FENet_imagenet.log

[2022-06-14 13:31:47,712] Namespace(auto_augment=False, batch_size=1024, cutout=False, data_dir='/dataset/public/ImageNetOrigin/', epoch=480, lr=0.6, nesterov=True, reduction=1.375, results_dir='./results/', resume=None)
[2022-06-14 13:31:47,713] ==> Preparing data..
[2022-06-14 13:31:56,749] Training / Testing data number: 50000 / 1281167
[2022-06-14 13:31:56,750] Using path: ./results/14133147/
[2022-06-14 13:31:56,750] ==> Building model..
[2022-06-14 13:32:02,766] DataParallel(
  (module): FENet(
    (conv1): Conv2d(3, 22, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
    (bn1): BatchNorm2d(22, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (ibssl): IBSSL(
      (conv1): Conv2d(22, 88, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(88, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shift2): SSL2d()
      (conv2): Conv2d(88, 22, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(22, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (ibpool): IBPool(
      (conv1): Conv2d(22, 220, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(220, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shift2): AvgPool2d(kernel_size=2, stride=2, padding=0)
      (conv2): Conv2d(220, 44, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn2): BatchNorm2d(44, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (feblock1): FEBlock3n2s(
      (resibssl_1): ResIBSSL(
        (conv1): Conv2d(11, 66, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(66, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(66, 11, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(11, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (resibssl_2): ResIBSSL(
        (conv1): Conv2d(22, 132, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(132, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(132, 22, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(22, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (ibpool): IBPool(
        (conv1): Conv2d(44, 528, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(528, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): AvgPool2d(kernel_size=2, stride=2, padding=0)
        (conv2): Conv2d(528, 88, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(88, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (feblock2): FEBlock4n2s(
      (resibssl_1): ResIBSSL(
        (conv1): Conv2d(11, 66, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(66, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(66, 11, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(11, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (resibssl_2): ResIBSSL(
        (conv1): Conv2d(22, 132, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(132, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(132, 22, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(22, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (resibssl_3): ResIBSSL(
        (conv1): Conv2d(44, 264, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(264, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(264, 44, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(44, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (ibpool): IBPool(
        (conv1): Conv2d(88, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(1056, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): AvgPool2d(kernel_size=2, stride=2, padding=0)
        (conv2): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(176, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (feblock3): FEBlock4n1s(
      (resibssl_1): ResIBSSL(
        (conv1): Conv2d(22, 132, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(132, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(132, 22, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(22, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (resibssl_2): ResIBSSL(
        (conv1): Conv2d(44, 264, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(264, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(264, 44, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(44, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (resibssl_3): ResIBSSL(
        (conv1): Conv2d(88, 528, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(528, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(528, 88, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(88, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (ibssl): IBSSL(
        (conv1): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(1056, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(176, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (feblock4): FEBlock4n2s(
      (resibssl_1): ResIBSSL(
        (conv1): Conv2d(22, 132, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(132, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(132, 22, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(22, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (resibssl_2): ResIBSSL(
        (conv1): Conv2d(44, 264, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(264, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(264, 44, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(44, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (resibssl_3): ResIBSSL(
        (conv1): Conv2d(88, 528, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(528, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(528, 88, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(88, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (ibpool): IBPool(
        (conv1): Conv2d(176, 2112, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(2112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): AvgPool2d(kernel_size=2, stride=2, padding=0)
        (conv2): Conv2d(2112, 352, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (feblock5): FEBlock3n1s(
      (resibssl_1): ResIBSSL(
        (conv1): Conv2d(88, 528, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(528, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(528, 88, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(88, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (resibssl_2): ResIBSSL(
        (conv1): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(1056, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(176, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (ibssl): IBSSL(
        (conv1): Conv2d(352, 2112, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(2112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (shift2): SSL2d()
        (conv2): Conv2d(2112, 352, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (conv2): Conv2d(352, 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 13:32:02,775] Epoch: 0
[2022-06-14 13:43:13,366] Train: Loss: 5.624 | Acc: 5.652 (72411/1281167) | Lr: 0.6
[2022-06-14 13:43:58,082] Test: Loss: 5.094 | Acc: 9.416 (4708/50000)
[2022-06-14 13:43:58,082] Saving..
[2022-06-14 13:43:58,194] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-14 13:43:58,194] Epoch: 1
[2022-06-14 13:54:33,611] Train: Loss: 4.000 | Acc: 21.036 (269504/1281167) | Lr: 0.5999935746063304
[2022-06-14 13:55:16,124] Test: Loss: 3.953 | Acc: 21.048 (10524/50000)
[2022-06-14 13:55:16,124] Saving..
[2022-06-14 13:55:16,213] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-14 13:55:16,213] Epoch: 2
[2022-06-14 14:05:41,313] Train: Loss: 3.395 | Acc: 29.920 (383330/1281167) | Lr: 0.5999742987005642
[2022-06-14 14:06:26,271] Test: Loss: 3.882 | Acc: 23.036 (11518/50000)
[2022-06-14 14:06:26,272] Saving..
[2022-06-14 14:06:26,366] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-14 14:06:26,366] Epoch: 3
[2022-06-14 14:16:59,658] Train: Loss: 3.076 | Acc: 35.194 (450900/1281167) | Lr: 0.599942173108417
[2022-06-14 14:17:43,553] Test: Loss: 3.348 | Acc: 30.914 (15457/50000)
[2022-06-14 14:17:43,553] Saving..
[2022-06-14 14:17:43,627] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-14 14:17:43,627] Epoch: 4
[2022-06-14 14:28:19,790] Train: Loss: 2.891 | Acc: 38.242 (489938/1281167) | Lr: 0.5998971992060422
[2022-06-14 14:29:04,393] Test: Loss: 2.864 | Acc: 37.842 (18921/50000)
[2022-06-14 14:29:04,393] Saving..
[2022-06-14 14:29:04,489] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-14 14:29:04,490] Epoch: 5
[2022-06-14 14:39:52,025] Train: Loss: 2.750 | Acc: 40.683 (521223/1281167) | Lr: 0.5998393789199723
[2022-06-14 14:40:33,955] Test: Loss: 2.716 | Acc: 39.920 (19960/50000)
[2022-06-14 14:40:33,956] Saving..
[2022-06-14 14:40:34,047] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-14 14:40:34,047] Epoch: 6
[2022-06-14 14:51:31,977] Train: Loss: 2.646 | Acc: 42.441 (543737/1281167) | Lr: 0.5997687147270356
[2022-06-14 14:52:13,598] Test: Loss: 2.803 | Acc: 39.512 (19756/50000)
[2022-06-14 14:52:13,599] Epoch: 7
[2022-06-14 15:02:58,481] Train: Loss: 2.570 | Acc: 43.805 (561213/1281167) | Lr: 0.5996852096542512
[2022-06-14 15:03:44,069] Test: Loss: 3.103 | Acc: 34.052 (17026/50000)
[2022-06-14 15:03:44,070] Epoch: 8
[2022-06-14 15:14:27,628] Train: Loss: 2.510 | Acc: 44.855 (574662/1281167) | Lr: 0.5995888672786983
[2022-06-14 15:15:08,360] Test: Loss: 3.075 | Acc: 35.408 (17704/50000)
[2022-06-14 15:15:08,360] Epoch: 9
[2022-06-14 15:25:46,619] Train: Loss: 2.461 | Acc: 45.707 (585586/1281167) | Lr: 0.5994796917273638
[2022-06-14 15:26:27,740] Test: Loss: 2.554 | Acc: 43.052 (21526/50000)
[2022-06-14 15:26:27,741] Saving..
[2022-06-14 15:26:27,979] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-14 15:26:27,979] Epoch: 10
[2022-06-14 15:37:08,320] Train: Loss: 2.428 | Acc: 46.388 (594308/1281167) | Lr: 0.5993576876769647
[2022-06-14 15:37:48,632] Test: Loss: 2.423 | Acc: 44.948 (22474/50000)
[2022-06-14 15:37:48,632] Saving..
[2022-06-14 15:37:48,844] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-14 15:37:48,844] Epoch: 11
[2022-06-14 15:48:33,237] Train: Loss: 2.393 | Acc: 47.005 (602211/1281167) | Lr: 0.5992228603537487
[2022-06-14 15:49:13,749] Test: Loss: 2.526 | Acc: 43.596 (21798/50000)
[2022-06-14 15:49:13,750] Epoch: 12
[2022-06-14 16:00:00,291] Train: Loss: 2.362 | Acc: 47.520 (608813/1281167) | Lr: 0.5990752155332696
[2022-06-14 16:00:40,616] Test: Loss: 2.337 | Acc: 46.844 (23422/50000)
[2022-06-14 16:00:40,616] Saving..
[2022-06-14 16:00:40,694] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-14 16:00:40,694] Epoch: 13
[2022-06-14 16:11:18,434] Train: Loss: 2.345 | Acc: 47.883 (613460/1281167) | Lr: 0.5989147595401398
[2022-06-14 16:11:59,200] Test: Loss: 2.523 | Acc: 42.970 (21485/50000)
[2022-06-14 16:11:59,200] Epoch: 14
[2022-06-14 16:22:41,525] Train: Loss: 2.326 | Acc: 48.259 (618279/1281167) | Lr: 0.5987414992477603
[2022-06-14 16:23:27,119] Test: Loss: 2.312 | Acc: 47.038 (23519/50000)
[2022-06-14 16:23:27,120] Saving..
[2022-06-14 16:23:27,197] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-14 16:23:27,197] Epoch: 15
[2022-06-14 16:34:14,094] Train: Loss: 2.306 | Acc: 48.583 (622427/1281167) | Lr: 0.5985554420780254
[2022-06-14 16:34:55,070] Test: Loss: 2.409 | Acc: 45.420 (22710/50000)
[2022-06-14 16:34:55,070] Epoch: 16
[2022-06-14 16:45:43,335] Train: Loss: 2.291 | Acc: 48.858 (625955/1281167) | Lr: 0.5983565960010048
[2022-06-14 16:46:23,523] Test: Loss: 2.375 | Acc: 46.404 (23202/50000)
[2022-06-14 16:46:23,524] Epoch: 17
[2022-06-14 16:57:12,860] Train: Loss: 2.278 | Acc: 49.148 (629672/1281167) | Lr: 0.5981449695346027
[2022-06-14 16:57:55,657] Test: Loss: 2.660 | Acc: 41.692 (20846/50000)
[2022-06-14 16:57:55,657] Epoch: 18
[2022-06-14 17:08:51,907] Train: Loss: 2.258 | Acc: 49.456 (633611/1281167) | Lr: 0.5979205717441928
[2022-06-14 17:09:32,772] Test: Loss: 2.337 | Acc: 46.910 (23455/50000)
[2022-06-14 17:09:32,772] Epoch: 19
[2022-06-14 17:20:13,012] Train: Loss: 2.253 | Acc: 49.618 (635692/1281167) | Lr: 0.5976834122422292
[2022-06-14 17:20:53,306] Test: Loss: 2.661 | Acc: 42.094 (21047/50000)
[2022-06-14 17:20:53,306] Epoch: 20
[2022-06-14 17:31:33,757] Train: Loss: 2.236 | Acc: 49.924 (639610/1281167) | Lr: 0.5974335011878359
[2022-06-14 17:32:14,986] Test: Loss: 2.401 | Acc: 46.084 (23042/50000)
[2022-06-14 17:32:14,986] Epoch: 21
[2022-06-14 17:42:51,104] Train: Loss: 2.230 | Acc: 50.039 (641080/1281167) | Lr: 0.5971708492863705
[2022-06-14 17:43:31,331] Test: Loss: 2.297 | Acc: 47.752 (23876/50000)
[2022-06-14 17:43:31,331] Saving..
[2022-06-14 17:43:31,410] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-14 17:43:31,410] Epoch: 22
[2022-06-14 17:54:06,303] Train: Loss: 2.225 | Acc: 50.118 (642095/1281167) | Lr: 0.5968954677889666
[2022-06-14 17:54:46,616] Test: Loss: 2.394 | Acc: 46.472 (23236/50000)
[2022-06-14 17:54:46,617] Epoch: 23
[2022-06-14 18:05:24,163] Train: Loss: 2.214 | Acc: 50.337 (644899/1281167) | Lr: 0.5966073684920506
[2022-06-14 18:06:03,818] Test: Loss: 2.287 | Acc: 47.892 (23946/50000)
[2022-06-14 18:06:03,819] Saving..
[2022-06-14 18:06:03,893] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-14 18:06:03,894] Epoch: 24
[2022-06-14 18:16:32,445] Train: Loss: 2.209 | Acc: 50.383 (645487/1281167) | Lr: 0.596306563736838
[2022-06-14 18:17:12,486] Test: Loss: 2.442 | Acc: 45.324 (22662/50000)
[2022-06-14 18:17:12,487] Epoch: 25
[2022-06-14 18:27:53,546] Train: Loss: 2.198 | Acc: 50.715 (649741/1281167) | Lr: 0.5959930664088029
[2022-06-14 18:28:34,324] Test: Loss: 2.223 | Acc: 48.950 (24475/50000)
[2022-06-14 18:28:34,325] Saving..
[2022-06-14 18:28:34,398] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-14 18:28:34,399] Epoch: 26
[2022-06-14 18:39:16,584] Train: Loss: 2.192 | Acc: 50.713 (649715/1281167) | Lr: 0.5956668899371277
[2022-06-14 18:39:56,759] Test: Loss: 2.535 | Acc: 43.870 (21935/50000)
[2022-06-14 18:39:56,759] Epoch: 27
[2022-06-14 18:50:32,616] Train: Loss: 2.186 | Acc: 50.841 (651355/1281167) | Lr: 0.5953280482941267
[2022-06-14 18:51:12,807] Test: Loss: 2.360 | Acc: 46.996 (23498/50000)
[2022-06-14 18:51:12,807] Epoch: 28
[2022-06-14 19:01:58,573] Train: Loss: 2.180 | Acc: 50.907 (652209/1281167) | Lr: 0.5949765559946483
[2022-06-14 19:02:44,850] Test: Loss: 2.486 | Acc: 44.584 (22292/50000)
[2022-06-14 19:02:44,850] Epoch: 29
[2022-06-14 19:13:27,571] Train: Loss: 2.174 | Acc: 51.075 (654354/1281167) | Lr: 0.5946124280954524
[2022-06-14 19:14:05,987] Test: Loss: 2.387 | Acc: 47.232 (23616/50000)
[2022-06-14 19:14:05,988] Epoch: 30
[2022-06-14 19:24:44,550] Train: Loss: 2.166 | Acc: 51.210 (656090/1281167) | Lr: 0.5942356801945667
[2022-06-14 19:25:25,812] Test: Loss: 2.297 | Acc: 47.504 (23752/50000)
[2022-06-14 19:25:25,813] Epoch: 31
[2022-06-14 19:35:57,331] Train: Loss: 2.166 | Acc: 51.212 (656112/1281167) | Lr: 0.5938463284306172
[2022-06-14 19:36:38,698] Test: Loss: 2.429 | Acc: 45.842 (22921/50000)
[2022-06-14 19:36:38,698] Epoch: 32
[2022-06-14 19:47:14,234] Train: Loss: 2.158 | Acc: 51.391 (658401/1281167) | Lr: 0.5934443894821377
[2022-06-14 19:47:56,573] Test: Loss: 2.264 | Acc: 48.432 (24216/50000)
[2022-06-14 19:47:56,574] Epoch: 33
[2022-06-14 19:58:42,256] Train: Loss: 2.154 | Acc: 51.474 (659466/1281167) | Lr: 0.5930298805668548
[2022-06-14 19:59:22,889] Test: Loss: 2.315 | Acc: 47.422 (23711/50000)
[2022-06-14 19:59:22,890] Epoch: 34
[2022-06-14 20:10:02,784] Train: Loss: 2.153 | Acc: 51.499 (659794/1281167) | Lr: 0.592602819440951
[2022-06-14 20:10:42,358] Test: Loss: 2.860 | Acc: 39.660 (19830/50000)
[2022-06-14 20:10:42,358] Epoch: 35
[2022-06-14 20:21:21,158] Train: Loss: 2.146 | Acc: 51.637 (661556/1281167) | Lr: 0.5921632243983034
[2022-06-14 20:22:02,392] Test: Loss: 2.305 | Acc: 47.618 (23809/50000)
[2022-06-14 20:22:02,392] Epoch: 36
[2022-06-14 20:32:46,833] Train: Loss: 2.142 | Acc: 51.696 (662310/1281167) | Lr: 0.5917111142697007
[2022-06-14 20:33:28,184] Test: Loss: 2.907 | Acc: 38.938 (19469/50000)
[2022-06-14 20:33:28,184] Epoch: 37
[2022-06-14 20:44:03,198] Train: Loss: 2.140 | Acc: 51.725 (662678/1281167) | Lr: 0.591246508422036
[2022-06-14 20:44:43,101] Test: Loss: 2.433 | Acc: 46.006 (23003/50000)
[2022-06-14 20:44:43,102] Epoch: 38
[2022-06-14 20:55:21,813] Train: Loss: 2.140 | Acc: 51.702 (662388/1281167) | Lr: 0.5907694267574775
[2022-06-14 20:56:01,441] Test: Loss: 2.418 | Acc: 46.190 (23095/50000)
[2022-06-14 20:56:01,441] Epoch: 39
[2022-06-14 21:06:35,311] Train: Loss: 2.127 | Acc: 52.005 (666276/1281167) | Lr: 0.5902798897126158
[2022-06-14 21:07:16,707] Test: Loss: 2.239 | Acc: 48.852 (24426/50000)
[2022-06-14 21:07:16,707] Epoch: 40
[2022-06-14 21:17:53,365] Train: Loss: 2.130 | Acc: 51.972 (665845/1281167) | Lr: 0.5897779182575887
[2022-06-14 21:18:34,314] Test: Loss: 2.492 | Acc: 44.890 (22445/50000)
[2022-06-14 21:18:34,314] Epoch: 41
[2022-06-14 21:29:15,872] Train: Loss: 2.129 | Acc: 51.932 (665337/1281167) | Lr: 0.5892635338951826
[2022-06-14 21:29:56,470] Test: Loss: 2.681 | Acc: 41.712 (20856/50000)
[2022-06-14 21:29:56,470] Epoch: 42
[2022-06-14 21:40:33,363] Train: Loss: 2.124 | Acc: 51.977 (665913/1281167) | Lr: 0.5887367586599115
[2022-06-14 21:41:13,666] Test: Loss: 2.217 | Acc: 49.368 (24684/50000)
[2022-06-14 21:41:13,666] Saving..
[2022-06-14 21:41:13,739] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-14 21:41:13,739] Epoch: 43
[2022-06-14 21:52:03,206] Train: Loss: 2.118 | Acc: 52.148 (668102/1281167) | Lr: 0.5881976151170734
[2022-06-14 21:52:44,490] Test: Loss: 2.387 | Acc: 45.804 (22902/50000)
[2022-06-14 21:52:44,491] Epoch: 44
[2022-06-14 22:03:23,469] Train: Loss: 2.116 | Acc: 52.153 (668168/1281167) | Lr: 0.5876461263617831
[2022-06-14 22:04:03,755] Test: Loss: 2.073 | Acc: 51.616 (25808/50000)
[2022-06-14 22:04:03,755] Saving..
