FENet_imagenet.log

[2022-06-14 09:10:54,700] Namespace(auto_augment=True, batch_size=1024, data_dir='/dataset/public/ImageNetOrigin/', epoch=480, lr=0.6, mode='Train', nesterov=True, reduction=1.0, results_dir='./results/', resume=None)
[2022-06-14 09:10:54,700] ==> Preparing data..
[2022-06-14 09:11:01,410] Training / Testing data number: 50000 / 1281167
[2022-06-14 09:11:01,411] Using path: ./results/14091054/
[2022-06-14 09:11:01,411] ==> Building model..
[2022-06-14 09:11:04,586] DataParallel(
  (module): FENet(
    (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
    (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (ibssl): IBSSL(
      (conv1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shift2): SSL2d()
      (conv2): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (ibpool): IBPool(
      (conv1): Conv2d(16, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shift2): AvgPool2d(kernel_size=2, stride=2, padding=0)
      (conv2): Conv2d(160, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (feblock1): FEBlock3n2s(
      (resibssl_1): ResIBSSL(
        (conv1): Conv2d(8, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(48, 8, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (resibssl_2): ResIBSSL(
        (conv1): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(96, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (ibpool): IBPool(
        (conv1): Conv2d(32, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): AvgPool2d(kernel_size=2, stride=2, padding=0)
        (conv2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (feblock2): FEBlock4n2s(
      (resibssl_1): ResIBSSL(
        (conv1): Conv2d(8, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(48, 8, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (resibssl_2): ResIBSSL(
        (conv1): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(96, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (resibssl_3): ResIBSSL(
        (conv1): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (ibpool): IBPool(
        (conv1): Conv2d(64, 768, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): AvgPool2d(kernel_size=2, stride=2, padding=0)
        (conv2): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (feblock3): FEBlock4n1s(
      (resibssl_1): ResIBSSL(
        (conv1): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(96, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (resibssl_2): ResIBSSL(
        (conv1): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (resibssl_3): ResIBSSL(
        (conv1): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (ibssl): IBSSL(
        (conv1): Conv2d(128, 768, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (feblock4): FEBlock4n2s(
      (resibssl_1): ResIBSSL(
        (conv1): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(96, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (resibssl_2): ResIBSSL(
        (conv1): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (resibssl_3): ResIBSSL(
        (conv1): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (ibpool): IBPool(
        (conv1): Conv2d(128, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): AvgPool2d(kernel_size=2, stride=2, padding=0)
        (conv2): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (feblock5): FEBlock3n1s(
      (resibssl_1): ResIBSSL(
        (conv1): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (resibssl_2): ResIBSSL(
        (conv1): Conv2d(128, 768, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (ibssl): IBSSL(
        (conv1): Conv2d(256, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(1536, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (conv2): Conv2d(256, 1932, kernel_size=(1, 1), stride=(1, 1), bias=False)
    (bn2): BatchNorm2d(1932, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (gap): AdaptiveAvgPool2d(output_size=(1, 1))
    (dropout): Dropout(p=0.2, inplace=False)
    (fc): Conv2d(1932, 1000, kernel_size=(1, 1), stride=(1, 1))
  )
)
[2022-06-14 09:11:04,595] Epoch: 0
[2022-06-14 09:27:42,148] Train: Loss: 5.780 | Acc: 4.340 (55609/1281167) | Lr: 0.6
[2022-06-14 09:28:26,033] Test: Loss: 5.324 | Acc: 7.348 (3674/50000)
[2022-06-14 09:28:26,033] Saving..
[2022-06-14 09:28:26,133] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-14 09:28:26,133] Epoch: 1
[2022-06-14 09:44:57,546] Train: Loss: 4.409 | Acc: 15.950 (204351/1281167) | Lr: 0.5999935746063304
[2022-06-14 09:45:39,562] Test: Loss: 4.024 | Acc: 19.648 (9824/50000)
[2022-06-14 09:45:39,562] Saving..
[2022-06-14 09:45:39,632] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-14 09:45:39,632] Epoch: 2
[2022-06-14 10:02:37,675] Train: Loss: 3.863 | Acc: 23.286 (298336/1281167) | Lr: 0.5999742987005642
[2022-06-14 10:03:20,447] Test: Loss: 3.732 | Acc: 24.910 (12455/50000)
[2022-06-14 10:03:20,447] Saving..
[2022-06-14 10:03:20,525] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-14 10:03:20,525] Epoch: 3
[2022-06-14 10:20:21,975] Train: Loss: 3.543 | Acc: 28.110 (360136/1281167) | Lr: 0.599942173108417
[2022-06-14 10:21:07,375] Test: Loss: 3.507 | Acc: 27.184 (13592/50000)
[2022-06-14 10:21:07,375] Saving..
[2022-06-14 10:21:07,445] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-14 10:21:07,446] Epoch: 4
[2022-06-14 10:38:07,584] Train: Loss: 3.344 | Acc: 31.124 (398751/1281167) | Lr: 0.5998971992060422
[2022-06-14 10:38:51,800] Test: Loss: 3.551 | Acc: 27.488 (13744/50000)
[2022-06-14 10:38:51,801] Saving..
[2022-06-14 10:38:51,866] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-14 10:38:51,866] Epoch: 5
[2022-06-14 10:55:57,373] Train: Loss: 3.215 | Acc: 33.172 (424984/1281167) | Lr: 0.5998393789199723
[2022-06-14 10:56:40,344] Test: Loss: 3.385 | Acc: 30.018 (15009/50000)
[2022-06-14 10:56:40,344] Saving..
[2022-06-14 10:56:40,494] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-14 10:56:40,494] Epoch: 6
[2022-06-14 11:14:49,225] Train: Loss: 3.116 | Acc: 34.860 (446609/1281167) | Lr: 0.5997687147270356
[2022-06-14 11:15:33,228] Test: Loss: 3.019 | Acc: 34.836 (17418/50000)
[2022-06-14 11:15:33,229] Saving..
[2022-06-14 11:15:33,313] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-14 11:15:33,313] Epoch: 7
[2022-06-14 11:32:15,629] Train: Loss: 3.045 | Acc: 36.067 (462079/1281167) | Lr: 0.5996852096542512
[2022-06-14 11:32:56,835] Test: Loss: 3.185 | Acc: 32.452 (16226/50000)
[2022-06-14 11:32:56,835] Epoch: 8
[2022-06-14 11:49:56,155] Train: Loss: 2.988 | Acc: 36.932 (473157/1281167) | Lr: 0.5995888672786983
[2022-06-14 11:50:39,194] Test: Loss: 3.293 | Acc: 31.002 (15501/50000)
[2022-06-14 11:50:39,195] Epoch: 9
[2022-06-14 12:07:38,031] Train: Loss: 2.943 | Acc: 37.720 (483257/1281167) | Lr: 0.5994796917273638
[2022-06-14 12:08:24,960] Test: Loss: 3.107 | Acc: 34.154 (17077/50000)
[2022-06-14 12:08:24,961] Epoch: 10
[2022-06-14 12:25:00,189] Train: Loss: 2.905 | Acc: 38.370 (491583/1281167) | Lr: 0.5993576876769647
[2022-06-14 12:25:42,683] Test: Loss: 2.846 | Acc: 37.692 (18846/50000)
[2022-06-14 12:25:42,684] Saving..
[2022-06-14 12:25:42,768] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-14 12:25:42,768] Epoch: 11
[2022-06-14 12:42:38,370] Train: Loss: 2.875 | Acc: 38.931 (498774/1281167) | Lr: 0.5992228603537487
[2022-06-14 12:43:20,622] Test: Loss: 3.054 | Acc: 34.886 (17443/50000)
[2022-06-14 12:43:20,623] Epoch: 12
[2022-06-14 12:59:58,356] Train: Loss: 2.843 | Acc: 39.456 (505498/1281167) | Lr: 0.5990752155332696
[2022-06-14 13:00:44,748] Test: Loss: 2.864 | Acc: 37.528 (18764/50000)
[2022-06-14 13:00:44,749] Epoch: 13
[2022-06-14 13:17:17,445] Train: Loss: 2.819 | Acc: 39.884 (510984/1281167) | Lr: 0.5989147595401398
[2022-06-14 13:18:03,922] Test: Loss: 2.978 | Acc: 35.702 (17851/50000)
[2022-06-14 13:18:03,923] Epoch: 14
[2022-06-14 13:34:57,074] Train: Loss: 2.800 | Acc: 40.169 (514634/1281167) | Lr: 0.5987414992477603
[2022-06-14 13:35:42,688] Test: Loss: 2.529 | Acc: 42.902 (21451/50000)
[2022-06-14 13:35:42,689] Saving..
[2022-06-14 13:35:42,755] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-14 13:35:42,756] Epoch: 15
[2022-06-14 13:52:38,535] Train: Loss: 2.780 | Acc: 40.471 (518496/1281167) | Lr: 0.5985554420780254
[2022-06-14 13:53:24,853] Test: Loss: 2.569 | Acc: 42.410 (21205/50000)
[2022-06-14 13:53:24,853] Epoch: 16
[2022-06-14 14:10:28,452] Train: Loss: 2.764 | Acc: 40.811 (522855/1281167) | Lr: 0.5983565960010048
[2022-06-14 14:11:13,978] Test: Loss: 2.714 | Acc: 41.212 (20606/50000)
[2022-06-14 14:11:13,979] Epoch: 17
[2022-06-14 14:29:11,354] Train: Loss: 2.751 | Acc: 40.962 (524793/1281167) | Lr: 0.5981449695346027
[2022-06-14 14:29:59,693] Test: Loss: 2.975 | Acc: 36.218 (18109/50000)
[2022-06-14 14:29:59,693] Epoch: 18
[2022-06-14 14:48:52,697] Train: Loss: 2.743 | Acc: 41.144 (527117/1281167) | Lr: 0.5979205717441928
[2022-06-14 14:49:40,700] Test: Loss: 2.748 | Acc: 39.880 (19940/50000)
[2022-06-14 14:49:40,700] Epoch: 19
[2022-06-14 15:07:11,277] Train: Loss: 2.727 | Acc: 41.421 (530667/1281167) | Lr: 0.5976834122422292
[2022-06-14 15:07:53,474] Test: Loss: 2.734 | Acc: 39.944 (19972/50000)
[2022-06-14 15:07:53,474] Epoch: 20
[2022-06-14 15:25:14,478] Train: Loss: 2.716 | Acc: 41.620 (533220/1281167) | Lr: 0.5974335011878359
[2022-06-14 15:26:01,829] Test: Loss: 3.029 | Acc: 35.920 (17960/50000)
[2022-06-14 15:26:01,829] Epoch: 21
[2022-06-14 15:44:07,301] Train: Loss: 2.706 | Acc: 41.798 (535507/1281167) | Lr: 0.5971708492863705
[2022-06-14 15:44:53,606] Test: Loss: 2.930 | Acc: 36.938 (18469/50000)
[2022-06-14 15:44:53,607] Epoch: 22
[2022-06-14 16:02:26,539] Train: Loss: 2.699 | Acc: 41.940 (537321/1281167) | Lr: 0.5968954677889666
[2022-06-14 16:03:16,636] Test: Loss: 2.737 | Acc: 40.064 (20032/50000)
[2022-06-14 16:03:16,637] Epoch: 23
[2022-06-14 16:21:35,806] Train: Loss: 2.691 | Acc: 42.117 (539585/1281167) | Lr: 0.5966073684920506
[2022-06-14 16:22:19,092] Test: Loss: 2.795 | Acc: 38.896 (19448/50000)
[2022-06-14 16:22:19,092] Epoch: 24
[2022-06-14 16:39:10,129] Train: Loss: 2.684 | Acc: 42.184 (540445/1281167) | Lr: 0.596306563736838
[2022-06-14 16:39:52,447] Test: Loss: 3.687 | Acc: 27.644 (13822/50000)
[2022-06-14 16:39:52,448] Epoch: 25
[2022-06-14 16:56:13,355] Train: Loss: 2.673 | Acc: 42.352 (542595/1281167) | Lr: 0.5959930664088029
[2022-06-14 16:56:57,942] Test: Loss: 2.606 | Acc: 41.844 (20922/50000)
[2022-06-14 16:56:57,942] Epoch: 26
[2022-06-14 17:15:28,517] Train: Loss: 2.666 | Acc: 42.531 (544889/1281167) | Lr: 0.5956668899371277
[2022-06-14 17:16:12,556] Test: Loss: 2.788 | Acc: 39.290 (19645/50000)
[2022-06-14 17:16:12,557] Epoch: 27
[2022-06-14 17:34:50,968] Train: Loss: 2.664 | Acc: 42.538 (544978/1281167) | Lr: 0.5953280482941267
[2022-06-14 17:35:34,820] Test: Loss: 2.711 | Acc: 40.498 (20249/50000)
[2022-06-14 17:35:34,821] Epoch: 28
[2022-06-14 17:53:28,448] Train: Loss: 2.657 | Acc: 42.647 (546380/1281167) | Lr: 0.5949765559946483
[2022-06-14 17:54:11,538] Test: Loss: 2.513 | Acc: 43.348 (21674/50000)
[2022-06-14 17:54:11,538] Saving..
[2022-06-14 17:54:11,610] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-14 17:54:11,611] Epoch: 29
[2022-06-14 18:12:52,543] Train: Loss: 2.655 | Acc: 42.743 (547615/1281167) | Lr: 0.5946124280954524
[2022-06-14 18:13:32,449] Test: Loss: 2.638 | Acc: 41.952 (20976/50000)
[2022-06-14 18:13:32,450] Epoch: 30
[2022-06-14 18:31:06,361] Train: Loss: 2.648 | Acc: 42.820 (548590/1281167) | Lr: 0.5942356801945667
[2022-06-14 18:31:49,454] Test: Loss: 2.436 | Acc: 45.110 (22555/50000)
[2022-06-14 18:31:49,454] Saving..
[2022-06-14 18:31:49,518] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-14 18:31:49,519] Epoch: 31
[2022-06-14 18:48:39,972] Train: Loss: 2.643 | Acc: 42.939 (550116/1281167) | Lr: 0.5938463284306172
[2022-06-14 18:49:22,310] Test: Loss: 2.465 | Acc: 44.638 (22319/50000)
[2022-06-14 18:49:22,310] Epoch: 32
[2022-06-14 19:06:13,064] Train: Loss: 2.636 | Acc: 43.018 (551132/1281167) | Lr: 0.5934443894821377
[2022-06-14 19:06:55,233] Test: Loss: 2.645 | Acc: 41.262 (20631/50000)
[2022-06-14 19:06:55,234] Epoch: 33
[2022-06-14 19:23:49,538] Train: Loss: 2.629 | Acc: 43.145 (552764/1281167) | Lr: 0.5930298805668548
[2022-06-14 19:24:33,092] Test: Loss: 2.509 | Acc: 43.428 (21714/50000)
[2022-06-14 19:24:33,093] Epoch: 34
[2022-06-14 19:43:16,843] Train: Loss: 2.629 | Acc: 43.154 (552878/1281167) | Lr: 0.592602819440951
[2022-06-14 19:44:00,837] Test: Loss: 2.403 | Acc: 45.478 (22739/50000)
[2022-06-14 19:44:00,837] Saving..
[2022-06-14 19:44:00,908] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-14 19:44:00,908] Epoch: 35
[2022-06-14 20:02:26,027] Train: Loss: 2.628 | Acc: 43.161 (552970/1281167) | Lr: 0.5921632243983034
[2022-06-14 20:03:10,600] Test: Loss: 2.328 | Acc: 46.868 (23434/50000)
[2022-06-14 20:03:10,600] Saving..
[2022-06-14 20:03:10,690] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-14 20:03:10,691] Epoch: 36
[2022-06-14 20:21:45,323] Train: Loss: 2.622 | Acc: 43.305 (554803/1281167) | Lr: 0.5917111142697007
[2022-06-14 20:22:32,203] Test: Loss: 2.731 | Acc: 39.536 (19768/50000)
[2022-06-14 20:22:32,203] Epoch: 37
[2022-06-14 20:40:40,505] Train: Loss: 2.617 | Acc: 43.366 (555585/1281167) | Lr: 0.591246508422036
[2022-06-14 20:41:23,148] Test: Loss: 2.683 | Acc: 40.746 (20373/50000)
[2022-06-14 20:41:23,148] Epoch: 38
[2022-06-14 20:58:35,559] Train: Loss: 2.611 | Acc: 43.468 (556895/1281167) | Lr: 0.5907694267574775
[2022-06-14 20:59:17,984] Test: Loss: 2.572 | Acc: 42.462 (21231/50000)
[2022-06-14 20:59:17,984] Epoch: 39
[2022-06-14 21:17:48,292] Train: Loss: 2.615 | Acc: 43.458 (556764/1281167) | Lr: 0.5902798897126158
[2022-06-14 21:18:32,459] Test: Loss: 2.685 | Acc: 40.706 (20353/50000)
[2022-06-14 21:18:32,459] Epoch: 40
[2022-06-14 21:36:46,533] Train: Loss: 2.613 | Acc: 43.469 (556906/1281167) | Lr: 0.5897779182575887
[2022-06-14 21:37:30,870] Test: Loss: 2.555 | Acc: 43.076 (21538/50000)
[2022-06-14 21:37:30,871] Epoch: 41
[2022-06-14 21:54:01,179] Train: Loss: 2.607 | Acc: 43.544 (557868/1281167) | Lr: 0.5892635338951826
[2022-06-14 21:54:44,038] Test: Loss: 2.677 | Acc: 41.642 (20821/50000)
[2022-06-14 21:54:44,038] Epoch: 42
[2022-06-14 22:11:59,166] Train: Loss: 2.605 | Acc: 43.605 (558653/1281167) | Lr: 0.5887367586599115
[2022-06-14 22:12:41,988] Test: Loss: 2.476 | Acc: 44.630 (22315/50000)
[2022-06-14 22:12:41,988] Epoch: 43
[2022-06-14 22:30:59,329] Train: Loss: 2.600 | Acc: 43.655 (559293/1281167) | Lr: 0.5881976151170734
[2022-06-14 22:31:45,693] Test: Loss: 2.863 | Acc: 38.222 (19111/50000)
[2022-06-14 22:31:45,694] Epoch: 44
[2022-06-14 22:48:36,192] Train: Loss: 2.599 | Acc: 43.704 (559917/1281167) | Lr: 0.5876461263617831
[2022-06-14 22:49:21,045] Test: Loss: 2.650 | Acc: 41.730 (20865/50000)
[2022-06-14 22:49:21,046] Epoch: 45
[2022-06-14 23:06:22,530] Train: Loss: 2.596 | Acc: 43.761 (560649/1281167) | Lr: 0.5870823160179836
[2022-06-14 23:07:07,609] Test: Loss: 2.418 | Acc: 45.470 (22735/50000)
[2022-06-14 23:07:07,610] Epoch: 46
[2022-06-14 23:24:24,829] Train: Loss: 2.594 | Acc: 43.806 (561232/1281167) | Lr: 0.5865062082374333
[2022-06-14 23:25:08,598] Test: Loss: 2.586 | Acc: 42.654 (21327/50000)
[2022-06-14 23:25:08,599] Epoch: 47
[2022-06-14 23:42:31,048] Train: Loss: 2.594 | Acc: 43.769 (560754/1281167) | Lr: 0.5859178276986722
[2022-06-14 23:43:13,481] Test: Loss: 2.470 | Acc: 44.692 (22346/50000)
[2022-06-14 23:43:13,482] Epoch: 48
[2022-06-14 23:59:54,278] Train: Loss: 2.590 | Acc: 43.843 (561699/1281167) | Lr: 0.5853171996059642
[2022-06-15 00:00:35,278] Test: Loss: 2.803 | Acc: 39.082 (19541/50000)
[2022-06-15 00:00:35,278] Epoch: 49
[2022-06-15 00:17:31,640] Train: Loss: 2.584 | Acc: 43.955 (563135/1281167) | Lr: 0.5847043496882178
[2022-06-15 00:18:14,185] Test: Loss: 2.426 | Acc: 45.596 (22798/50000)
[2022-06-15 00:18:14,185] Epoch: 50
[2022-06-15 00:35:18,097] Train: Loss: 2.585 | Acc: 43.973 (563373/1281167) | Lr: 0.5840793041978839
[2022-06-15 00:36:02,151] Test: Loss: 2.831 | Acc: 39.842 (19921/50000)
[2022-06-15 00:36:02,151] Epoch: 51
[2022-06-15 00:54:51,587] Train: Loss: 2.581 | Acc: 43.974 (563381/1281167) | Lr: 0.5834420899098308
[2022-06-15 00:55:34,704] Test: Loss: 2.471 | Acc: 45.228 (22614/50000)
[2022-06-15 00:55:34,705] Epoch: 52
[2022-06-15 01:13:39,720] Train: Loss: 2.585 | Acc: 43.951 (563090/1281167) | Lr: 0.5827927341201978
[2022-06-15 01:14:22,550] Test: Loss: 3.009 | Acc: 37.188 (18594/50000)
[2022-06-15 01:14:22,550] Epoch: 53
[2022-06-15 01:32:48,533] Train: Loss: 2.579 | Acc: 44.059 (564466/1281167) | Lr: 0.5821312646452258
[2022-06-15 01:33:30,518] Test: Loss: 2.668 | Acc: 41.504 (20752/50000)
[2022-06-15 01:33:30,518] Epoch: 54
[2022-06-15 01:50:29,410] Train: Loss: 2.574 | Acc: 44.124 (565301/1281167) | Lr: 0.5814577098200655
[2022-06-15 01:51:15,069] Test: Loss: 2.274 | Acc: 48.018 (24009/50000)
[2022-06-15 01:51:15,069] Saving..
