.5495, lr: 0.0004, train/prefecth_time: 0.0037 
[2023-08-14 12:56:58,859 INFO] 52224 iteration USE_EMA: False, train/sup_loss: 0.0016, train/unsup_loss: 0.0067, train/total_loss: 0.0083, train/util_ratio: 0.8750, train/run_time: 0.5234, lr: 0.0004, train/prefecth_time: 0.0032 
[2023-08-14 12:59:30,939 INFO] 52480 iteration USE_EMA: False, train/sup_loss: 0.0008, train/unsup_loss: 0.0025, train/total_loss: 0.0033, train/util_ratio: 0.8750, train/run_time: 0.5495, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 13:01:59,794 INFO] 52736 iteration USE_EMA: False, train/sup_loss: 0.0026, train/unsup_loss: 0.0033, train/total_loss: 0.0058, train/util_ratio: 0.7500, train/run_time: 0.5426, lr: 0.0004, train/prefecth_time: 0.0046 
[2023-08-14 13:04:27,630 INFO] 52992 iteration USE_EMA: False, train/sup_loss: 0.2552, train/unsup_loss: 0.0048, train/total_loss: 0.2599, train/util_ratio: 0.7500, train/run_time: 0.5132, lr: 0.0004, train/prefecth_time: 0.0031 
[2023-08-14 13:06:57,439 INFO] validating...
[2023-08-14 13:07:06,146 INFO] confusion matrix:
[[0.88461538 0.         0.         0.         0.         0.
  0.         0.01923077 0.01923077 0.         0.         0.
  0.         0.         0.01923077 0.         0.         0.
  0.05769231 0.        ]
 [0.01428571 0.85714286 0.         0.         0.         0.01428571
  0.         0.01428571 0.         0.         0.         0.
  0.         0.         0.02857143 0.         0.         0.01428571
  0.04285714 0.01428571]
 [0.         0.05357143 0.32142857 0.         0.         0.14285714
  0.01785714 0.         0.         0.08928571 0.03571429 0.07142857
  0.01785714 0.07142857 0.01785714 0.         0.         0.16071429
  0.         0.        ]
 [0.         0.         0.         0.3943662  0.         0.
  0.         0.         0.1971831  0.         0.08450704 0.
  0.         0.29577465 0.01408451 0.         0.         0.
  0.01408451 0.        ]
 [0.         0.01923077 0.01923077 0.         0.75       0.
  0.         0.         0.01923077 0.         0.03846154 0.
  0.         0.         0.         0.         0.13461538 0.01923077
  0.         0.        ]
 [0.03773585 0.03773585 0.         0.         0.         0.86792453
  0.         0.         0.         0.         0.         0.01886792
  0.         0.         0.         0.01886792 0.         0.01886792
  0.         0.        ]
 [0.07017544 0.01754386 0.         0.         0.         0.05263158
  0.63157895 0.01754386 0.01754386 0.         0.01754386 0.01754386
  0.01754386 0.         0.01754386 0.         0.         0.12280702
  0.         0.        ]
 [0.02777778 0.         0.02777778 0.         0.         0.
  0.         0.86111111 0.         0.         0.05555556 0.02777778
  0.         0.         0.         0.         0.         0.
  0.         0.        ]
 [0.05       0.025      0.025      0.         0.         0.
  0.         0.15       0.325      0.         0.35       0.025
  0.05       0.         0.         0.         0.         0.
  0.         0.        ]
 [0.         0.         0.04166667 0.         0.         0.01388889
  0.         0.         0.02777778 0.45833333 0.04166667 0.
  0.         0.36111111 0.02777778 0.         0.         0.02777778
  0.         0.        ]
 [0.         0.06122449 0.         0.         0.         0.02040816
  0.         0.06122449 0.10204082 0.         0.73469388 0.
  0.         0.         0.         0.         0.         0.02040816
  0.         0.        ]
 [0.         0.12962963 0.09259259 0.         0.         0.
  0.         0.03703704 0.         0.01851852 0.         0.68518519
  0.         0.         0.         0.         0.         0.03703704
  0.         0.        ]
 [0.03703704 0.         0.         0.         0.         0.
  0.         0.12962963 0.37037037 0.         0.16666667 0.03703704
  0.25925926 0.         0.         0.         0.         0.
  0.         0.        ]
 [0.         0.         0.04761905 0.02380952 0.         0.02380952
  0.         0.         0.02380952 0.02380952 0.02380952 0.04761905
  0.         0.78571429 0.         0.         0.         0.
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 [0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.8        0.         0.         0.2
  0.         0.        ]
 [0.1        0.         0.         0.         0.         0.1
  0.06666667 0.         0.         0.03333333 0.16666667 0.03333333
  0.03333333 0.         0.03333333 0.2        0.         0.2
  0.03333333 0.        ]
 [0.05263158 0.02631579 0.13157895 0.         0.07894737 0.
  0.02631579 0.02631579 0.         0.02631579 0.02631579 0.02631579
  0.         0.         0.02631579 0.         0.52631579 0.02631579
  0.         0.        ]
 [0.         0.03225806 0.         0.         0.         0.
  0.         0.         0.         0.03225806 0.09677419 0.06451613
  0.         0.         0.         0.         0.         0.5483871
  0.22580645 0.        ]
 [0.         0.         0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.06666667 0.         0.         0.06666667
  0.83333333 0.        ]
 [0.03333333 0.         0.         0.         0.         0.
  0.         0.03333333 0.06666667 0.         0.03333333 0.
  0.         0.         0.         0.         0.         0.03333333
  0.         0.8       ]]
[2023-08-14 13:07:08,090 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 13:07:08,091 INFO] 53248 iteration, USE_EMA: False, train/sup_loss: 0.0020, train/unsup_loss: 0.0032, train/total_loss: 0.0051, train/util_ratio: 0.5000, train/run_time: 0.5564, eval/loss: 4.1703, eval/top-1-acc: 0.6188, eval/balanced_acc: 0.6262, eval/precision: 0.6711, eval/recall: 0.6262, eval/F1: 0.6096, lr: 0.0004, train/prefecth_time: 0.0045 BEST_EVAL_ACC: 0.6484, at 51200 iters
[2023-08-14 13:09:43,523 INFO] 53504 iteration USE_EMA: False, train/sup_loss: 0.0004, train/unsup_loss: 0.0000, train/total_loss: 0.0004, train/util_ratio: 0.3750, train/run_time: 0.5103, lr: 0.0004, train/prefecth_time: 0.0025 
[2023-08-14 13:12:12,557 INFO] 53760 iteration USE_EMA: False, train/sup_loss: 0.4190, train/unsup_loss: 0.0102, train/total_loss: 0.4292, train/util_ratio: 0.5000, train/run_time: 0.4957, lr: 0.0004, train/prefecth_time: 0.0054 
[2023-08-14 13:14:42,980 INFO] 54016 iteration USE_EMA: False, train/sup_loss: 0.0015, train/unsup_loss: 0.0048, train/total_loss: 0.0063, train/util_ratio: 0.8750, train/run_time: 0.6085, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 13:17:11,828 INFO] 54272 iteration USE_EMA: False, train/sup_loss: 1.3171, train/unsup_loss: 0.0003, train/total_loss: 1.3174, train/util_ratio: 0.6250, train/run_time: 0.5658, lr: 0.0004, train/prefecth_time: 0.0024 
[2023-08-14 13:19:45,701 INFO] 54528 iteration USE_EMA: False, train/sup_loss: 0.0015, train/unsup_loss: 0.0126, train/total_loss: 0.0140, train/util_ratio: 0.7500, train/run_time: 0.5598, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 13:22:13,364 INFO] 54784 iteration USE_EMA: False, train/sup_loss: 0.0603, train/unsup_loss: 0.0025, train/total_loss: 0.0628, train/util_ratio: 0.6250, train/run_time: 0.5159, lr: 0.0004, train/prefecth_time: 0.0025 
[2023-08-14 13:24:41,058 INFO] 55040 iteration USE_EMA: False, train/sup_loss: 0.0013, train/unsup_loss: 0.0059, train/total_loss: 0.0072, train/util_ratio: 0.7500, train/run_time: 0.3071, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 13:27:09,887 INFO] validating...
[2023-08-14 13:27:19,407 INFO] confusion matrix:
[[0.63461538 0.01923077 0.03846154 0.         0.         0.
  0.         0.03846154 0.01923077 0.         0.         0.03846154
  0.01923077 0.         0.07692308 0.03846154 0.         0.
  0.01923077 0.05769231]
 [0.         0.91428571 0.01428571 0.         0.         0.01428571
  0.         0.         0.         0.         0.         0.02857143
  0.         0.01428571 0.         0.         0.         0.
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 [0.         0.01785714 0.44642857 0.         0.         0.07142857
  0.03571429 0.         0.         0.07142857 0.03571429 0.17857143
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 [0.         0.         0.         0.35211268 0.         0.
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  0.01408451 0.22535211 0.01408451 0.         0.         0.02816901
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 [0.         0.         0.03846154 0.         0.67307692 0.
  0.         0.         0.03846154 0.01923077 0.03846154 0.07692308
  0.         0.         0.         0.         0.11538462 0.
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 [0.01886792 0.01886792 0.01886792 0.         0.         0.88679245
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 [0.         0.05263158 0.12280702 0.         0.         0.
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  0.         0.        ]
 [0.         0.05555556 0.         0.         0.         0.
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  0.02777778 0.         0.         0.         0.         0.
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 [0.         0.05       0.025      0.         0.         0.
  0.         0.15       0.125      0.         0.4        0.
  0.175      0.         0.         0.         0.         0.05
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 [0.         0.         0.11111111 0.         0.         0.
  0.         0.         0.02777778 0.41666667 0.         0.02777778
  0.         0.36111111 0.         0.01388889 0.         0.04166667
  0.         0.        ]
 [0.         0.08163265 0.         0.         0.         0.02040816
  0.         0.04081633 0.06122449 0.02040816 0.75510204 0.02040816
  0.         0.         0.         0.         0.         0.
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 [0.         0.07407407 0.09259259 0.         0.         0.
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  0.         0.         0.         0.         0.         0.
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 [0.         0.03703704 0.         0.         0.         0.
  0.         0.01851852 0.14814815 0.01851852 0.24074074 0.03703704
  0.42592593 0.         0.03703704 0.01851852 0.         0.
  0.         0.01851852]
 [0.         0.         0.07142857 0.04761905 0.         0.
  0.02380952 0.         0.         0.         0.         0.
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 [0.         0.03333333 0.         0.         0.         0.
  0.         0.         0.         0.03333333 0.         0.
  0.         0.         0.8        0.         0.         0.13333333
  0.         0.        ]
 [0.         0.06666667 0.         0.         0.         0.
  0.1        0.         0.06666667 0.         0.1        0.2
  0.         0.         0.06666667 0.23333333 0.         0.13333333
  0.03333333 0.        ]
 [0.         0.         0.21052632 0.         0.10526316 0.
  0.         0.         0.02631579 0.02631579 0.02631579 0.10526316
  0.         0.         0.         0.         0.5        0.
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 [0.         0.09677419 0.         0.         0.         0.03225806
  0.         0.         0.         0.03225806 0.         0.09677419
  0.         0.         0.06451613 0.         0.         0.64516129
  0.03225806 0.        ]
 [0.         0.16666667 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.06666667 0.         0.         0.16666667
  0.56666667 0.        ]
 [0.         0.03333333 0.         0.         0.         0.
  0.         0.         0.         0.         0.06666667 0.
  0.03333333 0.         0.         0.03333333 0.         0.
  0.03333333 0.8       ]]
[2023-08-14 13:27:21,562 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 13:27:21,565 INFO] 55296 iteration, USE_EMA: False, train/sup_loss: 0.0017, train/unsup_loss: 0.0054, train/total_loss: 0.0071, train/util_ratio: 0.7500, train/run_time: 0.4889, eval/loss: 3.9804, eval/top-1-acc: 0.6019, eval/balanced_acc: 0.6045, eval/precision: 0.6414, eval/recall: 0.6045, eval/F1: 0.5934, lr: 0.0004, train/prefecth_time: 0.0045 BEST_EVAL_ACC: 0.6484, at 51200 iters
[2023-08-14 13:29:56,548 INFO] 55552 iteration USE_EMA: False, train/sup_loss: 0.0110, train/unsup_loss: 0.0016, train/total_loss: 0.0126, train/util_ratio: 0.5000, train/run_time: 0.5034, lr: 0.0004, train/prefecth_time: 0.0025 
[2023-08-14 13:32:24,754 INFO] 55808 iteration USE_EMA: False, train/sup_loss: 1.9677, train/unsup_loss: 0.0009, train/total_loss: 1.9686, train/util_ratio: 0.7500, train/run_time: 0.5696, lr: 0.0004, train/prefecth_time: 0.0024 
[2023-08-14 13:34:52,947 INFO] 56064 iteration USE_EMA: False, train/sup_loss: 0.0018, train/unsup_loss: 0.0037, train/total_loss: 0.0054, train/util_ratio: 0.7500, train/run_time: 0.5773, lr: 0.0004, train/prefecth_time: 0.0024 
[2023-08-14 13:37:22,709 INFO] 56320 iteration USE_EMA: False, train/sup_loss: 0.0007, train/unsup_loss: 0.0105, train/total_loss: 0.0112, train/util_ratio: 0.7500, train/run_time: 0.5853, lr: 0.0004, train/prefecth_time: 0.0011 
[2023-08-14 13:39:53,564 INFO] 56576 iteration USE_EMA: False, train/sup_loss: 0.0014, train/unsup_loss: 0.0005, train/total_loss: 0.0018, train/util_ratio: 1.0000, train/run_time: 0.6156, lr: 0.0004, train/prefecth_time: 0.0024 
[2023-08-14 13:42:22,419 INFO] 56832 iteration USE_EMA: False, train/sup_loss: 0.0271, train/unsup_loss: 0.0066, train/total_loss: 0.0337, train/util_ratio: 0.7500, train/run_time: 0.5201, lr: 0.0004, train/prefecth_time: 0.0047 
[2023-08-14 13:44:52,212 INFO] 57088 iteration USE_EMA: False, train/sup_loss: 0.0008, train/unsup_loss: 0.0012, train/total_loss: 0.0021, train/util_ratio: 1.0000, train/run_time: 0.5246, lr: 0.0004, train/prefecth_time: 0.0042 
[2023-08-14 13:47:22,258 INFO] validating...
[2023-08-14 13:47:30,952 INFO] confusion matrix:
[[0.76923077 0.         0.         0.         0.         0.
  0.         0.         0.03846154 0.         0.01923077 0.01923077
  0.01923077 0.         0.         0.07692308 0.         0.
  0.05769231 0.        ]
 [0.         0.85714286 0.01428571 0.         0.         0.04285714
  0.         0.         0.         0.         0.         0.01428571
  0.01428571 0.01428571 0.         0.01428571 0.         0.
  0.02857143 0.        ]
 [0.         0.01785714 0.28571429 0.         0.         0.14285714
  0.01785714 0.01785714 0.         0.14285714 0.01785714 0.125
  0.03571429 0.07142857 0.         0.         0.01785714 0.05357143
  0.05357143 0.        ]
 [0.         0.         0.         0.4084507  0.         0.
  0.         0.         0.02816901 0.05633803 0.05633803 0.18309859
  0.         0.23943662 0.01408451 0.         0.         0.
  0.01408451 0.        ]
 [0.01923077 0.01923077 0.03846154 0.         0.75       0.
  0.         0.         0.         0.         0.         0.07692308
  0.         0.01923077 0.         0.         0.07692308 0.
  0.         0.        ]
 [0.03773585 0.         0.         0.         0.         0.88679245
  0.         0.         0.         0.         0.         0.01886792
  0.03773585 0.01886792 0.         0.         0.         0.
  0.         0.        ]
 [0.15789474 0.01754386 0.07017544 0.         0.         0.05263158
  0.36842105 0.         0.01754386 0.         0.         0.05263158
  0.         0.         0.         0.0877193  0.01754386 0.10526316
  0.05263158 0.        ]
 [0.         0.02777778 0.02777778 0.02777778 0.         0.
  0.         0.66666667 0.         0.         0.08333333 0.08333333
  0.         0.05555556 0.         0.         0.         0.02777778
  0.         0.        ]
 [0.         0.         0.05       0.         0.         0.
  0.         0.075      0.15       0.         0.4        0.
  0.225      0.025      0.         0.         0.         0.025
  0.         0.05      ]
 [0.         0.         0.02777778 0.         0.         0.01388889
  0.         0.         0.01388889 0.61111111 0.         0.
  0.         0.26388889 0.         0.         0.         0.06944444
  0.         0.        ]
 [0.         0.04081633 0.         0.         0.         0.02040816
  0.         0.02040816 0.04081633 0.04081633 0.63265306 0.06122449
  0.06122449 0.         0.         0.04081633 0.         0.02040816
  0.02040816 0.        ]
 [0.         0.09259259 0.01851852 0.         0.         0.
  0.         0.         0.         0.01851852 0.         0.74074074
  0.03703704 0.         0.         0.         0.         0.09259259
  0.         0.        ]
 [0.         0.01851852 0.01851852 0.         0.         0.
  0.         0.01851852 0.07407407 0.         0.2037037  0.09259259
  0.53703704 0.         0.01851852 0.         0.         0.
  0.         0.01851852]
 [0.         0.         0.02380952 0.07142857 0.         0.02380952
  0.         0.         0.         0.02380952 0.         0.02380952
  0.         0.76190476 0.         0.         0.         0.07142857
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.03333333
  0.         0.         0.83333333 0.         0.         0.06666667
  0.06666667 0.        ]
 [0.         0.03333333 0.03333333 0.         0.         0.
  0.06666667 0.         0.         0.         0.         0.2
  0.13333333 0.         0.         0.4        0.         0.06666667
  0.06666667 0.        ]
 [0.02631579 0.05263158 0.13157895 0.         0.13157895 0.02631579
  0.         0.         0.         0.         0.         0.05263158
  0.         0.05263158 0.02631579 0.         0.47368421 0.02631579
  0.         0.        ]
 [0.         0.03225806 0.         0.         0.         0.09677419
  0.         0.         0.         0.03225806 0.         0.09677419
  0.03225806 0.         0.         0.         0.         0.38709677
  0.25806452 0.06451613]
 [0.03333333 0.         0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.03333333 0.         0.         0.03333333
  0.83333333 0.03333333]
 [0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.03333333 0.03333333
  0.03333333 0.         0.         0.03333333 0.         0.
  0.1        0.76666667]]
[2023-08-14 13:47:33,075 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 13:47:33,076 INFO] 57344 iteration, USE_EMA: False, train/sup_loss: 0.4525, train/unsup_loss: 0.0112, train/total_loss: 0.4637, train/util_ratio: 0.7500, train/run_time: 0.5869, eval/loss: 3.2883, eval/top-1-acc: 0.6051, eval/balanced_acc: 0.6060, eval/precision: 0.6289, eval/recall: 0.6060, eval/F1: 0.5935, lr: 0.0004, train/prefecth_time: 0.0041 BEST_EVAL_ACC: 0.6484, at 51200 iters
[2023-08-14 13:50:07,978 INFO] 57600 iteration USE_EMA: False, train/sup_loss: 0.0007, train/unsup_loss: 0.0003, train/total_loss: 0.0010, train/util_ratio: 0.7500, train/run_time: 0.4710, lr: 0.0004, train/prefecth_time: 0.0024 
[2023-08-14 13:52:38,504 INFO] 57856 iteration USE_EMA: False, train/sup_loss: 0.0302, train/unsup_loss: 0.0012, train/total_loss: 0.0314, train/util_ratio: 1.0000, train/run_time: 0.5016, lr: 0.0004, train/prefecth_time: 0.0025 
[2023-08-14 13:55:06,633 INFO] 58112 iteration USE_EMA: False, train/sup_loss: 0.0014, train/unsup_loss: 0.0033, train/total_loss: 0.0048, train/util_ratio: 0.8750, train/run_time: 0.5686, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 13:57:35,338 INFO] 58368 iteration USE_EMA: False, train/sup_loss: 0.0014, train/unsup_loss: 0.0111, train/total_loss: 0.0125, train/util_ratio: 1.0000, train/run_time: 0.5608, lr: 0.0004, train/prefecth_time: 0.0024 
[2023-08-14 14:00:07,873 INFO] 58624 iteration USE_EMA: False, train/sup_loss: 0.0003, train/unsup_loss: 0.0006, train/total_loss: 0.0009, train/util_ratio: 0.5000, train/run_time: 0.5069, lr: 0.0004, train/prefecth_time: 0.0025 
[2023-08-14 14:02:37,004 INFO] 58880 iteration USE_EMA: False, train/sup_loss: 0.0056, train/unsup_loss: 0.0032, train/total_loss: 0.0089, train/util_ratio: 0.6250, train/run_time: 0.5426, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 14:05:07,878 INFO] 59136 iteration USE_EMA: False, train/sup_loss: 0.0305, train/unsup_loss: 0.0115, train/total_loss: 0.0421, train/util_ratio: 0.8750, train/run_time: 0.5398, lr: 0.0004, train/prefecth_time: 0.0125 
[2023-08-14 14:07:36,604 INFO] validating...
