[2023-08-29 03:54:16,484 INFO] Use GPU: 0 for training
[2023-08-29 03:54:16,848 INFO] unlabeled data number: 21588, labeled data number 40
[2023-08-29 03:54:23,334 INFO] Create train and test data loaders
[2023-08-29 03:54:23,337 INFO] [!] data loader keys: dict_keys(['train_lb', 'train_ulb', 'eval'])
[2023-08-29 03:54:24,039 INFO] Create optimizer and scheduler
[2023-08-29 03:54:24,054 INFO] Number of Trainable Params: 21402250
[2023-08-29 03:54:28,850 INFO] Arguments: Namespace(save_dir='./saved_models/usb_cv/', save_name='pseudolabel_eurosat_40_0', resume=True, load_path='./saved_models/usb_cv//pseudolabel_eurosat_40_0/latest_model.pth', overwrite=True, use_tensorboard=True, use_wandb=False, use_aim=False, epoch=200, num_train_iter=204800, num_warmup_iter=5120, num_eval_iter=2048, num_log_iter=256, num_labels=40, batch_size=1, uratio=1, eval_batch_size=16, ema_m=0.9999, ulb_loss_ratio=1.0, optim='AdamW', lr=5e-05, momentum=0.9, weight_decay=0.0005, layer_decay=1.0, net='vit_small_patch2_32', net_from_name=False, use_pretrain=True, pretrain_path='https://github.com/microsoft/Semi-supervised-learning/releases/download/v.0.0.0/vit_small_patch2_32_mlp_im_1k_32.pth', algorithm='pseudolabel', use_cat=True, amp=False, clip_grad=0, imb_algorithm=None, data_dir='/usr/data/data', dataset='eurosat', num_classes=10, 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=4.0, sample_rate=16000, world_size=8, rank=0, dist_url='tcp://127.0.0.1:19591', dist_backend='nccl', seed=0, gpu=0, multiprocessing_distributed=True, c='/usr/data/jwy/otherbaseline-main/config/usb_cv/pseudolabel/pseudolabel_eurosat_40_0.yaml', p_cutoff=0.95, unsup_warm_up=0.4, clip=0.0, distributed=True, ulb_dest_len=21588, lb_dest_len=40)
[2023-08-29 03:54:28,850 INFO] Resume load path ./saved_models/usb_cv//pseudolabel_eurosat_40_0/latest_model.pth does not exist
[2023-08-29 03:54:28,850 INFO] Model training
[2023-08-29 03:56:07,196 INFO] 256 iteration USE_EMA: True, train/sup_loss: 4.9111, train/unsup_loss: 0.0000, train/total_loss: 4.9111, train/util_ratio: 0.0000, train/run_time: 0.1864, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-29 03:57:04,790 INFO] 512 iteration USE_EMA: True, train/sup_loss: 3.0066, train/unsup_loss: 0.0000, train/total_loss: 3.0066, train/util_ratio: 0.0000, train/run_time: 0.1973, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-29 03:58:02,350 INFO] 768 iteration USE_EMA: True, train/sup_loss: 1.9173, train/unsup_loss: 0.0000, train/total_loss: 1.9173, train/util_ratio: 0.0000, train/run_time: 0.2057, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 03:58:59,396 INFO] 1024 iteration USE_EMA: True, train/sup_loss: 0.6512, train/unsup_loss: 0.0000, train/total_loss: 0.6512, train/util_ratio: 0.0000, train/run_time: 0.1864, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-29 04:00:39,982 INFO] 1280 iteration USE_EMA: True, train/sup_loss: 0.6711, train/unsup_loss: 0.0000, train/total_loss: 0.6711, train/util_ratio: 0.0000, train/run_time: 0.1833, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-29 04:01:36,587 INFO] 1536 iteration USE_EMA: True, train/sup_loss: 0.2474, train/unsup_loss: 0.0000, train/total_loss: 0.2474, train/util_ratio: 0.0000, train/run_time: 0.2007, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-29 04:02:33,629 INFO] 1792 iteration USE_EMA: True, train/sup_loss: 0.1732, train/unsup_loss: 0.0000, train/total_loss: 0.1732, train/util_ratio: 0.0000, train/run_time: 0.1908, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-29 04:03:30,572 INFO] validating...
[2023-08-29 04:03:54,955 INFO] confusion matrix:
[[1.         0.         0.         0.         0.         0.
  0.         0.         0.         0.        ]
 [1.         0.         0.         0.         0.         0.
  0.         0.         0.         0.        ]
 [0.99833333 0.         0.         0.         0.         0.
  0.         0.00166667 0.         0.        ]
 [1.         0.         0.         0.         0.         0.
  0.         0.         0.         0.        ]
 [0.986      0.         0.         0.         0.         0.
  0.         0.014      0.         0.        ]
 [0.9975     0.         0.         0.         0.         0.
  0.         0.0025     0.         0.        ]
 [0.996      0.         0.         0.         0.         0.
  0.         0.004      0.         0.        ]
 [1.         0.         0.         0.         0.         0.
  0.         0.         0.         0.        ]
 [1.         0.         0.         0.         0.         0.
  0.         0.         0.         0.        ]
 [1.         0.         0.         0.         0.         0.
  0.         0.         0.         0.        ]]
[2023-08-29 04:03:55,647 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-29 04:03:56,496 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-29 04:03:56,497 INFO] 2048 iteration, USE_EMA: True, train/sup_loss: 0.0884, train/unsup_loss: 0.0040, train/total_loss: 0.0885, train/util_ratio: 0.1250, train/run_time: 0.1824, eval/loss: 4.1717, eval/top-1-acc: 0.1111, eval/balanced_acc: 0.1000, eval/precision: 0.0111, eval/recall: 0.1000, eval/F1: 0.0200, lr: 0.0000, train/prefecth_time: 0.0028 BEST_EVAL_ACC: 0.1111, at 2048 iters
[2023-08-29 04:05:35,561 INFO] 2304 iteration USE_EMA: True, train/sup_loss: 0.0509, train/unsup_loss: 0.0000, train/total_loss: 0.0509, train/util_ratio: 0.0000, train/run_time: 0.1889, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-29 04:06:32,518 INFO] 2560 iteration USE_EMA: True, train/sup_loss: 0.0488, train/unsup_loss: 0.0051, train/total_loss: 0.0490, train/util_ratio: 0.2500, train/run_time: 0.1985, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 04:07:29,830 INFO] 2816 iteration USE_EMA: True, train/sup_loss: 0.2478, train/unsup_loss: 0.0076, train/total_loss: 0.2481, train/util_ratio: 0.5000, train/run_time: 0.2223, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 04:08:27,146 INFO] 3072 iteration USE_EMA: True, train/sup_loss: 0.0280, train/unsup_loss: 0.0114, train/total_loss: 0.0285, train/util_ratio: 0.5000, train/run_time: 0.1906, lr: 0.0000, train/prefecth_time: 0.0021 
[2023-08-29 04:10:07,129 INFO] 3328 iteration USE_EMA: True, train/sup_loss: 0.0147, train/unsup_loss: 0.0047, train/total_loss: 0.0149, train/util_ratio: 0.1250, train/run_time: 0.1990, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 04:11:04,305 INFO] 3584 iteration USE_EMA: True, train/sup_loss: 0.1352, train/unsup_loss: 0.0089, train/total_loss: 0.1356, train/util_ratio: 0.5000, train/run_time: 0.1782, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-29 04:12:00,766 INFO] 3840 iteration USE_EMA: True, train/sup_loss: 0.0249, train/unsup_loss: 0.0034, train/total_loss: 0.0251, train/util_ratio: 0.3750, train/run_time: 0.2095, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-29 04:12:57,489 INFO] validating...
[2023-08-29 04:13:22,027 INFO] confusion matrix:
[[1.         0.         0.         0.         0.         0.
  0.         0.         0.         0.        ]
 [0.99       0.         0.         0.00166667 0.         0.005
  0.         0.00333333 0.         0.        ]
 [0.85833333 0.         0.         0.025      0.         0.02333333
  0.         0.09333333 0.         0.        ]
 [0.878      0.         0.         0.094      0.         0.
  0.         0.028      0.         0.        ]
 [0.734      0.         0.         0.066      0.         0.
  0.002      0.198      0.         0.        ]
 [0.895      0.         0.         0.0225     0.         0.0675
  0.         0.015      0.         0.        ]
 [0.858      0.         0.         0.018      0.         0.
  0.044      0.08       0.         0.        ]
 [0.71833333 0.         0.         0.00333333 0.         0.
  0.         0.27833333 0.         0.        ]
 [0.96392786 0.         0.         0.03006012 0.         0.00601202
  0.         0.         0.         0.        ]
 [1.         0.         0.         0.         0.         0.
  0.         0.         0.         0.        ]]
[2023-08-29 04:13:22,763 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-29 04:13:23,724 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-29 04:13:23,725 INFO] 4096 iteration, USE_EMA: True, train/sup_loss: 0.0081, train/unsup_loss: 0.0106, train/total_loss: 0.0087, train/util_ratio: 0.6250, train/run_time: 0.1958, eval/loss: 3.0796, eval/top-1-acc: 0.1598, eval/balanced_acc: 0.1484, eval/precision: 0.2449, eval/recall: 0.1484, eval/F1: 0.0915, lr: 0.0000, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.1598, at 4096 iters
[2023-08-29 04:15:02,574 INFO] 4352 iteration USE_EMA: True, train/sup_loss: 0.0118, train/unsup_loss: 0.0093, train/total_loss: 0.0123, train/util_ratio: 0.5000, train/run_time: 0.2090, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-29 04:15:59,037 INFO] 4608 iteration USE_EMA: True, train/sup_loss: 0.0028, train/unsup_loss: 0.0076, train/total_loss: 0.0032, train/util_ratio: 0.6250, train/run_time: 0.2102, lr: 0.0000, train/prefecth_time: 0.0041 
[2023-08-29 04:16:55,745 INFO] 4864 iteration USE_EMA: True, train/sup_loss: 0.0025, train/unsup_loss: 0.0132, train/total_loss: 0.0033, train/util_ratio: 0.6250, train/run_time: 0.2161, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-29 04:17:52,426 INFO] 5120 iteration USE_EMA: True, train/sup_loss: 0.0025, train/unsup_loss: 0.0109, train/total_loss: 0.0032, train/util_ratio: 0.7500, train/run_time: 0.1988, lr: 0.0001, train/prefecth_time: 0.0022 
[2023-08-29 04:19:31,863 INFO] 5376 iteration USE_EMA: True, train/sup_loss: 0.0038, train/unsup_loss: 0.0111, train/total_loss: 0.0046, train/util_ratio: 0.5000, train/run_time: 0.1938, lr: 0.0000, train/prefecth_time: 0.0034 
[2023-08-29 04:20:28,365 INFO] 5632 iteration USE_EMA: True, train/sup_loss: 0.0457, train/unsup_loss: 0.0140, train/total_loss: 0.0467, train/util_ratio: 0.6250, train/run_time: 0.1894, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-29 04:21:25,229 INFO] 5888 iteration USE_EMA: True, train/sup_loss: 0.0114, train/unsup_loss: 0.0052, train/total_loss: 0.0118, train/util_ratio: 0.5000, train/run_time: 0.1919, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 04:22:21,498 INFO] validating...
[2023-08-29 04:22:46,012 INFO] confusion matrix:
[[0.96333333 0.         0.         0.00333333 0.         0.00833333
  0.025      0.         0.         0.        ]
 [0.69166667 0.         0.         0.00333333 0.         0.27833333
  0.         0.02333333 0.00333333 0.        ]
 [0.26666667 0.         0.01166667 0.045      0.         0.31666667
  0.01833333 0.34       0.00166667 0.        ]
 [0.346      0.         0.         0.5        0.         0.012
  0.016      0.12       0.006      0.        ]
 [0.18       0.         0.         0.21       0.046      0.
  0.064      0.5        0.         0.        ]
 [0.225      0.         0.         0.0775     0.         0.62
  0.0575     0.015      0.005      0.        ]
 [0.144      0.         0.002      0.02       0.         0.006
  0.668      0.156      0.004      0.        ]
 [0.06166667 0.         0.         0.00166667 0.         0.
  0.         0.93666667 0.         0.        ]
 [0.71543086 0.         0.         0.18036072 0.         0.06613226
  0.00400802 0.02004008 0.01402806 0.        ]
 [0.85166667 0.         0.         0.         0.         0.00833333
  0.         0.00333333 0.         0.13666667]]
[2023-08-29 04:22:46,761 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-29 04:22:47,549 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-29 04:22:47,551 INFO] 6144 iteration, USE_EMA: True, train/sup_loss: 0.0027, train/unsup_loss: 0.0122, train/total_loss: 0.0036, train/util_ratio: 0.7500, train/run_time: 0.1844, eval/loss: 1.9322, eval/top-1-acc: 0.3873, eval/balanced_acc: 0.3896, eval/precision: 0.5639, eval/recall: 0.3896, eval/F1: 0.3065, lr: 0.0000, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.3873, at 6144 iters
[2023-08-29 04:24:26,445 INFO] 6400 iteration USE_EMA: True, train/sup_loss: 0.0011, train/unsup_loss: 0.0022, train/total_loss: 0.0012, train/util_ratio: 0.7500, train/run_time: 0.1912, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-29 04:25:23,316 INFO] 6656 iteration USE_EMA: True, train/sup_loss: 0.0301, train/unsup_loss: 0.0007, train/total_loss: 0.0302, train/util_ratio: 0.5000, train/run_time: 0.2054, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 04:26:19,764 INFO] 6912 iteration USE_EMA: True, train/sup_loss: 0.0003, train/unsup_loss: 0.0022, train/total_loss: 0.0005, train/util_ratio: 0.7500, train/run_time: 0.2372, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-29 04:27:16,571 INFO] 7168 iteration USE_EMA: True, train/sup_loss: 0.0305, train/unsup_loss: 0.0054, train/total_loss: 0.0310, train/util_ratio: 0.7500, train/run_time: 0.1938, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-29 04:28:57,211 INFO] 7424 iteration USE_EMA: True, train/sup_loss: 0.0004, train/unsup_loss: 0.0012, train/total_loss: 0.0005, train/util_ratio: 0.6250, train/run_time: 0.1803, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-29 04:29:54,033 INFO] 7680 iteration USE_EMA: True, train/sup_loss: 0.0088, train/unsup_loss: 0.0010, train/total_loss: 0.0089, train/util_ratio: 0.2500, train/run_time: 0.1890, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 04:30:51,156 INFO] 7936 iteration USE_EMA: True, train/sup_loss: 0.0003, train/unsup_loss: 0.0023, train/total_loss: 0.0005, train/util_ratio: 0.6250, train/run_time: 0.1953, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-29 04:31:48,225 INFO] validating...
[2023-08-29 04:32:12,379 INFO] confusion matrix:
[[0.86166667 0.         0.00333333 0.00666667 0.         0.01666667
  0.11       0.         0.00166667 0.        ]
 [0.075      0.60333333 0.         0.         0.         0.255
  0.         0.02833333 0.03666667 0.00166667]
 [0.01833333 0.         0.26666667 0.01333333 0.         0.36666667
  0.04333333 0.27       0.02166667 0.        ]
 [0.062      0.         0.004      0.572      0.006      0.006
  0.048      0.12       0.182      0.        ]
 [0.014      0.         0.         0.088      0.586      0.
  0.04       0.272      0.         0.        ]
 [0.0225     0.0025     0.         0.0325     0.         0.76
  0.14       0.005      0.0375     0.        ]
 [0.022      0.         0.046      0.01       0.         0.006
  0.792      0.114      0.01       0.        ]
 [0.005      0.         0.         0.         0.         0.
  0.         0.995      0.         0.        ]
 [0.27054108 0.         0.         0.17635271 0.00601202 0.14228457
  0.04408818 0.03607214 0.3246493  0.        ]
 [0.23833333 0.         0.         0.00833333 0.         0.035
  0.         0.00166667 0.         0.71666667]]
[2023-08-29 04:32:13,104 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-29 04:32:13,874 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-29 04:32:13,876 INFO] 8192 iteration, USE_EMA: True, train/sup_loss: 0.0006, train/unsup_loss: 0.0027, train/total_loss: 0.0009, train/util_ratio: 0.6250, train/run_time: 0.2016, eval/loss: 1.1333, eval/top-1-acc: 0.6496, eval/balanced_acc: 0.6478, eval/precision: 0.7158, eval/recall: 0.6478, eval/F1: 0.6361, lr: 0.0000, train/prefecth_time: 0.0023 BEST_EVAL_ACC: 0.6496, at 8192 iters
[2023-08-29 04:33:52,643 INFO] 8448 iteration USE_EMA: True, train/sup_loss: 0.0005, train/unsup_loss: 0.0008, train/total_loss: 0.0006, train/util_ratio: 0.7500, train/run_time: 0.1869, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-29 04:34:49,227 INFO] 8704 iteration USE_EMA: True, train/sup_loss: 0.0228, train/unsup_loss: 0.0074, train/total_loss: 0.0236, train/util_ratio: 0.7500, train/run_time: 0.1820, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-29 04:35:46,169 INFO] 8960 iteration USE_EMA: True, train/sup_loss: 0.0005, train/unsup_loss: 0.0037, train/total_loss: 0.0009, train/util_ratio: 0.7500, train/run_time: 0.1975, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 04:36:42,491 INFO] 9216 iteration USE_EMA: True, train/sup_loss: 0.0002, train/unsup_loss: 0.0021, train/total_loss: 0.0005, train/util_ratio: 0.6250, train/run_time: 0.1846, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 04:38:22,345 INFO] 9472 iteration USE_EMA: True, train/sup_loss: 0.0005, train/unsup_loss: 0.0003, train/total_loss: 0.0005, train/util_ratio: 0.7500, train/run_time: 0.1945, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-29 04:39:19,235 INFO] 9728 iteration USE_EMA: True, train/sup_loss: 0.0009, train/unsup_loss: 0.0008, train/total_loss: 0.0010, train/util_ratio: 0.6250, train/run_time: 0.2143, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-29 04:40:15,907 INFO] 9984 iteration USE_EMA: True, train/sup_loss: 0.0008, train/unsup_loss: 0.0064, train/total_loss: 0.0016, train/util_ratio: 0.7500, train/run_time: 0.1810, lr: 0.0000, train/prefecth_time: 0.0039 
[2023-08-29 04:41:13,293 INFO] validating...
[2023-08-29 04:41:38,085 INFO] confusion matrix:
[[0.78333333 0.         0.005      0.01333333 0.         0.01833333
  0.17       0.         0.01       0.        ]
 [0.005      0.79833333 0.00333333 0.00166667 0.         0.13833333
  0.         0.01666667 0.03666667 0.        ]
 [0.         0.         0.385      0.00333333 0.         0.295
  0.04       0.23666667 0.03833333 0.00166667]
 [0.022      0.         0.006      0.496      0.024      0.004
  0.05       0.11       0.288      0.        ]
 [0.002      0.         0.         0.018      0.82       0.
  0.012      0.148      0.         0.        ]
 [0.0025     0.005      0.         0.0075     0.         0.8075
  0.125      0.0025     0.05       0.        ]
 [0.004      0.         0.086      0.004      0.         0.008
  0.788      0.092      0.018      0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         1.         0.         0.        ]
 [0.08016032 0.00400802 0.         0.1242485  0.02004008 0.12825651
  0.0501002  0.02204409 0.57114228 0.        ]
 [0.05833333 0.00666667 0.         0.00666667 0.         0.065
  0.         0.00333333 0.01166667 0.84833333]]
[2023-08-29 04:41:39,163 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-29 04:41:40,432 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-29 04:41:40,434 INFO] 10240 iteration, USE_EMA: True, train/sup_loss: 0.0004, train/unsup_loss: 0.0002, train/total_loss: 0.0004, train/util_ratio: 0.8750, train/run_time: 0.2048, eval/loss: 0.8484, eval/top-1-acc: 0.7314, eval/balanced_acc: 0.7298, eval/precision: 0.7595, eval/recall: 0.7298, eval/F1: 0.7227, lr: 0.0000, train/prefecth_time: 0.0030 BEST_EVAL_ACC: 0.7314, at 10240 iters
[2023-08-29 04:43:19,214 INFO] 10496 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0029, train/total_loss: 0.0005, train/util_ratio: 0.8750, train/run_time: 0.1788, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-29 04:44:16,530 INFO] 10752 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 0.6250, train/run_time: 0.1935, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-29 04:45:13,739 INFO] 11008 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0011, train/total_loss: 0.0002, train/util_ratio: 0.7500, train/run_time: 0.1849, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-29 04:46:10,832 INFO] 11264 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0023, train/total_loss: 0.0004, train/util_ratio: 1.0000, train/run_time: 0.1939, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-29 04:47:50,915 INFO] 11520 iteration USE_EMA: True, train/sup_loss: 0.0003, train/unsup_loss: 0.0056, train/total_loss: 0.0011, train/util_ratio: 0.7500, train/run_time: 0.1870, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-29 04:48:47,718 INFO] 11776 iteration USE_EMA: True, train/sup_loss: 0.0003, train/unsup_loss: 0.0079, train/total_loss: 0.0015, train/util_ratio: 0.7500, train/run_time: 0.1817, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-29 04:49:46,535 INFO] 12032 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0026, train/total_loss: 0.0005, train/util_ratio: 1.0000, train/run_time: 0.2389, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-29 04:50:57,263 INFO] validating...
[2023-08-29 04:51:21,457 INFO] confusion matrix:
[[0.79166667 0.00166667 0.00666667 0.00833333 0.         0.02333333
  0.15333333 0.00166667 0.01333333 0.        ]
 [0.         0.85333333 0.00166667 0.00166667 0.         0.10833333
  0.         0.01333333 0.02166667 0.        ]
 [0.         0.00166667 0.47333333 0.00166667 0.         0.265
  0.03       0.2        0.02833333 0.        ]
 [0.006      0.         0.006      0.484      0.024      0.006
  0.044      0.088      0.342      0.        ]
 [0.         0.         0.         0.002      0.89       0.
  0.002      0.106      0.         0.        ]
 [0.0025     0.005      0.         0.0025     0.         0.8525
  0.0875     0.0025     0.0475     0.        ]
 [0.004      0.         0.118      0.002      0.002      0.008
  0.78       0.068      0.018      0.        ]
 [0.         0.         0.         0.         0.         0.
  0.         1.         0.         0.        ]
 [0.04809619 0.00400802 0.         0.1002004  0.02004008 0.12825651
  0.05811623 0.01603206 0.6252505  0.        ]
 [0.02833333 0.02666667 0.00333333 0.005      0.         0.06166667
  0.         0.         0.015      0.86      ]]
[2023-08-29 04:51:22,335 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-29 04:51:23,473 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-29 04:51:23,475 INFO] 12288 iteration, USE_EMA: True, train/sup_loss: 0.0002, train/unsup_loss: 0.0006, train/total_loss: 0.0003, train/util_ratio: 0.6250, train/run_time: 0.2418, eval/loss: 0.8255, eval/top-1-acc: 0.7625, eval/balanced_acc: 0.7610, eval/precision: 0.7828, eval/recall: 0.7610, eval/F1: 0.7543, lr: 0.0000, train/prefecth_time: 0.0021 BEST_EVAL_ACC: 0.7625, at 12288 iters
[2023-08-29 04:53:15,150 INFO] 12544 iteration USE_EMA: True, train/sup_loss: 0.0002, train/unsup_loss: 0.0044, train/total_loss: 0.0008, train/util_ratio: 0.7500, train/run_time: 0.2444, lr: 0.0000, train/prefecth_time: 0.0034 
[2023-08-29 04:54:26,040 INFO] 12800 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0053, train/total_loss: 0.0009, train/util_ratio: 0.8750, train/run_time: 0.2396, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-29 04:55:36,733 INFO] 13056 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0009, train/total_loss: 0.0003, train/util_ratio: 0.7500, train/run_time: 0.2676, lr: 0.0000, train/prefecth_time: 0.0039 
[2023-08-29 04:56:46,867 INFO] 13312 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0055, train/total_loss: 0.0010, train/util_ratio: 0.5000, train/run_time: 0.2328, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 04:58:39,356 INFO] 13568 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0010, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.2353, lr: 0.0000, train/prefecth_time: 0.0050 
[2023-08-29 04:59:50,028 INFO] 13824 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0073, train/total_loss: 0.0013, train/util_ratio: 0.8750, train/run_time: 0.2378, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-29 05:01:00,542 INFO] 14080 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0041, train/total_loss: 0.0007, train/util_ratio: 0.6250, train/run_time: 0.2255, lr: 0.0000, train/prefecth_time: 0.0039 
[2023-08-29 05:02:10,738 INFO] validating...
[2023-08-29 05:02:35,500 INFO] confusion matrix:
[[0.80833333 0.005      0.00833333 0.005      0.         0.02333333
  0.13       0.00166667 0.01666667 0.00166667]
 [0.         0.86333333 0.         0.         0.         0.10333333
  0.         0.01       0.02333333 0.        ]
 [0.         0.00333333 0.56166667 0.00166667 0.         0.23666667
  0.01833333 0.15       0.02833333 0.        ]
 [0.004      0.         0.014      0.462      0.034      0.006
  0.032      0.07       0.378      0.        ]
 [0.         0.         0.         0.002      0.924      0.
  0.002      0.072      0.         0.        ]
 [0.0025     0.005      0.         0.         0.         0.8775
  0.07       0.0025     0.0425     0.        ]
 [0.004      0.         0.144      0.         0.01       0.014
  0.748      0.058      0.022      0.        ]
 [0.         0.00166667 0.00166667 0.         0.         0.
  0.         0.99666667 0.         0.        ]
 [0.04208417 0.00400802 0.         0.0761523  0.02204409 0.11823647
  0.04609218 0.01202405 0.67935872 0.        ]
 [0.01666667 0.03333333 0.005      0.00333333 0.         0.05166667
  0.         0.         0.02       0.87      ]]
[2023-08-29 05:02:36,506 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-29 05:02:37,489 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-29 05:02:37,492 INFO] 14336 iteration, USE_EMA: True, train/sup_loss: 0.0007, train/unsup_loss: 0.0010, train/total_loss: 0.0009, train/util_ratio: 0.8750, train/run_time: 0.2649, eval/loss: 0.8886, eval/top-1-acc: 0.7811, eval/balanced_acc: 0.7791, eval/precision: 0.7969, eval/recall: 0.7791, eval/F1: 0.7726, lr: 0.0000, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.7811, at 14336 iters
[2023-08-29 05:04:28,787 INFO] 14592 iteration USE_EMA: True, train/sup_loss: 0.0054, train/unsup_loss: 0.0001, train/total_loss: 0.0054, train/util_ratio: 1.0000, train/run_time: 0.2270, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 05:05:38,373 INFO] 14848 iteration USE_EMA: True, train/sup_loss: 0.0013, train/unsup_loss: 0.0004, train/total_loss: 0.0014, train/util_ratio: 0.6250, train/run_time: 0.2332, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-29 05:06:48,049 INFO] 15104 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0047, train/total_loss: 0.0009, train/util_ratio: 1.0000, train/run_time: 0.2540, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-29 05:07:57,934 INFO] 15360 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0077, train/total_loss: 0.0015, train/util_ratio: 0.8750, train/run_time: 0.2782, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 05:09:51,826 INFO] 15616 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0029, train/total_loss: 0.0006, train/util_ratio: 0.8750, train/run_time: 0.2693, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-29 05:11:02,333 INFO] 15872 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.2369, lr: 0.0000, train/prefecth_time: 0.0042 
[2023-08-29 05:12:11,996 INFO] 16128 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0034, train/total_loss: 0.0007, train/util_ratio: 1.0000, train/run_time: 0.2597, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-29 05:13:22,362 INFO] validating...
[2023-08-29 05:13:47,166 INFO] confusion matrix:
[[0.815      0.005      0.01166667 0.005      0.         0.025
  0.11833333 0.00166667 0.01833333 0.        ]
 [0.         0.87166667 0.00166667 0.         0.         0.095
  0.         0.01       0.02166667 0.        ]
 [0.         0.005      0.59833333 0.         0.         0.22666667
  0.02       0.125      0.025      0.        ]
 [0.002      0.         0.02       0.462      0.05       0.006
  0.024      0.06       0.376      0.        ]
 [0.         0.         0.         0.002      0.956      0.
  0.002      0.04       0.         0.        ]
 [0.         0.01       0.         0.         0.         0.895
  0.0625     0.         0.0325     0.        ]
 [0.006      0.         0.164      0.         0.012      0.02
  0.748      0.038      0.012      0.        ]
 [0.         0.00333333 0.00333333 0.         0.00333333 0.
  0.         0.99       0.         0.        ]
 [0.04609218 0.00400802 0.         0.07014028 0.0240481  0.12825651
  0.04809619 0.00801603 0.67134269 0.        ]
 [0.015      0.035      0.005      0.005      0.         0.04166667
  0.         0.         0.02166667 0.87666667]]
[2023-08-29 05:13:47,980 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-29 05:13:49,255 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-29 05:13:49,256 INFO] 16384 iteration, USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0002, train/total_loss: 0.0002, train/util_ratio: 0.7500, train/run_time: 0.2628, eval/loss: 0.9899, eval/top-1-acc: 0.7903, eval/balanced_acc: 0.7884, eval/precision: 0.8016, eval/recall: 0.7884, eval/F1: 0.7812, lr: 0.0000, train/prefecth_time: 0.0027 BEST_EVAL_ACC: 0.7903, at 16384 iters
[2023-08-29 05:15:41,930 INFO] 16640 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0001, train/util_ratio: 0.8750, train/run_time: 0.2708, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-29 05:16:52,322 INFO] 16896 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0016, train/total_loss: 0.0004, train/util_ratio: 0.7500, train/run_time: 0.2431, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-29 05:18:02,170 INFO] 17152 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0023, train/total_loss: 0.0005, train/util_ratio: 0.8750, train/run_time: 0.2379, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-29 05:19:11,960 INFO] 17408 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0015, train/total_loss: 0.0003, train/util_ratio: 0.7500, train/run_time: 0.2260, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-29 05:21:04,973 INFO] 17664 iteration USE_EMA: True, train/sup_loss: 0.0002, train/unsup_loss: 0.0002, train/total_loss: 0.0003, train/util_ratio: 1.0000, train/run_time: 0.2462, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-29 05:22:14,515 INFO] 17920 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0004, train/total_loss: 0.0001, train/util_ratio: 0.7500, train/run_time: 0.2541, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-29 05:23:24,144 INFO] 18176 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0027, train/total_loss: 0.0006, train/util_ratio: 1.0000, train/run_time: 0.2299, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-29 05:24:33,472 INFO] validating...
[2023-08-29 05:24:57,381 INFO] confusion matrix:
[[0.825      0.00666667 0.00666667 0.005      0.         0.03
  0.11       0.         0.01666667 0.        ]
 [0.         0.87833333 0.00333333 0.         0.         0.095
  0.         0.00666667 0.01666667 0.        ]
 [0.         0.005      0.62833333 0.         0.00166667 0.21666667
  0.02166667 0.10166667 0.025      0.        ]
 [0.         0.         0.018      0.458      0.042      0.006
  0.024      0.04       0.412      0.        ]
 [0.         0.         0.         0.002      0.962      0.
  0.002      0.034      0.         0.        ]
 [0.         0.01       0.         0.         0.         0.9125
  0.05       0.         0.0275     0.        ]
 [0.008      0.         0.166      0.         0.01       0.024
  0.754      0.026      0.012      0.        ]
 [0.         0.00333333 0.005      0.         0.00333333 0.
  0.         0.98833333 0.         0.        ]
 [0.05611222 0.00200401 0.         0.06613226 0.02204409 0.12625251
  0.04208417 0.00400802 0.68136273 0.        ]
 [0.01333333 0.03333333 0.00333333 0.00333333 0.         0.04166667
  0.         0.         0.02333333 0.88166667]]
[2023-08-29 05:24:58,186 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-29 05:24:59,128 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-29 05:24:59,130 INFO] 18432 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0012, train/total_loss: 0.0003, train/util_ratio: 0.7500, train/run_time: 0.2259, eval/loss: 1.0964, eval/top-1-acc: 0.7989, eval/balanced_acc: 0.7970, eval/precision: 0.8100, eval/recall: 0.7970, eval/F1: 0.7898, lr: 0.0000, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.7989, at 18432 iters
[2023-08-29 05:26:51,733 INFO] 18688 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.2276, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 05:28:01,198 INFO] 18944 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0027, train/total_loss: 0.0007, train/util_ratio: 0.7500, train/run_time: 0.2358, lr: 0.0000, train/prefecth_time: 0.0042 
[2023-08-29 05:29:10,872 INFO] 19200 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0005, train/total_loss: 0.0001, train/util_ratio: 0.8750, train/run_time: 0.2361, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-29 05:30:20,171 INFO] 19456 iteration USE_EMA: True, train/sup_loss: 0.0396, train/unsup_loss: 0.0003, train/total_loss: 0.0396, train/util_ratio: 1.0000, train/run_time: 0.2694, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-29 05:32:13,631 INFO] 19712 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0059, train/total_loss: 0.0016, train/util_ratio: 0.7500, train/run_time: 0.2707, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-29 05:33:24,071 INFO] 19968 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0026, train/total_loss: 0.0007, train/util_ratio: 0.7500, train/run_time: 0.2333, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-29 05:35:08,715 INFO] 20224 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0001, train/util_ratio: 0.8750, train/run_time: 0.3999, lr: 0.0000, train/prefecth_time: 0.0035 
[2023-08-29 05:37:00,945 INFO] validating...