[2022-06-15 01:51:15,158] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-15 01:51:15,158] Epoch: 55
[2022-06-15 02:09:48,393] Train: Loss: 2.575 | Acc: 44.133 (565415/1281167) | Lr: 0.5807720984975637
[2022-06-15 02:10:32,118] Test: Loss: 2.642 | Acc: 41.036 (20518/50000)
[2022-06-15 02:10:32,118] Epoch: 56
[2022-06-15 02:29:00,591] Train: Loss: 2.570 | Acc: 44.162 (565791/1281167) | Lr: 0.5800744600470279
[2022-06-15 02:29:43,785] Test: Loss: 2.541 | Acc: 43.564 (21782/50000)
[2022-06-15 02:29:43,786] Epoch: 57
[2022-06-15 02:47:46,806] Train: Loss: 2.571 | Acc: 44.124 (565298/1281167) | Lr: 0.5793648243529671
[2022-06-15 02:48:29,409] Test: Loss: 2.435 | Acc: 45.066 (22533/50000)
[2022-06-15 02:48:29,409] Epoch: 58
[2022-06-15 03:06:55,059] Train: Loss: 2.569 | Acc: 44.214 (566452/1281167) | Lr: 0.5786432218138128
[2022-06-15 03:07:37,084] Test: Loss: 2.625 | Acc: 41.752 (20876/50000)
[2022-06-15 03:07:37,084] Epoch: 59
[2022-06-15 03:26:12,289] Train: Loss: 2.568 | Acc: 44.272 (567201/1281167) | Lr: 0.5779096833406159
[2022-06-15 03:26:54,576] Test: Loss: 2.825 | Acc: 38.804 (19402/50000)
[2022-06-15 03:26:54,576] Epoch: 60
[2022-06-15 03:44:46,417] Train: Loss: 2.564 | Acc: 44.308 (567662/1281167) | Lr: 0.5771642403557232
[2022-06-15 03:45:32,362] Test: Loss: 2.399 | Acc: 45.354 (22677/50000)
[2022-06-15 03:45:32,363] Epoch: 61
[2022-06-15 04:03:29,490] Train: Loss: 2.561 | Acc: 44.400 (568844/1281167) | Lr: 0.5764069247914314
[2022-06-15 04:04:12,669] Test: Loss: 2.763 | Acc: 39.894 (19947/50000)
[2022-06-15 04:04:12,669] Epoch: 62
[2022-06-15 04:22:43,141] Train: Loss: 2.565 | Acc: 44.324 (567862/1281167) | Lr: 0.5756377690886185
[2022-06-15 04:23:23,353] Test: Loss: 2.551 | Acc: 43.876 (21938/50000)
[2022-06-15 04:23:23,353] Epoch: 63
[2022-06-15 04:40:26,587] Train: Loss: 2.562 | Acc: 44.338 (568043/1281167) | Lr: 0.574856806195355
[2022-06-15 04:41:09,546] Test: Loss: 2.697 | Acc: 41.064 (20532/50000)
[2022-06-15 04:41:09,547] Epoch: 64
[2022-06-15 04:58:44,500] Train: Loss: 2.559 | Acc: 44.447 (569446/1281167) | Lr: 0.5740640695654917
[2022-06-15 04:59:32,469] Test: Loss: 2.683 | Acc: 40.900 (20450/50000)
[2022-06-15 04:59:32,470] Epoch: 65
[2022-06-15 05:18:01,264] Train: Loss: 2.558 | Acc: 44.443 (569386/1281167) | Lr: 0.5732595931572279
[2022-06-15 05:18:42,435] Test: Loss: 2.336 | Acc: 47.222 (23611/50000)
[2022-06-15 05:18:42,435] Epoch: 66
[2022-06-15 05:37:15,718] Train: Loss: 2.558 | Acc: 44.424 (569150/1281167) | Lr: 0.572443411431655
[2022-06-15 05:37:59,871] Test: Loss: 2.697 | Acc: 42.416 (21208/50000)
[2022-06-15 05:37:59,871] Epoch: 67
[2022-06-15 05:56:29,386] Train: Loss: 2.553 | Acc: 44.511 (570255/1281167) | Lr: 0.5716155593512818
[2022-06-15 05:57:13,008] Test: Loss: 2.249 | Acc: 48.318 (24159/50000)
[2022-06-15 05:57:13,008] Saving..
[2022-06-15 05:57:13,112] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-15 05:57:13,112] Epoch: 68
[2022-06-15 06:14:49,511] Train: Loss: 2.555 | Acc: 44.466 (569689/1281167) | Lr: 0.5707760723785362
[2022-06-15 06:15:30,801] Test: Loss: 2.483 | Acc: 44.794 (22397/50000)
[2022-06-15 06:15:30,801] Epoch: 69
[2022-06-15 06:34:02,148] Train: Loss: 2.551 | Acc: 44.524 (570432/1281167) | Lr: 0.5699249864742459
[2022-06-15 06:34:44,117] Test: Loss: 2.430 | Acc: 45.482 (22741/50000)
[2022-06-15 06:34:44,118] Epoch: 70
[2022-06-15 06:53:19,049] Train: Loss: 2.553 | Acc: 44.547 (570718/1281167) | Lr: 0.5690623380960986
[2022-06-15 06:54:06,005] Test: Loss: 2.338 | Acc: 46.976 (23488/50000)
[2022-06-15 06:54:06,006] Epoch: 71
[2022-06-15 07:12:06,128] Train: Loss: 2.546 | Acc: 44.628 (571756/1281167) | Lr: 0.5681881641970796
[2022-06-15 07:12:49,981] Test: Loss: 2.684 | Acc: 41.316 (20658/50000)
[2022-06-15 07:12:49,981] Epoch: 72
[2022-06-15 07:30:49,160] Train: Loss: 2.548 | Acc: 44.578 (571123/1281167) | Lr: 0.5673025022238892
[2022-06-15 07:31:32,928] Test: Loss: 2.777 | Acc: 40.232 (20116/50000)
[2022-06-15 07:31:32,928] Epoch: 73
[2022-06-15 07:49:34,142] Train: Loss: 2.545 | Acc: 44.700 (572687/1281167) | Lr: 0.5664053901153387
[2022-06-15 07:50:17,932] Test: Loss: 2.484 | Acc: 44.544 (22272/50000)
[2022-06-15 07:50:17,932] Epoch: 74
[2022-06-15 08:08:34,812] Train: Loss: 2.543 | Acc: 44.652 (572063/1281167) | Lr: 0.565496866300725
[2022-06-15 08:09:20,720] Test: Loss: 2.564 | Acc: 43.028 (21514/50000)
[2022-06-15 08:09:20,720] Epoch: 75
[2022-06-15 08:27:52,265] Train: Loss: 2.543 | Acc: 44.673 (572340/1281167) | Lr: 0.5645769696981845
[2022-06-15 08:28:40,761] Test: Loss: 2.352 | Acc: 46.578 (23289/50000)
[2022-06-15 08:28:40,762] Epoch: 76
[2022-06-15 08:47:10,729] Train: Loss: 2.542 | Acc: 44.772 (573609/1281167) | Lr: 0.563645739713026
[2022-06-15 08:47:53,640] Test: Loss: 2.351 | Acc: 46.370 (23185/50000)
[2022-06-15 08:47:53,640] Epoch: 77
[2022-06-15 09:05:21,392] Train: Loss: 2.537 | Acc: 44.871 (574875/1281167) | Lr: 0.5627032162360428
[2022-06-15 09:06:04,099] Test: Loss: 2.595 | Acc: 42.848 (21424/50000)
[2022-06-15 09:06:04,099] Epoch: 78
[2022-06-15 09:23:31,969] Train: Loss: 2.534 | Acc: 44.875 (574922/1281167) | Lr: 0.5617494396418036
[2022-06-15 09:24:14,740] Test: Loss: 2.592 | Acc: 42.466 (21233/50000)
[2022-06-15 09:24:14,741] Epoch: 79
[2022-06-15 09:42:05,636] Train: Loss: 2.536 | Acc: 44.824 (574274/1281167) | Lr: 0.5607844507869232
[2022-06-15 09:42:48,547] Test: Loss: 2.748 | Acc: 39.832 (19916/50000)
[2022-06-15 09:42:48,548] Epoch: 80
[2022-06-15 10:01:22,074] Train: Loss: 2.531 | Acc: 44.906 (575317/1281167) | Lr: 0.5598082910083125
[2022-06-15 10:02:06,127] Test: Loss: 2.462 | Acc: 44.214 (22107/50000)
[2022-06-15 10:02:06,127] Epoch: 81
[2022-06-15 10:20:32,638] Train: Loss: 2.535 | Acc: 44.801 (573980/1281167) | Lr: 0.5588210021214074
[2022-06-15 10:21:13,951] Test: Loss: 2.379 | Acc: 45.898 (22949/50000)
[2022-06-15 10:21:13,952] Epoch: 82
[2022-06-15 10:39:21,423] Train: Loss: 2.527 | Acc: 44.969 (576133/1281167) | Lr: 0.5578226264183781
[2022-06-15 10:40:04,857] Test: Loss: 2.577 | Acc: 43.110 (21555/50000)
[2022-06-15 10:40:04,858] Epoch: 83
[2022-06-15 10:58:09,125] Train: Loss: 2.530 | Acc: 44.925 (575559/1281167) | Lr: 0.5568132066663166
[2022-06-15 10:58:51,893] Test: Loss: 2.712 | Acc: 42.148 (21074/50000)
[2022-06-15 10:58:51,893] Epoch: 84
[2022-06-15 11:17:23,496] Train: Loss: 2.526 | Acc: 44.980 (576266/1281167) | Lr: 0.5557927861054056
[2022-06-15 11:18:12,165] Test: Loss: 2.220 | Acc: 48.750 (24375/50000)
[2022-06-15 11:18:12,165] Saving..
[2022-06-15 11:18:12,247] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-15 11:18:12,247] Epoch: 85
[2022-06-15 11:36:41,828] Train: Loss: 2.529 | Acc: 44.966 (576087/1281167) | Lr: 0.5547614084470658
[2022-06-15 11:37:24,807] Test: Loss: 2.525 | Acc: 43.942 (21971/50000)
[2022-06-15 11:37:24,808] Epoch: 86
[2022-06-15 11:55:51,563] Train: Loss: 2.523 | Acc: 45.078 (577529/1281167) | Lr: 0.5537191178720833
[2022-06-15 11:56:35,970] Test: Loss: 2.346 | Acc: 46.708 (23354/50000)
[2022-06-15 11:56:35,971] Epoch: 87
[2022-06-15 12:14:05,175] Train: Loss: 2.520 | Acc: 45.127 (578158/1281167) | Lr: 0.5526659590287172
[2022-06-15 12:14:47,765] Test: Loss: 2.745 | Acc: 41.100 (20550/50000)
[2022-06-15 12:14:47,766] Epoch: 88
[2022-06-15 12:32:09,915] Train: Loss: 2.520 | Acc: 45.110 (577937/1281167) | Lr: 0.5516019770307873
[2022-06-15 12:32:56,327] Test: Loss: 2.599 | Acc: 43.538 (21769/50000)
[2022-06-15 12:32:56,327] Epoch: 89
[2022-06-15 12:49:36,436] Train: Loss: 2.519 | Acc: 45.083 (577588/1281167) | Lr: 0.5505272174557411
[2022-06-15 12:50:20,155] Test: Loss: 2.583 | Acc: 43.680 (21840/50000)
[2022-06-15 12:50:20,155] Epoch: 90
[2022-06-15 13:08:21,309] Train: Loss: 2.519 | Acc: 45.143 (578355/1281167) | Lr: 0.5494417263427018
[2022-06-15 13:09:02,481] Test: Loss: 2.310 | Acc: 47.290 (23645/50000)
[2022-06-15 13:09:02,482] Epoch: 91
[2022-06-15 13:27:31,680] Train: Loss: 2.516 | Acc: 45.242 (579624/1281167) | Lr: 0.5483455501904958
[2022-06-15 13:28:15,023] Test: Loss: 3.018 | Acc: 36.360 (18180/50000)
[2022-06-15 13:28:15,024] Epoch: 92
[2022-06-15 13:46:45,038] Train: Loss: 2.518 | Acc: 45.161 (578590/1281167) | Lr: 0.5472387359556613
[2022-06-15 13:47:33,898] Test: Loss: 2.399 | Acc: 45.544 (22772/50000)
[2022-06-15 13:47:33,899] Epoch: 93
[2022-06-15 14:04:08,039] Train: Loss: 2.514 | Acc: 45.201 (579099/1281167) | Lr: 0.5461213310504361
[2022-06-15 14:04:49,799] Test: Loss: 2.726 | Acc: 41.672 (20836/50000)
[2022-06-15 14:04:49,799] Epoch: 94
[2022-06-15 14:22:51,881] Train: Loss: 2.512 | Acc: 45.279 (580100/1281167) | Lr: 0.5449933833407276
[2022-06-15 14:23:35,758] Test: Loss: 2.526 | Acc: 43.978 (21989/50000)
[2022-06-15 14:23:35,758] Epoch: 95
[2022-06-15 14:42:03,854] Train: Loss: 2.510 | Acc: 45.284 (580160/1281167) | Lr: 0.5438549411440613
[2022-06-15 14:42:51,386] Test: Loss: 2.456 | Acc: 44.926 (22463/50000)
[2022-06-15 14:42:51,386] Epoch: 96
[2022-06-15 15:01:23,121] Train: Loss: 2.513 | Acc: 45.213 (579260/1281167) | Lr: 0.542706053227512
[2022-06-15 15:02:05,572] Test: Loss: 2.459 | Acc: 44.890 (22445/50000)
[2022-06-15 15:02:05,572] Epoch: 97
[2022-06-15 15:20:35,094] Train: Loss: 2.514 | Acc: 45.275 (580054/1281167) | Lr: 0.5415467688056143
[2022-06-15 15:21:18,043] Test: Loss: 2.271 | Acc: 48.474 (24237/50000)
[2022-06-15 15:21:18,043] Epoch: 98
[2022-06-15 15:37:38,776] Train: Loss: 2.503 | Acc: 45.410 (581772/1281167) | Lr: 0.5403771375382543
[2022-06-15 15:38:21,468] Test: Loss: 2.442 | Acc: 45.154 (22577/50000)
[2022-06-15 15:38:21,468] Epoch: 99
[2022-06-15 15:56:52,651] Train: Loss: 2.506 | Acc: 45.417 (581862/1281167) | Lr: 0.5391972095285429
[2022-06-15 15:57:35,026] Test: Loss: 2.478 | Acc: 44.794 (22397/50000)
[2022-06-15 15:57:35,026] Epoch: 100
[2022-06-15 16:15:24,230] Train: Loss: 2.503 | Acc: 45.439 (582145/1281167) | Lr: 0.5380070353206687
[2022-06-15 16:16:07,206] Test: Loss: 2.331 | Acc: 46.902 (23451/50000)
[2022-06-15 16:16:07,206] Epoch: 101
[2022-06-15 16:34:35,015] Train: Loss: 2.506 | Acc: 45.328 (580727/1281167) | Lr: 0.5368066658977336
[2022-06-15 16:35:17,674] Test: Loss: 2.458 | Acc: 44.690 (22345/50000)
[2022-06-15 16:35:17,674] Epoch: 102
[2022-06-15 16:52:35,026] Train: Loss: 2.506 | Acc: 45.358 (581118/1281167) | Lr: 0.5355961526795687
[2022-06-15 16:53:17,790] Test: Loss: 2.304 | Acc: 47.630 (23815/50000)
[2022-06-15 16:53:17,790] Epoch: 103
[2022-06-15 17:11:04,872] Train: Loss: 2.503 | Acc: 45.474 (582597/1281167) | Lr: 0.5343755475205313
[2022-06-15 17:11:45,434] Test: Loss: 2.360 | Acc: 46.816 (23408/50000)
[2022-06-15 17:11:45,435] Epoch: 104
[2022-06-15 17:29:09,182] Train: Loss: 2.503 | Acc: 45.394 (581572/1281167) | Lr: 0.5331449027072837
[2022-06-15 17:29:52,214] Test: Loss: 2.239 | Acc: 48.764 (24382/50000)
[2022-06-15 17:29:52,214] Saving..
[2022-06-15 17:29:52,309] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-15 17:29:52,309] Epoch: 105
[2022-06-15 17:47:36,025] Train: Loss: 2.495 | Acc: 45.557 (583656/1281167) | Lr: 0.5319042709565539
[2022-06-15 17:48:19,336] Test: Loss: 2.209 | Acc: 49.116 (24558/50000)
[2022-06-15 17:48:19,336] Saving..
[2022-06-15 17:48:19,403] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-15 17:48:19,403] Epoch: 106
[2022-06-15 18:06:44,467] Train: Loss: 2.497 | Acc: 45.495 (582865/1281167) | Lr: 0.5306537054128772
[2022-06-15 18:07:29,280] Test: Loss: 2.314 | Acc: 47.160 (23580/50000)
[2022-06-15 18:07:29,280] Epoch: 107
[2022-06-15 18:25:47,892] Train: Loss: 2.492 | Acc: 45.558 (583674/1281167) | Lr: 0.529393259646319
[2022-06-15 18:26:29,722] Test: Loss: 2.454 | Acc: 45.028 (22514/50000)
[2022-06-15 18:26:29,722] Epoch: 108
[2022-06-15 18:44:51,580] Train: Loss: 2.495 | Acc: 45.611 (584347/1281167) | Lr: 0.528122987650181
[2022-06-15 18:45:35,464] Test: Loss: 2.536 | Acc: 43.524 (21762/50000)
[2022-06-15 18:45:35,464] Epoch: 109
[2022-06-15 19:03:53,708] Train: Loss: 2.492 | Acc: 45.617 (584429/1281167) | Lr: 0.5268429438386876
[2022-06-15 19:04:37,124] Test: Loss: 2.567 | Acc: 42.712 (21356/50000)
[2022-06-15 19:04:37,125] Epoch: 110
[2022-06-15 19:22:54,552] Train: Loss: 2.494 | Acc: 45.643 (584759/1281167) | Lr: 0.5255531830446555
[2022-06-15 19:23:38,396] Test: Loss: 2.503 | Acc: 45.294 (22647/50000)
[2022-06-15 19:23:38,396] Epoch: 111
[2022-06-15 19:42:00,302] Train: Loss: 2.488 | Acc: 45.680 (585232/1281167) | Lr: 0.5242537605171443
[2022-06-15 19:42:41,613] Test: Loss: 2.453 | Acc: 45.026 (22513/50000)
[2022-06-15 19:42:41,613] Epoch: 112
[2022-06-15 20:00:28,481] Train: Loss: 2.492 | Acc: 45.635 (584655/1281167) | Lr: 0.5229447319190905
[2022-06-15 20:01:13,568] Test: Loss: 2.433 | Acc: 45.734 (22867/50000)
[2022-06-15 20:01:13,568] Epoch: 113
[2022-06-15 20:19:34,963] Train: Loss: 2.487 | Acc: 45.712 (585649/1281167) | Lr: 0.5216261533249222
[2022-06-15 20:20:18,494] Test: Loss: 2.315 | Acc: 47.530 (23765/50000)
[2022-06-15 20:20:18,494] Epoch: 114
[2022-06-15 20:38:04,464] Train: Loss: 2.485 | Acc: 45.771 (586404/1281167) | Lr: 0.5202980812181581
[2022-06-15 20:38:43,648] Test: Loss: 2.354 | Acc: 46.270 (23135/50000)
[2022-06-15 20:38:43,648] Epoch: 115
[2022-06-15 20:57:04,666] Train: Loss: 2.486 | Acc: 45.698 (585465/1281167) | Lr: 0.5189605724889867
[2022-06-15 20:57:47,380] Test: Loss: 2.378 | Acc: 46.392 (23196/50000)
[2022-06-15 20:57:47,380] Epoch: 116
[2022-06-15 21:16:04,491] Train: Loss: 2.482 | Acc: 45.764 (586312/1281167) | Lr: 0.5176136844318308
[2022-06-15 21:16:48,143] Test: Loss: 2.353 | Acc: 46.646 (23323/50000)
[2022-06-15 21:16:48,144] Epoch: 117
[2022-06-15 21:34:31,833] Train: Loss: 2.481 | Acc: 45.813 (586944/1281167) | Lr: 0.5162574747428917
[2022-06-15 21:35:15,446] Test: Loss: 2.216 | Acc: 49.054 (24527/50000)
[2022-06-15 21:35:15,446] Epoch: 118
[2022-06-15 21:53:07,123] Train: Loss: 2.481 | Acc: 45.817 (586989/1281167) | Lr: 0.5148920015176788
[2022-06-15 21:53:51,027] Test: Loss: 2.159 | Acc: 50.344 (25172/50000)
[2022-06-15 21:53:51,027] Saving..