[2023-08-14 14:07:45,242 INFO] confusion matrix:
[[0.75       0.         0.         0.         0.         0.01923077
  0.         0.         0.01923077 0.         0.         0.
  0.05769231 0.         0.01923077 0.01923077 0.         0.01923077
  0.01923077 0.07692308]
 [0.         0.95714286 0.         0.         0.         0.01428571
  0.01428571 0.         0.         0.         0.         0.
  0.         0.01428571 0.         0.         0.         0.
  0.         0.        ]
 [0.         0.01785714 0.23214286 0.         0.01785714 0.17857143
  0.07142857 0.         0.         0.125      0.03571429 0.10714286
  0.01785714 0.03571429 0.         0.         0.01785714 0.08928571
  0.         0.05357143]
 [0.         0.         0.         0.4084507  0.         0.01408451
  0.         0.02816901 0.01408451 0.07042254 0.04225352 0.09859155
  0.01408451 0.28169014 0.01408451 0.         0.         0.01408451
  0.         0.        ]
 [0.         0.         0.         0.         0.69230769 0.
  0.01923077 0.01923077 0.         0.         0.01923077 0.01923077
  0.         0.         0.         0.         0.21153846 0.
  0.         0.01923077]
 [0.         0.         0.         0.         0.         1.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.        ]
 [0.01754386 0.05263158 0.         0.         0.         0.01754386
  0.80701754 0.         0.         0.         0.         0.01754386
  0.01754386 0.         0.         0.         0.03508772 0.03508772
  0.         0.        ]
 [0.08333333 0.02777778 0.         0.         0.         0.
  0.02777778 0.75       0.02777778 0.         0.         0.08333333
  0.         0.         0.         0.         0.         0.
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.025
  0.         0.1        0.2        0.         0.325      0.
  0.225      0.         0.         0.         0.         0.
  0.         0.125     ]
 [0.         0.         0.01388889 0.         0.01388889 0.01388889
  0.         0.         0.         0.48611111 0.01388889 0.
  0.         0.36111111 0.04166667 0.         0.         0.05555556
  0.         0.        ]
 [0.         0.08163265 0.         0.02040816 0.02040816 0.06122449
  0.         0.02040816 0.08163265 0.         0.65306122 0.
  0.04081633 0.         0.         0.02040816 0.         0.
  0.         0.        ]
 [0.         0.05555556 0.         0.         0.         0.
  0.         0.         0.         0.01851852 0.         0.87037037
  0.03703704 0.         0.         0.         0.         0.01851852
  0.         0.        ]
 [0.         0.03703704 0.         0.         0.         0.05555556
  0.         0.03703704 0.11111111 0.         0.16666667 0.03703704
  0.48148148 0.01851852 0.         0.         0.         0.
  0.         0.05555556]
 [0.         0.         0.         0.04761905 0.         0.0952381
  0.02380952 0.         0.         0.         0.         0.04761905
  0.         0.71428571 0.         0.         0.         0.07142857
  0.         0.        ]
 [0.03333333 0.1        0.         0.         0.         0.
  0.         0.         0.         0.         0.03333333 0.
  0.         0.         0.73333333 0.         0.         0.06666667
  0.03333333 0.        ]
 [0.         0.03333333 0.         0.         0.03333333 0.03333333
  0.16666667 0.         0.         0.         0.06666667 0.06666667
  0.1        0.         0.         0.36666667 0.         0.06666667
  0.06666667 0.        ]
 [0.         0.02631579 0.         0.         0.02631579 0.
  0.         0.02631579 0.         0.02631579 0.         0.07894737
  0.         0.         0.02631579 0.         0.73684211 0.
  0.         0.05263158]
 [0.         0.         0.         0.         0.         0.06451613
  0.         0.         0.         0.06451613 0.06451613 0.03225806
  0.09677419 0.         0.03225806 0.         0.03225806 0.4516129
  0.12903226 0.03225806]
 [0.         0.1        0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.03333333 0.         0.03333333 0.         0.         0.03333333
  0.6        0.16666667]
 [0.06666667 0.         0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.03333333 0.
  0.         0.         0.         0.         0.         0.
  0.         0.86666667]]
[2023-08-14 14:07:47,124 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 14:07:47,125 INFO] 59392 iteration, USE_EMA: False, train/sup_loss: 0.0003, train/unsup_loss: 0.0012, train/total_loss: 0.0015, train/util_ratio: 1.0000, train/run_time: 0.5536, eval/loss: 4.1605, eval/top-1-acc: 0.6410, eval/balanced_acc: 0.6379, eval/precision: 0.6656, eval/recall: 0.6379, eval/F1: 0.6193, lr: 0.0004, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.6484, at 51200 iters
[2023-08-14 14:10:19,768 INFO] 59648 iteration USE_EMA: False, train/sup_loss: 1.0120, train/unsup_loss: 0.0091, train/total_loss: 1.0210, train/util_ratio: 0.7500, train/run_time: 0.5392, lr: 0.0004, train/prefecth_time: 0.0045 
[2023-08-14 14:12:50,255 INFO] 59904 iteration USE_EMA: False, train/sup_loss: 0.0015, train/unsup_loss: 0.0005, train/total_loss: 0.0020, train/util_ratio: 0.7500, train/run_time: 0.6097, lr: 0.0004, train/prefecth_time: 0.0026 
[2023-08-14 14:15:19,523 INFO] 60160 iteration USE_EMA: False, train/sup_loss: 0.0022, train/unsup_loss: 0.0088, train/total_loss: 0.0110, train/util_ratio: 0.3750, train/run_time: 0.4734, lr: 0.0004, train/prefecth_time: 0.0046 
[2023-08-14 14:17:47,709 INFO] 60416 iteration USE_EMA: False, train/sup_loss: 0.0089, train/unsup_loss: 0.0073, train/total_loss: 0.0162, train/util_ratio: 0.8750, train/run_time: 0.5412, lr: 0.0004, train/prefecth_time: 0.0024 
[2023-08-14 14:20:20,971 INFO] 60672 iteration USE_EMA: False, train/sup_loss: 0.0068, train/unsup_loss: 0.0010, train/total_loss: 0.0079, train/util_ratio: 0.6250, train/run_time: 0.5642, lr: 0.0004, train/prefecth_time: 0.0025 
[2023-08-14 14:22:52,414 INFO] 60928 iteration USE_EMA: False, train/sup_loss: 0.0013, train/unsup_loss: 0.0034, train/total_loss: 0.0047, train/util_ratio: 0.6250, train/run_time: 0.6144, lr: 0.0004, train/prefecth_time: 0.0046 
[2023-08-14 14:25:18,991 INFO] 61184 iteration USE_EMA: False, train/sup_loss: 0.0032, train/unsup_loss: 0.0075, train/total_loss: 0.0107, train/util_ratio: 1.0000, train/run_time: 0.5270, lr: 0.0004, train/prefecth_time: 0.0046 
[2023-08-14 14:27:49,816 INFO] validating...
[2023-08-14 14:27:58,774 INFO] confusion matrix:
[[0.82692308 0.01923077 0.01923077 0.         0.         0.
  0.         0.         0.         0.         0.         0.01923077
  0.01923077 0.         0.01923077 0.01923077 0.         0.01923077
  0.         0.03846154]
 [0.         0.92857143 0.         0.         0.         0.01428571
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 [0.         0.01785714 0.35714286 0.         0.01785714 0.10714286
  0.03571429 0.         0.         0.10714286 0.01785714 0.125
  0.01785714 0.05357143 0.         0.         0.01785714 0.10714286
  0.         0.01785714]
 [0.         0.         0.01408451 0.30985915 0.         0.
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  0.         0.29577465 0.01408451 0.         0.         0.01408451
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 [0.         0.         0.03846154 0.         0.61538462 0.
  0.01923077 0.01923077 0.         0.01923077 0.         0.03846154
  0.         0.01923077 0.         0.         0.23076923 0.
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 [0.01886792 0.01886792 0.         0.         0.         0.9245283
  0.         0.         0.         0.         0.         0.
  0.         0.         0.01886792 0.         0.         0.
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 [0.01754386 0.03508772 0.01754386 0.         0.         0.05263158
  0.68421053 0.         0.         0.         0.01754386 0.01754386
  0.         0.         0.01754386 0.03508772 0.03508772 0.05263158
  0.         0.01754386]
 [0.05555556 0.02777778 0.02777778 0.         0.         0.
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  0.         0.         0.         0.         0.         0.
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 [0.025      0.         0.025      0.         0.025      0.
  0.         0.075      0.15       0.         0.5        0.
  0.125      0.         0.         0.         0.         0.
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 [0.         0.         0.06944444 0.         0.         0.01388889
  0.         0.         0.         0.54166667 0.01388889 0.
  0.         0.29166667 0.         0.01388889 0.         0.05555556
  0.         0.        ]
 [0.         0.08163265 0.         0.02040816 0.02040816 0.02040816
  0.         0.         0.02040816 0.02040816 0.7755102  0.02040816
  0.         0.         0.         0.02040816 0.         0.
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 [0.         0.11111111 0.         0.         0.         0.
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  0.         0.         0.         0.         0.         0.01851852
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 [0.         0.07407407 0.         0.         0.         0.03703704
  0.03703704 0.01851852 0.11111111 0.03703704 0.2962963  0.05555556
  0.27777778 0.01851852 0.         0.         0.         0.
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 [0.         0.         0.         0.02380952 0.         0.
  0.         0.         0.         0.04761905 0.         0.02380952
  0.         0.83333333 0.         0.02380952 0.         0.04761905
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 [0.03333333 0.1        0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.73333333 0.         0.03333333 0.03333333
  0.06666667 0.        ]
 [0.03333333 0.1        0.03333333 0.         0.03333333 0.
  0.1        0.         0.         0.         0.06666667 0.13333333
  0.         0.         0.         0.36666667 0.         0.06666667
  0.03333333 0.03333333]
 [0.         0.02631579 0.07894737 0.         0.02631579 0.
  0.         0.         0.         0.         0.02631579 0.
  0.         0.02631579 0.02631579 0.         0.73684211 0.
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 [0.         0.12903226 0.03225806 0.         0.         0.03225806
  0.         0.         0.         0.03225806 0.         0.12903226
  0.         0.         0.         0.         0.         0.41935484
  0.16129032 0.06451613]
 [0.         0.06666667 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
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 [0.03333333 0.         0.03333333 0.         0.         0.
  0.         0.03333333 0.         0.         0.06666667 0.
  0.         0.         0.         0.         0.03333333 0.
  0.         0.8       ]]
[2023-08-14 14:28:01,079 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 14:28:01,081 INFO] 61440 iteration, USE_EMA: False, train/sup_loss: 0.0006, train/unsup_loss: 0.0010, train/total_loss: 0.0016, train/util_ratio: 0.8750, train/run_time: 0.5542, eval/loss: 4.0235, eval/top-1-acc: 0.6283, eval/balanced_acc: 0.6286, eval/precision: 0.6456, eval/recall: 0.6286, eval/F1: 0.6078, lr: 0.0003, train/prefecth_time: 0.0045 BEST_EVAL_ACC: 0.6484, at 51200 iters
[2023-08-14 14:30:35,926 INFO] 61696 iteration USE_EMA: False, train/sup_loss: 0.0081, train/unsup_loss: 0.0004, train/total_loss: 0.0084, train/util_ratio: 0.5000, train/run_time: 0.4755, lr: 0.0003, train/prefecth_time: 0.0045 
[2023-08-14 14:33:06,359 INFO] 61952 iteration USE_EMA: False, train/sup_loss: 0.0004, train/unsup_loss: 0.0011, train/total_loss: 0.0015, train/util_ratio: 0.7500, train/run_time: 0.5326, lr: 0.0003, train/prefecth_time: 0.0046 
[2023-08-14 14:35:34,167 INFO] 62208 iteration USE_EMA: False, train/sup_loss: 0.0004, train/unsup_loss: 0.0044, train/total_loss: 0.0048, train/util_ratio: 0.6250, train/run_time: 0.6084, lr: 0.0003, train/prefecth_time: 0.0046 
[2023-08-14 14:38:02,524 INFO] 62464 iteration USE_EMA: False, train/sup_loss: 0.0034, train/unsup_loss: 0.0035, train/total_loss: 0.0069, train/util_ratio: 0.8750, train/run_time: 0.5612, lr: 0.0003, train/prefecth_time: 0.0025 
[2023-08-14 14:40:38,542 INFO] 62720 iteration USE_EMA: False, train/sup_loss: 0.0932, train/unsup_loss: 0.0046, train/total_loss: 0.0977, train/util_ratio: 0.7500, train/run_time: 0.5440, lr: 0.0003, train/prefecth_time: 0.0045 
[2023-08-14 14:43:08,878 INFO] 62976 iteration USE_EMA: False, train/sup_loss: 0.0013, train/unsup_loss: 0.0006, train/total_loss: 0.0019, train/util_ratio: 0.7500, train/run_time: 0.5248, lr: 0.0003, train/prefecth_time: 0.0066 
[2023-08-14 14:45:38,011 INFO] 63232 iteration USE_EMA: False, train/sup_loss: 0.0013, train/unsup_loss: 0.0027, train/total_loss: 0.0039, train/util_ratio: 0.8750, train/run_time: 0.5834, lr: 0.0003, train/prefecth_time: 0.0024 
[2023-08-14 14:48:08,830 INFO] validating...
[2023-08-14 14:48:17,630 INFO] confusion matrix:
[[0.88461538 0.         0.         0.         0.         0.
  0.         0.01923077 0.         0.         0.         0.01923077
  0.         0.         0.01923077 0.01923077 0.         0.01923077
  0.01923077 0.        ]
 [0.         0.95714286 0.         0.         0.         0.
  0.         0.         0.         0.         0.01428571 0.01428571
  0.         0.01428571 0.         0.         0.         0.
  0.         0.        ]
 [0.01785714 0.08928571 0.25       0.01785714 0.01785714 0.17857143
  0.03571429 0.         0.         0.10714286 0.01785714 0.07142857
  0.01785714 0.03571429 0.         0.         0.01785714 0.10714286
  0.01785714 0.        ]
 [0.         0.         0.         0.49295775 0.         0.
  0.         0.01408451 0.04225352 0.04225352 0.14084507 0.
  0.         0.22535211 0.01408451 0.         0.         0.02816901
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 [0.         0.         0.         0.         0.75       0.
  0.01923077 0.         0.         0.01923077 0.01923077 0.
  0.         0.         0.         0.         0.17307692 0.
  0.         0.01923077]
 [0.0754717  0.01886792 0.01886792 0.         0.         0.88679245
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
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 [0.07017544 0.0877193  0.01754386 0.         0.01754386 0.05263158
  0.54385965 0.01754386 0.         0.         0.03508772 0.
  0.01754386 0.         0.         0.05263158 0.03508772 0.01754386
  0.03508772 0.        ]
 [0.08333333 0.02777778 0.         0.         0.         0.
  0.         0.80555556 0.02777778 0.         0.02777778 0.02777778
  0.         0.         0.         0.         0.         0.
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 [0.05       0.025      0.         0.         0.         0.
  0.         0.175      0.1        0.         0.475      0.
  0.125      0.         0.         0.         0.         0.05
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 [0.         0.         0.01388889 0.         0.         0.02777778
  0.         0.         0.01388889 0.48611111 0.         0.
  0.         0.375      0.01388889 0.         0.         0.06944444
  0.         0.        ]
 [0.         0.08163265 0.         0.02040816 0.02040816 0.04081633
  0.         0.02040816 0.06122449 0.         0.67346939 0.
  0.06122449 0.         0.02040816 0.         0.         0.
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 [0.         0.05555556 0.01851852 0.         0.         0.
  0.         0.         0.         0.01851852 0.         0.85185185
  0.03703704 0.         0.         0.         0.         0.01851852
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 [0.         0.11111111 0.         0.         0.         0.
  0.         0.01851852 0.12962963 0.         0.22222222 0.03703704
  0.40740741 0.01851852 0.01851852 0.         0.         0.01851852
  0.01851852 0.        ]
 [0.         0.         0.02380952 0.02380952 0.02380952 0.
  0.         0.         0.         0.04761905 0.         0.
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 [0.03333333 0.03333333 0.         0.         0.         0.
  0.         0.         0.         0.         0.03333333 0.
  0.         0.         0.76666667 0.         0.         0.03333333
  0.1        0.        ]
 [0.03333333 0.13333333 0.         0.         0.         0.
  0.03333333 0.         0.         0.         0.1        0.03333333
  0.06666667 0.         0.         0.43333333 0.         0.13333333
  0.03333333 0.        ]
 [0.02631579 0.05263158 0.07894737 0.         0.02631579 0.
  0.         0.02631579 0.         0.02631579 0.02631579 0.
  0.         0.02631579 0.         0.         0.68421053 0.02631579
  0.         0.        ]
 [0.         0.03225806 0.         0.         0.         0.06451613
  0.         0.         0.         0.06451613 0.06451613 0.03225806
  0.06451613 0.         0.03225806 0.         0.         0.48387097
  0.16129032 0.        ]
 [0.         0.06666667 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.03333333
  0.86666667 0.        ]
 [0.1        0.         0.         0.         0.         0.
  0.         0.03333333 0.         0.         0.06666667 0.
  0.         0.         0.         0.         0.         0.03333333
  0.03333333 0.73333333]]
[2023-08-14 14:48:19,512 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 14:48:19,513 INFO] 63488 iteration, USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0049, train/total_loss: 0.0051, train/util_ratio: 0.7500, train/run_time: 0.4834, eval/loss: 4.1320, eval/top-1-acc: 0.6431, eval/balanced_acc: 0.6457, eval/precision: 0.6658, eval/recall: 0.6457, eval/F1: 0.6303, lr: 0.0003, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.6484, at 51200 iters
[2023-08-14 14:50:54,862 INFO] 63744 iteration USE_EMA: False, train/sup_loss: 0.0013, train/unsup_loss: 0.0035, train/total_loss: 0.0048, train/util_ratio: 0.8750, train/run_time: 0.5589, lr: 0.0003, train/prefecth_time: 0.0046 
[2023-08-14 14:53:24,208 INFO] 64000 iteration USE_EMA: False, train/sup_loss: 0.0004, train/unsup_loss: 0.0012, train/total_loss: 0.0016, train/util_ratio: 1.0000, train/run_time: 0.5708, lr: 0.0003, train/prefecth_time: 0.0046 
[2023-08-14 14:55:54,866 INFO] 64256 iteration USE_EMA: False, train/sup_loss: 0.0015, train/unsup_loss: 0.0035, train/total_loss: 0.0050, train/util_ratio: 0.6250, train/run_time: 0.5373, lr: 0.0003, train/prefecth_time: 0.0025 
[2023-08-14 14:58:24,633 INFO] 64512 iteration USE_EMA: False, train/sup_loss: 0.0011, train/unsup_loss: 0.0039, train/total_loss: 0.0051, train/util_ratio: 0.8750, train/run_time: 0.6076, lr: 0.0003, train/prefecth_time: 0.0033 
[2023-08-14 15:00:57,335 INFO] 64768 iteration USE_EMA: False, train/sup_loss: 0.2750, train/unsup_loss: 0.0029, train/total_loss: 0.2779, train/util_ratio: 0.7500, train/run_time: 0.6068, lr: 0.0003, train/prefecth_time: 0.0045 
[2023-08-14 15:03:28,821 INFO] 65024 iteration USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0095, train/total_loss: 0.0097, train/util_ratio: 1.0000, train/run_time: 0.5238, lr: 0.0003, train/prefecth_time: 0.0043 
[2023-08-14 15:06:02,055 INFO] 65280 iteration USE_EMA: False, train/sup_loss: 0.0017, train/unsup_loss: 0.0025, train/total_loss: 0.0042, train/util_ratio: 0.8750, train/run_time: 0.5607, lr: 0.0003, train/prefecth_time: 0.0025 
[2023-08-14 15:08:31,688 INFO] validating...
[2023-08-14 15:08:40,561 INFO] confusion matrix:
[[0.84615385 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.03846154 0.
  0.         0.         0.01923077 0.01923077 0.         0.01923077
  0.05769231 0.        ]
 [0.         0.97142857 0.         0.         0.         0.01428571
  0.         0.         0.         0.         0.         0.
  0.         0.01428571 0.         0.         0.         0.
  0.         0.        ]
 [0.01785714 0.10714286 0.28571429 0.         0.         0.10714286
  0.05357143 0.         0.         0.08928571 0.03571429 0.07142857
  0.01785714 0.07142857 0.         0.01785714 0.01785714 0.08928571
  0.01785714 0.        ]
 [0.         0.         0.02816901 0.45070423 0.         0.
  0.         0.01408451 0.04225352 0.05633803 0.05633803 0.07042254
  0.         0.22535211 0.01408451 0.         0.         0.01408451
  0.02816901 0.        ]
 [0.01923077 0.         0.         0.         0.75       0.
  0.01923077 0.         0.         0.01923077 0.01923077 0.