[2022-06-14 22:04:03,834] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-14 22:04:03,834] Epoch: 45
[2022-06-14 22:14:41,855] Train: Loss: 2.114 | Acc: 52.202 (668795/1281167) | Lr: 0.5870823160179836
[2022-06-14 22:15:22,589] Test: Loss: 2.192 | Acc: 49.550 (24775/50000)
[2022-06-14 22:15:22,590] Epoch: 46
[2022-06-14 22:26:05,911] Train: Loss: 2.112 | Acc: 52.306 (670129/1281167) | Lr: 0.5865062082374333
[2022-06-14 22:26:49,889] Test: Loss: 2.313 | Acc: 47.052 (23526/50000)
[2022-06-14 22:26:49,889] Epoch: 47
[2022-06-14 22:37:32,133] Train: Loss: 2.108 | Acc: 52.362 (670847/1281167) | Lr: 0.5859178276986722
[2022-06-14 22:38:13,156] Test: Loss: 2.213 | Acc: 49.102 (24551/50000)
[2022-06-14 22:38:13,157] Epoch: 48
[2022-06-14 22:48:57,027] Train: Loss: 2.107 | Acc: 52.360 (670815/1281167) | Lr: 0.5853171996059642
[2022-06-14 22:49:37,432] Test: Loss: 2.262 | Acc: 47.944 (23972/50000)
[2022-06-14 22:49:37,432] Epoch: 49
[2022-06-14 23:00:25,114] Train: Loss: 2.106 | Acc: 52.364 (670870/1281167) | Lr: 0.5847043496882178
[2022-06-14 23:01:06,841] Test: Loss: 2.468 | Acc: 45.482 (22741/50000)
[2022-06-14 23:01:06,842] Epoch: 50
[2022-06-14 23:12:08,716] Train: Loss: 2.102 | Acc: 52.487 (672448/1281167) | Lr: 0.5840793041978839
[2022-06-14 23:12:49,715] Test: Loss: 2.049 | Acc: 52.140 (26070/50000)
[2022-06-14 23:12:49,715] Saving..
[2022-06-14 23:12:49,804] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-14 23:12:49,804] Epoch: 51
[2022-06-14 23:23:38,169] Train: Loss: 2.101 | Acc: 52.444 (671889/1281167) | Lr: 0.5834420899098308
[2022-06-14 23:24:19,272] Test: Loss: 2.112 | Acc: 51.202 (25601/50000)
[2022-06-14 23:24:19,272] Epoch: 52
[2022-06-14 23:35:06,887] Train: Loss: 2.101 | Acc: 52.474 (672278/1281167) | Lr: 0.5827927341201978
[2022-06-14 23:35:47,784] Test: Loss: 2.245 | Acc: 48.602 (24301/50000)
[2022-06-14 23:35:47,784] Epoch: 53
[2022-06-14 23:46:32,633] Train: Loss: 2.095 | Acc: 52.630 (674278/1281167) | Lr: 0.5821312646452258
[2022-06-14 23:47:13,474] Test: Loss: 2.167 | Acc: 50.070 (25035/50000)
[2022-06-14 23:47:13,474] Epoch: 54
[2022-06-14 23:57:46,934] Train: Loss: 2.096 | Acc: 52.522 (672895/1281167) | Lr: 0.5814577098200655
[2022-06-14 23:58:26,220] Test: Loss: 2.312 | Acc: 47.138 (23569/50000)
[2022-06-14 23:58:26,221] Epoch: 55
[2022-06-15 00:09:13,917] Train: Loss: 2.093 | Acc: 52.615 (674091/1281167) | Lr: 0.5807720984975637
[2022-06-15 00:09:55,319] Test: Loss: 2.241 | Acc: 48.794 (24397/50000)
[2022-06-15 00:09:55,319] Epoch: 56
[2022-06-15 00:20:29,212] Train: Loss: 2.087 | Acc: 52.672 (674815/1281167) | Lr: 0.5800744600470279
[2022-06-15 00:21:10,466] Test: Loss: 2.312 | Acc: 47.118 (23559/50000)
[2022-06-15 00:21:10,466] Epoch: 57
[2022-06-15 00:31:45,167] Train: Loss: 2.092 | Acc: 52.728 (675535/1281167) | Lr: 0.5793648243529671
[2022-06-15 00:32:24,803] Test: Loss: 2.423 | Acc: 45.846 (22923/50000)
[2022-06-15 00:32:24,803] Epoch: 58
[2022-06-15 00:42:57,920] Train: Loss: 2.087 | Acc: 52.740 (675693/1281167) | Lr: 0.5786432218138128
[2022-06-15 00:43:38,186] Test: Loss: 2.411 | Acc: 46.062 (23031/50000)
[2022-06-15 00:43:38,186] Epoch: 59
[2022-06-15 00:54:15,610] Train: Loss: 2.083 | Acc: 52.877 (677437/1281167) | Lr: 0.5779096833406159
[2022-06-15 00:54:56,329] Test: Loss: 2.276 | Acc: 48.172 (24086/50000)
[2022-06-15 00:54:56,330] Epoch: 60
[2022-06-15 01:05:52,334] Train: Loss: 2.086 | Acc: 52.705 (675237/1281167) | Lr: 0.5771642403557232
[2022-06-15 01:06:32,587] Test: Loss: 2.210 | Acc: 49.414 (24707/50000)
[2022-06-15 01:06:32,588] Epoch: 61
[2022-06-15 01:17:11,868] Train: Loss: 2.080 | Acc: 52.930 (678118/1281167) | Lr: 0.5764069247914314
[2022-06-15 01:17:52,057] Test: Loss: 2.279 | Acc: 48.170 (24085/50000)
[2022-06-15 01:17:52,057] Epoch: 62
[2022-06-15 01:28:29,818] Train: Loss: 2.078 | Acc: 52.980 (678757/1281167) | Lr: 0.5756377690886185
[2022-06-15 01:29:09,544] Test: Loss: 2.377 | Acc: 46.064 (23032/50000)
[2022-06-15 01:29:09,558] Epoch: 63
[2022-06-15 01:39:44,155] Train: Loss: 2.076 | Acc: 52.958 (678485/1281167) | Lr: 0.574856806195355
[2022-06-15 01:40:25,624] Test: Loss: 2.297 | Acc: 48.286 (24143/50000)
[2022-06-15 01:40:25,624] Epoch: 64
[2022-06-15 01:51:13,750] Train: Loss: 2.076 | Acc: 52.979 (678751/1281167) | Lr: 0.5740640695654917
[2022-06-15 01:51:53,786] Test: Loss: 2.231 | Acc: 48.922 (24461/50000)
[2022-06-15 01:51:53,786] Epoch: 65
[2022-06-15 02:02:23,125] Train: Loss: 2.075 | Acc: 53.040 (679529/1281167) | Lr: 0.5732595931572279
[2022-06-15 02:03:03,038] Test: Loss: 3.069 | Acc: 36.336 (18168/50000)
[2022-06-15 02:03:03,039] Epoch: 66
[2022-06-15 02:13:37,237] Train: Loss: 2.076 | Acc: 52.929 (678110/1281167) | Lr: 0.572443411431655
[2022-06-15 02:14:16,772] Test: Loss: 2.312 | Acc: 48.162 (24081/50000)
[2022-06-15 02:14:16,772] Epoch: 67
[2022-06-15 02:24:48,938] Train: Loss: 2.066 | Acc: 53.176 (681268/1281167) | Lr: 0.5716155593512818
[2022-06-15 02:25:34,053] Test: Loss: 2.241 | Acc: 49.058 (24529/50000)
[2022-06-15 02:25:34,054] Epoch: 68
[2022-06-15 02:36:09,204] Train: Loss: 2.074 | Acc: 53.043 (679570/1281167) | Lr: 0.5707760723785362
[2022-06-15 02:36:48,890] Test: Loss: 2.344 | Acc: 47.094 (23547/50000)
[2022-06-15 02:36:48,891] Epoch: 69
[2022-06-15 02:47:24,622] Train: Loss: 2.065 | Acc: 53.222 (681859/1281167) | Lr: 0.5699249864742459
[2022-06-15 02:48:05,196] Test: Loss: 2.472 | Acc: 45.394 (22697/50000)
[2022-06-15 02:48:05,197] Epoch: 70
[2022-06-15 02:58:38,339] Train: Loss: 2.066 | Acc: 53.179 (681309/1281167) | Lr: 0.5690623380960986
[2022-06-15 02:59:19,561] Test: Loss: 2.359 | Acc: 47.150 (23575/50000)
[2022-06-15 02:59:19,561] Epoch: 71
[2022-06-15 03:10:08,880] Train: Loss: 2.066 | Acc: 53.164 (681116/1281167) | Lr: 0.5681881641970796
[2022-06-15 03:10:48,785] Test: Loss: 2.216 | Acc: 49.556 (24778/50000)
[2022-06-15 03:10:48,785] Epoch: 72
[2022-06-15 03:21:47,983] Train: Loss: 2.059 | Acc: 53.341 (683383/1281167) | Lr: 0.5673025022238892
[2022-06-15 03:22:28,592] Test: Loss: 2.100 | Acc: 51.326 (25663/50000)
[2022-06-15 03:22:28,592] Epoch: 73
[2022-06-15 03:33:28,849] Train: Loss: 2.061 | Acc: 53.260 (682351/1281167) | Lr: 0.5664053901153387
[2022-06-15 03:34:09,694] Test: Loss: 2.212 | Acc: 49.788 (24894/50000)
[2022-06-15 03:34:09,695] Epoch: 74
[2022-06-15 03:45:01,076] Train: Loss: 2.062 | Acc: 53.183 (681357/1281167) | Lr: 0.565496866300725
[2022-06-15 03:45:40,374] Test: Loss: 2.217 | Acc: 49.942 (24971/50000)
[2022-06-15 03:45:40,374] Epoch: 75
[2022-06-15 03:56:34,918] Train: Loss: 2.062 | Acc: 53.273 (682521/1281167) | Lr: 0.5645769696981845
[2022-06-15 03:57:14,636] Test: Loss: 2.435 | Acc: 45.828 (22914/50000)
[2022-06-15 03:57:14,636] Epoch: 76
[2022-06-15 04:08:06,768] Train: Loss: 2.061 | Acc: 53.254 (682267/1281167) | Lr: 0.563645739713026
[2022-06-15 04:08:46,981] Test: Loss: 2.180 | Acc: 50.168 (25084/50000)
[2022-06-15 04:08:46,981] Epoch: 77
[2022-06-15 04:19:39,862] Train: Loss: 2.054 | Acc: 53.332 (683273/1281167) | Lr: 0.5627032162360428
[2022-06-15 04:20:23,016] Test: Loss: 2.051 | Acc: 52.368 (26184/50000)
[2022-06-15 04:20:23,017] Saving..
[2022-06-15 04:20:23,119] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-15 04:20:23,119] Epoch: 78
[2022-06-15 04:31:00,275] Train: Loss: 2.053 | Acc: 53.418 (684374/1281167) | Lr: 0.5617494396418036
[2022-06-15 04:31:39,532] Test: Loss: 2.076 | Acc: 51.810 (25905/50000)
[2022-06-15 04:31:39,532] Epoch: 79
[2022-06-15 04:42:19,896] Train: Loss: 2.050 | Acc: 53.479 (685152/1281167) | Lr: 0.5607844507869232
[2022-06-15 04:43:00,629] Test: Loss: 2.510 | Acc: 44.480 (22240/50000)
[2022-06-15 04:43:00,629] Epoch: 80
[2022-06-15 04:53:43,462] Train: Loss: 2.053 | Acc: 53.427 (684484/1281167) | Lr: 0.5598082910083125
[2022-06-15 04:54:25,343] Test: Loss: 2.214 | Acc: 48.890 (24445/50000)
[2022-06-15 04:54:25,343] Epoch: 81
[2022-06-15 05:05:06,384] Train: Loss: 2.050 | Acc: 53.465 (684982/1281167) | Lr: 0.5588210021214074
[2022-06-15 05:05:46,609] Test: Loss: 2.675 | Acc: 41.876 (20938/50000)
[2022-06-15 05:05:46,610] Epoch: 82
[2022-06-15 05:16:27,616] Train: Loss: 2.045 | Acc: 53.597 (686664/1281167) | Lr: 0.5578226264183781
[2022-06-15 05:17:08,380] Test: Loss: 2.793 | Acc: 41.190 (20595/50000)
[2022-06-15 05:17:08,380] Epoch: 83
[2022-06-15 05:27:56,403] Train: Loss: 2.048 | Acc: 53.500 (685430/1281167) | Lr: 0.5568132066663166
[2022-06-15 05:28:36,435] Test: Loss: 1.943 | Acc: 54.436 (27218/50000)
[2022-06-15 05:28:36,436] Saving..
[2022-06-15 05:28:36,521] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-15 05:28:36,521] Epoch: 84
[2022-06-15 05:39:13,223] Train: Loss: 2.046 | Acc: 53.584 (686499/1281167) | Lr: 0.5557927861054056
[2022-06-15 05:39:54,074] Test: Loss: 2.254 | Acc: 48.316 (24158/50000)
[2022-06-15 05:39:54,075] Epoch: 85
[2022-06-15 05:50:48,002] Train: Loss: 2.047 | Acc: 53.604 (686762/1281167) | Lr: 0.5547614084470658
[2022-06-15 05:51:28,550] Test: Loss: 2.199 | Acc: 49.300 (24650/50000)
[2022-06-15 05:51:28,550] Epoch: 86
[2022-06-15 06:02:28,163] Train: Loss: 2.038 | Acc: 53.677 (687692/1281167) | Lr: 0.5537191178720833
[2022-06-15 06:03:08,843] Test: Loss: 1.991 | Acc: 53.910 (26955/50000)
[2022-06-15 06:03:08,844] Epoch: 87
[2022-06-15 06:13:56,449] Train: Loss: 2.041 | Acc: 53.679 (687714/1281167) | Lr: 0.5526659590287172
[2022-06-15 06:14:38,282] Test: Loss: 2.082 | Acc: 51.716 (25858/50000)
[2022-06-15 06:14:38,282] Epoch: 88
[2022-06-15 06:25:44,671] Train: Loss: 2.039 | Acc: 53.673 (687636/1281167) | Lr: 0.5516019770307873
[2022-06-15 06:26:24,419] Test: Loss: 2.124 | Acc: 51.216 (25608/50000)
[2022-06-15 06:26:24,419] Epoch: 89
[2022-06-15 06:37:02,289] Train: Loss: 2.039 | Acc: 53.696 (687932/1281167) | Lr: 0.5505272174557411
[2022-06-15 06:37:42,504] Test: Loss: 2.023 | Acc: 52.822 (26411/50000)
[2022-06-15 06:37:42,504] Epoch: 90
[2022-06-15 06:48:29,286] Train: Loss: 2.036 | Acc: 53.719 (688230/1281167) | Lr: 0.5494417263427018
[2022-06-15 06:49:15,293] Test: Loss: 2.425 | Acc: 46.160 (23080/50000)
[2022-06-15 06:49:15,293] Epoch: 91
[2022-06-15 06:59:48,958] Train: Loss: 2.037 | Acc: 53.702 (688010/1281167) | Lr: 0.5483455501904958
[2022-06-15 07:00:29,446] Test: Loss: 2.253 | Acc: 48.754 (24377/50000)
[2022-06-15 07:00:29,447] Epoch: 92
[2022-06-15 07:11:11,285] Train: Loss: 2.033 | Acc: 53.873 (690199/1281167) | Lr: 0.5472387359556613
[2022-06-15 07:11:51,064] Test: Loss: 2.065 | Acc: 52.526 (26263/50000)
[2022-06-15 07:11:51,065] Epoch: 93
[2022-06-15 07:22:29,680] Train: Loss: 2.032 | Acc: 53.773 (688926/1281167) | Lr: 0.5461213310504361
[2022-06-15 07:23:09,816] Test: Loss: 2.283 | Acc: 48.244 (24122/50000)
[2022-06-15 07:23:09,817] Epoch: 94
[2022-06-15 07:33:56,995] Train: Loss: 2.032 | Acc: 53.853 (689948/1281167) | Lr: 0.5449933833407276
[2022-06-15 07:34:37,449] Test: Loss: 2.217 | Acc: 49.482 (24741/50000)
[2022-06-15 07:34:37,449] Epoch: 95
[2022-06-15 07:45:23,691] Train: Loss: 2.028 | Acc: 53.931 (690944/1281167) | Lr: 0.5438549411440613
[2022-06-15 07:46:04,801] Test: Loss: 2.061 | Acc: 51.634 (25817/50000)
[2022-06-15 07:46:04,801] Epoch: 96
[2022-06-15 07:57:09,902] Train: Loss: 2.036 | Acc: 53.709 (688103/1281167) | Lr: 0.542706053227512
[2022-06-15 07:57:49,452] Test: Loss: 1.990 | Acc: 53.702 (26851/50000)
[2022-06-15 07:57:49,452] Epoch: 97
[2022-06-15 08:08:37,489] Train: Loss: 2.025 | Acc: 53.917 (690769/1281167) | Lr: 0.5415467688056143
[2022-06-15 08:09:19,398] Test: Loss: 2.125 | Acc: 51.676 (25838/50000)
[2022-06-15 08:09:19,398] Epoch: 98
[2022-06-15 08:20:03,847] Train: Loss: 2.024 | Acc: 53.968 (691419/1281167) | Lr: 0.5403771375382543
[2022-06-15 08:20:44,959] Test: Loss: 2.606 | Acc: 42.560 (21280/50000)
[2022-06-15 08:20:44,959] Epoch: 99
[2022-06-15 08:31:19,281] Train: Loss: 2.026 | Acc: 53.935 (690995/1281167) | Lr: 0.5391972095285429
[2022-06-15 08:31:59,799] Test: Loss: 2.268 | Acc: 48.702 (24351/50000)
[2022-06-15 08:31:59,800] Epoch: 100
[2022-06-15 08:42:35,830] Train: Loss: 2.019 | Acc: 54.097 (693069/1281167) | Lr: 0.5380070353206687
[2022-06-15 08:43:18,098] Test: Loss: 2.019 | Acc: 52.980 (26490/50000)
[2022-06-15 08:43:18,099] Epoch: 101
[2022-06-15 08:54:00,059] Train: Loss: 2.022 | Acc: 54.023 (692131/1281167) | Lr: 0.5368066658977336
[2022-06-15 08:54:41,851] Test: Loss: 2.119 | Acc: 50.794 (25397/50000)
[2022-06-15 08:54:41,852] Epoch: 102
[2022-06-15 09:05:20,237] Train: Loss: 2.020 | Acc: 54.045 (692401/1281167) | Lr: 0.5355961526795687
[2022-06-15 09:06:01,072] Test: Loss: 2.187 | Acc: 49.976 (24988/50000)
[2022-06-15 09:06:01,072] Epoch: 103
[2022-06-15 09:16:50,497] Train: Loss: 2.016 | Acc: 54.195 (694330/1281167) | Lr: 0.5343755475205313
[2022-06-15 09:17:29,903] Test: Loss: 1.969 | Acc: 54.008 (27004/50000)
[2022-06-15 09:17:29,903] Epoch: 104
[2022-06-15 09:28:04,975] Train: Loss: 2.018 | Acc: 54.141 (693634/1281167) | Lr: 0.5331449027072837
[2022-06-15 09:28:50,214] Test: Loss: 2.035 | Acc: 52.808 (26404/50000)
[2022-06-15 09:28:50,214] Epoch: 105
[2022-06-15 09:39:25,319] Train: Loss: 2.019 | Acc: 54.120 (693369/1281167) | Lr: 0.5319042709565539
[2022-06-15 09:40:05,842] Test: Loss: 2.387 | Acc: 46.494 (23247/50000)
[2022-06-15 09:40:05,843] Epoch: 106
[2022-06-15 09:50:48,971] Train: Loss: 2.017 | Acc: 54.130 (693491/1281167) | Lr: 0.5306537054128772
[2022-06-15 09:51:29,313] Test: Loss: 2.201 | Acc: 49.538 (24769/50000)
[2022-06-15 09:51:29,314] Epoch: 107
[2022-06-15 10:02:10,020] Train: Loss: 2.015 | Acc: 54.105 (693170/1281167) | Lr: 0.529393259646319
[2022-06-15 10:02:50,925] Test: Loss: 1.911 | Acc: 55.182 (27591/50000)
[2022-06-15 10:02:50,926] Saving..