[2022-06-15 21:53:51,101] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-15 21:53:51,101] Epoch: 119
[2022-06-15 22:11:31,835] Train: Loss: 2.477 | Acc: 45.900 (588054/1281167) | Lr: 0.5135173232485203
[2022-06-15 22:12:14,174] Test: Loss: 2.938 | Acc: 37.932 (18966/50000)
[2022-06-15 22:12:14,175] Epoch: 120
[2022-06-15 22:30:35,930] Train: Loss: 2.480 | Acc: 45.846 (587358/1281167) | Lr: 0.5121334988220579
[2022-06-15 22:31:19,931] Test: Loss: 2.343 | Acc: 47.246 (23623/50000)
[2022-06-15 22:31:19,932] Epoch: 121
[2022-06-15 22:49:37,308] Train: Loss: 2.477 | Acc: 45.888 (587896/1281167) | Lr: 0.5107405875167246
[2022-06-15 22:50:19,321] Test: Loss: 2.419 | Acc: 45.894 (22947/50000)
[2022-06-15 22:50:19,323] Epoch: 122
[2022-06-15 23:08:33,084] Train: Loss: 2.474 | Acc: 45.949 (588682/1281167) | Lr: 0.5093386490002044
[2022-06-15 23:09:15,946] Test: Loss: 2.225 | Acc: 49.308 (24654/50000)
[2022-06-15 23:09:15,947] Epoch: 123
[2022-06-15 23:27:37,648] Train: Loss: 2.475 | Acc: 45.955 (588759/1281167) | Lr: 0.5079277433268776
[2022-06-15 23:28:20,435] Test: Loss: 2.238 | Acc: 48.764 (24382/50000)
[2022-06-15 23:28:20,435] Epoch: 124
[2022-06-15 23:46:09,843] Train: Loss: 2.474 | Acc: 45.963 (588866/1281167) | Lr: 0.5065079309352473
[2022-06-15 23:46:52,016] Test: Loss: 2.350 | Acc: 46.834 (23417/50000)
[2022-06-15 23:46:52,016] Epoch: 125
[2022-06-16 00:05:10,848] Train: Loss: 2.473 | Acc: 46.036 (589794/1281167) | Lr: 0.5050792726453508
[2022-06-16 00:05:54,787] Test: Loss: 2.344 | Acc: 46.976 (23488/50000)
[2022-06-16 00:05:54,787] Epoch: 126
[2022-06-16 00:24:16,665] Train: Loss: 2.468 | Acc: 46.091 (590507/1281167) | Lr: 0.5036418296561543
[2022-06-16 00:25:00,217] Test: Loss: 2.288 | Acc: 48.212 (24106/50000)
[2022-06-16 00:25:00,217] Epoch: 127
[2022-06-16 00:42:48,157] Train: Loss: 2.468 | Acc: 46.031 (589740/1281167) | Lr: 0.5021956635429314
[2022-06-16 00:43:30,958] Test: Loss: 2.458 | Acc: 44.940 (22470/50000)
[2022-06-16 00:43:30,959] Epoch: 128
[2022-06-16 01:01:44,413] Train: Loss: 2.466 | Acc: 46.090 (590491/1281167) | Lr: 0.5007408362546251
[2022-06-16 01:02:25,516] Test: Loss: 2.103 | Acc: 51.298 (25649/50000)
[2022-06-16 01:02:25,516] Saving..
[2022-06-16 01:02:25,601] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-16 01:02:25,602] Epoch: 129
[2022-06-16 01:20:17,839] Train: Loss: 2.466 | Acc: 46.096 (590570/1281167) | Lr: 0.4992774101111944
[2022-06-16 01:21:01,214] Test: Loss: 2.527 | Acc: 44.274 (22137/50000)
[2022-06-16 01:21:01,215] Epoch: 130
[2022-06-16 01:39:21,908] Train: Loss: 2.461 | Acc: 46.174 (591560/1281167) | Lr: 0.4978054478009446
[2022-06-16 01:40:04,669] Test: Loss: 2.106 | Acc: 50.990 (25495/50000)
[2022-06-16 01:40:04,670] Epoch: 131
[2022-06-16 01:58:21,881] Train: Loss: 2.465 | Acc: 46.158 (591359/1281167) | Lr: 0.49632501237784193
[2022-06-16 01:59:05,478] Test: Loss: 2.321 | Acc: 47.116 (23558/50000)
[2022-06-16 01:59:05,479] Epoch: 132
[2022-06-16 02:17:26,166] Train: Loss: 2.462 | Acc: 46.133 (591036/1281167) | Lr: 0.49483616725881285
[2022-06-16 02:18:08,984] Test: Loss: 2.524 | Acc: 44.026 (22013/50000)
[2022-06-16 02:18:08,984] Epoch: 133
[2022-06-16 02:36:24,047] Train: Loss: 2.456 | Acc: 46.287 (593014/1281167) | Lr: 0.49333897622102685
[2022-06-16 02:37:08,928] Test: Loss: 2.754 | Acc: 41.066 (20533/50000)
[2022-06-16 02:37:08,929] Epoch: 134
[2022-06-16 02:55:29,878] Train: Loss: 2.454 | Acc: 46.300 (593185/1281167) | Lr: 0.49183350339916493
[2022-06-16 02:56:12,166] Test: Loss: 2.430 | Acc: 45.202 (22601/50000)
[2022-06-16 02:56:12,167] Epoch: 135
[2022-06-16 03:14:31,341] Train: Loss: 2.454 | Acc: 46.320 (593440/1281167) | Lr: 0.4903198132826722
[2022-06-16 03:15:14,790] Test: Loss: 2.557 | Acc: 43.300 (21650/50000)
[2022-06-16 03:15:14,790] Epoch: 136
[2022-06-16 03:33:05,764] Train: Loss: 2.454 | Acc: 46.278 (592896/1281167) | Lr: 0.4887979707129954
[2022-06-16 03:33:48,433] Test: Loss: 2.150 | Acc: 50.200 (25100/50000)
[2022-06-16 03:33:48,434] Epoch: 137
[2022-06-16 03:52:27,149] Train: Loss: 2.455 | Acc: 46.329 (593551/1281167) | Lr: 0.487268040880805
[2022-06-16 03:53:08,815] Test: Loss: 2.252 | Acc: 48.190 (24095/50000)
[2022-06-16 03:53:08,815] Epoch: 138
[2022-06-16 04:11:54,603] Train: Loss: 2.450 | Acc: 46.347 (593785/1281167) | Lr: 0.485730089323203
[2022-06-16 04:12:46,307] Test: Loss: 2.339 | Acc: 47.100 (23550/50000)
[2022-06-16 04:12:46,307] Epoch: 139
[2022-06-16 04:31:32,945] Train: Loss: 2.446 | Acc: 46.467 (595319/1281167) | Lr: 0.48418418192091556
[2022-06-16 04:32:16,718] Test: Loss: 2.285 | Acc: 47.664 (23832/50000)
[2022-06-16 04:32:16,719] Epoch: 140
[2022-06-16 04:50:30,379] Train: Loss: 2.451 | Acc: 46.364 (593998/1281167) | Lr: 0.48263038489547055
[2022-06-16 04:51:14,528] Test: Loss: 2.272 | Acc: 48.216 (24108/50000)
[2022-06-16 04:51:14,529] Epoch: 141
[2022-06-16 05:09:26,092] Train: Loss: 2.448 | Acc: 46.414 (594639/1281167) | Lr: 0.48106876480636107
[2022-06-16 05:10:13,179] Test: Loss: 2.250 | Acc: 48.370 (24185/50000)
[2022-06-16 05:10:13,179] Epoch: 142
[2022-06-16 05:28:47,139] Train: Loss: 2.446 | Acc: 46.454 (595154/1281167) | Lr: 0.47949938854819424
[2022-06-16 05:29:29,908] Test: Loss: 2.212 | Acc: 49.324 (24662/50000)
[2022-06-16 05:29:29,909] Epoch: 143
[2022-06-16 05:48:10,809] Train: Loss: 2.440 | Acc: 46.545 (596322/1281167) | Lr: 0.47792232334782575
[2022-06-16 05:48:53,982] Test: Loss: 2.253 | Acc: 48.584 (24292/50000)
[2022-06-16 05:48:53,982] Epoch: 144
[2022-06-16 06:07:35,046] Train: Loss: 2.444 | Acc: 46.525 (596067/1281167) | Lr: 0.47633763676147983
[2022-06-16 06:08:18,629] Test: Loss: 2.315 | Acc: 47.056 (23528/50000)
[2022-06-16 06:08:18,630] Epoch: 145
[2022-06-16 06:26:52,520] Train: Loss: 2.441 | Acc: 46.568 (596609/1281167) | Lr: 0.47474539667185567
[2022-06-16 06:27:35,172] Test: Loss: 2.714 | Acc: 40.914 (20457/50000)
[2022-06-16 06:27:35,172] Epoch: 146
[2022-06-16 06:45:45,937] Train: Loss: 2.439 | Acc: 46.519 (595980/1281167) | Lr: 0.4731456712852192
[2022-06-16 06:46:29,012] Test: Loss: 2.359 | Acc: 46.588 (23294/50000)
[2022-06-16 06:46:29,012] Epoch: 147
[2022-06-16 07:05:14,820] Train: Loss: 2.440 | Acc: 46.533 (596171/1281167) | Lr: 0.47153852912848176
[2022-06-16 07:05:58,468] Test: Loss: 2.202 | Acc: 49.628 (24814/50000)
[2022-06-16 07:05:58,468] Epoch: 148
[2022-06-16 07:24:42,766] Train: Loss: 2.434 | Acc: 46.720 (598564/1281167) | Lr: 0.4699240390462645
[2022-06-16 07:25:26,283] Test: Loss: 2.224 | Acc: 48.890 (24445/50000)
[2022-06-16 07:25:26,284] Epoch: 149
[2022-06-16 07:44:04,755] Train: Loss: 2.435 | Acc: 46.691 (598192/1281167) | Lr: 0.4683022701979489
[2022-06-16 07:44:46,824] Test: Loss: 2.154 | Acc: 50.332 (25166/50000)
[2022-06-16 07:44:46,826] Epoch: 150
[2022-06-16 08:02:54,570] Train: Loss: 2.437 | Acc: 46.641 (597551/1281167) | Lr: 0.4666732920547148
[2022-06-16 08:03:38,988] Test: Loss: 2.723 | Acc: 41.484 (20742/50000)
[2022-06-16 08:03:38,988] Epoch: 151
[2022-06-16 08:22:21,302] Train: Loss: 2.429 | Acc: 46.775 (599267/1281167) | Lr: 0.46503717439656433
[2022-06-16 08:23:05,335] Test: Loss: 2.355 | Acc: 46.966 (23483/50000)
[2022-06-16 08:23:05,336] Epoch: 152
[2022-06-16 08:41:36,328] Train: Loss: 2.434 | Acc: 46.743 (598859/1281167) | Lr: 0.46339398730933234
[2022-06-16 08:42:18,428] Test: Loss: 2.091 | Acc: 51.002 (25501/50000)
[2022-06-16 08:42:18,428] Epoch: 153
[2022-06-16 09:00:38,399] Train: Loss: 2.431 | Acc: 46.737 (598784/1281167) | Lr: 0.46174380118168473
[2022-06-16 09:01:22,176] Test: Loss: 2.318 | Acc: 46.986 (23493/50000)
[2022-06-16 09:01:22,177] Epoch: 154
[2022-06-16 09:18:26,059] Train: Loss: 2.424 | Acc: 46.852 (600258/1281167) | Lr: 0.4600866867021032
[2022-06-16 09:19:10,402] Test: Loss: 2.269 | Acc: 48.732 (24366/50000)
[2022-06-16 09:19:10,403] Epoch: 155
[2022-06-16 09:36:30,699] Train: Loss: 2.424 | Acc: 46.862 (600381/1281167) | Lr: 0.45842271485585645
[2022-06-16 09:37:14,866] Test: Loss: 2.356 | Acc: 46.448 (23224/50000)
[2022-06-16 09:37:14,867] Epoch: 156
[2022-06-16 09:55:41,757] Train: Loss: 2.421 | Acc: 46.943 (601413/1281167) | Lr: 0.45675195692196036
[2022-06-16 09:56:25,072] Test: Loss: 2.426 | Acc: 45.546 (22773/50000)
[2022-06-16 09:56:25,072] Epoch: 157
[2022-06-16 10:14:40,435] Train: Loss: 2.424 | Acc: 46.874 (600529/1281167) | Lr: 0.4550744844701241
[2022-06-16 10:15:26,806] Test: Loss: 2.180 | Acc: 50.096 (25048/50000)
[2022-06-16 10:15:26,806] Epoch: 158
[2022-06-16 10:33:37,385] Train: Loss: 2.421 | Acc: 46.912 (601027/1281167) | Lr: 0.4533903693576845
[2022-06-16 10:34:20,429] Test: Loss: 2.102 | Acc: 51.440 (25720/50000)
[2022-06-16 10:34:20,429] Saving..
[2022-06-16 10:34:20,508] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-16 10:34:20,508] Epoch: 159
[2022-06-16 10:52:31,062] Train: Loss: 2.420 | Acc: 46.888 (600719/1281167) | Lr: 0.4516996837265278
[2022-06-16 10:53:14,317] Test: Loss: 2.109 | Acc: 51.148 (25574/50000)
[2022-06-16 10:53:14,317] Epoch: 160
[2022-06-16 11:11:26,387] Train: Loss: 2.419 | Acc: 46.956 (601588/1281167) | Lr: 0.4500024999999993
[2022-06-16 11:12:09,100] Test: Loss: 2.215 | Acc: 49.150 (24575/50000)
[2022-06-16 11:12:09,101] Epoch: 161
[2022-06-16 11:29:51,708] Train: Loss: 2.413 | Acc: 47.037 (602628/1281167) | Lr: 0.44829889087980124
[2022-06-16 11:30:37,987] Test: Loss: 2.361 | Acc: 46.872 (23436/50000)
[2022-06-16 11:30:37,988] Epoch: 162
[2022-06-16 11:49:34,355] Train: Loss: 2.413 | Acc: 47.113 (603593/1281167) | Lr: 0.4465889293428783
[2022-06-16 11:50:20,160] Test: Loss: 2.052 | Acc: 52.164 (26082/50000)
[2022-06-16 11:50:20,161] Saving..
[2022-06-16 11:50:20,362] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-16 11:50:20,362] Epoch: 163
[2022-06-16 12:08:53,015] Train: Loss: 2.413 | Acc: 47.096 (603384/1281167) | Lr: 0.44487268863829144
[2022-06-16 12:09:45,408] Test: Loss: 2.489 | Acc: 44.876 (22438/50000)
[2022-06-16 12:09:45,408] Epoch: 164
[2022-06-16 12:28:02,488] Train: Loss: 2.407 | Acc: 47.197 (604667/1281167) | Lr: 0.44315024228408056
[2022-06-16 12:28:47,335] Test: Loss: 2.092 | Acc: 51.604 (25802/50000)
[2022-06-16 12:28:47,337] Epoch: 165
[2022-06-16 12:46:51,637] Train: Loss: 2.410 | Acc: 47.211 (604855/1281167) | Lr: 0.44142166406411454
[2022-06-16 12:47:37,266] Test: Loss: 2.011 | Acc: 52.588 (26294/50000)
[2022-06-16 12:47:37,266] Saving..
[2022-06-16 12:47:37,332] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-16 12:47:37,332] Epoch: 166
[2022-06-16 13:05:50,860] Train: Loss: 2.409 | Acc: 47.190 (604588/1281167) | Lr: 0.4396870280249311
[2022-06-16 13:06:40,011] Test: Loss: 2.108 | Acc: 51.478 (25739/50000)
[2022-06-16 13:06:40,011] Epoch: 167
[2022-06-16 13:25:17,334] Train: Loss: 2.404 | Acc: 47.247 (605308/1281167) | Lr: 0.437946408472565
[2022-06-16 13:25:59,572] Test: Loss: 2.086 | Acc: 51.606 (25803/50000)
[2022-06-16 13:25:59,572] Epoch: 168
[2022-06-16 13:44:42,663] Train: Loss: 2.403 | Acc: 47.217 (604923/1281167) | Lr: 0.43619987996936466
[2022-06-16 13:45:30,171] Test: Loss: 2.289 | Acc: 47.862 (23931/50000)
[2022-06-16 13:45:30,171] Epoch: 169
[2022-06-16 14:04:46,308] Train: Loss: 2.401 | Acc: 47.298 (605971/1281167) | Lr: 0.4344475173307981
[2022-06-16 14:05:30,951] Test: Loss: 2.132 | Acc: 50.680 (25340/50000)
[2022-06-16 14:05:30,951] Epoch: 170
[2022-06-16 14:23:38,293] Train: Loss: 2.395 | Acc: 47.320 (606244/1281167) | Lr: 0.4326893956222486
[2022-06-16 14:24:20,734] Test: Loss: 2.127 | Acc: 50.776 (25388/50000)
[2022-06-16 14:24:20,734] Epoch: 171
[2022-06-16 14:41:30,718] Train: Loss: 2.400 | Acc: 47.347 (606598/1281167) | Lr: 0.4309255901557986
[2022-06-16 14:42:15,864] Test: Loss: 2.321 | Acc: 47.722 (23861/50000)
[2022-06-16 14:42:15,864] Epoch: 172
[2022-06-16 15:00:14,433] Train: Loss: 2.394 | Acc: 47.408 (607372/1281167) | Lr: 0.4291561764870039
[2022-06-16 15:01:00,103] Test: Loss: 2.102 | Acc: 51.398 (25699/50000)
[2022-06-16 15:01:00,103] Epoch: 173
[2022-06-16 15:19:50,251] Train: Loss: 2.397 | Acc: 47.359 (606748/1281167) | Lr: 0.42738123041165693
[2022-06-16 15:20:35,082] Test: Loss: 2.301 | Acc: 47.690 (23845/50000)
[2022-06-16 15:20:35,082] Epoch: 174
[2022-06-16 15:38:23,902] Train: Loss: 2.393 | Acc: 47.464 (608095/1281167) | Lr: 0.4256008279625401
[2022-06-16 15:39:05,095] Test: Loss: 2.192 | Acc: 49.590 (24795/50000)
[2022-06-16 15:39:05,096] Epoch: 175
[2022-06-16 15:57:32,871] Train: Loss: 2.391 | Acc: 47.454 (607960/1281167) | Lr: 0.4238150454061688
[2022-06-16 15:58:19,460] Test: Loss: 2.263 | Acc: 48.116 (24058/50000)
[2022-06-16 15:58:19,461] Epoch: 176
[2022-06-16 16:17:04,559] Train: Loss: 2.388 | Acc: 47.527 (608898/1281167) | Lr: 0.4220239592395241
[2022-06-16 16:17:49,221] Test: Loss: 2.226 | Acc: 49.314 (24657/50000)
[2022-06-16 16:17:49,222] Epoch: 177
[2022-06-16 16:35:39,061] Train: Loss: 2.385 | Acc: 47.610 (609960/1281167) | Lr: 0.4202276461867761
[2022-06-16 16:36:33,478] Test: Loss: 2.265 | Acc: 47.902 (23951/50000)
[2022-06-16 16:36:33,479] Epoch: 178
[2022-06-16 16:55:07,579] Train: Loss: 2.382 | Acc: 47.688 (610958/1281167) | Lr: 0.4184261831959976
[2022-06-16 16:55:54,425] Test: Loss: 2.202 | Acc: 49.782 (24891/50000)
[2022-06-16 16:55:54,426] Epoch: 179
[2022-06-16 17:15:25,959] Train: Loss: 2.384 | Acc: 47.579 (609569/1281167) | Lr: 0.4166196474358673
[2022-06-16 17:16:17,415] Test: Loss: 2.064 | Acc: 51.986 (25993/50000)
[2022-06-16 17:16:17,416] Epoch: 180
[2022-06-16 17:34:48,472] Train: Loss: 2.386 | Acc: 47.567 (609410/1281167) | Lr: 0.4148081162923645
[2022-06-16 17:35:34,168] Test: Loss: 2.010 | Acc: 53.034 (26517/50000)
[2022-06-16 17:35:34,168] Saving..