[2023-08-29 05:37:25,468 INFO] confusion matrix:
[[0.83666667 0.00666667 0.00666667 0.00666667 0.         0.03
  0.09166667 0.         0.02166667 0.        ]
 [0.         0.89166667 0.00333333 0.         0.         0.08
  0.         0.00166667 0.01833333 0.005     ]
 [0.00166667 0.00666667 0.655      0.00166667 0.005      0.20666667
  0.02       0.07666667 0.02666667 0.        ]
 [0.         0.         0.012      0.456      0.032      0.006
  0.022      0.026      0.446      0.        ]
 [0.         0.         0.         0.002      0.962      0.
  0.002      0.034      0.         0.        ]
 [0.         0.0175     0.         0.         0.         0.8975
  0.055      0.         0.03       0.        ]
 [0.01       0.         0.166      0.         0.01       0.024
  0.754      0.02       0.016      0.        ]
 [0.         0.00666667 0.00666667 0.         0.00166667 0.
  0.         0.98333333 0.00166667 0.        ]
 [0.06412826 0.00200401 0.         0.05611222 0.02004008 0.1002004
  0.03807615 0.00400802 0.71342685 0.00200401]
 [0.01666667 0.03333333 0.00333333 0.00333333 0.         0.04
  0.         0.         0.025      0.87833333]]
[2023-08-29 05:37:26,232 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-29 05:37:27,392 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-29 05:37:27,393 INFO] 20480 iteration, USE_EMA: True, train/sup_loss: 0.0027, train/unsup_loss: 0.0001, train/total_loss: 0.0027, train/util_ratio: 1.0000, train/run_time: 0.4145, eval/loss: 1.2000, eval/top-1-acc: 0.8053, eval/balanced_acc: 0.8028, eval/precision: 0.8161, eval/recall: 0.8028, eval/F1: 0.7965, lr: 0.0000, train/prefecth_time: 0.0026 BEST_EVAL_ACC: 0.8053, at 20480 iters
[2023-08-29 05:39:57,630 INFO] 20736 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0061, train/total_loss: 0.0016, train/util_ratio: 0.8750, train/run_time: 0.4621, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-29 05:41:48,052 INFO] 20992 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0004, train/total_loss: 0.0002, train/util_ratio: 0.8750, train/run_time: 0.3652, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-29 05:43:38,228 INFO] 21248 iteration USE_EMA: True, train/sup_loss: 0.0006, train/unsup_loss: 0.0000, train/total_loss: 0.0006, train/util_ratio: 0.8750, train/run_time: 0.4261, lr: 0.0000, train/prefecth_time: 0.0033 
[2023-08-29 05:45:26,322 INFO] 21504 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0050, train/total_loss: 0.0013, train/util_ratio: 0.8750, train/run_time: 0.3589, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-29 05:47:58,758 INFO] 21760 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0013, train/total_loss: 0.0004, train/util_ratio: 1.0000, train/run_time: 0.3645, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-29 05:49:48,451 INFO] 22016 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0018, train/total_loss: 0.0005, train/util_ratio: 1.0000, train/run_time: 0.3539, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 05:51:38,874 INFO] 22272 iteration USE_EMA: True, train/sup_loss: 0.0228, train/unsup_loss: 0.0032, train/total_loss: 0.0237, train/util_ratio: 0.8750, train/run_time: 0.3696, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-29 05:53:28,471 INFO] validating...
[2023-08-29 05:53:52,722 INFO] confusion matrix:
[[0.845      0.00666667 0.00833333 0.005      0.         0.02666667
  0.085      0.         0.02333333 0.        ]
 [0.         0.90333333 0.00333333 0.         0.         0.07
  0.         0.         0.01833333 0.005     ]
 [0.00166667 0.00666667 0.69166667 0.00166667 0.00666667 0.18166667
  0.025      0.055      0.03       0.        ]
 [0.         0.         0.01       0.466      0.032      0.004
  0.018      0.024      0.446      0.        ]
 [0.         0.         0.         0.002      0.968      0.
  0.004      0.026      0.         0.        ]
 [0.0025     0.015      0.         0.0025     0.         0.895
  0.055      0.         0.03       0.        ]
 [0.012      0.         0.168      0.         0.006      0.016
  0.758      0.022      0.018      0.        ]
 [0.         0.00833333 0.01       0.         0.00166667 0.
  0.         0.97833333 0.00166667 0.        ]
 [0.06412826 0.00200401 0.         0.05210421 0.02004008 0.08016032
  0.03807615 0.00200401 0.74148297 0.        ]
 [0.015      0.03833333 0.00333333 0.00166667 0.         0.04
  0.         0.         0.03       0.87166667]]
[2023-08-29 05:53:53,486 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-29 05:53:54,732 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-29 05:53:54,742 INFO] 22528 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0013, train/total_loss: 0.0004, train/util_ratio: 0.8750, train/run_time: 0.4108, eval/loss: 1.3014, eval/top-1-acc: 0.8146, eval/balanced_acc: 0.8118, eval/precision: 0.8244, eval/recall: 0.8118, eval/F1: 0.8063, lr: 0.0000, train/prefecth_time: 0.0023 BEST_EVAL_ACC: 0.8146, at 22528 iters
[2023-08-29 05:56:25,762 INFO] 22784 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0116, train/total_loss: 0.0033, train/util_ratio: 0.8750, train/run_time: 0.4427, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-29 05:58:14,310 INFO] 23040 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4133, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-29 06:00:02,797 INFO] 23296 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0033, train/total_loss: 0.0010, train/util_ratio: 0.7500, train/run_time: 0.4231, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-29 06:01:52,391 INFO] 23552 iteration USE_EMA: True, train/sup_loss: 0.0072, train/unsup_loss: 0.0036, train/total_loss: 0.0082, train/util_ratio: 1.0000, train/run_time: 0.4103, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-29 06:04:25,603 INFO] 23808 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0026, train/total_loss: 0.0008, train/util_ratio: 1.0000, train/run_time: 0.4325, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-29 06:06:14,482 INFO] 24064 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0001, train/util_ratio: 0.8750, train/run_time: 0.3698, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-29 06:08:04,400 INFO] 24320 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0037, train/total_loss: 0.0011, train/util_ratio: 0.8750, train/run_time: 0.3563, lr: 0.0000, train/prefecth_time: 0.0033 
[2023-08-29 06:09:52,678 INFO] validating...
[2023-08-29 06:10:17,174 INFO] confusion matrix:
[[0.85166667 0.01       0.00666667 0.00333333 0.         0.025
  0.08166667 0.         0.02166667 0.        ]
 [0.         0.91       0.00333333 0.         0.         0.06
  0.         0.00166667 0.02166667 0.00333333]
 [0.00166667 0.00833333 0.68833333 0.00166667 0.00666667 0.17166667
  0.03833333 0.05666667 0.02666667 0.        ]
 [0.         0.         0.008      0.484      0.038      0.004
  0.014      0.032      0.42       0.        ]
 [0.         0.         0.         0.006      0.97       0.
  0.002      0.022      0.         0.        ]
 [0.0025     0.0125     0.         0.0025     0.         0.8975
  0.0525     0.         0.0325     0.        ]
 [0.012      0.         0.166      0.002      0.008      0.016
  0.756      0.02       0.02       0.        ]
 [0.         0.01       0.01       0.         0.         0.
  0.         0.97833333 0.00166667 0.        ]
 [0.06212425 0.00200401 0.         0.05410822 0.02004008 0.0761523
  0.03406814 0.00200401 0.749499   0.        ]
 [0.01333333 0.03666667 0.00333333 0.         0.         0.04166667
  0.         0.         0.04666667 0.85833333]]
[2023-08-29 06:10:18,175 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-29 06:10:19,450 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-29 06:10:19,451 INFO] 24576 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0006, train/total_loss: 0.0002, train/util_ratio: 0.8750, train/run_time: 0.4139, eval/loss: 1.4154, eval/top-1-acc: 0.8168, eval/balanced_acc: 0.8144, eval/precision: 0.8258, eval/recall: 0.8144, eval/F1: 0.8090, lr: 0.0000, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.8168, at 24576 iters
[2023-08-29 06:12:51,161 INFO] 24832 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0001, train/util_ratio: 0.8750, train/run_time: 0.3574, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-29 06:14:39,705 INFO] 25088 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4219, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-29 06:16:27,665 INFO] 25344 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.3638, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-29 06:18:15,892 INFO] 25600 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0003, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.3673, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-29 06:20:47,263 INFO] 25856 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3565, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-29 06:22:34,753 INFO] 26112 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4178, lr: 0.0000, train/prefecth_time: 0.0035 
[2023-08-29 06:24:22,719 INFO] 26368 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3563, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-29 06:26:09,676 INFO] validating...
[2023-08-29 06:26:34,122 INFO] confusion matrix:
[[0.85666667 0.00333333 0.00666667 0.00333333 0.         0.02166667
  0.075      0.         0.03333333 0.        ]
 [0.         0.90666667 0.00333333 0.         0.         0.05666667
  0.         0.00166667 0.02833333 0.00333333]
 [0.00166667 0.00666667 0.66333333 0.00166667 0.00666667 0.18166667
  0.04       0.06666667 0.03166667 0.        ]
 [0.         0.         0.006      0.5        0.04       0.002
  0.016      0.034      0.402      0.        ]
 [0.         0.         0.         0.006      0.962      0.
  0.002      0.03       0.         0.        ]
 [0.0025     0.0125     0.         0.0025     0.         0.8825
  0.0525     0.         0.0475     0.        ]
 [0.012      0.         0.174      0.002      0.01       0.018
  0.746      0.02       0.018      0.        ]
 [0.         0.01       0.00833333 0.         0.         0.
  0.         0.98       0.00166667 0.        ]
 [0.06412826 0.         0.         0.05410822 0.02004008 0.06613226
  0.03006012 0.         0.76553106 0.        ]
 [0.01333333 0.035      0.00333333 0.         0.         0.04166667
  0.         0.         0.05833333 0.84833333]]
[2023-08-29 06:26:34,881 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-29 06:26:34,882 INFO] 26624 iteration, USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0076, train/total_loss: 0.0026, train/util_ratio: 1.0000, train/run_time: 0.4178, eval/loss: 1.5739, eval/top-1-acc: 0.8135, eval/balanced_acc: 0.8111, eval/precision: 0.8239, eval/recall: 0.8111, eval/F1: 0.8064, lr: 0.0000, train/prefecth_time: 0.0028 BEST_EVAL_ACC: 0.8168, at 24576 iters
[2023-08-29 06:29:05,930 INFO] 26880 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0031, train/total_loss: 0.0011, train/util_ratio: 1.0000, train/run_time: 0.3982, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-29 06:30:54,024 INFO] 27136 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0001, train/util_ratio: 0.8750, train/run_time: 0.3732, lr: 0.0000, train/prefecth_time: 0.0034 
[2023-08-29 06:32:44,961 INFO] 27392 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0065, train/total_loss: 0.0022, train/util_ratio: 1.0000, train/run_time: 0.3687, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-29 06:34:34,647 INFO] 27648 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0003, train/total_loss: 0.0001, train/util_ratio: 0.7500, train/run_time: 0.3569, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-29 06:37:06,417 INFO] 27904 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0005, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.3984, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-29 06:38:56,202 INFO] 28160 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0015, train/total_loss: 0.0005, train/util_ratio: 0.8750, train/run_time: 0.4670, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-29 06:40:46,766 INFO] 28416 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0030, train/total_loss: 0.0011, train/util_ratio: 0.7500, train/run_time: 0.4205, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-29 06:42:36,302 INFO] validating...
[2023-08-29 06:43:00,188 INFO] confusion matrix:
[[0.85333333 0.00333333 0.00666667 0.005      0.         0.02833333
  0.07666667 0.         0.02666667 0.        ]
 [0.         0.90166667 0.00333333 0.         0.         0.065
  0.         0.00166667 0.025      0.00333333]
 [0.00166667 0.00666667 0.64333333 0.         0.005      0.20333333
  0.045      0.065      0.03       0.        ]
 [0.         0.         0.004      0.518      0.034      0.002
  0.018      0.036      0.388      0.        ]
 [0.         0.         0.         0.006      0.962      0.
  0.002      0.03       0.         0.        ]
 [0.0025     0.0125     0.         0.0025     0.         0.895
  0.05       0.         0.0375     0.        ]
 [0.012      0.         0.166      0.002      0.006      0.018
  0.762      0.012      0.022      0.        ]
 [0.         0.01166667 0.00833333 0.         0.00166667 0.
  0.         0.97666667 0.00166667 0.        ]
 [0.06813627 0.00200401 0.         0.05210421 0.02004008 0.06613226
  0.03607214 0.         0.75551102 0.        ]
 [0.01166667 0.03666667 0.00333333 0.         0.         0.045
  0.         0.         0.06166667 0.84166667]]
[2023-08-29 06:43:00,974 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-29 06:43:00,975 INFO] 28672 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0001, train/util_ratio: 0.8750, train/run_time: 0.4032, eval/loss: 1.6921, eval/top-1-acc: 0.8124, eval/balanced_acc: 0.8109, eval/precision: 0.8236, eval/recall: 0.8109, eval/F1: 0.8059, lr: 0.0000, train/prefecth_time: 0.0027 BEST_EVAL_ACC: 0.8168, at 24576 iters
[2023-08-29 06:45:34,311 INFO] 28928 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0009, train/total_loss: 0.0003, train/util_ratio: 1.0000, train/run_time: 0.4206, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 06:47:29,297 INFO] 29184 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0003, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4406, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 06:49:19,694 INFO] 29440 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0004, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.3702, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-29 06:51:08,621 INFO] 29696 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.3561, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-29 06:53:40,456 INFO] 29952 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0036, train/total_loss: 0.0013, train/util_ratio: 0.8750, train/run_time: 0.3584, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 06:55:30,713 INFO] 30208 iteration USE_EMA: True, train/sup_loss: 0.1313, train/unsup_loss: 0.0002, train/total_loss: 0.1314, train/util_ratio: 0.7500, train/run_time: 0.3574, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-29 06:57:20,666 INFO] 30464 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3567, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-29 06:59:10,158 INFO] validating...
[2023-08-29 06:59:34,870 INFO] confusion matrix:
[[0.86       0.00666667 0.00333333 0.005      0.         0.03
  0.06833333 0.         0.025      0.00166667]
 [0.         0.90666667 0.00333333 0.         0.         0.06333333
  0.         0.         0.025      0.00166667]
 [0.00166667 0.01       0.63166667 0.         0.005      0.22
  0.04333333 0.06       0.02833333 0.        ]
 [0.         0.         0.006      0.528      0.034      0.004
  0.018      0.026      0.384      0.        ]
 [0.         0.         0.         0.006      0.966      0.
  0.002      0.026      0.         0.        ]
 [0.0025     0.015      0.         0.0025     0.         0.89
  0.0475     0.         0.0425     0.        ]
 [0.014      0.         0.172      0.002      0.01       0.024
  0.742      0.012      0.024      0.        ]
 [0.         0.01166667 0.01333333 0.         0.005      0.
  0.         0.96833333 0.00166667 0.        ]
 [0.06613226 0.00400802 0.00200401 0.05410822 0.02004008 0.08416834
  0.0260521  0.         0.74348697 0.        ]
 [0.01166667 0.04833333 0.00333333 0.         0.         0.05
  0.         0.         0.06       0.82666667]]
[2023-08-29 06:59:35,733 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-29 06:59:35,735 INFO] 30720 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.7500, train/run_time: 0.3588, eval/loss: 1.8319, eval/top-1-acc: 0.8077, eval/balanced_acc: 0.8063, eval/precision: 0.8196, eval/recall: 0.8063, eval/F1: 0.8015, lr: 0.0000, train/prefecth_time: 0.0023 BEST_EVAL_ACC: 0.8168, at 24576 iters
[2023-08-29 07:02:08,100 INFO] 30976 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4142, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-29 07:03:58,283 INFO] 31232 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0006, train/total_loss: 0.0002, train/util_ratio: 0.8750, train/run_time: 0.4123, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 07:05:49,930 INFO] 31488 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0010, train/total_loss: 0.0004, train/util_ratio: 0.8750, train/run_time: 0.3564, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-29 07:07:39,469 INFO] 31744 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0003, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.3556, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-29 07:10:12,622 INFO] 32000 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4252, lr: 0.0000, train/prefecth_time: 0.0041 
[2023-08-29 07:12:01,420 INFO] 32256 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0009, train/total_loss: 0.0003, train/util_ratio: 1.0000, train/run_time: 0.4131, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-29 07:13:51,341 INFO] 32512 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0008, train/total_loss: 0.0003, train/util_ratio: 1.0000, train/run_time: 0.3704, lr: 0.0000, train/prefecth_time: 0.0041 
[2023-08-29 07:15:41,097 INFO] validating...
[2023-08-29 07:16:05,606 INFO] confusion matrix:
[[0.87166667 0.00833333 0.00333333 0.00333333 0.         0.025
  0.06166667 0.         0.025      0.00166667]
 [0.         0.91666667 0.00333333 0.         0.         0.05666667
  0.         0.         0.02166667 0.00166667]
 [0.00333333 0.01       0.61166667 0.00166667 0.00833333 0.23166667
  0.04       0.06       0.03333333 0.        ]
 [0.004      0.         0.006      0.514      0.032      0.004
  0.018      0.02       0.402      0.        ]
 [0.         0.         0.         0.006      0.972      0.
  0.004      0.018      0.         0.        ]
 [0.005      0.025      0.         0.0025     0.         0.8575
  0.0575     0.         0.0525     0.        ]
 [0.014      0.         0.17       0.004      0.012      0.024
  0.736      0.006      0.034      0.        ]
 [0.         0.01333333 0.01333333 0.         0.005      0.
  0.         0.96333333 0.005      0.        ]
 [0.07014028 0.00801603 0.00200401 0.04809619 0.02204409 0.0761523
  0.02204409 0.         0.75150301 0.        ]
 [0.01666667 0.05166667 0.00166667 0.         0.         0.055
  0.         0.         0.05833333 0.81666667]]
[2023-08-29 07:16:06,941 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-29 07:16:06,942 INFO] 32768 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0010, train/total_loss: 0.0004, train/util_ratio: 0.7500, train/run_time: 0.4146, eval/loss: 2.0173, eval/top-1-acc: 0.8033, eval/balanced_acc: 0.8011, eval/precision: 0.8161, eval/recall: 0.8011, eval/F1: 0.7965, lr: 0.0000, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.8168, at 24576 iters
[2023-08-29 07:18:38,761 INFO] 33024 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0008, train/total_loss: 0.0003, train/util_ratio: 1.0000, train/run_time: 0.4091, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-29 07:20:31,922 INFO] 33280 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4351, lr: 0.0000, train/prefecth_time: 0.0045 
[2023-08-29 07:22:24,993 INFO] 33536 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0015, train/total_loss: 0.0006, train/util_ratio: 0.8750, train/run_time: 0.4113, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-29 07:24:13,971 INFO] 33792 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0055, train/total_loss: 0.0023, train/util_ratio: 1.0000, train/run_time: 0.4005, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-29 07:26:49,465 INFO] 34048 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0033, train/total_loss: 0.0014, train/util_ratio: 0.6250, train/run_time: 0.4139, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 07:28:41,229 INFO] 34304 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0022, train/total_loss: 0.0009, train/util_ratio: 0.7500, train/run_time: 0.3794, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-29 07:30:32,703 INFO] 34560 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3557, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-29 07:32:22,893 INFO] validating...
[2023-08-29 07:32:47,603 INFO] confusion matrix:
[[0.87666667 0.005      0.00333333 0.00166667 0.         0.02666667
  0.06166667 0.00166667 0.02166667 0.00166667]
 [0.         0.915      0.00333333 0.         0.         0.05833333
  0.         0.         0.02       0.00333333]
 [0.00333333 0.00833333 0.61166667 0.00166667 0.01       0.21666667
  0.045      0.06666667 0.03666667 0.        ]
 [0.004      0.         0.008      0.506      0.03       0.004
  0.014      0.022      0.412      0.        ]
 [0.         0.         0.         0.006      0.972      0.
  0.004      0.018      0.         0.        ]
 [0.0075     0.02       0.         0.0025     0.         0.8175
  0.0925     0.         0.06       0.        ]
 [0.014      0.         0.164      0.004      0.01       0.016
  0.744      0.008      0.04       0.        ]
 [0.         0.01333333 0.01       0.         0.005      0.
  0.         0.965      0.00666667 0.        ]
 [0.0741483  0.00801603 0.00200401 0.03807615 0.02204409 0.08016032
  0.03807615 0.         0.73747495 0.        ]
 [0.01666667 0.05333333 0.00333333 0.         0.         0.06
  0.         0.         0.055      0.81166667]]
[2023-08-29 07:32:48,442 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-29 07:32:48,444 INFO] 34816 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0001, train/util_ratio: 0.6250, train/run_time: 0.4156, eval/loss: 2.1799, eval/top-1-acc: 0.7990, eval/balanced_acc: 0.7957, eval/precision: 0.8123, eval/recall: 0.7957, eval/F1: 0.7918, lr: 0.0000, train/prefecth_time: 0.0026 BEST_EVAL_ACC: 0.8168, at 24576 iters
[2023-08-29 07:35:20,151 INFO] 35072 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0005, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.4203, lr: 0.0000, train/prefecth_time: 0.0038 
[2023-08-29 07:37:11,145 INFO] 35328 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0005, train/total_loss: 0.0002, train/util_ratio: 0.8750, train/run_time: 0.3788, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-29 07:39:01,210 INFO] 35584 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0003, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4079, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-29 07:40:53,946 INFO] 35840 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0022, train/total_loss: 0.0010, train/util_ratio: 0.8750, train/run_time: 0.4122, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 07:43:27,197 INFO] 36096 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4386, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-29 07:45:16,465 INFO] 36352 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.3586, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-29 07:47:06,452 INFO] 36608 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0056, train/total_loss: 0.0025, train/util_ratio: 0.8750, train/run_time: 0.3599, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-29 07:48:55,158 INFO] validating...
[2023-08-29 07:49:19,316 INFO] confusion matrix:
[[0.86833333 0.005      0.00333333 0.00166667 0.         0.02666667
  0.06666667 0.00166667 0.025      0.00166667]
 [0.         0.91166667 0.00166667 0.         0.         0.06333333
  0.         0.         0.02       0.00333333]
 [0.00333333 0.00833333 0.585      0.005      0.01166667 0.22166667
  0.04       0.085      0.04       0.        ]
 [0.004      0.         0.008      0.494      0.03       0.008
  0.014      0.024      0.418      0.        ]
 [0.         0.         0.         0.004      0.968      0.
  0.004      0.024      0.         0.        ]
 [0.0075     0.02       0.         0.0025     0.         0.795
  0.11       0.         0.065      0.        ]
 [0.014      0.         0.162      0.004      0.012      0.016
  0.734      0.012      0.046      0.        ]
 [0.         0.01166667 0.00666667 0.         0.00333333 0.
  0.         0.97166667 0.00666667 0.        ]
 [0.07014028 0.01002004 0.00200401 0.03206413 0.02004008 0.0761523
  0.04408818 0.         0.74549098 0.        ]
 [0.01666667 0.05833333 0.00166667 0.         0.         0.06
  0.         0.         0.055      0.80833333]]
[2023-08-29 07:49:20,123 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-29 07:49:20,125 INFO] 36864 iteration, USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0003, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.4300, eval/loss: 2.3880, eval/top-1-acc: 0.7918, eval/balanced_acc: 0.7881, eval/precision: 0.8068, eval/recall: 0.7881, eval/F1: 0.7840, lr: 0.0000, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.8168, at 24576 iters
[2023-08-29 07:51:53,036 INFO] 37120 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4161, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-29 07:53:43,295 INFO] 37376 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0000, train/util_ratio: 0.7500, train/run_time: 0.4291, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-29 07:55:32,765 INFO] 37632 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4129, lr: 0.0000, train/prefecth_time: 0.0035 
[2023-08-29 07:57:21,652 INFO] 37888 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3735, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-29 07:59:53,672 INFO] 38144 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4050, lr: 0.0000, train/prefecth_time: 0.0099 
[2023-08-29 08:01:44,603 INFO] 38400 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0005, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.3660, lr: 0.0000, train/prefecth_time: 0.0035 
[2023-08-29 08:03:38,263 INFO] 38656 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0024, train/total_loss: 0.0011, train/util_ratio: 1.0000, train/run_time: 0.4134, lr: 0.0000, train/prefecth_time: 0.0036 
[2023-08-29 08:05:27,303 INFO] validating...
[2023-08-29 08:05:51,220 INFO] confusion matrix:
[[0.87166667 0.005      0.00333333 0.         0.         0.02666667
  0.06666667 0.00166667 0.02333333 0.00166667]
 [0.         0.905      0.00333333 0.         0.         0.06833333
  0.         0.         0.02       0.00333333]
 [0.00333333 0.00666667 0.6        0.005      0.00833333 0.21166667
  0.03833333 0.07833333 0.04666667 0.00166667]
 [0.004      0.         0.01       0.474      0.028      0.01
  0.014      0.024      0.436      0.        ]
 [0.         0.         0.         0.002      0.968      0.
  0.004      0.026      0.         0.        ]
 [0.0075     0.02       0.         0.0025     0.         0.7875
  0.1225     0.         0.06       0.        ]
 [0.016      0.         0.162      0.002      0.012      0.016
  0.724      0.014      0.054      0.        ]
 [0.         0.01166667 0.00666667 0.         0.005      0.
  0.         0.97       0.00666667 0.        ]
 [0.07815631 0.00801603 0.00200401 0.0260521  0.02004008 0.0741483
  0.04609218 0.         0.74549098 0.        ]
 [0.01666667 0.06       0.00333333 0.         0.         0.06166667
  0.         0.         0.05666667 0.80166667]]
[2023-08-30 00:50:22,008 INFO] Use GPU: 0 for training
[2023-08-30 00:50:22,371 INFO] unlabeled data number: 21588, labeled data number 40
[2023-08-30 00:50:28,767 INFO] Create train and test data loaders
[2023-08-30 00:50:28,769 INFO] [!] data loader keys: dict_keys(['train_lb', 'train_ulb', 'eval'])
[2023-08-30 00:50:29,462 INFO] Create optimizer and scheduler
[2023-08-30 00:50:29,465 INFO] Number of Trainable Params: 21402250
[2023-08-30 00:50:34,275 INFO] Arguments: Namespace(save_dir='./saved_models/usb_cv/', save_name='pseudolabel_eurosat_40_0', resume=True, load_path='/usr/saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth', overwrite=True, use_tensorboard=True, use_wandb=False, use_aim=False, epoch=200, num_train_iter=204800, num_warmup_iter=5120, num_eval_iter=2048, num_log_iter=256, num_labels=40, batch_size=1, uratio=1, eval_batch_size=16, ema_m=0.9999, ulb_loss_ratio=1.0, optim='AdamW', lr=5e-05, momentum=0.9, weight_decay=0.0005, layer_decay=1.0, net='vit_small_patch2_32', net_from_name=False, use_pretrain=True, pretrain_path='https://github.com/microsoft/Semi-supervised-learning/releases/download/v.0.0.0/vit_small_patch2_32_mlp_im_1k_32.pth', algorithm='fixmatch', use_cat=True, amp=False, clip_grad=0, imb_algorithm=None, data_dir='/usr/data/data', dataset='eurosat', num_classes=10, 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=4.0, sample_rate=16000, world_size=8, rank=0, dist_url='tcp://127.0.0.1:20647', dist_backend='nccl', seed=0, gpu=0, multiprocessing_distributed=True, c='/usr/data/jwy/otherbaseline-main/config/usb_cv/pseudolabel/pseudolabel_eurosat_40_0.yaml', hard_label=True, T=0.5, p_cutoff=0.95, unsup_warm_up=0.4, clip=0.0, distributed=True, ulb_dest_len=21588, lb_dest_len=40)
[2023-08-30 00:50:39,007 INFO] Model loaded
[2023-08-30 00:50:39,018 INFO] Model training
[2023-08-30 00:52:09,064 INFO] 24832 iteration USE_EMA: True, train/sup_loss: 0.0011, train/unsup_loss: 0.7515, train/total_loss: 0.7526, train/util_ratio: 0.6250, train/run_time: 0.1601, lr: 0.0000, train/prefecth_time: 0.0040 
[2023-08-30 00:52:57,839 INFO] 25088 iteration USE_EMA: True, train/sup_loss: 0.0173, train/unsup_loss: 0.6068, train/total_loss: 0.6241, train/util_ratio: 1.0000, train/run_time: 0.1650, lr: 0.0000, train/prefecth_time: 0.0033 
[2023-08-30 00:53:46,587 INFO] 25344 iteration USE_EMA: True, train/sup_loss: 0.0005, train/unsup_loss: 0.2149, train/total_loss: 0.2153, train/util_ratio: 0.7500, train/run_time: 0.1728, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 00:54:35,417 INFO] 25600 iteration USE_EMA: True, train/sup_loss: 0.0007, train/unsup_loss: 0.5868, train/total_loss: 0.5875, train/util_ratio: 0.8750, train/run_time: 0.1596, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 00:56:08,184 INFO] 25856 iteration USE_EMA: True, train/sup_loss: 0.0002, train/unsup_loss: 0.2545, train/total_loss: 0.2548, train/util_ratio: 0.7500, train/run_time: 0.1534, lr: 0.0000, train/prefecth_time: 0.0033 
[2023-08-30 00:56:56,950 INFO] 26112 iteration USE_EMA: True, train/sup_loss: 0.0007, train/unsup_loss: 0.3952, train/total_loss: 0.3959, train/util_ratio: 1.0000, train/run_time: 0.1708, lr: 0.0000, train/prefecth_time: 0.0033 
[2023-08-30 00:57:45,709 INFO] 26368 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 1.1444, train/total_loss: 1.1445, train/util_ratio: 1.0000, train/run_time: 0.1569, lr: 0.0000, train/prefecth_time: 0.0035 
[2023-08-30 00:58:34,531 INFO] validating...
[2023-08-30 00:58:59,092 INFO] confusion matrix:
[[0.87333333 0.00333333 0.00666667 0.00333333 0.         0.015
  0.06666667 0.00166667 0.03       0.        ]
 [0.         0.91166667 0.00333333 0.         0.         0.05333333
  0.         0.005      0.025      0.00166667]
 [0.00166667 0.005      0.58833333 0.00166667 0.00833333 0.19166667
  0.07166667 0.09       0.04166667 0.        ]
 [0.004      0.         0.004      0.496      0.034      0.002
  0.014      0.03       0.416      0.        ]
 [0.         0.         0.         0.004      0.976      0.
  0.002      0.018      0.         0.        ]
 [0.005      0.01       0.         0.005      0.         0.86
  0.0825     0.         0.0375     0.        ]
 [0.012      0.         0.126      0.002      0.006      0.006
  0.816      0.014      0.018      0.        ]
 [0.         0.00833333 0.005      0.         0.00166667 0.
  0.         0.985      0.         0.        ]
 [0.06412826 0.         0.         0.04408818 0.02004008 0.05210421
  0.03607214 0.         0.78356713 0.        ]
 [0.01666667 0.03166667 0.00333333 0.         0.         0.04
  0.         0.         0.06       0.84833333]]
[2023-08-30 00:58:59,936 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 00:58:59,937 INFO] 26624 iteration, USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.2637, train/total_loss: 0.2638, train/util_ratio: 1.0000, train/run_time: 0.1600, eval/loss: 1.4553, eval/top-1-acc: 0.8155, eval/balanced_acc: 0.8138, eval/precision: 0.8273, eval/recall: 0.8138, eval/F1: 0.8076, lr: 0.0000, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.8168, at 24576 iters
[2023-08-30 01:00:31,294 INFO] 26880 iteration USE_EMA: True, train/sup_loss: 0.0009, train/unsup_loss: 0.0024, train/total_loss: 0.0033, train/util_ratio: 0.8750, train/run_time: 0.1673, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 01:01:20,025 INFO] 27136 iteration USE_EMA: True, train/sup_loss: 0.0044, train/unsup_loss: 0.0936, train/total_loss: 0.0980, train/util_ratio: 0.8750, train/run_time: 0.1580, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 01:02:08,781 INFO] 27392 iteration USE_EMA: True, train/sup_loss: 0.0024, train/unsup_loss: 0.0222, train/total_loss: 0.0246, train/util_ratio: 0.8750, train/run_time: 0.1567, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 01:02:57,727 INFO] 27648 iteration USE_EMA: True, train/sup_loss: 0.0010, train/unsup_loss: 0.0426, train/total_loss: 0.0436, train/util_ratio: 0.8750, train/run_time: 0.1642, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 01:04:29,844 INFO] 27904 iteration USE_EMA: True, train/sup_loss: 0.0002, train/unsup_loss: 0.0977, train/total_loss: 0.0979, train/util_ratio: 0.8750, train/run_time: 0.1557, lr: 0.0000, train/prefecth_time: 0.0038 
[2023-08-30 01:05:18,658 INFO] 28160 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0376, train/total_loss: 0.0377, train/util_ratio: 1.0000, train/run_time: 0.1580, lr: 0.0000, train/prefecth_time: 0.0035 
[2023-08-30 01:06:07,350 INFO] 28416 iteration USE_EMA: True, train/sup_loss: 0.0008, train/unsup_loss: 0.4210, train/total_loss: 0.4218, train/util_ratio: 1.0000, train/run_time: 0.1635, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 01:06:56,202 INFO] validating...