[2022-06-15 10:02:51,037] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-15 10:02:51,037] Epoch: 108
[2022-06-15 10:13:46,806] Train: Loss: 2.012 | Acc: 54.174 (694054/1281167) | Lr: 0.528122987650181
[2022-06-15 10:14:26,176] Test: Loss: 2.098 | Acc: 51.816 (25908/50000)
[2022-06-15 10:14:26,176] Epoch: 109
[2022-06-15 10:25:25,326] Train: Loss: 2.010 | Acc: 54.277 (695384/1281167) | Lr: 0.5268429438386876
[2022-06-15 10:26:04,459] Test: Loss: 2.169 | Acc: 50.458 (25229/50000)
[2022-06-15 10:26:04,460] Epoch: 110
[2022-06-15 10:36:58,808] Train: Loss: 2.009 | Acc: 54.291 (695558/1281167) | Lr: 0.5255531830446555
[2022-06-15 10:37:39,615] Test: Loss: 1.985 | Acc: 53.538 (26769/50000)
[2022-06-15 10:37:39,616] Epoch: 111
[2022-06-15 10:48:31,737] Train: Loss: 2.002 | Acc: 54.412 (697110/1281167) | Lr: 0.5242537605171443
[2022-06-15 10:49:12,032] Test: Loss: 2.097 | Acc: 51.954 (25977/50000)
[2022-06-15 10:49:12,032] Epoch: 112
[2022-06-15 10:59:50,962] Train: Loss: 2.008 | Acc: 54.297 (695640/1281167) | Lr: 0.5229447319190905
[2022-06-15 11:00:32,119] Test: Loss: 2.153 | Acc: 50.892 (25446/50000)
[2022-06-15 11:00:32,120] Epoch: 113
[2022-06-15 11:11:08,139] Train: Loss: 2.005 | Acc: 54.299 (695657/1281167) | Lr: 0.5216261533249222
[2022-06-15 11:11:49,224] Test: Loss: 2.153 | Acc: 50.546 (25273/50000)
[2022-06-15 11:11:49,225] Epoch: 114
[2022-06-15 11:22:32,077] Train: Loss: 2.001 | Acc: 54.441 (697480/1281167) | Lr: 0.5202980812181581
[2022-06-15 11:23:16,608] Test: Loss: 2.332 | Acc: 48.596 (24298/50000)
[2022-06-15 11:23:16,614] Epoch: 115
[2022-06-15 11:34:02,602] Train: Loss: 2.002 | Acc: 54.437 (697427/1281167) | Lr: 0.5189605724889867
[2022-06-15 11:34:42,931] Test: Loss: 1.996 | Acc: 53.418 (26709/50000)
[2022-06-15 11:34:42,931] Epoch: 116
[2022-06-15 11:45:16,872] Train: Loss: 1.999 | Acc: 54.411 (697101/1281167) | Lr: 0.5176136844318308
[2022-06-15 11:45:57,562] Test: Loss: 2.114 | Acc: 51.006 (25503/50000)
[2022-06-15 11:45:57,563] Epoch: 117
[2022-06-15 11:56:27,665] Train: Loss: 2.000 | Acc: 54.487 (698071/1281167) | Lr: 0.5162574747428917
[2022-06-15 11:57:07,348] Test: Loss: 1.989 | Acc: 53.698 (26849/50000)
[2022-06-15 11:57:07,348] Epoch: 118
[2022-06-15 12:07:49,552] Train: Loss: 1.997 | Acc: 54.484 (698028/1281167) | Lr: 0.5148920015176788
[2022-06-15 12:08:30,620] Test: Loss: 2.137 | Acc: 51.340 (25670/50000)
[2022-06-15 12:08:30,620] Epoch: 119
[2022-06-15 12:19:11,181] Train: Loss: 2.001 | Acc: 54.399 (696940/1281167) | Lr: 0.5135173232485203
[2022-06-15 12:19:50,926] Test: Loss: 2.132 | Acc: 50.958 (25479/50000)
[2022-06-15 12:19:50,927] Epoch: 120
[2022-06-15 12:30:35,060] Train: Loss: 1.992 | Acc: 54.597 (699474/1281167) | Lr: 0.5121334988220579
[2022-06-15 12:31:16,014] Test: Loss: 2.019 | Acc: 53.018 (26509/50000)
[2022-06-15 12:31:16,015] Epoch: 121
[2022-06-15 12:41:57,860] Train: Loss: 1.990 | Acc: 54.667 (700378/1281167) | Lr: 0.5107405875167246
[2022-06-15 12:42:43,218] Test: Loss: 2.186 | Acc: 50.326 (25163/50000)
[2022-06-15 12:42:43,218] Epoch: 122
[2022-06-15 12:53:20,682] Train: Loss: 1.991 | Acc: 54.607 (699608/1281167) | Lr: 0.5093386490002044
[2022-06-15 12:53:59,870] Test: Loss: 2.098 | Acc: 51.678 (25839/50000)
[2022-06-15 12:53:59,870] Epoch: 123
[2022-06-15 13:04:46,489] Train: Loss: 1.986 | Acc: 54.703 (700839/1281167) | Lr: 0.5079277433268776
[2022-06-15 13:05:26,176] Test: Loss: 2.334 | Acc: 47.528 (23764/50000)
[2022-06-15 13:05:26,176] Epoch: 124
[2022-06-15 13:16:08,470] Train: Loss: 1.988 | Acc: 54.735 (701243/1281167) | Lr: 0.5065079309352473
[2022-06-15 13:16:48,191] Test: Loss: 2.037 | Acc: 52.274 (26137/50000)
[2022-06-15 13:16:48,191] Epoch: 125
[2022-06-15 13:27:41,222] Train: Loss: 1.986 | Acc: 54.682 (700562/1281167) | Lr: 0.5050792726453508
[2022-06-15 13:28:22,322] Test: Loss: 2.064 | Acc: 52.352 (26176/50000)
[2022-06-15 13:28:22,322] Epoch: 126
[2022-06-15 13:39:07,423] Train: Loss: 1.987 | Acc: 54.746 (701383/1281167) | Lr: 0.5036418296561543
[2022-06-15 13:39:47,455] Test: Loss: 1.997 | Acc: 53.636 (26818/50000)
[2022-06-15 13:39:47,456] Epoch: 127
[2022-06-15 13:50:28,404] Train: Loss: 1.983 | Acc: 54.734 (701234/1281167) | Lr: 0.5021956635429314
[2022-06-15 13:51:10,625] Test: Loss: 2.735 | Acc: 42.718 (21359/50000)
[2022-06-15 13:51:10,625] Epoch: 128
[2022-06-15 14:01:50,347] Train: Loss: 1.981 | Acc: 54.760 (701565/1281167) | Lr: 0.5007408362546251
[2022-06-15 14:02:38,627] Test: Loss: 2.044 | Acc: 52.804 (26402/50000)
[2022-06-15 14:02:38,628] Epoch: 129
[2022-06-15 14:13:15,322] Train: Loss: 1.979 | Acc: 54.868 (702947/1281167) | Lr: 0.4992774101111944
[2022-06-15 14:13:54,246] Test: Loss: 2.015 | Acc: 52.998 (26499/50000)
[2022-06-15 14:13:54,247] Epoch: 130
[2022-06-15 14:24:41,431] Train: Loss: 1.978 | Acc: 54.887 (703192/1281167) | Lr: 0.4978054478009446
[2022-06-15 14:25:21,311] Test: Loss: 2.268 | Acc: 48.768 (24384/50000)
[2022-06-15 14:25:21,311] Epoch: 131
[2022-06-15 14:36:02,951] Train: Loss: 1.979 | Acc: 54.854 (702774/1281167) | Lr: 0.49632501237784193
[2022-06-15 14:36:42,116] Test: Loss: 1.965 | Acc: 54.312 (27156/50000)
[2022-06-15 14:36:42,116] Epoch: 132
[2022-06-15 14:47:17,666] Train: Loss: 1.975 | Acc: 54.907 (703455/1281167) | Lr: 0.49483616725881285
[2022-06-15 14:48:03,544] Test: Loss: 2.063 | Acc: 52.312 (26156/50000)
[2022-06-15 14:48:03,545] Epoch: 133
[2022-06-15 14:58:41,363] Train: Loss: 1.975 | Acc: 54.973 (704298/1281167) | Lr: 0.49333897622102685
[2022-06-15 14:59:21,461] Test: Loss: 1.868 | Acc: 56.280 (28140/50000)
[2022-06-15 14:59:21,462] Saving..
[2022-06-15 14:59:21,554] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-15 14:59:21,554] Epoch: 134
[2022-06-15 15:10:00,455] Train: Loss: 1.975 | Acc: 54.946 (703949/1281167) | Lr: 0.49183350339916493
[2022-06-15 15:10:41,848] Test: Loss: 2.273 | Acc: 48.774 (24387/50000)
[2022-06-15 15:10:41,848] Epoch: 135
[2022-06-15 15:21:53,142] Train: Loss: 1.974 | Acc: 54.984 (704438/1281167) | Lr: 0.4903198132826722
[2022-06-15 15:22:34,689] Test: Loss: 2.108 | Acc: 51.906 (25953/50000)
[2022-06-15 15:22:34,689] Epoch: 136
[2022-06-15 15:33:27,695] Train: Loss: 1.971 | Acc: 55.036 (705109/1281167) | Lr: 0.4887979707129954
[2022-06-15 15:34:08,210] Test: Loss: 2.120 | Acc: 51.368 (25684/50000)
[2022-06-15 15:34:08,211] Epoch: 137
[2022-06-15 15:44:54,783] Train: Loss: 1.968 | Acc: 55.092 (705817/1281167) | Lr: 0.487268040880805
[2022-06-15 15:45:36,112] Test: Loss: 1.930 | Acc: 54.338 (27169/50000)
[2022-06-15 15:45:36,113] Epoch: 138
[2022-06-15 15:56:32,898] Train: Loss: 1.968 | Acc: 55.066 (705488/1281167) | Lr: 0.485730089323203
[2022-06-15 15:57:14,591] Test: Loss: 1.867 | Acc: 56.072 (28036/50000)
[2022-06-15 15:57:14,592] Epoch: 139
[2022-06-15 16:08:11,028] Train: Loss: 1.964 | Acc: 55.119 (706169/1281167) | Lr: 0.48418418192091556
[2022-06-15 16:08:52,066] Test: Loss: 2.147 | Acc: 51.042 (25521/50000)
[2022-06-15 16:08:52,067] Epoch: 140
[2022-06-15 16:19:34,464] Train: Loss: 1.967 | Acc: 55.087 (705757/1281167) | Lr: 0.48263038489547055
[2022-06-15 16:20:14,446] Test: Loss: 2.350 | Acc: 47.324 (23662/50000)
[2022-06-15 16:20:14,447] Epoch: 141
[2022-06-15 16:30:50,426] Train: Loss: 1.963 | Acc: 55.238 (707690/1281167) | Lr: 0.48106876480636107
[2022-06-15 16:31:30,437] Test: Loss: 2.030 | Acc: 52.966 (26483/50000)
[2022-06-15 16:31:30,437] Epoch: 142
[2022-06-15 16:42:02,802] Train: Loss: 1.966 | Acc: 55.166 (706773/1281167) | Lr: 0.47949938854819424
[2022-06-15 16:42:42,935] Test: Loss: 2.414 | Acc: 46.548 (23274/50000)
[2022-06-15 16:42:42,935] Epoch: 143
[2022-06-15 16:53:15,678] Train: Loss: 1.963 | Acc: 55.168 (706797/1281167) | Lr: 0.47792232334782575
[2022-06-15 16:53:56,206] Test: Loss: 1.957 | Acc: 54.090 (27045/50000)
[2022-06-15 16:53:56,206] Epoch: 144
[2022-06-15 17:04:32,152] Train: Loss: 1.960 | Acc: 55.245 (707784/1281167) | Lr: 0.47633763676147983
[2022-06-15 17:05:12,813] Test: Loss: 2.658 | Acc: 42.330 (21165/50000)
[2022-06-15 17:05:12,813] Epoch: 145
[2022-06-15 17:15:49,434] Train: Loss: 1.961 | Acc: 55.168 (706792/1281167) | Lr: 0.47474539667185567
[2022-06-15 17:16:30,429] Test: Loss: 2.034 | Acc: 53.006 (26503/50000)
[2022-06-15 17:16:30,430] Epoch: 146
[2022-06-15 17:26:59,767] Train: Loss: 1.954 | Acc: 55.340 (709000/1281167) | Lr: 0.4731456712852192
[2022-06-15 17:27:40,579] Test: Loss: 2.143 | Acc: 51.282 (25641/50000)
[2022-06-15 17:27:40,580] Epoch: 147
[2022-06-15 17:38:12,284] Train: Loss: 1.952 | Acc: 55.384 (709558/1281167) | Lr: 0.47153852912848176
[2022-06-15 17:38:55,373] Test: Loss: 1.935 | Acc: 54.794 (27397/50000)
[2022-06-15 17:38:55,373] Epoch: 148
[2022-06-15 17:49:21,461] Train: Loss: 1.953 | Acc: 55.398 (709744/1281167) | Lr: 0.4699240390462645
[2022-06-15 17:50:01,761] Test: Loss: 1.978 | Acc: 54.066 (27033/50000)
[2022-06-15 17:50:01,761] Epoch: 149
[2022-06-15 18:00:30,772] Train: Loss: 1.952 | Acc: 55.417 (709981/1281167) | Lr: 0.4683022701979489
[2022-06-15 18:01:11,648] Test: Loss: 1.901 | Acc: 55.504 (27752/50000)
[2022-06-15 18:01:11,648] Epoch: 150
[2022-06-15 18:11:38,421] Train: Loss: 1.951 | Acc: 55.382 (709540/1281167) | Lr: 0.4666732920547148
[2022-06-15 18:12:19,533] Test: Loss: 1.992 | Acc: 53.928 (26964/50000)
[2022-06-15 18:12:19,533] Epoch: 151
[2022-06-15 18:22:49,842] Train: Loss: 1.948 | Acc: 55.499 (711030/1281167) | Lr: 0.46503717439656433
[2022-06-15 18:23:30,473] Test: Loss: 1.887 | Acc: 55.666 (27833/50000)
[2022-06-15 18:23:30,473] Epoch: 152
[2022-06-15 18:34:05,620] Train: Loss: 1.944 | Acc: 55.524 (711359/1281167) | Lr: 0.46339398730933234
[2022-06-15 18:34:46,154] Test: Loss: 1.946 | Acc: 54.240 (27120/50000)
[2022-06-15 18:34:46,154] Epoch: 153
[2022-06-15 18:45:25,295] Train: Loss: 1.943 | Acc: 55.586 (712148/1281167) | Lr: 0.46174380118168473
[2022-06-15 18:46:06,710] Test: Loss: 1.974 | Acc: 53.842 (26921/50000)
[2022-06-15 18:46:06,710] Epoch: 154
[2022-06-15 18:56:47,718] Train: Loss: 1.940 | Acc: 55.606 (712402/1281167) | Lr: 0.4600866867021032
[2022-06-15 18:57:26,911] Test: Loss: 1.972 | Acc: 53.590 (26795/50000)
[2022-06-15 18:57:26,911] Epoch: 155
[2022-06-15 19:08:03,419] Train: Loss: 1.943 | Acc: 55.635 (712775/1281167) | Lr: 0.45842271485585645
[2022-06-15 19:08:45,768] Test: Loss: 1.860 | Acc: 56.322 (28161/50000)
[2022-06-15 19:08:45,768] Saving..
[2022-06-15 19:08:45,880] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-15 19:08:45,880] Epoch: 156
[2022-06-15 19:19:39,461] Train: Loss: 1.937 | Acc: 55.702 (713640/1281167) | Lr: 0.45675195692196036
[2022-06-15 19:20:20,851] Test: Loss: 2.056 | Acc: 52.540 (26270/50000)
[2022-06-15 19:20:20,851] Epoch: 157
[2022-06-15 19:31:06,651] Train: Loss: 1.940 | Acc: 55.635 (712778/1281167) | Lr: 0.4550744844701241
[2022-06-15 19:31:47,779] Test: Loss: 1.857 | Acc: 56.542 (28271/50000)
[2022-06-15 19:31:47,780] Saving..
[2022-06-15 19:31:47,850] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-15 19:31:47,851] Epoch: 158
[2022-06-15 19:42:40,560] Train: Loss: 1.931 | Acc: 55.834 (715332/1281167) | Lr: 0.4533903693576845
[2022-06-15 19:43:20,980] Test: Loss: 2.066 | Acc: 51.940 (25970/50000)
[2022-06-15 19:43:20,981] Epoch: 159
[2022-06-15 19:53:47,585] Train: Loss: 1.930 | Acc: 55.793 (714803/1281167) | Lr: 0.4516996837265278
[2022-06-15 19:54:28,295] Test: Loss: 1.836 | Acc: 56.300 (28150/50000)
[2022-06-15 19:54:28,296] Epoch: 160
[2022-06-15 20:05:03,199] Train: Loss: 1.931 | Acc: 55.845 (715468/1281167) | Lr: 0.4500024999999993
[2022-06-15 20:05:43,110] Test: Loss: 1.858 | Acc: 56.178 (28089/50000)
[2022-06-15 20:05:43,110] Epoch: 161
[2022-06-15 20:16:17,665] Train: Loss: 1.928 | Acc: 55.887 (716011/1281167) | Lr: 0.44829889087980124
[2022-06-15 20:16:58,390] Test: Loss: 2.097 | Acc: 52.234 (26117/50000)
[2022-06-15 20:16:58,391] Epoch: 162
[2022-06-15 20:27:37,526] Train: Loss: 1.928 | Acc: 55.877 (715873/1281167) | Lr: 0.4465889293428783
[2022-06-15 20:28:17,202] Test: Loss: 2.203 | Acc: 49.804 (24902/50000)
[2022-06-15 20:28:17,203] Epoch: 163
[2022-06-15 20:38:57,497] Train: Loss: 1.928 | Acc: 55.830 (715277/1281167) | Lr: 0.44487268863829144
[2022-06-15 20:39:37,157] Test: Loss: 1.948 | Acc: 54.560 (27280/50000)
[2022-06-15 20:39:37,158] Epoch: 164
[2022-06-15 20:50:08,780] Train: Loss: 1.927 | Acc: 55.880 (715914/1281167) | Lr: 0.44315024228408056
[2022-06-15 20:50:49,395] Test: Loss: 2.073 | Acc: 52.376 (26188/50000)
[2022-06-15 20:50:49,396] Epoch: 165
[2022-06-15 21:01:25,722] Train: Loss: 1.926 | Acc: 55.934 (716611/1281167) | Lr: 0.44142166406411454
[2022-06-15 21:02:05,787] Test: Loss: 1.859 | Acc: 55.972 (27986/50000)
[2022-06-15 21:02:05,788] Epoch: 166
[2022-06-15 21:12:53,116] Train: Loss: 1.921 | Acc: 55.985 (717255/1281167) | Lr: 0.4396870280249311
[2022-06-15 21:13:33,847] Test: Loss: 1.966 | Acc: 54.354 (27177/50000)
[2022-06-15 21:13:33,847] Epoch: 167
[2022-06-15 21:24:09,964] Train: Loss: 1.921 | Acc: 56.059 (718209/1281167) | Lr: 0.437946408472565
[2022-06-15 21:24:49,659] Test: Loss: 1.984 | Acc: 54.372 (27186/50000)
[2022-06-15 21:24:49,660] Epoch: 168
[2022-06-15 21:35:25,418] Train: Loss: 1.923 | Acc: 56.003 (717498/1281167) | Lr: 0.43619987996936466
[2022-06-15 21:36:06,021] Test: Loss: 1.955 | Acc: 54.610 (27305/50000)
[2022-06-15 21:36:06,021] Epoch: 169
[2022-06-15 21:46:41,277] Train: Loss: 1.916 | Acc: 56.104 (718788/1281167) | Lr: 0.4344475173307981
[2022-06-15 21:47:22,080] Test: Loss: 1.981 | Acc: 54.110 (27055/50000)
[2022-06-15 21:47:22,080] Epoch: 170
[2022-06-15 21:58:03,785] Train: Loss: 1.915 | Acc: 56.074 (718405/1281167) | Lr: 0.4326893956222486
[2022-06-15 21:58:44,642] Test: Loss: 1.821 | Acc: 57.090 (28545/50000)
[2022-06-15 21:58:44,642] Saving..