[2022-06-16 17:35:34,242] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-16 17:35:34,242] Epoch: 181
[2022-06-16 17:55:11,124] Train: Loss: 2.381 | Acc: 47.675 (610791/1281167) | Lr: 0.4129916673654542
[2022-06-16 17:55:55,939] Test: Loss: 2.241 | Acc: 48.856 (24428/50000)
[2022-06-16 17:55:55,939] Epoch: 182
[2022-06-16 18:15:28,678] Train: Loss: 2.374 | Acc: 47.755 (611825/1281167) | Lr: 0.4111703784657627
[2022-06-16 18:16:24,867] Test: Loss: 2.345 | Acc: 47.354 (23677/50000)
[2022-06-16 18:16:24,867] Epoch: 183
[2022-06-16 18:34:17,456] Train: Loss: 2.376 | Acc: 47.745 (611696/1281167) | Lr: 0.409344327611245
[2022-06-16 18:35:00,795] Test: Loss: 2.108 | Acc: 51.750 (25875/50000)
[2022-06-16 18:35:00,795] Epoch: 184
[2022-06-16 18:54:03,063] Train: Loss: 2.373 | Acc: 47.775 (612079/1281167) | Lr: 0.4075135930238419
[2022-06-16 18:54:48,703] Test: Loss: 2.054 | Acc: 52.168 (26084/50000)
[2022-06-16 18:54:48,703] Epoch: 185
[2022-06-16 19:13:55,466] Train: Loss: 2.371 | Acc: 47.871 (613304/1281167) | Lr: 0.40567825312612993
[2022-06-16 19:14:40,398] Test: Loss: 2.157 | Acc: 50.528 (25264/50000)
[2022-06-16 19:14:40,398] Epoch: 186
[2022-06-16 19:34:07,885] Train: Loss: 2.372 | Acc: 47.854 (613086/1281167) | Lr: 0.403838386537962
[2022-06-16 19:34:53,012] Test: Loss: 2.181 | Acc: 50.032 (25016/50000)
[2022-06-16 19:34:53,013] Epoch: 187
[2022-06-16 19:52:45,868] Train: Loss: 2.366 | Acc: 47.958 (614419/1281167) | Lr: 0.4019940720730991
[2022-06-16 19:53:33,897] Test: Loss: 2.458 | Acc: 45.150 (22575/50000)
[2022-06-16 19:53:33,898] Epoch: 188
[2022-06-16 20:12:17,091] Train: Loss: 2.369 | Acc: 47.877 (613388/1281167) | Lr: 0.4001453887358346
[2022-06-16 20:13:01,594] Test: Loss: 2.088 | Acc: 51.656 (25828/50000)
[2022-06-16 20:13:01,594] Epoch: 189
[2022-06-16 20:30:11,851] Train: Loss: 2.362 | Acc: 48.007 (615055/1281167) | Lr: 0.39829241571760976
[2022-06-16 20:30:57,666] Test: Loss: 2.054 | Acc: 52.380 (26190/50000)
[2022-06-16 20:30:57,666] Epoch: 190
[2022-06-16 20:47:55,388] Train: Loss: 2.360 | Acc: 48.073 (615901/1281167) | Lr: 0.3964352323936215
[2022-06-16 20:48:38,412] Test: Loss: 2.170 | Acc: 49.852 (24926/50000)
[2022-06-16 20:48:38,413] Epoch: 191
[2022-06-16 21:06:56,717] Train: Loss: 2.359 | Acc: 48.114 (616418/1281167) | Lr: 0.39457391831942223
[2022-06-16 21:07:38,750] Test: Loss: 2.107 | Acc: 51.114 (25557/50000)
[2022-06-16 21:07:38,751] Epoch: 192
[2022-06-16 21:24:59,264] Train: Loss: 2.360 | Acc: 48.081 (615998/1281167) | Lr: 0.3927085532275119
[2022-06-16 21:25:49,172] Test: Loss: 2.541 | Acc: 43.934 (21967/50000)
[2022-06-16 21:25:49,172] Epoch: 193
[2022-06-16 21:43:37,872] Train: Loss: 2.356 | Acc: 48.146 (616829/1281167) | Lr: 0.39083921702392277
[2022-06-16 21:44:21,732] Test: Loss: 2.192 | Acc: 50.146 (25073/50000)
[2022-06-16 21:44:21,732] Epoch: 194
[2022-06-16 22:03:09,220] Train: Loss: 2.355 | Acc: 48.125 (616558/1281167) | Lr: 0.388965989784796
[2022-06-16 22:03:52,836] Test: Loss: 2.200 | Acc: 49.550 (24775/50000)
[2022-06-16 22:03:52,836] Epoch: 195
[2022-06-16 22:21:10,401] Train: Loss: 2.353 | Acc: 48.179 (617249/1281167) | Lr: 0.38708895175295205
[2022-06-16 22:21:52,893] Test: Loss: 2.082 | Acc: 51.650 (25825/50000)
[2022-06-16 22:21:52,894] Epoch: 196
[2022-06-16 22:39:24,221] Train: Loss: 2.350 | Acc: 48.241 (618045/1281167) | Lr: 0.3852081833344529
[2022-06-16 22:40:10,991] Test: Loss: 2.163 | Acc: 50.250 (25125/50000)
[2022-06-16 22:40:10,992] Epoch: 197
[2022-06-16 22:57:20,071] Train: Loss: 2.349 | Acc: 48.218 (617755/1281167) | Lr: 0.38332376509515786
[2022-06-16 22:58:01,590] Test: Loss: 2.082 | Acc: 51.516 (25758/50000)
[2022-06-16 22:58:01,590] Epoch: 198
[2022-06-16 23:15:11,210] Train: Loss: 2.349 | Acc: 48.257 (618258/1281167) | Lr: 0.3814357777572725
[2022-06-16 23:15:52,569] Test: Loss: 2.174 | Acc: 49.934 (24967/50000)
[2022-06-16 23:15:52,571] Epoch: 199
[2022-06-16 23:33:07,303] Train: Loss: 2.345 | Acc: 48.308 (618910/1281167) | Lr: 0.37954430219589075
[2022-06-16 23:33:50,379] Test: Loss: 2.245 | Acc: 48.694 (24347/50000)
[2022-06-16 23:33:50,379] Epoch: 200
[2022-06-16 23:51:11,850] Train: Loss: 2.346 | Acc: 48.347 (619407/1281167) | Lr: 0.37764941943553026
[2022-06-16 23:51:55,841] Test: Loss: 2.232 | Acc: 48.912 (24456/50000)
[2022-06-16 23:51:55,842] Epoch: 201
[2022-06-17 00:09:25,259] Train: Loss: 2.346 | Acc: 48.276 (618492/1281167) | Lr: 0.37575121064666184
[2022-06-17 00:10:18,937] Test: Loss: 2.089 | Acc: 51.520 (25760/50000)
[2022-06-17 00:10:18,937] Epoch: 202
[2022-06-17 00:27:42,276] Train: Loss: 2.342 | Acc: 48.366 (619646/1281167) | Lr: 0.37384975714223234
[2022-06-17 00:28:28,748] Test: Loss: 2.112 | Acc: 51.798 (25899/50000)
[2022-06-17 00:28:28,748] Epoch: 203
[2022-06-17 00:46:09,666] Train: Loss: 2.340 | Acc: 48.427 (620430/1281167) | Lr: 0.37194514037418125
[2022-06-17 00:46:55,831] Test: Loss: 2.016 | Acc: 53.456 (26728/50000)
[2022-06-17 00:46:55,831] Saving..
[2022-06-17 00:46:55,905] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-17 00:46:55,905] Epoch: 204
[2022-06-17 01:04:30,319] Train: Loss: 2.332 | Acc: 48.584 (622437/1281167) | Lr: 0.3700374419299519
[2022-06-17 01:05:12,424] Test: Loss: 2.219 | Acc: 49.316 (24658/50000)
[2022-06-17 01:05:12,424] Epoch: 205
[2022-06-17 01:22:32,660] Train: Loss: 2.331 | Acc: 48.570 (622258/1281167) | Lr: 0.3681267435289963
[2022-06-17 01:23:20,694] Test: Loss: 1.948 | Acc: 54.278 (27139/50000)
[2022-06-17 01:23:20,695] Saving..
[2022-06-17 01:23:20,885] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-17 01:23:20,885] Epoch: 206
[2022-06-17 01:40:43,824] Train: Loss: 2.331 | Acc: 48.586 (622465/1281167) | Lr: 0.3662131270192749
[2022-06-17 01:41:28,392] Test: Loss: 2.059 | Acc: 52.260 (26130/50000)
[2022-06-17 01:41:28,393] Epoch: 207
[2022-06-17 01:59:49,740] Train: Loss: 2.334 | Acc: 48.606 (622720/1281167) | Lr: 0.3642966743737495
[2022-06-17 02:00:33,052] Test: Loss: 2.099 | Acc: 51.686 (25843/50000)
[2022-06-17 02:00:33,053] Epoch: 208
[2022-06-17 02:18:42,845] Train: Loss: 2.332 | Acc: 48.569 (622249/1281167) | Lr: 0.36237746768687323
[2022-06-17 02:19:26,489] Test: Loss: 2.515 | Acc: 44.290 (22145/50000)
[2022-06-17 02:19:26,489] Epoch: 209
[2022-06-17 02:37:14,357] Train: Loss: 2.328 | Acc: 48.645 (623227/1281167) | Lr: 0.360455589171073
[2022-06-17 02:37:57,200] Test: Loss: 2.066 | Acc: 52.188 (26094/50000)
[2022-06-17 02:37:57,201] Epoch: 210
[2022-06-17 02:55:44,521] Train: Loss: 2.323 | Acc: 48.715 (624124/1281167) | Lr: 0.358531121153228
[2022-06-17 02:56:25,632] Test: Loss: 2.165 | Acc: 50.438 (25219/50000)
[2022-06-17 02:56:25,632] Epoch: 211
[2022-06-17 03:14:19,864] Train: Loss: 2.322 | Acc: 48.760 (624695/1281167) | Lr: 0.3566041460711427
[2022-06-17 03:15:03,861] Test: Loss: 2.080 | Acc: 51.470 (25735/50000)
[2022-06-17 03:15:03,861] Epoch: 212
[2022-06-17 03:33:20,421] Train: Loss: 2.319 | Acc: 48.824 (625522/1281167) | Lr: 0.35467474647001634
[2022-06-17 03:34:01,555] Test: Loss: 2.293 | Acc: 48.194 (24097/50000)
[2022-06-17 03:34:01,555] Epoch: 213
[2022-06-17 03:52:24,959] Train: Loss: 2.322 | Acc: 48.738 (624411/1281167) | Lr: 0.3527430049989062
[2022-06-17 03:53:08,465] Test: Loss: 2.442 | Acc: 45.526 (22763/50000)
[2022-06-17 03:53:08,466] Epoch: 214
[2022-06-17 04:11:03,045] Train: Loss: 2.316 | Acc: 48.842 (625753/1281167) | Lr: 0.3508090044071877
[2022-06-17 04:11:52,219] Test: Loss: 2.190 | Acc: 50.026 (25013/50000)
[2022-06-17 04:11:52,219] Epoch: 215
[2022-06-17 04:29:05,228] Train: Loss: 2.315 | Acc: 48.908 (626594/1281167) | Lr: 0.34887282754100923
[2022-06-17 04:29:48,721] Test: Loss: 2.152 | Acc: 50.452 (25226/50000)
[2022-06-17 04:29:48,722] Epoch: 216
[2022-06-17 04:48:07,381] Train: Loss: 2.311 | Acc: 48.927 (626832/1281167) | Lr: 0.3469345573397436
[2022-06-17 04:48:50,790] Test: Loss: 2.018 | Acc: 52.852 (26426/50000)
[2022-06-17 04:48:50,790] Epoch: 217
[2022-06-17 05:06:00,038] Train: Loss: 2.310 | Acc: 48.960 (627255/1281167) | Lr: 0.3449942768324353
[2022-06-17 05:06:43,381] Test: Loss: 2.106 | Acc: 51.454 (25727/50000)
[2022-06-17 05:06:43,381] Epoch: 218
[2022-06-17 05:24:08,522] Train: Loss: 2.313 | Acc: 48.897 (626455/1281167) | Lr: 0.34305206913424346
[2022-06-17 05:24:53,399] Test: Loss: 2.021 | Acc: 52.598 (26299/50000)
[2022-06-17 05:24:53,399] Epoch: 219
[2022-06-17 05:42:12,483] Train: Loss: 2.306 | Acc: 49.064 (628596/1281167) | Lr: 0.3411080174428815
[2022-06-17 05:42:57,720] Test: Loss: 2.033 | Acc: 52.448 (26224/50000)
[2022-06-17 05:42:57,720] Epoch: 220
[2022-06-17 06:00:42,666] Train: Loss: 2.304 | Acc: 49.063 (628583/1281167) | Lr: 0.3391622050350539
[2022-06-17 06:01:27,575] Test: Loss: 2.159 | Acc: 50.932 (25466/50000)
[2022-06-17 06:01:27,575] Epoch: 221
[2022-06-17 06:18:57,514] Train: Loss: 2.303 | Acc: 49.134 (629493/1281167) | Lr: 0.3372147152628879
[2022-06-17 06:19:39,236] Test: Loss: 2.177 | Acc: 50.338 (25169/50000)
[2022-06-17 06:19:39,237] Epoch: 222
[2022-06-17 06:37:25,124] Train: Loss: 2.299 | Acc: 49.197 (630299/1281167) | Lr: 0.33526563155036354
[2022-06-17 06:38:08,642] Test: Loss: 2.025 | Acc: 52.658 (26329/50000)
[2022-06-17 06:38:08,642] Epoch: 223
[2022-06-17 06:55:42,544] Train: Loss: 2.295 | Acc: 49.228 (630693/1281167) | Lr: 0.33331503738974005
[2022-06-17 06:56:26,093] Test: Loss: 2.140 | Acc: 50.764 (25382/50000)
[2022-06-17 06:56:26,094] Epoch: 224
[2022-06-17 07:15:05,504] Train: Loss: 2.295 | Acc: 49.301 (631627/1281167) | Lr: 0.33136301633797927
[2022-06-17 07:15:50,110] Test: Loss: 2.520 | Acc: 44.784 (22392/50000)
[2022-06-17 07:15:50,110] Epoch: 225
[2022-06-17 07:34:37,933] Train: Loss: 2.297 | Acc: 49.189 (630191/1281167) | Lr: 0.3294096520131662
[2022-06-17 07:35:21,820] Test: Loss: 1.949 | Acc: 54.388 (27194/50000)
[2022-06-17 07:35:21,820] Saving..
[2022-06-17 07:35:21,886] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-17 07:35:21,887] Epoch: 226
[2022-06-17 07:54:06,903] Train: Loss: 2.293 | Acc: 49.379 (632632/1281167) | Lr: 0.327455028090927
[2022-06-17 07:54:48,648] Test: Loss: 2.192 | Acc: 50.386 (25193/50000)
[2022-06-17 07:54:48,648] Epoch: 227
[2022-06-17 08:12:49,216] Train: Loss: 2.287 | Acc: 49.426 (633232/1281167) | Lr: 0.32549922830084527
[2022-06-17 08:13:30,974] Test: Loss: 2.085 | Acc: 51.490 (25745/50000)
[2022-06-17 08:13:30,974] Epoch: 228
[2022-06-17 08:31:26,884] Train: Loss: 2.286 | Acc: 49.465 (633731/1281167) | Lr: 0.3235423364228745
[2022-06-17 08:32:12,756] Test: Loss: 2.045 | Acc: 52.740 (26370/50000)
[2022-06-17 08:32:12,756] Epoch: 229
[2022-06-17 08:49:46,877] Train: Loss: 2.280 | Acc: 49.511 (634320/1281167) | Lr: 0.3215844362837498
[2022-06-17 08:50:31,401] Test: Loss: 2.158 | Acc: 50.752 (25376/50000)
[2022-06-17 08:50:31,401] Epoch: 230
[2022-06-17 09:08:49,139] Train: Loss: 2.282 | Acc: 49.541 (634702/1281167) | Lr: 0.31962561175339643
[2022-06-17 09:09:35,422] Test: Loss: 1.931 | Acc: 54.508 (27254/50000)
[2022-06-17 09:09:35,423] Saving..
[2022-06-17 09:09:35,497] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-17 09:09:35,497] Epoch: 231
[2022-06-17 09:27:28,737] Train: Loss: 2.282 | Acc: 49.533 (634605/1281167) | Lr: 0.3176659467413381
[2022-06-17 09:28:13,409] Test: Loss: 2.059 | Acc: 52.052 (26026/50000)
[2022-06-17 09:28:13,410] Epoch: 232
[2022-06-17 09:47:04,579] Train: Loss: 2.275 | Acc: 49.642 (636003/1281167) | Lr: 0.3157055251931016
[2022-06-17 09:47:47,340] Test: Loss: 2.055 | Acc: 52.276 (26138/50000)
[2022-06-17 09:47:47,340] Epoch: 233
[2022-06-17 10:05:20,097] Train: Loss: 2.277 | Acc: 49.590 (635331/1281167) | Lr: 0.3137444310866212
[2022-06-17 10:06:01,494] Test: Loss: 2.004 | Acc: 53.286 (26643/50000)
[2022-06-17 10:06:01,494] Epoch: 234
[2022-06-17 10:23:34,005] Train: Loss: 2.273 | Acc: 49.650 (636095/1281167) | Lr: 0.31178274842864145
[2022-06-17 10:24:18,198] Test: Loss: 2.235 | Acc: 49.174 (24587/50000)
[2022-06-17 10:24:18,198] Epoch: 235
[2022-06-17 10:42:30,919] Train: Loss: 2.271 | Acc: 49.693 (636656/1281167) | Lr: 0.30982056125111845
[2022-06-17 10:43:15,177] Test: Loss: 2.009 | Acc: 52.876 (26438/50000)
[2022-06-17 10:43:15,178] Epoch: 236
[2022-06-17 11:01:10,046] Train: Loss: 2.271 | Acc: 49.715 (636938/1281167) | Lr: 0.3078579536076201
[2022-06-17 11:01:57,464] Test: Loss: 2.196 | Acc: 49.852 (24926/50000)
[2022-06-17 11:01:57,465] Epoch: 237
[2022-06-17 11:19:16,325] Train: Loss: 2.268 | Acc: 49.830 (638411/1281167) | Lr: 0.30589500956972593
[2022-06-17 11:19:59,892] Test: Loss: 2.019 | Acc: 53.270 (26635/50000)
[2022-06-17 11:19:59,892] Epoch: 238
[2022-06-17 11:37:34,957] Train: Loss: 2.260 | Acc: 49.944 (639870/1281167) | Lr: 0.3039318132234252
[2022-06-17 11:38:16,964] Test: Loss: 1.983 | Acc: 53.766 (26883/50000)
[2022-06-17 11:38:16,965] Epoch: 239
[2022-06-17 11:55:06,683] Train: Loss: 2.263 | Acc: 49.826 (638350/1281167) | Lr: 0.3019684486655154
[2022-06-17 11:55:57,103] Test: Loss: 2.163 | Acc: 50.944 (25472/50000)
[2022-06-17 11:55:57,104] Epoch: 240
[2022-06-17 12:13:26,916] Train: Loss: 2.223 | Acc: 50.610 (648395/1281167) | Lr: 0.30000499999999974
[2022-06-17 12:14:09,968] Test: Loss: 2.044 | Acc: 52.536 (26268/50000)
[2022-06-17 12:14:09,969] Epoch: 241
[2022-06-17 12:31:44,325] Train: Loss: 2.208 | Acc: 50.930 (652504/1281167) | Lr: 0.29804155133448396
[2022-06-17 12:32:27,623] Test: Loss: 1.869 | Acc: 55.920 (27960/50000)
[2022-06-17 12:32:27,623] Saving..