[2023-08-30 01:07:20,929 INFO] confusion matrix:
[[0.885      0.00333333 0.005      0.00166667 0.         0.015
  0.05833333 0.00166667 0.03       0.        ]
 [0.         0.92833333 0.005      0.         0.         0.04
  0.         0.005      0.02       0.00166667]
 [0.         0.00666667 0.52333333 0.00166667 0.005      0.19166667
  0.11166667 0.10666667 0.05333333 0.        ]
 [0.002      0.         0.002      0.494      0.026      0.
  0.006      0.026      0.444      0.        ]
 [0.         0.         0.         0.004      0.98       0.
  0.004      0.012      0.         0.        ]
 [0.005      0.0125     0.         0.005      0.         0.8325
  0.115      0.         0.03       0.        ]
 [0.012      0.         0.09       0.002      0.004      0.006
  0.858      0.018      0.01       0.        ]
 [0.         0.00333333 0.00166667 0.         0.00166667 0.
  0.         0.99333333 0.         0.        ]
 [0.06813627 0.         0.         0.02805611 0.01603206 0.0240481
  0.01002004 0.         0.85370741 0.        ]
 [0.01833333 0.045      0.005      0.         0.         0.03666667
  0.         0.         0.06666667 0.82833333]]
[2023-08-30 01:07:21,855 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 01:07:23,258 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-30 01:07:23,259 INFO] 28672 iteration, USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0027, train/total_loss: 0.0028, train/util_ratio: 0.7500, train/run_time: 0.1576, eval/loss: 1.4071, eval/top-1-acc: 0.8187, eval/balanced_acc: 0.8177, eval/precision: 0.8347, eval/recall: 0.8177, eval/F1: 0.8097, lr: 0.0000, train/prefecth_time: 0.0023 BEST_EVAL_ACC: 0.8187, at 28672 iters
[2023-08-30 01:08:53,909 INFO] 28928 iteration USE_EMA: True, train/sup_loss: 0.0004, train/unsup_loss: 0.0009, train/total_loss: 0.0012, train/util_ratio: 0.8750, train/run_time: 0.1634, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 01:09:42,636 INFO] 29184 iteration USE_EMA: True, train/sup_loss: 0.0003, train/unsup_loss: 0.0722, train/total_loss: 0.0725, train/util_ratio: 0.8750, train/run_time: 0.1564, lr: 0.0000, train/prefecth_time: 0.0038 
[2023-08-30 01:10:31,415 INFO] 29440 iteration USE_EMA: True, train/sup_loss: 0.0002, train/unsup_loss: 0.0120, train/total_loss: 0.0121, train/util_ratio: 0.7500, train/run_time: 0.1552, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 01:11:20,237 INFO] 29696 iteration USE_EMA: True, train/sup_loss: 0.0008, train/unsup_loss: 0.4475, train/total_loss: 0.4483, train/util_ratio: 0.8750, train/run_time: 0.1595, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 01:12:52,871 INFO] 29952 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.1199, train/total_loss: 0.1200, train/util_ratio: 0.8750, train/run_time: 0.1620, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-30 01:13:41,616 INFO] 30208 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0172, train/total_loss: 0.0172, train/util_ratio: 0.7500, train/run_time: 0.1732, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 01:14:30,627 INFO] 30464 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0817, train/total_loss: 0.0818, train/util_ratio: 0.7500, train/run_time: 0.1657, lr: 0.0000, train/prefecth_time: 0.0034 
[2023-08-30 01:15:19,695 INFO] validating...
[2023-08-30 01:15:44,104 INFO] confusion matrix:
[[0.90333333 0.00333333 0.00166667 0.         0.         0.01333333
  0.05333333 0.00166667 0.02333333 0.        ]
 [0.         0.95166667 0.005      0.         0.         0.03166667
  0.         0.005      0.00666667 0.        ]
 [0.00166667 0.005      0.46       0.00166667 0.00666667 0.17666667
  0.15833333 0.13666667 0.05333333 0.        ]
 [0.         0.         0.002      0.546      0.022      0.
  0.002      0.022      0.406      0.        ]
 [0.         0.         0.         0.002      0.984      0.
  0.004      0.01       0.         0.        ]
 [0.005      0.0125     0.         0.005      0.         0.84
  0.1175     0.         0.02       0.        ]
 [0.014      0.         0.048      0.         0.002      0.002
  0.912      0.016      0.006      0.        ]
 [0.         0.00166667 0.         0.         0.00333333 0.
  0.         0.995      0.         0.        ]
 [0.06412826 0.         0.         0.01603206 0.01002004 0.01603206
  0.00601202 0.         0.88777555 0.        ]
 [0.02166667 0.05333333 0.00666667 0.         0.         0.02666667
  0.         0.         0.065      0.82666667]]
[2023-08-30 01:15:45,126 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 01:15:46,859 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-30 01:15:46,860 INFO] 30720 iteration, USE_EMA: True, train/sup_loss: 0.0002, train/unsup_loss: 0.5321, train/total_loss: 0.5322, train/util_ratio: 0.8750, train/run_time: 0.1607, eval/loss: 1.2264, eval/top-1-acc: 0.8302, eval/balanced_acc: 0.8306, eval/precision: 0.8480, eval/recall: 0.8306, eval/F1: 0.8203, lr: 0.0000, train/prefecth_time: 0.0027 BEST_EVAL_ACC: 0.8302, at 30720 iters
[2023-08-30 01:17:17,257 INFO] 30976 iteration USE_EMA: True, train/sup_loss: 0.0004, train/unsup_loss: 0.7104, train/total_loss: 0.7108, train/util_ratio: 1.0000, train/run_time: 0.1546, lr: 0.0000, train/prefecth_time: 0.0047 
[2023-08-30 01:18:06,061 INFO] 31232 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0061, train/total_loss: 0.0061, train/util_ratio: 0.8750, train/run_time: 0.1572, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 01:18:55,189 INFO] 31488 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.2598, train/total_loss: 0.2599, train/util_ratio: 0.8750, train/run_time: 0.1595, lr: 0.0000, train/prefecth_time: 0.0035 
[2023-08-30 01:19:44,204 INFO] 31744 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0208, train/total_loss: 0.0208, train/util_ratio: 0.6250, train/run_time: 0.1653, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 01:21:16,191 INFO] 32000 iteration USE_EMA: True, train/sup_loss: 0.0013, train/unsup_loss: 0.0299, train/total_loss: 0.0312, train/util_ratio: 0.7500, train/run_time: 0.1636, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 01:22:05,414 INFO] 32256 iteration USE_EMA: True, train/sup_loss: 0.0002, train/unsup_loss: 0.8755, train/total_loss: 0.8757, train/util_ratio: 0.7500, train/run_time: 0.1709, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 01:22:55,007 INFO] 32512 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0062, train/total_loss: 0.0063, train/util_ratio: 1.0000, train/run_time: 0.1588, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 01:23:43,969 INFO] validating...
[2023-08-30 01:24:08,115 INFO] confusion matrix:
[[0.92       0.00333333 0.         0.         0.         0.00833333
  0.04333333 0.00166667 0.02333333 0.        ]
 [0.         0.96       0.005      0.         0.         0.02166667
  0.         0.00666667 0.00666667 0.        ]
 [0.00166667 0.005      0.43       0.00166667 0.00666667 0.155
  0.185      0.155      0.06       0.        ]
 [0.         0.         0.002      0.636      0.016      0.
  0.002      0.02       0.324      0.        ]
 [0.         0.         0.         0.004      0.986      0.
  0.004      0.006      0.         0.        ]
 [0.005      0.01       0.         0.0075     0.         0.8425
  0.1225     0.         0.0125     0.        ]
 [0.014      0.         0.038      0.         0.002      0.
  0.932      0.012      0.002      0.        ]
 [0.         0.         0.         0.         0.00333333 0.
  0.         0.99666667 0.         0.        ]
 [0.04008016 0.         0.         0.01002004 0.00601202 0.00200401
  0.         0.         0.94188377 0.        ]
 [0.02166667 0.06333333 0.01       0.         0.         0.025
  0.         0.         0.05333333 0.82666667]]
[2023-08-30 01:24:09,063 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 01:24:10,413 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-30 01:24:10,415 INFO] 32768 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0289, train/total_loss: 0.0289, train/util_ratio: 0.8750, train/run_time: 0.1572, eval/loss: 0.9838, eval/top-1-acc: 0.8453, eval/balanced_acc: 0.8472, eval/precision: 0.8606, eval/recall: 0.8472, eval/F1: 0.8360, lr: 0.0000, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.8453, at 32768 iters
[2023-08-30 01:25:40,671 INFO] 33024 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.4463, train/total_loss: 0.4464, train/util_ratio: 0.8750, train/run_time: 0.1834, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 01:26:29,321 INFO] 33280 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0271, train/total_loss: 0.0272, train/util_ratio: 1.0000, train/run_time: 0.1610, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 01:27:18,088 INFO] 33536 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.2639, train/total_loss: 0.2640, train/util_ratio: 0.8750, train/run_time: 0.1578, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 01:28:06,594 INFO] 33792 iteration USE_EMA: True, train/sup_loss: 0.0003, train/unsup_loss: 0.1276, train/total_loss: 0.1279, train/util_ratio: 1.0000, train/run_time: 0.1567, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 01:29:38,476 INFO] 34048 iteration USE_EMA: True, train/sup_loss: 0.0003, train/unsup_loss: 0.0465, train/total_loss: 0.0468, train/util_ratio: 0.7500, train/run_time: 0.1594, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 01:30:27,262 INFO] 34304 iteration USE_EMA: True, train/sup_loss: 0.0029, train/unsup_loss: 0.0011, train/total_loss: 0.0039, train/util_ratio: 0.7500, train/run_time: 0.1644, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 01:31:16,122 INFO] 34560 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0003, train/total_loss: 0.0004, train/util_ratio: 0.8750, train/run_time: 0.1652, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 01:32:05,115 INFO] validating...
[2023-08-30 01:32:29,843 INFO] confusion matrix:
[[0.93166667 0.00333333 0.         0.         0.         0.005
  0.04       0.00166667 0.01833333 0.        ]
 [0.         0.97166667 0.005      0.         0.         0.01166667
  0.         0.00833333 0.00333333 0.        ]
 [0.00166667 0.00333333 0.43666667 0.00166667 0.00666667 0.14666667
  0.17166667 0.16333333 0.06833333 0.        ]
 [0.         0.         0.002      0.77       0.01       0.
  0.002      0.018      0.198      0.        ]
 [0.         0.         0.         0.004      0.986      0.
  0.004      0.006      0.         0.        ]
 [0.005      0.01       0.         0.0075     0.         0.84
  0.1275     0.         0.01       0.        ]
 [0.012      0.         0.04       0.002      0.002      0.002
  0.938      0.004      0.         0.        ]
 [0.         0.         0.         0.         0.00333333 0.
  0.         0.99666667 0.         0.        ]
 [0.0240481  0.         0.         0.00601202 0.00400802 0.
  0.         0.         0.96392786 0.00200401]
 [0.02333333 0.06666667 0.01       0.         0.         0.02
  0.         0.         0.03666667 0.84333333]]
[2023-08-30 01:32:31,003 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 01:32:32,099 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-30 01:32:32,100 INFO] 34816 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.5947, train/total_loss: 0.5947, train/util_ratio: 0.7500, train/run_time: 0.1580, eval/loss: 0.7783, eval/top-1-acc: 0.8653, eval/balanced_acc: 0.8678, eval/precision: 0.8745, eval/recall: 0.8678, eval/F1: 0.8573, lr: 0.0000, train/prefecth_time: 0.0043 BEST_EVAL_ACC: 0.8653, at 34816 iters
[2023-08-30 01:34:02,589 INFO] 35072 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0070, train/total_loss: 0.0071, train/util_ratio: 1.0000, train/run_time: 0.1579, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 01:34:51,624 INFO] 35328 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.1565, train/total_loss: 0.1566, train/util_ratio: 0.8750, train/run_time: 0.1676, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 01:35:40,490 INFO] 35584 iteration USE_EMA: True, train/sup_loss: 0.0024, train/unsup_loss: 0.0236, train/total_loss: 0.0260, train/util_ratio: 1.0000, train/run_time: 0.1548, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 01:36:29,531 INFO] 35840 iteration USE_EMA: True, train/sup_loss: 0.0003, train/unsup_loss: 0.0450, train/total_loss: 0.0453, train/util_ratio: 0.8750, train/run_time: 0.1709, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 01:38:01,515 INFO] 36096 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.1536, train/total_loss: 0.1536, train/util_ratio: 1.0000, train/run_time: 0.1584, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 01:38:50,918 INFO] 36352 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0024, train/total_loss: 0.0024, train/util_ratio: 0.8750, train/run_time: 0.1690, lr: 0.0000, train/prefecth_time: 0.0037 
[2023-08-30 01:39:39,694 INFO] 36608 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0112, train/total_loss: 0.0113, train/util_ratio: 1.0000, train/run_time: 0.1606, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-30 01:40:28,430 INFO] validating...
[2023-08-30 01:40:52,794 INFO] confusion matrix:
[[0.945      0.00333333 0.         0.         0.         0.005
  0.035      0.00166667 0.01       0.        ]
 [0.         0.975      0.005      0.         0.         0.01166667
  0.         0.00833333 0.         0.        ]
 [0.         0.00333333 0.48       0.00166667 0.00333333 0.14166667
  0.14333333 0.155      0.07166667 0.        ]
 [0.         0.         0.002      0.872      0.006      0.
  0.002      0.016      0.102      0.        ]
 [0.         0.         0.         0.004      0.986      0.
  0.004      0.006      0.         0.        ]
 [0.005      0.0075     0.         0.0075     0.         0.855
  0.1175     0.         0.0075     0.        ]
 [0.012      0.         0.036      0.004      0.002      0.002
  0.94       0.004      0.         0.        ]
 [0.         0.         0.         0.         0.00166667 0.
  0.         0.99833333 0.         0.        ]
 [0.01803607 0.         0.         0.         0.00200401 0.
  0.         0.         0.97795591 0.00200401]
 [0.01833333 0.05833333 0.01333333 0.         0.         0.01166667
  0.         0.         0.02666667 0.87166667]]
[2023-08-30 01:40:53,622 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 01:40:54,666 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-30 01:40:54,667 INFO] 36864 iteration, USE_EMA: True, train/sup_loss: 0.0007, train/unsup_loss: 0.1330, train/total_loss: 0.1336, train/util_ratio: 0.8750, train/run_time: 0.1639, eval/loss: 0.6332, eval/top-1-acc: 0.8874, eval/balanced_acc: 0.8901, eval/precision: 0.8920, eval/recall: 0.8901, eval/F1: 0.8809, lr: 0.0000, train/prefecth_time: 0.0028 BEST_EVAL_ACC: 0.8874, at 36864 iters
[2023-08-30 01:42:05,915 INFO] Use GPU: 0 for training
[2023-08-30 01:42:06,273 INFO] unlabeled data number: 21588, labeled data number 40
[2023-08-30 01:42:12,732 INFO] Create train and test data loaders
[2023-08-30 01:42:12,733 INFO] [!] data loader keys: dict_keys(['train_lb', 'train_ulb', 'eval'])
[2023-08-30 01:42:13,396 INFO] Create optimizer and scheduler
[2023-08-30 01:42:13,407 INFO] Number of Trainable Params: 21402250
[2023-08-30 01:42:18,392 INFO] Arguments: Namespace(save_dir='./saved_models/usb_cv/', save_name='pseudolabel_eurosat_40_0', resume=True, load_path='/usr/saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth', overwrite=True, use_tensorboard=True, use_wandb=False, use_aim=False, epoch=200, num_train_iter=204800, num_warmup_iter=5120, num_eval_iter=2048, num_log_iter=256, num_labels=40, batch_size=1, uratio=1, eval_batch_size=16, ema_m=0.9999, ulb_loss_ratio=1.0, optim='AdamW', lr=5e-05, momentum=0.9, weight_decay=0.0005, layer_decay=1.0, net='vit_small_patch2_32', net_from_name=False, use_pretrain=True, pretrain_path='https://github.com/microsoft/Semi-supervised-learning/releases/download/v.0.0.0/vit_small_patch2_32_mlp_im_1k_32.pth', algorithm='pseudolabel', use_cat=True, amp=False, clip_grad=0, imb_algorithm=None, data_dir='/usr/data/data', dataset='eurosat', num_classes=10, 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=4.0, sample_rate=16000, world_size=8, rank=0, dist_url='tcp://127.0.0.1:21210', dist_backend='nccl', seed=0, gpu=0, multiprocessing_distributed=True, c='/usr/data/jwy/otherbaseline-main/config/usb_cv/pseudolabel/pseudolabel_eurosat_40_0.yaml', p_cutoff=0.95, unsup_warm_up=0.4, clip=0.0, distributed=True, ulb_dest_len=21588, lb_dest_len=40)
[2023-08-30 01:42:23,051 INFO] Model loaded
[2023-08-30 01:42:23,053 INFO] Model training
[2023-08-30 01:44:55,219 INFO] 37120 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0016, train/total_loss: 0.0008, train/util_ratio: 0.8750, train/run_time: 0.4124, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 01:46:47,866 INFO] 37376 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0038, train/total_loss: 0.0018, train/util_ratio: 0.8750, train/run_time: 0.4403, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 01:48:40,654 INFO] 37632 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4191, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 01:50:32,074 INFO] 37888 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0006, train/total_loss: 0.0003, train/util_ratio: 0.8750, train/run_time: 0.4147, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 01:53:07,667 INFO] 38144 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0003, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.4184, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 01:55:00,444 INFO] 38400 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4195, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 01:56:52,946 INFO] 38656 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0027, train/total_loss: 0.0013, train/util_ratio: 1.0000, train/run_time: 0.4035, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 01:58:44,713 INFO] validating...
[2023-08-30 01:59:09,252 INFO] confusion matrix:
[[0.95333333 0.00333333 0.         0.         0.         0.005
  0.03       0.00166667 0.00666667 0.        ]
 [0.         0.97666667 0.005      0.         0.         0.01
  0.         0.00833333 0.         0.        ]
 [0.         0.00333333 0.53833333 0.00166667 0.00333333 0.13166667
  0.09833333 0.14833333 0.075      0.        ]
 [0.         0.         0.         0.944      0.002      0.
  0.         0.008      0.046      0.        ]
 [0.         0.         0.         0.002      0.992      0.
  0.004      0.002      0.         0.        ]
 [0.005      0.0075     0.         0.0075     0.         0.86
  0.1175     0.         0.0025     0.        ]
 [0.012      0.         0.04       0.004      0.002      0.002
  0.936      0.004      0.         0.        ]
 [0.         0.         0.         0.00166667 0.00166667 0.
  0.         0.99666667 0.         0.        ]
 [0.01603206 0.         0.         0.         0.         0.
  0.         0.         0.98196393 0.00200401]
 [0.015      0.04833333 0.01166667 0.         0.         0.00833333
  0.         0.00166667 0.01833333 0.89666667]]
[2023-08-30 01:59:10,235 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 01:59:11,274 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-30 01:59:11,276 INFO] 38912 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3620, eval/loss: 0.5515, eval/top-1-acc: 0.9052, eval/balanced_acc: 0.9076, eval/precision: 0.9067, eval/recall: 0.9076, eval/F1: 0.9001, lr: 0.0000, train/prefecth_time: 0.0022 BEST_EVAL_ACC: 0.9052, at 38912 iters
[2023-08-30 02:01:45,068 INFO] 39168 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0024, train/total_loss: 0.0011, train/util_ratio: 1.0000, train/run_time: 0.3553, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 02:03:37,845 INFO] 39424 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3602, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 02:05:28,674 INFO] 39680 iteration USE_EMA: True, train/sup_loss: 0.0011, train/unsup_loss: 0.0000, train/total_loss: 0.0011, train/util_ratio: 1.0000, train/run_time: 0.4324, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 02:07:19,547 INFO] 39936 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4202, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 02:09:53,305 INFO] 40192 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4184, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 02:11:44,367 INFO] 40448 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4186, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 02:13:37,051 INFO] 40704 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0006, train/total_loss: 0.0003, train/util_ratio: 0.7500, train/run_time: 0.4284, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 02:15:29,123 INFO] validating...
[2023-08-30 02:15:53,546 INFO] confusion matrix:
[[0.95666667 0.00333333 0.         0.         0.         0.005
  0.02666667 0.00166667 0.00666667 0.        ]
 [0.         0.97666667 0.00666667 0.         0.         0.00833333
  0.         0.00833333 0.         0.        ]
 [0.         0.00333333 0.57666667 0.00333333 0.00333333 0.12833333
  0.07333333 0.13333333 0.07833333 0.        ]
 [0.         0.         0.002      0.96       0.002      0.
  0.         0.006      0.03       0.        ]
 [0.         0.         0.         0.002      0.994      0.
  0.002      0.002      0.         0.        ]
 [0.005      0.0075     0.         0.0075     0.         0.865
  0.1125     0.         0.0025     0.        ]
 [0.01       0.         0.044      0.004      0.002      0.002
  0.936      0.002      0.         0.        ]
 [0.         0.         0.         0.00166667 0.00166667 0.
  0.         0.99666667 0.         0.        ]
 [0.01002004 0.         0.         0.00400802 0.         0.
  0.         0.         0.98396794 0.00200401]
 [0.01666667 0.04       0.01       0.         0.         0.00833333
  0.         0.00166667 0.015      0.90833333]]
[2023-08-30 02:15:54,469 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 02:15:55,383 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-30 02:15:55,384 INFO] 40960 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0005, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.3828, eval/loss: 0.5313, eval/top-1-acc: 0.9133, eval/balanced_acc: 0.9154, eval/precision: 0.9131, eval/recall: 0.9154, eval/F1: 0.9088, lr: 0.0000, train/prefecth_time: 0.0027 BEST_EVAL_ACC: 0.9133, at 40960 iters
[2023-08-30 02:18:26,561 INFO] 41216 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.7500, train/run_time: 0.3983, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 02:20:15,078 INFO] 41472 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0004, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.4016, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 02:22:04,937 INFO] 41728 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4326, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 02:23:55,276 INFO] 41984 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3552, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 02:26:30,120 INFO] 42240 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3838, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 02:28:18,930 INFO] 42496 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 0.8750, train/run_time: 0.3905, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 02:30:07,860 INFO] 42752 iteration USE_EMA: True, train/sup_loss: 0.0016, train/unsup_loss: 0.0000, train/total_loss: 0.0016, train/util_ratio: 0.8750, train/run_time: 0.4314, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 02:31:56,947 INFO] validating...
[2023-08-30 02:32:20,948 INFO] confusion matrix:
[[0.955      0.00333333 0.         0.         0.         0.005
  0.02666667 0.00166667 0.00833333 0.        ]
 [0.         0.96833333 0.00666667 0.         0.         0.01666667
  0.         0.00833333 0.         0.        ]
 [0.         0.00166667 0.59833333 0.00666667 0.00333333 0.14666667
  0.06333333 0.10833333 0.07166667 0.        ]
 [0.         0.         0.002      0.97       0.002      0.
  0.         0.004      0.022      0.        ]
 [0.         0.         0.         0.002      0.994      0.
  0.002      0.002      0.         0.        ]
 [0.005      0.0075     0.         0.0075     0.         0.875
  0.1025     0.         0.0025     0.        ]
 [0.014      0.         0.054      0.004      0.002      0.002
  0.922      0.002      0.         0.        ]
 [0.         0.         0.         0.00333333 0.00166667 0.
  0.         0.995      0.         0.        ]
 [0.00601202 0.         0.         0.00200401 0.         0.
  0.         0.         0.98997996 0.00200401]
 [0.01333333 0.03666667 0.01       0.         0.         0.00833333
  0.         0.00166667 0.01166667 0.91833333]]
[2023-08-30 02:32:21,762 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 02:32:22,630 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-30 02:32:22,632 INFO] 43008 iteration, USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0002, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.3954, eval/loss: 0.5609, eval/top-1-acc: 0.9165, eval/balanced_acc: 0.9186, eval/precision: 0.9148, eval/recall: 0.9186, eval/F1: 0.9122, lr: 0.0000, train/prefecth_time: 0.0023 BEST_EVAL_ACC: 0.9165, at 43008 iters
[2023-08-30 02:34:53,170 INFO] 43264 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4195, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 02:36:42,014 INFO] 43520 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0012, train/total_loss: 0.0006, train/util_ratio: 1.0000, train/run_time: 0.4040, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 02:38:31,977 INFO] 43776 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4176, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 02:40:21,526 INFO] 44032 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0007, train/total_loss: 0.0004, train/util_ratio: 0.8750, train/run_time: 0.4092, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 02:42:53,236 INFO] 44288 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0018, train/total_loss: 0.0010, train/util_ratio: 1.0000, train/run_time: 0.4245, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 02:44:43,285 INFO] 44544 iteration USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0041, train/total_loss: 0.0023, train/util_ratio: 0.6250, train/run_time: 0.4178, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 02:46:32,217 INFO] 44800 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4347, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 02:48:21,366 INFO] validating...
[2023-08-30 02:48:45,410 INFO] confusion matrix:
[[0.95666667 0.00333333 0.         0.         0.         0.00666667
  0.025      0.00166667 0.00666667 0.        ]
 [0.         0.96833333 0.00666667 0.         0.         0.01666667
  0.         0.00833333 0.         0.        ]
 [0.         0.00166667 0.62       0.00666667 0.005      0.15666667
  0.05333333 0.095      0.06166667 0.        ]
 [0.         0.         0.002      0.976      0.002      0.
  0.         0.         0.02       0.        ]
 [0.         0.         0.         0.002      0.994      0.
  0.002      0.002      0.         0.        ]
 [0.005      0.0075     0.0025     0.01       0.         0.875
  0.0975     0.         0.0025     0.        ]
 [0.012      0.         0.074      0.006      0.004      0.002
  0.902      0.         0.         0.        ]
 [0.         0.         0.         0.00333333 0.005      0.
  0.         0.99166667 0.         0.        ]
 [0.00400802 0.         0.         0.00200401 0.         0.
  0.         0.         0.99198397 0.00200401]
 [0.01166667 0.03166667 0.01       0.         0.         0.00833333
  0.         0.00166667 0.01       0.92666667]]
[2023-08-30 02:48:46,293 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 02:48:47,241 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-30 02:48:47,243 INFO] 45056 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3705, eval/loss: 0.6014, eval/top-1-acc: 0.9185, eval/balanced_acc: 0.9202, eval/precision: 0.9153, eval/recall: 0.9202, eval/F1: 0.9143, lr: 0.0000, train/prefecth_time: 0.0032 BEST_EVAL_ACC: 0.9185, at 45056 iters
[2023-08-30 02:51:19,304 INFO] 45312 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4143, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 02:53:09,347 INFO] 45568 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3916, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 02:54:59,170 INFO] 45824 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4124, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 02:56:49,548 INFO] 46080 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0025, train/total_loss: 0.0014, train/util_ratio: 1.0000, train/run_time: 0.3526, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 02:59:21,741 INFO] 46336 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3747, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 03:01:10,529 INFO] 46592 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0003, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.3636, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 03:02:59,580 INFO] 46848 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3567, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 03:04:48,026 INFO] validating...
[2023-08-30 03:05:12,498 INFO] confusion matrix:
[[0.95166667 0.00333333 0.         0.         0.         0.00666667
  0.03       0.00166667 0.005      0.00166667]
 [0.         0.96666667 0.00666667 0.         0.         0.01833333
  0.         0.00833333 0.         0.        ]
 [0.         0.00166667 0.61       0.00833333 0.00666667 0.16333333
  0.05666667 0.09833333 0.055      0.        ]
 [0.         0.         0.002      0.976      0.004      0.
  0.         0.         0.018      0.        ]
 [0.         0.         0.         0.002      0.994      0.
  0.002      0.002      0.         0.        ]
 [0.005      0.0025     0.         0.01       0.         0.8925
  0.0875     0.         0.0025     0.        ]
 [0.01       0.         0.084      0.006      0.004      0.002
  0.892      0.         0.002      0.        ]
 [0.         0.         0.         0.00333333 0.00333333 0.
  0.         0.99333333 0.         0.        ]
 [0.00400802 0.         0.         0.00200401 0.         0.
  0.         0.         0.99198397 0.00200401]
 [0.01333333 0.02833333 0.01166667 0.         0.         0.00833333
  0.         0.         0.00833333 0.93      ]]
[2023-08-30 03:05:13,281 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 03:05:13,283 INFO] 47104 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4295, eval/loss: 0.6588, eval/top-1-acc: 0.9176, eval/balanced_acc: 0.9198, eval/precision: 0.9143, eval/recall: 0.9198, eval/F1: 0.9135, lr: 0.0000, train/prefecth_time: 0.0021 BEST_EVAL_ACC: 0.9185, at 45056 iters
[2023-08-30 03:07:46,294 INFO] 47360 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4133, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 03:09:35,280 INFO] 47616 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0045, train/total_loss: 0.0026, train/util_ratio: 1.0000, train/run_time: 0.3802, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 03:11:23,299 INFO] 47872 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0010, train/total_loss: 0.0006, train/util_ratio: 0.8750, train/run_time: 0.3916, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 03:13:11,914 INFO] 48128 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0067, train/total_loss: 0.0039, train/util_ratio: 0.8750, train/run_time: 0.3560, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 03:15:47,781 INFO] 48384 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4082, lr: 0.0000, train/prefecth_time: 0.0038 
[2023-08-30 03:17:38,373 INFO] 48640 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0006, train/total_loss: 0.0004, train/util_ratio: 1.0000, train/run_time: 0.3617, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 03:19:27,024 INFO] 48896 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.3843, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 03:21:15,401 INFO] validating...
[2023-08-30 03:21:40,009 INFO] confusion matrix:
[[0.95166667 0.00333333 0.         0.         0.         0.00666667
  0.03       0.00166667 0.00666667 0.        ]
 [0.         0.97       0.00666667 0.         0.         0.015
  0.         0.00833333 0.         0.        ]
 [0.         0.00166667 0.59833333 0.01       0.00666667 0.165
  0.05666667 0.10666667 0.055      0.        ]
 [0.         0.         0.         0.982      0.002      0.
  0.         0.         0.016      0.        ]
 [0.         0.         0.         0.002      0.994      0.
  0.002      0.002      0.         0.        ]
 [0.005      0.0025     0.         0.0125     0.         0.895
  0.0825     0.         0.0025     0.        ]
 [0.008      0.         0.086      0.006      0.002      0.002
  0.892      0.002      0.002      0.        ]
 [0.         0.         0.         0.00333333 0.005      0.
  0.         0.99166667 0.         0.        ]
 [0.00400802 0.         0.         0.00400802 0.         0.
  0.         0.         0.98997996 0.00200401]
 [0.01333333 0.025      0.005      0.00166667 0.         0.00666667
  0.         0.         0.00666667 0.94166667]]
[2023-08-30 03:21:40,897 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 03:21:40,899 INFO] 49152 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0003, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.3556, eval/loss: 0.7177, eval/top-1-acc: 0.9183, eval/balanced_acc: 0.9206, eval/precision: 0.9151, eval/recall: 0.9206, eval/F1: 0.9140, lr: 0.0000, train/prefecth_time: 0.0021 BEST_EVAL_ACC: 0.9185, at 45056 iters
[2023-08-30 03:24:11,599 INFO] 49408 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0038, train/total_loss: 0.0023, train/util_ratio: 0.8750, train/run_time: 0.4176, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 03:26:00,843 INFO] 49664 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.6250, train/run_time: 0.4087, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 03:27:49,858 INFO] 49920 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0084, train/total_loss: 0.0051, train/util_ratio: 0.8750, train/run_time: 0.4129, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 03:29:38,917 INFO] 50176 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4138, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 03:32:11,934 INFO] 50432 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4323, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 03:34:01,497 INFO] 50688 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4569, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 03:35:51,592 INFO] 50944 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0099, train/total_loss: 0.0062, train/util_ratio: 1.0000, train/run_time: 0.3996, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 03:37:40,284 INFO] validating...
[2023-08-30 03:38:05,171 INFO] confusion matrix:
[[0.94833333 0.00333333 0.         0.         0.         0.00833333
  0.03166667 0.00166667 0.00666667 0.        ]
 [0.         0.97       0.00833333 0.         0.         0.015
  0.         0.00666667 0.         0.        ]
 [0.         0.00166667 0.60666667 0.00833333 0.00666667 0.16333333
  0.05333333 0.105      0.055      0.        ]
 [0.         0.         0.         0.98       0.004      0.
  0.         0.         0.016      0.        ]
 [0.         0.         0.         0.002      0.992      0.
  0.002      0.004      0.         0.        ]
 [0.005      0.01       0.         0.0125     0.         0.9
  0.07       0.         0.0025     0.        ]
 [0.008      0.         0.096      0.008      0.002      0.002
  0.88       0.002      0.002      0.        ]
 [0.         0.         0.00166667 0.00166667 0.005      0.
  0.         0.99166667 0.         0.        ]
 [0.00400802 0.         0.         0.00400802 0.         0.