  0.         0.         0.01923077 0.01923077 0.13461538 0.
  0.         0.        ]
 [0.0754717  0.03773585 0.01886792 0.         0.         0.81132075
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.03773585 0.         0.01886792
  0.         0.        ]
 [0.0877193  0.10526316 0.01754386 0.         0.         0.07017544
  0.54385965 0.         0.01754386 0.         0.01754386 0.
  0.         0.         0.03508772 0.07017544 0.         0.
  0.03508772 0.        ]
 [0.11111111 0.05555556 0.         0.         0.         0.02777778
  0.         0.63888889 0.         0.         0.11111111 0.02777778
  0.         0.         0.         0.         0.         0.02777778
  0.         0.        ]
 [0.05       0.075      0.         0.         0.         0.
  0.         0.075      0.1        0.         0.5        0.
  0.125      0.         0.         0.         0.         0.075
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.01388889
  0.         0.         0.01388889 0.59722222 0.         0.01388889
  0.         0.27777778 0.01388889 0.         0.         0.06944444
  0.         0.        ]
 [0.         0.06122449 0.         0.02040816 0.02040816 0.02040816
  0.         0.02040816 0.02040816 0.02040816 0.75510204 0.
  0.         0.02040816 0.02040816 0.02040816 0.         0.
  0.         0.        ]
 [0.         0.05555556 0.         0.         0.         0.
  0.         0.         0.         0.01851852 0.         0.87037037
  0.01851852 0.         0.         0.         0.         0.03703704
  0.         0.        ]
 [0.05555556 0.09259259 0.         0.         0.         0.01851852
  0.01851852 0.03703704 0.05555556 0.         0.18518519 0.07407407
  0.25925926 0.         0.07407407 0.03703704 0.         0.01851852
  0.07407407 0.        ]
 [0.02380952 0.         0.         0.02380952 0.02380952 0.
  0.         0.         0.         0.07142857 0.         0.
  0.         0.80952381 0.         0.         0.         0.04761905
  0.         0.        ]
 [0.06666667 0.06666667 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.73333333 0.         0.         0.03333333
  0.1        0.        ]
 [0.06666667 0.03333333 0.03333333 0.         0.         0.
  0.06666667 0.         0.         0.         0.06666667 0.06666667
  0.         0.         0.06666667 0.5        0.         0.06666667
  0.03333333 0.        ]
 [0.13157895 0.07894737 0.07894737 0.         0.07894737 0.
  0.         0.         0.         0.         0.         0.05263158
  0.         0.         0.05263158 0.         0.47368421 0.
  0.02631579 0.02631579]
 [0.         0.03225806 0.03225806 0.         0.         0.
  0.         0.         0.         0.06451613 0.         0.03225806
  0.         0.         0.         0.         0.         0.48387097
  0.35483871 0.        ]
 [0.         0.03333333 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.03333333
  0.93333333 0.        ]
 [0.06666667 0.         0.         0.         0.         0.
  0.03333333 0.         0.         0.         0.03333333 0.
  0.         0.         0.         0.         0.         0.
  0.1        0.76666667]]
[2023-08-14 15:08:42,695 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 15:08:42,696 INFO] 65536 iteration, USE_EMA: False, train/sup_loss: 0.0073, train/unsup_loss: 0.0051, train/total_loss: 0.0124, train/util_ratio: 0.6250, train/run_time: 0.6365, eval/loss: 3.6469, eval/top-1-acc: 0.6294, eval/balanced_acc: 0.6290, eval/precision: 0.6456, eval/recall: 0.6290, eval/F1: 0.6062, lr: 0.0003, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.6484, at 51200 iters
[2023-08-14 15:11:18,945 INFO] 65792 iteration USE_EMA: False, train/sup_loss: 0.0006, train/unsup_loss: 0.0097, train/total_loss: 0.0103, train/util_ratio: 1.0000, train/run_time: 0.5591, lr: 0.0003, train/prefecth_time: 0.0024 
[2023-08-14 15:13:49,106 INFO] 66048 iteration USE_EMA: False, train/sup_loss: 0.0007, train/unsup_loss: 0.0019, train/total_loss: 0.0027, train/util_ratio: 0.8750, train/run_time: 0.5382, lr: 0.0003, train/prefecth_time: 0.0046 
[2023-08-14 15:16:21,058 INFO] 66304 iteration USE_EMA: False, train/sup_loss: 0.0025, train/unsup_loss: 0.0014, train/total_loss: 0.0039, train/util_ratio: 0.6250, train/run_time: 0.5880, lr: 0.0003, train/prefecth_time: 0.0024 
[2023-08-14 15:18:52,099 INFO] 66560 iteration USE_EMA: False, train/sup_loss: 0.0012, train/unsup_loss: 0.0070, train/total_loss: 0.0081, train/util_ratio: 0.7500, train/run_time: 0.5583, lr: 0.0003, train/prefecth_time: 0.0025 
[2023-08-14 15:21:20,785 INFO] 66816 iteration USE_EMA: False, train/sup_loss: 0.0004, train/unsup_loss: 0.0050, train/total_loss: 0.0054, train/util_ratio: 0.3750, train/run_time: 0.2703, lr: 0.0003, train/prefecth_time: 0.0046 
[2023-08-14 15:22:44,514 INFO] 67072 iteration USE_EMA: False, train/sup_loss: 0.0026, train/unsup_loss: 0.0069, train/total_loss: 0.0095, train/util_ratio: 0.6250, train/run_time: 0.2828, lr: 0.0003, train/prefecth_time: 0.0069 
[2023-08-14 15:24:07,135 INFO] 67328 iteration USE_EMA: False, train/sup_loss: 0.0005, train/unsup_loss: 0.0020, train/total_loss: 0.0025, train/util_ratio: 1.0000, train/run_time: 0.3029, lr: 0.0003, train/prefecth_time: 0.0044 
[2023-08-14 15:25:29,891 INFO] validating...
[2023-08-14 15:25:35,332 INFO] confusion matrix:
[[0.88461538 0.01923077 0.         0.         0.         0.
  0.         0.01923077 0.         0.         0.         0.01923077
  0.         0.         0.01923077 0.01923077 0.         0.01923077
  0.         0.        ]
 [0.         0.88571429 0.01428571 0.         0.         0.01428571
  0.         0.01428571 0.         0.         0.         0.01428571
  0.01428571 0.         0.02857143 0.01428571 0.         0.
  0.         0.        ]
 [0.         0.07142857 0.30357143 0.         0.         0.16071429
  0.05357143 0.         0.         0.10714286 0.03571429 0.05357143
  0.01785714 0.05357143 0.         0.01785714 0.01785714 0.08928571
  0.01785714 0.        ]
 [0.         0.         0.         0.35211268 0.         0.
  0.         0.02816901 0.04225352 0.11267606 0.02816901 0.11267606
  0.01408451 0.25352113 0.         0.         0.         0.05633803
  0.         0.        ]
 [0.         0.         0.11538462 0.         0.40384615 0.
  0.01923077 0.01923077 0.         0.03846154 0.         0.01923077
  0.         0.         0.         0.         0.32692308 0.05769231
  0.         0.        ]
 [0.01886792 0.         0.         0.         0.         0.96226415
  0.         0.01886792 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.        ]
 [0.05263158 0.05263158 0.01754386 0.         0.         0.05263158
  0.66666667 0.         0.01754386 0.         0.         0.
  0.01754386 0.         0.         0.05263158 0.         0.05263158
  0.01754386 0.        ]
 [0.02777778 0.02777778 0.         0.         0.         0.
  0.         0.86111111 0.         0.         0.02777778 0.02777778
  0.         0.         0.         0.         0.         0.02777778
  0.         0.        ]
 [0.05       0.05       0.025      0.         0.         0.
  0.         0.2        0.075      0.025      0.375      0.
  0.125      0.         0.         0.         0.         0.05
  0.         0.025     ]
 [0.         0.         0.02777778 0.         0.         0.01388889
  0.         0.         0.         0.69444444 0.         0.
  0.         0.19444444 0.         0.         0.         0.06944444
  0.         0.        ]
 [0.         0.02040816 0.         0.02040816 0.         0.04081633
  0.         0.06122449 0.02040816 0.04081633 0.67346939 0.
  0.02040816 0.02040816 0.         0.02040816 0.         0.06122449
  0.         0.        ]
 [0.         0.07407407 0.09259259 0.         0.         0.
  0.         0.         0.         0.         0.         0.75925926
  0.01851852 0.         0.         0.         0.         0.05555556
  0.         0.        ]
 [0.05555556 0.05555556 0.         0.         0.         0.
  0.         0.11111111 0.09259259 0.         0.22222222 0.03703704
  0.27777778 0.         0.05555556 0.03703704 0.         0.03703704
  0.01851852 0.        ]
 [0.         0.         0.02380952 0.02380952 0.         0.02380952
  0.         0.         0.         0.0952381  0.         0.
  0.02380952 0.78571429 0.         0.         0.         0.02380952
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.76666667 0.         0.         0.1
  0.13333333 0.        ]
 [0.         0.         0.03333333 0.         0.         0.
  0.1        0.         0.         0.         0.06666667 0.06666667
  0.06666667 0.         0.03333333 0.56666667 0.         0.
  0.06666667 0.        ]
 [0.02631579 0.02631579 0.10526316 0.         0.         0.02631579
  0.         0.05263158 0.02631579 0.         0.         0.
  0.         0.         0.         0.         0.68421053 0.
  0.         0.05263158]
 [0.         0.03225806 0.03225806 0.         0.         0.06451613
  0.         0.         0.         0.06451613 0.03225806 0.03225806
  0.06451613 0.         0.         0.         0.         0.48387097
  0.16129032 0.03225806]
 [0.03333333 0.03333333 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.06666667
  0.8        0.03333333]
 [0.06666667 0.         0.         0.         0.         0.
  0.         0.03333333 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.1
  0.06666667 0.73333333]]
[2023-08-14 15:25:37,386 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 15:25:37,388 INFO] 67584 iteration, USE_EMA: False, train/sup_loss: 0.0013, train/unsup_loss: 0.0044, train/total_loss: 0.0056, train/util_ratio: 0.6250, train/run_time: 0.2730, eval/loss: 4.0090, eval/top-1-acc: 0.6262, eval/balanced_acc: 0.6310, eval/precision: 0.6323, eval/recall: 0.6310, eval/F1: 0.6060, lr: 0.0003, train/prefecth_time: 0.0040 BEST_EVAL_ACC: 0.6484, at 51200 iters
[2023-08-14 15:27:04,937 INFO] 67840 iteration USE_EMA: False, train/sup_loss: 0.0003, train/unsup_loss: 0.0015, train/total_loss: 0.0018, train/util_ratio: 1.0000, train/run_time: 0.3311, lr: 0.0003, train/prefecth_time: 0.0051 
[2023-08-14 15:28:28,374 INFO] 68096 iteration USE_EMA: False, train/sup_loss: 0.0012, train/unsup_loss: 0.0011, train/total_loss: 0.0023, train/util_ratio: 1.0000, train/run_time: 0.3098, lr: 0.0003, train/prefecth_time: 0.0071 
[2023-08-14 15:29:52,560 INFO] 68352 iteration USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0020, train/total_loss: 0.0023, train/util_ratio: 0.6250, train/run_time: 0.2901, lr: 0.0003, train/prefecth_time: 0.0044 
[2023-08-14 15:31:15,251 INFO] 68608 iteration USE_EMA: False, train/sup_loss: 0.0004, train/unsup_loss: 0.0003, train/total_loss: 0.0007, train/util_ratio: 0.8750, train/run_time: 0.2838, lr: 0.0003, train/prefecth_time: 0.0041 
[2023-08-14 15:32:43,054 INFO] 68864 iteration USE_EMA: False, train/sup_loss: 0.0019, train/unsup_loss: 0.0024, train/total_loss: 0.0043, train/util_ratio: 0.3750, train/run_time: 0.2721, lr: 0.0003, train/prefecth_time: 0.0038 
[2023-08-14 15:34:06,553 INFO] 69120 iteration USE_EMA: False, train/sup_loss: 0.0006, train/unsup_loss: 0.0015, train/total_loss: 0.0022, train/util_ratio: 0.5000, train/run_time: 0.3048, lr: 0.0003, train/prefecth_time: 0.0042 
[2023-08-14 15:35:29,675 INFO] 69376 iteration USE_EMA: False, train/sup_loss: 0.0003, train/unsup_loss: 0.0004, train/total_loss: 0.0007, train/util_ratio: 0.8750, train/run_time: 0.3132, lr: 0.0003, train/prefecth_time: 0.0038 
[2023-08-14 15:36:52,867 INFO] validating...
[2023-08-14 15:36:58,259 INFO] confusion matrix:
[[0.75       0.         0.01923077 0.         0.         0.
  0.         0.         0.01923077 0.         0.         0.03846154
  0.         0.         0.         0.03846154 0.         0.01923077
  0.01923077 0.09615385]
 [0.         0.85714286 0.02857143 0.         0.         0.
  0.         0.         0.         0.         0.         0.04285714
  0.         0.         0.01428571 0.         0.         0.02857143
  0.01428571 0.01428571]
 [0.         0.01785714 0.41071429 0.         0.         0.03571429
  0.05357143 0.         0.         0.08928571 0.01785714 0.07142857
  0.05357143 0.05357143 0.         0.07142857 0.01785714 0.07142857
  0.01785714 0.01785714]
 [0.         0.         0.01408451 0.35211268 0.         0.01408451
  0.         0.01408451 0.11267606 0.05633803 0.04225352 0.08450704
  0.01408451 0.25352113 0.         0.         0.         0.02816901
  0.         0.01408451]
 [0.01923077 0.         0.09615385 0.         0.42307692 0.
  0.         0.         0.         0.05769231 0.01923077 0.
  0.         0.         0.         0.         0.34615385 0.01923077
  0.         0.01923077]
 [0.01886792 0.03773585 0.03773585 0.         0.         0.8490566
  0.         0.         0.         0.         0.         0.01886792
  0.         0.         0.         0.03773585 0.         0.
  0.         0.        ]
 [0.01754386 0.         0.01754386 0.         0.         0.03508772
  0.71929825 0.         0.         0.         0.         0.
  0.03508772 0.         0.01754386 0.03508772 0.01754386 0.0877193
  0.01754386 0.        ]
 [0.02777778 0.02777778 0.02777778 0.         0.         0.
  0.         0.61111111 0.02777778 0.         0.05555556 0.02777778
  0.05555556 0.         0.         0.         0.         0.11111111
  0.         0.02777778]
 [0.         0.025      0.         0.         0.         0.
  0.         0.025      0.325      0.025      0.325      0.
  0.125      0.         0.         0.         0.         0.05
  0.         0.1       ]
 [0.         0.         0.02777778 0.         0.         0.
  0.         0.         0.         0.54166667 0.         0.
  0.         0.33333333 0.         0.02777778 0.         0.06944444
  0.         0.        ]
 [0.         0.02040816 0.         0.02040816 0.         0.04081633
  0.         0.02040816 0.08163265 0.02040816 0.55102041 0.06122449
  0.04081633 0.04081633 0.         0.         0.         0.08163265
  0.         0.02040816]
 [0.         0.03703704 0.05555556 0.         0.         0.
  0.         0.         0.         0.         0.         0.83333333
  0.01851852 0.         0.         0.         0.         0.05555556
  0.         0.        ]
 [0.05555556 0.03703704 0.         0.         0.         0.
  0.         0.01851852 0.2037037  0.01851852 0.09259259 0.03703704
  0.27777778 0.         0.09259259 0.01851852 0.         0.03703704
  0.         0.11111111]
 [0.         0.         0.         0.02380952 0.         0.
  0.02380952 0.         0.         0.02380952 0.         0.04761905
  0.         0.83333333 0.         0.         0.         0.04761905
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.06666667 0.         0.03333333
  0.         0.         0.76666667 0.         0.         0.03333333
  0.1        0.        ]
 [0.03333333 0.         0.03333333 0.         0.         0.
  0.1        0.         0.         0.03333333 0.03333333 0.1
  0.03333333 0.         0.         0.6        0.         0.
  0.03333333 0.        ]
 [0.         0.02631579 0.23684211 0.         0.         0.
  0.         0.         0.         0.         0.         0.13157895
  0.         0.         0.         0.         0.55263158 0.
  0.         0.05263158]
 [0.         0.         0.09677419 0.         0.         0.
  0.         0.         0.         0.03225806 0.         0.09677419
  0.         0.         0.03225806 0.03225806 0.         0.48387097
  0.16129032 0.06451613]
 [0.         0.03333333 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.03333333 0.         0.06666667
  0.76666667 0.1       ]
 [0.06666667 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.03333333 0.
  0.         0.         0.         0.         0.         0.03333333
  0.         0.86666667]]
[2023-08-14 15:37:00,280 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 15:37:00,281 INFO] 69632 iteration, USE_EMA: False, train/sup_loss: 0.0008, train/unsup_loss: 0.0007, train/total_loss: 0.0015, train/util_ratio: 0.8750, train/run_time: 0.3216, eval/loss: 4.0876, eval/top-1-acc: 0.6093, eval/balanced_acc: 0.6186, eval/precision: 0.6344, eval/recall: 0.6186, eval/F1: 0.6020, lr: 0.0003, train/prefecth_time: 0.0051 BEST_EVAL_ACC: 0.6484, at 51200 iters
[2023-08-14 15:38:26,982 INFO] 69888 iteration USE_EMA: False, train/sup_loss: 0.0005, train/unsup_loss: 0.0097, train/total_loss: 0.0103, train/util_ratio: 1.0000, train/run_time: 0.2884, lr: 0.0003, train/prefecth_time: 0.0046 
[2023-08-14 15:39:50,150 INFO] 70144 iteration USE_EMA: False, train/sup_loss: 0.0007, train/unsup_loss: 0.0021, train/total_loss: 0.0028, train/util_ratio: 0.8750, train/run_time: 0.2933, lr: 0.0003, train/prefecth_time: 0.0039 
[2023-08-14 15:41:13,125 INFO] 70400 iteration USE_EMA: False, train/sup_loss: 0.0001, train/unsup_loss: 0.0005, train/total_loss: 0.0006, train/util_ratio: 0.8750, train/run_time: 0.2861, lr: 0.0003, train/prefecth_time: 0.0040 
[2023-08-14 15:42:36,461 INFO] 70656 iteration USE_EMA: False, train/sup_loss: 0.5640, train/unsup_loss: 0.0002, train/total_loss: 0.5642, train/util_ratio: 0.8750, train/run_time: 0.2732, lr: 0.0003, train/prefecth_time: 0.0034 
[2023-08-14 15:44:04,621 INFO] 70912 iteration USE_EMA: False, train/sup_loss: 0.0055, train/unsup_loss: 0.0050, train/total_loss: 0.0105, train/util_ratio: 0.6250, train/run_time: 0.2809, lr: 0.0003, train/prefecth_time: 0.0040 
[2023-08-14 15:45:28,009 INFO] 71168 iteration USE_EMA: False, train/sup_loss: 0.0118, train/unsup_loss: 0.0007, train/total_loss: 0.0124, train/util_ratio: 0.8750, train/run_time: 0.2954, lr: 0.0003, train/prefecth_time: 0.0047 
[2023-08-14 15:46:51,388 INFO] 71424 iteration USE_EMA: False, train/sup_loss: 0.2528, train/unsup_loss: 0.0044, train/total_loss: 0.2573, train/util_ratio: 0.8750, train/run_time: 0.2554, lr: 0.0003, train/prefecth_time: 0.0044 
[2023-08-14 15:48:14,288 INFO] validating...
[2023-08-14 15:48:19,692 INFO] confusion matrix:
[[0.84615385 0.         0.01923077 0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.01923077 0.03846154 0.         0.01923077
  0.03846154 0.01923077]
 [0.         0.92857143 0.01428571 0.         0.         0.
  0.         0.         0.         0.         0.01428571 0.01428571
  0.         0.         0.         0.         0.         0.01428571
  0.01428571 0.        ]
 [0.         0.01785714 0.32142857 0.         0.         0.125
  0.03571429 0.01785714 0.         0.10714286 0.03571429 0.125
  0.01785714 0.03571429 0.         0.         0.01785714 0.125
  0.01785714 0.        ]
 [0.         0.         0.         0.35211268 0.         0.
  0.         0.01408451 0.05633803 0.04225352 0.08450704 0.11267606
  0.04225352 0.25352113 0.01408451 0.         0.         0.02816901
  0.         0.        ]
 [0.         0.         0.17307692 0.         0.53846154 0.
  0.         0.         0.         0.03846154 0.03846154 0.01923077
  0.         0.         0.         0.         0.13461538 0.05769231
  0.         0.        ]
 [0.01886792 0.         0.         0.         0.         0.96226415
  0.         0.         0.         0.         0.         0.01886792
  0.         0.         0.         0.         0.         0.
  0.         0.        ]
 [0.05263158 0.03508772 0.01754386 0.         0.         0.05263158
  0.57894737 0.         0.         0.         0.         0.
  0.01754386 0.         0.01754386 0.07017544 0.03508772 0.0877193
  0.03508772 0.        ]
 [0.02777778 0.05555556 0.         0.         0.         0.