[2022-06-15 21:58:44,729] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-15 21:58:44,729] Epoch: 171
[2022-06-15 22:09:21,436] Train: Loss: 1.915 | Acc: 56.120 (718991/1281167) | Lr: 0.4309255901557986
[2022-06-15 22:10:02,029] Test: Loss: 2.068 | Acc: 52.446 (26223/50000)
[2022-06-15 22:10:02,030] Epoch: 172
[2022-06-15 22:20:41,284] Train: Loss: 1.915 | Acc: 56.141 (719257/1281167) | Lr: 0.4291561764870039
[2022-06-15 22:21:21,731] Test: Loss: 2.069 | Acc: 53.004 (26502/50000)
[2022-06-15 22:21:21,733] Epoch: 173
[2022-06-15 22:31:55,051] Train: Loss: 1.913 | Acc: 56.109 (718848/1281167) | Lr: 0.42738123041165693
[2022-06-15 22:32:35,916] Test: Loss: 1.857 | Acc: 56.184 (28092/50000)
[2022-06-15 22:32:35,917] Epoch: 174
[2022-06-15 22:43:05,387] Train: Loss: 1.907 | Acc: 56.326 (721635/1281167) | Lr: 0.4256008279625401
[2022-06-15 22:43:46,276] Test: Loss: 1.755 | Acc: 57.972 (28986/50000)
[2022-06-15 22:43:46,277] Saving..
[2022-06-15 22:43:46,348] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-15 22:43:46,348] Epoch: 175
[2022-06-15 22:54:17,714] Train: Loss: 1.905 | Acc: 56.316 (721497/1281167) | Lr: 0.4238150454061688
[2022-06-15 22:54:57,548] Test: Loss: 1.931 | Acc: 54.862 (27431/50000)
[2022-06-15 22:54:57,549] Epoch: 176
[2022-06-15 23:05:29,128] Train: Loss: 1.903 | Acc: 56.367 (722152/1281167) | Lr: 0.4220239592395241
[2022-06-15 23:06:08,418] Test: Loss: 1.963 | Acc: 54.216 (27108/50000)
[2022-06-15 23:06:08,418] Epoch: 177
[2022-06-15 23:16:47,067] Train: Loss: 1.903 | Acc: 56.394 (722505/1281167) | Lr: 0.4202276461867761
[2022-06-15 23:17:28,177] Test: Loss: 1.943 | Acc: 54.842 (27421/50000)
[2022-06-15 23:17:28,178] Epoch: 178
[2022-06-15 23:28:06,668] Train: Loss: 1.897 | Acc: 56.474 (723531/1281167) | Lr: 0.4184261831959976
[2022-06-15 23:28:46,705] Test: Loss: 1.984 | Acc: 54.118 (27059/50000)
[2022-06-15 23:28:46,705] Epoch: 179
[2022-06-15 23:39:19,487] Train: Loss: 1.899 | Acc: 56.415 (722771/1281167) | Lr: 0.4166196474358673
[2022-06-15 23:39:58,782] Test: Loss: 2.018 | Acc: 53.068 (26534/50000)
[2022-06-15 23:39:58,782] Epoch: 180
[2022-06-15 23:50:45,777] Train: Loss: 1.898 | Acc: 56.483 (723639/1281167) | Lr: 0.4148081162923645
[2022-06-15 23:51:26,425] Test: Loss: 1.858 | Acc: 56.138 (28069/50000)
[2022-06-15 23:51:26,425] Epoch: 181
[2022-06-16 00:01:58,153] Train: Loss: 1.897 | Acc: 56.499 (723849/1281167) | Lr: 0.4129916673654542
[2022-06-16 00:02:39,327] Test: Loss: 2.004 | Acc: 53.380 (26690/50000)
[2022-06-16 00:02:39,327] Epoch: 182
[2022-06-16 00:13:17,442] Train: Loss: 1.890 | Acc: 56.651 (725789/1281167) | Lr: 0.4111703784657627
[2022-06-16 00:13:59,573] Test: Loss: 1.878 | Acc: 55.672 (27836/50000)
[2022-06-16 00:13:59,573] Epoch: 183
[2022-06-16 00:24:32,421] Train: Loss: 1.891 | Acc: 56.578 (724857/1281167) | Lr: 0.409344327611245
[2022-06-16 00:25:12,695] Test: Loss: 1.812 | Acc: 57.424 (28712/50000)
[2022-06-16 00:25:12,696] Epoch: 184
[2022-06-16 00:35:43,799] Train: Loss: 1.888 | Acc: 56.661 (725926/1281167) | Lr: 0.4075135930238419
[2022-06-16 00:36:24,500] Test: Loss: 1.910 | Acc: 55.370 (27685/50000)
[2022-06-16 00:36:24,501] Epoch: 185
[2022-06-16 00:46:56,089] Train: Loss: 1.885 | Acc: 56.778 (727427/1281167) | Lr: 0.40567825312612993
[2022-06-16 00:47:36,511] Test: Loss: 1.883 | Acc: 56.086 (28043/50000)
[2022-06-16 00:47:36,511] Epoch: 186
[2022-06-16 00:58:15,441] Train: Loss: 1.887 | Acc: 56.681 (726176/1281167) | Lr: 0.403838386537962
[2022-06-16 00:58:56,110] Test: Loss: 2.004 | Acc: 53.802 (26901/50000)
[2022-06-16 00:58:56,111] Epoch: 187
[2022-06-16 01:09:27,870] Train: Loss: 1.888 | Acc: 56.673 (726072/1281167) | Lr: 0.4019940720730991
[2022-06-16 01:10:08,037] Test: Loss: 1.918 | Acc: 55.336 (27668/50000)
[2022-06-16 01:10:08,038] Epoch: 188
[2022-06-16 01:20:45,360] Train: Loss: 1.882 | Acc: 56.780 (727443/1281167) | Lr: 0.4001453887358346
[2022-06-16 01:21:25,971] Test: Loss: 2.362 | Acc: 47.726 (23863/50000)
[2022-06-16 01:21:25,972] Epoch: 189
[2022-06-16 01:32:03,311] Train: Loss: 1.878 | Acc: 56.854 (728389/1281167) | Lr: 0.39829241571760976
[2022-06-16 01:32:44,065] Test: Loss: 2.024 | Acc: 52.986 (26493/50000)
[2022-06-16 01:32:44,066] Epoch: 190
[2022-06-16 01:43:25,104] Train: Loss: 1.877 | Acc: 56.905 (729053/1281167) | Lr: 0.3964352323936215
[2022-06-16 01:44:05,375] Test: Loss: 1.821 | Acc: 57.012 (28506/50000)
[2022-06-16 01:44:05,375] Epoch: 191
[2022-06-16 01:54:39,114] Train: Loss: 1.873 | Acc: 56.950 (729619/1281167) | Lr: 0.39457391831942223
[2022-06-16 01:55:19,005] Test: Loss: 1.822 | Acc: 57.050 (28525/50000)
[2022-06-16 01:55:19,005] Epoch: 192
[2022-06-16 02:05:49,440] Train: Loss: 1.877 | Acc: 56.874 (728647/1281167) | Lr: 0.3927085532275119
[2022-06-16 02:06:30,043] Test: Loss: 2.045 | Acc: 52.114 (26057/50000)
[2022-06-16 02:06:30,043] Epoch: 193
[2022-06-16 02:17:07,666] Train: Loss: 1.874 | Acc: 56.981 (730026/1281167) | Lr: 0.39083921702392277
[2022-06-16 02:17:47,176] Test: Loss: 2.084 | Acc: 52.928 (26464/50000)
[2022-06-16 02:17:47,177] Epoch: 194
[2022-06-16 02:28:34,844] Train: Loss: 1.869 | Acc: 57.078 (731270/1281167) | Lr: 0.388965989784796
[2022-06-16 02:29:15,883] Test: Loss: 1.881 | Acc: 55.748 (27874/50000)
[2022-06-16 02:29:15,883] Epoch: 195
[2022-06-16 02:39:50,346] Train: Loss: 1.868 | Acc: 57.122 (731828/1281167) | Lr: 0.38708895175295205
[2022-06-16 02:40:30,669] Test: Loss: 1.714 | Acc: 59.296 (29648/50000)
[2022-06-16 02:40:30,669] Saving..
[2022-06-16 02:40:30,767] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-16 02:40:30,767] Epoch: 196
[2022-06-16 02:51:13,193] Train: Loss: 1.867 | Acc: 57.058 (731013/1281167) | Lr: 0.3852081833344529
[2022-06-16 02:51:53,444] Test: Loss: 1.929 | Acc: 54.966 (27483/50000)
[2022-06-16 02:51:53,445] Epoch: 197
[2022-06-16 03:02:24,825] Train: Loss: 1.863 | Acc: 57.219 (733070/1281167) | Lr: 0.38332376509515786
[2022-06-16 03:03:05,674] Test: Loss: 1.929 | Acc: 55.018 (27509/50000)
[2022-06-16 03:03:05,674] Epoch: 198
[2022-06-16 03:13:37,649] Train: Loss: 1.864 | Acc: 57.157 (732280/1281167) | Lr: 0.3814357777572725
[2022-06-16 03:14:17,358] Test: Loss: 2.170 | Acc: 50.918 (25459/50000)
[2022-06-16 03:14:17,359] Epoch: 199
[2022-06-16 03:24:54,271] Train: Loss: 1.861 | Acc: 57.224 (733134/1281167) | Lr: 0.37954430219589075
[2022-06-16 03:25:34,360] Test: Loss: 2.191 | Acc: 50.440 (25220/50000)
[2022-06-16 03:25:34,360] Epoch: 200
[2022-06-16 03:36:07,309] Train: Loss: 1.855 | Acc: 57.294 (734035/1281167) | Lr: 0.37764941943553026
[2022-06-16 03:36:47,570] Test: Loss: 1.805 | Acc: 57.260 (28630/50000)
[2022-06-16 03:36:47,571] Epoch: 201
[2022-06-16 03:47:35,523] Train: Loss: 1.857 | Acc: 57.299 (734097/1281167) | Lr: 0.37575121064666184
[2022-06-16 03:48:30,606] Test: Loss: 1.916 | Acc: 55.298 (27649/50000)
[2022-06-16 03:48:30,606] Epoch: 202
[2022-06-16 03:59:24,148] Train: Loss: 1.852 | Acc: 57.352 (734779/1281167) | Lr: 0.37384975714223234
[2022-06-16 04:00:04,383] Test: Loss: 1.764 | Acc: 58.042 (29021/50000)
[2022-06-16 04:00:04,384] Epoch: 203
[2022-06-16 04:11:14,447] Train: Loss: 1.849 | Acc: 57.385 (735195/1281167) | Lr: 0.37194514037418125
[2022-06-16 04:12:04,941] Test: Loss: 1.714 | Acc: 59.278 (29639/50000)
[2022-06-16 04:12:04,941] Epoch: 204
[2022-06-16 04:23:12,278] Train: Loss: 1.849 | Acc: 57.480 (736410/1281167) | Lr: 0.3700374419299519
[2022-06-16 04:23:51,642] Test: Loss: 1.744 | Acc: 58.530 (29265/50000)
[2022-06-16 04:23:51,643] Epoch: 205
[2022-06-16 04:34:59,438] Train: Loss: 1.845 | Acc: 57.545 (737249/1281167) | Lr: 0.3681267435289963
[2022-06-16 04:35:44,273] Test: Loss: 2.122 | Acc: 51.804 (25902/50000)
[2022-06-16 04:35:44,273] Epoch: 206
[2022-06-16 04:46:34,928] Train: Loss: 1.847 | Acc: 57.465 (736217/1281167) | Lr: 0.3662131270192749
[2022-06-16 04:47:18,926] Test: Loss: 1.937 | Acc: 55.056 (27528/50000)
[2022-06-16 04:47:18,927] Epoch: 207
[2022-06-16 04:58:22,348] Train: Loss: 1.843 | Acc: 57.534 (737102/1281167) | Lr: 0.3642966743737495
[2022-06-16 04:59:02,061] Test: Loss: 1.748 | Acc: 58.528 (29264/50000)
[2022-06-16 04:59:02,062] Epoch: 208
[2022-06-16 05:09:59,754] Train: Loss: 1.841 | Acc: 57.599 (737937/1281167) | Lr: 0.36237746768687323
[2022-06-16 05:10:44,826] Test: Loss: 1.807 | Acc: 57.252 (28626/50000)
[2022-06-16 05:10:44,826] Epoch: 209
[2022-06-16 05:21:50,389] Train: Loss: 1.840 | Acc: 57.609 (738064/1281167) | Lr: 0.360455589171073
[2022-06-16 05:22:29,776] Test: Loss: 1.811 | Acc: 57.074 (28537/50000)
[2022-06-16 05:22:29,776] Epoch: 210
[2022-06-16 05:33:42,948] Train: Loss: 1.838 | Acc: 57.688 (739079/1281167) | Lr: 0.358531121153228
[2022-06-16 05:34:26,397] Test: Loss: 1.713 | Acc: 59.074 (29537/50000)
[2022-06-16 05:34:26,397] Epoch: 211
[2022-06-16 05:45:26,399] Train: Loss: 1.835 | Acc: 57.735 (739679/1281167) | Lr: 0.3566041460711427
[2022-06-16 05:46:07,380] Test: Loss: 1.865 | Acc: 56.180 (28090/50000)
[2022-06-16 05:46:07,381] Epoch: 212
[2022-06-16 05:57:09,915] Train: Loss: 1.832 | Acc: 57.803 (740553/1281167) | Lr: 0.35467474647001634
[2022-06-16 05:57:57,847] Test: Loss: 1.762 | Acc: 58.184 (29092/50000)
[2022-06-16 05:57:57,848] Epoch: 213
[2022-06-16 06:09:03,817] Train: Loss: 1.829 | Acc: 57.848 (741133/1281167) | Lr: 0.3527430049989062
[2022-06-16 06:09:43,439] Test: Loss: 1.721 | Acc: 58.948 (29474/50000)
[2022-06-16 06:09:43,439] Epoch: 214
[2022-06-16 06:20:43,592] Train: Loss: 1.825 | Acc: 57.905 (741857/1281167) | Lr: 0.3508090044071877
[2022-06-16 06:21:23,951] Test: Loss: 1.966 | Acc: 54.380 (27190/50000)
[2022-06-16 06:21:23,951] Epoch: 215
[2022-06-16 06:32:22,028] Train: Loss: 1.824 | Acc: 57.880 (741543/1281167) | Lr: 0.34887282754100923
[2022-06-16 06:33:08,880] Test: Loss: 1.832 | Acc: 56.916 (28458/50000)
[2022-06-16 06:33:08,880] Epoch: 216
[2022-06-16 06:44:09,973] Train: Loss: 1.828 | Acc: 57.876 (741491/1281167) | Lr: 0.3469345573397436
[2022-06-16 06:44:51,450] Test: Loss: 2.056 | Acc: 53.250 (26625/50000)
[2022-06-16 06:44:51,451] Epoch: 217
[2022-06-16 06:55:49,527] Train: Loss: 1.823 | Acc: 57.933 (742217/1281167) | Lr: 0.3449942768324353
[2022-06-16 06:56:43,927] Test: Loss: 1.764 | Acc: 58.282 (29141/50000)
[2022-06-16 06:56:43,927] Epoch: 218
[2022-06-16 07:07:40,726] Train: Loss: 1.821 | Acc: 57.971 (742709/1281167) | Lr: 0.34305206913424346
[2022-06-16 07:08:21,215] Test: Loss: 1.660 | Acc: 60.080 (30040/50000)
[2022-06-16 07:08:21,215] Saving..
[2022-06-16 07:08:21,291] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-16 07:08:21,291] Epoch: 219
[2022-06-16 07:19:06,675] Train: Loss: 1.815 | Acc: 58.098 (744332/1281167) | Lr: 0.3411080174428815
[2022-06-16 07:19:48,259] Test: Loss: 1.729 | Acc: 58.830 (29415/50000)
[2022-06-16 07:19:48,259] Epoch: 220
[2022-06-16 07:30:50,903] Train: Loss: 1.814 | Acc: 58.105 (744426/1281167) | Lr: 0.3391622050350539
[2022-06-16 07:31:32,928] Test: Loss: 2.004 | Acc: 53.610 (26805/50000)
[2022-06-16 07:31:32,928] Epoch: 221
[2022-06-16 07:42:22,038] Train: Loss: 1.815 | Acc: 58.139 (744854/1281167) | Lr: 0.3372147152628879
[2022-06-16 07:43:02,669] Test: Loss: 1.812 | Acc: 57.594 (28797/50000)
[2022-06-16 07:43:02,669] Epoch: 222
[2022-06-16 07:54:05,852] Train: Loss: 1.810 | Acc: 58.237 (746113/1281167) | Lr: 0.33526563155036354
[2022-06-16 07:54:52,818] Test: Loss: 1.793 | Acc: 57.690 (28845/50000)
[2022-06-16 07:54:52,818] Epoch: 223
[2022-06-16 08:06:10,769] Train: Loss: 1.810 | Acc: 58.212 (745797/1281167) | Lr: 0.33331503738974005
[2022-06-16 08:06:50,887] Test: Loss: 1.761 | Acc: 57.958 (28979/50000)
[2022-06-16 08:06:50,887] Epoch: 224
[2022-06-16 08:17:43,213] Train: Loss: 1.806 | Acc: 58.254 (746326/1281167) | Lr: 0.33136301633797927
[2022-06-16 08:18:39,991] Test: Loss: 1.865 | Acc: 56.156 (28078/50000)
[2022-06-16 08:18:39,991] Epoch: 225
[2022-06-16 08:29:36,519] Train: Loss: 1.805 | Acc: 58.325 (747243/1281167) | Lr: 0.3294096520131662
[2022-06-16 08:30:16,221] Test: Loss: 1.766 | Acc: 57.986 (28993/50000)
[2022-06-16 08:30:16,221] Epoch: 226
[2022-06-16 08:41:07,833] Train: Loss: 1.800 | Acc: 58.369 (747801/1281167) | Lr: 0.327455028090927
[2022-06-16 08:41:48,741] Test: Loss: 1.787 | Acc: 57.482 (28741/50000)
[2022-06-16 08:41:48,742] Epoch: 227
[2022-06-16 08:52:43,027] Train: Loss: 1.797 | Acc: 58.441 (748728/1281167) | Lr: 0.32549922830084527
[2022-06-16 08:53:31,702] Test: Loss: 1.806 | Acc: 57.240 (28620/50000)
[2022-06-16 08:53:31,703] Epoch: 228
[2022-06-16 09:04:32,629] Train: Loss: 1.801 | Acc: 58.374 (747872/1281167) | Lr: 0.3235423364228745
[2022-06-16 09:05:12,435] Test: Loss: 1.744 | Acc: 58.540 (29270/50000)
[2022-06-16 09:05:12,435] Epoch: 229
[2022-06-16 09:16:00,923] Train: Loss: 1.795 | Acc: 58.516 (749691/1281167) | Lr: 0.3215844362837498
[2022-06-16 09:16:41,308] Test: Loss: 1.744 | Acc: 58.624 (29312/50000)
[2022-06-16 09:16:41,309] Epoch: 230
[2022-06-16 09:27:45,734] Train: Loss: 1.788 | Acc: 58.624 (751072/1281167) | Lr: 0.31962561175339643
[2022-06-16 09:28:25,172] Test: Loss: 1.779 | Acc: 57.718 (28859/50000)
[2022-06-16 09:28:25,172] Epoch: 231
[2022-06-16 09:39:17,699] Train: Loss: 1.792 | Acc: 58.593 (750680/1281167) | Lr: 0.3176659467413381
[2022-06-16 09:39:55,946] Test: Loss: 1.712 | Acc: 59.068 (29534/50000)
[2022-06-16 09:39:55,946] Epoch: 232
[2022-06-16 09:50:49,416] Train: Loss: 1.789 | Acc: 58.571 (750392/1281167) | Lr: 0.3157055251931016
[2022-06-16 09:51:30,111] Test: Loss: 1.658 | Acc: 60.472 (30236/50000)
[2022-06-16 09:51:30,112] Saving..