  0.         0.         0.98997996 0.00200401]
 [0.01166667 0.01666667 0.005      0.00333333 0.         0.00666667
  0.         0.         0.00666667 0.95      ]]
[2023-08-30 03:38:05,933 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 03:38:07,176 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/model_best.pth
[2023-08-30 03:38:07,177 INFO] 51200 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0000, train/util_ratio: 0.7500, train/run_time: 0.3618, eval/loss: 0.7599, eval/top-1-acc: 0.9187, eval/balanced_acc: 0.9209, eval/precision: 0.9150, eval/recall: 0.9209, eval/F1: 0.9145, lr: 0.0000, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 03:40:38,739 INFO] 51456 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4338, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 03:42:29,065 INFO] 51712 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0027, train/total_loss: 0.0017, train/util_ratio: 0.8750, train/run_time: 0.4159, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 03:44:16,942 INFO] 51968 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4137, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 03:46:07,689 INFO] 52224 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4180, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 03:48:39,293 INFO] 52480 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0035, train/total_loss: 0.0023, train/util_ratio: 0.8750, train/run_time: 0.3893, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 03:50:27,232 INFO] 52736 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0003, train/total_loss: 0.0002, train/util_ratio: 0.8750, train/run_time: 0.3671, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 03:52:16,716 INFO] 52992 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3555, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 03:54:06,764 INFO] validating...
[2023-08-30 03:54:30,818 INFO] confusion matrix:
[[0.95       0.00333333 0.         0.         0.         0.00833333
  0.03166667 0.00166667 0.005      0.        ]
 [0.         0.96666667 0.00833333 0.         0.         0.015
  0.         0.00666667 0.00166667 0.00166667]
 [0.00166667 0.00166667 0.59166667 0.00833333 0.00666667 0.17
  0.05666667 0.105      0.05833333 0.        ]
 [0.         0.         0.         0.986      0.004      0.
  0.         0.         0.01       0.        ]
 [0.         0.         0.         0.004      0.99       0.
  0.002      0.004      0.         0.        ]
 [0.005      0.015      0.         0.0175     0.         0.885
  0.075      0.         0.0025     0.        ]
 [0.01       0.         0.104      0.008      0.002      0.002
  0.87       0.002      0.002      0.        ]
 [0.         0.         0.00166667 0.00166667 0.00333333 0.
  0.         0.99333333 0.         0.        ]
 [0.00400802 0.         0.         0.01002004 0.         0.
  0.         0.         0.98396794 0.00200401]
 [0.01166667 0.01166667 0.00333333 0.00333333 0.         0.00666667
  0.         0.         0.00666667 0.95666667]]
[2023-08-30 03:54:31,723 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 03:54:31,724 INFO] 53248 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4119, eval/loss: 0.8285, eval/top-1-acc: 0.9155, eval/balanced_acc: 0.9173, eval/precision: 0.9113, eval/recall: 0.9173, eval/F1: 0.9108, lr: 0.0000, train/prefecth_time: 0.0030 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 03:57:03,916 INFO] 53504 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 0.8750, train/run_time: 0.4103, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-30 03:58:53,526 INFO] 53760 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.7500, train/run_time: 0.4304, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 04:00:42,817 INFO] 54016 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4168, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 04:02:32,827 INFO] 54272 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4302, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 04:05:05,822 INFO] 54528 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4082, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-30 04:06:55,688 INFO] 54784 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0055, train/total_loss: 0.0037, train/util_ratio: 0.8750, train/run_time: 0.3613, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 04:08:46,889 INFO] 55040 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0023, train/total_loss: 0.0015, train/util_ratio: 1.0000, train/run_time: 0.4304, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 04:10:34,949 INFO] validating...
[2023-08-30 04:10:59,600 INFO] confusion matrix:
[[0.94833333 0.00333333 0.         0.         0.         0.00833333
  0.03166667 0.00166667 0.00666667 0.        ]
 [0.         0.965      0.00833333 0.         0.         0.01833333
  0.         0.005      0.00166667 0.00166667]
 [0.00166667 0.00166667 0.61       0.00833333 0.00333333 0.16
  0.055      0.095      0.065      0.        ]
 [0.         0.         0.         0.984      0.004      0.
  0.         0.         0.012      0.        ]
 [0.         0.         0.         0.004      0.99       0.
  0.002      0.004      0.         0.        ]
 [0.005      0.0175     0.         0.02       0.         0.8725
  0.0825     0.         0.0025     0.        ]
 [0.018      0.         0.106      0.012      0.002      0.002
  0.856      0.002      0.002      0.        ]
 [0.         0.         0.00166667 0.00166667 0.00333333 0.
  0.         0.99333333 0.         0.        ]
 [0.00400802 0.         0.         0.00801603 0.         0.
  0.         0.         0.98597194 0.00200401]
 [0.01166667 0.02166667 0.00333333 0.00333333 0.         0.00333333
  0.         0.         0.00666667 0.95      ]]
[2023-08-30 04:11:00,480 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 04:11:00,482 INFO] 55296 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3568, eval/loss: 0.8778, eval/top-1-acc: 0.9142, eval/balanced_acc: 0.9155, eval/precision: 0.9098, eval/recall: 0.9155, eval/F1: 0.9096, lr: 0.0000, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 04:13:33,300 INFO] 55552 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3954, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-30 04:15:21,060 INFO] 55808 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0003, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.3613, lr: 0.0000, train/prefecth_time: 0.0036 
[2023-08-30 04:17:11,667 INFO] 56064 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3606, lr: 0.0000, train/prefecth_time: 0.0042 
[2023-08-30 04:19:01,130 INFO] 56320 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.3571, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 04:21:32,848 INFO] 56576 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4341, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 04:23:21,907 INFO] 56832 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3744, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 04:25:12,043 INFO] 57088 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4051, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 04:27:01,729 INFO] validating...
[2023-08-30 04:27:25,712 INFO] confusion matrix:
[[0.935      0.00333333 0.         0.         0.         0.00833333
  0.04166667 0.00166667 0.01       0.        ]
 [0.         0.96166667 0.00833333 0.         0.         0.02166667
  0.         0.00333333 0.005      0.        ]
 [0.00166667 0.00166667 0.62833333 0.00833333 0.00333333 0.15333333
  0.04333333 0.08666667 0.07333333 0.        ]
 [0.         0.         0.         0.984      0.004      0.
  0.         0.         0.012      0.        ]
 [0.         0.         0.         0.004      0.988      0.
  0.002      0.004      0.002      0.        ]
 [0.0025     0.02       0.         0.0225     0.         0.8675
  0.085      0.         0.0025     0.        ]
 [0.018      0.         0.12       0.008      0.002      0.002
  0.846      0.002      0.002      0.        ]
 [0.         0.         0.00166667 0.00333333 0.00333333 0.
  0.         0.99166667 0.         0.        ]
 [0.00400802 0.         0.         0.00601202 0.         0.
  0.         0.         0.98997996 0.        ]
 [0.01166667 0.035      0.00166667 0.00333333 0.         0.005
  0.         0.         0.00666667 0.93666667]]
[2023-08-30 04:27:26,504 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 04:27:26,505 INFO] 57344 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 0.8750, train/run_time: 0.4196, eval/loss: 0.9375, eval/top-1-acc: 0.9117, eval/balanced_acc: 0.9129, eval/precision: 0.9073, eval/recall: 0.9129, eval/F1: 0.9074, lr: 0.0000, train/prefecth_time: 0.0023 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 04:29:59,088 INFO] 57600 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3752, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 04:31:49,509 INFO] 57856 iteration USE_EMA: True, train/sup_loss: 0.0031, train/unsup_loss: 0.0038, train/total_loss: 0.0058, train/util_ratio: 1.0000, train/run_time: 0.3600, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 04:33:39,139 INFO] 58112 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3607, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 04:35:27,547 INFO] 58368 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0032, train/total_loss: 0.0023, train/util_ratio: 1.0000, train/run_time: 0.4924, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 04:37:59,565 INFO] 58624 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3654, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 04:39:49,689 INFO] 58880 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4801, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 04:41:38,621 INFO] 59136 iteration USE_EMA: True, train/sup_loss: 0.0033, train/unsup_loss: 0.0046, train/total_loss: 0.0066, train/util_ratio: 1.0000, train/run_time: 0.4406, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 04:43:31,079 INFO] validating...
[2023-08-30 04:43:55,215 INFO] confusion matrix:
[[0.93       0.00333333 0.         0.         0.         0.01166667
  0.045      0.00166667 0.00833333 0.        ]
 [0.         0.95166667 0.00666667 0.         0.         0.03333333
  0.         0.00166667 0.00666667 0.        ]
 [0.00166667 0.00166667 0.60166667 0.00833333 0.00333333 0.18166667
  0.04666667 0.07833333 0.07666667 0.        ]
 [0.         0.         0.         0.986      0.002      0.
  0.         0.         0.012      0.        ]
 [0.         0.         0.         0.008      0.986      0.
  0.002      0.002      0.002      0.        ]
 [0.         0.015      0.         0.015      0.         0.9
  0.0675     0.         0.0025     0.        ]
 [0.016      0.         0.12       0.006      0.002      0.006
  0.846      0.002      0.002      0.        ]
 [0.         0.         0.00166667 0.00333333 0.00833333 0.
  0.         0.98333333 0.00333333 0.        ]
 [0.00400802 0.         0.         0.00601202 0.         0.
  0.         0.         0.98997996 0.        ]
 [0.01       0.04166667 0.00333333 0.00333333 0.         0.00666667
  0.         0.         0.015      0.92      ]]
[2023-08-30 04:43:56,111 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 04:43:56,112 INFO] 59392 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 0.8750, train/run_time: 0.4085, eval/loss: 1.0594, eval/top-1-acc: 0.9066, eval/balanced_acc: 0.9095, eval/precision: 0.9032, eval/recall: 0.9095, eval/F1: 0.9025, lr: 0.0000, train/prefecth_time: 0.0022 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 04:46:27,729 INFO] 59648 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0028, train/total_loss: 0.0020, train/util_ratio: 1.0000, train/run_time: 0.4781, lr: 0.0000, train/prefecth_time: 0.0051 
[2023-08-30 04:48:16,955 INFO] 59904 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4128, lr: 0.0000, train/prefecth_time: 0.0038 
[2023-08-30 04:50:07,835 INFO] 60160 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0063, train/total_loss: 0.0046, train/util_ratio: 1.0000, train/run_time: 0.4520, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 04:51:56,417 INFO] 60416 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3935, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 04:54:27,315 INFO] 60672 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3603, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 04:56:17,022 INFO] 60928 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3583, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 04:58:05,173 INFO] 61184 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3794, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 04:59:53,709 INFO] validating...
[2023-08-30 05:00:18,260 INFO] confusion matrix:
[[0.92166667 0.00333333 0.         0.         0.         0.01666667
  0.04833333 0.00166667 0.00833333 0.        ]
 [0.         0.95       0.00666667 0.         0.         0.035
  0.         0.00166667 0.00666667 0.        ]
 [0.00166667 0.00166667 0.60166667 0.00833333 0.00333333 0.18333333
  0.04833333 0.07166667 0.08       0.        ]
 [0.         0.         0.         0.986      0.002      0.
  0.         0.         0.012      0.        ]
 [0.         0.         0.         0.008      0.986      0.
  0.002      0.002      0.002      0.        ]
 [0.         0.015      0.         0.015      0.         0.9025
  0.065      0.         0.0025     0.        ]
 [0.014      0.         0.122      0.006      0.002      0.01
  0.842      0.002      0.002      0.        ]
 [0.         0.         0.00166667 0.00333333 0.01       0.
  0.         0.98166667 0.00333333 0.        ]
 [0.00400802 0.         0.         0.00801603 0.         0.00200401
  0.00200401 0.         0.98396794 0.        ]
 [0.01       0.04833333 0.00333333 0.00333333 0.         0.01166667
  0.         0.         0.02       0.90333333]]
[2023-08-30 05:00:19,444 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 05:00:19,446 INFO] 61440 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4094, eval/loss: 1.1499, eval/top-1-acc: 0.9028, eval/balanced_acc: 0.9059, eval/precision: 0.8998, eval/recall: 0.9059, eval/F1: 0.8987, lr: 0.0000, train/prefecth_time: 0.0023 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 05:02:50,110 INFO] 61696 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4138, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 05:04:39,518 INFO] 61952 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4147, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 05:06:28,327 INFO] 62208 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4168, lr: 0.0000, train/prefecth_time: 0.0056 
[2023-08-30 05:08:17,447 INFO] 62464 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3900, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 05:10:49,714 INFO] 62720 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.7500, train/run_time: 0.3601, lr: 0.0000, train/prefecth_time: 0.0037 
[2023-08-30 05:12:38,475 INFO] 62976 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3810, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 05:14:25,496 INFO] 63232 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.3605, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 05:16:14,667 INFO] validating...
[2023-08-30 05:16:39,475 INFO] confusion matrix:
[[0.91166667 0.00333333 0.         0.         0.         0.025
  0.05       0.00166667 0.00833333 0.        ]
 [0.         0.94833333 0.005      0.         0.         0.03833333
  0.         0.00166667 0.00666667 0.        ]
 [0.00166667 0.00166667 0.59166667 0.01       0.00666667 0.185
  0.055      0.07       0.07833333 0.        ]
 [0.         0.         0.         0.988      0.002      0.
  0.         0.         0.01       0.        ]
 [0.         0.         0.         0.008      0.984      0.
  0.002      0.004      0.002      0.        ]
 [0.         0.015      0.         0.015      0.         0.905
  0.0625     0.         0.0025     0.        ]
 [0.018      0.         0.114      0.006      0.002      0.01
  0.844      0.004      0.002      0.        ]
 [0.         0.         0.00166667 0.005      0.01       0.
  0.         0.98       0.00333333 0.        ]
 [0.00601202 0.         0.         0.00801603 0.         0.00601202
  0.00400802 0.         0.9759519  0.        ]
 [0.01166667 0.04666667 0.00333333 0.00333333 0.         0.01333333
  0.         0.         0.025      0.89666667]]
[2023-08-30 05:16:40,600 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 05:16:40,602 INFO] 63488 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3593, eval/loss: 1.2433, eval/top-1-acc: 0.8991, eval/balanced_acc: 0.9025, eval/precision: 0.8967, eval/recall: 0.9025, eval/F1: 0.8949, lr: 0.0000, train/prefecth_time: 0.0023 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 05:19:10,761 INFO] 63744 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0019, train/total_loss: 0.0014, train/util_ratio: 1.0000, train/run_time: 0.3741, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 05:20:59,386 INFO] 64000 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4246, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 05:22:46,655 INFO] 64256 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3711, lr: 0.0000, train/prefecth_time: 0.0034 
[2023-08-30 05:24:35,591 INFO] 64512 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4154, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 05:27:08,461 INFO] 64768 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4011, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 05:28:58,274 INFO] 65024 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4234, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 05:30:47,144 INFO] 65280 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0038, train/total_loss: 0.0030, train/util_ratio: 1.0000, train/run_time: 0.3601, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 05:32:38,805 INFO] validating...
[2023-08-30 05:33:02,698 INFO] confusion matrix:
[[0.895      0.00333333 0.         0.         0.         0.03333333
  0.05833333 0.00166667 0.00833333 0.        ]
 [0.         0.94666667 0.005      0.         0.         0.04166667
  0.         0.         0.00666667 0.        ]
 [0.00166667 0.         0.57666667 0.01       0.00666667 0.19666667
  0.06       0.06833333 0.08       0.        ]
 [0.         0.         0.         0.986      0.002      0.002
  0.         0.         0.01       0.        ]
 [0.         0.         0.         0.008      0.984      0.
  0.002      0.004      0.002      0.        ]
 [0.         0.0075     0.         0.015      0.         0.9125
  0.065      0.         0.         0.        ]
 [0.014      0.         0.116      0.004      0.004      0.01
  0.846      0.004      0.002      0.        ]
 [0.         0.         0.         0.005      0.00666667 0.
  0.         0.985      0.00333333 0.        ]
 [0.00801603 0.         0.         0.00801603 0.         0.01202405
  0.00400802 0.         0.96793587 0.        ]
 [0.01166667 0.04166667 0.00333333 0.00333333 0.         0.02333333
  0.         0.         0.02166667 0.895     ]]
[2023-08-30 05:33:03,611 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 05:33:03,612 INFO] 65536 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.3569, eval/loss: 1.3466, eval/top-1-acc: 0.8955, eval/balanced_acc: 0.8995, eval/precision: 0.8943, eval/recall: 0.8995, eval/F1: 0.8914, lr: 0.0000, train/prefecth_time: 0.0022 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 05:35:38,425 INFO] 65792 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4173, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 05:37:32,065 INFO] 66048 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4206, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 05:39:21,008 INFO] 66304 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4081, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 05:41:10,562 INFO] 66560 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3576, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 05:43:42,481 INFO] 66816 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4101, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 05:45:34,241 INFO] 67072 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4549, lr: 0.0000, train/prefecth_time: 0.0038 
[2023-08-30 05:47:24,733 INFO] 67328 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0003, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.4255, lr: 0.0000, train/prefecth_time: 0.0035 
[2023-08-30 05:49:13,424 INFO] validating...
[2023-08-30 05:49:37,877 INFO] confusion matrix:
[[0.88333333 0.00333333 0.00166667 0.         0.         0.03666667
  0.06333333 0.00166667 0.01       0.        ]
 [0.         0.94166667 0.005      0.00166667 0.         0.04666667
  0.         0.         0.005      0.        ]
 [0.00166667 0.         0.59       0.01       0.00833333 0.19666667
  0.06166667 0.06       0.07166667 0.        ]
 [0.         0.         0.         0.986      0.002      0.004
  0.         0.         0.008      0.        ]
 [0.         0.         0.         0.008      0.984      0.
  0.002      0.004      0.002      0.        ]
 [0.         0.0075     0.         0.015      0.         0.9175
  0.06       0.         0.         0.        ]
 [0.012      0.         0.122      0.004      0.004      0.01
  0.842      0.004      0.002      0.        ]
 [0.         0.         0.         0.005      0.01166667 0.
  0.         0.98       0.00333333 0.        ]
 [0.00801603 0.         0.         0.00801603 0.         0.01803607
  0.00400802 0.         0.96192385 0.        ]
 [0.01333333 0.04166667 0.00333333 0.00333333 0.         0.03333333
  0.         0.         0.01833333 0.88666667]]
[2023-08-30 05:49:38,786 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 05:49:38,787 INFO] 67584 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 0.8750, train/run_time: 0.4033, eval/loss: 1.4093, eval/top-1-acc: 0.8931, eval/balanced_acc: 0.8973, eval/precision: 0.8926, eval/recall: 0.8973, eval/F1: 0.8895, lr: 0.0000, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 05:52:09,318 INFO] 67840 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0012, train/total_loss: 0.0010, train/util_ratio: 1.0000, train/run_time: 0.3696, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 05:53:58,938 INFO] 68096 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3770, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 05:55:48,211 INFO] 68352 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4013, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 05:57:37,604 INFO] 68608 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4064, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 06:00:12,800 INFO] 68864 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4294, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 06:02:04,027 INFO] 69120 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4091, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 06:03:57,952 INFO] 69376 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4127, lr: 0.0000, train/prefecth_time: 0.0035 
[2023-08-30 06:05:49,993 INFO] validating...
[2023-08-30 06:06:13,953 INFO] confusion matrix:
[[0.88333333 0.00333333 0.00166667 0.         0.         0.03833333
  0.06166667 0.00166667 0.01       0.        ]
 [0.         0.93833333 0.00666667 0.00166667 0.         0.04666667
  0.         0.         0.005      0.00166667]
 [0.         0.00166667 0.6        0.01       0.005      0.19333333
  0.07333333 0.05333333 0.06333333 0.        ]
 [0.         0.         0.004      0.974      0.         0.01
  0.002      0.         0.01       0.        ]
 [0.         0.         0.         0.008      0.988      0.
  0.002      0.         0.002      0.        ]
 [0.         0.0075     0.         0.015      0.         0.9175
  0.06       0.         0.         0.        ]
 [0.012      0.         0.116      0.004      0.004      0.01
  0.85       0.002      0.002      0.        ]
 [0.         0.         0.00333333 0.005      0.01166667 0.
  0.         0.97666667 0.00333333 0.        ]
 [0.01402806 0.         0.         0.00801603 0.         0.03406814
  0.00400802 0.         0.93787575 0.00200401]
 [0.01       0.035      0.00333333 0.00333333 0.         0.035
  0.         0.         0.01833333 0.895     ]]
[2023-08-30 06:06:14,904 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 06:06:14,905 INFO] 69632 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0063, train/total_loss: 0.0053, train/util_ratio: 1.0000, train/run_time: 0.3582, eval/loss: 1.4476, eval/top-1-acc: 0.8922, eval/balanced_acc: 0.8961, eval/precision: 0.8923, eval/recall: 0.8961, eval/F1: 0.8888, lr: 0.0000, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 06:08:46,917 INFO] 69888 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3718, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 06:10:38,428 INFO] 70144 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4283, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 06:12:29,387 INFO] 70400 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4256, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 06:14:21,415 INFO] 70656 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0050, train/total_loss: 0.0043, train/util_ratio: 1.0000, train/run_time: 0.4196, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 06:16:56,764 INFO] 70912 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4161, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 06:18:50,138 INFO] 71168 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0039, train/total_loss: 0.0034, train/util_ratio: 1.0000, train/run_time: 0.3578, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 06:20:43,166 INFO] 71424 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4310, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 06:22:34,474 INFO] validating...
[2023-08-30 06:22:59,213 INFO] confusion matrix:
[[0.88333333 0.00333333 0.00166667 0.         0.         0.04166667
  0.05833333 0.00166667 0.01       0.        ]
 [0.00166667 0.935      0.00666667 0.00166667 0.         0.04666667
  0.         0.         0.00666667 0.00166667]
 [0.         0.00166667 0.62       0.01       0.00833333 0.18166667
  0.07333333 0.04666667 0.05833333 0.        ]
 [0.         0.         0.004      0.97       0.         0.012
  0.002      0.         0.012      0.        ]
 [0.         0.         0.         0.006      0.99       0.
  0.002      0.         0.002      0.        ]
 [0.         0.0075     0.         0.015      0.         0.9125
  0.065      0.         0.         0.        ]
 [0.012      0.         0.128      0.004      0.004      0.008
  0.842      0.         0.002      0.        ]
 [0.         0.         0.00166667 0.005      0.015      0.
  0.         0.975      0.00333333 0.        ]
 [0.01603206 0.         0.         0.01202405 0.         0.03607214
  0.00400802 0.         0.92985972 0.00200401]
 [0.01166667 0.04166667 0.00333333 0.00333333 0.         0.035
  0.         0.         0.02       0.885     ]]
[2023-08-30 06:23:00,077 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 06:23:00,079 INFO] 71680 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0016, train/total_loss: 0.0014, train/util_ratio: 1.0000, train/run_time: 0.4135, eval/loss: 1.4958, eval/top-1-acc: 0.8907, eval/balanced_acc: 0.8943, eval/precision: 0.8905, eval/recall: 0.8943, eval/F1: 0.8876, lr: 0.0000, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 06:25:32,461 INFO] 71936 iteration USE_EMA: True, train/sup_loss: 1.5576, train/unsup_loss: 0.0000, train/total_loss: 1.5576, train/util_ratio: 1.0000, train/run_time: 0.4010, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 06:27:24,342 INFO] 72192 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4139, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 06:29:13,767 INFO] 72448 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3952, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 06:31:04,400 INFO] 72704 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3654, lr: 0.0000, train/prefecth_time: 0.0021 
[2023-08-30 06:33:38,433 INFO] 72960 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4088, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 06:35:28,693 INFO] 73216 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4298, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 06:37:19,317 INFO] 73472 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0024, train/total_loss: 0.0021, train/util_ratio: 1.0000, train/run_time: 0.4231, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 06:39:08,278 INFO] validating...
[2023-08-30 06:39:33,057 INFO] confusion matrix:
[[0.88666667 0.00333333 0.00166667 0.         0.         0.04166667
  0.05666667 0.         0.01       0.        ]
 [0.00166667 0.935      0.00666667 0.         0.         0.04666667
  0.         0.         0.00833333 0.00166667]
 [0.         0.00166667 0.63166667 0.01       0.00833333 0.17
  0.085      0.04166667 0.05166667 0.        ]
 [0.         0.         0.004      0.968      0.         0.012
  0.002      0.         0.014      0.        ]
 [0.         0.         0.         0.006      0.99       0.
  0.002      0.         0.002      0.        ]
 [0.         0.0075     0.         0.015      0.         0.905
  0.0725     0.         0.         0.        ]
 [0.012      0.         0.12       0.002      0.004      0.008
  0.852      0.         0.002      0.        ]
 [0.         0.00333333 0.00166667 0.005      0.01       0.
  0.         0.97833333 0.00166667 0.        ]
 [0.01803607 0.         0.         0.00801603 0.         0.03406814
  0.00400802 0.         0.93386774 0.00200401]
 [0.01166667 0.04666667 0.00333333 0.00333333 0.         0.03166667
  0.         0.         0.02       0.88333333]]
[2023-08-30 06:39:33,947 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 06:39:33,948 INFO] 73728 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4881, eval/loss: 1.5255, eval/top-1-acc: 0.8931, eval/balanced_acc: 0.8964, eval/precision: 0.8928, eval/recall: 0.8964, eval/F1: 0.8901, lr: 0.0000, train/prefecth_time: 0.0028 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 06:42:06,341 INFO] 73984 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0012, train/total_loss: 0.0011, train/util_ratio: 0.8750, train/run_time: 0.4096, lr: 0.0000, train/prefecth_time: 0.0033 
[2023-08-30 06:43:54,817 INFO] 74240 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4298, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 06:45:47,603 INFO] 74496 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3586, lr: 0.0000, train/prefecth_time: 0.0033 
[2023-08-30 06:47:37,588 INFO] 74752 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0004, train/total_loss: 0.0004, train/util_ratio: 1.0000, train/run_time: 0.4144, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 06:50:11,927 INFO] 75008 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4094, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 06:52:04,549 INFO] 75264 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3580, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 06:53:55,499 INFO] 75520 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4385, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 06:55:45,186 INFO] validating...
[2023-08-30 06:56:09,139 INFO] confusion matrix:
[[0.885      0.00333333 0.00166667 0.         0.         0.04166667
  0.05833333 0.         0.01       0.        ]
 [0.00166667 0.93166667 0.00666667 0.         0.         0.05
  0.         0.         0.00666667 0.00333333]
 [0.         0.00333333 0.625      0.01       0.00833333 0.16
  0.10833333 0.03833333 0.04666667 0.        ]
 [0.         0.         0.004      0.962      0.002      0.014
  0.006      0.         0.012      0.        ]
 [0.         0.         0.         0.006      0.99       0.
  0.002      0.         0.002      0.        ]
 [0.         0.0075     0.         0.0125     0.         0.89
  0.09       0.         0.         0.        ]
 [0.014      0.         0.116      0.002      0.004      0.006
  0.856      0.         0.002      0.        ]
 [0.         0.005      0.00166667 0.00333333 0.01       0.
  0.         0.97833333 0.00166667 0.        ]
 [0.01803607 0.         0.         0.00801603 0.         0.03607214
  0.00601202 0.         0.92985972 0.00200401]
 [0.00666667 0.045      0.00166667 0.00166667 0.         0.03166667
  0.         0.         0.02166667 0.89166667]]
[2023-08-30 06:56:10,007 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 06:56:10,008 INFO] 75776 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0038, train/total_loss: 0.0035, train/util_ratio: 0.8750, train/run_time: 0.3568, eval/loss: 1.5519, eval/top-1-acc: 0.8911, eval/balanced_acc: 0.8940, eval/precision: 0.8911, eval/recall: 0.8940, eval/F1: 0.8880, lr: 0.0000, train/prefecth_time: 0.0022 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 06:58:45,547 INFO] 76032 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4144, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-30 07:00:35,470 INFO] 76288 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0050, train/total_loss: 0.0047, train/util_ratio: 1.0000, train/run_time: 0.4566, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 07:02:26,533 INFO] 76544 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3653, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 07:04:16,173 INFO] 76800 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3562, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 07:06:51,972 INFO] 77056 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0058, train/total_loss: 0.0055, train/util_ratio: 1.0000, train/run_time: 0.4116, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 07:08:43,343 INFO] 77312 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4184, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 07:10:32,956 INFO] 77568 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4119, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 07:12:23,532 INFO] validating...
[2023-08-30 07:12:48,129 INFO] confusion matrix:
[[0.88833333 0.00333333 0.00166667 0.         0.         0.04666667
  0.05166667 0.         0.00833333 0.        ]
 [0.00166667 0.93       0.005      0.         0.         0.05666667
  0.         0.         0.00333333 0.00333333]
 [0.         0.00333333 0.65166667 0.01166667 0.00833333 0.16833333
  0.09       0.03       0.03666667 0.        ]
 [0.002      0.         0.004      0.952      0.004      0.014
  0.006      0.         0.018      0.        ]
 [0.         0.         0.         0.006      0.986      0.
  0.002      0.004      0.002      0.        ]
 [0.         0.0075     0.         0.0125     0.         0.8975
  0.0825     0.         0.         0.        ]
 [0.014      0.         0.124      0.002      0.         0.01
  0.846      0.002      0.002      0.        ]
 [0.         0.005      0.00333333 0.00333333 0.005      0.
  0.         0.98166667 0.00166667 0.        ]
 [0.01803607 0.00200401 0.         0.01002004 0.         0.04609218
  0.00601202 0.         0.91583166 0.00200401]
 [0.005      0.04166667 0.         0.00166667 0.         0.035
  0.         0.         0.01166667 0.905     ]]
[2023-08-30 07:12:49,143 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 07:12:49,144 INFO] 77824 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4107, eval/loss: 1.5676, eval/top-1-acc: 0.8931, eval/balanced_acc: 0.8954, eval/precision: 0.8940, eval/recall: 0.8954, eval/F1: 0.8903, lr: 0.0000, train/prefecth_time: 0.0021 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 07:15:22,424 INFO] 78080 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0058, train/total_loss: 0.0055, train/util_ratio: 1.0000, train/run_time: 0.4296, lr: 0.0000, train/prefecth_time: 0.0034 
[2023-08-30 07:17:14,225 INFO] 78336 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4204, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 07:19:07,379 INFO] 78592 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0025, train/total_loss: 0.0024, train/util_ratio: 1.0000, train/run_time: 0.4135, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 07:21:01,847 INFO] 78848 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0005, train/total_loss: 0.0005, train/util_ratio: 1.0000, train/run_time: 0.4103, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 07:23:36,794 INFO] 79104 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4334, lr: 0.0000, train/prefecth_time: 0.0034 
[2023-08-30 07:25:29,664 INFO] 79360 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0019, train/total_loss: 0.0018, train/util_ratio: 1.0000, train/run_time: 0.4210, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 07:27:22,871 INFO] 79616 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4092, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 07:29:15,378 INFO] validating...
[2023-08-30 07:29:39,825 INFO] confusion matrix:
[[0.89333333 0.005      0.00166667 0.         0.         0.04333333
  0.05       0.         0.00666667 0.        ]
 [0.00166667 0.93       0.005      0.         0.         0.05666667
  0.         0.         0.00333333 0.00333333]
 [0.         0.00333333 0.67166667 0.01       0.00833333 0.15166667
  0.08666667 0.02833333 0.04       0.        ]
 [0.004      0.         0.004      0.95       0.004      0.012
  0.006      0.         0.02       0.        ]
 [0.         0.         0.         0.006      0.986      0.
  0.002      0.004      0.002      0.        ]
 [0.         0.01       0.         0.015      0.         0.8775
  0.0975     0.         0.         0.        ]
 [0.016      0.         0.122      0.002      0.         0.008
  0.85       0.         0.002      0.        ]
 [0.         0.01166667 0.00333333 0.00333333 0.005      0.
  0.         0.975      0.00166667 0.        ]
 [0.01603206 0.00200401 0.         0.00801603 0.         0.04008016
  0.00400802 0.         0.92785571 0.00200401]
 [0.00666667 0.03833333 0.         0.         0.         0.03166667
  0.         0.         0.015      0.90833333]]
[2023-08-30 07:29:40,623 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 07:29:40,623 INFO] 79872 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4096, eval/loss: 1.5650, eval/top-1-acc: 0.8954, eval/balanced_acc: 0.8970, eval/precision: 0.8951, eval/recall: 0.8970, eval/F1: 0.8925, lr: 0.0000, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 07:32:16,502 INFO] 80128 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4333, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 07:34:06,246 INFO] 80384 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3587, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 07:35:57,430 INFO] 80640 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4104, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 07:37:47,576 INFO] 80896 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3601, lr: 0.0000, train/prefecth_time: 0.0021 
[2023-08-30 07:40:22,247 INFO] 81152 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4299, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 07:42:12,814 INFO] 81408 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4226, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 07:44:03,815 INFO] 81664 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4162, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 07:45:55,761 INFO] validating...
[2023-08-30 07:46:20,558 INFO] confusion matrix:
[[0.9        0.005      0.00166667 0.         0.         0.03666667
  0.05       0.         0.00666667 0.        ]
 [0.00166667 0.91666667 0.005      0.         0.         0.06833333
  0.         0.         0.005      0.00333333]
 [0.         0.00333333 0.685      0.01166667 0.00833333 0.12833333
  0.09333333 0.02666667 0.04333333 0.        ]
 [0.004      0.         0.002      0.952      0.006      0.012
  0.004      0.         0.02       0.        ]
 [0.         0.         0.         0.006      0.984      0.
  0.002      0.006      0.002      0.        ]
 [0.         0.0075     0.         0.015      0.         0.87
  0.1075     0.         0.         0.        ]
 [0.022      0.         0.122      0.002      0.         0.008
  0.842      0.002      0.002      0.        ]
 [0.         0.00666667 0.00333333 0.00333333 0.00333333 0.