  0.         0.75       0.         0.         0.11111111 0.02777778
  0.         0.         0.         0.         0.         0.02777778
  0.         0.        ]
 [0.025      0.05       0.         0.         0.         0.
  0.         0.175      0.175      0.025      0.4        0.
  0.1        0.         0.025      0.         0.         0.025
  0.         0.        ]
 [0.         0.         0.02777778 0.         0.         0.
  0.         0.         0.01388889 0.54166667 0.         0.
  0.         0.29166667 0.02777778 0.         0.         0.09722222
  0.         0.        ]
 [0.         0.04081633 0.         0.02040816 0.         0.02040816
  0.         0.04081633 0.04081633 0.04081633 0.73469388 0.02040816
  0.         0.         0.02040816 0.         0.         0.
  0.02040816 0.        ]
 [0.         0.05555556 0.03703704 0.         0.         0.
  0.         0.         0.         0.         0.         0.87037037
  0.01851852 0.         0.         0.         0.         0.01851852
  0.         0.        ]
 [0.03703704 0.07407407 0.         0.         0.         0.01851852
  0.         0.12962963 0.2037037  0.         0.18518519 0.03703704
  0.22222222 0.         0.05555556 0.01851852 0.         0.
  0.         0.01851852]
 [0.         0.         0.         0.04761905 0.         0.02380952
  0.02380952 0.         0.         0.         0.         0.02380952
  0.         0.73809524 0.         0.         0.         0.14285714
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.8        0.         0.         0.03333333
  0.16666667 0.        ]
 [0.03333333 0.03333333 0.         0.         0.         0.06666667
  0.06666667 0.         0.         0.         0.13333333 0.1
  0.         0.         0.03333333 0.36666667 0.         0.1
  0.06666667 0.        ]
 [0.02631579 0.05263158 0.18421053 0.         0.05263158 0.05263158
  0.         0.         0.         0.         0.         0.05263158
  0.         0.         0.02631579 0.         0.52631579 0.
  0.         0.02631579]
 [0.         0.03225806 0.         0.         0.         0.03225806
  0.         0.         0.         0.03225806 0.         0.03225806
  0.         0.         0.         0.         0.         0.70967742
  0.12903226 0.03225806]
 [0.         0.03333333 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.13333333
  0.76666667 0.03333333]
 [0.         0.         0.03333333 0.         0.         0.
  0.         0.06666667 0.         0.         0.03333333 0.
  0.         0.         0.         0.         0.         0.03333333
  0.03333333 0.8       ]]
[2023-08-14 15:48:21,669 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 15:48:21,671 INFO] 71680 iteration, USE_EMA: False, train/sup_loss: 0.0006, train/unsup_loss: 0.0020, train/total_loss: 0.0026, train/util_ratio: 0.7500, train/run_time: 0.2421, eval/loss: 4.4854, eval/top-1-acc: 0.6199, eval/balanced_acc: 0.6265, eval/precision: 0.6343, eval/recall: 0.6265, eval/F1: 0.6020, lr: 0.0003, train/prefecth_time: 0.0031 BEST_EVAL_ACC: 0.6484, at 51200 iters
[2023-08-14 15:49:47,886 INFO] 71936 iteration USE_EMA: False, train/sup_loss: 0.0009, train/unsup_loss: 0.0038, train/total_loss: 0.0048, train/util_ratio: 1.0000, train/run_time: 0.2769, lr: 0.0003, train/prefecth_time: 0.0039 
[2023-08-14 15:51:09,129 INFO] 72192 iteration USE_EMA: False, train/sup_loss: 0.0019, train/unsup_loss: 0.0074, train/total_loss: 0.0093, train/util_ratio: 0.8750, train/run_time: 0.2834, lr: 0.0003, train/prefecth_time: 0.0027 
[2023-08-14 15:52:29,485 INFO] 72448 iteration USE_EMA: False, train/sup_loss: 0.0054, train/unsup_loss: 0.0059, train/total_loss: 0.0113, train/util_ratio: 0.6250, train/run_time: 0.2727, lr: 0.0003, train/prefecth_time: 0.0032 
[2023-08-14 15:53:51,293 INFO] 72704 iteration USE_EMA: False, train/sup_loss: 0.0007, train/unsup_loss: 0.0114, train/total_loss: 0.0121, train/util_ratio: 0.7500, train/run_time: 0.2787, lr: 0.0003, train/prefecth_time: 0.0034 
[2023-08-14 15:55:17,068 INFO] 72960 iteration USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0002, train/total_loss: 0.0004, train/util_ratio: 0.6250, train/run_time: 0.2943, lr: 0.0003, train/prefecth_time: 0.0037 
[2023-08-14 15:56:38,064 INFO] 73216 iteration USE_EMA: False, train/sup_loss: 0.0001, train/unsup_loss: 0.0077, train/total_loss: 0.0077, train/util_ratio: 0.8750, train/run_time: 0.2921, lr: 0.0003, train/prefecth_time: 0.0044 
[2023-08-14 15:57:59,684 INFO] 73472 iteration USE_EMA: False, train/sup_loss: 0.0014, train/unsup_loss: 0.0155, train/total_loss: 0.0169, train/util_ratio: 0.8750, train/run_time: 0.2772, lr: 0.0003, train/prefecth_time: 0.0036 
[2023-08-14 15:59:22,454 INFO] validating...
[2023-08-14 15:59:28,131 INFO] confusion matrix:
[[0.88461538 0.01923077 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.01923077 0.         0.01923077 0.01923077 0.         0.01923077
  0.         0.01923077]
 [0.01428571 0.82857143 0.01428571 0.         0.         0.02857143
  0.         0.         0.         0.         0.         0.07142857
  0.         0.         0.         0.02857143 0.         0.
  0.         0.01428571]
 [0.         0.         0.25       0.         0.01785714 0.08928571
  0.03571429 0.         0.         0.08928571 0.01785714 0.21428571
  0.01785714 0.05357143 0.03571429 0.03571429 0.01785714 0.07142857
  0.03571429 0.01785714]
 [0.         0.01408451 0.         0.29577465 0.         0.
  0.         0.         0.         0.04225352 0.02816901 0.30985915
  0.         0.26760563 0.01408451 0.01408451 0.         0.
  0.01408451 0.        ]
 [0.01923077 0.         0.03846154 0.         0.57692308 0.
  0.01923077 0.         0.         0.01923077 0.         0.01923077
  0.         0.         0.01923077 0.         0.28846154 0.
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 [0.0754717  0.         0.         0.         0.         0.8490566
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.05660377 0.         0.01886792
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 [0.07017544 0.         0.         0.         0.         0.03508772
  0.50877193 0.         0.         0.         0.01754386 0.05263158
  0.01754386 0.         0.03508772 0.14035088 0.03508772 0.03508772
  0.05263158 0.        ]
 [0.05555556 0.02777778 0.         0.         0.         0.
  0.         0.72222222 0.         0.         0.11111111 0.02777778
  0.         0.         0.02777778 0.         0.         0.02777778
  0.         0.        ]
 [0.025      0.         0.         0.         0.         0.
  0.         0.175      0.15       0.         0.3        0.075
  0.125      0.         0.         0.         0.         0.15
  0.         0.        ]
 [0.         0.         0.04166667 0.         0.         0.01388889
  0.         0.         0.         0.61111111 0.         0.09722222
  0.         0.18055556 0.         0.02777778 0.         0.02777778
  0.         0.        ]
 [0.         0.02040816 0.         0.02040816 0.02040816 0.02040816
  0.         0.         0.04081633 0.02040816 0.75510204 0.02040816
  0.02040816 0.         0.         0.02040816 0.         0.02040816
  0.02040816 0.        ]
 [0.         0.03703704 0.01851852 0.         0.         0.
  0.         0.         0.         0.         0.         0.92592593
  0.         0.         0.         0.         0.         0.01851852
  0.         0.        ]
 [0.01851852 0.03703704 0.         0.         0.         0.01851852
  0.         0.07407407 0.03703704 0.         0.2037037  0.05555556
  0.33333333 0.         0.12962963 0.05555556 0.         0.
  0.03703704 0.        ]
 [0.         0.         0.         0.02380952 0.         0.02380952
  0.         0.         0.         0.11904762 0.         0.04761905
  0.         0.69047619 0.         0.07142857 0.         0.02380952
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.8        0.         0.         0.1
  0.1        0.        ]
 [0.03333333 0.         0.         0.         0.         0.
  0.03333333 0.         0.06666667 0.03333333 0.03333333 0.03333333
  0.03333333 0.         0.         0.66666667 0.         0.
  0.06666667 0.        ]
 [0.02631579 0.02631579 0.07894737 0.         0.05263158 0.02631579
  0.         0.         0.         0.         0.         0.10526316
  0.         0.         0.         0.         0.63157895 0.
  0.         0.05263158]
 [0.         0.         0.         0.         0.         0.03225806
  0.         0.         0.         0.         0.03225806 0.22580645
  0.         0.         0.03225806 0.09677419 0.         0.35483871
  0.19354839 0.03225806]
 [0.         0.         0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.03333333 0.         0.         0.06666667
  0.86666667 0.        ]
 [0.06666667 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.06666667 0.
  0.         0.         0.         0.         0.         0.03333333
  0.06666667 0.76666667]]
[2023-08-14 15:59:30,313 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 15:59:30,314 INFO] 73728 iteration, USE_EMA: False, train/sup_loss: 0.0014, train/unsup_loss: 0.0022, train/total_loss: 0.0035, train/util_ratio: 1.0000, train/run_time: 0.2814, eval/loss: 4.2290, eval/top-1-acc: 0.6135, eval/balanced_acc: 0.6234, eval/precision: 0.6366, eval/recall: 0.6234, eval/F1: 0.5948, lr: 0.0003, train/prefecth_time: 0.0038 BEST_EVAL_ACC: 0.6484, at 51200 iters
[2023-08-14 16:00:57,563 INFO] 73984 iteration USE_EMA: False, train/sup_loss: 0.0011, train/unsup_loss: 0.0024, train/total_loss: 0.0035, train/util_ratio: 0.8750, train/run_time: 0.3046, lr: 0.0003, train/prefecth_time: 0.0043 
[2023-08-14 16:02:21,000 INFO] 74240 iteration USE_EMA: False, train/sup_loss: 0.0410, train/unsup_loss: 0.0000, train/total_loss: 0.0410, train/util_ratio: 1.0000, train/run_time: 0.2571, lr: 0.0003, train/prefecth_time: 0.0046 
[2023-08-14 16:03:44,219 INFO] 74496 iteration USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0025, train/total_loss: 0.0027, train/util_ratio: 0.7500, train/run_time: 0.2834, lr: 0.0003, train/prefecth_time: 0.0036 
[2023-08-14 16:05:07,514 INFO] 74752 iteration USE_EMA: False, train/sup_loss: 0.0005, train/unsup_loss: 0.0021, train/total_loss: 0.0026, train/util_ratio: 0.7500, train/run_time: 0.2977, lr: 0.0003, train/prefecth_time: 0.0047 
[2023-08-14 16:06:35,713 INFO] 75008 iteration USE_EMA: False, train/sup_loss: 0.0003, train/unsup_loss: 0.0055, train/total_loss: 0.0058, train/util_ratio: 0.8750, train/run_time: 0.2723, lr: 0.0003, train/prefecth_time: 0.0046 
[2023-08-14 16:07:58,827 INFO] 75264 iteration USE_EMA: False, train/sup_loss: 0.0014, train/unsup_loss: 0.0011, train/total_loss: 0.0025, train/util_ratio: 0.6250, train/run_time: 0.2801, lr: 0.0003, train/prefecth_time: 0.0067 
[2023-08-14 16:09:22,616 INFO] 75520 iteration USE_EMA: False, train/sup_loss: 0.0032, train/unsup_loss: 0.0008, train/total_loss: 0.0040, train/util_ratio: 0.8750, train/run_time: 0.2982, lr: 0.0003, train/prefecth_time: 0.0065 
[2023-08-14 16:10:45,938 INFO] validating...
[2023-08-14 16:10:51,482 INFO] confusion matrix:
[[0.88461538 0.         0.01923077 0.         0.         0.
  0.         0.         0.         0.         0.01923077 0.
  0.01923077 0.         0.         0.         0.         0.
  0.05769231 0.        ]
 [0.         0.9        0.         0.         0.         0.01428571
  0.         0.         0.         0.         0.01428571 0.02857143
  0.         0.         0.01428571 0.         0.         0.01428571
  0.         0.01428571]
 [0.         0.01785714 0.35714286 0.         0.         0.14285714
  0.03571429 0.         0.         0.10714286 0.05357143 0.07142857
  0.01785714 0.01785714 0.         0.         0.01785714 0.14285714
  0.01785714 0.        ]
 [0.         0.         0.         0.26760563 0.         0.
  0.         0.         0.01408451 0.07042254 0.09859155 0.14084507
  0.01408451 0.30985915 0.         0.         0.         0.07042254
  0.01408451 0.        ]
 [0.         0.         0.17307692 0.         0.57692308 0.
  0.01923077 0.         0.         0.         0.05769231 0.
  0.         0.         0.         0.         0.17307692 0.
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.88679245
  0.         0.         0.         0.         0.05660377 0.
  0.         0.         0.         0.         0.         0.
  0.05660377 0.        ]
 [0.10526316 0.01754386 0.03508772 0.         0.         0.03508772
  0.56140351 0.         0.         0.         0.01754386 0.
  0.01754386 0.         0.         0.05263158 0.03508772 0.10526316
  0.01754386 0.        ]
 [0.         0.02777778 0.         0.         0.         0.
  0.         0.61111111 0.02777778 0.         0.16666667 0.02777778
  0.05555556 0.         0.02777778 0.         0.         0.05555556
  0.         0.        ]
 [0.075      0.         0.         0.         0.         0.
  0.         0.075      0.125      0.         0.475      0.025
  0.1        0.         0.025      0.         0.         0.1
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 [0.         0.         0.01388889 0.         0.02777778 0.01388889
  0.         0.         0.         0.52777778 0.         0.02777778
  0.         0.27777778 0.01388889 0.         0.         0.09722222
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 [0.         0.02040816 0.         0.         0.02040816 0.02040816
  0.         0.         0.02040816 0.02040816 0.85714286 0.
  0.02040816 0.         0.         0.         0.         0.02040816
  0.         0.        ]
 [0.         0.07407407 0.03703704 0.         0.         0.
  0.         0.         0.         0.         0.         0.81481481
  0.01851852 0.         0.         0.         0.         0.05555556
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 [0.01851852 0.03703704 0.         0.         0.         0.
  0.         0.01851852 0.01851852 0.         0.37037037 0.03703704
  0.44444444 0.         0.03703704 0.         0.         0.
  0.01851852 0.        ]
 [0.         0.         0.         0.04761905 0.         0.07142857
  0.02380952 0.         0.         0.02380952 0.07142857 0.04761905
  0.         0.66666667 0.         0.         0.         0.04761905
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 [0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.03333333 0.         0.
  0.         0.         0.76666667 0.         0.         0.06666667
  0.13333333 0.        ]
 [0.03333333 0.03333333 0.         0.         0.         0.
  0.16666667 0.         0.         0.         0.06666667 0.2
  0.03333333 0.         0.         0.36666667 0.         0.03333333
  0.06666667 0.        ]
 [0.         0.02631579 0.18421053 0.         0.05263158 0.02631579
  0.         0.         0.02631579 0.         0.         0.02631579
  0.         0.         0.         0.         0.60526316 0.02631579
  0.         0.02631579]
 [0.         0.         0.         0.         0.         0.03225806
  0.         0.         0.         0.         0.         0.06451613
  0.         0.         0.03225806 0.         0.         0.64516129
  0.22580645 0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.06666667 0.         0.         0.1
  0.83333333 0.        ]
 [0.06666667 0.         0.03333333 0.         0.         0.
  0.         0.         0.         0.         0.06666667 0.
  0.         0.         0.         0.         0.         0.06666667
  0.06666667 0.7       ]]
[2023-08-14 16:10:53,553 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 16:10:53,556 INFO] 75776 iteration, USE_EMA: False, train/sup_loss: 0.0007, train/unsup_loss: 0.0025, train/total_loss: 0.0031, train/util_ratio: 1.0000, train/run_time: 0.2711, eval/loss: 4.5473, eval/top-1-acc: 0.6156, eval/balanced_acc: 0.6199, eval/precision: 0.6641, eval/recall: 0.6199, eval/F1: 0.6042, lr: 0.0003, train/prefecth_time: 0.0033 BEST_EVAL_ACC: 0.6484, at 51200 iters
[2023-08-14 16:12:21,990 INFO] 76032 iteration USE_EMA: False, train/sup_loss: 0.1170, train/unsup_loss: 0.0015, train/total_loss: 0.1185, train/util_ratio: 0.6250, train/run_time: 0.2379, lr: 0.0003, train/prefecth_time: 0.0038 
[2023-08-14 16:13:45,889 INFO] 76288 iteration USE_EMA: False, train/sup_loss: 0.0088, train/unsup_loss: 0.0042, train/total_loss: 0.0130, train/util_ratio: 0.6250, train/run_time: 0.2667, lr: 0.0003, train/prefecth_time: 0.0041 
[2023-08-14 16:15:08,635 INFO] 76544 iteration USE_EMA: False, train/sup_loss: 0.0162, train/unsup_loss: 0.0059, train/total_loss: 0.0221, train/util_ratio: 0.6250, train/run_time: 0.2738, lr: 0.0003, train/prefecth_time: 0.0036 
[2023-08-14 16:16:31,633 INFO] 76800 iteration USE_EMA: False, train/sup_loss: 0.0013, train/unsup_loss: 0.0074, train/total_loss: 0.0087, train/util_ratio: 0.7500, train/run_time: 0.2503, lr: 0.0003, train/prefecth_time: 0.0035 
[2023-08-14 16:17:57,609 INFO] 77056 iteration USE_EMA: False, train/sup_loss: 0.0020, train/unsup_loss: 0.0012, train/total_loss: 0.0032, train/util_ratio: 0.6250, train/run_time: 0.2617, lr: 0.0003, train/prefecth_time: 0.0032 
[2023-08-14 16:19:19,402 INFO] 77312 iteration USE_EMA: False, train/sup_loss: 0.0039, train/unsup_loss: 0.0007, train/total_loss: 0.0046, train/util_ratio: 1.0000, train/run_time: 0.2981, lr: 0.0003, train/prefecth_time: 0.0036 
[2023-08-14 16:20:41,476 INFO] 77568 iteration USE_EMA: False, train/sup_loss: 0.0008, train/unsup_loss: 0.0027, train/total_loss: 0.0036, train/util_ratio: 1.0000, train/run_time: 0.2730, lr: 0.0003, train/prefecth_time: 0.0051 
[2023-08-14 16:22:02,583 INFO] validating...
[2023-08-14 16:22:08,196 INFO] confusion matrix:
[[0.98076923 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.01923077 0.        ]
 [0.         0.94285714 0.         0.         0.         0.02857143
  0.         0.         0.         0.         0.         0.01428571
  0.         0.         0.         0.         0.         0.
  0.         0.01428571]
 [0.         0.01785714 0.23214286 0.         0.01785714 0.17857143
  0.05357143 0.         0.         0.10714286 0.01785714 0.19642857
  0.01785714 0.03571429 0.         0.01785714 0.01785714 0.05357143
  0.01785714 0.01785714]
 [0.02816901 0.         0.         0.28169014 0.         0.
  0.         0.01408451 0.02816901 0.07042254 0.02816901 0.08450704
  0.02816901 0.42253521 0.         0.         0.         0.01408451
  0.         0.        ]
 [0.         0.         0.01923077 0.         0.59615385 0.
  0.01923077 0.         0.         0.03846154 0.         0.03846154
  0.         0.         0.         0.         0.26923077 0.
  0.         0.01923077]
 [0.01886792 0.         0.         0.         0.         0.96226415
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.01886792 0.        ]
 [0.0877193  0.03508772 0.         0.         0.         0.05263158
  0.71929825 0.         0.         0.         0.         0.01754386
  0.01754386 0.         0.         0.01754386 0.         0.03508772
  0.01754386 0.        ]
 [0.11111111 0.02777778 0.02777778 0.         0.         0.
  0.         0.52777778 0.         0.         0.22222222 0.05555556
  0.02777778 0.         0.         0.         0.         0.
  0.         0.        ]
 [0.175      0.025      0.025      0.         0.         0.
  0.         0.025      0.275      0.025      0.25       0.
  0.15       0.         0.         0.         0.         0.05
  0.         0.        ]
 [0.         0.         0.01388889 0.         0.         0.02777778
  0.         0.         0.01388889 0.58333333 0.         0.01388889
  0.         0.30555556 0.01388889 0.         0.         0.02777778
  0.         0.        ]
 [0.         0.02040816 0.02040816 0.02040816 0.02040816 0.04081633
  0.         0.         0.04081633 0.04081633 0.67346939 0.
  0.04081633 0.         0.         0.02040816 0.         0.02040816
  0.02040816 0.02040816]
 [0.         0.09259259 0.         0.         0.         0.