[2022-06-17 12:32:27,705] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-17 12:32:27,706] Epoch: 242
[2022-06-17 12:50:08,226] Train: Loss: 2.204 | Acc: 50.956 (652832/1281167) | Lr: 0.29607818677657416
[2022-06-17 12:50:49,287] Test: Loss: 1.955 | Acc: 54.234 (27117/50000)
[2022-06-17 12:50:49,288] Epoch: 243
[2022-06-17 13:08:23,266] Train: Loss: 2.202 | Acc: 51.017 (653610/1281167) | Lr: 0.29411499043027345
[2022-06-17 13:09:04,936] Test: Loss: 1.979 | Acc: 53.564 (26782/50000)
[2022-06-17 13:09:04,937] Epoch: 244
[2022-06-17 13:25:58,169] Train: Loss: 2.194 | Acc: 51.116 (654887/1281167) | Lr: 0.2921520463923793
[2022-06-17 13:26:43,975] Test: Loss: 1.884 | Acc: 55.690 (27845/50000)
[2022-06-17 13:26:43,976] Epoch: 245
[2022-06-17 13:44:29,010] Train: Loss: 2.193 | Acc: 51.146 (655268/1281167) | Lr: 0.290189438748881
[2022-06-17 13:45:10,753] Test: Loss: 1.860 | Acc: 56.364 (28182/50000)
[2022-06-17 13:45:10,754] Saving..
[2022-06-17 13:45:10,831] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-17 13:45:10,832] Epoch: 246
[2022-06-17 14:03:19,572] Train: Loss: 2.189 | Acc: 51.309 (657352/1281167) | Lr: 0.2882272515713579
[2022-06-17 14:04:02,241] Test: Loss: 2.103 | Acc: 51.704 (25852/50000)
[2022-06-17 14:04:02,242] Epoch: 247
[2022-06-17 14:22:09,365] Train: Loss: 2.181 | Acc: 51.422 (658802/1281167) | Lr: 0.2862655689133781
[2022-06-17 14:22:52,604] Test: Loss: 2.233 | Acc: 49.656 (24828/50000)
[2022-06-17 14:22:52,605] Epoch: 248
[2022-06-17 14:41:05,882] Train: Loss: 2.181 | Acc: 51.360 (658002/1281167) | Lr: 0.2843044748068978
[2022-06-17 14:41:53,388] Test: Loss: 2.097 | Acc: 51.554 (25777/50000)
[2022-06-17 14:41:53,389] Epoch: 249
[2022-06-17 15:00:01,757] Train: Loss: 2.178 | Acc: 51.437 (658992/1281167) | Lr: 0.2823440532586613
[2022-06-17 15:00:44,164] Test: Loss: 1.956 | Acc: 54.238 (27119/50000)
[2022-06-17 15:00:44,165] Epoch: 250
[2022-06-17 15:18:55,005] Train: Loss: 2.171 | Acc: 51.569 (660688/1281167) | Lr: 0.280384388246603
[2022-06-17 15:19:38,068] Test: Loss: 1.797 | Acc: 57.094 (28547/50000)
[2022-06-17 15:19:38,068] Saving..
[2022-06-17 15:19:38,156] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-17 15:19:38,157] Epoch: 251
[2022-06-17 15:37:31,247] Train: Loss: 2.170 | Acc: 51.579 (660807/1281167) | Lr: 0.27842556371624966
[2022-06-17 15:38:12,619] Test: Loss: 1.971 | Acc: 54.100 (27050/50000)
[2022-06-17 15:38:12,621] Epoch: 252
[2022-06-17 15:56:19,737] Train: Loss: 2.168 | Acc: 51.632 (661496/1281167) | Lr: 0.27646766357712493
[2022-06-17 15:57:01,902] Test: Loss: 1.767 | Acc: 57.802 (28901/50000)
[2022-06-17 15:57:01,903] Saving..
[2022-06-17 15:57:01,974] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-17 15:57:01,975] Epoch: 253
[2022-06-17 16:14:37,416] Train: Loss: 2.167 | Acc: 51.598 (661060/1281167) | Lr: 0.2745107716991541
[2022-06-17 16:15:21,090] Test: Loss: 1.784 | Acc: 57.494 (28747/50000)
[2022-06-17 16:15:21,090] Epoch: 254
[2022-06-17 16:32:48,440] Train: Loss: 2.164 | Acc: 51.691 (662245/1281167) | Lr: 0.27255497190907235
[2022-06-17 16:33:30,158] Test: Loss: 1.861 | Acc: 55.984 (27992/50000)
[2022-06-17 16:33:30,159] Epoch: 255
[2022-06-17 16:50:48,317] Train: Loss: 2.158 | Acc: 51.859 (664394/1281167) | Lr: 0.2706003479868332
[2022-06-17 16:51:31,760] Test: Loss: 1.752 | Acc: 58.714 (29357/50000)
[2022-06-17 16:51:31,760] Saving..
[2022-06-17 16:51:31,855] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-17 16:51:31,856] Epoch: 256
[2022-06-17 17:09:24,753] Train: Loss: 2.156 | Acc: 51.844 (664207/1281167) | Lr: 0.2686469836620201
[2022-06-17 17:10:08,385] Test: Loss: 1.862 | Acc: 55.980 (27990/50000)
[2022-06-17 17:10:08,386] Epoch: 257
[2022-06-17 17:27:10,118] Train: Loss: 2.155 | Acc: 51.883 (664712/1281167) | Lr: 0.26669496261025927
[2022-06-17 17:27:56,225] Test: Loss: 1.829 | Acc: 56.378 (28189/50000)
[2022-06-17 17:27:56,227] Epoch: 258
[2022-06-17 17:45:22,324] Train: Loss: 2.148 | Acc: 52.002 (666238/1281167) | Lr: 0.2647443684496358
[2022-06-17 17:46:07,866] Test: Loss: 1.862 | Acc: 56.194 (28097/50000)
[2022-06-17 17:46:07,867] Epoch: 259
[2022-06-17 18:03:19,951] Train: Loss: 2.150 | Acc: 51.962 (665725/1281167) | Lr: 0.2627952847371114
[2022-06-17 18:04:01,886] Test: Loss: 1.818 | Acc: 56.870 (28435/50000)
[2022-06-17 18:04:01,888] Epoch: 260
[2022-06-17 18:21:36,558] Train: Loss: 2.141 | Acc: 52.129 (667855/1281167) | Lr: 0.2608477949649454
[2022-06-17 18:22:19,377] Test: Loss: 1.748 | Acc: 58.454 (29227/50000)
[2022-06-17 18:22:19,378] Epoch: 261
[2022-06-17 18:40:08,640] Train: Loss: 2.139 | Acc: 52.226 (669107/1281167) | Lr: 0.25890198255711777
[2022-06-17 18:40:52,022] Test: Loss: 1.819 | Acc: 56.906 (28453/50000)
[2022-06-17 18:40:52,023] Epoch: 262
[2022-06-17 18:58:21,935] Train: Loss: 2.138 | Acc: 52.272 (669692/1281167) | Lr: 0.25695793086575586
[2022-06-17 18:59:09,260] Test: Loss: 1.926 | Acc: 55.076 (27538/50000)
[2022-06-17 18:59:09,261] Epoch: 263
[2022-06-17 19:16:44,501] Train: Loss: 2.135 | Acc: 52.292 (669949/1281167) | Lr: 0.25501572316756393
[2022-06-17 19:17:33,332] Test: Loss: 1.756 | Acc: 58.278 (29139/50000)
[2022-06-17 19:17:33,335] Epoch: 264
[2022-06-17 19:35:23,123] Train: Loss: 2.133 | Acc: 52.284 (669842/1281167) | Lr: 0.25307544266025567
[2022-06-17 19:36:09,392] Test: Loss: 1.854 | Acc: 56.138 (28069/50000)
[2022-06-17 19:36:09,393] Epoch: 265
[2022-06-17 19:54:00,143] Train: Loss: 2.129 | Acc: 52.403 (671372/1281167) | Lr: 0.2511371724589901
[2022-06-17 19:54:47,505] Test: Loss: 1.868 | Acc: 55.948 (27974/50000)
[2022-06-17 19:54:47,506] Epoch: 266
[2022-06-17 20:12:12,216] Train: Loss: 2.130 | Acc: 52.351 (670705/1281167) | Lr: 0.24920099559281159
[2022-06-17 20:12:58,105] Test: Loss: 1.754 | Acc: 58.852 (29426/50000)
[2022-06-17 20:12:58,106] Saving..
[2022-06-17 20:12:58,182] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-17 20:12:58,184] Epoch: 267
[2022-06-17 20:30:43,122] Train: Loss: 2.125 | Acc: 52.461 (672115/1281167) | Lr: 0.2472669950010931
[2022-06-17 20:31:32,679] Test: Loss: 1.837 | Acc: 57.086 (28543/50000)
[2022-06-17 20:31:32,680] Epoch: 268
[2022-06-17 20:49:00,327] Train: Loss: 2.117 | Acc: 52.639 (674393/1281167) | Lr: 0.245335253529983
[2022-06-17 20:49:43,987] Test: Loss: 1.917 | Acc: 55.296 (27648/50000)
[2022-06-17 20:49:43,988] Epoch: 269
[2022-06-17 21:07:28,973] Train: Loss: 2.118 | Acc: 52.606 (673968/1281167) | Lr: 0.24340585392885664
[2022-06-17 21:08:11,906] Test: Loss: 1.690 | Acc: 59.576 (29788/50000)
[2022-06-17 21:08:11,907] Saving..
[2022-06-17 21:08:11,972] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-17 21:08:11,973] Epoch: 270
[2022-06-17 21:25:10,580] Train: Loss: 2.117 | Acc: 52.674 (674841/1281167) | Lr: 0.24147887884677136
[2022-06-17 21:25:53,981] Test: Loss: 1.750 | Acc: 58.328 (29164/50000)
[2022-06-17 21:25:53,982] Epoch: 271
[2022-06-17 21:43:00,244] Train: Loss: 2.114 | Acc: 52.640 (674402/1281167) | Lr: 0.23955441082892628
[2022-06-17 21:43:43,009] Test: Loss: 1.841 | Acc: 56.418 (28209/50000)
[2022-06-17 21:43:43,012] Epoch: 272
[2022-06-17 22:01:07,398] Train: Loss: 2.116 | Acc: 52.589 (673750/1281167) | Lr: 0.237632532313126
[2022-06-17 22:01:50,535] Test: Loss: 1.780 | Acc: 57.826 (28913/50000)
[2022-06-17 22:01:50,536] Epoch: 273
[2022-06-17 22:19:22,012] Train: Loss: 2.109 | Acc: 52.773 (676106/1281167) | Lr: 0.2357133256262498
[2022-06-17 22:20:05,529] Test: Loss: 1.773 | Acc: 57.834 (28917/50000)
[2022-06-17 22:20:05,530] Epoch: 274
[2022-06-17 22:37:25,433] Train: Loss: 2.105 | Acc: 52.806 (676535/1281167) | Lr: 0.23379687298072446
[2022-06-17 22:38:08,382] Test: Loss: 1.675 | Acc: 59.876 (29938/50000)
[2022-06-17 22:38:08,382] Saving..
[2022-06-17 22:38:08,513] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-17 22:38:08,514] Epoch: 275
[2022-06-17 22:55:24,698] Train: Loss: 2.104 | Acc: 52.859 (677207/1281167) | Lr: 0.23188325647100297
[2022-06-17 22:56:09,800] Test: Loss: 1.701 | Acc: 59.260 (29630/50000)
[2022-06-17 22:56:09,801] Epoch: 276
[2022-06-17 23:13:38,393] Train: Loss: 2.097 | Acc: 52.981 (678775/1281167) | Lr: 0.2299725580700474
[2022-06-17 23:14:34,066] Test: Loss: 1.744 | Acc: 58.602 (29301/50000)
[2022-06-17 23:14:34,067] Epoch: 277
[2022-06-17 23:32:07,602] Train: Loss: 2.097 | Acc: 53.002 (679043/1281167) | Lr: 0.22806485962581804
[2022-06-17 23:32:52,555] Test: Loss: 1.766 | Acc: 58.152 (29076/50000)
[2022-06-17 23:32:52,556] Epoch: 278
[2022-06-17 23:50:13,312] Train: Loss: 2.094 | Acc: 53.026 (679356/1281167) | Lr: 0.22616024285776695
[2022-06-17 23:51:05,851] Test: Loss: 1.731 | Acc: 58.694 (29347/50000)
[2022-06-17 23:51:05,852] Epoch: 279
[2022-06-18 00:07:51,718] Train: Loss: 2.091 | Acc: 53.071 (679923/1281167) | Lr: 0.22425878935333746
[2022-06-18 00:08:34,924] Test: Loss: 1.756 | Acc: 58.056 (29028/50000)
[2022-06-18 00:08:34,925] Epoch: 280
[2022-06-18 00:25:53,932] Train: Loss: 2.089 | Acc: 53.149 (680924/1281167) | Lr: 0.22236058056446906
[2022-06-18 00:26:36,980] Test: Loss: 1.838 | Acc: 56.820 (28410/50000)
[2022-06-18 00:26:36,981] Epoch: 281
[2022-06-18 00:43:28,884] Train: Loss: 2.088 | Acc: 53.145 (680872/1281167) | Lr: 0.22046569780410857
[2022-06-18 00:44:10,985] Test: Loss: 1.888 | Acc: 55.686 (27843/50000)
[2022-06-18 00:44:10,987] Epoch: 282
[2022-06-18 01:02:38,744] Train: Loss: 2.083 | Acc: 53.238 (682062/1281167) | Lr: 0.2185742222427268
[2022-06-18 01:03:20,359] Test: Loss: 1.743 | Acc: 58.468 (29234/50000)
[2022-06-18 01:03:20,360] Epoch: 283
[2022-06-18 01:22:51,701] Train: Loss: 2.085 | Acc: 53.211 (681725/1281167) | Lr: 0.2166862349048415
[2022-06-18 01:23:33,798] Test: Loss: 1.865 | Acc: 56.274 (28137/50000)
[2022-06-18 01:23:33,799] Epoch: 284
[2022-06-18 01:40:06,214] Train: Loss: 2.079 | Acc: 53.340 (683369/1281167) | Lr: 0.21480181666554649
[2022-06-18 01:40:50,189] Test: Loss: 1.738 | Acc: 58.758 (29379/50000)
[2022-06-18 01:40:50,191] Epoch: 285
[2022-06-18 01:57:54,237] Train: Loss: 2.074 | Acc: 53.414 (684324/1281167) | Lr: 0.21292104824704733
[2022-06-18 01:58:38,073] Test: Loss: 1.630 | Acc: 60.716 (30358/50000)
[2022-06-18 01:58:38,073] Saving..
[2022-06-18 01:58:38,148] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-18 01:58:38,149] Epoch: 286
[2022-06-18 02:15:38,161] Train: Loss: 2.070 | Acc: 53.521 (685690/1281167) | Lr: 0.21104401021520333
[2022-06-18 02:16:23,357] Test: Loss: 1.653 | Acc: 60.150 (30075/50000)
[2022-06-18 02:16:23,358] Epoch: 287
[2022-06-18 02:33:39,210] Train: Loss: 2.072 | Acc: 53.482 (685191/1281167) | Lr: 0.20917078297607666
[2022-06-18 02:34:21,810] Test: Loss: 1.707 | Acc: 59.196 (29598/50000)
[2022-06-18 02:34:21,812] Epoch: 288
[2022-06-18 02:51:43,878] Train: Loss: 2.067 | Acc: 53.559 (686182/1281167) | Lr: 0.20730144677248746
[2022-06-18 02:52:37,375] Test: Loss: 1.758 | Acc: 58.224 (29112/50000)
[2022-06-18 02:52:37,376] Epoch: 289
[2022-06-18 03:09:52,462] Train: Loss: 2.062 | Acc: 53.613 (686873/1281167) | Lr: 0.20543608168057714
[2022-06-18 03:10:36,724] Test: Loss: 1.698 | Acc: 59.372 (29686/50000)
[2022-06-18 03:10:36,726] Epoch: 290
[2022-06-18 03:27:58,735] Train: Loss: 2.062 | Acc: 53.664 (687528/1281167) | Lr: 0.2035747676063779
[2022-06-18 03:28:42,126] Test: Loss: 1.677 | Acc: 59.782 (29891/50000)
[2022-06-18 03:28:42,127] Epoch: 291
[2022-06-18 03:45:48,343] Train: Loss: 2.057 | Acc: 53.838 (689759/1281167) | Lr: 0.2017175842823896
[2022-06-18 03:46:30,830] Test: Loss: 1.653 | Acc: 60.438 (30219/50000)
[2022-06-18 03:46:30,833] Epoch: 292
[2022-06-18 04:03:33,247] Train: Loss: 2.059 | Acc: 53.759 (688743/1281167) | Lr: 0.1998646112641647
[2022-06-18 04:04:13,805] Test: Loss: 1.677 | Acc: 59.666 (29833/50000)
[2022-06-18 04:04:13,806] Epoch: 293
[2022-06-18 04:21:06,720] Train: Loss: 2.054 | Acc: 53.843 (689822/1281167) | Lr: 0.1980159279269002
[2022-06-18 04:21:47,452] Test: Loss: 1.695 | Acc: 59.262 (29631/50000)
[2022-06-18 04:21:47,453] Epoch: 294
[2022-06-18 04:39:08,677] Train: Loss: 2.053 | Acc: 53.820 (689524/1281167) | Lr: 0.1961716134620373
[2022-06-18 04:40:00,140] Test: Loss: 1.668 | Acc: 59.876 (29938/50000)
[2022-06-18 04:40:00,143] Epoch: 295
[2022-06-18 04:57:17,795] Train: Loss: 2.043 | Acc: 54.040 (692340/1281167) | Lr: 0.19433174687386934
[2022-06-18 04:58:00,022] Test: Loss: 1.710 | Acc: 59.220 (29610/50000)
[2022-06-18 04:58:00,023] Epoch: 296
[2022-06-18 05:15:00,140] Train: Loss: 2.043 | Acc: 54.035 (692276/1281167) | Lr: 0.19249640697615744
[2022-06-18 05:15:43,770] Test: Loss: 1.613 | Acc: 61.112 (30556/50000)
[2022-06-18 05:15:43,770] Saving..
[2022-06-18 05:15:43,849] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-18 05:15:43,850] Epoch: 297
[2022-06-18 05:33:02,950] Train: Loss: 2.038 | Acc: 54.150 (693754/1281167) | Lr: 0.1906656723887543
[2022-06-18 05:33:45,252] Test: Loss: 1.714 | Acc: 59.204 (29602/50000)
[2022-06-18 05:33:45,253] Epoch: 298
[2022-06-18 05:50:30,071] Train: Loss: 2.034 | Acc: 54.250 (695038/1281167) | Lr: 0.1888396215342365
[2022-06-18 05:51:12,448] Test: Loss: 1.817 | Acc: 56.828 (28414/50000)
[2022-06-18 05:51:12,449] Epoch: 299
[2022-06-18 06:08:06,365] Train: Loss: 2.035 | Acc: 54.187 (694220/1281167) | Lr: 0.18701833263454504
[2022-06-18 06:08:49,321] Test: Loss: 1.721 | Acc: 58.890 (29445/50000)
[2022-06-18 06:08:49,323] Epoch: 300
[2022-06-18 06:25:49,942] Train: Loss: 2.032 | Acc: 54.234 (694824/1281167) | Lr: 0.1852018837076347
[2022-06-18 06:26:30,103] Test: Loss: 1.649 | Acc: 60.714 (30357/50000)
[2022-06-18 06:26:30,104] Epoch: 301
[2022-06-18 06:42:40,470] Train: Loss: 2.031 | Acc: 54.257 (695129/1281167) | Lr: 0.1833903525641319
[2022-06-18 06:43:21,389] Test: Loss: 1.703 | Acc: 59.624 (29812/50000)
[2022-06-18 06:43:21,390] Epoch: 302
[2022-06-18 06:59:49,576] Train: Loss: 2.025 | Acc: 54.381 (696709/1281167) | Lr: 0.1815838168040016
[2022-06-18 07:00:29,852] Test: Loss: 1.603 | Acc: 61.318 (30659/50000)
[2022-06-18 07:00:29,853] Saving..