  0.         0.98166667 0.00166667 0.        ]
 [0.01202405 0.00200401 0.         0.00601202 0.         0.04208417
  0.00400802 0.         0.93186373 0.00200401]
 [0.00666667 0.02833333 0.         0.         0.         0.035
  0.         0.         0.01       0.92      ]]
[2023-08-30 07:46:21,600 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 07:46:21,601 INFO] 81920 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4088, eval/loss: 1.5645, eval/top-1-acc: 0.8972, eval/balanced_acc: 0.8983, eval/precision: 0.8964, eval/recall: 0.8983, eval/F1: 0.8942, lr: 0.0000, train/prefecth_time: 0.0027 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 07:48:52,589 INFO] 82176 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4187, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 07:50:44,160 INFO] 82432 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4202, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 07:52:35,919 INFO] 82688 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4106, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 07:54:26,287 INFO] 82944 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4104, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 07:56:58,992 INFO] 83200 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3588, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 07:58:50,469 INFO] 83456 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3843, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 08:00:41,143 INFO] 83712 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4166, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 08:02:33,546 INFO] validating...
[2023-08-30 08:02:57,346 INFO] confusion matrix:
[[0.89166667 0.005      0.00166667 0.         0.         0.03833333
  0.055      0.         0.00833333 0.        ]
 [0.00166667 0.91333333 0.005      0.         0.         0.07333333
  0.         0.         0.00333333 0.00333333]
 [0.         0.00333333 0.68666667 0.01166667 0.00833333 0.12666667
  0.085      0.03333333 0.045      0.        ]
 [0.004      0.         0.002      0.95       0.006      0.01
  0.004      0.002      0.022      0.        ]
 [0.         0.         0.         0.004      0.986      0.
  0.002      0.006      0.002      0.        ]
 [0.         0.0075     0.         0.015      0.         0.87
  0.1075     0.         0.         0.        ]
 [0.018      0.         0.124      0.002      0.         0.008
  0.842      0.004      0.002      0.        ]
 [0.         0.00666667 0.00333333 0.00333333 0.00333333 0.
  0.         0.98166667 0.00166667 0.        ]
 [0.01002004 0.00200401 0.         0.00400802 0.         0.03607214
  0.00400802 0.         0.94188377 0.00200401]
 [0.00666667 0.03333333 0.00166667 0.00166667 0.         0.03333333
  0.         0.         0.00833333 0.915     ]]
[2023-08-30 08:02:58,175 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 08:02:58,176 INFO] 83968 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4088, eval/loss: 1.5967, eval/top-1-acc: 0.8965, eval/balanced_acc: 0.8978, eval/precision: 0.8957, eval/recall: 0.8978, eval/F1: 0.8936, lr: 0.0000, train/prefecth_time: 0.0026 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 08:05:31,055 INFO] 84224 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 0.8750, train/run_time: 0.3600, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 08:07:23,430 INFO] 84480 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4156, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 08:09:16,562 INFO] 84736 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4097, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 08:11:08,316 INFO] 84992 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0005, train/total_loss: 0.0005, train/util_ratio: 0.8750, train/run_time: 0.4152, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 08:13:41,690 INFO] 85248 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3571, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 08:15:32,669 INFO] 85504 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.3563, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 08:17:24,033 INFO] 85760 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4184, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 08:19:15,435 INFO] validating...
[2023-08-30 08:19:39,464 INFO] confusion matrix:
[[0.905      0.005      0.00166667 0.         0.         0.03166667
  0.04666667 0.00166667 0.00833333 0.        ]
 [0.00166667 0.92166667 0.005      0.         0.         0.065
  0.         0.         0.00333333 0.00333333]
 [0.         0.00666667 0.68166667 0.01333333 0.01       0.13166667
  0.08       0.03666667 0.04       0.        ]
 [0.004      0.         0.002      0.95       0.008      0.008
  0.006      0.         0.022      0.        ]
 [0.         0.         0.         0.002      0.99       0.
  0.002      0.004      0.002      0.        ]
 [0.         0.0075     0.         0.015      0.         0.875
  0.1025     0.         0.         0.        ]
 [0.028      0.         0.126      0.004      0.         0.008
  0.828      0.004      0.002      0.        ]
 [0.         0.00666667 0.00166667 0.00333333 0.00333333 0.
  0.         0.98333333 0.00166667 0.        ]
 [0.01402806 0.00400802 0.         0.00200401 0.         0.03406814
  0.00200401 0.         0.94188377 0.00200401]
 [0.00833333 0.03833333 0.00166667 0.00166667 0.         0.03
  0.         0.         0.01       0.91      ]]
[2023-08-30 08:19:40,230 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 08:19:40,231 INFO] 86016 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4102, eval/loss: 1.5755, eval/top-1-acc: 0.8974, eval/balanced_acc: 0.8987, eval/precision: 0.8957, eval/recall: 0.8987, eval/F1: 0.8943, lr: 0.0000, train/prefecth_time: 0.0023 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 08:22:15,019 INFO] 86272 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4647, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 08:24:06,937 INFO] 86528 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0035, train/total_loss: 0.0035, train/util_ratio: 1.0000, train/run_time: 0.4161, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 08:25:59,407 INFO] 86784 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4180, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 08:27:51,420 INFO] 87040 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4006, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 08:30:29,312 INFO] 87296 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0009, train/total_loss: 0.0009, train/util_ratio: 1.0000, train/run_time: 0.3806, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 08:32:19,960 INFO] 87552 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4378, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 08:34:10,139 INFO] 87808 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3579, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 08:36:00,266 INFO] validating...
[2023-08-30 08:36:24,672 INFO] confusion matrix:
[[0.91166667 0.005      0.         0.         0.         0.02833333
  0.04666667 0.         0.00833333 0.        ]
 [0.00166667 0.93166667 0.00666667 0.         0.         0.055
  0.         0.         0.00166667 0.00333333]
 [0.         0.005      0.70333333 0.01333333 0.01       0.13
  0.07166667 0.03666667 0.03       0.        ]
 [0.004      0.         0.002      0.944      0.012      0.01
  0.006      0.004      0.018      0.        ]
 [0.         0.         0.         0.004      0.986      0.
  0.002      0.006      0.002      0.        ]
 [0.         0.01       0.         0.015      0.         0.8675
  0.1075     0.         0.         0.        ]
 [0.03       0.         0.12       0.         0.002      0.008
  0.832      0.006      0.002      0.        ]
 [0.         0.00833333 0.00166667 0.00166667 0.00333333 0.
  0.         0.98333333 0.00166667 0.        ]
 [0.01402806 0.00200401 0.         0.00200401 0.         0.03006012
  0.00200401 0.         0.94789579 0.00200401]
 [0.00833333 0.045      0.00333333 0.00166667 0.         0.02666667
  0.         0.         0.01       0.905     ]]
[2023-08-30 08:36:25,506 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 08:36:25,507 INFO] 88064 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4320, eval/loss: 1.5034, eval/top-1-acc: 0.9005, eval/balanced_acc: 0.9012, eval/precision: 0.8986, eval/recall: 0.9012, eval/F1: 0.8975, lr: 0.0000, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 08:39:00,652 INFO] 88320 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0054, train/total_loss: 0.0054, train/util_ratio: 0.8750, train/run_time: 0.4330, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 08:40:53,688 INFO] 88576 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.7500, train/run_time: 0.4156, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 08:42:42,543 INFO] 88832 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0005, train/total_loss: 0.0005, train/util_ratio: 1.0000, train/run_time: 0.4167, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 08:44:35,977 INFO] 89088 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3581, lr: 0.0000, train/prefecth_time: 0.0021 
[2023-08-30 08:47:10,554 INFO] 89344 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3581, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 08:49:00,550 INFO] 89600 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4444, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 08:50:50,843 INFO] 89856 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0045, train/total_loss: 0.0045, train/util_ratio: 1.0000, train/run_time: 0.4196, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 08:52:40,727 INFO] validating...
[2023-08-30 08:53:05,289 INFO] confusion matrix:
[[0.915      0.005      0.         0.         0.         0.025
  0.04666667 0.         0.00833333 0.        ]
 [0.00166667 0.93166667 0.00666667 0.         0.         0.05333333
  0.         0.         0.00333333 0.00333333]
 [0.         0.005      0.705      0.01333333 0.01       0.12833333
  0.08333333 0.03       0.025      0.        ]
 [0.004      0.         0.002      0.942      0.018      0.006
  0.004      0.004      0.02       0.        ]
 [0.         0.         0.         0.002      0.992      0.
  0.002      0.004      0.         0.        ]
 [0.0025     0.01       0.         0.0125     0.         0.8625
  0.1125     0.         0.         0.        ]
 [0.034      0.         0.11       0.         0.004      0.006
  0.838      0.006      0.002      0.        ]
 [0.         0.00833333 0.00333333 0.00166667 0.00333333 0.
  0.         0.98166667 0.00166667 0.        ]
 [0.01402806 0.         0.         0.         0.         0.02805611
  0.00200401 0.         0.95390782 0.00200401]
 [0.00833333 0.03833333 0.00333333 0.00166667 0.         0.025
  0.         0.         0.00833333 0.915     ]]
[2023-08-30 08:53:06,228 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 08:53:06,229 INFO] 90112 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3605, eval/loss: 1.4789, eval/top-1-acc: 0.9031, eval/balanced_acc: 0.9037, eval/precision: 0.9009, eval/recall: 0.9037, eval/F1: 0.9000, lr: 0.0000, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 08:55:38,944 INFO] 90368 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3688, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 08:57:29,513 INFO] 90624 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4162, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 08:59:23,090 INFO] 90880 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0003, train/total_loss: 0.0003, train/util_ratio: 1.0000, train/run_time: 0.4098, lr: 0.0000, train/prefecth_time: 0.0038 
[2023-08-30 09:01:16,982 INFO] 91136 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0009, train/total_loss: 0.0009, train/util_ratio: 0.8750, train/run_time: 0.3580, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 09:03:52,576 INFO] 91392 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4272, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 09:05:45,343 INFO] 91648 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4271, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-30 09:07:37,455 INFO] 91904 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4297, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 09:09:29,495 INFO] validating...
[2023-08-30 09:09:54,103 INFO] confusion matrix:
[[0.90333333 0.00666667 0.         0.         0.         0.035
  0.04666667 0.         0.00833333 0.        ]
 [0.         0.93666667 0.00666667 0.         0.         0.05166667
  0.         0.         0.00333333 0.00166667]
 [0.         0.00333333 0.72666667 0.01333333 0.015      0.125
  0.06666667 0.03166667 0.01833333 0.        ]
 [0.004      0.         0.002      0.928      0.026      0.006
  0.004      0.01       0.02       0.        ]
 [0.         0.         0.         0.         0.996      0.
  0.         0.004      0.         0.        ]
 [0.         0.01       0.         0.0075     0.         0.8925
  0.09       0.         0.         0.        ]
 [0.03       0.         0.118      0.         0.008      0.01
  0.826      0.006      0.002      0.        ]
 [0.         0.005      0.005      0.00166667 0.005      0.
  0.         0.98166667 0.00166667 0.        ]
 [0.01402806 0.         0.         0.00200401 0.         0.03006012
  0.         0.         0.95190381 0.00200401]
 [0.01       0.05833333 0.00333333 0.00166667 0.         0.025
  0.         0.         0.00833333 0.89333333]]
[2023-08-30 09:09:54,911 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 09:09:54,913 INFO] 92160 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 0.8750, train/run_time: 0.3700, eval/loss: 1.5235, eval/top-1-acc: 0.9024, eval/balanced_acc: 0.9036, eval/precision: 0.9008, eval/recall: 0.9036, eval/F1: 0.8997, lr: 0.0000, train/prefecth_time: 0.0020 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 09:12:29,100 INFO] 92416 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0028, train/total_loss: 0.0028, train/util_ratio: 0.8750, train/run_time: 0.4228, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 09:14:19,166 INFO] 92672 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4624, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 09:16:09,925 INFO] 92928 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4108, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 09:17:59,805 INFO] 93184 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4338, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 09:20:32,890 INFO] 93440 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.3582, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 09:22:22,726 INFO] 93696 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4184, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 09:24:13,365 INFO] 93952 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0009, train/total_loss: 0.0009, train/util_ratio: 1.0000, train/run_time: 0.3621, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 09:26:04,002 INFO] validating...
[2023-08-30 09:26:28,503 INFO] confusion matrix:
[[0.89833333 0.005      0.         0.00166667 0.         0.03666667
  0.04833333 0.         0.01       0.        ]
 [0.         0.93166667 0.00833333 0.         0.         0.05333333
  0.         0.         0.00333333 0.00333333]
 [0.         0.00333333 0.75166667 0.01166667 0.015      0.11
  0.06166667 0.02833333 0.01833333 0.        ]
 [0.004      0.         0.002      0.924      0.026      0.01
  0.004      0.012      0.018      0.        ]
 [0.         0.         0.         0.         0.996      0.
  0.         0.004      0.         0.        ]
 [0.         0.01       0.         0.005      0.         0.905
  0.08       0.         0.         0.        ]
 [0.026      0.         0.124      0.         0.008      0.01
  0.822      0.008      0.002      0.        ]
 [0.         0.005      0.005      0.         0.00333333 0.
  0.         0.985      0.00166667 0.        ]
 [0.01603206 0.         0.         0.00801603 0.         0.03607214
  0.         0.         0.93787575 0.00200401]
 [0.00833333 0.06       0.00333333 0.00166667 0.         0.02833333
  0.         0.         0.01       0.88833333]]
[2023-08-30 09:26:29,466 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 09:26:29,467 INFO] 94208 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3602, eval/loss: 1.5330, eval/top-1-acc: 0.9028, eval/balanced_acc: 0.9040, eval/precision: 0.9015, eval/recall: 0.9040, eval/F1: 0.9004, lr: 0.0000, train/prefecth_time: 0.0026 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 09:29:02,222 INFO] 94464 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4325, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 09:30:53,563 INFO] 94720 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4103, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 09:32:45,965 INFO] 94976 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4098, lr: 0.0000, train/prefecth_time: 0.0036 
[2023-08-30 09:34:38,326 INFO] 95232 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0021, train/total_loss: 0.0021, train/util_ratio: 0.8750, train/run_time: 0.3572, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 09:37:15,547 INFO] 95488 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3727, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 09:39:08,482 INFO] 95744 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4216, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 09:40:59,650 INFO] 96000 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3683, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 09:42:50,472 INFO] validating...
[2023-08-30 09:43:14,938 INFO] confusion matrix:
[[0.895      0.005      0.         0.00166667 0.         0.03166667
  0.05       0.         0.01666667 0.        ]
 [0.         0.94166667 0.00833333 0.         0.         0.04333333
  0.         0.         0.00333333 0.00333333]
 [0.         0.00333333 0.785      0.005      0.015      0.09166667
  0.05333333 0.02833333 0.01833333 0.        ]
 [0.004      0.         0.002      0.904      0.038      0.01
  0.002      0.022      0.018      0.        ]
 [0.         0.         0.         0.         0.994      0.
  0.         0.006      0.         0.        ]
 [0.         0.0225     0.         0.005      0.         0.885
  0.0875     0.         0.         0.        ]
 [0.024      0.         0.122      0.         0.01       0.012
  0.822      0.008      0.002      0.        ]
 [0.         0.00333333 0.00166667 0.         0.005      0.
  0.         0.98833333 0.00166667 0.        ]
 [0.01002004 0.00200401 0.         0.01202405 0.         0.04008016
  0.         0.         0.93386774 0.00200401]
 [0.01166667 0.06       0.005      0.         0.         0.02666667
  0.         0.         0.01       0.88666667]]
[2023-08-30 09:43:15,940 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 09:43:15,942 INFO] 96256 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4341, eval/loss: 1.5405, eval/top-1-acc: 0.9035, eval/balanced_acc: 0.9036, eval/precision: 0.9017, eval/recall: 0.9036, eval/F1: 0.9007, lr: 0.0000, train/prefecth_time: 0.0026 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 09:45:49,004 INFO] 96512 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0004, train/total_loss: 0.0004, train/util_ratio: 0.8750, train/run_time: 0.3619, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 09:47:40,985 INFO] 96768 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0025, train/total_loss: 0.0025, train/util_ratio: 0.8750, train/run_time: 0.4205, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 09:49:33,425 INFO] 97024 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4064, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 09:51:24,191 INFO] 97280 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4171, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 09:53:59,891 INFO] 97536 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4952, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 09:55:51,447 INFO] 97792 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3700, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 09:57:44,346 INFO] 98048 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3748, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 09:59:39,070 INFO] validating...
[2023-08-30 10:00:03,770 INFO] confusion matrix:
[[0.89       0.005      0.         0.00166667 0.00333333 0.03666667
  0.05       0.         0.01333333 0.        ]
 [0.         0.92666667 0.00833333 0.         0.         0.05666667
  0.         0.         0.00333333 0.005     ]
 [0.         0.00333333 0.78833333 0.005      0.01666667 0.09333333
  0.05333333 0.02833333 0.01166667 0.        ]
 [0.004      0.         0.002      0.888      0.05       0.016
  0.002      0.022      0.016      0.        ]
 [0.         0.         0.         0.         0.996      0.
  0.         0.004      0.         0.        ]
 [0.         0.0125     0.         0.0025     0.         0.8975
  0.0875     0.         0.         0.        ]
 [0.024      0.         0.12       0.         0.01       0.012
  0.824      0.008      0.002      0.        ]
 [0.         0.00333333 0.00166667 0.         0.00833333 0.
  0.         0.985      0.00166667 0.        ]
 [0.01202405 0.00400802 0.         0.01202405 0.         0.05611222
  0.00200401 0.         0.91182365 0.00200401]
 [0.01333333 0.05666667 0.00666667 0.         0.         0.02833333
  0.         0.         0.01       0.885     ]]
[2023-08-30 10:00:04,605 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 10:00:04,606 INFO] 98304 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4105, eval/loss: 1.6303, eval/top-1-acc: 0.8989, eval/balanced_acc: 0.8992, eval/precision: 0.8984, eval/recall: 0.8992, eval/F1: 0.8962, lr: 0.0000, train/prefecth_time: 0.0023 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 10:02:38,326 INFO] 98560 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.4374, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 10:04:30,973 INFO] 98816 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0080, train/total_loss: 0.0080, train/util_ratio: 1.0000, train/run_time: 0.4310, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 10:06:21,669 INFO] 99072 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4344, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 10:08:13,076 INFO] 99328 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4109, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 10:10:47,099 INFO] 99584 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0005, train/total_loss: 0.0005, train/util_ratio: 1.0000, train/run_time: 0.3584, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 10:12:38,126 INFO] 99840 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4103, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 10:14:27,655 INFO] 100096 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4373, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 10:16:19,256 INFO] validating...
[2023-08-30 10:16:43,730 INFO] confusion matrix:
[[0.88       0.005      0.         0.00166667 0.00333333 0.04166667
  0.05166667 0.         0.01666667 0.        ]
 [0.         0.90166667 0.00666667 0.         0.         0.08166667
  0.         0.         0.005      0.005     ]
 [0.         0.00333333 0.75666667 0.00833333 0.025      0.105
  0.05666667 0.03333333 0.01166667 0.        ]
 [0.002      0.         0.         0.888      0.054      0.014
  0.006      0.02       0.016      0.        ]
 [0.         0.         0.         0.         0.998      0.
  0.         0.002      0.         0.        ]
 [0.         0.01       0.         0.005      0.         0.8925
  0.0925     0.         0.         0.        ]
 [0.022      0.         0.096      0.         0.01       0.012
  0.85       0.008      0.002      0.        ]
 [0.         0.00333333 0.00166667 0.         0.00833333 0.
  0.         0.985      0.00166667 0.        ]
 [0.01002004 0.00200401 0.         0.01202405 0.00200401 0.05611222
  0.00200401 0.         0.91382766 0.00200401]
 [0.015      0.04833333 0.00333333 0.         0.         0.03166667
  0.         0.         0.01166667 0.89      ]]
[2023-08-30 10:16:44,631 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 10:16:44,632 INFO] 100352 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4163, eval/loss: 1.7307, eval/top-1-acc: 0.8944, eval/balanced_acc: 0.8956, eval/precision: 0.8951, eval/recall: 0.8956, eval/F1: 0.8918, lr: 0.0000, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 10:19:19,231 INFO] 100608 iteration USE_EMA: True, train/sup_loss: 0.0007, train/unsup_loss: 0.0000, train/total_loss: 0.0007, train/util_ratio: 1.0000, train/run_time: 0.4169, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 10:21:08,728 INFO] 100864 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4395, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 10:23:01,873 INFO] 101120 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0019, train/total_loss: 0.0019, train/util_ratio: 1.0000, train/run_time: 0.4334, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 10:24:53,061 INFO] 101376 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3921, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 10:27:25,901 INFO] 101632 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3595, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 10:29:15,178 INFO] 101888 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4905, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 10:31:04,754 INFO] 102144 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4502, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 10:32:54,781 INFO] validating...
[2023-08-30 10:33:19,586 INFO] confusion matrix:
[[0.875      0.00333333 0.         0.         0.00166667 0.04666667
  0.05       0.         0.02333333 0.        ]
 [0.         0.885      0.00666667 0.         0.         0.09666667
  0.         0.00166667 0.005      0.005     ]
 [0.         0.00333333 0.74833333 0.01166667 0.035      0.1
  0.04666667 0.03833333 0.01666667 0.        ]
 [0.002      0.         0.         0.89       0.058      0.012
  0.006      0.016      0.016      0.        ]
 [0.         0.         0.         0.         0.998      0.
  0.         0.002      0.         0.        ]
 [0.0025     0.005      0.         0.005      0.         0.8975
  0.09       0.         0.         0.        ]
 [0.022      0.         0.09       0.004      0.022      0.012
  0.836      0.01       0.004      0.        ]
 [0.         0.00166667 0.00166667 0.         0.01       0.
  0.         0.985      0.00166667 0.        ]
 [0.00801603 0.         0.         0.01202405 0.00200401 0.05611222
  0.         0.         0.91983968 0.00200401]
 [0.01833333 0.03833333 0.00166667 0.         0.         0.03666667
  0.         0.         0.015      0.89      ]]
[2023-08-30 10:33:20,527 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 10:33:20,528 INFO] 102400 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4171, eval/loss: 1.8453, eval/top-1-acc: 0.8909, eval/balanced_acc: 0.8925, eval/precision: 0.8920, eval/recall: 0.8925, eval/F1: 0.8881, lr: 0.0000, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 10:35:55,631 INFO] 102656 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4407, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 10:37:47,013 INFO] 102912 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4185, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 10:39:36,779 INFO] 103168 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4177, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 10:41:30,515 INFO] 103424 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4087, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 10:44:06,883 INFO] 103680 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0003, train/total_loss: 0.0003, train/util_ratio: 0.8750, train/run_time: 0.3683, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 10:45:56,537 INFO] 103936 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4311, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 10:47:46,176 INFO] 104192 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.3582, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 10:49:35,602 INFO] validating...
[2023-08-30 10:50:00,109 INFO] confusion matrix:
[[0.87166667 0.005      0.         0.         0.00166667 0.06
  0.03833333 0.00166667 0.02166667 0.        ]
 [0.         0.885      0.005      0.         0.         0.1
  0.         0.00166667 0.00333333 0.005     ]
 [0.         0.00333333 0.735      0.00833333 0.03166667 0.09666667
  0.05333333 0.04833333 0.02333333 0.        ]
 [0.002      0.         0.         0.88       0.054      0.01
  0.006      0.02       0.028      0.        ]
 [0.         0.         0.         0.         0.998      0.
  0.         0.002      0.         0.        ]
 [0.0025     0.005      0.         0.005      0.         0.9075
  0.08       0.         0.         0.        ]
 [0.022      0.         0.09       0.004      0.016      0.012
  0.84       0.012      0.004      0.        ]
 [0.         0.00166667 0.00166667 0.         0.00333333 0.
  0.         0.99166667 0.00166667 0.        ]
 [0.01002004 0.         0.         0.01402806 0.00200401 0.06212425
  0.         0.         0.90981964 0.00200401]
 [0.01833333 0.05       0.00166667 0.         0.         0.04
  0.         0.         0.01333333 0.87666667]]
[2023-08-30 10:50:01,056 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 10:50:01,057 INFO] 104448 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0012, train/total_loss: 0.0012, train/util_ratio: 0.8750, train/run_time: 0.4114, eval/loss: 1.9209, eval/top-1-acc: 0.8876, eval/balanced_acc: 0.8895, eval/precision: 0.8897, eval/recall: 0.8895, eval/F1: 0.8850, lr: 0.0000, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 10:52:35,221 INFO] 104704 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4340, lr: 0.0000, train/prefecth_time: 0.0035 
[2023-08-30 10:54:26,062 INFO] 104960 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4035, lr: 0.0000, train/prefecth_time: 0.0034 
[2023-08-30 10:56:16,234 INFO] 105216 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3611, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 10:58:05,926 INFO] 105472 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4123, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 11:00:39,420 INFO] 105728 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4120, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 11:02:30,508 INFO] 105984 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4210, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 11:04:20,311 INFO] 106240 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4220, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 11:06:13,043 INFO] validating...
[2023-08-30 11:06:37,545 INFO] confusion matrix:
[[0.86666667 0.005      0.         0.         0.00166667 0.06166667
  0.04       0.00166667 0.02333333 0.        ]
 [0.         0.87833333 0.005      0.         0.         0.105
  0.         0.00166667 0.00333333 0.00666667]
 [0.         0.00333333 0.71333333 0.00833333 0.02333333 0.09166667
  0.06333333 0.06       0.03666667 0.        ]
 [0.004      0.         0.         0.86       0.048      0.01
  0.004      0.024      0.05       0.        ]
 [0.         0.         0.         0.002      0.992      0.
  0.         0.006      0.         0.        ]
 [0.0025     0.0075     0.         0.0025     0.         0.9125
  0.0725     0.         0.0025     0.        ]
 [0.022      0.         0.09       0.004      0.008      0.012
  0.846      0.014      0.004      0.        ]
 [0.         0.00166667 0.00166667 0.         0.00166667 0.
  0.         0.99333333 0.00166667 0.        ]
 [0.00801603 0.         0.         0.01202405 0.         0.06012024
  0.         0.         0.91783567 0.00200401]
 [0.01666667 0.04       0.00166667 0.         0.         0.03833333
  0.         0.         0.015      0.88833333]]
[2023-08-30 11:06:38,608 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 11:06:38,610 INFO] 106496 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4083, eval/loss: 1.9885, eval/top-1-acc: 0.8846, eval/balanced_acc: 0.8868, eval/precision: 0.8872, eval/recall: 0.8868, eval/F1: 0.8820, lr: 0.0000, train/prefecth_time: 0.0027 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 11:09:10,842 INFO] 106752 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4411, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 11:11:00,499 INFO] 107008 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0003, train/total_loss: 0.0003, train/util_ratio: 1.0000, train/run_time: 0.4171, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 11:12:49,976 INFO] 107264 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4093, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 11:14:40,282 INFO] 107520 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3608, lr: 0.0000, train/prefecth_time: 0.0021 
[2023-08-30 11:17:15,171 INFO] 107776 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4182, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 11:19:05,730 INFO] 108032 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0005, train/total_loss: 0.0005, train/util_ratio: 1.0000, train/run_time: 0.4205, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 11:20:56,262 INFO] 108288 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4222, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 11:22:46,491 INFO] validating...
[2023-08-30 11:23:10,818 INFO] confusion matrix:
[[0.87333333 0.00333333 0.         0.         0.         0.06666667
  0.04       0.00166667 0.015      0.        ]
 [0.         0.87166667 0.005      0.         0.         0.11333333
  0.         0.         0.00333333 0.00666667]
 [0.         0.00333333 0.72       0.00666667 0.02666667 0.09833333
  0.05166667 0.06333333 0.03       0.        ]
 [0.004      0.         0.         0.886      0.042      0.014
  0.002      0.022      0.03       0.        ]
 [0.         0.         0.         0.002      0.994      0.
  0.         0.004      0.         0.        ]
 [0.0025     0.005      0.0025     0.0025     0.         0.9225
  0.065      0.         0.         0.        ]
 [0.022      0.         0.094      0.004      0.01       0.014
  0.834      0.018      0.004      0.        ]
 [0.         0.00166667 0.00166667 0.         0.00166667 0.
  0.         0.99333333 0.00166667 0.        ]
 [0.01402806 0.         0.         0.01202405 0.00200401 0.07214429
  0.00200401 0.         0.89579158 0.00200401]
 [0.02       0.04       0.00333333 0.         0.         0.04166667
  0.         0.         0.01333333 0.88166667]]
[2023-08-30 11:23:11,687 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 11:23:11,688 INFO] 108544 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3652, eval/loss: 2.0222, eval/top-1-acc: 0.8848, eval/balanced_acc: 0.8872, eval/precision: 0.8888, eval/recall: 0.8872, eval/F1: 0.8827, lr: 0.0000, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 11:25:46,937 INFO] 108800 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4395, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 11:27:37,285 INFO] 109056 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3614, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 11:29:29,127 INFO] 109312 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0025, train/total_loss: 0.0025, train/util_ratio: 1.0000, train/run_time: 0.4185, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 11:31:20,484 INFO] 109568 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3558, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 11:33:51,750 INFO] 109824 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4142, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 11:35:42,272 INFO] 110080 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3661, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 11:37:33,374 INFO] 110336 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4325, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 11:39:23,826 INFO] validating...
[2023-08-30 11:39:48,188 INFO] confusion matrix:
[[0.865      0.00333333 0.         0.         0.         0.08166667
  0.035      0.00166667 0.01333333 0.        ]
 [0.         0.86666667 0.005      0.         0.         0.11833333
  0.         0.         0.00333333 0.00666667]
 [0.         0.00333333 0.7        0.00833333 0.025      0.11666667
  0.05833333 0.05833333 0.03       0.        ]
 [0.004      0.         0.         0.896      0.034      0.016
  0.002      0.02       0.028      0.        ]
 [0.         0.         0.         0.004      0.99       0.
  0.         0.006      0.         0.        ]
 [0.0025     0.005      0.0025     0.005      0.         0.94
  0.045      0.         0.         0.        ]
 [0.026      0.         0.1        0.006      0.008      0.018
  0.822      0.016      0.004      0.        ]
 [0.         0.00166667 0.00166667 0.         0.         0.
  0.         0.995      0.00166667 0.        ]
 [0.01803607 0.         0.         0.01202405 0.00200401 0.08016032
  0.00400802 0.         0.88176353 0.00200401]
 [0.01833333 0.03166667 0.00333333 0.         0.         0.04666667
  0.         0.         0.015      0.885     ]]
[2023-08-30 11:39:49,001 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 11:39:49,002 INFO] 110592 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0008, train/total_loss: 0.0008, train/util_ratio: 1.0000, train/run_time: 0.3932, eval/loss: 2.0635, eval/top-1-acc: 0.8811, eval/balanced_acc: 0.8841, eval/precision: 0.8874, eval/recall: 0.8841, eval/F1: 0.8794, lr: 0.0000, train/prefecth_time: 0.0023 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 11:42:23,226 INFO] 110848 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4111, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 11:44:14,337 INFO] 111104 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3724, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 11:46:05,370 INFO] 111360 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0063, train/total_loss: 0.0063, train/util_ratio: 1.0000, train/run_time: 0.3820, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 11:47:55,785 INFO] 111616 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4153, lr: 0.0000, train/prefecth_time: 0.0020 
[2023-08-30 11:50:30,884 INFO] 111872 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4137, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 11:52:21,707 INFO] 112128 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3587, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 11:54:15,344 INFO] 112384 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3983, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 11:56:06,180 INFO] validating...
[2023-08-30 11:56:30,616 INFO] confusion matrix:
[[0.855      0.00333333 0.         0.         0.         0.09166667
  0.03333333 0.00166667 0.015      0.        ]
 [0.         0.87166667 0.00833333 0.         0.         0.10666667
  0.         0.         0.00333333 0.01      ]
 [0.         0.00333333 0.745      0.00833333 0.025      0.09833333
  0.04333333 0.05833333 0.01833333 0.        ]
 [0.004      0.         0.002      0.902      0.024      0.016
  0.002      0.02       0.03       0.        ]
 [0.         0.         0.         0.004      0.99       0.
  0.         0.006      0.         0.        ]
 [0.         0.005      0.0025     0.0025     0.         0.9425
  0.0475     0.         0.         0.        ]
 [0.02       0.         0.128      0.004      0.012      0.018
  0.792      0.022      0.004      0.        ]
 [0.         0.00166667 0.00166667 0.00166667 0.         0.
  0.         0.99333333 0.00166667 0.        ]
 [0.02004008 0.         0.         0.01202405 0.00200401 0.08216433
  0.00400802 0.         0.87775551 0.00200401]
 [0.015      0.025      0.00166667 0.         0.         0.04
  0.         0.         0.015      0.90333333]]
[2023-08-30 11:56:31,615 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 11:56:31,616 INFO] 112640 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3909, eval/loss: 2.0268, eval/top-1-acc: 0.8850, eval/balanced_acc: 0.8873, eval/precision: 0.8902, eval/recall: 0.8873, eval/F1: 0.8832, lr: 0.0000, train/prefecth_time: 0.0023 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 11:59:07,447 INFO] 112896 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4114, lr: 0.0000, train/prefecth_time: 0.0033 
[2023-08-30 12:01:01,167 INFO] 113152 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4094, lr: 0.0000, train/prefecth_time: 0.0038 
[2023-08-30 12:02:52,531 INFO] 113408 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0011, train/total_loss: 0.0011, train/util_ratio: 1.0000, train/run_time: 0.4074, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 12:04:45,427 INFO] 113664 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4139, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 12:07:18,598 INFO] 113920 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0006, train/total_loss: 0.0006, train/util_ratio: 1.0000, train/run_time: 0.4350, lr: 0.0000, train/prefecth_time: 0.0035 
[2023-08-30 12:09:11,826 INFO] 114176 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4080, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 12:11:03,325 INFO] 114432 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3885, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 12:12:54,657 INFO] validating...