  0.         0.         0.         0.01851852 0.         0.87037037
  0.         0.         0.         0.         0.         0.01851852
  0.         0.        ]
 [0.09259259 0.03703704 0.         0.         0.         0.01851852
  0.         0.01851852 0.18518519 0.         0.11111111 0.05555556
  0.44444444 0.         0.03703704 0.         0.         0.
  0.         0.        ]
 [0.         0.         0.         0.02380952 0.         0.04761905
  0.02380952 0.         0.         0.04761905 0.02380952 0.02380952
  0.         0.78571429 0.         0.         0.         0.02380952
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.03333333 0.         0.
  0.         0.         0.76666667 0.         0.         0.06666667
  0.13333333 0.        ]
 [0.06666667 0.03333333 0.         0.         0.         0.1
  0.06666667 0.         0.         0.03333333 0.         0.06666667
  0.         0.         0.         0.46666667 0.         0.06666667
  0.06666667 0.03333333]
 [0.05263158 0.02631579 0.10526316 0.         0.02631579 0.07894737
  0.02631579 0.         0.         0.         0.         0.07894737
  0.         0.         0.         0.         0.55263158 0.
  0.02631579 0.02631579]
 [0.         0.         0.         0.         0.         0.03225806
  0.         0.         0.         0.03225806 0.         0.06451613
  0.         0.         0.03225806 0.         0.         0.48387097
  0.32258065 0.03225806]
 [0.03333333 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.03333333 0.         0.         0.
  0.9        0.03333333]
 [0.1        0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.03333333 0.
  0.03333333 0.         0.         0.         0.         0.
  0.03333333 0.8       ]]
[2023-08-14 16:22:10,166 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 16:22:10,167 INFO] 77824 iteration, USE_EMA: False, train/sup_loss: 0.0011, train/unsup_loss: 0.0065, train/total_loss: 0.0076, train/util_ratio: 1.0000, train/run_time: 0.3015, eval/loss: 3.9616, eval/top-1-acc: 0.6399, eval/balanced_acc: 0.6423, eval/precision: 0.6681, eval/recall: 0.6423, eval/F1: 0.6231, lr: 0.0003, train/prefecth_time: 0.0032 BEST_EVAL_ACC: 0.6484, at 51200 iters
[2023-08-14 16:23:36,750 INFO] 78080 iteration USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0006, train/total_loss: 0.0009, train/util_ratio: 1.0000, train/run_time: 0.3257, lr: 0.0003, train/prefecth_time: 0.0035 
[2023-08-14 16:24:58,941 INFO] 78336 iteration USE_EMA: False, train/sup_loss: 0.0027, train/unsup_loss: 0.0059, train/total_loss: 0.0086, train/util_ratio: 0.8750, train/run_time: 0.2799, lr: 0.0003, train/prefecth_time: 0.0036 
[2023-08-14 16:26:21,178 INFO] 78592 iteration USE_EMA: False, train/sup_loss: 0.4350, train/unsup_loss: 0.0016, train/total_loss: 0.4366, train/util_ratio: 1.0000, train/run_time: 0.2909, lr: 0.0003, train/prefecth_time: 0.0032 
[2023-08-14 16:27:43,789 INFO] 78848 iteration USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0013, train/total_loss: 0.0014, train/util_ratio: 0.8750, train/run_time: 0.3129, lr: 0.0003, train/prefecth_time: 0.0038 
[2023-08-14 16:29:11,082 INFO] 79104 iteration USE_EMA: False, train/sup_loss: 0.0032, train/unsup_loss: 0.0030, train/total_loss: 0.0062, train/util_ratio: 0.8750, train/run_time: 0.2909, lr: 0.0003, train/prefecth_time: 0.0050 
[2023-08-14 16:30:34,039 INFO] 79360 iteration USE_EMA: False, train/sup_loss: 0.0006, train/unsup_loss: 0.0027, train/total_loss: 0.0032, train/util_ratio: 1.0000, train/run_time: 0.2924, lr: 0.0002, train/prefecth_time: 0.0055 
[2023-08-14 16:31:56,840 INFO] 79616 iteration USE_EMA: False, train/sup_loss: 0.0804, train/unsup_loss: 0.0036, train/total_loss: 0.0840, train/util_ratio: 0.8750, train/run_time: 0.3039, lr: 0.0002, train/prefecth_time: 0.0040 
[2023-08-14 16:33:19,488 INFO] validating...
[2023-08-14 16:33:25,218 INFO] confusion matrix:
[[0.88461538 0.         0.01923077 0.         0.         0.
  0.         0.01923077 0.         0.         0.         0.
  0.         0.         0.         0.01923077 0.         0.
  0.03846154 0.01923077]
 [0.         0.88571429 0.02857143 0.         0.         0.
  0.01428571 0.         0.         0.         0.01428571 0.01428571
  0.01428571 0.01428571 0.         0.         0.         0.
  0.01428571 0.        ]
 [0.         0.03571429 0.51785714 0.         0.01785714 0.
  0.05357143 0.         0.         0.08928571 0.05357143 0.05357143
  0.         0.07142857 0.         0.         0.         0.08928571
  0.01785714 0.        ]
 [0.         0.         0.02816901 0.52112676 0.         0.
  0.         0.01408451 0.04225352 0.01408451 0.04225352 0.02816901
  0.         0.28169014 0.01408451 0.         0.         0.01408451
  0.         0.        ]
 [0.         0.         0.11538462 0.         0.75       0.
  0.         0.         0.         0.         0.03846154 0.
  0.         0.         0.         0.         0.09615385 0.
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 [0.03773585 0.01886792 0.05660377 0.         0.         0.66037736
  0.         0.         0.         0.         0.11320755 0.01886792
  0.         0.         0.         0.05660377 0.         0.01886792
  0.01886792 0.        ]
 [0.03508772 0.03508772 0.05263158 0.         0.         0.01754386
  0.68421053 0.         0.         0.         0.03508772 0.
  0.         0.         0.         0.03508772 0.05263158 0.
  0.03508772 0.01754386]
 [0.02777778 0.02777778 0.         0.         0.         0.
  0.         0.77777778 0.         0.         0.08333333 0.05555556
  0.02777778 0.         0.         0.         0.         0.
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 [0.075      0.025      0.         0.         0.         0.
  0.         0.125      0.15       0.         0.4        0.05
  0.125      0.         0.         0.         0.         0.025
  0.025      0.        ]
 [0.         0.         0.02777778 0.         0.01388889 0.
  0.         0.         0.02777778 0.44444444 0.         0.
  0.         0.43055556 0.         0.         0.         0.05555556
  0.         0.        ]
 [0.         0.04081633 0.04081633 0.02040816 0.         0.
  0.         0.02040816 0.06122449 0.         0.73469388 0.
  0.         0.04081633 0.         0.02040816 0.         0.02040816
  0.         0.        ]
 [0.         0.07407407 0.07407407 0.         0.         0.
  0.         0.         0.         0.03703704 0.         0.77777778
  0.         0.         0.         0.         0.         0.03703704
  0.         0.        ]
 [0.03703704 0.         0.01851852 0.         0.         0.
  0.         0.01851852 0.07407407 0.         0.2037037  0.
  0.55555556 0.         0.         0.01851852 0.         0.
  0.07407407 0.        ]
 [0.         0.         0.02380952 0.04761905 0.         0.
  0.02380952 0.         0.         0.02380952 0.         0.
  0.         0.85714286 0.         0.         0.         0.02380952
  0.         0.        ]
 [0.03333333 0.06666667 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.73333333 0.         0.         0.06666667
  0.1        0.        ]
 [0.03333333 0.         0.06666667 0.         0.         0.
  0.1        0.         0.         0.03333333 0.         0.13333333
  0.         0.         0.         0.5        0.         0.06666667
  0.06666667 0.        ]
 [0.         0.02631579 0.31578947 0.         0.07894737 0.
  0.         0.         0.         0.         0.         0.
  0.         0.02631579 0.         0.         0.5        0.
  0.02631579 0.02631579]
 [0.         0.         0.06451613 0.         0.         0.
  0.         0.         0.         0.06451613 0.03225806 0.03225806
  0.         0.         0.         0.         0.         0.48387097
  0.32258065 0.        ]
 [0.         0.         0.03333333 0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.03333333
  0.93333333 0.        ]
 [0.03333333 0.         0.03333333 0.         0.         0.
  0.         0.03333333 0.         0.         0.06666667 0.
  0.03333333 0.         0.         0.         0.         0.03333333
  0.03333333 0.73333333]]
[2023-08-14 16:33:27,327 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 16:33:29,324 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/model_best.pth
[2023-08-14 16:33:29,326 INFO] 79872 iteration, USE_EMA: False, train/sup_loss: 0.0022, train/unsup_loss: 0.0040, train/total_loss: 0.0062, train/util_ratio: 0.6250, train/run_time: 0.2742, eval/loss: 3.3698, eval/top-1-acc: 0.6526, eval/balanced_acc: 0.6543, eval/precision: 0.6889, eval/recall: 0.6543, eval/F1: 0.6485, lr: 0.0002, train/prefecth_time: 0.0037 BEST_EVAL_ACC: 0.6526, at 79872 iters
[2023-08-14 16:34:57,340 INFO] 80128 iteration USE_EMA: False, train/sup_loss: 0.0009, train/unsup_loss: 0.0003, train/total_loss: 0.0012, train/util_ratio: 0.8750, train/run_time: 0.2843, lr: 0.0002, train/prefecth_time: 0.0039 
[2023-08-14 16:36:20,209 INFO] 80384 iteration USE_EMA: False, train/sup_loss: 0.0006, train/unsup_loss: 0.0001, train/total_loss: 0.0007, train/util_ratio: 0.8750, train/run_time: 0.2860, lr: 0.0002, train/prefecth_time: 0.0036 
[2023-08-14 16:37:43,388 INFO] 80640 iteration USE_EMA: False, train/sup_loss: 0.0182, train/unsup_loss: 0.0044, train/total_loss: 0.0227, train/util_ratio: 1.0000, train/run_time: 0.3042, lr: 0.0002, train/prefecth_time: 0.0043 
[2023-08-14 16:39:06,950 INFO] 80896 iteration USE_EMA: False, train/sup_loss: 0.0021, train/unsup_loss: 0.0057, train/total_loss: 0.0078, train/util_ratio: 0.8750, train/run_time: 0.3179, lr: 0.0002, train/prefecth_time: 0.0041 
[2023-08-14 16:40:34,489 INFO] 81152 iteration USE_EMA: False, train/sup_loss: 0.0006, train/unsup_loss: 0.0015, train/total_loss: 0.0021, train/util_ratio: 0.8750, train/run_time: 0.2936, lr: 0.0002, train/prefecth_time: 0.0035 
[2023-08-14 16:41:57,653 INFO] 81408 iteration USE_EMA: False, train/sup_loss: 0.0005, train/unsup_loss: 0.0006, train/total_loss: 0.0011, train/util_ratio: 0.6250, train/run_time: 0.2784, lr: 0.0002, train/prefecth_time: 0.0046 
[2023-08-14 16:43:19,748 INFO] 81664 iteration USE_EMA: False, train/sup_loss: 0.0007, train/unsup_loss: 0.0006, train/total_loss: 0.0013, train/util_ratio: 0.5000, train/run_time: 0.2613, lr: 0.0002, train/prefecth_time: 0.0025 
[2023-08-14 16:44:41,502 INFO] validating...
[2023-08-14 16:44:47,051 INFO] confusion matrix:
[[0.90384615 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.01923077
  0.01923077 0.         0.         0.01923077 0.         0.
  0.03846154 0.        ]
 [0.         0.84285714 0.01428571 0.         0.         0.01428571
  0.         0.         0.         0.         0.01428571 0.02857143
  0.02857143 0.01428571 0.         0.         0.         0.
  0.04285714 0.        ]
 [0.         0.03571429 0.35714286 0.         0.01785714 0.10714286
  0.05357143 0.         0.         0.10714286 0.01785714 0.16071429
  0.         0.05357143 0.         0.01785714 0.         0.03571429
  0.03571429 0.        ]
 [0.         0.         0.01408451 0.3943662  0.         0.
  0.         0.         0.         0.04225352 0.04225352 0.08450704
  0.02816901 0.36619718 0.         0.         0.         0.01408451
  0.01408451 0.        ]
 [0.         0.         0.         0.         0.63461538 0.
  0.01923077 0.         0.         0.         0.03846154 0.01923077
  0.         0.         0.         0.         0.28846154 0.
  0.         0.        ]
 [0.         0.         0.         0.         0.         0.88679245
  0.         0.         0.01886792 0.         0.03773585 0.
  0.01886792 0.         0.         0.03773585 0.         0.
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 [0.         0.03508772 0.         0.         0.         0.03508772
  0.61403509 0.         0.         0.         0.03508772 0.07017544
  0.03508772 0.         0.         0.10526316 0.01754386 0.
  0.05263158 0.        ]
 [0.02777778 0.02777778 0.         0.         0.         0.
  0.         0.80555556 0.         0.         0.02777778 0.05555556
  0.02777778 0.         0.         0.         0.         0.
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 [0.025      0.025      0.         0.         0.         0.
  0.         0.125      0.2        0.         0.325      0.025
  0.225      0.         0.         0.         0.         0.025
  0.025      0.        ]
 [0.         0.         0.02777778 0.         0.01388889 0.
  0.         0.         0.02777778 0.43055556 0.01388889 0.02777778
  0.         0.41666667 0.         0.         0.         0.04166667
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 [0.         0.04081633 0.         0.02040816 0.         0.
  0.         0.04081633 0.06122449 0.02040816 0.75510204 0.
  0.         0.02040816 0.         0.02040816 0.         0.
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 [0.         0.05555556 0.01851852 0.         0.         0.
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 [0.         0.         0.         0.         0.         0.
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  0.59259259 0.01851852 0.         0.01851852 0.         0.
  0.05555556 0.        ]
 [0.         0.         0.         0.02380952 0.         0.
  0.02380952 0.         0.         0.         0.         0.04761905
  0.         0.88095238 0.         0.         0.         0.02380952
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 [0.         0.03333333 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.03333333
  0.         0.         0.76666667 0.         0.         0.06666667
  0.1        0.        ]
 [0.         0.         0.03333333 0.         0.         0.
  0.03333333 0.         0.         0.         0.06666667 0.16666667
  0.06666667 0.         0.         0.56666667 0.         0.
  0.06666667 0.        ]
 [0.         0.02631579 0.18421053 0.         0.02631579 0.02631579
  0.02631579 0.         0.         0.         0.         0.02631579
  0.         0.02631579 0.         0.         0.63157895 0.
  0.         0.02631579]
 [0.         0.03225806 0.06451613 0.         0.         0.03225806
  0.         0.         0.03225806 0.06451613 0.03225806 0.12903226
  0.         0.         0.03225806 0.         0.         0.35483871
  0.22580645 0.        ]
 [0.         0.         0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
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 [0.         0.         0.03333333 0.         0.         0.
  0.         0.         0.         0.         0.06666667 0.03333333
  0.03333333 0.         0.         0.         0.         0.
  0.1        0.73333333]]
[2023-08-14 16:44:49,231 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 16:44:49,232 INFO] 81920 iteration, USE_EMA: False, train/sup_loss: 0.0001, train/unsup_loss: 0.0002, train/total_loss: 0.0004, train/util_ratio: 0.8750, train/run_time: 0.2878, eval/loss: 4.5276, eval/top-1-acc: 0.6505, eval/balanced_acc: 0.6579, eval/precision: 0.6820, eval/recall: 0.6579, eval/F1: 0.6442, lr: 0.0002, train/prefecth_time: 0.0038 BEST_EVAL_ACC: 0.6526, at 79872 iters
[2023-08-14 16:46:17,459 INFO] 82176 iteration USE_EMA: False, train/sup_loss: 0.0006, train/unsup_loss: 0.0037, train/total_loss: 0.0043, train/util_ratio: 0.7500, train/run_time: 0.2550, lr: 0.0002, train/prefecth_time: 0.0038 
[2023-08-14 16:47:40,453 INFO] 82432 iteration USE_EMA: False, train/sup_loss: 0.0019, train/unsup_loss: 0.0166, train/total_loss: 0.0184, train/util_ratio: 1.0000, train/run_time: 0.2684, lr: 0.0002, train/prefecth_time: 0.0042 
[2023-08-14 16:49:02,653 INFO] 82688 iteration USE_EMA: False, train/sup_loss: 0.0004, train/unsup_loss: 0.0009, train/total_loss: 0.0013, train/util_ratio: 0.5000, train/run_time: 0.2796, lr: 0.0002, train/prefecth_time: 0.0035 
[2023-08-14 16:50:26,041 INFO] 82944 iteration USE_EMA: False, train/sup_loss: 0.0001, train/unsup_loss: 0.0092, train/total_loss: 0.0094, train/util_ratio: 0.7500, train/run_time: 0.2535, lr: 0.0002, train/prefecth_time: 0.0038 
[2023-08-14 16:51:52,291 INFO] 83200 iteration USE_EMA: False, train/sup_loss: 0.0149, train/unsup_loss: 0.0022, train/total_loss: 0.0172, train/util_ratio: 0.5000, train/run_time: 0.2963, lr: 0.0002, train/prefecth_time: 0.0040 
[2023-08-14 16:53:14,867 INFO] 83456 iteration USE_EMA: False, train/sup_loss: 0.0007, train/unsup_loss: 0.0007, train/total_loss: 0.0014, train/util_ratio: 1.0000, train/run_time: 0.2722, lr: 0.0002, train/prefecth_time: 0.0109 
[2023-08-14 16:54:38,602 INFO] 83712 iteration USE_EMA: False, train/sup_loss: 0.0018, train/unsup_loss: 0.0002, train/total_loss: 0.0020, train/util_ratio: 1.0000, train/run_time: 0.2878, lr: 0.0002, train/prefecth_time: 0.0027 
[2023-08-14 16:56:01,924 INFO] validating...
[2023-08-14 16:56:07,546 INFO] confusion matrix:
[[0.94230769 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.05769231 0.        ]
 [0.         0.87142857 0.01428571 0.         0.         0.01428571
  0.         0.         0.         0.         0.01428571 0.02857143
  0.01428571 0.01428571 0.         0.         0.         0.01428571
  0.01428571 0.        ]
 [0.         0.03571429 0.48214286 0.         0.         0.03571429
  0.01785714 0.         0.         0.10714286 0.03571429 0.08928571
  0.         0.03571429 0.03571429 0.         0.01785714 0.08928571
  0.01785714 0.        ]
 [0.         0.         0.02816901 0.45070423 0.         0.
  0.         0.         0.01408451 0.05633803 0.02816901 0.14084507
  0.         0.23943662 0.         0.         0.         0.02816901
  0.01408451 0.        ]
 [0.         0.         0.01923077 0.         0.65384615 0.
  0.01923077 0.         0.         0.01923077 0.03846154 0.01923077
  0.         0.         0.         0.         0.23076923 0.
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 [0.         0.         0.         0.         0.         0.88679245
  0.         0.         0.01886792 0.         0.05660377 0.
  0.         0.         0.         0.01886792 0.         0.
  0.01886792 0.        ]
 [0.05263158 0.         0.05263158 0.         0.         0.05263158
  0.61403509 0.         0.         0.         0.         0.14035088
  0.         0.         0.         0.01754386 0.01754386 0.03508772
  0.01754386 0.        ]
 [0.05555556 0.02777778 0.02777778 0.         0.         0.
  0.         0.80555556 0.         0.         0.02777778 0.02777778
  0.02777778 0.         0.         0.         0.         0.
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 [0.075      0.         0.025      0.         0.         0.
  0.         0.125      0.15       0.025      0.3        0.075
  0.2        0.         0.         0.         0.         0.
  0.025      0.        ]
 [0.         0.         0.04166667 0.         0.         0.01388889
  0.         0.         0.01388889 0.52777778 0.         0.
  0.         0.36111111 0.         0.         0.         0.04166667
  0.         0.        ]
 [0.         0.         0.02040816 0.02040816 0.         0.
  0.         0.02040816 0.04081633 0.02040816 0.83673469 0.
  0.         0.         0.         0.02040816 0.         0.
  0.02040816 0.        ]
 [0.         0.03703704 0.01851852 0.         0.         0.
  0.         0.         0.         0.01851852 0.         0.90740741
  0.         0.         0.         0.         0.         0.01851852
  0.         0.        ]
 [0.03703704 0.         0.         0.         0.         0.
  0.         0.05555556 0.07407407 0.01851852 0.14814815 0.09259259
  0.46296296 0.         0.         0.01851852 0.         0.01851852
  0.07407407 0.        ]
 [0.         0.         0.02380952 0.07142857 0.         0.
  0.02380952 0.         0.         0.02380952 0.         0.04761905
  0.         0.80952381 0.         0.         0.         0.
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 [0.         0.1        0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.03333333
  0.         0.         0.7        0.         0.         0.06666667
  0.1        0.        ]
 [0.         0.         0.06666667 0.         0.         0.03333333
  0.1        0.         0.         0.         0.06666667 0.16666667
  0.03333333 0.         0.         0.4        0.03333333 0.03333333
  0.06666667 0.        ]
 [0.02631579 0.07894737 0.26315789 0.         0.07894737 0.