[2022-06-16 09:51:30,183] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-16 09:51:30,183] Epoch: 233
[2022-06-16 10:02:26,448] Train: Loss: 1.786 | Acc: 58.663 (751576/1281167) | Lr: 0.3137444310866212
[2022-06-16 10:03:07,111] Test: Loss: 2.049 | Acc: 52.978 (26489/50000)
[2022-06-16 10:03:07,112] Epoch: 234
[2022-06-16 10:13:56,087] Train: Loss: 1.782 | Acc: 58.772 (752970/1281167) | Lr: 0.31178274842864145
[2022-06-16 10:14:39,515] Test: Loss: 1.842 | Acc: 56.782 (28391/50000)
[2022-06-16 10:14:39,515] Epoch: 235
[2022-06-16 10:25:40,230] Train: Loss: 1.780 | Acc: 58.808 (753426/1281167) | Lr: 0.30982056125111845
[2022-06-16 10:26:20,484] Test: Loss: 1.766 | Acc: 58.278 (29139/50000)
[2022-06-16 10:26:20,485] Epoch: 236
[2022-06-16 10:37:14,390] Train: Loss: 1.776 | Acc: 58.827 (753666/1281167) | Lr: 0.3078579536076201
[2022-06-16 10:37:53,682] Test: Loss: 1.688 | Acc: 59.354 (29677/50000)
[2022-06-16 10:37:53,682] Epoch: 237
[2022-06-16 10:48:40,837] Train: Loss: 1.777 | Acc: 58.895 (754544/1281167) | Lr: 0.30589500956972593
[2022-06-16 10:49:22,843] Test: Loss: 1.842 | Acc: 57.086 (28543/50000)
[2022-06-16 10:49:22,844] Epoch: 238
[2022-06-16 11:00:22,654] Train: Loss: 1.773 | Acc: 58.975 (755571/1281167) | Lr: 0.3039318132234252
[2022-06-16 11:01:01,546] Test: Loss: 1.835 | Acc: 56.938 (28469/50000)
[2022-06-16 11:01:01,546] Epoch: 239
[2022-06-16 11:11:58,143] Train: Loss: 1.772 | Acc: 59.014 (756074/1281167) | Lr: 0.3019684486655154
[2022-06-16 11:12:37,412] Test: Loss: 1.812 | Acc: 57.248 (28624/50000)
[2022-06-16 11:12:37,432] Epoch: 240
[2022-06-16 11:23:21,395] Train: Loss: 1.743 | Acc: 59.499 (762277/1281167) | Lr: 0.30000499999999974
[2022-06-16 11:24:13,981] Test: Loss: 1.925 | Acc: 55.618 (27809/50000)
[2022-06-16 11:24:13,981] Epoch: 241
[2022-06-16 11:35:15,161] Train: Loss: 1.741 | Acc: 59.559 (763056/1281167) | Lr: 0.29804155133448396
[2022-06-16 11:35:56,081] Test: Loss: 1.645 | Acc: 60.530 (30265/50000)
[2022-06-16 11:35:56,082] Saving..
[2022-06-16 11:35:56,150] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-16 11:35:56,150] Epoch: 242
[2022-06-16 11:47:12,763] Train: Loss: 1.732 | Acc: 59.723 (765153/1281167) | Lr: 0.29607818677657416
[2022-06-16 11:47:53,505] Test: Loss: 1.737 | Acc: 58.962 (29481/50000)
[2022-06-16 11:47:53,505] Epoch: 243
[2022-06-16 11:58:40,620] Train: Loss: 1.727 | Acc: 59.855 (766843/1281167) | Lr: 0.29411499043027345
[2022-06-16 11:59:21,724] Test: Loss: 1.642 | Acc: 60.896 (30448/50000)
[2022-06-16 11:59:21,725] Saving..
[2022-06-16 11:59:21,827] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-16 11:59:21,827] Epoch: 244
[2022-06-16 12:10:21,071] Train: Loss: 1.728 | Acc: 59.822 (766423/1281167) | Lr: 0.2921520463923793
[2022-06-16 12:11:08,448] Test: Loss: 1.690 | Acc: 59.876 (29938/50000)
[2022-06-16 12:11:08,449] Epoch: 245
[2022-06-16 12:22:11,546] Train: Loss: 1.719 | Acc: 60.054 (769397/1281167) | Lr: 0.290189438748881
[2022-06-16 12:22:51,553] Test: Loss: 2.290 | Acc: 49.460 (24730/50000)
[2022-06-16 12:22:51,553] Epoch: 246
[2022-06-16 12:33:52,634] Train: Loss: 1.716 | Acc: 60.054 (769387/1281167) | Lr: 0.2882272515713579
[2022-06-16 12:34:33,548] Test: Loss: 1.588 | Acc: 61.886 (30943/50000)
[2022-06-16 12:34:33,548] Saving..
[2022-06-16 12:34:33,620] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-16 12:34:33,621] Epoch: 247
[2022-06-16 12:45:32,145] Train: Loss: 1.714 | Acc: 60.133 (770407/1281167) | Lr: 0.2862655689133781
[2022-06-16 12:46:15,610] Test: Loss: 1.696 | Acc: 59.646 (29823/50000)
[2022-06-16 12:46:15,610] Epoch: 248
[2022-06-16 12:57:16,100] Train: Loss: 1.710 | Acc: 60.197 (771222/1281167) | Lr: 0.2843044748068978
[2022-06-16 12:57:57,052] Test: Loss: 1.771 | Acc: 58.278 (29139/50000)
[2022-06-16 12:57:57,052] Epoch: 249
[2022-06-16 13:08:59,334] Train: Loss: 1.710 | Acc: 60.233 (771680/1281167) | Lr: 0.2823440532586613
[2022-06-16 13:09:40,093] Test: Loss: 1.633 | Acc: 60.930 (30465/50000)
[2022-06-16 13:09:40,093] Epoch: 250
[2022-06-16 13:20:39,022] Train: Loss: 1.704 | Acc: 60.299 (772535/1281167) | Lr: 0.280384388246603
[2022-06-16 13:21:19,124] Test: Loss: 1.579 | Acc: 61.862 (30931/50000)
[2022-06-16 13:21:19,124] Epoch: 251
[2022-06-16 13:32:28,844] Train: Loss: 1.701 | Acc: 60.403 (773867/1281167) | Lr: 0.27842556371624966
[2022-06-16 13:33:16,889] Test: Loss: 1.590 | Acc: 61.640 (30820/50000)
[2022-06-16 13:33:16,890] Epoch: 252
[2022-06-16 13:44:21,477] Train: Loss: 1.695 | Acc: 60.410 (773949/1281167) | Lr: 0.27646766357712493
[2022-06-16 13:45:02,204] Test: Loss: 1.575 | Acc: 62.302 (31151/50000)
[2022-06-16 13:45:02,204] Saving..
[2022-06-16 13:45:02,284] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-16 13:45:02,284] Epoch: 253
[2022-06-16 13:56:14,888] Train: Loss: 1.694 | Acc: 60.518 (775331/1281167) | Lr: 0.2745107716991541
[2022-06-16 13:56:54,577] Test: Loss: 1.579 | Acc: 61.862 (30931/50000)
[2022-06-16 13:56:54,577] Epoch: 254
[2022-06-16 14:08:11,860] Train: Loss: 1.691 | Acc: 60.625 (776713/1281167) | Lr: 0.27255497190907235
[2022-06-16 14:08:52,875] Test: Loss: 1.680 | Acc: 60.002 (30001/50000)
[2022-06-16 14:08:52,875] Epoch: 255
[2022-06-16 14:20:03,379] Train: Loss: 1.688 | Acc: 60.660 (777160/1281167) | Lr: 0.2706003479868332
[2022-06-16 14:20:42,914] Test: Loss: 1.635 | Acc: 60.582 (30291/50000)
[2022-06-16 14:20:42,915] Epoch: 256
[2022-06-16 14:31:46,073] Train: Loss: 1.688 | Acc: 60.637 (776866/1281167) | Lr: 0.2686469836620201
[2022-06-16 14:32:24,873] Test: Loss: 1.638 | Acc: 60.968 (30484/50000)
[2022-06-16 14:32:24,873] Epoch: 257
[2022-06-16 14:43:32,821] Train: Loss: 1.688 | Acc: 60.666 (777239/1281167) | Lr: 0.26669496261025927
[2022-06-16 14:44:14,534] Test: Loss: 1.689 | Acc: 59.732 (29866/50000)
[2022-06-16 14:44:14,534] Epoch: 258
[2022-06-16 14:55:13,598] Train: Loss: 1.680 | Acc: 60.855 (779649/1281167) | Lr: 0.2647443684496358
[2022-06-16 14:55:54,922] Test: Loss: 1.704 | Acc: 59.600 (29800/50000)
[2022-06-16 14:55:54,923] Epoch: 259
[2022-06-16 15:06:53,024] Train: Loss: 1.679 | Acc: 60.808 (779050/1281167) | Lr: 0.2627952847371114
[2022-06-16 15:07:34,013] Test: Loss: 1.961 | Acc: 55.442 (27721/50000)
[2022-06-16 15:07:34,014] Epoch: 260
[2022-06-16 15:18:50,823] Train: Loss: 1.674 | Acc: 60.922 (780510/1281167) | Lr: 0.2608477949649454
[2022-06-16 15:19:31,393] Test: Loss: 1.649 | Acc: 60.798 (30399/50000)
[2022-06-16 15:19:31,394] Epoch: 261
[2022-06-16 15:30:30,827] Train: Loss: 1.675 | Acc: 60.921 (780498/1281167) | Lr: 0.25890198255711777
[2022-06-16 15:31:11,578] Test: Loss: 1.914 | Acc: 56.378 (28189/50000)
[2022-06-16 15:31:11,578] Epoch: 262
[2022-06-16 15:42:09,956] Train: Loss: 1.670 | Acc: 61.008 (781611/1281167) | Lr: 0.25695793086575586
[2022-06-16 15:42:50,515] Test: Loss: 2.010 | Acc: 54.004 (27002/50000)
[2022-06-16 15:42:50,516] Epoch: 263
[2022-06-16 15:53:55,142] Train: Loss: 1.666 | Acc: 61.091 (782679/1281167) | Lr: 0.25501572316756393
[2022-06-16 15:54:34,661] Test: Loss: 1.716 | Acc: 59.114 (29557/50000)
[2022-06-16 15:54:34,661] Epoch: 264
[2022-06-16 16:05:41,623] Train: Loss: 1.666 | Acc: 61.136 (783252/1281167) | Lr: 0.25307544266025567
[2022-06-16 16:06:24,012] Test: Loss: 1.516 | Acc: 63.282 (31641/50000)
[2022-06-16 16:06:24,013] Saving..
[2022-06-16 16:06:24,116] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-16 16:06:24,117] Epoch: 265
[2022-06-16 16:17:29,970] Train: Loss: 1.661 | Acc: 61.225 (784400/1281167) | Lr: 0.2511371724589901
[2022-06-16 16:18:10,624] Test: Loss: 1.613 | Acc: 61.166 (30583/50000)
[2022-06-16 16:18:10,624] Epoch: 266
[2022-06-16 16:29:23,047] Train: Loss: 1.660 | Acc: 61.300 (785351/1281167) | Lr: 0.24920099559281159
[2022-06-16 16:30:05,283] Test: Loss: 1.887 | Acc: 56.476 (28238/50000)
[2022-06-16 16:30:05,283] Epoch: 267
[2022-06-16 16:41:13,496] Train: Loss: 1.659 | Acc: 61.309 (785465/1281167) | Lr: 0.2472669950010931
[2022-06-16 16:41:57,942] Test: Loss: 1.797 | Acc: 58.228 (29114/50000)
[2022-06-16 16:41:57,942] Epoch: 268
[2022-06-16 16:53:28,866] Train: Loss: 1.654 | Acc: 61.319 (785597/1281167) | Lr: 0.245335253529983
[2022-06-16 16:54:11,576] Test: Loss: 1.548 | Acc: 62.790 (31395/50000)
[2022-06-16 16:54:11,576] Epoch: 269
[2022-06-16 17:05:12,115] Train: Loss: 1.653 | Acc: 61.424 (786950/1281167) | Lr: 0.24340585392885664
[2022-06-16 17:05:58,118] Test: Loss: 1.805 | Acc: 57.856 (28928/50000)
[2022-06-16 17:05:58,118] Epoch: 270
[2022-06-16 17:17:06,643] Train: Loss: 1.647 | Acc: 61.437 (787106/1281167) | Lr: 0.24147887884677136
[2022-06-16 17:17:51,904] Test: Loss: 1.723 | Acc: 59.040 (29520/50000)
[2022-06-16 17:17:51,904] Epoch: 271
[2022-06-16 17:28:55,623] Train: Loss: 1.646 | Acc: 61.504 (787968/1281167) | Lr: 0.23955441082892628
[2022-06-16 17:29:36,709] Test: Loss: 1.621 | Acc: 61.382 (30691/50000)
[2022-06-16 17:29:36,710] Epoch: 272
[2022-06-16 17:40:37,667] Train: Loss: 1.640 | Acc: 61.633 (789624/1281167) | Lr: 0.237632532313126
[2022-06-16 17:41:19,278] Test: Loss: 1.518 | Acc: 63.210 (31605/50000)
[2022-06-16 17:41:19,278] Epoch: 273
[2022-06-16 17:52:21,252] Train: Loss: 1.641 | Acc: 61.645 (789770/1281167) | Lr: 0.2357133256262498
[2022-06-16 17:53:02,039] Test: Loss: 1.512 | Acc: 63.394 (31697/50000)
[2022-06-16 17:53:02,040] Saving..
[2022-06-16 17:53:02,137] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-16 17:53:02,137] Epoch: 274
[2022-06-16 18:04:09,649] Train: Loss: 1.636 | Acc: 61.738 (790969/1281167) | Lr: 0.23379687298072446
[2022-06-16 18:04:50,006] Test: Loss: 1.540 | Acc: 62.672 (31336/50000)
[2022-06-16 18:04:50,006] Epoch: 275
[2022-06-16 18:16:01,744] Train: Loss: 1.630 | Acc: 61.831 (792164/1281167) | Lr: 0.23188325647100297
[2022-06-16 18:16:54,733] Test: Loss: 1.700 | Acc: 59.854 (29927/50000)
[2022-06-16 18:16:54,733] Epoch: 276
[2022-06-16 18:28:19,359] Train: Loss: 1.632 | Acc: 61.749 (791102/1281167) | Lr: 0.2299725580700474
[2022-06-16 18:29:00,073] Test: Loss: 1.526 | Acc: 63.138 (31569/50000)
[2022-06-16 18:29:00,074] Epoch: 277
[2022-06-16 18:40:23,614] Train: Loss: 1.628 | Acc: 61.916 (793253/1281167) | Lr: 0.22806485962581804
[2022-06-16 18:41:05,972] Test: Loss: 1.677 | Acc: 60.154 (30077/50000)
[2022-06-16 18:41:05,973] Epoch: 278
[2022-06-16 18:52:21,381] Train: Loss: 1.625 | Acc: 61.953 (793718/1281167) | Lr: 0.22616024285776695
[2022-06-16 18:53:03,100] Test: Loss: 1.515 | Acc: 63.614 (31807/50000)
[2022-06-16 18:53:03,100] Saving..
[2022-06-16 18:53:03,203] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-16 18:53:03,203] Epoch: 279
[2022-06-16 19:04:14,990] Train: Loss: 1.621 | Acc: 62.039 (794829/1281167) | Lr: 0.22425878935333746
[2022-06-16 19:04:56,465] Test: Loss: 1.518 | Acc: 62.974 (31487/50000)
[2022-06-16 19:04:56,465] Epoch: 280
[2022-06-16 19:16:00,440] Train: Loss: 1.620 | Acc: 62.017 (794539/1281167) | Lr: 0.22236058056446906
[2022-06-16 19:16:41,067] Test: Loss: 1.604 | Acc: 61.462 (30731/50000)
[2022-06-16 19:16:41,067] Epoch: 281
[2022-06-16 19:27:42,476] Train: Loss: 1.617 | Acc: 62.047 (794923/1281167) | Lr: 0.22046569780410857
[2022-06-16 19:28:22,864] Test: Loss: 1.573 | Acc: 62.346 (31173/50000)
[2022-06-16 19:28:22,864] Epoch: 282
[2022-06-16 19:39:22,922] Train: Loss: 1.616 | Acc: 62.147 (796203/1281167) | Lr: 0.2185742222427268
[2022-06-16 19:40:02,936] Test: Loss: 1.561 | Acc: 62.110 (31055/50000)
[2022-06-16 19:40:02,936] Epoch: 283
[2022-06-16 19:51:22,100] Train: Loss: 1.609 | Acc: 62.279 (797902/1281167) | Lr: 0.2166862349048415
[2022-06-16 19:52:07,532] Test: Loss: 1.504 | Acc: 63.206 (31603/50000)
[2022-06-16 19:52:07,532] Epoch: 284
[2022-06-16 20:03:09,670] Train: Loss: 1.613 | Acc: 62.183 (796674/1281167) | Lr: 0.21480181666554649
[2022-06-16 20:03:51,234] Test: Loss: 1.559 | Acc: 62.580 (31290/50000)
[2022-06-16 20:03:51,234] Epoch: 285
[2022-06-16 20:14:56,459] Train: Loss: 1.605 | Acc: 62.335 (798614/1281167) | Lr: 0.21292104824704733
[2022-06-16 20:15:37,341] Test: Loss: 1.500 | Acc: 63.770 (31885/50000)
[2022-06-16 20:15:37,341] Saving..