[2023-08-30 12:13:19,124 INFO] confusion matrix:
[[0.85333333 0.00333333 0.         0.         0.         0.09666667
  0.03       0.00166667 0.015      0.        ]
 [0.         0.87       0.00666667 0.         0.         0.11
  0.         0.         0.00333333 0.01      ]
 [0.         0.005      0.74833333 0.00833333 0.02333333 0.10166667
  0.04       0.055      0.01833333 0.        ]
 [0.004      0.         0.002      0.922      0.016      0.016
  0.         0.012      0.028      0.        ]
 [0.         0.         0.         0.006      0.99       0.
  0.         0.004      0.         0.        ]
 [0.         0.005      0.0025     0.005      0.         0.9375
  0.05       0.         0.         0.        ]
 [0.014      0.         0.146      0.006      0.012      0.02
  0.784      0.014      0.004      0.        ]
 [0.         0.00166667 0.00166667 0.00166667 0.         0.
  0.         0.99333333 0.00166667 0.        ]
 [0.01803607 0.         0.         0.01202405 0.00200401 0.09018036
  0.00400802 0.         0.87174349 0.00200401]
 [0.01166667 0.02       0.         0.         0.         0.035
  0.         0.         0.015      0.91833333]]
[2023-08-30 12:13:20,192 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 12:13:20,194 INFO] 114688 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4113, eval/loss: 2.0181, eval/top-1-acc: 0.8868, eval/balanced_acc: 0.8889, eval/precision: 0.8922, eval/recall: 0.8889, eval/F1: 0.8850, lr: 0.0000, train/prefecth_time: 0.0034 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 12:15:53,609 INFO] 114944 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3801, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 12:17:44,004 INFO] 115200 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3740, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 12:19:34,921 INFO] 115456 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4103, lr: 0.0000, train/prefecth_time: 0.0034 
[2023-08-30 12:21:26,706 INFO] 115712 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0006, train/total_loss: 0.0006, train/util_ratio: 1.0000, train/run_time: 0.4163, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 12:23:59,037 INFO] 115968 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0010, train/total_loss: 0.0010, train/util_ratio: 1.0000, train/run_time: 0.3609, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 12:25:52,005 INFO] 116224 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4063, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 12:27:44,294 INFO] 116480 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4237, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 12:29:36,886 INFO] validating...
[2023-08-30 12:30:01,289 INFO] confusion matrix:
[[0.845      0.00333333 0.         0.         0.         0.105
  0.02666667 0.00166667 0.01833333 0.        ]
 [0.         0.88333333 0.005      0.         0.         0.09833333
  0.         0.         0.00333333 0.01      ]
 [0.         0.005      0.74833333 0.01166667 0.01666667 0.10666667
  0.035      0.055      0.02166667 0.        ]
 [0.004      0.         0.002      0.92       0.014      0.014
  0.         0.01       0.036      0.        ]
 [0.         0.         0.         0.006      0.986      0.
  0.         0.008      0.         0.        ]
 [0.         0.005      0.005      0.0075     0.         0.945
  0.0375     0.         0.         0.        ]
 [0.014      0.         0.144      0.01       0.012      0.02
  0.778      0.018      0.004      0.        ]
 [0.         0.00166667 0.00166667 0.00166667 0.         0.
  0.         0.99333333 0.00166667 0.        ]
 [0.01603206 0.         0.         0.01202405 0.00200401 0.08416834
  0.00400802 0.         0.87975952 0.00200401]
 [0.01       0.01666667 0.         0.         0.         0.03166667
  0.         0.         0.01666667 0.925     ]]
[2023-08-30 12:30:02,183 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 12:30:02,184 INFO] 116736 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.7500, train/run_time: 0.4110, eval/loss: 2.0619, eval/top-1-acc: 0.8883, eval/balanced_acc: 0.8904, eval/precision: 0.8935, eval/recall: 0.8904, eval/F1: 0.8864, lr: 0.0000, train/prefecth_time: 0.0021 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 12:32:37,991 INFO] 116992 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0022, train/total_loss: 0.0022, train/util_ratio: 1.0000, train/run_time: 0.4133, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 12:34:29,143 INFO] 117248 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3573, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 12:36:19,447 INFO] 117504 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4088, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 12:38:11,497 INFO] 117760 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4131, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 12:40:45,674 INFO] 118016 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0015, train/total_loss: 0.0015, train/util_ratio: 1.0000, train/run_time: 0.4417, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 12:42:38,044 INFO] 118272 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3557, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 12:44:31,756 INFO] 118528 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4108, lr: 0.0000, train/prefecth_time: 0.0036 
[2023-08-30 12:46:23,901 INFO] validating...
[2023-08-30 12:46:48,291 INFO] confusion matrix:
[[0.83833333 0.005      0.00166667 0.         0.         0.11
  0.025      0.00166667 0.01833333 0.        ]
 [0.         0.885      0.005      0.         0.         0.095
  0.         0.         0.005      0.01      ]
 [0.         0.005      0.735      0.00833333 0.01833333 0.11333333
  0.02666667 0.06333333 0.03       0.        ]
 [0.004      0.         0.002      0.91       0.012      0.014
  0.         0.016      0.042      0.        ]
 [0.         0.         0.         0.006      0.98       0.
  0.         0.014      0.         0.        ]
 [0.         0.015      0.005      0.005      0.         0.9325
  0.0425     0.         0.         0.        ]
 [0.014      0.         0.16       0.012      0.012      0.02
  0.756      0.024      0.002      0.        ]
 [0.         0.00166667 0.00166667 0.00166667 0.         0.
  0.         0.99333333 0.00166667 0.        ]
 [0.01803607 0.         0.         0.01202405 0.00400802 0.08016032
  0.00400802 0.         0.87975952 0.00200401]
 [0.01166667 0.01666667 0.         0.         0.         0.03166667
  0.         0.         0.02       0.92      ]]
[2023-08-30 12:46:49,334 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 12:46:49,335 INFO] 118784 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3600, eval/loss: 2.1849, eval/top-1-acc: 0.8813, eval/balanced_acc: 0.8830, eval/precision: 0.8869, eval/recall: 0.8830, eval/F1: 0.8792, lr: 0.0000, train/prefecth_time: 0.0026 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 12:49:24,841 INFO] 119040 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3586, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 12:51:19,162 INFO] 119296 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4175, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 12:53:12,177 INFO] 119552 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.4154, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 12:55:05,141 INFO] 119808 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4147, lr: 0.0000, train/prefecth_time: 0.0021 
[2023-08-30 12:57:41,820 INFO] 120064 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0007, train/total_loss: 0.0007, train/util_ratio: 1.0000, train/run_time: 0.4342, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 12:59:35,648 INFO] 120320 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0009, train/total_loss: 0.0009, train/util_ratio: 1.0000, train/run_time: 0.4152, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 13:01:27,715 INFO] 120576 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0024, train/total_loss: 0.0024, train/util_ratio: 1.0000, train/run_time: 0.4400, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 13:03:20,588 INFO] validating...
[2023-08-30 13:03:44,960 INFO] confusion matrix:
[[0.83833333 0.005      0.00333333 0.         0.         0.09833333
  0.03       0.00166667 0.02333333 0.        ]
 [0.         0.89666667 0.005      0.         0.         0.07833333
  0.         0.         0.01       0.01      ]
 [0.         0.005      0.69333333 0.01166667 0.02166667 0.12833333
  0.02666667 0.07166667 0.04166667 0.        ]
 [0.004      0.         0.002      0.898      0.014      0.014
  0.002      0.016      0.05       0.        ]
 [0.         0.         0.         0.004      0.988      0.
  0.         0.008      0.         0.        ]
 [0.         0.025      0.0025     0.0075     0.         0.9175
  0.045      0.         0.0025     0.        ]
 [0.018      0.         0.166      0.012      0.018      0.018
  0.74       0.024      0.004      0.        ]
 [0.         0.00166667 0.         0.00166667 0.         0.
  0.         0.995      0.00166667 0.        ]
 [0.01603206 0.         0.         0.01402806 0.00601202 0.07214429
  0.00200401 0.         0.88777555 0.00200401]
 [0.01166667 0.02       0.         0.         0.         0.035
  0.         0.         0.02166667 0.91166667]]
[2023-08-30 13:03:45,901 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 13:03:45,902 INFO] 120832 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0006, train/total_loss: 0.0006, train/util_ratio: 0.8750, train/run_time: 0.4948, eval/loss: 2.3157, eval/top-1-acc: 0.8750, eval/balanced_acc: 0.8766, eval/precision: 0.8789, eval/recall: 0.8766, eval/F1: 0.8721, lr: 0.0000, train/prefecth_time: 0.0022 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 13:06:21,946 INFO] 121088 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0009, train/total_loss: 0.0009, train/util_ratio: 1.0000, train/run_time: 0.4846, lr: 0.0000, train/prefecth_time: 0.0035 
[2023-08-30 13:08:16,497 INFO] 121344 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4182, lr: 0.0000, train/prefecth_time: 0.0034 
[2023-08-30 13:10:11,316 INFO] 121600 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0044, train/total_loss: 0.0044, train/util_ratio: 1.0000, train/run_time: 0.4200, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 13:12:04,859 INFO] 121856 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4287, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 13:14:43,000 INFO] 122112 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4503, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 13:16:37,335 INFO] 122368 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0046, train/total_loss: 0.0046, train/util_ratio: 1.0000, train/run_time: 0.4150, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 13:18:29,816 INFO] 122624 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4343, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 13:20:20,856 INFO] validating...
[2023-08-30 13:20:45,224 INFO] confusion matrix:
[[0.85333333 0.005      0.00333333 0.         0.         0.08666667
  0.02833333 0.00166667 0.02166667 0.        ]
 [0.         0.9        0.005      0.         0.         0.075
  0.         0.         0.00833333 0.01166667]
 [0.         0.005      0.68166667 0.01       0.02       0.135
  0.03       0.07       0.04833333 0.        ]
 [0.004      0.         0.004      0.88       0.03       0.014
  0.004      0.016      0.048      0.        ]
 [0.         0.         0.         0.004      0.99       0.
  0.         0.006      0.         0.        ]
 [0.         0.03       0.         0.0075     0.         0.9125
  0.0475     0.         0.0025     0.        ]
 [0.016      0.         0.174      0.01       0.022      0.02
  0.73       0.024      0.004      0.        ]
 [0.         0.         0.         0.00166667 0.005      0.
  0.         0.99166667 0.00166667 0.        ]
 [0.01603206 0.         0.         0.01402806 0.01002004 0.07214429
  0.00200401 0.         0.88376754 0.00200401]
 [0.01166667 0.01666667 0.         0.         0.         0.03333333
  0.         0.         0.01666667 0.92166667]]
[2023-08-30 13:20:46,062 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 13:20:46,063 INFO] 122880 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0007, train/total_loss: 0.0007, train/util_ratio: 1.0000, train/run_time: 0.3579, eval/loss: 2.4099, eval/top-1-acc: 0.8733, eval/balanced_acc: 0.8745, eval/precision: 0.8762, eval/recall: 0.8745, eval/F1: 0.8698, lr: 0.0000, train/prefecth_time: 0.0022 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 13:23:21,338 INFO] 123136 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4155, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 13:25:14,909 INFO] 123392 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3928, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 13:27:07,806 INFO] 123648 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4212, lr: 0.0000, train/prefecth_time: 0.0058 
[2023-08-30 13:29:00,585 INFO] 123904 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0045, train/total_loss: 0.0045, train/util_ratio: 1.0000, train/run_time: 0.4224, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 13:31:35,837 INFO] 124160 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3583, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 13:33:26,639 INFO] 124416 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4279, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 13:35:17,617 INFO] 124672 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0030, train/total_loss: 0.0030, train/util_ratio: 1.0000, train/run_time: 0.4251, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 13:37:09,449 INFO] validating...
[2023-08-30 13:37:33,820 INFO] confusion matrix:
[[0.85833333 0.005      0.00333333 0.         0.         0.08666667
  0.025      0.00166667 0.02       0.        ]
 [0.         0.89333333 0.00333333 0.         0.         0.08333333
  0.         0.00166667 0.00333333 0.015     ]
 [0.         0.005      0.65166667 0.01       0.02333333 0.15166667
  0.03333333 0.075      0.05       0.        ]
 [0.004      0.         0.004      0.868      0.044      0.014
  0.002      0.022      0.042      0.        ]
 [0.         0.         0.         0.002      0.99       0.
  0.         0.008      0.         0.        ]
 [0.         0.03       0.         0.0075     0.         0.9175
  0.0425     0.         0.0025     0.        ]
 [0.016      0.         0.162      0.01       0.022      0.034
  0.728      0.024      0.004      0.        ]
 [0.         0.         0.         0.         0.005      0.
  0.         0.99333333 0.00166667 0.        ]
 [0.01803607 0.         0.         0.01002004 0.02004008 0.08216433
  0.00400802 0.00200401 0.86172345 0.00200401]
 [0.01166667 0.01666667 0.         0.         0.         0.035
  0.         0.         0.01666667 0.92      ]]
[2023-08-30 13:37:34,586 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 13:37:34,586 INFO] 124928 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4096, eval/loss: 2.5851, eval/top-1-acc: 0.8668, eval/balanced_acc: 0.8682, eval/precision: 0.8720, eval/recall: 0.8682, eval/F1: 0.8632, lr: 0.0000, train/prefecth_time: 0.0023 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 13:40:09,514 INFO] 125184 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4206, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 13:42:00,068 INFO] 125440 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4240, lr: 0.0000, train/prefecth_time: 0.0034 
[2023-08-30 13:43:51,760 INFO] 125696 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4162, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 13:45:43,297 INFO] 125952 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4166, lr: 0.0000, train/prefecth_time: 0.0021 
[2023-08-30 13:48:20,416 INFO] 126208 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4132, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 13:50:13,442 INFO] 126464 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3580, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 13:52:04,770 INFO] 126720 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4218, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 13:53:57,969 INFO] validating...
[2023-08-30 13:54:22,414 INFO] confusion matrix:
[[0.85666667 0.00333333 0.00333333 0.         0.         0.09166667
  0.025      0.         0.02       0.        ]
 [0.00166667 0.88166667 0.00833333 0.         0.         0.08666667
  0.         0.005      0.00333333 0.01333333]
 [0.         0.005      0.67       0.00833333 0.02166667 0.15333333
  0.03333333 0.06666667 0.04166667 0.        ]
 [0.004      0.         0.004      0.868      0.046      0.014
  0.002      0.022      0.04       0.        ]
 [0.         0.         0.         0.002      0.99       0.
  0.         0.008      0.         0.        ]
 [0.         0.025      0.0025     0.01       0.         0.9225
  0.0375     0.         0.0025     0.        ]
 [0.022      0.         0.154      0.008      0.018      0.038
  0.73       0.026      0.004      0.        ]
 [0.         0.         0.         0.         0.00333333 0.
  0.         0.995      0.00166667 0.        ]
 [0.02004008 0.         0.         0.01002004 0.0260521  0.08817635
  0.00200401 0.00601202 0.84569138 0.00200401]
 [0.01333333 0.02333333 0.         0.         0.00166667 0.035
  0.         0.         0.01833333 0.90833333]]
[2023-08-30 13:54:23,433 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 13:54:23,434 INFO] 126976 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4147, eval/loss: 2.6615, eval/top-1-acc: 0.8653, eval/balanced_acc: 0.8668, eval/precision: 0.8719, eval/recall: 0.8668, eval/F1: 0.8622, lr: 0.0000, train/prefecth_time: 0.0023 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 13:57:00,311 INFO] 127232 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4187, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 13:58:53,222 INFO] 127488 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3695, lr: 0.0000, train/prefecth_time: 0.0036 
[2023-08-30 14:00:45,760 INFO] 127744 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0020, train/total_loss: 0.0020, train/util_ratio: 1.0000, train/run_time: 0.4254, lr: 0.0000, train/prefecth_time: 0.0039 
[2023-08-30 14:02:37,753 INFO] 128000 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4128, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 14:05:13,927 INFO] 128256 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0106, train/total_loss: 0.0106, train/util_ratio: 1.0000, train/run_time: 0.3690, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 14:07:05,821 INFO] 128512 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4086, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 14:08:57,574 INFO] 128768 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4295, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 14:10:51,613 INFO] validating...
[2023-08-30 14:11:15,751 INFO] confusion matrix:
[[0.85833333 0.005      0.005      0.00333333 0.         0.09166667
  0.025      0.         0.01166667 0.        ]
 [0.00166667 0.86       0.00833333 0.         0.         0.11
  0.         0.00333333 0.00333333 0.01333333]
 [0.         0.005      0.64833333 0.01       0.02833333 0.15833333
  0.04833333 0.07       0.03166667 0.        ]
 [0.004      0.         0.004      0.872      0.048      0.012
  0.002      0.024      0.034      0.        ]
 [0.         0.         0.         0.002      0.988      0.
  0.         0.01       0.         0.        ]
 [0.         0.0175     0.0025     0.01       0.         0.9325
  0.0375     0.         0.         0.        ]
 [0.024      0.         0.136      0.012      0.018      0.036
  0.75       0.022      0.002      0.        ]
 [0.         0.         0.         0.         0.005      0.
  0.         0.995      0.         0.        ]
 [0.02004008 0.         0.         0.02004008 0.03006012 0.1002004
  0.00200401 0.00601202 0.81963928 0.00200401]
 [0.01333333 0.02166667 0.         0.         0.00166667 0.035
  0.         0.         0.01666667 0.91166667]]
[2023-08-30 14:11:16,569 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 14:11:16,570 INFO] 129024 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4084, eval/loss: 2.7613, eval/top-1-acc: 0.8615, eval/balanced_acc: 0.8635, eval/precision: 0.8695, eval/recall: 0.8635, eval/F1: 0.8585, lr: 0.0000, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 14:13:52,338 INFO] 129280 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4113, lr: 0.0000, train/prefecth_time: 0.0037 
[2023-08-30 14:15:44,341 INFO] 129536 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0006, train/total_loss: 0.0006, train/util_ratio: 0.8750, train/run_time: 0.4157, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-30 14:17:35,540 INFO] 129792 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4231, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 14:19:27,599 INFO] 130048 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4105, lr: 0.0000, train/prefecth_time: 0.0021 
[2023-08-30 14:22:03,164 INFO] 130304 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4190, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 14:23:53,817 INFO] 130560 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4215, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 14:25:44,854 INFO] 130816 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0050, train/total_loss: 0.0050, train/util_ratio: 1.0000, train/run_time: 0.4136, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 14:27:38,365 INFO] validating...
[2023-08-30 14:28:02,864 INFO] confusion matrix:
[[0.85833333 0.00666667 0.005      0.00333333 0.         0.09
  0.03       0.         0.00666667 0.        ]
 [0.00166667 0.855      0.01       0.         0.         0.10833333
  0.         0.005      0.00333333 0.01666667]
 [0.         0.005      0.66       0.01       0.02833333 0.15833333
  0.04833333 0.06333333 0.02666667 0.        ]
 [0.004      0.         0.004      0.868      0.054      0.012
  0.002      0.022      0.034      0.        ]
 [0.         0.         0.         0.002      0.988      0.
  0.         0.01       0.         0.        ]
 [0.         0.02       0.0025     0.0125     0.005      0.915
  0.045      0.         0.         0.        ]
 [0.014      0.         0.14       0.012      0.016      0.03
  0.766      0.02       0.002      0.        ]
 [0.         0.         0.         0.00166667 0.00333333 0.
  0.         0.995      0.         0.        ]
 [0.02805611 0.00200401 0.         0.02805611 0.03807615 0.11222445
  0.01002004 0.00601202 0.77354709 0.00200401]
 [0.01166667 0.01833333 0.         0.         0.00166667 0.03833333
  0.         0.         0.01666667 0.91333333]]
[2023-08-30 14:28:03,893 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 14:28:03,894 INFO] 131072 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.4141, eval/loss: 2.7962, eval/top-1-acc: 0.8579, eval/balanced_acc: 0.8592, eval/precision: 0.8656, eval/recall: 0.8592, eval/F1: 0.8547, lr: 0.0000, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 14:30:37,089 INFO] 131328 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0022, train/total_loss: 0.0022, train/util_ratio: 1.0000, train/run_time: 0.4104, lr: 0.0000, train/prefecth_time: 0.0034 
[2023-08-30 14:32:30,052 INFO] 131584 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4056, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 14:34:22,003 INFO] 131840 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0003, train/total_loss: 0.0003, train/util_ratio: 1.0000, train/run_time: 0.3905, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 14:36:16,273 INFO] 132096 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4173, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 14:38:51,244 INFO] 132352 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4148, lr: 0.0000, train/prefecth_time: 0.0039 
[2023-08-30 14:40:43,618 INFO] 132608 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4294, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 14:42:35,200 INFO] 132864 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4158, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 14:44:28,181 INFO] validating...
[2023-08-30 14:44:52,136 INFO] confusion matrix:
[[0.85333333 0.00833333 0.00666667 0.00333333 0.         0.09166667
  0.03166667 0.         0.005      0.        ]
 [0.         0.86833333 0.01       0.         0.         0.09333333
  0.         0.00333333 0.00166667 0.02333333]
 [0.         0.005      0.665      0.01333333 0.02166667 0.16333333
  0.04666667 0.06166667 0.02333333 0.        ]
 [0.004      0.         0.004      0.878      0.044      0.012
  0.002      0.024      0.032      0.        ]
 [0.         0.         0.         0.008      0.972      0.
  0.002      0.018      0.         0.        ]
 [0.         0.02       0.0025     0.015      0.005      0.905
  0.0525     0.         0.         0.        ]
 [0.012      0.         0.144      0.014      0.016      0.03
  0.764      0.018      0.002      0.        ]
 [0.         0.         0.         0.00166667 0.00333333 0.
  0.         0.995      0.         0.        ]
 [0.03006012 0.00400802 0.         0.03406814 0.04008016 0.13226453
  0.01402806 0.00601202 0.73747495 0.00200401]
 [0.01       0.01833333 0.         0.         0.00166667 0.04
  0.         0.         0.01166667 0.91833333]]
[2023-08-30 14:44:52,921 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 14:44:52,922 INFO] 133120 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0060, train/total_loss: 0.0060, train/util_ratio: 1.0000, train/run_time: 0.4155, eval/loss: 2.8709, eval/top-1-acc: 0.8552, eval/balanced_acc: 0.8556, eval/precision: 0.8629, eval/recall: 0.8556, eval/F1: 0.8516, lr: 0.0000, train/prefecth_time: 0.0022 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 14:47:28,344 INFO] 133376 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3832, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 14:49:20,318 INFO] 133632 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4164, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 14:51:12,379 INFO] 133888 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3756, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 14:53:03,374 INFO] 134144 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3591, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 14:55:37,792 INFO] 134400 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3676, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-30 14:57:29,309 INFO] 134656 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4155, lr: 0.0000, train/prefecth_time: 0.0038 
[2023-08-30 14:59:20,974 INFO] 134912 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4105, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 15:01:14,598 INFO] validating...
[2023-08-30 15:01:39,016 INFO] confusion matrix:
[[0.855      0.01       0.00666667 0.00333333 0.         0.08833333
  0.03333333 0.         0.00333333 0.        ]
 [0.         0.85833333 0.00833333 0.         0.         0.1
  0.         0.00166667 0.00166667 0.03      ]
 [0.         0.00666667 0.67833333 0.01333333 0.01833333 0.165
  0.05       0.04833333 0.02       0.        ]
 [0.002      0.         0.004      0.886      0.042      0.014
  0.002      0.02       0.03       0.        ]
 [0.         0.         0.         0.008      0.972      0.
  0.002      0.018      0.         0.        ]
 [0.0025     0.0125     0.0025     0.0125     0.005      0.915
  0.05       0.         0.         0.        ]
 [0.014      0.         0.15       0.014      0.014      0.032
  0.762      0.014      0.         0.        ]
 [0.         0.         0.00166667 0.00166667 0.00333333 0.
  0.         0.99333333 0.         0.        ]
 [0.03607214 0.00601202 0.00200401 0.03406814 0.04008016 0.13827655
  0.01803607 0.00801603 0.71543086 0.00200401]
 [0.01       0.02       0.         0.         0.00166667 0.03166667
  0.         0.         0.00666667 0.93      ]]
[2023-08-30 15:01:39,805 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 15:01:39,806 INFO] 135168 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0034, train/total_loss: 0.0034, train/util_ratio: 0.8750, train/run_time: 0.4157, eval/loss: 2.8951, eval/top-1-acc: 0.8561, eval/balanced_acc: 0.8565, eval/precision: 0.8643, eval/recall: 0.8565, eval/F1: 0.8525, lr: 0.0000, train/prefecth_time: 0.0028 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 15:04:11,840 INFO] 135424 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4295, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 15:06:04,546 INFO] 135680 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4183, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 15:07:56,602 INFO] 135936 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3600, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 15:09:48,117 INFO] 136192 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4205, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 15:12:24,289 INFO] 136448 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0007, train/total_loss: 0.0007, train/util_ratio: 1.0000, train/run_time: 0.4118, lr: 0.0000, train/prefecth_time: 0.0035 
[2023-08-30 15:14:15,760 INFO] 136704 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4405, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 15:16:07,912 INFO] 136960 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3804, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 15:18:01,358 INFO] validating...
[2023-08-30 15:18:25,323 INFO] confusion matrix:
[[0.86166667 0.00833333 0.00666667 0.00333333 0.         0.085
  0.03333333 0.         0.00166667 0.        ]
 [0.         0.83833333 0.00666667 0.00166667 0.         0.12666667
  0.         0.00166667 0.00166667 0.02333333]
 [0.         0.005      0.66333333 0.01666667 0.02333333 0.18666667
  0.03833333 0.05333333 0.01333333 0.        ]
 [0.002      0.         0.004      0.89       0.042      0.014
  0.002      0.02       0.026      0.        ]
 [0.         0.         0.         0.01       0.966      0.
  0.         0.024      0.         0.        ]
 [0.0025     0.01       0.0025     0.015      0.0075     0.9075
  0.055      0.         0.         0.        ]
 [0.022      0.         0.166      0.016      0.016      0.038
  0.726      0.016      0.         0.        ]
 [0.         0.         0.         0.00166667 0.00333333 0.
  0.         0.995      0.         0.        ]
 [0.04008016 0.00601202 0.00200401 0.04208417 0.04208417 0.14629259
  0.01603206 0.00601202 0.69739479 0.00200401]
 [0.00833333 0.01666667 0.00166667 0.         0.00166667 0.035
  0.         0.         0.00666667 0.93      ]]
[2023-08-30 15:18:26,068 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 15:18:26,069 INFO] 137216 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4540, eval/loss: 3.0862, eval/top-1-acc: 0.8474, eval/balanced_acc: 0.8475, eval/precision: 0.8591, eval/recall: 0.8475, eval/F1: 0.8438, lr: 0.0000, train/prefecth_time: 0.0021 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 15:21:01,604 INFO] 137472 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4201, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 15:22:54,232 INFO] 137728 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4198, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 15:24:46,976 INFO] 137984 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.3640, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 15:26:41,156 INFO] 138240 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4082, lr: 0.0000, train/prefecth_time: 0.0021 
[2023-08-30 15:29:17,217 INFO] 138496 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4133, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 15:31:08,914 INFO] 138752 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3652, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 15:32:59,648 INFO] 139008 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0028, train/total_loss: 0.0028, train/util_ratio: 1.0000, train/run_time: 0.4394, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 15:34:51,551 INFO] validating...
[2023-08-30 15:35:15,881 INFO] confusion matrix:
[[0.86833333 0.00666667 0.00666667 0.00166667 0.         0.085
  0.03       0.         0.00166667 0.        ]
 [0.00166667 0.81666667 0.00666667 0.         0.         0.14833333
  0.         0.00166667 0.00166667 0.02333333]
 [0.         0.005      0.68       0.01       0.02       0.19666667
  0.03       0.04666667 0.01166667 0.        ]
 [0.004      0.         0.004      0.888      0.046      0.014
  0.         0.02       0.024      0.        ]
 [0.002      0.         0.         0.01       0.96       0.
  0.         0.028      0.         0.        ]
 [0.0025     0.005      0.0025     0.0125     0.0075     0.915
  0.055      0.         0.         0.        ]
 [0.028      0.         0.19       0.016      0.016      0.044
  0.696      0.01       0.         0.        ]
 [0.         0.00166667 0.00166667 0.00166667 0.00333333 0.
  0.         0.99166667 0.         0.        ]
 [0.03807615 0.00400802 0.00200401 0.03807615 0.04208417 0.14629259
  0.01402806 0.00601202 0.70741483 0.00200401]
 [0.00833333 0.01833333 0.00166667 0.         0.00166667 0.04
  0.         0.         0.005      0.925     ]]
[2023-08-30 15:35:16,758 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 15:35:16,759 INFO] 139264 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4121, eval/loss: 3.1923, eval/top-1-acc: 0.8446, eval/balanced_acc: 0.8448, eval/precision: 0.8596, eval/recall: 0.8448, eval/F1: 0.8419, lr: 0.0000, train/prefecth_time: 0.0022 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 15:37:52,652 INFO] 139520 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3520, lr: 0.0000, train/prefecth_time: 0.0035 
[2023-08-30 15:39:44,009 INFO] 139776 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4293, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 15:41:36,059 INFO] 140032 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4141, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 15:43:28,797 INFO] 140288 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4157, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 15:46:04,176 INFO] 140544 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0003, train/total_loss: 0.0003, train/util_ratio: 1.0000, train/run_time: 0.4174, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-30 15:47:55,490 INFO] 140800 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0005, train/total_loss: 0.0005, train/util_ratio: 1.0000, train/run_time: 0.4077, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 15:49:47,163 INFO] 141056 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4326, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 15:51:38,275 INFO] validating...
[2023-08-30 15:52:02,766 INFO] confusion matrix:
[[0.88333333 0.00666667 0.005      0.00166667 0.         0.07333333
  0.02833333 0.         0.00166667 0.        ]
 [0.00166667 0.82166667 0.00666667 0.00166667 0.         0.145
  0.         0.00166667 0.00166667 0.02      ]
 [0.         0.005      0.66666667 0.01833333 0.01333333 0.19333333
  0.035      0.05666667 0.01166667 0.        ]
 [0.004      0.         0.004      0.916      0.032      0.014
  0.         0.014      0.016      0.        ]
 [0.002      0.         0.         0.012      0.954      0.
  0.         0.032      0.         0.        ]
 [0.0025     0.0075     0.0025     0.015      0.01       0.8875
  0.075      0.         0.         0.        ]
 [0.032      0.         0.182      0.016      0.014      0.028
  0.712      0.016      0.         0.        ]
 [0.         0.00166667 0.00166667 0.00166667 0.00333333 0.
  0.         0.99166667 0.         0.        ]
 [0.04208417 0.00400802 0.00200401 0.04408818 0.04609218 0.13627255
  0.01402806 0.00601202 0.70340681 0.00200401]
 [0.00833333 0.02166667 0.00166667 0.         0.00166667 0.04333333
  0.         0.         0.005      0.91833333]]
[2023-08-30 15:52:03,602 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 15:52:03,603 INFO] 141312 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3579, eval/loss: 3.1852, eval/top-1-acc: 0.8457, eval/balanced_acc: 0.8455, eval/precision: 0.8580, eval/recall: 0.8455, eval/F1: 0.8424, lr: 0.0000, train/prefecth_time: 0.0021 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 15:54:36,937 INFO] 141568 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3612, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 15:56:28,222 INFO] 141824 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4130, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 15:58:18,790 INFO] 142080 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3736, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 16:00:10,069 INFO] 142336 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4096, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 16:02:45,687 INFO] 142592 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4345, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 16:04:36,668 INFO] 142848 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4415, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 16:06:30,450 INFO] 143104 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4150, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 16:08:22,418 INFO] validating...
[2023-08-30 16:08:47,005 INFO] confusion matrix:
[[0.89333333 0.00833333 0.00333333 0.00166667 0.         0.06333333
  0.02833333 0.00166667 0.         0.        ]
 [0.00166667 0.84166667 0.00833333 0.00166667 0.         0.13166667
  0.         0.00166667 0.00166667 0.01166667]
 [0.         0.00666667 0.66666667 0.02166667 0.01166667 0.185
  0.035      0.06333333 0.01       0.        ]
 [0.004      0.         0.004      0.928      0.026      0.012
  0.         0.01       0.016      0.        ]
 [0.002      0.         0.002      0.012      0.95       0.
  0.         0.034      0.         0.        ]
 [0.0025     0.0075     0.0025     0.015      0.01       0.8725
  0.09       0.         0.         0.        ]
 [0.032      0.         0.188      0.018      0.014      0.024
  0.706      0.018      0.         0.        ]
 [0.         0.00333333 0.00166667 0.00166667 0.00333333 0.