[2022-06-18 07:00:29,944] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-18 07:00:29,945] Epoch: 303
[2022-06-18 07:16:32,416] Train: Loss: 2.018 | Acc: 54.457 (697682/1281167) | Lr: 0.17978235381322308
[2022-06-18 07:17:13,267] Test: Loss: 1.662 | Acc: 59.976 (29988/50000)
[2022-06-18 07:17:13,268] Epoch: 304
[2022-06-18 07:33:21,786] Train: Loss: 2.017 | Acc: 54.541 (698764/1281167) | Lr: 0.17798604076047517
[2022-06-18 07:34:03,282] Test: Loss: 1.649 | Acc: 60.478 (30239/50000)
[2022-06-18 07:34:03,283] Epoch: 305
[2022-06-18 07:50:12,808] Train: Loss: 2.021 | Acc: 54.467 (697812/1281167) | Lr: 0.17619495459383036
[2022-06-18 07:50:53,841] Test: Loss: 1.602 | Acc: 61.534 (30767/50000)
[2022-06-18 07:50:53,841] Saving..
[2022-06-18 07:50:53,942] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-18 07:50:53,943] Epoch: 306
[2022-06-18 08:07:04,013] Train: Loss: 2.013 | Acc: 54.611 (699654/1281167) | Lr: 0.17440917203745904
[2022-06-18 08:07:45,014] Test: Loss: 1.706 | Acc: 59.254 (29627/50000)
[2022-06-18 08:07:45,015] Epoch: 307
[2022-06-18 08:23:53,258] Train: Loss: 2.010 | Acc: 54.660 (700282/1281167) | Lr: 0.17262876958834228
[2022-06-18 08:24:34,237] Test: Loss: 1.851 | Acc: 56.482 (28241/50000)
[2022-06-18 08:24:34,238] Epoch: 308
[2022-06-18 08:40:46,176] Train: Loss: 2.005 | Acc: 54.787 (701911/1281167) | Lr: 0.1708538235129954
[2022-06-18 08:41:26,457] Test: Loss: 1.666 | Acc: 60.212 (30106/50000)
[2022-06-18 08:41:26,458] Epoch: 309
[2022-06-18 08:58:22,559] Train: Loss: 2.005 | Acc: 54.803 (702123/1281167) | Lr: 0.16908440984420062
[2022-06-18 08:59:05,991] Test: Loss: 1.602 | Acc: 61.532 (30766/50000)
[2022-06-18 08:59:05,992] Epoch: 310
[2022-06-18 09:16:48,941] Train: Loss: 2.001 | Acc: 54.875 (703039/1281167) | Lr: 0.16732060437775062
[2022-06-18 09:17:33,755] Test: Loss: 1.606 | Acc: 61.458 (30729/50000)
[2022-06-18 09:17:33,756] Epoch: 311
[2022-06-18 09:35:55,960] Train: Loss: 1.997 | Acc: 54.882 (703127/1281167) | Lr: 0.16556248266920107
[2022-06-18 09:36:40,244] Test: Loss: 1.547 | Acc: 62.416 (31208/50000)
[2022-06-18 09:36:40,244] Saving..
[2022-06-18 09:36:40,463] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-18 09:36:40,464] Epoch: 312
[2022-06-18 09:54:16,006] Train: Loss: 1.994 | Acc: 54.991 (704523/1281167) | Lr: 0.16381012003063453
[2022-06-18 09:54:59,486] Test: Loss: 1.592 | Acc: 61.728 (30864/50000)
[2022-06-18 09:54:59,487] Epoch: 313
[2022-06-18 10:13:18,886] Train: Loss: 1.994 | Acc: 55.035 (705092/1281167) | Lr: 0.16206359152743416
[2022-06-18 10:14:03,243] Test: Loss: 1.636 | Acc: 60.634 (30317/50000)
[2022-06-18 10:14:03,244] Epoch: 314
[2022-06-18 10:31:35,143] Train: Loss: 1.989 | Acc: 55.152 (706592/1281167) | Lr: 0.1603229719750681
[2022-06-18 10:32:21,235] Test: Loss: 1.568 | Acc: 62.258 (31129/50000)
[2022-06-18 10:32:21,236] Epoch: 315
[2022-06-18 10:51:57,010] Train: Loss: 1.981 | Acc: 55.186 (707030/1281167) | Lr: 0.1585883359358847
[2022-06-18 10:52:42,562] Test: Loss: 1.707 | Acc: 59.254 (29627/50000)
[2022-06-18 10:52:42,563] Epoch: 316
[2022-06-18 11:11:49,341] Train: Loss: 1.977 | Acc: 55.357 (709213/1281167) | Lr: 0.15685975771591865
[2022-06-18 11:12:33,568] Test: Loss: 1.576 | Acc: 61.788 (30894/50000)
[2022-06-18 11:12:33,569] Epoch: 317
[2022-06-18 11:31:36,226] Train: Loss: 1.976 | Acc: 55.402 (709787/1281167) | Lr: 0.15513731136170764
[2022-06-18 11:32:22,253] Test: Loss: 1.566 | Acc: 62.146 (31073/50000)
[2022-06-18 11:32:22,254] Epoch: 318
[2022-06-18 11:50:37,583] Train: Loss: 1.975 | Acc: 55.357 (709218/1281167) | Lr: 0.15342107065712082
[2022-06-18 11:51:21,801] Test: Loss: 1.713 | Acc: 59.582 (29791/50000)
[2022-06-18 11:51:21,802] Epoch: 319
[2022-06-18 12:10:40,645] Train: Loss: 1.972 | Acc: 55.394 (709692/1281167) | Lr: 0.151711109120198
[2022-06-18 12:11:24,747] Test: Loss: 1.803 | Acc: 57.232 (28616/50000)
[2022-06-18 12:11:24,749] Epoch: 320
[2022-06-18 12:30:32,280] Train: Loss: 1.967 | Acc: 55.462 (710565/1281167) | Lr: 0.15000749999999993
[2022-06-18 12:31:20,602] Test: Loss: 1.580 | Acc: 61.820 (30910/50000)
[2022-06-18 12:31:20,603] Epoch: 321
[2022-06-18 12:48:50,428] Train: Loss: 1.963 | Acc: 55.614 (712504/1281167) | Lr: 0.14831031627347144
[2022-06-18 12:49:35,519] Test: Loss: 1.607 | Acc: 61.238 (30619/50000)
[2022-06-18 12:49:35,520] Epoch: 322
[2022-06-18 13:07:06,528] Train: Loss: 1.959 | Acc: 55.643 (712875/1281167) | Lr: 0.1466196306423147
[2022-06-18 13:07:49,788] Test: Loss: 1.628 | Acc: 60.938 (30469/50000)
[2022-06-18 13:07:49,790] Epoch: 323
[2022-06-18 13:25:33,538] Train: Loss: 1.961 | Acc: 55.643 (712881/1281167) | Lr: 0.14493551552987505
[2022-06-18 13:26:17,597] Test: Loss: 1.607 | Acc: 61.298 (30649/50000)
[2022-06-18 13:26:17,598] Epoch: 324
[2022-06-18 13:44:31,786] Train: Loss: 1.961 | Acc: 55.684 (713403/1281167) | Lr: 0.14325804307803883
[2022-06-18 13:45:18,984] Test: Loss: 1.594 | Acc: 61.588 (30794/50000)
[2022-06-18 13:45:18,986] Epoch: 325
[2022-06-18 14:04:17,337] Train: Loss: 1.949 | Acc: 55.901 (716189/1281167) | Lr: 0.14158728514414276
[2022-06-18 14:05:03,359] Test: Loss: 1.569 | Acc: 62.194 (31097/50000)
[2022-06-18 14:05:03,361] Epoch: 326
[2022-06-18 14:24:37,954] Train: Loss: 1.951 | Acc: 55.797 (714847/1281167) | Lr: 0.13992331329789603
[2022-06-18 14:25:24,110] Test: Loss: 1.682 | Acc: 59.946 (29973/50000)
[2022-06-18 14:25:24,111] Epoch: 327
[2022-06-18 14:44:30,022] Train: Loss: 1.943 | Acc: 55.903 (716207/1281167) | Lr: 0.13826619881831437
[2022-06-18 14:45:21,558] Test: Loss: 1.488 | Acc: 63.412 (31706/50000)
[2022-06-18 14:45:21,558] Saving..
[2022-06-18 14:45:21,642] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-18 14:45:21,643] Epoch: 328
[2022-06-18 15:04:58,622] Train: Loss: 1.945 | Acc: 55.993 (717366/1281167) | Lr: 0.13661601269066695
[2022-06-18 15:05:43,751] Test: Loss: 1.507 | Acc: 63.398 (31699/50000)
[2022-06-18 15:05:43,752] Epoch: 329
[2022-06-18 15:25:23,512] Train: Loss: 1.937 | Acc: 56.125 (719050/1281167) | Lr: 0.13497282560343488
[2022-06-18 15:26:17,903] Test: Loss: 1.592 | Acc: 61.524 (30762/50000)
[2022-06-18 15:26:17,904] Epoch: 330
[2022-06-18 15:43:57,167] Train: Loss: 1.940 | Acc: 56.029 (717825/1281167) | Lr: 0.1333367079452844
[2022-06-18 15:44:43,495] Test: Loss: 1.717 | Acc: 59.198 (29599/50000)
[2022-06-18 15:44:43,496] Epoch: 331
[2022-06-18 16:04:02,347] Train: Loss: 1.935 | Acc: 56.158 (719481/1281167) | Lr: 0.13170772980205034
[2022-06-18 16:04:46,474] Test: Loss: 1.523 | Acc: 63.002 (31501/50000)
[2022-06-18 16:04:46,475] Epoch: 332
[2022-06-18 16:22:04,118] Train: Loss: 1.929 | Acc: 56.298 (721268/1281167) | Lr: 0.13008596095373462
[2022-06-18 16:22:48,634] Test: Loss: 1.498 | Acc: 63.540 (31770/50000)
[2022-06-18 16:22:48,634] Saving..
[2022-06-18 16:22:48,700] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-18 16:22:48,700] Epoch: 333
[2022-06-18 16:40:06,669] Train: Loss: 1.927 | Acc: 56.343 (721848/1281167) | Lr: 0.12847147087151742
[2022-06-18 16:40:51,360] Test: Loss: 1.501 | Acc: 63.680 (31840/50000)
[2022-06-18 16:40:51,360] Saving..
[2022-06-18 16:40:51,422] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-18 16:40:51,423] Epoch: 334
[2022-06-18 16:57:58,221] Train: Loss: 1.926 | Acc: 56.319 (721543/1281167) | Lr: 0.12686432871477996
[2022-06-18 16:58:44,716] Test: Loss: 1.454 | Acc: 64.600 (32300/50000)
[2022-06-18 16:58:44,716] Saving..
[2022-06-18 16:58:44,792] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-18 16:58:44,793] Epoch: 335
[2022-06-18 17:18:05,005] Train: Loss: 1.918 | Acc: 56.470 (723478/1281167) | Lr: 0.12526460332814365
[2022-06-18 17:18:51,107] Test: Loss: 1.515 | Acc: 63.146 (31573/50000)
[2022-06-18 17:18:51,108] Epoch: 336
[2022-06-18 17:38:04,169] Train: Loss: 1.916 | Acc: 56.510 (723992/1281167) | Lr: 0.12367236323851946
[2022-06-18 17:38:49,538] Test: Loss: 1.499 | Acc: 63.530 (31765/50000)
[2022-06-18 17:38:49,539] Epoch: 337
[2022-06-18 17:57:13,199] Train: Loss: 1.913 | Acc: 56.629 (725517/1281167) | Lr: 0.12208767665217356
[2022-06-18 17:57:59,565] Test: Loss: 1.541 | Acc: 62.648 (31324/50000)
[2022-06-18 17:57:59,566] Epoch: 338
[2022-06-18 18:17:14,248] Train: Loss: 1.907 | Acc: 56.698 (726398/1281167) | Lr: 0.12051061145180504
[2022-06-18 18:17:59,935] Test: Loss: 1.473 | Acc: 64.090 (32045/50000)
[2022-06-18 18:17:59,936] Epoch: 339
[2022-06-18 18:36:38,197] Train: Loss: 1.907 | Acc: 56.708 (726528/1281167) | Lr: 0.11894123519363835
[2022-06-18 18:37:22,033] Test: Loss: 1.534 | Acc: 63.098 (31549/50000)
[2022-06-18 18:37:22,034] Epoch: 340
[2022-06-18 18:56:03,721] Train: Loss: 1.902 | Acc: 56.791 (727589/1281167) | Lr: 0.11737961510452875
[2022-06-18 18:56:46,745] Test: Loss: 1.488 | Acc: 63.604 (31802/50000)
[2022-06-18 18:56:46,746] Epoch: 341
[2022-06-18 19:15:47,159] Train: Loss: 1.899 | Acc: 56.867 (728564/1281167) | Lr: 0.1158258180790838
[2022-06-18 19:16:32,235] Test: Loss: 1.508 | Acc: 63.178 (31589/50000)
[2022-06-18 19:16:32,236] Epoch: 342
[2022-06-18 19:33:49,950] Train: Loss: 1.897 | Acc: 56.903 (729020/1281167) | Lr: 0.11427991067679634
[2022-06-18 19:34:36,055] Test: Loss: 1.677 | Acc: 59.508 (29754/50000)
[2022-06-18 19:34:36,056] Epoch: 343
[2022-06-18 19:54:04,028] Train: Loss: 1.891 | Acc: 57.012 (730413/1281167) | Lr: 0.1127419591191943
[2022-06-18 19:54:51,391] Test: Loss: 1.448 | Acc: 64.600 (32300/50000)
[2022-06-18 19:54:51,392] Epoch: 344
[2022-06-18 20:14:10,788] Train: Loss: 1.888 | Acc: 57.106 (731619/1281167) | Lr: 0.11121202928700398
[2022-06-18 20:14:56,260] Test: Loss: 1.519 | Acc: 63.214 (31607/50000)
[2022-06-18 20:14:56,261] Epoch: 345
[2022-06-18 20:33:39,857] Train: Loss: 1.886 | Acc: 57.082 (731316/1281167) | Lr: 0.10969018671732703
[2022-06-18 20:34:25,802] Test: Loss: 1.463 | Acc: 64.670 (32335/50000)
[2022-06-18 20:34:25,803] Saving..
[2022-06-18 20:34:25,880] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-18 20:34:25,882] Epoch: 346
[2022-06-18 20:51:37,010] Train: Loss: 1.886 | Acc: 57.105 (731613/1281167) | Lr: 0.10817649660083442
[2022-06-18 20:52:22,375] Test: Loss: 1.511 | Acc: 63.444 (31722/50000)
[2022-06-18 20:52:22,376] Epoch: 347
[2022-06-18 21:11:10,249] Train: Loss: 1.875 | Acc: 57.302 (734140/1281167) | Lr: 0.10667102377897254
[2022-06-18 21:11:56,130] Test: Loss: 1.499 | Acc: 63.582 (31791/50000)
[2022-06-18 21:11:56,131] Epoch: 348
[2022-06-18 21:31:13,328] Train: Loss: 1.875 | Acc: 57.355 (734809/1281167) | Lr: 0.10517383274118651
[2022-06-18 21:31:57,969] Test: Loss: 1.468 | Acc: 64.310 (32155/50000)
[2022-06-18 21:31:57,970] Epoch: 349
[2022-06-18 21:49:16,107] Train: Loss: 1.871 | Acc: 57.386 (735208/1281167) | Lr: 0.10368498762215736
[2022-06-18 21:50:01,947] Test: Loss: 1.460 | Acc: 64.510 (32255/50000)
[2022-06-18 21:50:01,947] Epoch: 350
[2022-06-18 22:09:21,403] Train: Loss: 1.868 | Acc: 57.496 (736624/1281167) | Lr: 0.10220455219905486
[2022-06-18 22:10:05,776] Test: Loss: 1.469 | Acc: 64.298 (32149/50000)
[2022-06-18 22:10:05,777] Epoch: 351
[2022-06-18 22:27:42,862] Train: Loss: 1.867 | Acc: 57.497 (736631/1281167) | Lr: 0.10073258988880494
[2022-06-18 22:28:28,547] Test: Loss: 1.431 | Acc: 65.162 (32581/50000)
[2022-06-18 22:28:28,547] Saving..
[2022-06-18 22:28:28,628] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-18 22:28:28,629] Epoch: 352
[2022-06-18 22:46:54,019] Train: Loss: 1.859 | Acc: 57.690 (739111/1281167) | Lr: 0.09926916374537434
[2022-06-18 22:47:39,362] Test: Loss: 1.418 | Acc: 65.446 (32723/50000)
[2022-06-18 22:47:39,363] Saving..
[2022-06-18 22:47:39,429] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-18 22:47:39,430] Epoch: 353
[2022-06-18 23:05:37,964] Train: Loss: 1.860 | Acc: 57.629 (738330/1281167) | Lr: 0.09781433645706791
[2022-06-18 23:06:22,330] Test: Loss: 1.416 | Acc: 65.482 (32741/50000)
[2022-06-18 23:06:22,331] Saving..
[2022-06-18 23:06:22,395] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-18 23:06:22,396] Epoch: 354
[2022-06-18 23:25:28,905] Train: Loss: 1.852 | Acc: 57.736 (739700/1281167) | Lr: 0.09636817034384504
[2022-06-18 23:26:14,346] Test: Loss: 1.443 | Acc: 65.056 (32528/50000)
[2022-06-18 23:26:14,347] Epoch: 355
[2022-06-18 23:45:34,668] Train: Loss: 1.849 | Acc: 57.817 (740730/1281167) | Lr: 0.09493072735464868
[2022-06-18 23:46:18,365] Test: Loss: 1.448 | Acc: 64.930 (32465/50000)
[2022-06-18 23:46:18,366] Epoch: 356
[2022-06-19 00:04:43,084] Train: Loss: 1.846 | Acc: 57.874 (741464/1281167) | Lr: 0.09350206906475214
[2022-06-19 00:05:28,129] Test: Loss: 1.442 | Acc: 64.664 (32332/50000)
[2022-06-19 00:05:28,130] Epoch: 357
[2022-06-19 00:23:30,628] Train: Loss: 1.840 | Acc: 58.022 (743357/1281167) | Lr: 0.09208225667312192
[2022-06-19 00:24:16,319] Test: Loss: 1.450 | Acc: 64.762 (32381/50000)
[2022-06-19 00:24:16,320] Epoch: 358
[2022-06-19 00:43:16,989] Train: Loss: 1.843 | Acc: 58.011 (743221/1281167) | Lr: 0.09067135099979513
[2022-06-19 00:44:03,136] Test: Loss: 1.383 | Acc: 66.226 (33113/50000)
[2022-06-19 00:44:03,137] Saving..
[2022-06-19 00:44:03,204] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 00:44:03,205] Epoch: 359
[2022-06-19 01:03:25,597] Train: Loss: 1.839 | Acc: 58.068 (743953/1281167) | Lr: 0.08926941248327502
[2022-06-19 01:04:10,534] Test: Loss: 1.502 | Acc: 63.450 (31725/50000)
[2022-06-19 01:04:10,535] Epoch: 360
[2022-06-19 01:23:26,549] Train: Loss: 1.833 | Acc: 58.187 (745476/1281167) | Lr: 0.08787650117794162
[2022-06-19 01:24:11,555] Test: Loss: 1.422 | Acc: 65.498 (32749/50000)
[2022-06-19 01:24:11,556] Epoch: 361
[2022-06-19 01:43:38,119] Train: Loss: 1.829 | Acc: 58.229 (746007/1281167) | Lr: 0.08649267675147938
[2022-06-19 01:44:24,495] Test: Loss: 1.375 | Acc: 66.204 (33102/50000)
[2022-06-19 01:44:24,496] Epoch: 362
[2022-06-19 02:03:51,983] Train: Loss: 1.827 | Acc: 58.360 (747689/1281167) | Lr: 0.08511799848232077
[2022-06-19 02:04:36,475] Test: Loss: 1.479 | Acc: 64.150 (32075/50000)
[2022-06-19 02:04:36,476] Epoch: 363
[2022-06-19 02:22:33,988] Train: Loss: 1.821 | Acc: 58.359 (747674/1281167) | Lr: 0.08375252525710779
[2022-06-19 02:23:19,244] Test: Loss: 1.412 | Acc: 65.544 (32772/50000)
[2022-06-19 02:23:19,245] Epoch: 364
[2022-06-19 02:40:37,920] Train: Loss: 1.819 | Acc: 58.393 (748106/1281167) | Lr: 0.08239631556816869
[2022-06-19 02:41:23,671] Test: Loss: 1.380 | Acc: 66.042 (33021/50000)
[2022-06-19 02:41:23,672] Epoch: 365
[2022-06-19 02:58:59,122] Train: Loss: 1.815 | Acc: 58.519 (749720/1281167) | Lr: 0.0810494275110127
[2022-06-19 02:59:43,533] Test: Loss: 1.403 | Acc: 65.738 (32869/50000)
[2022-06-19 02:59:43,534] Epoch: 366
[2022-06-19 03:18:39,225] Train: Loss: 1.807 | Acc: 58.732 (752460/1281167) | Lr: 0.0797119187818415
[2022-06-19 03:19:23,759] Test: Loss: 1.375 | Acc: 66.454 (33227/50000)
[2022-06-19 03:19:23,759] Saving..