[2022-06-16 20:15:37,413] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-16 20:15:37,414] Epoch: 286
[2022-06-16 20:26:36,270] Train: Loss: 1.602 | Acc: 62.448 (800057/1281167) | Lr: 0.21104401021520333
[2022-06-16 20:27:21,302] Test: Loss: 1.551 | Acc: 62.652 (31326/50000)
[2022-06-16 20:27:21,303] Epoch: 287
[2022-06-16 20:38:19,199] Train: Loss: 1.599 | Acc: 62.528 (801084/1281167) | Lr: 0.20917078297607666
[2022-06-16 20:38:59,663] Test: Loss: 1.540 | Acc: 62.644 (31322/50000)
[2022-06-16 20:38:59,663] Epoch: 288
[2022-06-16 20:50:02,879] Train: Loss: 1.596 | Acc: 62.579 (801741/1281167) | Lr: 0.20730144677248746
[2022-06-16 20:50:44,674] Test: Loss: 1.446 | Acc: 64.828 (32414/50000)
[2022-06-16 20:50:44,674] Saving..
[2022-06-16 20:50:44,797] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-16 20:50:44,797] Epoch: 289
[2022-06-16 21:02:06,265] Train: Loss: 1.591 | Acc: 62.651 (802667/1281167) | Lr: 0.20543608168057714
[2022-06-16 21:02:47,702] Test: Loss: 1.553 | Acc: 62.480 (31240/50000)
[2022-06-16 21:02:47,702] Epoch: 290
[2022-06-16 21:14:15,013] Train: Loss: 1.594 | Acc: 62.589 (801867/1281167) | Lr: 0.2035747676063779
[2022-06-16 21:14:55,582] Test: Loss: 1.503 | Acc: 63.388 (31694/50000)
[2022-06-16 21:14:55,583] Epoch: 291
[2022-06-16 21:26:07,259] Train: Loss: 1.587 | Acc: 62.742 (803828/1281167) | Lr: 0.2017175842823896
[2022-06-16 21:26:57,345] Test: Loss: 1.633 | Acc: 60.926 (30463/50000)
[2022-06-16 21:26:57,346] Epoch: 292
[2022-06-16 21:38:13,431] Train: Loss: 1.582 | Acc: 62.867 (805435/1281167) | Lr: 0.1998646112641647
[2022-06-16 21:38:57,513] Test: Loss: 1.602 | Acc: 61.790 (30895/50000)
[2022-06-16 21:38:57,514] Epoch: 293
[2022-06-16 21:50:20,198] Train: Loss: 1.582 | Acc: 62.822 (804849/1281167) | Lr: 0.1980159279269002
[2022-06-16 21:51:02,861] Test: Loss: 1.448 | Acc: 65.014 (32507/50000)
[2022-06-16 21:51:02,862] Saving..
[2022-06-16 21:51:02,929] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-16 21:51:02,929] Epoch: 294
[2022-06-16 22:02:21,444] Train: Loss: 1.576 | Acc: 62.951 (806513/1281167) | Lr: 0.1961716134620373
[2022-06-16 22:03:03,537] Test: Loss: 1.624 | Acc: 61.080 (30540/50000)
[2022-06-16 22:03:03,538] Epoch: 295
[2022-06-16 22:14:06,996] Train: Loss: 1.572 | Acc: 63.042 (807667/1281167) | Lr: 0.19433174687386934
[2022-06-16 22:14:50,549] Test: Loss: 1.457 | Acc: 64.460 (32230/50000)
[2022-06-16 22:14:50,549] Epoch: 296
[2022-06-16 22:25:50,568] Train: Loss: 1.571 | Acc: 63.022 (807421/1281167) | Lr: 0.19249640697615744
[2022-06-16 22:26:32,433] Test: Loss: 1.450 | Acc: 64.894 (32447/50000)
[2022-06-16 22:26:32,434] Epoch: 297
[2022-06-16 22:37:50,781] Train: Loss: 1.567 | Acc: 63.153 (809101/1281167) | Lr: 0.1906656723887543
[2022-06-16 22:38:37,611] Test: Loss: 1.489 | Acc: 64.136 (32068/50000)
[2022-06-16 22:38:37,612] Epoch: 298
[2022-06-16 22:49:42,603] Train: Loss: 1.562 | Acc: 63.239 (810194/1281167) | Lr: 0.1888396215342365
[2022-06-16 22:50:25,021] Test: Loss: 1.513 | Acc: 63.356 (31678/50000)
[2022-06-16 22:50:25,021] Epoch: 299
[2022-06-16 23:01:27,088] Train: Loss: 1.563 | Acc: 63.268 (810570/1281167) | Lr: 0.18701833263454504
[2022-06-16 23:02:07,764] Test: Loss: 1.578 | Acc: 62.502 (31251/50000)
[2022-06-16 23:02:07,765] Epoch: 300
[2022-06-16 23:13:13,221] Train: Loss: 1.561 | Acc: 63.311 (811118/1281167) | Lr: 0.1852018837076347
[2022-06-16 23:13:54,709] Test: Loss: 1.760 | Acc: 58.618 (29309/50000)
[2022-06-16 23:13:54,709] Epoch: 301
[2022-06-16 23:25:08,148] Train: Loss: 1.555 | Acc: 63.396 (812211/1281167) | Lr: 0.1833903525641319
[2022-06-16 23:25:48,379] Test: Loss: 1.447 | Acc: 64.940 (32470/50000)
[2022-06-16 23:25:48,379] Epoch: 302
[2022-06-16 23:36:54,834] Train: Loss: 1.553 | Acc: 63.379 (811989/1281167) | Lr: 0.1815838168040016
[2022-06-16 23:37:36,572] Test: Loss: 1.652 | Acc: 60.748 (30374/50000)
[2022-06-16 23:37:36,572] Epoch: 303
[2022-06-16 23:48:45,394] Train: Loss: 1.547 | Acc: 63.540 (814055/1281167) | Lr: 0.17978235381322308
[2022-06-16 23:49:29,013] Test: Loss: 1.513 | Acc: 63.400 (31700/50000)
[2022-06-16 23:49:29,014] Epoch: 304
[2022-06-17 00:00:46,384] Train: Loss: 1.546 | Acc: 63.548 (814151/1281167) | Lr: 0.17798604076047517
[2022-06-17 00:01:33,200] Test: Loss: 1.435 | Acc: 65.092 (32546/50000)
[2022-06-17 00:01:33,201] Saving..
[2022-06-17 00:01:33,303] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 00:01:33,303] Epoch: 305
[2022-06-17 00:12:42,680] Train: Loss: 1.545 | Acc: 63.629 (815194/1281167) | Lr: 0.17619495459383036
[2022-06-17 00:13:29,296] Test: Loss: 1.525 | Acc: 63.020 (31510/50000)
[2022-06-17 00:13:29,297] Epoch: 306
[2022-06-17 00:24:42,025] Train: Loss: 1.539 | Acc: 63.746 (816692/1281167) | Lr: 0.17440917203745904
[2022-06-17 00:25:22,175] Test: Loss: 1.432 | Acc: 65.016 (32508/50000)
[2022-06-17 00:25:22,176] Epoch: 307
[2022-06-17 00:36:27,065] Train: Loss: 1.541 | Acc: 63.687 (815941/1281167) | Lr: 0.17262876958834228
[2022-06-17 00:37:09,054] Test: Loss: 1.580 | Acc: 62.274 (31137/50000)
[2022-06-17 00:37:09,054] Epoch: 308
[2022-06-17 00:48:13,558] Train: Loss: 1.533 | Acc: 63.868 (818259/1281167) | Lr: 0.1708538235129954
[2022-06-17 00:48:56,769] Test: Loss: 1.436 | Acc: 64.944 (32472/50000)
[2022-06-17 00:48:56,769] Epoch: 309
[2022-06-17 01:00:01,029] Train: Loss: 1.528 | Acc: 63.937 (819146/1281167) | Lr: 0.16908440984420062
[2022-06-17 01:00:42,213] Test: Loss: 1.408 | Acc: 65.450 (32725/50000)
[2022-06-17 01:00:42,213] Saving..
[2022-06-17 01:00:42,311] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 01:00:42,311] Epoch: 310
[2022-06-17 01:11:44,441] Train: Loss: 1.528 | Acc: 63.960 (819436/1281167) | Lr: 0.16732060437775062
[2022-06-17 01:12:26,915] Test: Loss: 1.528 | Acc: 63.326 (31663/50000)
[2022-06-17 01:12:26,916] Epoch: 311
[2022-06-17 01:23:37,921] Train: Loss: 1.523 | Acc: 64.038 (820435/1281167) | Lr: 0.16556248266920107
[2022-06-17 01:24:24,079] Test: Loss: 1.403 | Acc: 65.644 (32822/50000)
[2022-06-17 01:24:24,079] Saving..
[2022-06-17 01:24:24,149] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 01:24:24,149] Epoch: 312
[2022-06-17 01:35:41,488] Train: Loss: 1.520 | Acc: 64.108 (821334/1281167) | Lr: 0.16381012003063453
[2022-06-17 01:36:36,134] Test: Loss: 1.436 | Acc: 64.862 (32431/50000)
[2022-06-17 01:36:36,134] Epoch: 313
[2022-06-17 01:47:43,004] Train: Loss: 1.517 | Acc: 64.202 (822533/1281167) | Lr: 0.16206359152743416
[2022-06-17 01:48:24,215] Test: Loss: 1.354 | Acc: 66.884 (33442/50000)
[2022-06-17 01:48:24,216] Saving..
[2022-06-17 01:48:24,289] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 01:48:24,289] Epoch: 314
[2022-06-17 01:59:34,143] Train: Loss: 1.515 | Acc: 64.234 (822950/1281167) | Lr: 0.1603229719750681
[2022-06-17 02:00:14,592] Test: Loss: 1.595 | Acc: 61.834 (30917/50000)
[2022-06-17 02:00:14,593] Epoch: 315
[2022-06-17 02:11:23,212] Train: Loss: 1.512 | Acc: 64.284 (823582/1281167) | Lr: 0.1585883359358847
[2022-06-17 02:12:02,480] Test: Loss: 1.417 | Acc: 65.478 (32739/50000)
[2022-06-17 02:12:02,480] Epoch: 316
[2022-06-17 02:23:13,332] Train: Loss: 1.505 | Acc: 64.433 (825500/1281167) | Lr: 0.15685975771591865
[2022-06-17 02:24:02,037] Test: Loss: 1.470 | Acc: 64.552 (32276/50000)
[2022-06-17 02:24:02,037] Epoch: 317
[2022-06-17 02:34:59,568] Train: Loss: 1.503 | Acc: 64.513 (826518/1281167) | Lr: 0.15513731136170764
[2022-06-17 02:35:40,532] Test: Loss: 1.368 | Acc: 66.506 (33253/50000)
[2022-06-17 02:35:40,532] Epoch: 318
[2022-06-17 02:46:49,175] Train: Loss: 1.502 | Acc: 64.517 (826574/1281167) | Lr: 0.15342107065712082
[2022-06-17 02:47:39,660] Test: Loss: 1.390 | Acc: 66.054 (33027/50000)
[2022-06-17 02:47:39,660] Epoch: 319
[2022-06-17 02:58:45,912] Train: Loss: 1.497 | Acc: 64.573 (827291/1281167) | Lr: 0.151711109120198
[2022-06-17 02:59:27,471] Test: Loss: 1.413 | Acc: 65.558 (32779/50000)
[2022-06-17 02:59:27,471] Epoch: 320
[2022-06-17 03:10:42,536] Train: Loss: 1.495 | Acc: 64.635 (828082/1281167) | Lr: 0.15000749999999993
[2022-06-17 03:11:22,960] Test: Loss: 1.372 | Acc: 66.600 (33300/50000)
[2022-06-17 03:11:22,961] Epoch: 321
[2022-06-17 03:22:19,694] Train: Loss: 1.492 | Acc: 64.699 (828897/1281167) | Lr: 0.14831031627347144
[2022-06-17 03:23:03,243] Test: Loss: 1.374 | Acc: 66.414 (33207/50000)
[2022-06-17 03:23:03,243] Epoch: 322
[2022-06-17 03:34:13,914] Train: Loss: 1.487 | Acc: 64.856 (830919/1281167) | Lr: 0.1466196306423147
[2022-06-17 03:34:55,831] Test: Loss: 1.430 | Acc: 65.326 (32663/50000)
[2022-06-17 03:34:55,832] Epoch: 323
[2022-06-17 03:46:05,435] Train: Loss: 1.485 | Acc: 64.814 (830376/1281167) | Lr: 0.14493551552987505
[2022-06-17 03:47:00,756] Test: Loss: 1.363 | Acc: 66.836 (33418/50000)
[2022-06-17 03:47:00,757] Epoch: 324
[2022-06-17 03:58:06,955] Train: Loss: 1.479 | Acc: 64.980 (832508/1281167) | Lr: 0.14325804307803883
[2022-06-17 03:58:50,712] Test: Loss: 1.364 | Acc: 66.650 (33325/50000)
[2022-06-17 03:58:50,713] Epoch: 325
[2022-06-17 04:10:13,351] Train: Loss: 1.475 | Acc: 65.100 (834037/1281167) | Lr: 0.14158728514414276
[2022-06-17 04:10:59,610] Test: Loss: 1.348 | Acc: 67.060 (33530/50000)
[2022-06-17 04:10:59,610] Saving..
[2022-06-17 04:10:59,729] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 04:10:59,729] Epoch: 326
[2022-06-17 04:22:22,867] Train: Loss: 1.473 | Acc: 65.098 (834017/1281167) | Lr: 0.13992331329789603
[2022-06-17 04:23:07,369] Test: Loss: 1.487 | Acc: 64.146 (32073/50000)
[2022-06-17 04:23:07,370] Epoch: 327
[2022-06-17 04:34:28,024] Train: Loss: 1.469 | Acc: 65.180 (835062/1281167) | Lr: 0.13826619881831437
[2022-06-17 04:35:09,150] Test: Loss: 1.343 | Acc: 67.362 (33681/50000)
[2022-06-17 04:35:09,150] Saving..
[2022-06-17 04:35:09,288] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 04:35:09,288] Epoch: 328
[2022-06-17 04:46:18,941] Train: Loss: 1.466 | Acc: 65.277 (836312/1281167) | Lr: 0.13661601269066695
[2022-06-17 04:47:02,854] Test: Loss: 1.523 | Acc: 63.064 (31532/50000)
[2022-06-17 04:47:02,854] Epoch: 329
[2022-06-17 04:58:03,606] Train: Loss: 1.463 | Acc: 65.347 (837204/1281167) | Lr: 0.13497282560343488
[2022-06-17 04:58:43,847] Test: Loss: 1.355 | Acc: 66.556 (33278/50000)
[2022-06-17 04:58:43,847] Epoch: 330
[2022-06-17 05:09:53,641] Train: Loss: 1.458 | Acc: 65.429 (838254/1281167) | Lr: 0.1333367079452844
[2022-06-17 05:10:34,708] Test: Loss: 1.409 | Acc: 65.802 (32901/50000)
[2022-06-17 05:10:34,708] Epoch: 331
[2022-06-17 05:21:47,483] Train: Loss: 1.455 | Acc: 65.467 (838743/1281167) | Lr: 0.13170772980205034
[2022-06-17 05:22:27,799] Test: Loss: 1.330 | Acc: 67.560 (33780/50000)
[2022-06-17 05:22:27,800] Saving..
[2022-06-17 05:22:27,891] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 05:22:27,891] Epoch: 332
[2022-06-17 05:33:38,111] Train: Loss: 1.452 | Acc: 65.570 (840067/1281167) | Lr: 0.13008596095373462
[2022-06-17 05:34:18,001] Test: Loss: 1.345 | Acc: 67.274 (33637/50000)
[2022-06-17 05:34:18,001] Epoch: 333
[2022-06-17 05:45:28,547] Train: Loss: 1.448 | Acc: 65.620 (840706/1281167) | Lr: 0.12847147087151742
[2022-06-17 05:46:08,477] Test: Loss: 1.385 | Acc: 65.854 (32927/50000)
[2022-06-17 05:46:08,478] Epoch: 334
[2022-06-17 05:57:12,762] Train: Loss: 1.442 | Acc: 65.704 (841782/1281167) | Lr: 0.12686432871477996
[2022-06-17 05:57:57,681] Test: Loss: 1.361 | Acc: 66.804 (33402/50000)
[2022-06-17 05:57:57,681] Epoch: 335
[2022-06-17 06:08:56,391] Train: Loss: 1.443 | Acc: 65.770 (842627/1281167) | Lr: 0.12526460332814365
[2022-06-17 06:09:37,821] Test: Loss: 1.352 | Acc: 67.164 (33582/50000)
[2022-06-17 06:09:37,822] Epoch: 336
[2022-06-17 06:20:50,255] Train: Loss: 1.436 | Acc: 65.893 (844205/1281167) | Lr: 0.12367236323851946
[2022-06-17 06:21:30,384] Test: Loss: 1.348 | Acc: 66.950 (33475/50000)
[2022-06-17 06:21:30,384] Epoch: 337
[2022-06-17 06:32:49,166] Train: Loss: 1.434 | Acc: 65.902 (844311/1281167) | Lr: 0.12208767665217356
[2022-06-17 06:33:30,351] Test: Loss: 1.364 | Acc: 66.752 (33376/50000)
[2022-06-17 06:33:30,352] Epoch: 338
[2022-06-17 06:45:16,943] Train: Loss: 1.429 | Acc: 66.036 (846036/1281167) | Lr: 0.12051061145180504
[2022-06-17 06:45:56,149] Test: Loss: 1.331 | Acc: 67.376 (33688/50000)
[2022-06-17 06:45:56,149] Epoch: 339
[2022-06-17 06:57:13,662] Train: Loss: 1.427 | Acc: 66.082 (846627/1281167) | Lr: 0.11894123519363835
[2022-06-17 06:57:54,164] Test: Loss: 1.399 | Acc: 65.914 (32957/50000)
[2022-06-17 06:57:54,164] Epoch: 340
[2022-06-17 07:09:15,560] Train: Loss: 1.421 | Acc: 66.178 (847848/1281167) | Lr: 0.11737961510452875
[2022-06-17 07:09:58,356] Test: Loss: 1.364 | Acc: 66.384 (33192/50000)
[2022-06-17 07:09:58,357] Epoch: 341
[2022-06-17 07:21:31,826] Train: Loss: 1.419 | Acc: 66.253 (848814/1281167) | Lr: 0.1158258180790838
[2022-06-17 07:22:14,330] Test: Loss: 1.410 | Acc: 65.682 (32841/50000)
[2022-06-17 07:22:14,331] Epoch: 342
[2022-06-17 07:33:31,257] Train: Loss: 1.414 | Acc: 66.316 (849617/1281167) | Lr: 0.11427991067679634
[2022-06-17 07:34:12,779] Test: Loss: 1.370 | Acc: 66.740 (33370/50000)
[2022-06-17 07:34:12,779] Epoch: 343
[2022-06-17 07:45:21,942] Train: Loss: 1.411 | Acc: 66.400 (850692/1281167) | Lr: 0.1127419591191943
[2022-06-17 07:46:02,668] Test: Loss: 1.406 | Acc: 65.682 (32841/50000)
[2022-06-17 07:46:02,669] Epoch: 344
[2022-06-17 07:57:10,027] Train: Loss: 1.408 | Acc: 66.478 (851691/1281167) | Lr: 0.11121202928700398
[2022-06-17 07:57:50,810] Test: Loss: 1.311 | Acc: 68.042 (34021/50000)
[2022-06-17 07:57:50,810] Saving..