  0.         0.         0.         0.02631579 0.         0.10526316
  0.         0.         0.         0.         0.39473684 0.
  0.         0.02631579]
 [0.         0.         0.03225806 0.         0.         0.06451613
  0.         0.         0.03225806 0.06451613 0.06451613 0.12903226
  0.         0.         0.         0.         0.         0.41935484
  0.19354839 0.        ]
 [0.         0.03333333 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.03333333 0.03333333
  0.86666667 0.        ]
 [0.03333333 0.         0.03333333 0.         0.         0.
  0.         0.         0.         0.         0.06666667 0.03333333
  0.         0.         0.         0.         0.         0.
  0.1        0.73333333]]
[2023-08-14 16:56:09,654 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 16:56:09,655 INFO] 83968 iteration, USE_EMA: False, train/sup_loss: 0.0003, train/unsup_loss: 0.0030, train/total_loss: 0.0033, train/util_ratio: 0.8750, train/run_time: 0.2890, eval/loss: 3.8251, eval/top-1-acc: 0.6505, eval/balanced_acc: 0.6458, eval/precision: 0.6760, eval/recall: 0.6458, eval/F1: 0.6357, lr: 0.0002, train/prefecth_time: 0.0026 BEST_EVAL_ACC: 0.6526, at 79872 iters
[2023-08-14 16:57:37,654 INFO] 84224 iteration USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0003, train/total_loss: 0.0005, train/util_ratio: 1.0000, train/run_time: 0.2965, lr: 0.0002, train/prefecth_time: 0.0052 
[2023-08-14 16:59:01,130 INFO] 84480 iteration USE_EMA: False, train/sup_loss: 0.0003, train/unsup_loss: 0.0023, train/total_loss: 0.0026, train/util_ratio: 0.8750, train/run_time: 0.2806, lr: 0.0002, train/prefecth_time: 0.0064 
[2023-08-14 17:00:24,206 INFO] 84736 iteration USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0023, train/total_loss: 0.0025, train/util_ratio: 0.8750, train/run_time: 0.2717, lr: 0.0002, train/prefecth_time: 0.0029 
[2023-08-14 17:01:46,591 INFO] 84992 iteration USE_EMA: False, train/sup_loss: 0.0009, train/unsup_loss: 0.0002, train/total_loss: 0.0011, train/util_ratio: 0.6250, train/run_time: 0.2978, lr: 0.0002, train/prefecth_time: 0.0036 
[2023-08-14 17:03:13,414 INFO] 85248 iteration USE_EMA: False, train/sup_loss: 0.0003, train/unsup_loss: 0.0000, train/total_loss: 0.0003, train/util_ratio: 0.8750, train/run_time: 0.2663, lr: 0.0002, train/prefecth_time: 0.0042 
[2023-08-14 17:04:36,253 INFO] 85504 iteration USE_EMA: False, train/sup_loss: 0.0001, train/unsup_loss: 0.0025, train/total_loss: 0.0026, train/util_ratio: 0.8750, train/run_time: 0.2857, lr: 0.0002, train/prefecth_time: 0.0037 
[2023-08-14 17:05:58,707 INFO] 85760 iteration USE_EMA: False, train/sup_loss: 0.0005, train/unsup_loss: 0.0025, train/total_loss: 0.0030, train/util_ratio: 0.7500, train/run_time: 0.2877, lr: 0.0002, train/prefecth_time: 0.0046 
[2023-08-14 17:07:21,698 INFO] validating...
[2023-08-14 17:07:27,322 INFO] confusion matrix:
[[0.82692308 0.         0.         0.         0.         0.
  0.         0.01923077 0.         0.         0.         0.01923077
  0.         0.         0.         0.03846154 0.         0.
  0.05769231 0.03846154]
 [0.         0.91428571 0.01428571 0.         0.         0.
  0.         0.         0.         0.         0.01428571 0.01428571
  0.         0.         0.         0.         0.         0.
  0.02857143 0.01428571]
 [0.         0.01785714 0.35714286 0.         0.01785714 0.16071429
  0.01785714 0.         0.         0.07142857 0.03571429 0.14285714
  0.         0.07142857 0.         0.         0.01785714 0.07142857
  0.01785714 0.        ]
 [0.         0.         0.01408451 0.42253521 0.         0.
  0.         0.         0.         0.04225352 0.04225352 0.15492958
  0.01408451 0.29577465 0.         0.         0.         0.
  0.01408451 0.        ]
 [0.         0.         0.01923077 0.         0.61538462 0.
  0.01923077 0.         0.         0.         0.03846154 0.03846154
  0.         0.         0.         0.         0.26923077 0.
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 [0.         0.         0.         0.         0.         0.94339623
  0.         0.         0.         0.         0.01886792 0.
  0.         0.         0.         0.         0.         0.
  0.03773585 0.        ]
 [0.03508772 0.0877193  0.03508772 0.         0.         0.0877193
  0.57894737 0.         0.         0.         0.         0.05263158
  0.         0.         0.         0.01754386 0.01754386 0.
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 [0.02777778 0.02777778 0.         0.         0.         0.
  0.         0.88888889 0.         0.         0.02777778 0.02777778
  0.         0.         0.         0.         0.         0.
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 [0.075      0.05       0.         0.         0.         0.
  0.         0.25       0.15       0.         0.275      0.05
  0.05       0.         0.         0.         0.         0.05
  0.025      0.025     ]
 [0.         0.         0.02777778 0.         0.         0.01388889
  0.         0.         0.01388889 0.44444444 0.01388889 0.
  0.         0.44444444 0.         0.         0.         0.04166667
  0.         0.        ]
 [0.         0.04081633 0.         0.         0.         0.02040816
  0.         0.04081633 0.02040816 0.         0.79591837 0.
  0.         0.04081633 0.         0.02040816 0.         0.
  0.02040816 0.        ]
 [0.         0.05555556 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.92592593
  0.         0.01851852 0.         0.         0.         0.
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 [0.01851852 0.03703704 0.         0.         0.         0.
  0.         0.12962963 0.07407407 0.         0.2037037  0.05555556
  0.35185185 0.         0.         0.01851852 0.         0.01851852
  0.07407407 0.01851852]
 [0.         0.         0.         0.07142857 0.         0.02380952
  0.02380952 0.         0.         0.02380952 0.         0.02380952
  0.         0.80952381 0.         0.         0.         0.02380952
  0.         0.        ]
 [0.         0.16666667 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.03333333
  0.         0.         0.7        0.         0.         0.03333333
  0.06666667 0.        ]
 [0.         0.06666667 0.03333333 0.         0.         0.06666667
  0.03333333 0.         0.         0.         0.06666667 0.13333333
  0.         0.         0.         0.46666667 0.         0.03333333
  0.1        0.        ]
 [0.         0.07894737 0.21052632 0.         0.05263158 0.02631579
  0.         0.         0.         0.         0.         0.13157895
  0.         0.02631579 0.         0.         0.44736842 0.
  0.         0.02631579]
 [0.         0.03225806 0.         0.         0.         0.06451613
  0.         0.         0.         0.06451613 0.         0.12903226
  0.         0.         0.         0.         0.         0.51612903
  0.16129032 0.03225806]
 [0.         0.03333333 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.86666667 0.06666667]
 [0.03333333 0.         0.03333333 0.         0.         0.
  0.         0.03333333 0.         0.         0.06666667 0.03333333
  0.         0.         0.         0.         0.         0.
  0.03333333 0.76666667]]
[2023-08-14 17:07:29,408 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 17:07:29,409 INFO] 86016 iteration, USE_EMA: False, train/sup_loss: 0.0013, train/unsup_loss: 0.0019, train/total_loss: 0.0031, train/util_ratio: 1.0000, train/run_time: 0.3123, eval/loss: 4.1913, eval/top-1-acc: 0.6346, eval/balanced_acc: 0.6394, eval/precision: 0.6778, eval/recall: 0.6394, eval/F1: 0.6198, lr: 0.0002, train/prefecth_time: 0.0031 BEST_EVAL_ACC: 0.6526, at 79872 iters
[2023-08-14 17:08:57,014 INFO] 86272 iteration USE_EMA: False, train/sup_loss: 0.0006, train/unsup_loss: 0.0085, train/total_loss: 0.0091, train/util_ratio: 0.7500, train/run_time: 0.2870, lr: 0.0002, train/prefecth_time: 0.0046 
[2023-08-14 17:10:20,366 INFO] 86528 iteration USE_EMA: False, train/sup_loss: 0.0001, train/unsup_loss: 0.0002, train/total_loss: 0.0003, train/util_ratio: 1.0000, train/run_time: 0.2807, lr: 0.0002, train/prefecth_time: 0.0037 
[2023-08-14 17:11:42,752 INFO] 86784 iteration USE_EMA: False, train/sup_loss: 0.0005, train/unsup_loss: 0.0066, train/total_loss: 0.0071, train/util_ratio: 0.8750, train/run_time: 0.2992, lr: 0.0002, train/prefecth_time: 0.0038 
[2023-08-14 17:13:05,348 INFO] 87040 iteration USE_EMA: False, train/sup_loss: 0.0030, train/unsup_loss: 0.0041, train/total_loss: 0.0071, train/util_ratio: 0.7500, train/run_time: 0.2679, lr: 0.0002, train/prefecth_time: 0.0033 
[2023-08-14 17:14:32,811 INFO] 87296 iteration USE_EMA: False, train/sup_loss: 0.0012, train/unsup_loss: 0.0052, train/total_loss: 0.0064, train/util_ratio: 0.7500, train/run_time: 0.2736, lr: 0.0002, train/prefecth_time: 0.0045 
[2023-08-14 17:15:55,492 INFO] 87552 iteration USE_EMA: False, train/sup_loss: 0.0604, train/unsup_loss: 0.0013, train/total_loss: 0.0618, train/util_ratio: 1.0000, train/run_time: 0.2773, lr: 0.0002, train/prefecth_time: 0.0042 
[2023-08-14 17:17:19,064 INFO] 87808 iteration USE_EMA: False, train/sup_loss: 0.0007, train/unsup_loss: 0.0029, train/total_loss: 0.0036, train/util_ratio: 0.8750, train/run_time: 0.2830, lr: 0.0002, train/prefecth_time: 0.0041 
[2023-08-14 17:18:42,448 INFO] validating...
[2023-08-14 17:18:47,968 INFO] confusion matrix:
[[0.76923077 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.01923077 0.         0.07692308 0.01923077 0.         0.01923077
  0.03846154 0.05769231]
 [0.         0.82857143 0.01428571 0.         0.         0.
  0.         0.         0.         0.         0.01428571 0.02857143
  0.         0.01428571 0.07142857 0.         0.         0.01428571
  0.01428571 0.        ]
 [0.         0.03571429 0.25       0.         0.01785714 0.07142857
  0.05357143 0.         0.         0.125      0.01785714 0.14285714
  0.         0.03571429 0.14285714 0.         0.         0.08928571
  0.01785714 0.        ]
 [0.         0.         0.         0.45070423 0.         0.
  0.         0.01408451 0.         0.08450704 0.02816901 0.07042254
  0.         0.28169014 0.01408451 0.         0.         0.05633803
  0.         0.        ]
 [0.         0.         0.         0.         0.73076923 0.
  0.01923077 0.         0.         0.01923077 0.         0.01923077
  0.         0.01923077 0.07692308 0.         0.11538462 0.
  0.         0.        ]
 [0.01886792 0.         0.         0.         0.         0.8490566
  0.         0.         0.         0.         0.01886792 0.
  0.         0.         0.05660377 0.         0.         0.01886792
  0.03773585 0.        ]
 [0.07017544 0.01754386 0.01754386 0.         0.         0.05263158
  0.61403509 0.         0.         0.         0.         0.01754386
  0.01754386 0.         0.03508772 0.03508772 0.01754386 0.07017544
  0.03508772 0.        ]
 [0.02777778 0.02777778 0.         0.         0.         0.
  0.         0.77777778 0.02777778 0.         0.08333333 0.02777778
  0.         0.         0.02777778 0.         0.         0.
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 [0.025      0.025      0.         0.         0.         0.
  0.         0.075      0.075      0.025      0.475      0.
  0.225      0.         0.025      0.         0.         0.05
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 [0.         0.         0.         0.         0.         0.
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  0.         0.47222222 0.04166667 0.         0.         0.02777778
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 [0.         0.02040816 0.         0.02040816 0.         0.
  0.         0.02040816 0.04081633 0.02040816 0.71428571 0.
  0.02040816 0.04081633 0.02040816 0.         0.         0.08163265
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 [0.         0.01851852 0.         0.         0.         0.
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 [0.         0.         0.         0.         0.         0.
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  0.51851852 0.         0.12962963 0.         0.         0.
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 [0.         0.         0.         0.07142857 0.         0.
  0.02380952 0.         0.         0.04761905 0.         0.04761905
  0.         0.80952381 0.         0.         0.         0.
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 [0.         0.         0.         0.         0.         0.
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  0.1        0.        ]
 [0.         0.         0.         0.         0.         0.
  0.03333333 0.         0.03333333 0.         0.16666667 0.26666667
  0.03333333 0.         0.23333333 0.2        0.         0.
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 [0.         0.05263158 0.13157895 0.         0.07894737 0.
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  0.         0.02631579 0.21052632 0.         0.39473684 0.
  0.         0.02631579]
 [0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.06451613 0.         0.09677419
  0.         0.         0.09677419 0.         0.         0.5483871
  0.16129032 0.03225806]
 [0.         0.03333333 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
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  0.83333333 0.        ]
 [0.         0.         0.         0.         0.         0.
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  0.03333333 0.         0.03333333 0.         0.         0.
  0.06666667 0.76666667]]
[2023-08-14 17:18:50,103 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 17:18:50,104 INFO] 88064 iteration, USE_EMA: False, train/sup_loss: 0.0011, train/unsup_loss: 0.0005, train/total_loss: 0.0016, train/util_ratio: 0.8750, train/run_time: 0.2926, eval/loss: 4.5992, eval/top-1-acc: 0.6146, eval/balanced_acc: 0.6168, eval/precision: 0.6475, eval/recall: 0.6168, eval/F1: 0.5924, lr: 0.0002, train/prefecth_time: 0.0043 BEST_EVAL_ACC: 0.6526, at 79872 iters
[2023-08-14 17:20:18,500 INFO] 88320 iteration USE_EMA: False, train/sup_loss: 0.0011, train/unsup_loss: 0.0013, train/total_loss: 0.0024, train/util_ratio: 1.0000, train/run_time: 0.3032, lr: 0.0002, train/prefecth_time: 0.0039 
[2023-08-14 17:21:40,815 INFO] 88576 iteration USE_EMA: False, train/sup_loss: 0.0003, train/unsup_loss: 0.0014, train/total_loss: 0.0016, train/util_ratio: 0.8750, train/run_time: 0.2841, lr: 0.0002, train/prefecth_time: 0.0047 
[2023-08-14 17:23:02,410 INFO] 88832 iteration USE_EMA: False, train/sup_loss: 0.0031, train/unsup_loss: 0.0004, train/total_loss: 0.0036, train/util_ratio: 0.8750, train/run_time: 0.2814, lr: 0.0002, train/prefecth_time: 0.0047 
[2023-08-14 17:24:24,994 INFO] 89088 iteration USE_EMA: False, train/sup_loss: 0.0009, train/unsup_loss: 0.0036, train/total_loss: 0.0045, train/util_ratio: 0.7500, train/run_time: 0.2982, lr: 0.0002, train/prefecth_time: 0.0037 
[2023-08-14 17:25:51,100 INFO] 89344 iteration USE_EMA: False, train/sup_loss: 0.0006, train/unsup_loss: 0.0001, train/total_loss: 0.0007, train/util_ratio: 0.8750, train/run_time: 0.2933, lr: 0.0002, train/prefecth_time: 0.0042 
[2023-08-14 17:27:12,985 INFO] 89600 iteration USE_EMA: False, train/sup_loss: 0.0001, train/unsup_loss: 0.0065, train/total_loss: 0.0067, train/util_ratio: 1.0000, train/run_time: 0.2786, lr: 0.0002, train/prefecth_time: 0.0031 
[2023-08-14 17:28:36,349 INFO] 89856 iteration USE_EMA: False, train/sup_loss: 0.0016, train/unsup_loss: 0.0018, train/total_loss: 0.0034, train/util_ratio: 1.0000, train/run_time: 0.3042, lr: 0.0002, train/prefecth_time: 0.0031 
[2023-08-14 17:29:59,535 INFO] validating...
[2023-08-14 17:30:05,029 INFO] confusion matrix:
[[0.90384615 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.01923077 0.         0.
  0.03846154 0.03846154]
 [0.         0.85714286 0.01428571 0.         0.         0.01428571
  0.01428571 0.         0.         0.         0.01428571 0.04285714
  0.01428571 0.         0.         0.         0.         0.
  0.01428571 0.01428571]
 [0.05357143 0.05357143 0.33928571 0.         0.         0.07142857
  0.125      0.         0.01785714 0.05357143 0.03571429 0.07142857
  0.         0.08928571 0.01785714 0.         0.01785714 0.03571429
  0.01785714 0.        ]
 [0.         0.         0.         0.32394366 0.01408451 0.
  0.         0.01408451 0.02816901 0.04225352 0.05633803 0.07042254
  0.01408451 0.42253521 0.         0.         0.         0.01408451
  0.         0.        ]
 [0.01923077 0.         0.         0.         0.61538462 0.
  0.03846154 0.         0.         0.01923077 0.         0.03846154
  0.         0.         0.         0.         0.26923077 0.
  0.         0.        ]
 [0.0754717  0.         0.         0.         0.         0.90566038
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.01886792 0.        ]
 [0.10526316 0.05263158 0.         0.         0.         0.03508772
  0.71929825 0.         0.         0.         0.01754386 0.
  0.         0.         0.         0.03508772 0.         0.
  0.03508772 0.        ]
 [0.13888889 0.         0.         0.         0.         0.02777778
  0.         0.66666667 0.         0.         0.11111111 0.02777778
  0.02777778 0.         0.         0.         0.         0.
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 [0.1        0.025      0.025      0.         0.025      0.
  0.         0.125      0.125      0.         0.4        0.
  0.175      0.         0.         0.         0.         0.
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 [0.         0.         0.02777778 0.         0.         0.01388889
  0.         0.         0.01388889 0.47222222 0.01388889 0.
  0.         0.44444444 0.         0.         0.         0.01388889
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 [0.         0.02040816 0.         0.         0.         0.02040816
  0.         0.02040816 0.02040816 0.02040816 0.83673469 0.
  0.02040816 0.02040816 0.         0.02040816 0.         0.
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 [0.         0.01851852 0.         0.         0.         0.
  0.         0.         0.         0.         0.01851852 0.94444444
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 [0.11111111 0.         0.         0.         0.         0.
  0.01851852 0.09259259 0.11111111 0.         0.2962963  0.03703704
  0.31481481 0.         0.         0.         0.         0.
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 [0.         0.         0.02380952 0.04761905 0.         0.
  0.02380952 0.         0.         0.         0.         0.02380952
  0.         0.85714286 0.         0.02380952 0.         0.
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 [0.03333333 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.76666667 0.         0.         0.03333333
  0.16666667 0.        ]
 [0.03333333 0.06666667 0.03333333 0.         0.         0.
  0.1        0.         0.         0.         0.06666667 0.16666667
  0.         0.         0.         0.46666667 0.         0.
  0.03333333 0.03333333]
 [0.07894737 0.02631579 0.13157895 0.         0.05263158 0.02631579
  0.         0.         0.         0.         0.         0.05263158
  0.         0.02631579 0.         0.         0.57894737 0.
  0.         0.02631579]
 [0.03225806 0.         0.03225806 0.         0.         0.03225806
  0.         0.         0.         0.06451613 0.03225806 0.12903226
  0.06451613 0.03225806 0.         0.03225806 0.         0.35483871
  0.16129032 0.03225806]
 [0.03333333 0.03333333 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.         0.
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  0.83333333 0.06666667]
 [0.1        0.         0.         0.         0.         0.