  0.         0.99       0.         0.        ]
 [0.05210421 0.00400802 0.00200401 0.04809619 0.04408818 0.12224449
  0.01002004 0.00601202 0.70941884 0.00200401]
 [0.00833333 0.02666667 0.00333333 0.00166667 0.00166667 0.04666667
  0.         0.         0.005      0.90666667]]
[2023-08-30 16:08:47,926 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 16:08:47,928 INFO] 143360 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4167, eval/loss: 3.1849, eval/top-1-acc: 0.8472, eval/balanced_acc: 0.8464, eval/precision: 0.8568, eval/recall: 0.8464, eval/F1: 0.8434, lr: 0.0000, train/prefecth_time: 0.0023 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 16:11:24,326 INFO] 143616 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4259, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 16:13:17,454 INFO] 143872 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3944, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 16:15:11,028 INFO] 144128 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 0.8750, train/run_time: 0.4023, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-30 16:17:01,028 INFO] 144384 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3583, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 16:19:36,789 INFO] 144640 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4080, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 16:21:27,807 INFO] 144896 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3645, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 16:23:20,102 INFO] 145152 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4020, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 16:25:10,074 INFO] validating...
[2023-08-30 16:25:34,204 INFO] confusion matrix:
[[0.89166667 0.00833333 0.00333333 0.00333333 0.         0.06333333
  0.02833333 0.00166667 0.         0.        ]
 [0.00166667 0.86166667 0.00666667 0.00166667 0.         0.115
  0.         0.00166667 0.00166667 0.01      ]
 [0.         0.00833333 0.67833333 0.01666667 0.01166667 0.17833333
  0.03       0.06666667 0.01       0.        ]
 [0.004      0.         0.004      0.938      0.018      0.012
  0.         0.01       0.014      0.        ]
 [0.002      0.         0.004      0.012      0.948      0.
  0.         0.034      0.         0.        ]
 [0.0025     0.01       0.0025     0.015      0.01       0.865
  0.095      0.         0.         0.        ]
 [0.032      0.         0.194      0.016      0.012      0.024
  0.704      0.018      0.         0.        ]
 [0.         0.005      0.00166667 0.00166667 0.00333333 0.
  0.         0.98833333 0.         0.        ]
 [0.04809619 0.00601202 0.00400802 0.04008016 0.03807615 0.1242485
  0.01002004 0.00400802 0.72344689 0.00200401]
 [0.01       0.04333333 0.00333333 0.00166667 0.00166667 0.05333333
  0.         0.         0.00666667 0.88      ]]
[2023-08-30 16:25:35,239 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 16:25:35,241 INFO] 145408 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0008, train/total_loss: 0.0008, train/util_ratio: 0.8750, train/run_time: 0.3564, eval/loss: 3.1909, eval/top-1-acc: 0.8487, eval/balanced_acc: 0.8478, eval/precision: 0.8578, eval/recall: 0.8478, eval/F1: 0.8452, lr: 0.0000, train/prefecth_time: 0.0028 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 16:28:10,395 INFO] 145664 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3985, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 16:30:01,313 INFO] 145920 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4299, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 16:31:52,117 INFO] 146176 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 0.8750, train/run_time: 0.3655, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 16:33:45,041 INFO] 146432 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0014, train/total_loss: 0.0014, train/util_ratio: 1.0000, train/run_time: 0.4143, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 16:36:20,142 INFO] 146688 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4740, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 16:38:11,951 INFO] 146944 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4117, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 16:40:04,243 INFO] 147200 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3608, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 16:41:56,119 INFO] validating...
[2023-08-30 16:42:20,395 INFO] confusion matrix:
[[0.89       0.01166667 0.00333333 0.00333333 0.         0.05833333
  0.03       0.00166667 0.00166667 0.        ]
 [0.00166667 0.86333333 0.00666667 0.00166667 0.         0.11666667
  0.         0.         0.00166667 0.00833333]
 [0.         0.00833333 0.67833333 0.01666667 0.01166667 0.18166667
  0.03333333 0.06       0.01       0.        ]
 [0.004      0.         0.004      0.938      0.022      0.012
  0.         0.01       0.01       0.        ]
 [0.002      0.         0.004      0.01       0.964      0.
  0.         0.02       0.         0.        ]
 [0.0025     0.0125     0.0025     0.015      0.01       0.8625
  0.095      0.         0.         0.        ]
 [0.036      0.         0.21       0.014      0.014      0.024
  0.688      0.014      0.         0.        ]
 [0.         0.005      0.00333333 0.00166667 0.00333333 0.
  0.         0.98666667 0.         0.        ]
 [0.04809619 0.00601202 0.00601202 0.03607214 0.03807615 0.1242485
  0.01202405 0.00400802 0.72344689 0.00200401]
 [0.01       0.06833333 0.00333333 0.00166667 0.00166667 0.05833333
  0.         0.         0.00666667 0.85      ]]
[2023-08-30 16:42:21,396 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 16:42:21,397 INFO] 147456 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4089, eval/loss: 3.2777, eval/top-1-acc: 0.8450, eval/balanced_acc: 0.8444, eval/precision: 0.8543, eval/recall: 0.8444, eval/F1: 0.8417, lr: 0.0000, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 16:44:54,192 INFO] 147712 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4306, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 16:46:46,112 INFO] 147968 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4093, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 16:48:37,698 INFO] 148224 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4299, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 16:50:28,320 INFO] 148480 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4130, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 16:53:03,012 INFO] 148736 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4202, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 16:54:56,637 INFO] 148992 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4664, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 16:56:48,548 INFO] 149248 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4218, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 16:58:41,965 INFO] validating...
[2023-08-30 16:59:06,439 INFO] confusion matrix:
[[0.88       0.01166667 0.00833333 0.00333333 0.         0.05666667
  0.03666667 0.00166667 0.00166667 0.        ]
 [0.         0.87       0.00666667 0.00166667 0.         0.11166667
  0.         0.         0.00166667 0.00833333]
 [0.         0.00833333 0.67333333 0.01333333 0.01833333 0.175
  0.04       0.05666667 0.015      0.        ]
 [0.006      0.         0.004      0.922      0.03       0.014
  0.         0.006      0.018      0.        ]
 [0.002      0.         0.002      0.008      0.976      0.
  0.         0.012      0.         0.        ]
 [0.0025     0.0125     0.0025     0.0125     0.01       0.8575
  0.1025     0.         0.         0.        ]
 [0.03       0.         0.21       0.012      0.014      0.02
  0.698      0.012      0.004      0.        ]
 [0.         0.00833333 0.005      0.00166667 0.00333333 0.
  0.         0.98       0.00166667 0.        ]
 [0.03607214 0.00400802 0.00400802 0.02805611 0.03607214 0.10821643
  0.01803607 0.00200401 0.76152305 0.00200401]
 [0.01       0.12666667 0.00333333 0.00166667 0.00166667 0.06166667
  0.         0.         0.00833333 0.78666667]]
[2023-08-30 16:59:07,250 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 16:59:07,252 INFO] 149504 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.4177, eval/loss: 3.3421, eval/top-1-acc: 0.8400, eval/balanced_acc: 0.8405, eval/precision: 0.8482, eval/recall: 0.8405, eval/F1: 0.8374, lr: 0.0000, train/prefecth_time: 0.0022 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 17:01:41,721 INFO] 149760 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.4214, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 17:03:35,433 INFO] 150016 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3596, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 17:05:25,958 INFO] 150272 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0010, train/total_loss: 0.0010, train/util_ratio: 1.0000, train/run_time: 0.4050, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 17:07:16,677 INFO] 150528 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0036, train/total_loss: 0.0036, train/util_ratio: 1.0000, train/run_time: 0.4166, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 17:09:52,967 INFO] 150784 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3976, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 17:11:47,470 INFO] 151040 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4100, lr: 0.0000, train/prefecth_time: 0.0036 
[2023-08-30 17:13:38,895 INFO] 151296 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4341, lr: 0.0000, train/prefecth_time: 0.0036 
[2023-08-30 17:15:30,392 INFO] validating...
[2023-08-30 17:15:54,919 INFO] confusion matrix:
[[0.86666667 0.01166667 0.015      0.00333333 0.         0.05833333
  0.04       0.00166667 0.00333333 0.        ]
 [0.         0.88833333 0.00666667 0.00166667 0.         0.09333333
  0.         0.         0.00166667 0.00833333]
 [0.         0.01166667 0.67166667 0.01166667 0.02       0.16166667
  0.04166667 0.06166667 0.02       0.        ]
 [0.006      0.         0.006      0.904      0.034      0.014
  0.         0.006      0.03       0.        ]
 [0.002      0.         0.         0.008      0.986      0.
  0.         0.004      0.         0.        ]
 [0.0025     0.015      0.0025     0.015      0.0125     0.85
  0.1025     0.         0.         0.        ]
 [0.02       0.         0.208      0.01       0.016      0.016
  0.71       0.016      0.004      0.        ]
 [0.         0.00833333 0.005      0.00166667 0.005      0.
  0.         0.97833333 0.00166667 0.        ]
 [0.02805611 0.00801603 0.00400802 0.0260521  0.03206413 0.10621242
  0.01402806 0.00200401 0.77755511 0.00200401]
 [0.01       0.16833333 0.005      0.00333333 0.00166667 0.06333333
  0.         0.         0.00833333 0.74      ]]
[2023-08-30 17:15:55,942 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 17:15:55,943 INFO] 151552 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4193, eval/loss: 3.4707, eval/top-1-acc: 0.8363, eval/balanced_acc: 0.8373, eval/precision: 0.8441, eval/recall: 0.8373, eval/F1: 0.8337, lr: 0.0000, train/prefecth_time: 0.0027 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 17:18:28,589 INFO] 151808 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.4319, lr: 0.0000, train/prefecth_time: 0.0037 
[2023-08-30 17:20:20,684 INFO] 152064 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4947, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 17:22:13,540 INFO] 152320 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4096, lr: 0.0000, train/prefecth_time: 0.0033 
[2023-08-30 17:24:07,320 INFO] 152576 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4162, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 17:26:42,114 INFO] 152832 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3596, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 17:28:33,678 INFO] 153088 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4093, lr: 0.0000, train/prefecth_time: 0.0040 
[2023-08-30 17:30:25,714 INFO] 153344 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4165, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 17:32:16,659 INFO] validating...
[2023-08-30 17:32:40,941 INFO] confusion matrix:
[[0.84666667 0.01166667 0.015      0.005      0.         0.06166667
  0.055      0.00166667 0.00333333 0.        ]
 [0.         0.89       0.00666667 0.00166667 0.         0.08833333
  0.         0.00166667 0.00166667 0.01      ]
 [0.         0.015      0.68333333 0.01       0.02       0.15666667
  0.04       0.065      0.01       0.        ]
 [0.004      0.         0.006      0.894      0.044      0.014
  0.         0.01       0.028      0.        ]
 [0.002      0.         0.         0.008      0.99       0.
  0.         0.         0.         0.        ]
 [0.0025     0.015      0.0025     0.0175     0.0125     0.835
  0.11       0.0025     0.0025     0.        ]
 [0.016      0.         0.22       0.014      0.02       0.016
  0.694      0.016      0.004      0.        ]
 [0.         0.01       0.005      0.00166667 0.00666667 0.
  0.         0.97666667 0.         0.        ]
 [0.02805611 0.00801603 0.00601202 0.0240481  0.03206413 0.10821643
  0.01603206 0.00200401 0.77354709 0.00200401]
 [0.00666667 0.205      0.00833333 0.00333333 0.00166667 0.06666667
  0.         0.         0.00833333 0.7       ]]
[2023-08-30 17:32:41,971 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 17:32:41,973 INFO] 153600 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3559, eval/loss: 3.7168, eval/top-1-acc: 0.8274, eval/balanced_acc: 0.8283, eval/precision: 0.8371, eval/recall: 0.8283, eval/F1: 0.8249, lr: 0.0000, train/prefecth_time: 0.0028 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 17:35:15,349 INFO] 153856 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4247, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 17:37:08,062 INFO] 154112 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4229, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 17:39:03,323 INFO] 154368 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4175, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 17:40:54,987 INFO] 154624 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4177, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 17:43:28,763 INFO] 154880 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4098, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 17:45:20,592 INFO] 155136 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3789, lr: 0.0000, train/prefecth_time: 0.0036 
[2023-08-30 17:47:14,626 INFO] 155392 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4248, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 17:49:10,043 INFO] validating...
[2023-08-30 17:49:34,533 INFO] confusion matrix:
[[0.825      0.01166667 0.01333333 0.00666667 0.         0.05833333
  0.08       0.00166667 0.00333333 0.        ]
 [0.         0.89       0.00333333 0.00166667 0.         0.09166667
  0.         0.         0.00166667 0.01166667]
 [0.         0.01166667 0.675      0.01       0.02166667 0.16
  0.05       0.06333333 0.00833333 0.        ]
 [0.004      0.         0.006      0.882      0.058      0.014
  0.         0.012      0.024      0.        ]
 [0.002      0.         0.         0.008      0.99       0.
  0.         0.         0.         0.        ]
 [0.0025     0.0125     0.0025     0.0175     0.0125     0.825
  0.1225     0.0025     0.0025     0.        ]
 [0.012      0.         0.2        0.012      0.022      0.014
  0.722      0.016      0.002      0.        ]
 [0.         0.01       0.005      0.00166667 0.00666667 0.
  0.         0.97666667 0.         0.        ]
 [0.02805611 0.01002004 0.00601202 0.0260521  0.04408818 0.11222445
  0.0240481  0.00400802 0.74348697 0.00200401]
 [0.00833333 0.22166667 0.005      0.00333333 0.00166667 0.07
  0.         0.         0.01166667 0.67833333]]
[2023-08-30 17:49:35,597 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 17:49:35,599 INFO] 155648 iteration, USE_EMA: True, train/sup_loss: 0.0001, train/unsup_loss: 0.0000, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4274, eval/loss: 3.9439, eval/top-1-acc: 0.8196, eval/balanced_acc: 0.8207, eval/precision: 0.8309, eval/recall: 0.8207, eval/F1: 0.8169, lr: 0.0000, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 17:52:11,248 INFO] 155904 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3581, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 17:54:00,832 INFO] 156160 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4043, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 17:55:55,055 INFO] 156416 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3908, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-30 17:57:47,463 INFO] 156672 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0047, train/total_loss: 0.0047, train/util_ratio: 1.0000, train/run_time: 0.4183, lr: 0.0000, train/prefecth_time: 0.0021 
[2023-08-30 18:00:22,400 INFO] 156928 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4158, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 18:02:15,022 INFO] 157184 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4225, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 18:04:07,680 INFO] 157440 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3576, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-30 18:06:00,327 INFO] validating...
[2023-08-30 18:06:24,967 INFO] confusion matrix:
[[0.82333333 0.01166667 0.00666667 0.00833333 0.         0.06
  0.085      0.00166667 0.00333333 0.        ]
 [0.         0.87       0.00333333 0.00166667 0.         0.11166667
  0.         0.         0.00166667 0.01166667]
 [0.         0.01166667 0.63       0.01166667 0.03166667 0.18166667
  0.065      0.065      0.00333333 0.        ]
 [0.002      0.         0.006      0.878      0.072      0.016
  0.         0.012      0.014      0.        ]
 [0.         0.         0.         0.004      0.996      0.
  0.         0.         0.         0.        ]
 [0.0025     0.0125     0.0025     0.015      0.015      0.83
  0.1225     0.         0.         0.        ]
 [0.012      0.         0.176      0.012      0.03       0.014
  0.736      0.02       0.         0.        ]
 [0.         0.01       0.005      0.00166667 0.01       0.
  0.         0.97333333 0.         0.        ]
 [0.03206413 0.01202405 0.00400802 0.03607214 0.05210421 0.12024048
  0.03206413 0.00400802 0.70541082 0.00200401]
 [0.00833333 0.225      0.005      0.005      0.00166667 0.07833333
  0.         0.         0.01333333 0.66333333]]
[2023-08-30 18:06:25,781 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 18:06:25,782 INFO] 157696 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4156, eval/loss: 4.2001, eval/top-1-acc: 0.8085, eval/balanced_acc: 0.8105, eval/precision: 0.8243, eval/recall: 0.8105, eval/F1: 0.8058, lr: 0.0000, train/prefecth_time: 0.0024 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 18:09:00,554 INFO] 157952 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4299, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 18:10:51,918 INFO] 158208 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3580, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 18:12:43,561 INFO] 158464 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4051, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 18:14:35,638 INFO] 158720 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4104, lr: 0.0000, train/prefecth_time: 0.0021 
[2023-08-30 18:17:09,359 INFO] 158976 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4101, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 18:19:02,049 INFO] 159232 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4266, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 18:20:53,865 INFO] 159488 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4283, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 18:22:47,393 INFO] validating...
[2023-08-30 18:23:11,747 INFO] confusion matrix:
[[0.82       0.01166667 0.00666667 0.00833333 0.00166667 0.06166667
  0.085      0.00166667 0.00333333 0.        ]
 [0.         0.86833333 0.00333333 0.00166667 0.         0.11166667
  0.         0.00166667 0.00166667 0.01166667]
 [0.         0.01       0.59333333 0.01166667 0.035      0.18833333
  0.08166667 0.07666667 0.00333333 0.        ]
 [0.004      0.         0.004      0.872      0.082      0.016
  0.         0.012      0.01       0.        ]
 [0.         0.         0.         0.004      0.996      0.
  0.         0.         0.         0.        ]
 [0.0025     0.0125     0.0025     0.0175     0.015      0.83
  0.12       0.         0.         0.        ]
 [0.012      0.         0.154      0.012      0.054      0.014
  0.73       0.024      0.         0.        ]
 [0.         0.01       0.00333333 0.00166667 0.01666667 0.
  0.         0.96833333 0.         0.        ]
 [0.04008016 0.01202405 0.00400802 0.04208417 0.06212425 0.11823647
  0.03406814 0.00400802 0.68136273 0.00200401]
 [0.01       0.235      0.00333333 0.005      0.00166667 0.08666667
  0.         0.         0.01166667 0.64666667]]
[2023-08-30 18:23:12,616 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 18:23:12,617 INFO] 159744 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4158, eval/loss: 4.4657, eval/top-1-acc: 0.7981, eval/balanced_acc: 0.8006, eval/precision: 0.8164, eval/recall: 0.8006, eval/F1: 0.7948, lr: 0.0000, train/prefecth_time: 0.0027 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 18:25:47,842 INFO] 160000 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 0.8750, train/run_time: 0.4106, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 18:27:40,486 INFO] 160256 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3638, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 18:29:34,232 INFO] 160512 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4106, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 18:31:26,593 INFO] 160768 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3638, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 18:34:01,055 INFO] 161024 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0015, train/total_loss: 0.0015, train/util_ratio: 1.0000, train/run_time: 0.3568, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 18:35:51,204 INFO] 161280 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3563, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 18:37:43,289 INFO] 161536 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.4370, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 18:39:35,397 INFO] validating...
[2023-08-30 18:39:59,669 INFO] confusion matrix:
[[0.83166667 0.01166667 0.00166667 0.00833333 0.00166667 0.06166667
  0.07833333 0.00166667 0.00333333 0.        ]
 [0.         0.86666667 0.00333333 0.00166667 0.         0.11
  0.         0.00166667 0.00166667 0.015     ]
 [0.         0.00833333 0.56166667 0.01833333 0.04333333 0.19
  0.09166667 0.08166667 0.00333333 0.00166667]
 [0.004      0.         0.004      0.862      0.096      0.014
  0.         0.012      0.008      0.        ]
 [0.         0.         0.         0.002      0.998      0.
  0.         0.         0.         0.        ]
 [0.0025     0.0125     0.0025     0.02       0.0175     0.825
  0.12       0.         0.         0.        ]
 [0.014      0.         0.13       0.01       0.092      0.012
  0.714      0.028      0.         0.        ]
 [0.         0.01       0.00333333 0.00166667 0.03333333 0.
  0.         0.95166667 0.         0.        ]
 [0.04408818 0.01002004 0.00400802 0.05210421 0.07014028 0.11422846
  0.03807615 0.00601202 0.65931864 0.00200401]
 [0.01       0.22833333 0.00166667 0.00666667 0.00333333 0.09166667
  0.         0.         0.01       0.64833333]]
[2023-08-30 18:40:00,428 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 18:40:00,429 INFO] 161792 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4184, eval/loss: 4.7524, eval/top-1-acc: 0.7894, eval/balanced_acc: 0.7918, eval/precision: 0.8092, eval/recall: 0.7918, eval/F1: 0.7851, lr: 0.0000, train/prefecth_time: 0.0022 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 18:42:33,869 INFO] 162048 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4184, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 18:44:24,096 INFO] 162304 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4232, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 18:46:15,780 INFO] 162560 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0007, train/total_loss: 0.0007, train/util_ratio: 1.0000, train/run_time: 0.4430, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-30 18:48:05,633 INFO] 162816 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0006, train/total_loss: 0.0006, train/util_ratio: 1.0000, train/run_time: 0.3575, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 18:50:39,132 INFO] 163072 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4174, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 18:52:28,845 INFO] 163328 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4451, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 18:54:21,492 INFO] 163584 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0044, train/total_loss: 0.0044, train/util_ratio: 1.0000, train/run_time: 0.3569, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 18:56:11,586 INFO] validating...
[2023-08-30 18:56:35,934 INFO] confusion matrix:
[[0.83333333 0.01166667 0.         0.01       0.00333333 0.06166667
  0.075      0.00166667 0.00333333 0.        ]
 [0.         0.87833333 0.00333333 0.00166667 0.         0.10333333
  0.         0.00166667 0.00166667 0.01      ]
 [0.         0.00833333 0.51833333 0.02       0.055      0.20333333
  0.09       0.10166667 0.00333333 0.        ]
 [0.006      0.         0.004      0.846      0.112      0.014
  0.         0.012      0.006      0.        ]
 [0.         0.         0.         0.         1.         0.
  0.         0.         0.         0.        ]
 [0.0025     0.0125     0.0025     0.02       0.02       0.8225
  0.12       0.         0.         0.        ]
 [0.012      0.         0.12       0.012      0.13       0.012
  0.68       0.034      0.         0.        ]
 [0.         0.00666667 0.00333333 0.00166667 0.05166667 0.
  0.         0.93666667 0.         0.        ]
 [0.04208417 0.01002004 0.00200401 0.05811623 0.07815631 0.11422846
  0.04408818 0.00601202 0.64529058 0.        ]
 [0.00833333 0.22666667 0.00166667 0.01       0.005      0.10166667
  0.         0.         0.01166667 0.635     ]]
[2023-08-30 18:56:36,735 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 18:56:36,736 INFO] 163840 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4163, eval/loss: 5.1044, eval/top-1-acc: 0.7770, eval/balanced_acc: 0.7795, eval/precision: 0.8009, eval/recall: 0.7795, eval/F1: 0.7717, lr: 0.0000, train/prefecth_time: 0.0023 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 18:59:10,758 INFO] 164096 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4205, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 19:01:00,973 INFO] 164352 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4003, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 19:02:51,651 INFO] 164608 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4224, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 19:04:41,162 INFO] 164864 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4220, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 19:07:14,307 INFO] 165120 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4137, lr: 0.0000, train/prefecth_time: 0.0036 
[2023-08-30 19:09:04,747 INFO] 165376 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4191, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 19:10:57,724 INFO] 165632 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4173, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 19:12:46,746 INFO] validating...
[2023-08-30 19:13:11,231 INFO] confusion matrix:
[[0.835      0.01333333 0.         0.00833333 0.00333333 0.05833333
  0.07833333 0.00166667 0.00166667 0.        ]
 [0.         0.89166667 0.00333333 0.00166667 0.         0.08833333
  0.         0.00333333 0.00166667 0.01      ]
 [0.         0.01166667 0.50166667 0.02       0.04833333 0.22166667
  0.09       0.10166667 0.005      0.        ]
 [0.006      0.         0.004      0.85       0.11       0.014
  0.         0.012      0.004      0.        ]
 [0.         0.         0.         0.         1.         0.
  0.         0.         0.         0.        ]
 [0.0025     0.02       0.0025     0.02       0.015      0.82
  0.1175     0.         0.0025     0.        ]
 [0.012      0.         0.116      0.014      0.12       0.012
  0.692      0.034      0.         0.        ]
 [0.         0.005      0.00333333 0.00166667 0.05833333 0.
  0.         0.93166667 0.         0.        ]
 [0.03807615 0.01002004 0.00200401 0.06412826 0.07815631 0.11623246
  0.04609218 0.00601202 0.63927856 0.        ]
 [0.00833333 0.21333333 0.00166667 0.01       0.00333333 0.11
  0.         0.         0.01666667 0.63666667]]
[2023-08-30 19:13:12,097 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 19:13:12,098 INFO] 165888 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4087, eval/loss: 5.1414, eval/top-1-acc: 0.7772, eval/balanced_acc: 0.7798, eval/precision: 0.8007, eval/recall: 0.7798, eval/F1: 0.7715, lr: 0.0000, train/prefecth_time: 0.0027 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 19:15:42,581 INFO] 166144 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4155, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 19:17:33,202 INFO] 166400 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4397, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 19:19:25,026 INFO] 166656 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4193, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 19:21:15,188 INFO] 166912 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3604, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 19:23:48,533 INFO] 167168 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0042, train/total_loss: 0.0042, train/util_ratio: 1.0000, train/run_time: 0.3608, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-30 19:25:37,324 INFO] 167424 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4587, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 19:27:26,686 INFO] 167680 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4098, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 19:29:15,554 INFO] validating...
[2023-08-30 19:29:39,689 INFO] confusion matrix:
[[0.84       0.01166667 0.         0.00833333 0.00333333 0.06
  0.07333333 0.00166667 0.00166667 0.        ]
 [0.         0.88666667 0.00333333 0.00166667 0.         0.08833333
  0.         0.00333333 0.00166667 0.015     ]
 [0.00166667 0.00833333 0.485      0.02       0.05       0.24166667
  0.09       0.09833333 0.00333333 0.00166667]
 [0.006      0.         0.004      0.864      0.098      0.016
  0.002      0.01       0.         0.        ]
 [0.         0.         0.         0.         1.         0.
  0.         0.         0.         0.        ]
 [0.0025     0.0225     0.0025     0.03       0.015      0.8075
  0.1175     0.         0.0025     0.        ]
 [0.014      0.         0.1        0.012      0.132      0.012
  0.698      0.032      0.         0.        ]
 [0.         0.005      0.00333333 0.00166667 0.05666667 0.
  0.         0.93333333 0.         0.        ]
 [0.03807615 0.01002004 0.00200401 0.07214429 0.0761523  0.11222445
  0.04408818 0.00601202 0.63927856 0.        ]
 [0.01       0.17333333 0.         0.01       0.00333333 0.115
  0.         0.         0.02       0.66833333]]
[2023-08-30 19:29:40,543 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 19:29:40,544 INFO] 167936 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0007, train/total_loss: 0.0007, train/util_ratio: 1.0000, train/run_time: 0.3608, eval/loss: 5.1039, eval/top-1-acc: 0.7800, eval/balanced_acc: 0.7822, eval/precision: 0.8030, eval/recall: 0.7822, eval/F1: 0.7739, lr: 0.0000, train/prefecth_time: 0.0022 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 19:32:11,936 INFO] 168192 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4319, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 19:34:00,369 INFO] 168448 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4029, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 19:35:50,558 INFO] 168704 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3648, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 19:37:41,220 INFO] 168960 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4184, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 19:40:14,071 INFO] 169216 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4665, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 19:42:05,154 INFO] 169472 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4261, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 19:43:56,000 INFO] 169728 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3722, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 19:45:45,497 INFO] validating...
[2023-08-30 19:46:09,684 INFO] confusion matrix:
[[0.87       0.01       0.         0.00666667 0.00166667 0.05
  0.05666667 0.00166667 0.00333333 0.        ]
 [0.         0.87666667 0.00333333 0.00166667 0.         0.09166667
  0.         0.00666667 0.00166667 0.01833333]
 [0.00166667 0.00666667 0.45166667 0.02       0.05333333 0.26666667
  0.1        0.095      0.00333333 0.00166667]
 [0.006      0.         0.004      0.87       0.094      0.014
  0.         0.008      0.004      0.        ]
 [0.         0.         0.         0.002      0.998      0.
  0.         0.         0.         0.        ]
 [0.0025     0.015      0.         0.03       0.015      0.8125
  0.1225     0.         0.0025     0.        ]
 [0.02       0.         0.076      0.012      0.132      0.01
  0.714      0.036      0.         0.        ]
 [0.         0.00333333 0.00333333 0.00166667 0.05333333 0.
  0.         0.93833333 0.         0.        ]
 [0.03807615 0.00801603 0.00200401 0.06813627 0.07214429 0.10821643
  0.04208417 0.00601202 0.65531062 0.        ]
 [0.015      0.11166667 0.         0.005      0.005      0.11166667
  0.         0.         0.02333333 0.72833333]]
[2023-08-30 19:46:10,456 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 19:46:10,457 INFO] 169984 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4308, eval/loss: 4.9387, eval/top-1-acc: 0.7894, eval/balanced_acc: 0.7915, eval/precision: 0.8106, eval/recall: 0.7915, eval/F1: 0.7826, lr: 0.0000, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 19:48:42,291 INFO] 170240 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4056, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 19:50:31,979 INFO] 170496 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3863, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 19:52:24,706 INFO] 170752 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4182, lr: 0.0000, train/prefecth_time: 0.0040 
[2023-08-30 19:54:17,785 INFO] 171008 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4160, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 19:56:51,181 INFO] 171264 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4231, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 19:58:45,230 INFO] 171520 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3578, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 20:00:35,083 INFO] 171776 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4377, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 20:02:27,527 INFO] validating...
[2023-08-30 20:02:52,057 INFO] confusion matrix:
[[0.885      0.01       0.         0.00666667 0.00166667 0.04666667
  0.04166667 0.00166667 0.00666667 0.        ]
 [0.         0.875      0.00333333 0.00166667 0.         0.095
  0.         0.00166667 0.00333333 0.02      ]
 [0.00333333 0.005      0.43       0.02333333 0.06166667 0.27666667
  0.09833333 0.08833333 0.00333333 0.01      ]
 [0.008      0.         0.004      0.868      0.096      0.014
  0.         0.006      0.004      0.        ]
 [0.         0.         0.         0.002      0.998      0.
  0.         0.         0.         0.        ]
 [0.0025     0.0175     0.         0.03       0.015      0.81
  0.12       0.         0.005      0.        ]
 [0.028      0.         0.066      0.014      0.142      0.01
  0.708      0.032      0.         0.        ]
 [0.         0.00333333 0.00166667 0.00166667 0.065      0.
  0.         0.92833333 0.         0.        ]
 [0.04008016 0.00801603 0.00200401 0.05811623 0.07014028 0.10420842
  0.03807615 0.00601202 0.67334669 0.        ]
 [0.01666667 0.075      0.         0.005      0.005      0.07166667
  0.         0.         0.02333333 0.80333333]]
[2023-08-30 20:02:52,931 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 20:02:52,932 INFO] 172032 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4116, eval/loss: 4.8514, eval/top-1-acc: 0.7964, eval/balanced_acc: 0.7979, eval/precision: 0.8148, eval/recall: 0.7979, eval/F1: 0.7886, lr: 0.0000, train/prefecth_time: 0.0026 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 20:05:25,935 INFO] 172288 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4240, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 20:07:16,533 INFO] 172544 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3585, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 20:09:08,204 INFO] 172800 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4165, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 20:11:00,715 INFO] 173056 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0058, train/total_loss: 0.0058, train/util_ratio: 1.0000, train/run_time: 0.3605, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 20:13:39,280 INFO] 173312 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4215, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 20:15:32,199 INFO] 173568 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4126, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 20:17:23,782 INFO] 173824 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0007, train/total_loss: 0.0007, train/util_ratio: 1.0000, train/run_time: 0.3957, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 20:19:15,018 INFO] validating...
[2023-08-30 20:19:39,121 INFO] confusion matrix:
[[0.895      0.00833333 0.         0.00666667 0.00166667 0.03833333
  0.04       0.00166667 0.00833333 0.        ]
 [0.         0.88166667 0.00333333 0.00166667 0.         0.07833333
  0.         0.         0.005      0.03      ]
 [0.00333333 0.01166667 0.43833333 0.02333333 0.065      0.27
  0.09333333 0.07666667 0.00666667 0.01166667]
 [0.008      0.         0.004      0.868      0.09       0.014
  0.         0.006      0.01       0.        ]
 [0.         0.         0.         0.002      0.998      0.
  0.         0.         0.         0.        ]
 [0.0025     0.025      0.         0.03       0.015      0.7875
  0.135      0.         0.005      0.        ]
 [0.028      0.         0.076      0.01       0.144      0.008
  0.704      0.03       0.         0.        ]
 [0.         0.005      0.00166667 0.00166667 0.06166667 0.
  0.         0.93       0.         0.        ]
 [0.04208417 0.00801603 0.00200401 0.04008016 0.05811623 0.09619238
  0.03206413 0.00400802 0.71743487 0.        ]
 [0.01333333 0.07166667 0.         0.00666667 0.00333333 0.04833333
  0.         0.         0.035      0.82166667]]
[2023-08-30 20:19:39,963 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 20:19:39,964 INFO] 174080 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3584, eval/loss: 4.6975, eval/top-1-acc: 0.8035, eval/balanced_acc: 0.8042, eval/precision: 0.8152, eval/recall: 0.8042, eval/F1: 0.7951, lr: 0.0000, train/prefecth_time: 0.0025 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 20:22:14,739 INFO] 174336 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4216, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 20:24:05,814 INFO] 174592 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0005, train/total_loss: 0.0005, train/util_ratio: 1.0000, train/run_time: 0.3597, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 20:25:57,003 INFO] 174848 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3964, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 20:27:49,960 INFO] 175104 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4160, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 20:30:24,146 INFO] 175360 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4425, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 20:32:17,397 INFO] 175616 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4236, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-30 20:34:09,265 INFO] 175872 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4433, lr: 0.0000, train/prefecth_time: 0.0035 
[2023-08-30 20:36:00,771 INFO] validating...