[2022-06-17 07:57:50,906] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 07:57:50,906] Epoch: 345
[2022-06-17 08:09:04,692] Train: Loss: 1.407 | Acc: 66.486 (851792/1281167) | Lr: 0.10969018671732703
[2022-06-17 08:09:46,472] Test: Loss: 1.291 | Acc: 68.142 (34071/50000)
[2022-06-17 08:09:46,473] Saving..
[2022-06-17 08:09:46,543] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 08:09:46,544] Epoch: 346
[2022-06-17 08:20:56,997] Train: Loss: 1.398 | Acc: 66.685 (854345/1281167) | Lr: 0.10817649660083442
[2022-06-17 08:21:38,809] Test: Loss: 1.317 | Acc: 67.834 (33917/50000)
[2022-06-17 08:21:38,809] Epoch: 347
[2022-06-17 08:32:47,110] Train: Loss: 1.397 | Acc: 66.712 (854691/1281167) | Lr: 0.10667102377897254
[2022-06-17 08:33:30,869] Test: Loss: 1.279 | Acc: 68.618 (34309/50000)
[2022-06-17 08:33:30,870] Saving..
[2022-06-17 08:33:30,960] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 08:33:30,960] Epoch: 348
[2022-06-17 08:44:38,198] Train: Loss: 1.392 | Acc: 66.817 (856035/1281167) | Lr: 0.10517383274118651
[2022-06-17 08:45:23,869] Test: Loss: 1.293 | Acc: 68.078 (34039/50000)
[2022-06-17 08:45:23,869] Epoch: 349
[2022-06-17 08:56:36,533] Train: Loss: 1.387 | Acc: 66.913 (857266/1281167) | Lr: 0.10368498762215736
[2022-06-17 08:57:17,523] Test: Loss: 1.316 | Acc: 67.690 (33845/50000)
[2022-06-17 08:57:17,523] Epoch: 350
[2022-06-17 09:08:35,727] Train: Loss: 1.383 | Acc: 66.983 (858159/1281167) | Lr: 0.10220455219905486
[2022-06-17 09:09:21,364] Test: Loss: 1.410 | Acc: 66.026 (33013/50000)
[2022-06-17 09:09:21,364] Epoch: 351
[2022-06-17 09:20:30,209] Train: Loss: 1.384 | Acc: 66.966 (857951/1281167) | Lr: 0.10073258988880494
[2022-06-17 09:21:22,685] Test: Loss: 1.338 | Acc: 67.238 (33619/50000)
[2022-06-17 09:21:22,686] Epoch: 352
[2022-06-17 09:32:39,863] Train: Loss: 1.378 | Acc: 67.118 (859892/1281167) | Lr: 0.09926916374537434
[2022-06-17 09:33:20,391] Test: Loss: 1.284 | Acc: 68.668 (34334/50000)
[2022-06-17 09:33:20,392] Saving..
[2022-06-17 09:33:20,501] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 09:33:20,501] Epoch: 353
[2022-06-17 09:44:30,116] Train: Loss: 1.373 | Acc: 67.178 (860666/1281167) | Lr: 0.09781433645706791
[2022-06-17 09:45:10,886] Test: Loss: 1.294 | Acc: 67.956 (33978/50000)
[2022-06-17 09:45:10,886] Epoch: 354
[2022-06-17 09:56:23,382] Train: Loss: 1.370 | Acc: 67.314 (862405/1281167) | Lr: 0.09636817034384504
[2022-06-17 09:57:04,321] Test: Loss: 1.297 | Acc: 68.230 (34115/50000)
[2022-06-17 09:57:04,321] Epoch: 355
[2022-06-17 10:08:22,273] Train: Loss: 1.368 | Acc: 67.357 (862952/1281167) | Lr: 0.09493072735464868
[2022-06-17 10:09:04,649] Test: Loss: 1.260 | Acc: 68.734 (34367/50000)
[2022-06-17 10:09:04,649] Saving..
[2022-06-17 10:09:04,767] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 10:09:04,768] Epoch: 356
[2022-06-17 10:20:20,540] Train: Loss: 1.361 | Acc: 67.492 (864681/1281167) | Lr: 0.09350206906475214
[2022-06-17 10:21:02,127] Test: Loss: 1.325 | Acc: 67.618 (33809/50000)
[2022-06-17 10:21:02,127] Epoch: 357
[2022-06-17 10:32:16,588] Train: Loss: 1.359 | Acc: 67.527 (865136/1281167) | Lr: 0.09208225667312192
[2022-06-17 10:32:58,550] Test: Loss: 1.329 | Acc: 67.528 (33764/50000)
[2022-06-17 10:32:58,550] Epoch: 358
[2022-06-17 10:44:19,017] Train: Loss: 1.353 | Acc: 67.636 (866528/1281167) | Lr: 0.09067135099979513
[2022-06-17 10:45:01,695] Test: Loss: 1.247 | Acc: 69.328 (34664/50000)
[2022-06-17 10:45:01,695] Saving..
[2022-06-17 10:45:01,791] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 10:45:01,792] Epoch: 359
[2022-06-17 10:56:21,302] Train: Loss: 1.350 | Acc: 67.692 (867253/1281167) | Lr: 0.08926941248327502
[2022-06-17 10:57:07,241] Test: Loss: 1.270 | Acc: 68.652 (34326/50000)
[2022-06-17 10:57:07,242] Epoch: 360
[2022-06-17 11:08:15,315] Train: Loss: 1.346 | Acc: 67.805 (868693/1281167) | Lr: 0.08787650117794162
[2022-06-17 11:08:59,141] Test: Loss: 1.348 | Acc: 67.124 (33562/50000)
[2022-06-17 11:08:59,141] Epoch: 361
[2022-06-17 11:20:20,405] Train: Loss: 1.343 | Acc: 67.869 (869509/1281167) | Lr: 0.08649267675147938
[2022-06-17 11:21:01,848] Test: Loss: 1.281 | Acc: 68.622 (34311/50000)
[2022-06-17 11:21:01,849] Epoch: 362
[2022-06-17 11:32:11,199] Train: Loss: 1.336 | Acc: 67.995 (871129/1281167) | Lr: 0.08511799848232077
[2022-06-17 11:32:52,909] Test: Loss: 1.272 | Acc: 68.816 (34408/50000)
[2022-06-17 11:32:52,909] Epoch: 363
[2022-06-17 11:44:10,045] Train: Loss: 1.334 | Acc: 68.060 (871964/1281167) | Lr: 0.08375252525710779
[2022-06-17 11:44:51,398] Test: Loss: 1.370 | Acc: 66.988 (33494/50000)
[2022-06-17 11:44:51,398] Epoch: 364
[2022-06-17 11:56:18,083] Train: Loss: 1.330 | Acc: 68.149 (873105/1281167) | Lr: 0.08239631556816869
[2022-06-17 11:57:08,177] Test: Loss: 1.325 | Acc: 67.622 (33811/50000)
[2022-06-17 11:57:08,177] Epoch: 365
[2022-06-17 12:08:21,269] Train: Loss: 1.325 | Acc: 68.255 (874462/1281167) | Lr: 0.0810494275110127
[2022-06-17 12:09:02,791] Test: Loss: 1.245 | Acc: 69.408 (34704/50000)
[2022-06-17 12:09:02,791] Saving..
[2022-06-17 12:09:02,862] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 12:09:02,862] Epoch: 366
[2022-06-17 12:20:16,692] Train: Loss: 1.321 | Acc: 68.352 (875697/1281167) | Lr: 0.0797119187818415
[2022-06-17 12:21:00,881] Test: Loss: 1.243 | Acc: 69.254 (34627/50000)
[2022-06-17 12:21:00,881] Epoch: 367
[2022-06-17 12:32:16,785] Train: Loss: 1.316 | Acc: 68.468 (877189/1281167) | Lr: 0.07838384667507721
[2022-06-17 12:32:57,840] Test: Loss: 1.213 | Acc: 70.178 (35089/50000)
[2022-06-17 12:32:57,840] Saving..
[2022-06-17 12:32:57,910] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 12:32:57,910] Epoch: 368
[2022-06-17 12:44:11,584] Train: Loss: 1.310 | Acc: 68.529 (877975/1281167) | Lr: 0.07706526808090909
[2022-06-17 12:44:54,240] Test: Loss: 1.257 | Acc: 69.214 (34607/50000)
[2022-06-17 12:44:54,240] Epoch: 369
[2022-06-17 12:56:13,548] Train: Loss: 1.309 | Acc: 68.612 (879032/1281167) | Lr: 0.07575623948285524
[2022-06-17 12:56:56,289] Test: Loss: 1.225 | Acc: 69.708 (34854/50000)
[2022-06-17 12:56:56,289] Epoch: 370
[2022-06-17 13:08:13,548] Train: Loss: 1.306 | Acc: 68.641 (879407/1281167) | Lr: 0.07445681695534413
[2022-06-17 13:08:53,274] Test: Loss: 1.230 | Acc: 69.754 (34877/50000)
[2022-06-17 13:08:53,275] Epoch: 371
[2022-06-17 13:20:10,679] Train: Loss: 1.299 | Acc: 68.782 (881218/1281167) | Lr: 0.073167056161312
[2022-06-17 13:20:58,269] Test: Loss: 1.201 | Acc: 70.496 (35248/50000)
[2022-06-17 13:20:58,269] Saving..
[2022-06-17 13:20:58,383] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 13:20:58,383] Epoch: 372
[2022-06-17 13:32:25,438] Train: Loss: 1.297 | Acc: 68.853 (882119/1281167) | Lr: 0.07188701234981865
[2022-06-17 13:33:11,766] Test: Loss: 1.218 | Acc: 70.052 (35026/50000)
[2022-06-17 13:33:11,767] Epoch: 373
[2022-06-17 13:44:24,984] Train: Loss: 1.292 | Acc: 68.935 (883171/1281167) | Lr: 0.07061674035368062
[2022-06-17 13:45:06,064] Test: Loss: 1.210 | Acc: 70.140 (35070/50000)
[2022-06-17 13:45:06,064] Epoch: 374
[2022-06-17 13:56:16,352] Train: Loss: 1.286 | Acc: 69.087 (885119/1281167) | Lr: 0.06935629458712246
[2022-06-17 13:57:02,504] Test: Loss: 1.229 | Acc: 69.802 (34901/50000)
[2022-06-17 13:57:02,504] Epoch: 375
[2022-06-17 14:08:19,714] Train: Loss: 1.281 | Acc: 69.170 (886188/1281167) | Lr: 0.06810572904344565
[2022-06-17 14:09:03,006] Test: Loss: 1.228 | Acc: 69.578 (34789/50000)
[2022-06-17 14:09:03,006] Epoch: 376
[2022-06-17 14:20:15,098] Train: Loss: 1.279 | Acc: 69.222 (886854/1281167) | Lr: 0.06686509729271595
[2022-06-17 14:20:55,948] Test: Loss: 1.260 | Acc: 69.012 (34506/50000)
[2022-06-17 14:20:55,948] Epoch: 377
[2022-06-17 14:32:06,238] Train: Loss: 1.277 | Acc: 69.276 (887539/1281167) | Lr: 0.06563445247946846
[2022-06-17 14:32:51,344] Test: Loss: 1.223 | Acc: 69.826 (34913/50000)
[2022-06-17 14:32:51,344] Epoch: 378
[2022-06-17 14:44:04,980] Train: Loss: 1.269 | Acc: 69.437 (889603/1281167) | Lr: 0.06441384732043082
[2022-06-17 14:44:47,550] Test: Loss: 1.201 | Acc: 70.374 (35187/50000)
[2022-06-17 14:44:47,550] Epoch: 379
[2022-06-17 14:55:54,468] Train: Loss: 1.263 | Acc: 69.599 (891679/1281167) | Lr: 0.06320333410226592
[2022-06-17 14:56:36,044] Test: Loss: 1.198 | Acc: 70.432 (35216/50000)
[2022-06-17 14:56:36,045] Epoch: 380
[2022-06-17 15:07:47,253] Train: Loss: 1.261 | Acc: 69.624 (891997/1281167) | Lr: 0.06200296467933081
[2022-06-17 15:08:29,572] Test: Loss: 1.196 | Acc: 70.462 (35231/50000)
[2022-06-17 15:08:29,573] Epoch: 381
[2022-06-17 15:19:50,137] Train: Loss: 1.255 | Acc: 69.727 (893322/1281167) | Lr: 0.0608127904714567
[2022-06-17 15:20:30,471] Test: Loss: 1.208 | Acc: 70.244 (35122/50000)
[2022-06-17 15:20:30,471] Epoch: 382
[2022-06-17 15:31:44,672] Train: Loss: 1.254 | Acc: 69.787 (894083/1281167) | Lr: 0.05963286246174523
[2022-06-17 15:32:24,505] Test: Loss: 1.215 | Acc: 70.196 (35098/50000)
[2022-06-17 15:32:24,506] Epoch: 383
[2022-06-17 15:43:41,414] Train: Loss: 1.246 | Acc: 69.920 (895792/1281167) | Lr: 0.0584632311943853
[2022-06-17 15:44:23,021] Test: Loss: 1.186 | Acc: 70.780 (35390/50000)
[2022-06-17 15:44:23,021] Saving..
[2022-06-17 15:44:23,095] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 15:44:23,095] Epoch: 384
[2022-06-17 15:55:39,514] Train: Loss: 1.244 | Acc: 69.996 (896770/1281167) | Lr: 0.05730394677248761
[2022-06-17 15:56:21,439] Test: Loss: 1.191 | Acc: 70.492 (35246/50000)
[2022-06-17 15:56:21,440] Epoch: 385
[2022-06-17 16:07:30,931] Train: Loss: 1.237 | Acc: 70.199 (899363/1281167) | Lr: 0.056155058855938356
[2022-06-17 16:08:13,914] Test: Loss: 1.182 | Acc: 70.614 (35307/50000)
[2022-06-17 16:08:13,915] Epoch: 386
[2022-06-17 16:19:19,552] Train: Loss: 1.232 | Acc: 70.264 (900204/1281167) | Lr: 0.05501661665927207
[2022-06-17 16:20:01,124] Test: Loss: 1.159 | Acc: 71.376 (35688/50000)
[2022-06-17 16:20:01,124] Saving..
[2022-06-17 16:20:01,216] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 16:20:01,216] Epoch: 387
[2022-06-17 16:31:13,182] Train: Loss: 1.229 | Acc: 70.315 (900852/1281167) | Lr: 0.05388866894956349
[2022-06-17 16:31:53,230] Test: Loss: 1.244 | Acc: 69.330 (34665/50000)
[2022-06-17 16:31:53,230] Epoch: 388
[2022-06-17 16:43:10,153] Train: Loss: 1.225 | Acc: 70.421 (902210/1281167) | Lr: 0.052771264044338406
[2022-06-17 16:43:49,127] Test: Loss: 1.157 | Acc: 71.428 (35714/50000)
[2022-06-17 16:43:49,128] Saving..
[2022-06-17 16:43:49,245] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 16:43:49,245] Epoch: 389
[2022-06-17 16:55:06,209] Train: Loss: 1.218 | Acc: 70.579 (904240/1281167) | Lr: 0.05166444980950378
[2022-06-17 16:55:44,711] Test: Loss: 1.176 | Acc: 70.936 (35468/50000)
[2022-06-17 16:55:44,711] Epoch: 390
[2022-06-17 17:07:06,378] Train: Loss: 1.215 | Acc: 70.671 (905409/1281167) | Lr: 0.050568273657297956
[2022-06-17 17:07:47,231] Test: Loss: 1.160 | Acc: 71.076 (35538/50000)
[2022-06-17 17:07:47,232] Epoch: 391
[2022-06-17 17:19:00,720] Train: Loss: 1.207 | Acc: 70.815 (907261/1281167) | Lr: 0.04948278254425858
[2022-06-17 17:19:45,930] Test: Loss: 1.219 | Acc: 70.078 (35039/50000)
[2022-06-17 17:19:45,931] Epoch: 392
[2022-06-17 17:30:55,403] Train: Loss: 1.203 | Acc: 70.912 (908499/1281167) | Lr: 0.04840802296921249
[2022-06-17 17:31:35,991] Test: Loss: 1.179 | Acc: 71.088 (35544/50000)
[2022-06-17 17:31:35,991] Epoch: 393
[2022-06-17 17:42:48,169] Train: Loss: 1.199 | Acc: 71.012 (909788/1281167) | Lr: 0.0473440409712826
[2022-06-17 17:43:29,885] Test: Loss: 1.161 | Acc: 71.452 (35726/50000)
[2022-06-17 17:43:29,886] Saving..
[2022-06-17 17:43:29,977] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 17:43:29,977] Epoch: 394
[2022-06-17 17:54:37,131] Train: Loss: 1.194 | Acc: 71.083 (910693/1281167) | Lr: 0.04629088212791651
[2022-06-17 17:55:18,403] Test: Loss: 1.150 | Acc: 71.690 (35845/50000)
[2022-06-17 17:55:18,403] Saving..
[2022-06-17 17:55:18,495] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 17:55:18,496] Epoch: 395
[2022-06-17 18:06:32,934] Train: Loss: 1.190 | Acc: 71.183 (911970/1281167) | Lr: 0.04524859155293395
[2022-06-17 18:07:13,875] Test: Loss: 1.181 | Acc: 70.998 (35499/50000)
[2022-06-17 18:07:13,875] Epoch: 396
[2022-06-17 18:18:24,870] Train: Loss: 1.185 | Acc: 71.297 (913433/1281167) | Lr: 0.04421721389459408
[2022-06-17 18:19:15,585] Test: Loss: 1.156 | Acc: 71.556 (35778/50000)
[2022-06-17 18:19:15,585] Epoch: 397
[2022-06-17 18:30:23,430] Train: Loss: 1.184 | Acc: 71.383 (914533/1281167) | Lr: 0.04319679333368313
[2022-06-17 18:31:04,428] Test: Loss: 1.130 | Acc: 72.284 (36142/50000)
[2022-06-17 18:31:04,428] Saving..
[2022-06-17 18:31:04,523] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 18:31:04,524] Epoch: 398
[2022-06-17 18:42:16,537] Train: Loss: 1.174 | Acc: 71.539 (916536/1281167) | Lr: 0.04218737358162167
[2022-06-17 18:42:57,564] Test: Loss: 1.134 | Acc: 72.010 (36005/50000)
[2022-06-17 18:42:57,565] Epoch: 399
[2022-06-17 18:54:03,100] Train: Loss: 1.171 | Acc: 71.617 (917537/1281167) | Lr: 0.04118899787859231
[2022-06-17 18:54:45,045] Test: Loss: 1.129 | Acc: 72.234 (36117/50000)
[2022-06-17 18:54:45,046] Epoch: 400
[2022-06-17 19:06:05,905] Train: Loss: 1.165 | Acc: 71.683 (918377/1281167) | Lr: 0.040201708991687284
[2022-06-17 19:06:45,984] Test: Loss: 1.132 | Acc: 71.868 (35934/50000)
[2022-06-17 19:06:45,984] Epoch: 401
[2022-06-17 19:18:07,318] Train: Loss: 1.165 | Acc: 71.739 (919101/1281167) | Lr: 0.039225549213076645
[2022-06-17 19:18:53,433] Test: Loss: 1.126 | Acc: 72.144 (36072/50000)
[2022-06-17 19:18:53,433] Epoch: 402
[2022-06-17 19:30:08,856] Train: Loss: 1.157 | Acc: 71.900 (921164/1281167) | Lr: 0.03826056035819619
[2022-06-17 19:30:53,766] Test: Loss: 1.127 | Acc: 72.286 (36143/50000)
[2022-06-17 19:30:53,766] Saving..