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  0.03333333 0.8       ]]
[2023-08-14 17:30:07,104 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 17:30:07,105 INFO] 90112 iteration, USE_EMA: False, train/sup_loss: 0.0024, train/unsup_loss: 0.0020, train/total_loss: 0.0044, train/util_ratio: 1.0000, train/run_time: 0.2935, eval/loss: 4.2766, eval/top-1-acc: 0.6304, eval/balanced_acc: 0.6341, eval/precision: 0.6623, eval/recall: 0.6341, eval/F1: 0.6146, lr: 0.0002, train/prefecth_time: 0.0037 BEST_EVAL_ACC: 0.6526, at 79872 iters
[2023-08-14 17:31:35,983 INFO] 90368 iteration USE_EMA: False, train/sup_loss: 0.0045, train/unsup_loss: 0.0011, train/total_loss: 0.0056, train/util_ratio: 1.0000, train/run_time: 0.2705, lr: 0.0002, train/prefecth_time: 0.0033 
[2023-08-14 17:32:59,200 INFO] 90624 iteration USE_EMA: False, train/sup_loss: 0.0001, train/unsup_loss: 0.0002, train/total_loss: 0.0003, train/util_ratio: 0.8750, train/run_time: 0.3019, lr: 0.0002, train/prefecth_time: 0.0028 
[2023-08-14 17:34:20,378 INFO] 90880 iteration USE_EMA: False, train/sup_loss: 0.0001, train/unsup_loss: 0.0001, train/total_loss: 0.0002, train/util_ratio: 0.8750, train/run_time: 0.3093, lr: 0.0002, train/prefecth_time: 0.0023 
[2023-08-14 17:35:41,675 INFO] 91136 iteration USE_EMA: False, train/sup_loss: 0.0005, train/unsup_loss: 0.0017, train/total_loss: 0.0022, train/util_ratio: 0.7500, train/run_time: 0.2723, lr: 0.0002, train/prefecth_time: 0.0023 
[2023-08-14 17:37:08,227 INFO] 91392 iteration USE_EMA: False, train/sup_loss: 0.0005, train/unsup_loss: 0.0017, train/total_loss: 0.0023, train/util_ratio: 0.8750, train/run_time: 0.3046, lr: 0.0002, train/prefecth_time: 0.0037 
[2023-08-14 17:38:30,906 INFO] 91648 iteration USE_EMA: False, train/sup_loss: 0.0001, train/unsup_loss: 0.0012, train/total_loss: 0.0012, train/util_ratio: 1.0000, train/run_time: 0.3018, lr: 0.0002, train/prefecth_time: 0.0036 
[2023-08-14 17:39:53,593 INFO] 91904 iteration USE_EMA: False, train/sup_loss: 0.0005, train/unsup_loss: 0.0009, train/total_loss: 0.0013, train/util_ratio: 1.0000, train/run_time: 0.2797, lr: 0.0002, train/prefecth_time: 0.0042 
[2023-08-14 17:41:16,251 INFO] validating...
[2023-08-14 17:41:21,688 INFO] confusion matrix:
[[0.90384615 0.         0.         0.         0.         0.
  0.         0.05769231 0.         0.         0.         0.
  0.         0.         0.         0.01923077 0.         0.
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 [0.         0.9        0.01428571 0.         0.         0.01428571
  0.01428571 0.         0.         0.         0.01428571 0.
  0.         0.         0.01428571 0.         0.         0.01428571
  0.         0.01428571]
 [0.         0.03571429 0.35714286 0.         0.         0.07142857
  0.17857143 0.         0.01785714 0.10714286 0.01785714 0.03571429
  0.01785714 0.03571429 0.03571429 0.         0.         0.07142857
  0.01785714 0.        ]
 [0.         0.         0.         0.42253521 0.01408451 0.02816901
  0.         0.05633803 0.07042254 0.01408451 0.08450704 0.05633803
  0.01408451 0.18309859 0.         0.01408451 0.         0.04225352
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 [0.         0.01923077 0.         0.         0.67307692 0.
  0.01923077 0.         0.         0.         0.03846154 0.01923077
  0.         0.         0.         0.         0.23076923 0.
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 [0.03773585 0.01886792 0.         0.         0.         0.81132075
  0.         0.         0.         0.         0.01886792 0.01886792
  0.         0.         0.         0.05660377 0.         0.
  0.03773585 0.        ]
 [0.05263158 0.         0.         0.         0.         0.
  0.85964912 0.         0.         0.         0.         0.
  0.         0.         0.         0.05263158 0.         0.01754386
  0.01754386 0.        ]
 [0.         0.02777778 0.         0.         0.         0.
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  0.         0.         0.         0.         0.         0.
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 [0.075      0.025      0.05       0.         0.         0.
  0.         0.175      0.25       0.         0.3        0.
  0.125      0.         0.         0.         0.         0.
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 [0.         0.         0.125      0.         0.01388889 0.01388889
  0.01388889 0.         0.02777778 0.51388889 0.01388889 0.
  0.         0.23611111 0.         0.         0.         0.04166667
  0.         0.        ]
 [0.         0.06122449 0.02040816 0.02040816 0.02040816 0.04081633
  0.         0.02040816 0.14285714 0.02040816 0.63265306 0.
  0.         0.         0.         0.02040816 0.         0.
  0.         0.        ]
 [0.         0.05555556 0.03703704 0.         0.         0.
  0.01851852 0.         0.         0.         0.         0.83333333
  0.         0.         0.         0.         0.         0.05555556
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 [0.09259259 0.03703704 0.         0.         0.         0.
  0.         0.11111111 0.14814815 0.         0.2037037  0.01851852
  0.35185185 0.         0.         0.03703704 0.         0.
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 [0.         0.         0.04761905 0.04761905 0.         0.
  0.02380952 0.         0.02380952 0.07142857 0.         0.02380952
  0.         0.71428571 0.         0.02380952 0.         0.02380952
  0.         0.        ]
 [0.03333333 0.03333333 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.76666667 0.         0.         0.03333333
  0.13333333 0.        ]
 [0.03333333 0.03333333 0.         0.         0.         0.
  0.13333333 0.         0.         0.         0.06666667 0.13333333
  0.         0.         0.         0.53333333 0.         0.03333333
  0.03333333 0.        ]
 [0.         0.07894737 0.13157895 0.         0.05263158 0.
  0.05263158 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.63157895 0.02631579
  0.02631579 0.        ]
 [0.         0.         0.03225806 0.         0.         0.
  0.         0.         0.         0.06451613 0.         0.
  0.         0.         0.         0.03225806 0.         0.64516129
  0.19354839 0.03225806]
 [0.         0.06666667 0.         0.         0.         0.
  0.         0.         0.03333333 0.         0.         0.
  0.         0.         0.         0.         0.         0.03333333
  0.86666667 0.        ]
 [0.03333333 0.         0.         0.         0.         0.
  0.03333333 0.03333333 0.         0.         0.06666667 0.
  0.         0.         0.         0.         0.         0.03333333
  0.03333333 0.76666667]]
[2023-08-14 17:41:23,664 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 17:41:25,738 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/model_best.pth
[2023-08-14 17:41:25,739 INFO] 92160 iteration, USE_EMA: False, train/sup_loss: 0.0001, train/unsup_loss: 0.0042, train/total_loss: 0.0043, train/util_ratio: 0.8750, train/run_time: 0.2717, eval/loss: 4.3818, eval/top-1-acc: 0.6579, eval/balanced_acc: 0.6661, eval/precision: 0.6678, eval/recall: 0.6661, eval/F1: 0.6499, lr: 0.0002, train/prefecth_time: 0.0037 BEST_EVAL_ACC: 0.6579, at 92160 iters
[2023-08-14 17:42:52,846 INFO] 92416 iteration USE_EMA: False, train/sup_loss: 0.0008, train/unsup_loss: 0.0038, train/total_loss: 0.0046, train/util_ratio: 0.8750, train/run_time: 0.2792, lr: 0.0002, train/prefecth_time: 0.0038 
[2023-08-14 17:44:16,887 INFO] 92672 iteration USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0043, train/total_loss: 0.0045, train/util_ratio: 1.0000, train/run_time: 0.2833, lr: 0.0002, train/prefecth_time: 0.0057 
[2023-08-14 17:45:39,758 INFO] 92928 iteration USE_EMA: False, train/sup_loss: 0.0006, train/unsup_loss: 0.0046, train/total_loss: 0.0053, train/util_ratio: 0.7500, train/run_time: 0.2732, lr: 0.0002, train/prefecth_time: 0.0038 
[2023-08-14 17:47:01,550 INFO] 93184 iteration USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0016, train/total_loss: 0.0017, train/util_ratio: 0.8750, train/run_time: 0.2647, lr: 0.0002, train/prefecth_time: 0.0036 
[2023-08-14 17:48:28,839 INFO] 93440 iteration USE_EMA: False, train/sup_loss: 0.0013, train/unsup_loss: 0.0060, train/total_loss: 0.0073, train/util_ratio: 0.8750, train/run_time: 0.3001, lr: 0.0002, train/prefecth_time: 0.0041 
[2023-08-14 17:49:52,150 INFO] 93696 iteration USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0004, train/total_loss: 0.0005, train/util_ratio: 0.8750, train/run_time: 0.2982, lr: 0.0002, train/prefecth_time: 0.0044 
[2023-08-14 17:51:15,737 INFO] 93952 iteration USE_EMA: False, train/sup_loss: 0.0006, train/unsup_loss: 0.0004, train/total_loss: 0.0010, train/util_ratio: 1.0000, train/run_time: 0.3075, lr: 0.0002, train/prefecth_time: 0.0041 
[2023-08-14 17:52:38,834 INFO] validating...
[2023-08-14 17:52:44,412 INFO] confusion matrix:
[[0.94230769 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.01923077 0.         0.         0.01923077 0.         0.
  0.01923077 0.        ]
 [0.         0.85714286 0.01428571 0.         0.         0.01428571
  0.01428571 0.         0.         0.         0.01428571 0.02857143
  0.         0.         0.01428571 0.         0.         0.01428571
  0.01428571 0.01428571]
 [0.         0.01785714 0.35714286 0.         0.         0.125
  0.07142857 0.         0.01785714 0.05357143 0.01785714 0.07142857
  0.         0.07142857 0.         0.         0.01785714 0.17857143
  0.         0.        ]
 [0.         0.         0.         0.38028169 0.         0.01408451
  0.         0.01408451 0.02816901 0.02816901 0.02816901 0.07042254
  0.01408451 0.38028169 0.01408451 0.         0.         0.02816901
  0.         0.        ]
 [0.         0.         0.01923077 0.         0.65384615 0.
  0.01923077 0.         0.         0.         0.         0.05769231
  0.         0.01923077 0.         0.         0.23076923 0.
  0.         0.        ]
 [0.01886792 0.         0.         0.         0.         0.9245283
  0.         0.         0.         0.         0.01886792 0.
  0.         0.         0.         0.01886792 0.         0.
  0.01886792 0.        ]
 [0.0877193  0.         0.01754386 0.         0.         0.03508772
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  0.01754386 0.01754386 0.01754386 0.07017544 0.03508772 0.05263158
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 [0.         0.05555556 0.         0.         0.         0.
  0.         0.77777778 0.         0.         0.05555556 0.02777778
  0.02777778 0.         0.         0.         0.         0.05555556
  0.         0.        ]
 [0.175      0.025      0.025      0.         0.         0.
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 [0.         0.         0.05555556 0.         0.         0.01388889
  0.         0.         0.01388889 0.36111111 0.01388889 0.
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 [0.         0.04081633 0.         0.02040816 0.02040816 0.02040816
  0.         0.02040816 0.06122449 0.02040816 0.69387755 0.02040816
  0.         0.04081633 0.02040816 0.         0.         0.02040816
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 [0.         0.01851852 0.03703704 0.         0.         0.
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 [0.07407407 0.         0.         0.         0.         0.01851852
  0.         0.03703704 0.03703704 0.         0.12962963 0.01851852
  0.61111111 0.         0.03703704 0.         0.         0.01851852
  0.01851852 0.        ]
 [0.         0.         0.04761905 0.04761905 0.         0.
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 [0.03333333 0.         0.         0.         0.         0.
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 [0.02631579 0.02631579 0.18421053 0.         0.02631579 0.
  0.02631579 0.         0.         0.         0.         0.
  0.         0.02631579 0.         0.         0.65789474 0.02631579
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 [0.         0.         0.03225806 0.         0.         0.03225806
  0.         0.         0.         0.03225806 0.         0.12903226
  0.         0.         0.06451613 0.         0.         0.5483871
  0.12903226 0.03225806]
 [0.03333333 0.         0.         0.         0.         0.03333333
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  0.76666667 0.        ]
 [0.1        0.         0.         0.         0.         0.
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  0.03333333 0.76666667]]
[2023-08-14 17:52:46,587 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 17:52:46,589 INFO] 94208 iteration, USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0003, train/total_loss: 0.0004, train/util_ratio: 1.0000, train/run_time: 0.2872, eval/loss: 4.5204, eval/top-1-acc: 0.6515, eval/balanced_acc: 0.6610, eval/precision: 0.6850, eval/recall: 0.6610, eval/F1: 0.6452, lr: 0.0002, train/prefecth_time: 0.0036 BEST_EVAL_ACC: 0.6579, at 92160 iters
[2023-08-14 17:54:13,530 INFO] 94464 iteration USE_EMA: False, train/sup_loss: 0.0001, train/unsup_loss: 0.0002, train/total_loss: 0.0004, train/util_ratio: 0.8750, train/run_time: 0.2933, lr: 0.0002, train/prefecth_time: 0.0030 
[2023-08-14 17:55:35,236 INFO] 94720 iteration USE_EMA: False, train/sup_loss: 0.0000, train/unsup_loss: 0.0004, train/total_loss: 0.0004, train/util_ratio: 1.0000, train/run_time: 0.2660, lr: 0.0002, train/prefecth_time: 0.0036 
[2023-08-14 17:56:56,999 INFO] 94976 iteration USE_EMA: False, train/sup_loss: 0.5180, train/unsup_loss: 0.0002, train/total_loss: 0.5182, train/util_ratio: 0.8750, train/run_time: 0.2630, lr: 0.0001, train/prefecth_time: 0.0035 
[2023-08-14 17:58:19,126 INFO] 95232 iteration USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0014, train/total_loss: 0.0015, train/util_ratio: 1.0000, train/run_time: 0.2649, lr: 0.0001, train/prefecth_time: 0.0032 
[2023-08-14 17:59:45,935 INFO] 95488 iteration USE_EMA: False, train/sup_loss: 0.0008, train/unsup_loss: 0.0014, train/total_loss: 0.0022, train/util_ratio: 0.8750, train/run_time: 0.2763, lr: 0.0001, train/prefecth_time: 0.0040 
[2023-08-14 18:01:07,501 INFO] 95744 iteration USE_EMA: False, train/sup_loss: 0.0004, train/unsup_loss: 0.0038, train/total_loss: 0.0042, train/util_ratio: 0.8750, train/run_time: 0.2585, lr: 0.0001, train/prefecth_time: 0.0035 
[2023-08-14 18:02:29,155 INFO] 96000 iteration USE_EMA: False, train/sup_loss: 0.0013, train/unsup_loss: 0.0027, train/total_loss: 0.0040, train/util_ratio: 0.8750, train/run_time: 0.2913, lr: 0.0001, train/prefecth_time: 0.0036 
[2023-08-14 18:03:52,284 INFO] validating...
[2023-08-14 18:03:57,856 INFO] confusion matrix:
[[0.92307692 0.         0.         0.         0.         0.
  0.         0.01923077 0.         0.         0.         0.
  0.         0.         0.         0.01923077 0.         0.
  0.01923077 0.01923077]
 [0.         0.88571429 0.         0.         0.         0.01428571
  0.02857143 0.         0.         0.         0.01428571 0.02857143
  0.         0.         0.01428571 0.         0.         0.
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 [0.         0.08928571 0.28571429 0.         0.         0.14285714
  0.08928571 0.         0.01785714 0.08928571 0.01785714 0.10714286
  0.         0.03571429 0.         0.         0.         0.08928571
  0.01785714 0.01785714]
 [0.         0.         0.         0.56338028 0.         0.02816901
  0.         0.         0.04225352 0.01408451 0.07042254 0.05633803
  0.01408451 0.21126761 0.         0.         0.         0.
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 [0.         0.         0.         0.         0.73076923 0.
  0.05769231 0.         0.         0.         0.         0.05769231
  0.         0.01923077 0.         0.         0.13461538 0.
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 [0.01886792 0.01886792 0.         0.         0.         0.90566038
  0.         0.         0.         0.         0.01886792 0.
  0.         0.         0.         0.01886792 0.         0.
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 [0.0877193  0.03508772 0.         0.         0.         0.05263158
  0.77192982 0.         0.         0.         0.         0.
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 [0.         0.05555556 0.         0.         0.         0.
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 [0.1        0.025      0.         0.         0.         0.
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 [0.         0.         0.01388889 0.         0.         0.
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  0.         0.38888889 0.         0.         0.         0.01388889
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 [0.         0.08163265 0.         0.02040816 0.02040816 0.02040816
  0.02040816 0.02040816 0.06122449 0.02040816 0.69387755 0.
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 [0.         0.05555556 0.01851852 0.         0.         0.
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 [0.05555556 0.01851852 0.         0.         0.         0.
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  0.55555556 0.         0.         0.01851852 0.         0.
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 [0.         0.         0.         0.04761905 0.         0.
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 [0.03333333 0.03333333 0.         0.         0.         0.
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 [0.03333333 0.06666667 0.         0.         0.         0.
  0.13333333 0.         0.         0.         0.06666667 0.2
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 [0.         0.05263158 0.18421053 0.         0.05263158 0.02631579
  0.07894737 0.         0.02631579 0.         0.         0.05263158
  0.         0.02631579 0.02631579 0.         0.44736842 0.
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 [0.         0.06451613 0.03225806 0.         0.         0.03225806
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 [0.1        0.         0.         0.         0.         0.
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  0.         0.         0.         0.         0.         0.
  0.         0.83333333]]
[2023-08-14 18:03:59,956 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 18:04:01,941 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/model_best.pth
[2023-08-14 18:04:01,943 INFO] 96256 iteration, USE_EMA: False, train/sup_loss: 0.0005, train/unsup_loss: 0.0057, train/total_loss: 0.0062, train/util_ratio: 0.7500, train/run_time: 0.3053, eval/loss: 3.9216, eval/top-1-acc: 0.6663, eval/balanced_acc: 0.6596, eval/precision: 0.6770, eval/recall: 0.6596, eval/F1: 0.6457, lr: 0.0001, train/prefecth_time: 0.0035 BEST_EVAL_ACC: 0.6663, at 96256 iters
[2023-08-14 18:05:27,963 INFO] 96512 iteration USE_EMA: False, train/sup_loss: 0.0004, train/unsup_loss: 0.0035, train/total_loss: 0.0039, train/util_ratio: 1.0000, train/run_time: 0.2755, lr: 0.0001, train/prefecth_time: 0.0042 
[2023-08-14 18:06:50,635 INFO] 96768 iteration USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0001, train/total_loss: 0.0003, train/util_ratio: 0.8750, train/run_time: 0.2846, lr: 0.0001, train/prefecth_time: 0.0039 
[2023-08-14 18:08:14,036 INFO] 97024 iteration USE_EMA: False, train/sup_loss: 0.0005, train/unsup_loss: 0.0012, train/total_loss: 0.0017, train/util_ratio: 0.8750, train/run_time: 0.2910, lr: 0.0001, train/prefecth_time: 0.0036 
[2023-08-14 18:09:36,194 INFO] 97280 iteration USE_EMA: False, train/sup_loss: 0.4866, train/unsup_loss: 0.0010, train/total_loss: 0.4875, train/util_ratio: 1.0000, train/run_time: 0.2637, lr: 0.0001, train/prefecth_time: 0.0032 
[2023-08-14 18:11:02,306 INFO] 97536 iteration USE_EMA: False, train/sup_loss: 0.0005, train/unsup_loss: 0.0122, train/total_loss: 0.0127, train/util_ratio: 0.7500, train/run_time: 0.2909, lr: 0.0001, train/prefecth_time: 0.0039 
[2023-08-14 18:12:24,756 INFO] 97792 iteration USE_EMA: False, train/sup_loss: 0.0019, train/unsup_loss: 0.0006, train/total_loss: 0.0024, train/util_ratio: 0.7500, train/run_time: 0.2853, lr: 0.0001, train/prefecth_time: 0.0037 
[2023-08-14 18:13:48,306 INFO] 98048 iteration USE_EMA: False, train/sup_loss: 0.0007, train/unsup_loss: 0.0004, train/total_loss: 0.0011, train/util_ratio: 1.0000, train/run_time: 0.3152, lr: 0.0001, train/prefecth_time: 0.0039 
[2023-08-14 18:15:11,473 INFO] validating...
[2023-08-14 18:15:16,955 INFO] confusion matrix:
[[0.92307692 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.01923077 0.
  0.         0.         0.         0.03846154 0.         0.
  0.01923077 0.        ]
 [0.         0.87142857 0.02857143 0.         0.         0.01428571
  0.01428571 0.         0.         0.         0.01428571 0.02857143
  0.         0.01428571 0.01428571 0.         0.         0.
  0.         0.        ]
 [0.         0.01785714 0.375      0.         0.         0.14285714
  0.08928571 0.         0.         0.07142857 0.01785714 0.07142857
  0.         0.05357143 0.01785714 0.         0.         0.125
  0.01785714 0.        ]
 [0.         0.         0.01408451 0.66197183 0.         0.
  0.         0.02816901 0.         0.02816901 0.09859155 0.01408451
  0.01408451 0.11267606 0.         0.         0.         0.01408451
  0.01408451 0.        ]
 [0.         0.         0.03846154 0.         0.65384615 0.
  0.03846154 0.         0.         0.         0.01923077 0.03846154
  0.         0.         0.         0.         0.21153846 0.