[2023-08-30 20:36:25,282 INFO] confusion matrix:
[[0.89833333 0.00833333 0.         0.00666667 0.         0.03833333
  0.035      0.00166667 0.01166667 0.        ]
 [0.         0.88333333 0.00333333 0.00166667 0.         0.07166667
  0.         0.         0.005      0.035     ]
 [0.00333333 0.01166667 0.45166667 0.02166667 0.07166667 0.26833333
  0.08       0.07       0.01       0.01166667]
 [0.008      0.         0.004      0.872      0.086      0.014
  0.         0.006      0.01       0.        ]
 [0.         0.         0.         0.002      0.998      0.
  0.         0.         0.         0.        ]
 [0.0025     0.03       0.         0.0275     0.015      0.7825
  0.135      0.         0.0075     0.        ]
 [0.028      0.         0.08       0.01       0.148      0.008
  0.698      0.028      0.         0.        ]
 [0.         0.005      0.00166667 0.00166667 0.05166667 0.
  0.         0.94       0.         0.        ]
 [0.04609218 0.00801603 0.00200401 0.03807615 0.0501002  0.09218437
  0.03206413 0.00400802 0.72745491 0.        ]
 [0.01333333 0.08       0.         0.005      0.00333333 0.045
  0.         0.         0.03666667 0.81666667]]
[2023-08-30 20:36:26,302 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 20:36:26,303 INFO] 176128 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4124, eval/loss: 4.6256, eval/top-1-acc: 0.8064, eval/balanced_acc: 0.8068, eval/precision: 0.8161, eval/recall: 0.8068, eval/F1: 0.7980, lr: 0.0000, train/prefecth_time: 0.0021 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 20:38:59,510 INFO] 176384 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4223, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 20:40:52,637 INFO] 176640 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3710, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 20:42:45,708 INFO] 176896 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3973, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 20:44:35,730 INFO] 177152 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0026, train/total_loss: 0.0026, train/util_ratio: 1.0000, train/run_time: 0.4314, lr: 0.0000, train/prefecth_time: 0.0021 
[2023-08-30 20:47:10,783 INFO] 177408 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4103, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 20:49:00,303 INFO] 177664 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0009, train/total_loss: 0.0009, train/util_ratio: 1.0000, train/run_time: 0.4172, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 20:50:52,861 INFO] 177920 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0015, train/total_loss: 0.0015, train/util_ratio: 1.0000, train/run_time: 0.4271, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 20:52:43,587 INFO] validating...
[2023-08-30 20:53:07,635 INFO] confusion matrix:
[[0.91       0.00833333 0.         0.00666667 0.         0.03666667
  0.03       0.00166667 0.00666667 0.        ]
 [0.         0.88166667 0.00333333 0.         0.         0.07666667
  0.         0.         0.005      0.03333333]
 [0.00166667 0.01166667 0.475      0.02166667 0.07333333 0.26833333
  0.06833333 0.06166667 0.00833333 0.01      ]
 [0.008      0.         0.004      0.882      0.08       0.014
  0.         0.004      0.008      0.        ]
 [0.         0.         0.         0.002      0.998      0.
  0.         0.         0.         0.        ]
 [0.005      0.025      0.         0.025      0.015      0.795
  0.13       0.         0.005      0.        ]
 [0.03       0.         0.09       0.008      0.15       0.008
  0.688      0.026      0.         0.        ]
 [0.         0.005      0.00166667 0.00166667 0.04333333 0.
  0.         0.94833333 0.         0.        ]
 [0.0501002  0.00801603 0.00200401 0.04208417 0.04408818 0.09418838
  0.02805611 0.00400802 0.72745491 0.        ]
 [0.015      0.07833333 0.         0.005      0.00333333 0.045
  0.         0.         0.03166667 0.82166667]]
[2023-08-30 20:53:08,426 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 20:53:08,427 INFO] 178176 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4160, eval/loss: 4.5232, eval/top-1-acc: 0.8126, eval/balanced_acc: 0.8127, eval/precision: 0.8220, eval/recall: 0.8127, eval/F1: 0.8046, lr: 0.0000, train/prefecth_time: 0.0022 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 20:55:42,195 INFO] 178432 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4830, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 20:57:33,181 INFO] 178688 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4133, lr: 0.0000, train/prefecth_time: 0.0034 
[2023-08-30 20:59:25,477 INFO] 178944 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4141, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 21:01:16,252 INFO] 179200 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3624, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 21:03:52,171 INFO] 179456 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3624, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 21:05:42,990 INFO] 179712 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3588, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 21:07:37,113 INFO] 179968 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.7500, train/run_time: 0.4182, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 21:09:27,727 INFO] validating...
[2023-08-30 21:09:51,950 INFO] confusion matrix:
[[0.91166667 0.01166667 0.         0.00666667 0.         0.04333333
  0.02666667 0.         0.         0.        ]
 [0.         0.89333333 0.00333333 0.         0.         0.07666667
  0.         0.         0.00333333 0.02333333]
 [0.00166667 0.01166667 0.46166667 0.02       0.07333333 0.29333333
  0.06166667 0.05833333 0.01       0.00833333]
 [0.008      0.         0.004      0.874      0.082      0.016
  0.         0.008      0.008      0.        ]
 [0.         0.         0.         0.002      0.996      0.
  0.         0.002      0.         0.        ]
 [0.005      0.02       0.         0.0225     0.0075     0.8275
  0.115      0.         0.0025     0.        ]
 [0.03       0.         0.088      0.01       0.142      0.02
  0.678      0.032      0.         0.        ]
 [0.         0.005      0.00166667 0.00166667 0.04166667 0.
  0.         0.95       0.         0.        ]
 [0.04809619 0.00801603 0.00200401 0.04609218 0.04609218 0.1002004
  0.02805611 0.00400802 0.71743487 0.        ]
 [0.015      0.085      0.         0.005      0.00333333 0.055
  0.         0.         0.03       0.80666667]]
[2023-08-30 21:09:52,827 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 21:09:52,828 INFO] 180224 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3582, eval/loss: 4.6234, eval/top-1-acc: 0.8107, eval/balanced_acc: 0.8116, eval/precision: 0.8240, eval/recall: 0.8116, eval/F1: 0.8030, lr: 0.0000, train/prefecth_time: 0.0026 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 21:12:23,558 INFO] 180480 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4879, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 21:14:13,098 INFO] 180736 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4129, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 21:16:02,414 INFO] 180992 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4256, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 21:17:53,202 INFO] 181248 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3688, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 21:20:28,365 INFO] 181504 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4131, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-30 21:22:20,269 INFO] 181760 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3610, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 21:24:09,066 INFO] 182016 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3603, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 21:25:57,539 INFO] validating...
[2023-08-30 21:26:21,948 INFO] confusion matrix:
[[0.90666667 0.01166667 0.         0.005      0.         0.05166667
  0.025      0.         0.         0.        ]
 [0.         0.87166667 0.00333333 0.         0.         0.09666667
  0.         0.         0.00333333 0.025     ]
 [0.00166667 0.01166667 0.435      0.015      0.07166667 0.33333333
  0.05666667 0.05666667 0.00833333 0.01      ]
 [0.008      0.         0.004      0.892      0.064      0.016
  0.         0.008      0.008      0.        ]
 [0.         0.         0.         0.002      0.996      0.
  0.         0.002      0.         0.        ]
 [0.005      0.015      0.         0.0175     0.         0.855
  0.105      0.0025     0.         0.        ]
 [0.026      0.         0.092      0.012      0.132      0.038
  0.682      0.018      0.         0.        ]
 [0.         0.00166667 0.00166667 0.00166667 0.04       0.
  0.         0.955      0.         0.        ]
 [0.0501002  0.00801603 0.00200401 0.05611222 0.04408818 0.1242485
  0.0260521  0.00400802 0.68537074 0.        ]
 [0.01666667 0.08166667 0.         0.005      0.00333333 0.055
  0.         0.         0.02666667 0.81166667]]
[2023-08-30 21:26:22,817 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 21:26:22,818 INFO] 182272 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3782, eval/loss: 4.7249, eval/top-1-acc: 0.8070, eval/balanced_acc: 0.8090, eval/precision: 0.8252, eval/recall: 0.8090, eval/F1: 0.7999, lr: 0.0000, train/prefecth_time: 0.0027 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 21:28:54,989 INFO] 182528 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3690, lr: 0.0000, train/prefecth_time: 0.0035 
[2023-08-30 21:30:44,107 INFO] 182784 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3583, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 21:32:34,624 INFO] 183040 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4273, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 21:34:26,022 INFO] 183296 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4151, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 21:36:59,092 INFO] 183552 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4357, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 21:38:50,765 INFO] 183808 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4293, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 21:40:41,926 INFO] 184064 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0006, train/total_loss: 0.0006, train/util_ratio: 1.0000, train/run_time: 0.3923, lr: 0.0000, train/prefecth_time: 0.0050 
[2023-08-30 21:42:33,456 INFO] validating...
[2023-08-30 21:42:57,562 INFO] confusion matrix:
[[0.905      0.01166667 0.         0.005      0.         0.05833333
  0.02       0.         0.         0.        ]
 [0.         0.87166667 0.005      0.         0.         0.095
  0.         0.         0.00333333 0.025     ]
 [0.00166667 0.015      0.44       0.01166667 0.07166667 0.34333333
  0.05166667 0.04833333 0.00666667 0.01      ]
 [0.01       0.         0.006      0.902      0.05       0.016
  0.         0.008      0.008      0.        ]
 [0.         0.         0.         0.002      0.996      0.
  0.         0.002      0.         0.        ]
 [0.0025     0.0125     0.         0.015      0.         0.8725
  0.0975     0.         0.         0.        ]
 [0.028      0.         0.09       0.014      0.128      0.04
  0.684      0.016      0.         0.        ]
 [0.         0.00166667 0.00166667 0.00166667 0.04333333 0.
  0.         0.95166667 0.         0.        ]
 [0.05210421 0.01202405 0.00200401 0.05210421 0.03807615 0.13026052
  0.0240481  0.00400802 0.68537074 0.        ]
 [0.01833333 0.08333333 0.         0.         0.00333333 0.06333333
  0.         0.         0.02       0.81166667]]
[2023-08-30 21:42:58,532 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 21:42:58,534 INFO] 184320 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4164, eval/loss: 4.7389, eval/top-1-acc: 0.8094, eval/balanced_acc: 0.8120, eval/precision: 0.8300, eval/recall: 0.8120, eval/F1: 0.8032, lr: 0.0000, train/prefecth_time: 0.0021 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 21:45:33,402 INFO] 184576 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3558, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 21:47:25,841 INFO] 184832 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4186, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 21:49:17,860 INFO] 185088 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3709, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 21:51:08,691 INFO] 185344 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4109, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 21:53:44,401 INFO] 185600 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4187, lr: 0.0000, train/prefecth_time: 0.0030 
[2023-08-30 21:55:35,298 INFO] 185856 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4391, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 21:57:25,785 INFO] 186112 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4007, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-30 21:59:18,212 INFO] validating...
[2023-08-30 21:59:42,955 INFO] confusion matrix:
[[0.90333333 0.01166667 0.         0.005      0.         0.06
  0.02       0.         0.         0.        ]
 [0.         0.88       0.005      0.         0.         0.085
  0.         0.         0.005      0.025     ]
 [0.00166667 0.01833333 0.42333333 0.01333333 0.07333333 0.34666667
  0.055      0.05333333 0.00666667 0.00833333]
 [0.01       0.         0.006      0.902      0.05       0.016
  0.         0.008      0.008      0.        ]
 [0.         0.         0.         0.002      0.996      0.
  0.         0.002      0.         0.        ]
 [0.0025     0.0125     0.         0.015      0.         0.8775
  0.0925     0.         0.         0.        ]
 [0.038      0.         0.074      0.014      0.126      0.044
  0.68       0.022      0.002      0.        ]
 [0.         0.00166667 0.00166667 0.00166667 0.04       0.
  0.         0.955      0.         0.        ]
 [0.05410822 0.01202405 0.         0.04609218 0.04008016 0.12625251
  0.02004008 0.00400802 0.69739479 0.        ]
 [0.01833333 0.08166667 0.         0.         0.00333333 0.06333333
  0.         0.         0.02333333 0.81      ]]
[2023-08-30 21:59:44,037 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 21:59:44,039 INFO] 186368 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4097, eval/loss: 4.7340, eval/top-1-acc: 0.8096, eval/balanced_acc: 0.8125, eval/precision: 0.8305, eval/recall: 0.8125, eval/F1: 0.8028, lr: 0.0000, train/prefecth_time: 0.0021 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 22:02:15,285 INFO] 186624 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0039, train/total_loss: 0.0039, train/util_ratio: 1.0000, train/run_time: 0.4244, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 22:04:05,262 INFO] 186880 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4024, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 22:05:55,185 INFO] 187136 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4403, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 22:07:46,699 INFO] 187392 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4257, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 22:10:21,772 INFO] 187648 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4266, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 22:12:15,002 INFO] 187904 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4221, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 22:14:08,800 INFO] 188160 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4241, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 22:15:59,800 INFO] validating...
[2023-08-30 22:16:24,263 INFO] confusion matrix:
[[0.9        0.01333333 0.         0.005      0.         0.065
  0.01666667 0.         0.         0.        ]
 [0.         0.88333333 0.00333333 0.         0.         0.085
  0.         0.         0.00333333 0.025     ]
 [0.00166667 0.02166667 0.39333333 0.01833333 0.06833333 0.36333333
  0.05833333 0.05833333 0.00833333 0.00833333]
 [0.008      0.         0.006      0.91       0.046      0.016
  0.         0.008      0.006      0.        ]
 [0.         0.         0.         0.002      0.996      0.
  0.         0.002      0.         0.        ]
 [0.0025     0.015      0.         0.015      0.         0.885
  0.0825     0.         0.         0.        ]
 [0.04       0.         0.07       0.016      0.118      0.048
  0.68       0.026      0.002      0.        ]
 [0.         0.         0.00166667 0.00166667 0.03       0.
  0.         0.96666667 0.         0.        ]
 [0.05210421 0.01202405 0.         0.04809619 0.03807615 0.13827655
  0.01603206 0.00400802 0.69138277 0.        ]
 [0.02       0.09333333 0.         0.         0.00333333 0.06833333
  0.         0.         0.01833333 0.79666667]]
[2023-08-30 22:16:25,161 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 22:16:25,162 INFO] 188416 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3546, eval/loss: 4.8178, eval/top-1-acc: 0.8068, eval/balanced_acc: 0.8102, eval/precision: 0.8302, eval/recall: 0.8102, eval/F1: 0.7994, lr: 0.0000, train/prefecth_time: 0.0022 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 22:19:00,806 INFO] 188672 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3601, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 22:20:50,072 INFO] 188928 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4260, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 22:22:38,746 INFO] 189184 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3894, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 22:24:29,083 INFO] 189440 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4235, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 22:27:02,905 INFO] 189696 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0007, train/total_loss: 0.0007, train/util_ratio: 1.0000, train/run_time: 0.4068, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 22:28:53,841 INFO] 189952 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4220, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 22:30:46,296 INFO] 190208 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4139, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 22:32:38,913 INFO] validating...
[2023-08-30 22:33:03,081 INFO] confusion matrix:
[[0.895      0.01333333 0.         0.005      0.         0.06833333
  0.01833333 0.         0.         0.        ]
 [0.         0.87666667 0.00333333 0.         0.         0.08666667
  0.         0.         0.00333333 0.03      ]
 [0.00166667 0.02333333 0.41       0.01333333 0.065      0.35833333
  0.05333333 0.05666667 0.01       0.00833333]
 [0.008      0.         0.006      0.908      0.048      0.018
  0.         0.008      0.004      0.        ]
 [0.         0.         0.         0.002      0.996      0.
  0.         0.002      0.         0.        ]
 [0.0025     0.02       0.         0.0125     0.         0.885
  0.075      0.0025     0.         0.0025    ]
 [0.034      0.         0.076      0.016      0.116      0.058
  0.664      0.034      0.002      0.        ]
 [0.         0.         0.00166667 0.00166667 0.02666667 0.
  0.         0.97       0.         0.        ]
 [0.0501002  0.01402806 0.         0.05210421 0.03807615 0.14228457
  0.01402806 0.00400802 0.68537074 0.        ]
 [0.01833333 0.09166667 0.         0.         0.00333333 0.07166667
  0.         0.         0.01833333 0.79666667]]
[2023-08-30 22:33:03,941 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 22:33:03,942 INFO] 190464 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4126, eval/loss: 4.8378, eval/top-1-acc: 0.8055, eval/balanced_acc: 0.8087, eval/precision: 0.8297, eval/recall: 0.8087, eval/F1: 0.7988, lr: 0.0000, train/prefecth_time: 0.0021 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 22:35:37,509 INFO] 190720 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3768, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 22:37:26,865 INFO] 190976 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3603, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 22:39:19,601 INFO] 191232 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4504, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 22:41:11,280 INFO] 191488 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4125, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 22:43:46,982 INFO] 191744 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0022, train/total_loss: 0.0022, train/util_ratio: 1.0000, train/run_time: 0.3693, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 22:45:36,691 INFO] 192000 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3592, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 22:47:28,900 INFO] 192256 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4281, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 22:49:21,882 INFO] validating...
[2023-08-30 22:49:46,646 INFO] confusion matrix:
[[0.89       0.01333333 0.         0.005      0.         0.075
  0.01666667 0.         0.         0.        ]
 [0.         0.86333333 0.00333333 0.         0.         0.09
  0.         0.         0.00333333 0.04      ]
 [0.00333333 0.025      0.44333333 0.01166667 0.06166667 0.33166667
  0.045      0.05333333 0.01       0.015     ]
 [0.008      0.         0.006      0.908      0.048      0.018
  0.         0.008      0.004      0.        ]
 [0.         0.         0.         0.002      0.996      0.
  0.         0.002      0.         0.        ]
 [0.0025     0.02       0.         0.0125     0.         0.8925
  0.0675     0.0025     0.         0.0025    ]
 [0.026      0.         0.092      0.018      0.11       0.06
  0.65       0.042      0.002      0.        ]
 [0.         0.         0.00166667 0.00166667 0.02666667 0.
  0.         0.97       0.         0.        ]
 [0.04809619 0.01402806 0.00400802 0.05811623 0.03006012 0.13627255
  0.01202405 0.00400802 0.69338677 0.        ]
 [0.01666667 0.09666667 0.         0.         0.00333333 0.065
  0.         0.         0.01833333 0.8       ]]
[2023-08-30 22:49:47,645 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 22:49:47,646 INFO] 192512 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0002, train/total_loss: 0.0002, train/util_ratio: 1.0000, train/run_time: 0.4097, eval/loss: 4.7847, eval/top-1-acc: 0.8076, eval/balanced_acc: 0.8107, eval/precision: 0.8292, eval/recall: 0.8107, eval/F1: 0.8016, lr: 0.0000, train/prefecth_time: 0.0023 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 22:52:19,977 INFO] 192768 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4247, lr: 0.0000, train/prefecth_time: 0.0042 
[2023-08-30 22:54:10,175 INFO] 193024 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3608, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 22:56:00,925 INFO] 193280 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4276, lr: 0.0000, train/prefecth_time: 0.0043 
[2023-08-30 22:57:50,791 INFO] 193536 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3545, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 23:00:25,448 INFO] 193792 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4362, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 23:02:15,704 INFO] 194048 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3865, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 23:04:06,901 INFO] 194304 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4108, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-30 23:05:59,524 INFO] validating...
[2023-08-30 23:06:23,907 INFO] confusion matrix:
[[0.88666667 0.01333333 0.         0.005      0.         0.07666667
  0.01666667 0.         0.00166667 0.        ]
 [0.         0.865      0.00333333 0.         0.         0.08666667
  0.         0.         0.00333333 0.04166667]
 [0.00333333 0.02666667 0.47166667 0.01       0.06166667 0.315
  0.035      0.05166667 0.01       0.015     ]
 [0.008      0.002      0.006      0.91       0.046      0.014
  0.         0.01       0.004      0.        ]
 [0.         0.         0.         0.004      0.99       0.
  0.         0.006      0.         0.        ]
 [0.0025     0.0275     0.         0.0125     0.         0.885
  0.065      0.005      0.         0.0025    ]
 [0.026      0.         0.11       0.018      0.108      0.056
  0.638      0.044      0.         0.        ]
 [0.         0.00166667 0.00166667 0.00166667 0.02833333 0.
  0.         0.96666667 0.         0.        ]
 [0.04809619 0.01402806 0.00400802 0.05811623 0.03006012 0.1242485
  0.01202405 0.00400802 0.70541082 0.        ]
 [0.015      0.1        0.         0.         0.00333333 0.065
  0.         0.         0.02166667 0.795     ]]
[2023-08-30 23:06:24,843 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 23:06:24,844 INFO] 194560 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4043, eval/loss: 4.7393, eval/top-1-acc: 0.8087, eval/balanced_acc: 0.8113, eval/precision: 0.8280, eval/recall: 0.8113, eval/F1: 0.8031, lr: 0.0000, train/prefecth_time: 0.0022 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 23:08:57,940 INFO] 194816 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4172, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 23:10:48,777 INFO] 195072 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4334, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 23:12:40,773 INFO] 195328 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4212, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 23:14:34,465 INFO] 195584 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4166, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 23:17:08,749 INFO] 195840 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3732, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 23:18:56,545 INFO] 196096 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4164, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 23:20:44,414 INFO] 196352 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3600, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 23:22:35,535 INFO] validating...
[2023-08-30 23:22:59,544 INFO] confusion matrix:
[[0.88       0.01333333 0.         0.005      0.         0.08333333
  0.01666667 0.         0.         0.00166667]
 [0.         0.85333333 0.005      0.         0.         0.10333333
  0.         0.         0.00166667 0.03666667]
 [0.005      0.025      0.49666667 0.00833333 0.05666667 0.305
  0.03666667 0.045      0.00666667 0.015     ]
 [0.006      0.002      0.006      0.922      0.038      0.012
  0.         0.01       0.004      0.        ]
 [0.         0.         0.         0.004      0.992      0.
  0.         0.004      0.         0.        ]
 [0.0025     0.02       0.         0.0125     0.         0.9025
  0.055      0.005      0.         0.0025    ]
 [0.026      0.         0.122      0.018      0.102      0.07
  0.626      0.036      0.         0.        ]
 [0.         0.00333333 0.00166667 0.00166667 0.02833333 0.
  0.         0.965      0.         0.        ]
 [0.0501002  0.01402806 0.00601202 0.07815631 0.03607214 0.13026052
  0.01202405 0.00400802 0.66933868 0.        ]
 [0.01333333 0.1        0.         0.         0.00166667 0.075
  0.         0.         0.01833333 0.79166667]]
[2023-08-30 23:23:00,455 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 23:23:00,456 INFO] 196608 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4159, eval/loss: 4.8554, eval/top-1-acc: 0.8070, eval/balanced_acc: 0.8099, eval/precision: 0.8292, eval/recall: 0.8099, eval/F1: 0.8021, lr: 0.0000, train/prefecth_time: 0.0020 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 23:25:31,808 INFO] 196864 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4582, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 23:27:22,745 INFO] 197120 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4163, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 23:29:11,881 INFO] 197376 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3607, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-30 23:31:02,360 INFO] 197632 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.3582, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 23:33:35,898 INFO] 197888 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3665, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-30 23:35:28,156 INFO] 198144 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4276, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 23:37:14,582 INFO] 198400 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3769, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 23:39:03,567 INFO] validating...
[2023-08-30 23:39:27,959 INFO] confusion matrix:
[[0.88833333 0.01333333 0.         0.005      0.         0.07833333
  0.015      0.         0.         0.        ]
 [0.         0.855      0.005      0.         0.         0.10166667
  0.         0.         0.00166667 0.03666667]
 [0.00666667 0.02333333 0.51833333 0.01333333 0.04833333 0.29333333
  0.035      0.03833333 0.005      0.01833333]
 [0.006      0.002      0.006      0.922      0.038      0.012
  0.         0.01       0.004      0.        ]
 [0.         0.         0.         0.004      0.992      0.
  0.         0.004      0.         0.        ]
 [0.0025     0.02       0.0025     0.0125     0.         0.9075
  0.0475     0.005      0.         0.0025    ]
 [0.04       0.         0.132      0.024      0.1        0.072
  0.6        0.032      0.         0.        ]
 [0.         0.00333333 0.00666667 0.00166667 0.02833333 0.
  0.         0.96       0.         0.        ]
 [0.0501002  0.01402806 0.00601202 0.10821643 0.03206413 0.13026052
  0.01402806 0.00400802 0.64128257 0.        ]
 [0.01666667 0.10333333 0.00333333 0.         0.00166667 0.07333333
  0.         0.         0.01833333 0.78333333]]
[2023-08-30 23:39:28,983 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 23:39:28,984 INFO] 198656 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3763, eval/loss: 4.9284, eval/top-1-acc: 0.8044, eval/balanced_acc: 0.8068, eval/precision: 0.8254, eval/recall: 0.8068, eval/F1: 0.7987, lr: 0.0000, train/prefecth_time: 0.0022 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 23:42:00,289 INFO] 198912 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3672, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-30 23:43:47,535 INFO] 199168 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4329, lr: 0.0000, train/prefecth_time: 0.0023 
[2023-08-30 23:45:36,087 INFO] 199424 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4221, lr: 0.0000, train/prefecth_time: 0.0027 
[2023-08-30 23:47:27,416 INFO] 199680 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4102, lr: 0.0000, train/prefecth_time: 0.0022 
[2023-08-30 23:49:59,721 INFO] 199936 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3591, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 23:51:51,463 INFO] 200192 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3710, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-30 23:53:42,150 INFO] 200448 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3641, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-30 23:55:31,811 INFO] validating...
[2023-08-30 23:55:56,341 INFO] confusion matrix:
[[0.89833333 0.01333333 0.         0.00833333 0.         0.06666667
  0.01333333 0.         0.         0.        ]
 [0.         0.855      0.005      0.00166667 0.         0.09666667
  0.         0.         0.00166667 0.04      ]
 [0.01       0.02       0.56166667 0.015      0.045      0.26166667
  0.03       0.035      0.005      0.01666667]
 [0.006      0.         0.004      0.93       0.036      0.012
  0.         0.008      0.004      0.        ]
 [0.         0.         0.         0.004      0.994      0.
  0.         0.002      0.         0.        ]
 [0.005      0.0225     0.0025     0.02       0.         0.87
  0.07       0.01       0.         0.        ]
 [0.056      0.         0.142      0.03       0.094      0.056
  0.594      0.028      0.         0.        ]
 [0.         0.00333333 0.00666667 0.00166667 0.04       0.
  0.         0.94833333 0.         0.        ]
 [0.0501002  0.01402806 0.00601202 0.11623246 0.03206413 0.11623246
  0.01402806 0.00400802 0.64729459 0.        ]
 [0.01666667 0.11       0.00333333 0.00166667 0.00166667 0.07333333
  0.         0.         0.01833333 0.775     ]]
[2023-08-30 23:55:57,322 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-30 23:55:57,324 INFO] 200704 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0001, train/total_loss: 0.0001, train/util_ratio: 1.0000, train/run_time: 0.4151, eval/loss: 4.8248, eval/top-1-acc: 0.8063, eval/balanced_acc: 0.8074, eval/precision: 0.8227, eval/recall: 0.8074, eval/F1: 0.8000, lr: 0.0000, train/prefecth_time: 0.0021 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-30 23:58:30,372 INFO] 200960 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4173, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-31 00:00:21,731 INFO] 201216 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4360, lr: 0.0000, train/prefecth_time: 0.0028 
[2023-08-31 00:02:13,515 INFO] 201472 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4234, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-31 00:04:06,644 INFO] 201728 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3563, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-31 00:06:37,624 INFO] 201984 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3936, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-31 00:08:30,002 INFO] 202240 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4153, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-31 00:10:21,395 INFO] 202496 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3567, lr: 0.0000, train/prefecth_time: 0.0029 
[2023-08-31 00:12:11,391 INFO] validating...
[2023-08-31 00:12:35,637 INFO] confusion matrix:
[[0.90666667 0.01333333 0.         0.00833333 0.         0.05833333
  0.01333333 0.         0.         0.        ]
 [0.         0.845      0.005      0.00166667 0.         0.09833333
  0.         0.         0.00166667 0.04833333]
 [0.01       0.01833333 0.58666667 0.01833333 0.04       0.23833333
  0.03166667 0.03333333 0.00666667 0.01666667]
 [0.006      0.         0.006      0.934      0.032      0.012
  0.         0.006      0.004      0.        ]
 [0.         0.         0.         0.004      0.994      0.
  0.         0.002      0.         0.        ]
 [0.005      0.0225     0.0025     0.0225     0.         0.865
  0.075      0.0075     0.         0.        ]
 [0.084      0.         0.146      0.032      0.08       0.05
  0.58       0.028      0.         0.        ]
 [0.         0.00333333 0.01666667 0.00166667 0.04333333 0.
  0.         0.935      0.         0.        ]
 [0.05410822 0.01402806 0.00801603 0.13026052 0.0260521  0.10821643
  0.01603206 0.00400802 0.63927856 0.        ]
 [0.01833333 0.10166667 0.00333333 0.005      0.00333333 0.065
  0.         0.         0.02       0.78333333]]
[2023-08-31 00:12:36,407 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-31 00:12:36,408 INFO] 202752 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4156, eval/loss: 4.8312, eval/top-1-acc: 0.8063, eval/balanced_acc: 0.8069, eval/precision: 0.8192, eval/recall: 0.8069, eval/F1: 0.7993, lr: 0.0000, train/prefecth_time: 0.0022 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-31 00:15:11,777 INFO] 203008 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.3593, lr: 0.0000, train/prefecth_time: 0.0031 
[2023-08-31 00:17:03,968 INFO] 203264 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4303, lr: 0.0000, train/prefecth_time: 0.0032 
[2023-08-31 00:18:54,914 INFO] 203520 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.3568, lr: 0.0000, train/prefecth_time: 0.0024 
[2023-08-31 00:20:45,859 INFO] 203776 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0008, train/total_loss: 0.0008, train/util_ratio: 1.0000, train/run_time: 0.3587, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-31 00:23:20,887 INFO] 204032 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4408, lr: 0.0000, train/prefecth_time: 0.0026 
[2023-08-31 00:25:13,450 INFO] 204288 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0003, train/total_loss: 0.0003, train/util_ratio: 1.0000, train/run_time: 0.4099, lr: 0.0000, train/prefecth_time: 0.0025 
[2023-08-31 00:27:08,618 INFO] 204544 iteration USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 0.8750, train/run_time: 0.4119, lr: 0.0000, train/prefecth_time: 0.0033 
[2023-08-31 00:28:59,622 INFO] validating...
[2023-08-31 00:29:23,272 INFO] confusion matrix:
[[0.91       0.01333333 0.         0.01       0.         0.05333333
  0.01333333 0.         0.         0.        ]
 [0.         0.83       0.005      0.00166667 0.         0.10833333
  0.         0.         0.00333333 0.05166667]
 [0.00666667 0.01333333 0.61       0.01833333 0.03333333 0.23
  0.03333333 0.03333333 0.005      0.01666667]
 [0.006      0.         0.006      0.938      0.03       0.012
  0.         0.006      0.002      0.        ]
 [0.         0.         0.         0.006      0.992      0.
  0.         0.002      0.         0.        ]
 [0.005      0.0225     0.0025     0.03       0.         0.8525
  0.08       0.0075     0.         0.        ]
 [0.082      0.         0.162      0.044      0.072      0.042
  0.57       0.028      0.         0.        ]
 [0.         0.00333333 0.01833333 0.00333333 0.03666667 0.
  0.         0.93833333 0.         0.        ]
 [0.05811623 0.01202405 0.00801603 0.14428858 0.0240481  0.10621242
  0.01202405 0.00400802 0.63126253 0.        ]
 [0.01833333 0.08666667 0.00333333 0.00666667 0.00333333 0.065
  0.         0.         0.02       0.79666667]]
[2023-08-31 00:29:24,134 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-31 00:29:24,135 INFO] 204800 iteration, USE_EMA: True, train/sup_loss: 0.0000, train/unsup_loss: 0.0000, train/total_loss: 0.0000, train/util_ratio: 1.0000, train/run_time: 0.4101, eval/loss: 4.7992, eval/top-1-acc: 0.8070, eval/balanced_acc: 0.8069, eval/precision: 0.8190, eval/recall: 0.8069, eval/F1: 0.7997, lr: 0.0000, train/prefecth_time: 0.0023 BEST_EVAL_ACC: 0.9187, at 51200 iters
[2023-08-31 00:29:27,206 INFO] model saved: ./saved_models/usb_cv/pseudolabel_eurosat_40_0/latest_model.pth
[2023-08-31 00:29:27,207 INFO] Model result - eval/best_acc : 0.918688646045564
[2023-08-31 00:29:27,207 INFO] Model result - eval/best_it : 51199