[2022-06-19 03:19:23,846] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 03:19:23,846] Epoch: 367
[2022-06-19 03:38:43,451] Train: Loss: 1.808 | Acc: 58.712 (752195/1281167) | Lr: 0.07838384667507721
[2022-06-19 03:39:29,421] Test: Loss: 1.445 | Acc: 64.760 (32380/50000)
[2022-06-19 03:39:29,422] Epoch: 368
[2022-06-19 03:58:50,611] Train: Loss: 1.797 | Acc: 58.938 (755098/1281167) | Lr: 0.07706526808090909
[2022-06-19 03:59:35,119] Test: Loss: 1.387 | Acc: 65.958 (32979/50000)
[2022-06-19 03:59:35,121] Epoch: 369
[2022-06-19 04:18:24,490] Train: Loss: 1.797 | Acc: 58.924 (754914/1281167) | Lr: 0.07575623948285524
[2022-06-19 04:19:08,972] Test: Loss: 1.432 | Acc: 65.010 (32505/50000)
[2022-06-19 04:19:08,973] Epoch: 370
[2022-06-19 04:36:20,250] Train: Loss: 1.796 | Acc: 58.943 (755162/1281167) | Lr: 0.07445681695534413
[2022-06-19 04:37:05,800] Test: Loss: 1.371 | Acc: 66.434 (33217/50000)
[2022-06-19 04:37:05,801] Epoch: 371
[2022-06-19 04:54:20,679] Train: Loss: 1.790 | Acc: 59.008 (755985/1281167) | Lr: 0.073167056161312
[2022-06-19 04:55:05,916] Test: Loss: 1.362 | Acc: 66.540 (33270/50000)
[2022-06-19 04:55:05,916] Saving..
[2022-06-19 04:55:05,984] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 04:55:05,985] Epoch: 372
[2022-06-19 05:13:12,344] Train: Loss: 1.787 | Acc: 59.096 (757119/1281167) | Lr: 0.07188701234981865
[2022-06-19 05:13:57,014] Test: Loss: 1.376 | Acc: 66.254 (33127/50000)
[2022-06-19 05:13:57,015] Epoch: 373
[2022-06-19 05:32:34,203] Train: Loss: 1.783 | Acc: 59.130 (757557/1281167) | Lr: 0.07061674035368062
[2022-06-19 05:33:20,320] Test: Loss: 1.408 | Acc: 65.554 (32777/50000)
[2022-06-19 05:33:20,321] Epoch: 374
[2022-06-19 05:50:36,060] Train: Loss: 1.777 | Acc: 59.226 (758786/1281167) | Lr: 0.06935629458712246
[2022-06-19 05:51:22,438] Test: Loss: 1.360 | Acc: 66.656 (33328/50000)
[2022-06-19 05:51:22,439] Saving..
[2022-06-19 05:51:22,513] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 05:51:22,513] Epoch: 375
[2022-06-19 06:09:45,730] Train: Loss: 1.776 | Acc: 59.374 (760685/1281167) | Lr: 0.06810572904344565
[2022-06-19 06:10:30,511] Test: Loss: 1.348 | Acc: 66.970 (33485/50000)
[2022-06-19 06:10:30,511] Saving..
[2022-06-19 06:10:30,573] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 06:10:30,574] Epoch: 376
[2022-06-19 06:28:36,837] Train: Loss: 1.774 | Acc: 59.306 (759812/1281167) | Lr: 0.06686509729271595
[2022-06-19 06:29:21,063] Test: Loss: 1.346 | Acc: 66.960 (33480/50000)
[2022-06-19 06:29:21,064] Epoch: 377
[2022-06-19 06:47:48,959] Train: Loss: 1.770 | Acc: 59.458 (761760/1281167) | Lr: 0.06563445247946846
[2022-06-19 06:48:35,340] Test: Loss: 1.330 | Acc: 67.188 (33594/50000)
[2022-06-19 06:48:35,340] Saving..
[2022-06-19 06:48:35,418] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 06:48:35,419] Epoch: 378
[2022-06-19 07:07:30,424] Train: Loss: 1.762 | Acc: 59.602 (763603/1281167) | Lr: 0.06441384732043082
[2022-06-19 07:08:13,955] Test: Loss: 1.467 | Acc: 64.296 (32148/50000)
[2022-06-19 07:08:13,956] Epoch: 379
[2022-06-19 07:26:10,139] Train: Loss: 1.761 | Acc: 59.589 (763429/1281167) | Lr: 0.06320333410226592
[2022-06-19 07:26:54,827] Test: Loss: 1.367 | Acc: 66.224 (33112/50000)
[2022-06-19 07:26:54,828] Epoch: 380
[2022-06-19 07:44:32,623] Train: Loss: 1.757 | Acc: 59.685 (764670/1281167) | Lr: 0.06200296467933081
[2022-06-19 07:45:17,184] Test: Loss: 1.314 | Acc: 67.706 (33853/50000)
[2022-06-19 07:45:17,184] Saving..
[2022-06-19 07:45:17,282] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 07:45:17,283] Epoch: 381
[2022-06-19 08:04:40,074] Train: Loss: 1.754 | Acc: 59.731 (765251/1281167) | Lr: 0.0608127904714567
[2022-06-19 08:05:24,019] Test: Loss: 1.322 | Acc: 67.522 (33761/50000)
[2022-06-19 08:05:24,020] Epoch: 382
[2022-06-19 08:24:17,934] Train: Loss: 1.750 | Acc: 59.864 (766958/1281167) | Lr: 0.05963286246174523
[2022-06-19 08:25:02,055] Test: Loss: 1.363 | Acc: 66.568 (33284/50000)
[2022-06-19 08:25:02,056] Epoch: 383
[2022-06-19 08:44:26,500] Train: Loss: 1.743 | Acc: 60.032 (769111/1281167) | Lr: 0.0584632311943853
[2022-06-19 08:45:10,750] Test: Loss: 1.354 | Acc: 66.854 (33427/50000)
[2022-06-19 08:45:10,751] Epoch: 384
[2022-06-19 09:03:39,755] Train: Loss: 1.739 | Acc: 60.048 (769311/1281167) | Lr: 0.05730394677248761
[2022-06-19 09:04:25,644] Test: Loss: 1.346 | Acc: 66.770 (33385/50000)
[2022-06-19 09:04:25,645] Epoch: 385
[2022-06-19 09:23:47,819] Train: Loss: 1.736 | Acc: 60.125 (770297/1281167) | Lr: 0.056155058855938356
[2022-06-19 09:24:34,466] Test: Loss: 1.307 | Acc: 67.824 (33912/50000)
[2022-06-19 09:24:34,466] Saving..
[2022-06-19 09:24:34,531] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 09:24:34,532] Epoch: 386
[2022-06-19 09:41:45,682] Train: Loss: 1.730 | Acc: 60.242 (771801/1281167) | Lr: 0.05501661665927207
[2022-06-19 09:42:30,635] Test: Loss: 1.320 | Acc: 67.370 (33685/50000)
[2022-06-19 09:42:30,636] Epoch: 387
[2022-06-19 10:01:51,456] Train: Loss: 1.727 | Acc: 60.306 (772623/1281167) | Lr: 0.05388866894956349
[2022-06-19 10:02:36,712] Test: Loss: 1.321 | Acc: 67.662 (33831/50000)
[2022-06-19 10:02:36,713] Epoch: 388
[2022-06-19 10:19:50,372] Train: Loss: 1.718 | Acc: 60.516 (775312/1281167) | Lr: 0.052771264044338406
[2022-06-19 10:20:36,239] Test: Loss: 1.322 | Acc: 67.502 (33751/50000)
[2022-06-19 10:20:36,240] Epoch: 389
[2022-06-19 10:39:25,890] Train: Loss: 1.717 | Acc: 60.505 (775164/1281167) | Lr: 0.05166444980950378
[2022-06-19 10:40:12,489] Test: Loss: 1.277 | Acc: 68.480 (34240/50000)
[2022-06-19 10:40:12,490] Saving..
[2022-06-19 10:40:12,549] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 10:40:12,551] Epoch: 390
[2022-06-19 10:59:39,693] Train: Loss: 1.716 | Acc: 60.519 (775348/1281167) | Lr: 0.050568273657297956
[2022-06-19 11:00:24,279] Test: Loss: 1.334 | Acc: 67.338 (33669/50000)
[2022-06-19 11:00:24,280] Epoch: 391
[2022-06-19 11:19:43,673] Train: Loss: 1.710 | Acc: 60.659 (777145/1281167) | Lr: 0.04948278254425858
[2022-06-19 11:20:28,961] Test: Loss: 1.311 | Acc: 67.660 (33830/50000)
[2022-06-19 11:20:28,962] Epoch: 392
[2022-06-19 11:39:21,920] Train: Loss: 1.707 | Acc: 60.712 (777825/1281167) | Lr: 0.04840802296921249
[2022-06-19 11:40:07,293] Test: Loss: 1.285 | Acc: 68.456 (34228/50000)
[2022-06-19 11:40:07,294] Epoch: 393
[2022-06-19 11:58:03,494] Train: Loss: 1.701 | Acc: 60.849 (779579/1281167) | Lr: 0.0473440409712826
[2022-06-19 11:58:47,075] Test: Loss: 1.294 | Acc: 68.240 (34120/50000)
[2022-06-19 11:58:47,076] Epoch: 394
[2022-06-19 12:16:08,303] Train: Loss: 1.701 | Acc: 60.832 (779357/1281167) | Lr: 0.04629088212791651
[2022-06-19 12:16:53,316] Test: Loss: 1.296 | Acc: 68.102 (34051/50000)
[2022-06-19 12:16:53,317] Epoch: 395
[2022-06-19 12:34:15,119] Train: Loss: 1.691 | Acc: 61.058 (782261/1281167) | Lr: 0.04524859155293395
[2022-06-19 12:35:00,776] Test: Loss: 1.354 | Acc: 66.932 (33466/50000)
[2022-06-19 12:35:00,777] Epoch: 396
[2022-06-19 12:53:53,653] Train: Loss: 1.691 | Acc: 61.011 (781657/1281167) | Lr: 0.04421721389459408
[2022-06-19 12:54:37,996] Test: Loss: 1.265 | Acc: 68.810 (34405/50000)
[2022-06-19 12:54:37,996] Saving..
[2022-06-19 12:54:38,079] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 12:54:38,080] Epoch: 397
[2022-06-19 13:12:42,637] Train: Loss: 1.684 | Acc: 61.211 (784219/1281167) | Lr: 0.04319679333368313
[2022-06-19 13:13:29,362] Test: Loss: 1.258 | Acc: 68.912 (34456/50000)
[2022-06-19 13:13:29,363] Saving..
[2022-06-19 13:13:29,429] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 13:13:29,429] Epoch: 398
[2022-06-19 13:32:24,569] Train: Loss: 1.679 | Acc: 61.241 (784603/1281167) | Lr: 0.04218737358162167
[2022-06-19 13:33:09,888] Test: Loss: 1.272 | Acc: 68.568 (34284/50000)
[2022-06-19 13:33:09,889] Epoch: 399
[2022-06-19 13:52:10,399] Train: Loss: 1.674 | Acc: 61.413 (786806/1281167) | Lr: 0.04118899787859231
[2022-06-19 13:52:56,240] Test: Loss: 1.276 | Acc: 68.582 (34291/50000)
[2022-06-19 13:52:56,241] Epoch: 400
[2022-06-19 14:12:11,647] Train: Loss: 1.670 | Acc: 61.435 (787080/1281167) | Lr: 0.040201708991687284
[2022-06-19 14:12:56,822] Test: Loss: 1.298 | Acc: 67.800 (33900/50000)
[2022-06-19 14:12:56,823] Epoch: 401
[2022-06-19 14:32:20,214] Train: Loss: 1.671 | Acc: 61.425 (786962/1281167) | Lr: 0.039225549213076645
[2022-06-19 14:33:05,689] Test: Loss: 1.246 | Acc: 69.196 (34598/50000)
[2022-06-19 14:33:05,689] Saving..
[2022-06-19 14:33:05,765] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 14:33:05,766] Epoch: 402
[2022-06-19 14:52:21,248] Train: Loss: 1.664 | Acc: 61.628 (789557/1281167) | Lr: 0.03826056035819619
[2022-06-19 14:53:04,540] Test: Loss: 1.251 | Acc: 69.186 (34593/50000)
[2022-06-19 14:53:04,541] Epoch: 403
[2022-06-19 15:10:25,748] Train: Loss: 1.656 | Acc: 61.800 (791764/1281167) | Lr: 0.037306783763956984
[2022-06-19 15:11:10,373] Test: Loss: 1.257 | Acc: 69.046 (34523/50000)
[2022-06-19 15:11:10,374] Epoch: 404
[2022-06-19 15:28:25,513] Train: Loss: 1.656 | Acc: 61.818 (791986/1281167) | Lr: 0.036364260286973704
[2022-06-19 15:29:09,421] Test: Loss: 1.247 | Acc: 69.156 (34578/50000)
[2022-06-19 15:29:09,422] Epoch: 405
[2022-06-19 15:47:15,096] Train: Loss: 1.649 | Acc: 61.901 (793061/1281167) | Lr: 0.03543303030181524
[2022-06-19 15:48:00,264] Test: Loss: 1.239 | Acc: 69.460 (34730/50000)
[2022-06-19 15:48:00,265] Saving..
[2022-06-19 15:48:00,335] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 15:48:00,336] Epoch: 406
[2022-06-19 16:05:09,395] Train: Loss: 1.645 | Acc: 61.985 (794131/1281167) | Lr: 0.034513133699274764
[2022-06-19 16:05:56,692] Test: Loss: 1.261 | Acc: 68.864 (34432/50000)
[2022-06-19 16:05:56,692] Epoch: 407
[2022-06-19 16:25:08,090] Train: Loss: 1.640 | Acc: 62.044 (794893/1281167) | Lr: 0.03360460988466101
[2022-06-19 16:25:53,255] Test: Loss: 1.286 | Acc: 68.238 (34119/50000)
[2022-06-19 16:25:53,256] Epoch: 408
[2022-06-19 16:43:52,298] Train: Loss: 1.639 | Acc: 62.110 (795738/1281167) | Lr: 0.03270749777611059
[2022-06-19 16:44:39,263] Test: Loss: 1.242 | Acc: 69.250 (34625/50000)
[2022-06-19 16:44:39,264] Epoch: 409
[2022-06-19 17:04:11,563] Train: Loss: 1.632 | Acc: 62.250 (797529/1281167) | Lr: 0.0318218358029202
[2022-06-19 17:04:57,396] Test: Loss: 1.227 | Acc: 69.438 (34719/50000)
[2022-06-19 17:04:57,397] Epoch: 410
[2022-06-19 17:24:19,983] Train: Loss: 1.627 | Acc: 62.335 (798620/1281167) | Lr: 0.030947661903901174
[2022-06-19 17:25:04,051] Test: Loss: 1.214 | Acc: 69.934 (34967/50000)
[2022-06-19 17:25:04,051] Saving..
[2022-06-19 17:25:04,127] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 17:25:04,127] Epoch: 411
[2022-06-19 17:44:00,891] Train: Loss: 1.624 | Acc: 62.348 (798784/1281167) | Lr: 0.030085013525753792
[2022-06-19 17:44:47,458] Test: Loss: 1.215 | Acc: 69.806 (34903/50000)
[2022-06-19 17:44:47,459] Epoch: 412
[2022-06-19 18:02:10,409] Train: Loss: 1.615 | Acc: 62.569 (801613/1281167) | Lr: 0.029233927621463613
[2022-06-19 18:02:54,294] Test: Loss: 1.232 | Acc: 69.594 (34797/50000)
[2022-06-19 18:02:54,295] Epoch: 413
[2022-06-19 18:22:05,012] Train: Loss: 1.613 | Acc: 62.659 (802762/1281167) | Lr: 0.02839444064871788
[2022-06-19 18:22:50,877] Test: Loss: 1.204 | Acc: 70.226 (35113/50000)
[2022-06-19 18:22:50,877] Saving..
[2022-06-19 18:22:50,967] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 18:22:50,968] Epoch: 414
[2022-06-19 18:42:14,570] Train: Loss: 1.608 | Acc: 62.777 (804276/1281167) | Lr: 0.02756658856834477
[2022-06-19 18:43:01,009] Test: Loss: 1.211 | Acc: 70.036 (35018/50000)
[2022-06-19 18:43:01,010] Epoch: 415
[2022-06-19 19:02:22,826] Train: Loss: 1.602 | Acc: 62.867 (805430/1281167) | Lr: 0.026750406842771888
[2022-06-19 19:03:08,107] Test: Loss: 1.186 | Acc: 70.506 (35253/50000)
[2022-06-19 19:03:08,107] Saving..
[2022-06-19 19:03:08,169] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 19:03:08,170] Epoch: 416
[2022-06-19 19:20:14,058] Train: Loss: 1.597 | Acc: 62.981 (806895/1281167) | Lr: 0.025945930434507904
[2022-06-19 19:20:58,757] Test: Loss: 1.216 | Acc: 69.946 (34973/50000)
[2022-06-19 19:20:58,758] Epoch: 417
[2022-06-19 19:39:58,594] Train: Loss: 1.594 | Acc: 63.086 (808237/1281167) | Lr: 0.025153193804644813
[2022-06-19 19:40:44,507] Test: Loss: 1.189 | Acc: 70.516 (35258/50000)
[2022-06-19 19:40:44,508] Saving..
[2022-06-19 19:40:44,572] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 19:40:44,573] Epoch: 418
[2022-06-19 20:00:01,247] Train: Loss: 1.593 | Acc: 63.081 (808170/1281167) | Lr: 0.024372230911381205
[2022-06-19 20:00:44,953] Test: Loss: 1.193 | Acc: 70.326 (35163/50000)
[2022-06-19 20:00:44,955] Epoch: 419
[2022-06-19 20:20:06,207] Train: Loss: 1.586 | Acc: 63.212 (809847/1281167) | Lr: 0.02360307520856838
[2022-06-19 20:20:51,793] Test: Loss: 1.188 | Acc: 70.722 (35361/50000)
[2022-06-19 20:20:51,793] Saving..
[2022-06-19 20:20:51,930] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 20:20:51,932] Epoch: 420
[2022-06-19 20:40:19,791] Train: Loss: 1.583 | Acc: 63.294 (810904/1281167) | Lr: 0.02284575964427652
[2022-06-19 20:41:03,716] Test: Loss: 1.168 | Acc: 71.054 (35527/50000)
[2022-06-19 20:41:03,716] Saving..
[2022-06-19 20:41:03,798] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 20:41:03,799] Epoch: 421
[2022-06-19 21:00:36,673] Train: Loss: 1.574 | Acc: 63.467 (813113/1281167) | Lr: 0.02210031665938393
[2022-06-19 21:01:21,550] Test: Loss: 1.176 | Acc: 70.924 (35462/50000)
[2022-06-19 21:01:21,551] Epoch: 422
[2022-06-19 21:19:48,191] Train: Loss: 1.573 | Acc: 63.515 (813739/1281167) | Lr: 0.021366778186187076
[2022-06-19 21:20:31,695] Test: Loss: 1.168 | Acc: 71.014 (35507/50000)
[2022-06-19 21:20:31,696] Epoch: 423
[2022-06-19 21:39:13,281] Train: Loss: 1.566 | Acc: 63.577 (814526/1281167) | Lr: 0.02064517564703278
[2022-06-19 21:39:57,438] Test: Loss: 1.190 | Acc: 70.460 (35230/50000)
[2022-06-19 21:39:57,439] Epoch: 424
[2022-06-19 21:59:06,723] Train: Loss: 1.561 | Acc: 63.709 (816225/1281167) | Lr: 0.019935539952971953
[2022-06-19 21:59:51,043] Test: Loss: 1.165 | Acc: 71.152 (35576/50000)
[2022-06-19 21:59:51,043] Saving..