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 [0.01886792 0.         0.         0.         0.         0.96226415
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.01886792 0.        ]
 [0.07017544 0.         0.         0.         0.         0.07017544
  0.68421053 0.         0.         0.         0.01754386 0.
  0.01754386 0.         0.01754386 0.05263158 0.         0.07017544
  0.         0.        ]
 [0.05555556 0.05555556 0.         0.         0.         0.
  0.         0.77777778 0.         0.         0.05555556 0.02777778
  0.         0.         0.         0.         0.         0.02777778
  0.         0.        ]
 [0.15       0.025      0.         0.         0.         0.
  0.         0.125      0.175      0.         0.425      0.
  0.075      0.         0.         0.         0.         0.025
  0.         0.        ]
 [0.         0.         0.11111111 0.         0.         0.01388889
  0.         0.         0.01388889 0.45833333 0.01388889 0.
  0.         0.34722222 0.01388889 0.         0.         0.02777778
  0.         0.        ]
 [0.         0.04081633 0.02040816 0.02040816 0.         0.02040816
  0.         0.02040816 0.08163265 0.02040816 0.73469388 0.
  0.         0.         0.         0.02040816 0.         0.
  0.02040816 0.        ]
 [0.         0.05555556 0.05555556 0.         0.         0.
  0.         0.         0.         0.01851852 0.         0.83333333
  0.         0.         0.         0.         0.         0.03703704
  0.         0.        ]
 [0.03703704 0.         0.         0.         0.         0.03703704
  0.         0.07407407 0.05555556 0.         0.22222222 0.03703704
  0.51851852 0.         0.         0.01851852 0.         0.
  0.         0.        ]
 [0.         0.         0.07142857 0.07142857 0.         0.
  0.02380952 0.         0.         0.04761905 0.         0.02380952
  0.         0.73809524 0.         0.02380952 0.         0.
  0.         0.        ]
 [0.03333333 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.76666667 0.         0.         0.1
  0.1        0.        ]
 [0.03333333 0.         0.03333333 0.         0.         0.
  0.1        0.         0.         0.         0.06666667 0.26666667
  0.         0.         0.         0.46666667 0.         0.
  0.03333333 0.        ]
 [0.         0.05263158 0.21052632 0.         0.05263158 0.02631579
  0.05263158 0.         0.         0.         0.         0.02631579
  0.02631579 0.02631579 0.         0.         0.5        0.02631579
  0.         0.        ]
 [0.         0.         0.03225806 0.         0.         0.06451613
  0.         0.         0.         0.06451613 0.         0.12903226
  0.         0.         0.03225806 0.         0.         0.48387097
  0.16129032 0.03225806]
 [0.         0.03333333 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.03333333 0.
  0.         0.         0.03333333 0.         0.         0.06666667
  0.8        0.        ]
 [0.2        0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.03333333 0.
  0.         0.         0.         0.         0.         0.
  0.         0.76666667]]
[2023-08-14 18:15:19,016 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 18:15:19,017 INFO] 98304 iteration, USE_EMA: False, train/sup_loss: 0.0003, train/unsup_loss: 0.0002, train/total_loss: 0.0006, train/util_ratio: 0.8750, train/run_time: 0.2927, eval/loss: 4.3122, eval/top-1-acc: 0.6621, eval/balanced_acc: 0.6576, eval/precision: 0.6753, eval/recall: 0.6576, eval/F1: 0.6485, lr: 0.0001, train/prefecth_time: 0.0038 BEST_EVAL_ACC: 0.6663, at 96256 iters
[2023-08-14 18:16:45,086 INFO] 98560 iteration USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0020, train/total_loss: 0.0022, train/util_ratio: 0.8750, train/run_time: 0.2935, lr: 0.0001, train/prefecth_time: 0.0033 
[2023-08-14 18:18:06,171 INFO] 98816 iteration USE_EMA: False, train/sup_loss: 0.0025, train/unsup_loss: 0.0003, train/total_loss: 0.0028, train/util_ratio: 1.0000, train/run_time: 0.2695, lr: 0.0001, train/prefecth_time: 0.0033 
[2023-08-14 18:19:29,091 INFO] 99072 iteration USE_EMA: False, train/sup_loss: 0.0007, train/unsup_loss: 0.0018, train/total_loss: 0.0026, train/util_ratio: 0.8750, train/run_time: 0.2625, lr: 0.0001, train/prefecth_time: 0.0051 
[2023-08-14 18:20:51,483 INFO] 99328 iteration USE_EMA: False, train/sup_loss: 0.0006, train/unsup_loss: 0.0006, train/total_loss: 0.0012, train/util_ratio: 0.8750, train/run_time: 0.3001, lr: 0.0001, train/prefecth_time: 0.0040 
[2023-08-14 18:22:18,685 INFO] 99584 iteration USE_EMA: False, train/sup_loss: 0.0005, train/unsup_loss: 0.0002, train/total_loss: 0.0006, train/util_ratio: 1.0000, train/run_time: 0.2807, lr: 0.0001, train/prefecth_time: 0.0038 
[2023-08-14 18:23:41,297 INFO] 99840 iteration USE_EMA: False, train/sup_loss: 0.0001, train/unsup_loss: 0.0002, train/total_loss: 0.0004, train/util_ratio: 1.0000, train/run_time: 0.2859, lr: 0.0001, train/prefecth_time: 0.0043 
[2023-08-14 18:25:04,280 INFO] 100096 iteration USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0031, train/total_loss: 0.0033, train/util_ratio: 0.8750, train/run_time: 0.2966, lr: 0.0001, train/prefecth_time: 0.0041 
[2023-08-14 18:26:26,811 INFO] validating...
[2023-08-14 18:26:32,254 INFO] confusion matrix:
[[0.88461538 0.         0.         0.         0.         0.
  0.         0.01923077 0.         0.         0.         0.
  0.01923077 0.         0.         0.05769231 0.         0.
  0.01923077 0.        ]
 [0.         0.91428571 0.         0.         0.         0.01428571
  0.         0.         0.         0.         0.01428571 0.02857143
  0.         0.01428571 0.01428571 0.         0.         0.
  0.         0.        ]
 [0.         0.03571429 0.25       0.         0.         0.14285714
  0.01785714 0.         0.         0.10714286 0.01785714 0.14285714
  0.         0.05357143 0.         0.         0.         0.21428571
  0.01785714 0.        ]
 [0.         0.         0.         0.46478873 0.         0.
  0.         0.02816901 0.02816901 0.07042254 0.09859155 0.09859155
  0.01408451 0.18309859 0.         0.         0.         0.01408451
  0.         0.        ]
 [0.         0.         0.         0.         0.69230769 0.
  0.01923077 0.         0.         0.05769231 0.05769231 0.05769231
  0.         0.         0.         0.         0.11538462 0.
  0.         0.        ]
 [0.01886792 0.         0.         0.         0.         0.86792453
  0.         0.         0.         0.         0.05660377 0.
  0.01886792 0.         0.         0.03773585 0.         0.
  0.         0.        ]
 [0.03508772 0.05263158 0.         0.         0.         0.03508772
  0.49122807 0.         0.         0.         0.03508772 0.01754386
  0.03508772 0.         0.03508772 0.10526316 0.03508772 0.12280702
  0.         0.        ]
 [0.         0.05555556 0.         0.         0.         0.
  0.         0.69444444 0.         0.         0.13888889 0.02777778
  0.02777778 0.         0.         0.         0.         0.05555556
  0.         0.        ]
 [0.075      0.025      0.         0.         0.         0.
  0.         0.05       0.1        0.         0.625      0.
  0.075      0.         0.         0.         0.         0.05
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 [0.         0.         0.01388889 0.         0.         0.
  0.         0.         0.01388889 0.58333333 0.05555556 0.01388889
  0.         0.30555556 0.         0.         0.         0.01388889
  0.         0.        ]
 [0.         0.04081633 0.         0.02040816 0.         0.02040816
  0.         0.02040816 0.02040816 0.02040816 0.7755102  0.
  0.02040816 0.02040816 0.         0.         0.         0.04081633
  0.         0.        ]
 [0.         0.03703704 0.         0.         0.         0.
  0.         0.         0.         0.01851852 0.         0.90740741
  0.         0.         0.         0.         0.         0.03703704
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 [0.01851852 0.         0.         0.         0.         0.
  0.         0.03703704 0.03703704 0.         0.37037037 0.05555556
  0.44444444 0.         0.01851852 0.01851852 0.         0.
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 [0.         0.         0.         0.04761905 0.         0.
  0.         0.         0.         0.0952381  0.         0.04761905
  0.         0.73809524 0.         0.04761905 0.         0.02380952
  0.         0.        ]
 [0.03333333 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.06666667 0.
  0.         0.         0.8        0.         0.         0.03333333
  0.06666667 0.        ]
 [0.03333333 0.03333333 0.         0.         0.         0.
  0.03333333 0.         0.         0.         0.23333333 0.16666667
  0.         0.         0.         0.43333333 0.03333333 0.
  0.03333333 0.        ]
 [0.         0.10526316 0.15789474 0.         0.02631579 0.02631579
  0.         0.         0.         0.07894737 0.         0.10526316
  0.02631579 0.02631579 0.02631579 0.         0.36842105 0.05263158
  0.         0.        ]
 [0.         0.03225806 0.03225806 0.         0.         0.03225806
  0.         0.         0.         0.06451613 0.06451613 0.12903226
  0.         0.         0.03225806 0.         0.         0.48387097
  0.12903226 0.        ]
 [0.         0.06666667 0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.06666667 0.
  0.         0.         0.03333333 0.         0.         0.13333333
  0.66666667 0.        ]
 [0.13333333 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.06666667 0.
  0.         0.         0.         0.         0.         0.06666667
  0.06666667 0.66666667]]
[2023-08-14 18:26:34,341 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 18:26:34,343 INFO] 100352 iteration, USE_EMA: False, train/sup_loss: 0.0005, train/unsup_loss: 0.0038, train/total_loss: 0.0043, train/util_ratio: 1.0000, train/run_time: 0.2933, eval/loss: 4.3820, eval/top-1-acc: 0.6188, eval/balanced_acc: 0.6114, eval/precision: 0.6632, eval/recall: 0.6114, eval/F1: 0.6045, lr: 0.0001, train/prefecth_time: 0.0044 BEST_EVAL_ACC: 0.6663, at 96256 iters
[2023-08-14 18:28:00,937 INFO] 100608 iteration USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0090, train/total_loss: 0.0092, train/util_ratio: 1.0000, train/run_time: 0.3111, lr: 0.0001, train/prefecth_time: 0.0040 
[2023-08-14 18:29:22,719 INFO] 100864 iteration USE_EMA: False, train/sup_loss: 0.0005, train/unsup_loss: 0.0003, train/total_loss: 0.0008, train/util_ratio: 0.8750, train/run_time: 0.2759, lr: 0.0001, train/prefecth_time: 0.0034 
[2023-08-14 18:30:43,938 INFO] 101120 iteration USE_EMA: False, train/sup_loss: 0.0006, train/unsup_loss: 0.0027, train/total_loss: 0.0033, train/util_ratio: 0.8750, train/run_time: 0.2606, lr: 0.0001, train/prefecth_time: 0.0038 
[2023-08-14 18:32:06,380 INFO] 101376 iteration USE_EMA: False, train/sup_loss: 0.0017, train/unsup_loss: 0.0058, train/total_loss: 0.0075, train/util_ratio: 1.0000, train/run_time: 0.3067, lr: 0.0001, train/prefecth_time: 0.0037 
[2023-08-14 18:33:33,152 INFO] 101632 iteration USE_EMA: False, train/sup_loss: 0.0001, train/unsup_loss: 0.0044, train/total_loss: 0.0045, train/util_ratio: 0.8750, train/run_time: 0.2550, lr: 0.0001, train/prefecth_time: 0.0027 
[2023-08-14 18:34:54,826 INFO] 101888 iteration USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0013, train/total_loss: 0.0015, train/util_ratio: 1.0000, train/run_time: 0.2928, lr: 0.0001, train/prefecth_time: 0.0045 
[2023-08-14 18:36:17,618 INFO] 102144 iteration USE_EMA: False, train/sup_loss: 0.0002, train/unsup_loss: 0.0072, train/total_loss: 0.0074, train/util_ratio: 1.0000, train/run_time: 0.2895, lr: 0.0001, train/prefecth_time: 0.0039 
[2023-08-14 18:37:40,084 INFO] validating...
[2023-08-14 18:37:45,630 INFO] confusion matrix:
[[0.90384615 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.01923077 0.
  0.         0.         0.         0.05769231 0.         0.
  0.01923077 0.        ]
 [0.         0.88571429 0.         0.         0.         0.01428571
  0.         0.         0.         0.         0.01428571 0.05714286
  0.         0.         0.01428571 0.         0.         0.
  0.         0.01428571]
 [0.         0.07142857 0.26785714 0.         0.         0.14285714
  0.05357143 0.         0.         0.07142857 0.01785714 0.125
  0.         0.05357143 0.         0.         0.01785714 0.14285714
  0.01785714 0.01785714]
 [0.         0.         0.01408451 0.5915493  0.         0.01408451
  0.         0.01408451 0.02816901 0.02816901 0.11267606 0.04225352
  0.01408451 0.11267606 0.         0.         0.         0.02816901
  0.         0.        ]
 [0.         0.         0.         0.         0.86538462 0.
  0.01923077 0.         0.         0.         0.01923077 0.03846154
  0.         0.         0.         0.         0.05769231 0.
  0.         0.        ]
 [0.01886792 0.         0.         0.         0.         0.90566038
  0.         0.         0.         0.         0.03773585 0.
  0.         0.         0.         0.03773585 0.         0.
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 [0.03508772 0.05263158 0.         0.         0.         0.03508772
  0.61403509 0.         0.         0.         0.03508772 0.
  0.01754386 0.         0.01754386 0.10526316 0.01754386 0.07017544
  0.         0.        ]
 [0.         0.05555556 0.         0.         0.         0.
  0.         0.77777778 0.         0.         0.02777778 0.02777778
  0.02777778 0.         0.         0.         0.         0.08333333
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 [0.075      0.025      0.         0.         0.         0.
  0.         0.075      0.175      0.         0.475      0.
  0.125      0.         0.         0.         0.         0.05
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 [0.         0.         0.09722222 0.         0.02777778 0.01388889
  0.         0.         0.01388889 0.52777778 0.05555556 0.01388889
  0.         0.22222222 0.         0.         0.         0.02777778
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 [0.         0.06122449 0.         0.02040816 0.02040816 0.02040816
  0.         0.02040816 0.06122449 0.02040816 0.73469388 0.
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 [0.         0.03703704 0.         0.         0.         0.
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 [0.01851852 0.01851852 0.         0.         0.         0.
  0.         0.01851852 0.16666667 0.         0.2037037  0.03703704
  0.5        0.         0.01851852 0.01851852 0.         0.
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 [0.         0.         0.02380952 0.04761905 0.         0.
  0.02380952 0.         0.         0.04761905 0.         0.07142857
  0.         0.73809524 0.         0.02380952 0.         0.02380952
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 [0.03333333 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.03333333 0.
  0.         0.         0.8        0.         0.         0.06666667
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 [0.03333333 0.03333333 0.         0.         0.         0.
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  0.         0.         0.         0.6        0.         0.03333333
  0.03333333 0.        ]
 [0.         0.02631579 0.13157895 0.         0.10526316 0.02631579
  0.05263158 0.         0.         0.         0.         0.07894737
  0.         0.02631579 0.         0.         0.52631579 0.02631579
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 [0.         0.         0.03225806 0.         0.         0.03225806
  0.         0.         0.         0.03225806 0.06451613 0.16129032
  0.         0.         0.03225806 0.         0.         0.51612903
  0.12903226 0.        ]
 [0.         0.1        0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.06666667 0.
  0.         0.         0.03333333 0.         0.         0.03333333
  0.7        0.03333333]
 [0.06666667 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.06666667 0.
  0.         0.         0.         0.         0.         0.
  0.06666667 0.8       ]]
[2023-08-14 18:37:47,573 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 18:37:49,620 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/model_best.pth
[2023-08-14 18:37:49,621 INFO] 102400 iteration, USE_EMA: False, train/sup_loss: 0.0001, train/unsup_loss: 0.0002, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.2650, eval/loss: 4.3099, eval/top-1-acc: 0.6705, eval/balanced_acc: 0.6687, eval/precision: 0.6840, eval/recall: 0.6687, eval/F1: 0.6592, lr: 0.0001, train/prefecth_time: 0.0021 BEST_EVAL_ACC: 0.6705, at 102400 iters
[2023-08-14 18:37:52,956 INFO] model saved: /liuzicheng/jwy/saved_models/usb_audio/pseudolabel_fsdnoisy_1773_0/latest_model.pth
[2023-08-14 18:38:00,396 INFO] Model loaded
[2023-08-14 18:38:06,012 INFO] confusion matrix:
[[0.90384615 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.01923077 0.
  0.         0.         0.         0.05769231 0.         0.
  0.01923077 0.        ]
 [0.         0.88571429 0.         0.         0.         0.01428571
  0.         0.         0.         0.         0.01428571 0.05714286
  0.         0.         0.01428571 0.         0.         0.
  0.         0.01428571]
 [0.         0.07142857 0.26785714 0.         0.         0.14285714
  0.05357143 0.         0.         0.07142857 0.01785714 0.125
  0.         0.05357143 0.         0.         0.01785714 0.14285714
  0.01785714 0.01785714]
 [0.         0.         0.01408451 0.5915493  0.         0.01408451
  0.         0.01408451 0.02816901 0.02816901 0.11267606 0.04225352
  0.01408451 0.11267606 0.         0.         0.         0.02816901
  0.         0.        ]
 [0.         0.         0.         0.         0.86538462 0.
  0.01923077 0.         0.         0.         0.01923077 0.03846154
  0.         0.         0.         0.         0.05769231 0.
  0.         0.        ]
 [0.01886792 0.         0.         0.         0.         0.90566038
  0.         0.         0.         0.         0.03773585 0.
  0.         0.         0.         0.03773585 0.         0.
  0.         0.        ]
 [0.03508772 0.05263158 0.         0.         0.         0.03508772
  0.61403509 0.         0.         0.         0.03508772 0.
  0.01754386 0.         0.01754386 0.10526316 0.01754386 0.07017544
  0.         0.        ]
 [0.         0.05555556 0.         0.         0.         0.
  0.         0.77777778 0.         0.         0.02777778 0.02777778
  0.02777778 0.         0.         0.         0.         0.08333333
  0.         0.        ]
 [0.075      0.025      0.         0.         0.         0.
  0.         0.075      0.175      0.         0.475      0.
  0.125      0.         0.         0.         0.         0.05
  0.         0.        ]
 [0.         0.         0.09722222 0.         0.02777778 0.01388889
  0.         0.         0.01388889 0.52777778 0.05555556 0.01388889
  0.         0.22222222 0.         0.         0.         0.02777778
  0.         0.        ]
 [0.         0.06122449 0.         0.02040816 0.02040816 0.02040816
  0.         0.02040816 0.06122449 0.02040816 0.73469388 0.
  0.         0.         0.         0.02040816 0.         0.02040816
  0.         0.        ]
 [0.         0.03703704 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.94444444
  0.         0.         0.         0.         0.         0.01851852
  0.         0.        ]
 [0.01851852 0.01851852 0.         0.         0.         0.
  0.         0.01851852 0.16666667 0.         0.2037037  0.03703704
  0.5        0.         0.01851852 0.01851852 0.         0.
  0.         0.        ]
 [0.         0.         0.02380952 0.04761905 0.         0.
  0.02380952 0.         0.         0.04761905 0.         0.07142857
  0.         0.73809524 0.         0.02380952 0.         0.02380952
  0.         0.        ]
 [0.03333333 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.03333333 0.
  0.         0.         0.8        0.         0.         0.06666667
  0.06666667 0.        ]
 [0.03333333 0.03333333 0.         0.         0.         0.
  0.06666667 0.         0.         0.         0.06666667 0.13333333
  0.         0.         0.         0.6        0.         0.03333333
  0.03333333 0.        ]
 [0.         0.02631579 0.13157895 0.         0.10526316 0.02631579
  0.05263158 0.         0.         0.         0.         0.07894737
  0.         0.02631579 0.         0.         0.52631579 0.02631579
  0.         0.        ]
 [0.         0.         0.03225806 0.         0.         0.03225806
  0.         0.         0.         0.03225806 0.06451613 0.16129032
  0.         0.         0.03225806 0.         0.         0.51612903
  0.12903226 0.        ]
 [0.         0.1        0.         0.         0.         0.03333333
  0.         0.         0.         0.         0.06666667 0.
  0.         0.         0.03333333 0.         0.         0.03333333
  0.7        0.03333333]
 [0.06666667 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.06666667 0.
  0.         0.         0.         0.         0.         0.
  0.06666667 0.8       ]]
[2023-08-14 18:38:06,018 INFO] Model result - eval/best_acc : 0.6705385427666315
[2023-08-14 18:38:06,018 INFO] Model result - eval/best_it : 102399