[2022-06-19 21:59:51,149] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 21:59:51,150] Epoch: 425
[2022-06-19 22:16:55,904] Train: Loss: 1.559 | Acc: 63.800 (817380/1281167) | Lr: 0.019237901502436115
[2022-06-19 22:17:38,988] Test: Loss: 1.179 | Acc: 70.968 (35484/50000)
[2022-06-19 22:17:38,989] Epoch: 426
[2022-06-19 22:34:42,305] Train: Loss: 1.550 | Acc: 63.980 (819692/1281167) | Lr: 0.018552290179934355
[2022-06-19 22:35:27,636] Test: Loss: 1.166 | Acc: 71.070 (35535/50000)
[2022-06-19 22:35:27,637] Epoch: 427
[2022-06-19 22:55:00,845] Train: Loss: 1.546 | Acc: 63.977 (819655/1281167) | Lr: 0.017878735354773998
[2022-06-19 22:55:44,132] Test: Loss: 1.177 | Acc: 70.750 (35375/50000)
[2022-06-19 22:55:44,137] Epoch: 428
[2022-06-19 23:15:13,055] Train: Loss: 1.541 | Acc: 64.103 (821270/1281167) | Lr: 0.017217265879801952
[2022-06-19 23:15:59,531] Test: Loss: 1.158 | Acc: 71.296 (35648/50000)
[2022-06-19 23:15:59,531] Saving..
[2022-06-19 23:15:59,611] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 23:15:59,612] Epoch: 429
[2022-06-19 23:35:06,713] Train: Loss: 1.538 | Acc: 64.245 (823089/1281167) | Lr: 0.01656791009016891
[2022-06-19 23:35:52,012] Test: Loss: 1.155 | Acc: 71.102 (35551/50000)
[2022-06-19 23:35:52,013] Epoch: 430
[2022-06-19 23:55:15,848] Train: Loss: 1.532 | Acc: 64.366 (824632/1281167) | Lr: 0.015930695802115792
[2022-06-19 23:56:01,639] Test: Loss: 1.149 | Acc: 71.430 (35715/50000)
[2022-06-19 23:56:01,639] Saving..
[2022-06-19 23:56:01,725] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-19 23:56:01,726] Epoch: 431
[2022-06-20 00:15:35,336] Train: Loss: 1.532 | Acc: 64.327 (824139/1281167) | Lr: 0.015305650311781776
[2022-06-20 00:16:19,247] Test: Loss: 1.144 | Acc: 71.554 (35777/50000)
[2022-06-20 00:16:19,247] Saving..
[2022-06-20 00:16:19,315] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 00:16:19,316] Epoch: 432
[2022-06-20 00:35:42,750] Train: Loss: 1.524 | Acc: 64.484 (826144/1281167) | Lr: 0.014692800394035406
[2022-06-20 00:36:28,886] Test: Loss: 1.145 | Acc: 71.556 (35778/50000)
[2022-06-20 00:36:28,886] Saving..
[2022-06-20 00:36:28,957] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 00:36:28,957] Epoch: 433
[2022-06-20 00:55:57,018] Train: Loss: 1.518 | Acc: 64.588 (827480/1281167) | Lr: 0.01409217230132743
[2022-06-20 00:56:42,354] Test: Loss: 1.147 | Acc: 71.540 (35770/50000)
[2022-06-20 00:56:42,355] Epoch: 434
[2022-06-20 01:15:17,911] Train: Loss: 1.514 | Acc: 64.681 (828670/1281167) | Lr: 0.01350379176256632
[2022-06-20 01:16:01,334] Test: Loss: 1.145 | Acc: 71.738 (35869/50000)
[2022-06-20 01:16:01,334] Saving..
[2022-06-20 01:16:01,413] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 01:16:01,414] Epoch: 435
[2022-06-20 01:33:15,824] Train: Loss: 1.508 | Acc: 64.856 (830909/1281167) | Lr: 0.012927683982016004
[2022-06-20 01:34:02,138] Test: Loss: 1.127 | Acc: 71.956 (35978/50000)
[2022-06-20 01:34:02,138] Saving..
[2022-06-20 01:34:02,205] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 01:34:02,205] Epoch: 436
[2022-06-20 01:53:33,696] Train: Loss: 1.504 | Acc: 64.920 (831728/1281167) | Lr: 0.01236387363821645
[2022-06-20 01:54:19,544] Test: Loss: 1.137 | Acc: 71.730 (35865/50000)
[2022-06-20 01:54:19,545] Epoch: 437
[2022-06-20 02:13:46,368] Train: Loss: 1.499 | Acc: 65.038 (833249/1281167) | Lr: 0.011812384882926191
[2022-06-20 02:14:29,897] Test: Loss: 1.127 | Acc: 71.968 (35984/50000)
[2022-06-20 02:14:29,897] Saving..
[2022-06-20 02:14:29,981] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 02:14:29,983] Epoch: 438
[2022-06-20 02:31:31,054] Train: Loss: 1.494 | Acc: 65.100 (834039/1281167) | Lr: 0.011273241340088076
[2022-06-20 02:32:17,234] Test: Loss: 1.126 | Acc: 71.942 (35971/50000)
[2022-06-20 02:32:17,235] Epoch: 439
[2022-06-20 02:51:19,709] Train: Loss: 1.489 | Acc: 65.232 (835725/1281167) | Lr: 0.01074646610481706
[2022-06-20 02:52:04,308] Test: Loss: 1.121 | Acc: 72.048 (36024/50000)
[2022-06-20 02:52:04,309] Saving..
[2022-06-20 02:52:04,384] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 02:52:04,385] Epoch: 440
[2022-06-20 03:09:18,825] Train: Loss: 1.483 | Acc: 65.369 (837492/1281167) | Lr: 0.010232081742410942
[2022-06-20 03:10:04,789] Test: Loss: 1.120 | Acc: 72.132 (36066/50000)
[2022-06-20 03:10:04,790] Saving..
[2022-06-20 03:10:04,855] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 03:10:04,856] Epoch: 441
[2022-06-20 03:27:17,706] Train: Loss: 1.479 | Acc: 65.426 (838218/1281167) | Lr: 0.009730110287383863
[2022-06-20 03:28:03,292] Test: Loss: 1.122 | Acc: 72.048 (36024/50000)
[2022-06-20 03:28:03,293] Epoch: 442
[2022-06-20 03:47:36,736] Train: Loss: 1.476 | Acc: 65.459 (838641/1281167) | Lr: 0.009240573242522235
[2022-06-20 03:48:22,851] Test: Loss: 1.106 | Acc: 72.568 (36284/50000)
[2022-06-20 03:48:22,851] Saving..
[2022-06-20 03:48:22,932] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 03:48:22,933] Epoch: 443
[2022-06-20 04:07:55,894] Train: Loss: 1.471 | Acc: 65.599 (840429/1281167) | Lr: 0.008763491577963696
[2022-06-20 04:08:41,565] Test: Loss: 1.108 | Acc: 72.584 (36292/50000)
[2022-06-20 04:08:41,565] Saving..
[2022-06-20 04:08:41,652] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 04:08:41,653] Epoch: 444
[2022-06-20 04:28:10,684] Train: Loss: 1.469 | Acc: 65.687 (841558/1281167) | Lr: 0.008298885730299011
[2022-06-20 04:28:54,934] Test: Loss: 1.099 | Acc: 72.668 (36334/50000)
[2022-06-20 04:28:54,935] Saving..
[2022-06-20 04:28:55,030] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 04:28:55,031] Epoch: 445
[2022-06-20 04:47:53,925] Train: Loss: 1.463 | Acc: 65.761 (842514/1281167) | Lr: 0.007846775601696289
[2022-06-20 04:48:39,910] Test: Loss: 1.097 | Acc: 72.534 (36267/50000)
[2022-06-20 04:48:39,911] Epoch: 446
[2022-06-20 05:08:14,779] Train: Loss: 1.454 | Acc: 65.974 (845236/1281167) | Lr: 0.007407180559048736
[2022-06-20 05:08:58,537] Test: Loss: 1.095 | Acc: 72.712 (36356/50000)
[2022-06-20 05:08:58,538] Saving..
[2022-06-20 05:08:58,615] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 05:08:58,615] Epoch: 447
[2022-06-20 05:27:51,077] Train: Loss: 1.453 | Acc: 66.011 (845708/1281167) | Lr: 0.006980119433144881
[2022-06-20 05:28:34,035] Test: Loss: 1.097 | Acc: 72.902 (36451/50000)
[2022-06-20 05:28:34,036] Saving..
[2022-06-20 05:28:34,123] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 05:28:34,124] Epoch: 448
[2022-06-20 05:48:02,537] Train: Loss: 1.450 | Acc: 66.112 (847004/1281167) | Lr: 0.006565610517861955
[2022-06-20 05:48:48,527] Test: Loss: 1.095 | Acc: 72.810 (36405/50000)
[2022-06-20 05:48:48,528] Epoch: 449
[2022-06-20 06:07:48,816] Train: Loss: 1.445 | Acc: 66.172 (847773/1281167) | Lr: 0.006163671569382373
[2022-06-20 06:08:34,929] Test: Loss: 1.089 | Acc: 72.864 (36432/50000)
[2022-06-20 06:08:34,930] Epoch: 450
[2022-06-20 06:27:37,714] Train: Loss: 1.443 | Acc: 66.216 (848340/1281167) | Lr: 0.005774319805432881
[2022-06-20 06:28:25,387] Test: Loss: 1.085 | Acc: 72.938 (36469/50000)
[2022-06-20 06:28:25,388] Saving..
[2022-06-20 06:28:25,474] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 06:28:25,475] Epoch: 451
[2022-06-20 06:48:00,326] Train: Loss: 1.436 | Acc: 66.452 (851364/1281167) | Lr: 0.005397571904547165
[2022-06-20 06:48:44,668] Test: Loss: 1.081 | Acc: 72.966 (36483/50000)
[2022-06-20 06:48:44,669] Saving..
[2022-06-20 06:48:44,731] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 06:48:44,732] Epoch: 452
[2022-06-20 07:07:17,213] Train: Loss: 1.430 | Acc: 66.473 (851631/1281167) | Lr: 0.005033444005351463
[2022-06-20 07:08:00,692] Test: Loss: 1.086 | Acc: 73.006 (36503/50000)
[2022-06-20 07:08:00,692] Saving..
[2022-06-20 07:08:00,776] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 07:08:00,776] Epoch: 453
[2022-06-20 07:26:04,862] Train: Loss: 1.428 | Acc: 66.525 (852301/1281167) | Lr: 0.004681951705872932
[2022-06-20 07:26:48,336] Test: Loss: 1.080 | Acc: 73.378 (36689/50000)
[2022-06-20 07:26:48,337] Saving..
[2022-06-20 07:26:48,419] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 07:26:48,420] Epoch: 454
[2022-06-20 07:46:15,759] Train: Loss: 1.425 | Acc: 66.589 (853118/1281167) | Lr: 0.004343110062871961
[2022-06-20 07:47:00,507] Test: Loss: 1.077 | Acc: 73.150 (36575/50000)
[2022-06-20 07:47:00,508] Epoch: 455
[2022-06-20 08:04:08,702] Train: Loss: 1.420 | Acc: 66.710 (854663/1281167) | Lr: 0.004016933591196727
[2022-06-20 08:04:55,040] Test: Loss: 1.077 | Acc: 73.220 (36610/50000)
[2022-06-20 08:04:55,041] Epoch: 456
[2022-06-20 08:24:31,013] Train: Loss: 1.418 | Acc: 66.767 (855397/1281167) | Lr: 0.003703436263161675
[2022-06-20 08:25:17,617] Test: Loss: 1.072 | Acc: 73.316 (36658/50000)
[2022-06-20 08:25:17,618] Epoch: 457
[2022-06-20 08:44:17,298] Train: Loss: 1.415 | Acc: 66.814 (856004/1281167) | Lr: 0.0034026315079488997
[2022-06-20 08:45:01,883] Test: Loss: 1.065 | Acc: 73.320 (36660/50000)
[2022-06-20 08:45:01,885] Epoch: 458
[2022-06-20 09:04:03,907] Train: Loss: 1.410 | Acc: 66.942 (857639/1281167) | Lr: 0.0031145322110330566
[2022-06-20 09:04:49,619] Test: Loss: 1.068 | Acc: 73.424 (36712/50000)
[2022-06-20 09:04:49,619] Saving..
[2022-06-20 09:04:49,704] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 09:04:49,705] Epoch: 459
[2022-06-20 09:24:20,701] Train: Loss: 1.407 | Acc: 67.003 (858415/1281167) | Lr: 0.0028391507136290403
[2022-06-20 09:25:06,830] Test: Loss: 1.067 | Acc: 73.486 (36743/50000)
[2022-06-20 09:25:06,830] Saving..
[2022-06-20 09:25:06,915] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 09:25:06,916] Epoch: 460
[2022-06-20 09:42:46,834] Train: Loss: 1.408 | Acc: 66.994 (858302/1281167) | Lr: 0.0025764988121637525
[2022-06-20 09:43:33,117] Test: Loss: 1.064 | Acc: 73.574 (36787/50000)
[2022-06-20 09:43:33,117] Saving..
[2022-06-20 09:43:33,266] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 09:43:33,267] Epoch: 461
[2022-06-20 10:03:12,853] Train: Loss: 1.401 | Acc: 67.072 (859299/1281167) | Lr: 0.0023265877577704455
[2022-06-20 10:03:56,620] Test: Loss: 1.066 | Acc: 73.478 (36739/50000)
[2022-06-20 10:03:56,621] Epoch: 462
[2022-06-20 10:22:26,860] Train: Loss: 1.396 | Acc: 67.185 (860749/1281167) | Lr: 0.002089428255806885
[2022-06-20 10:23:15,556] Test: Loss: 1.060 | Acc: 73.586 (36793/50000)
[2022-06-20 10:23:15,556] Saving..
[2022-06-20 10:23:15,644] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 10:23:15,645] Epoch: 463
[2022-06-20 10:43:21,399] Train: Loss: 1.396 | Acc: 67.224 (861253/1281167) | Lr: 0.0018650304653968564
[2022-06-20 10:44:09,350] Test: Loss: 1.063 | Acc: 73.444 (36722/50000)
[2022-06-20 10:44:09,352] Epoch: 464
[2022-06-20 11:04:18,376] Train: Loss: 1.392 | Acc: 67.331 (862622/1281167) | Lr: 0.00165340399899482
[2022-06-20 11:05:06,570] Test: Loss: 1.060 | Acc: 73.588 (36794/50000)
[2022-06-20 11:05:06,570] Saving..
[2022-06-20 11:05:06,918] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 11:05:06,919] Epoch: 465
[2022-06-20 11:25:13,597] Train: Loss: 1.393 | Acc: 67.301 (862236/1281167) | Lr: 0.0014545579219743146
[2022-06-20 11:26:01,870] Test: Loss: 1.057 | Acc: 73.752 (36876/50000)
[2022-06-20 11:26:01,871] Saving..
[2022-06-20 11:26:01,939] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 11:26:01,941] Epoch: 466
[2022-06-20 11:46:05,479] Train: Loss: 1.389 | Acc: 67.435 (863950/1281167) | Lr: 0.001268500752239375
[2022-06-20 11:46:53,831] Test: Loss: 1.056 | Acc: 73.740 (36870/50000)
[2022-06-20 11:46:53,832] Epoch: 467
[2022-06-20 12:06:58,192] Train: Loss: 1.383 | Acc: 67.434 (863948/1281167) | Lr: 0.001095240459859929
[2022-06-20 12:07:46,565] Test: Loss: 1.056 | Acc: 73.784 (36892/50000)
[2022-06-20 12:07:46,565] Saving..
[2022-06-20 12:07:46,629] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 12:07:46,630] Epoch: 468
[2022-06-20 12:27:40,596] Train: Loss: 1.384 | Acc: 67.473 (864440/1281167) | Lr: 0.000934784466730276
[2022-06-20 12:28:28,497] Test: Loss: 1.054 | Acc: 73.778 (36889/50000)
[2022-06-20 12:28:28,498] Epoch: 469
[2022-06-20 12:48:43,000] Train: Loss: 1.382 | Acc: 67.505 (864853/1281167) | Lr: 0.0007871396462511146
[2022-06-20 12:49:30,041] Test: Loss: 1.052 | Acc: 73.790 (36895/50000)
[2022-06-20 12:49:30,042] Saving..
[2022-06-20 12:49:30,111] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 12:49:30,112] Epoch: 470
[2022-06-20 13:08:45,163] Train: Loss: 1.376 | Acc: 67.650 (866712/1281167) | Lr: 0.0006523123230351489
[2022-06-20 13:09:33,440] Test: Loss: 1.052 | Acc: 73.848 (36924/50000)
[2022-06-20 13:09:33,440] Saving..
[2022-06-20 13:09:33,530] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 13:09:33,531] Epoch: 471
[2022-06-20 13:29:30,652] Train: Loss: 1.382 | Acc: 67.509 (864904/1281167) | Lr: 0.0005303082726361429
[2022-06-20 13:30:17,584] Test: Loss: 1.052 | Acc: 73.882 (36941/50000)
[2022-06-20 13:30:17,585] Saving..
[2022-06-20 13:30:17,667] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 13:30:17,668] Epoch: 472
[2022-06-20 13:50:28,437] Train: Loss: 1.379 | Acc: 67.635 (866520/1281167) | Lr: 0.00042113272130162266
[2022-06-20 13:51:16,312] Test: Loss: 1.051 | Acc: 73.956 (36978/50000)
[2022-06-20 13:51:16,312] Saving..
[2022-06-20 13:51:16,374] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 13:51:16,375] Epoch: 473
[2022-06-20 14:11:08,060] Train: Loss: 1.376 | Acc: 67.603 (866107/1281167) | Lr: 0.0003247903457487596
[2022-06-20 14:11:55,275] Test: Loss: 1.050 | Acc: 73.926 (36963/50000)
[2022-06-20 14:11:55,276] Epoch: 474
[2022-06-20 14:31:46,563] Train: Loss: 1.375 | Acc: 67.625 (866393/1281167) | Lr: 0.0002412852729643332
[2022-06-20 14:32:35,162] Test: Loss: 1.049 | Acc: 73.916 (36958/50000)
[2022-06-20 14:32:35,163] Epoch: 475
[2022-06-20 14:52:41,023] Train: Loss: 1.377 | Acc: 67.571 (865701/1281167) | Lr: 0.00017062108002767639
[2022-06-20 14:53:28,616] Test: Loss: 1.048 | Acc: 73.962 (36981/50000)
[2022-06-20 14:53:28,617] Saving..
[2022-06-20 14:53:28,687] * Saved checkpoint to ./results/14091054/FENet_imagenet.t7
[2022-06-20 14:53:28,689] Epoch: 476
[2022-06-20 15:13:18,871] Train: Loss: 1.377 | Acc: 67.674 (867015/1281167) | Lr: 0.00011280079395769976
[2022-06-20 15:14:05,472] Test: Loss: 1.048 | Acc: 73.896 (36948/50000)
[2022-06-20 15:14:05,473] Epoch: 477
[2022-06-20 15:33:50,250] Train: Loss: 1.380 | Acc: 67.545 (865366/1281167) | Lr: 6.782689158296475e-05
[2022-06-20 15:34:38,746] Test: Loss: 1.049 | Acc: 73.896 (36948/50000)
[2022-06-20 15:34:38,746] Epoch: 478
[2022-06-20 15:54:51,374] Train: Loss: 1.376 | Acc: 67.653 (866752/1281167) | Lr: 3.5701299435769944e-05
[2022-06-20 15:55:39,452] Test: Loss: 1.049 | Acc: 73.916 (36958/50000)
[2022-06-20 15:55:39,453] Epoch: 479
[2022-06-20 16:15:50,920] Train: Loss: 1.373 | Acc: 67.738 (867831/1281167) | Lr: 1.642539366955192e-05
[2022-06-20 16:16:39,848] Test: Loss: 1.049 | Acc: 73.952 (36976/50000)