[2022-06-17 19:30:53,868] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 19:30:53,868] Epoch: 403
[2022-06-17 19:42:17,524] Train: Loss: 1.151 | Acc: 72.024 (922746/1281167) | Lr: 0.037306783763956984
[2022-06-17 19:43:01,285] Test: Loss: 1.141 | Acc: 71.850 (35925/50000)
[2022-06-17 19:43:01,285] Epoch: 404
[2022-06-17 19:54:22,211] Train: Loss: 1.146 | Acc: 72.186 (924826/1281167) | Lr: 0.036364260286973704
[2022-06-17 19:55:07,523] Test: Loss: 1.125 | Acc: 72.126 (36063/50000)
[2022-06-17 19:55:07,524] Epoch: 405
[2022-06-17 20:06:24,182] Train: Loss: 1.142 | Acc: 72.274 (925949/1281167) | Lr: 0.03543303030181524
[2022-06-17 20:07:13,100] Test: Loss: 1.153 | Acc: 71.582 (35791/50000)
[2022-06-17 20:07:13,101] Epoch: 406
[2022-06-17 20:18:27,832] Train: Loss: 1.135 | Acc: 72.409 (927679/1281167) | Lr: 0.034513133699274764
[2022-06-17 20:19:08,440] Test: Loss: 1.119 | Acc: 72.552 (36276/50000)
[2022-06-17 20:19:08,441] Saving..
[2022-06-17 20:19:08,536] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 20:19:08,537] Epoch: 407
[2022-06-17 20:30:34,455] Train: Loss: 1.129 | Acc: 72.533 (929272/1281167) | Lr: 0.03360460988466101
[2022-06-17 20:31:24,240] Test: Loss: 1.138 | Acc: 71.804 (35902/50000)
[2022-06-17 20:31:24,240] Epoch: 408
[2022-06-17 20:42:40,286] Train: Loss: 1.124 | Acc: 72.689 (931262/1281167) | Lr: 0.03270749777611059
[2022-06-17 20:43:21,557] Test: Loss: 1.101 | Acc: 72.490 (36245/50000)
[2022-06-17 20:43:21,557] Epoch: 409
[2022-06-17 20:54:42,540] Train: Loss: 1.118 | Acc: 72.794 (932608/1281167) | Lr: 0.0318218358029202
[2022-06-17 20:55:26,116] Test: Loss: 1.106 | Acc: 72.564 (36282/50000)
[2022-06-17 20:55:26,132] Saving..
[2022-06-17 20:55:26,229] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 20:55:26,229] Epoch: 410
[2022-06-17 21:06:29,694] Train: Loss: 1.116 | Acc: 72.803 (932733/1281167) | Lr: 0.030947661903901174
[2022-06-17 21:07:11,196] Test: Loss: 1.123 | Acc: 72.568 (36284/50000)
[2022-06-17 21:07:11,197] Saving..
[2022-06-17 21:07:11,277] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 21:07:11,278] Epoch: 411
[2022-06-17 21:18:01,772] Train: Loss: 1.111 | Acc: 72.955 (934676/1281167) | Lr: 0.030085013525753792
[2022-06-17 21:18:41,525] Test: Loss: 1.147 | Acc: 71.842 (35921/50000)
[2022-06-17 21:18:41,526] Epoch: 412
[2022-06-17 21:29:36,991] Train: Loss: 1.103 | Acc: 73.104 (936588/1281167) | Lr: 0.029233927621463613
[2022-06-17 21:30:18,944] Test: Loss: 1.099 | Acc: 72.756 (36378/50000)
[2022-06-17 21:30:18,944] Saving..
[2022-06-17 21:30:19,037] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 21:30:19,037] Epoch: 413
[2022-06-17 21:41:18,923] Train: Loss: 1.102 | Acc: 73.207 (937898/1281167) | Lr: 0.02839444064871788
[2022-06-17 21:41:59,717] Test: Loss: 1.105 | Acc: 72.868 (36434/50000)
[2022-06-17 21:41:59,718] Saving..
[2022-06-17 21:41:59,786] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 21:41:59,786] Epoch: 414
[2022-06-17 21:52:47,596] Train: Loss: 1.094 | Acc: 73.289 (938951/1281167) | Lr: 0.02756658856834477
[2022-06-17 21:53:28,673] Test: Loss: 1.118 | Acc: 72.600 (36300/50000)
[2022-06-17 21:53:28,673] Epoch: 415
[2022-06-17 22:04:29,067] Train: Loss: 1.089 | Acc: 73.407 (940462/1281167) | Lr: 0.026750406842771888
[2022-06-17 22:05:09,874] Test: Loss: 1.090 | Acc: 73.124 (36562/50000)
[2022-06-17 22:05:09,875] Saving..
[2022-06-17 22:05:09,954] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 22:05:09,955] Epoch: 416
[2022-06-17 22:16:15,291] Train: Loss: 1.083 | Acc: 73.592 (942842/1281167) | Lr: 0.025945930434507904
[2022-06-17 22:16:57,025] Test: Loss: 1.095 | Acc: 72.900 (36450/50000)
[2022-06-17 22:16:57,025] Epoch: 417
[2022-06-17 22:28:00,343] Train: Loss: 1.081 | Acc: 73.617 (943163/1281167) | Lr: 0.025153193804644813
[2022-06-17 22:28:47,692] Test: Loss: 1.096 | Acc: 73.244 (36622/50000)
[2022-06-17 22:28:47,692] Saving..
[2022-06-17 22:28:47,763] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 22:28:47,763] Epoch: 418
[2022-06-17 22:39:44,837] Train: Loss: 1.073 | Acc: 73.783 (945279/1281167) | Lr: 0.024372230911381205
[2022-06-17 22:40:26,394] Test: Loss: 1.081 | Acc: 73.196 (36598/50000)
[2022-06-17 22:40:26,395] Epoch: 419
[2022-06-17 22:51:25,559] Train: Loss: 1.068 | Acc: 73.897 (946750/1281167) | Lr: 0.02360307520856838
[2022-06-17 22:52:06,675] Test: Loss: 1.086 | Acc: 73.538 (36769/50000)
[2022-06-17 22:52:06,675] Saving..
[2022-06-17 22:52:06,745] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 22:52:06,745] Epoch: 420
[2022-06-17 23:03:06,638] Train: Loss: 1.059 | Acc: 74.134 (949783/1281167) | Lr: 0.02284575964427652
[2022-06-17 23:03:47,913] Test: Loss: 1.072 | Acc: 73.648 (36824/50000)
[2022-06-17 23:03:47,913] Saving..
[2022-06-17 23:03:48,002] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 23:03:48,002] Epoch: 421
[2022-06-17 23:14:48,828] Train: Loss: 1.055 | Acc: 74.216 (950837/1281167) | Lr: 0.02210031665938393
[2022-06-17 23:15:42,862] Test: Loss: 1.085 | Acc: 73.176 (36588/50000)
[2022-06-17 23:15:42,863] Epoch: 422
[2022-06-17 23:26:34,616] Train: Loss: 1.051 | Acc: 74.289 (951764/1281167) | Lr: 0.021366778186187076
[2022-06-17 23:27:16,391] Test: Loss: 1.071 | Acc: 73.668 (36834/50000)
[2022-06-17 23:27:16,391] Saving..
[2022-06-17 23:27:16,465] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 23:27:16,465] Epoch: 423
[2022-06-17 23:38:19,549] Train: Loss: 1.046 | Acc: 74.415 (953375/1281167) | Lr: 0.02064517564703278
[2022-06-17 23:38:59,546] Test: Loss: 1.083 | Acc: 73.304 (36652/50000)
[2022-06-17 23:38:59,547] Epoch: 424
[2022-06-17 23:49:51,206] Train: Loss: 1.038 | Acc: 74.588 (955592/1281167) | Lr: 0.019935539952971953
[2022-06-17 23:50:44,306] Test: Loss: 1.064 | Acc: 73.750 (36875/50000)
[2022-06-17 23:50:44,306] Saving..
[2022-06-17 23:50:44,419] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-17 23:50:44,420] Epoch: 425
[2022-06-18 00:01:44,236] Train: Loss: 1.032 | Acc: 74.717 (957250/1281167) | Lr: 0.019237901502436115
[2022-06-18 00:02:24,041] Test: Loss: 1.060 | Acc: 73.826 (36913/50000)
[2022-06-18 00:02:24,042] Saving..
[2022-06-18 00:02:24,147] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 00:02:24,147] Epoch: 426
[2022-06-18 00:13:19,527] Train: Loss: 1.024 | Acc: 74.956 (960308/1281167) | Lr: 0.018552290179934355
[2022-06-18 00:14:13,795] Test: Loss: 1.080 | Acc: 73.470 (36735/50000)
[2022-06-18 00:14:13,796] Epoch: 427
[2022-06-18 00:25:19,044] Train: Loss: 1.023 | Acc: 74.941 (960119/1281167) | Lr: 0.017878735354773998
[2022-06-18 00:25:59,084] Test: Loss: 1.064 | Acc: 73.708 (36854/50000)
[2022-06-18 00:25:59,084] Epoch: 428
[2022-06-18 00:36:54,945] Train: Loss: 1.018 | Acc: 75.072 (961803/1281167) | Lr: 0.017217265879801952
[2022-06-18 00:37:35,510] Test: Loss: 1.063 | Acc: 73.868 (36934/50000)
[2022-06-18 00:37:35,510] Saving..
[2022-06-18 00:37:35,592] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 00:37:35,592] Epoch: 429
[2022-06-18 00:48:34,536] Train: Loss: 1.009 | Acc: 75.253 (964122/1281167) | Lr: 0.01656791009016891
[2022-06-18 00:49:15,182] Test: Loss: 1.065 | Acc: 73.682 (36841/50000)
[2022-06-18 00:49:15,183] Epoch: 430
[2022-06-18 01:00:17,468] Train: Loss: 1.007 | Acc: 75.269 (964322/1281167) | Lr: 0.015930695802115792
[2022-06-18 01:00:59,497] Test: Loss: 1.059 | Acc: 73.846 (36923/50000)
[2022-06-18 01:00:59,498] Epoch: 431
[2022-06-18 01:11:58,974] Train: Loss: 0.999 | Acc: 75.537 (967756/1281167) | Lr: 0.015305650311781776
[2022-06-18 01:12:39,212] Test: Loss: 1.073 | Acc: 73.564 (36782/50000)
[2022-06-18 01:12:39,213] Epoch: 432
[2022-06-18 01:24:29,900] Train: Loss: 0.992 | Acc: 75.650 (969203/1281167) | Lr: 0.014692800394035406
[2022-06-18 01:25:09,824] Test: Loss: 1.057 | Acc: 73.930 (36965/50000)
[2022-06-18 01:25:09,824] Saving..
[2022-06-18 01:25:09,914] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 01:25:09,915] Epoch: 433
[2022-06-18 01:35:52,912] Train: Loss: 0.989 | Acc: 75.753 (970518/1281167) | Lr: 0.01409217230132743
[2022-06-18 01:36:34,120] Test: Loss: 1.048 | Acc: 74.248 (37124/50000)
[2022-06-18 01:36:34,120] Saving..
[2022-06-18 01:36:34,191] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 01:36:34,191] Epoch: 434
[2022-06-18 01:47:20,790] Train: Loss: 0.982 | Acc: 75.864 (971944/1281167) | Lr: 0.01350379176256632
[2022-06-18 01:48:01,547] Test: Loss: 1.048 | Acc: 74.250 (37125/50000)
[2022-06-18 01:48:01,547] Saving..
[2022-06-18 01:48:01,639] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 01:48:01,639] Epoch: 435
[2022-06-18 01:58:53,908] Train: Loss: 0.977 | Acc: 76.003 (973723/1281167) | Lr: 0.012927683982016004
[2022-06-18 01:59:33,122] Test: Loss: 1.052 | Acc: 74.218 (37109/50000)
[2022-06-18 01:59:33,123] Epoch: 436
[2022-06-18 02:10:26,089] Train: Loss: 0.972 | Acc: 76.139 (975473/1281167) | Lr: 0.01236387363821645
[2022-06-18 02:11:07,520] Test: Loss: 1.042 | Acc: 74.370 (37185/50000)
[2022-06-18 02:11:07,520] Saving..
[2022-06-18 02:11:07,588] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 02:11:07,589] Epoch: 437
[2022-06-18 02:21:56,623] Train: Loss: 0.965 | Acc: 76.301 (977545/1281167) | Lr: 0.011812384882926191
[2022-06-18 02:22:37,128] Test: Loss: 1.044 | Acc: 74.330 (37165/50000)
[2022-06-18 02:22:37,129] Epoch: 438
[2022-06-18 02:33:24,332] Train: Loss: 0.962 | Acc: 76.389 (978666/1281167) | Lr: 0.011273241340088076
[2022-06-18 02:34:03,659] Test: Loss: 1.042 | Acc: 74.392 (37196/50000)
[2022-06-18 02:34:03,659] Saving..
[2022-06-18 02:34:03,744] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 02:34:03,744] Epoch: 439
[2022-06-18 02:44:51,225] Train: Loss: 0.954 | Acc: 76.580 (981112/1281167) | Lr: 0.01074646610481706
[2022-06-18 02:45:31,373] Test: Loss: 1.040 | Acc: 74.488 (37244/50000)
[2022-06-18 02:45:31,373] Saving..
[2022-06-18 02:45:31,462] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 02:45:31,462] Epoch: 440
[2022-06-18 02:56:17,239] Train: Loss: 0.950 | Acc: 76.638 (981862/1281167) | Lr: 0.010232081742410942
[2022-06-18 02:56:58,994] Test: Loss: 1.034 | Acc: 74.662 (37331/50000)
[2022-06-18 02:56:58,995] Saving..
[2022-06-18 02:56:59,104] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 02:56:59,104] Epoch: 441
[2022-06-18 03:07:54,913] Train: Loss: 0.944 | Acc: 76.781 (983691/1281167) | Lr: 0.009730110287383863
[2022-06-18 03:08:37,813] Test: Loss: 1.036 | Acc: 74.580 (37290/50000)
[2022-06-18 03:08:37,814] Epoch: 442
[2022-06-18 03:19:44,001] Train: Loss: 0.939 | Acc: 76.868 (984810/1281167) | Lr: 0.009240573242522235
[2022-06-18 03:20:21,750] Test: Loss: 1.039 | Acc: 74.424 (37212/50000)
[2022-06-18 03:20:21,750] Epoch: 443
[2022-06-18 03:31:20,027] Train: Loss: 0.934 | Acc: 76.974 (986161/1281167) | Lr: 0.008763491577963696
[2022-06-18 03:32:12,517] Test: Loss: 1.036 | Acc: 74.658 (37329/50000)
[2022-06-18 03:32:12,518] Epoch: 444
[2022-06-18 03:43:00,000] Train: Loss: 0.930 | Acc: 77.110 (987909/1281167) | Lr: 0.008298885730299011
[2022-06-18 03:43:39,798] Test: Loss: 1.034 | Acc: 74.634 (37317/50000)
[2022-06-18 03:43:39,799] Epoch: 445
[2022-06-18 03:54:31,754] Train: Loss: 0.924 | Acc: 77.263 (989871/1281167) | Lr: 0.007846775601696289
[2022-06-18 03:55:13,094] Test: Loss: 1.032 | Acc: 74.698 (37349/50000)
[2022-06-18 03:55:13,095] Saving..
[2022-06-18 03:55:13,185] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 03:55:13,185] Epoch: 446
[2022-06-18 04:05:59,063] Train: Loss: 0.918 | Acc: 77.388 (991472/1281167) | Lr: 0.007407180559048736
[2022-06-18 04:06:39,770] Test: Loss: 1.030 | Acc: 74.668 (37334/50000)
[2022-06-18 04:06:39,771] Epoch: 447
[2022-06-18 04:17:36,736] Train: Loss: 0.913 | Acc: 77.503 (992937/1281167) | Lr: 0.006980119433144881
[2022-06-18 04:18:16,634] Test: Loss: 1.023 | Acc: 74.936 (37468/50000)
[2022-06-18 04:18:16,634] Saving..
[2022-06-18 04:18:16,731] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 04:18:16,731] Epoch: 448
[2022-06-18 04:29:13,601] Train: Loss: 0.908 | Acc: 77.658 (994928/1281167) | Lr: 0.006565610517861955
[2022-06-18 04:29:55,349] Test: Loss: 1.029 | Acc: 74.898 (37449/50000)
[2022-06-18 04:29:55,349] Epoch: 449
[2022-06-18 04:40:50,945] Train: Loss: 0.903 | Acc: 77.774 (996419/1281167) | Lr: 0.006163671569382373
[2022-06-18 04:41:38,235] Test: Loss: 1.016 | Acc: 74.940 (37470/50000)
[2022-06-18 04:41:38,236] Saving..
[2022-06-18 04:41:38,347] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 04:41:38,347] Epoch: 450
[2022-06-18 04:52:35,198] Train: Loss: 0.898 | Acc: 77.873 (997678/1281167) | Lr: 0.005774319805432881
[2022-06-18 04:53:15,719] Test: Loss: 1.027 | Acc: 74.846 (37423/50000)
[2022-06-18 04:53:15,720] Epoch: 451
[2022-06-18 05:04:17,181] Train: Loss: 0.892 | Acc: 78.008 (999410/1281167) | Lr: 0.005397571904547165
[2022-06-18 05:05:12,692] Test: Loss: 1.014 | Acc: 75.172 (37586/50000)
[2022-06-18 05:05:12,692] Saving..
[2022-06-18 05:05:12,789] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 05:05:12,790] Epoch: 452
[2022-06-18 05:16:12,060] Train: Loss: 0.888 | Acc: 78.133 (1001018/1281167) | Lr: 0.005033444005351463
[2022-06-18 05:16:52,356] Test: Loss: 1.012 | Acc: 75.242 (37621/50000)
[2022-06-18 05:16:52,356] Saving..
[2022-06-18 05:16:52,437] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 05:16:52,437] Epoch: 453
[2022-06-18 05:27:47,078] Train: Loss: 0.886 | Acc: 78.171 (1001506/1281167) | Lr: 0.004681951705872932
[2022-06-18 05:28:28,331] Test: Loss: 1.011 | Acc: 75.062 (37531/50000)
[2022-06-18 05:28:28,332] Epoch: 454
[2022-06-18 05:39:25,300] Train: Loss: 0.878 | Acc: 78.345 (1003730/1281167) | Lr: 0.004343110062871961
[2022-06-18 05:40:10,295] Test: Loss: 1.013 | Acc: 75.216 (37608/50000)
[2022-06-18 05:40:10,296] Epoch: 455
[2022-06-18 05:50:57,540] Train: Loss: 0.875 | Acc: 78.424 (1004740/1281167) | Lr: 0.004016933591196727
[2022-06-18 05:51:38,347] Test: Loss: 1.016 | Acc: 75.378 (37689/50000)
[2022-06-18 05:51:38,348] Saving..
[2022-06-18 05:51:38,423] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 05:51:38,423] Epoch: 456
[2022-06-18 06:02:27,502] Train: Loss: 0.872 | Acc: 78.502 (1005747/1281167) | Lr: 0.003703436263161675
[2022-06-18 06:03:07,814] Test: Loss: 1.015 | Acc: 75.382 (37691/50000)
[2022-06-18 06:03:07,814] Saving..
[2022-06-18 06:03:07,908] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 06:03:07,908] Epoch: 457
[2022-06-18 06:14:01,071] Train: Loss: 0.869 | Acc: 78.632 (1007405/1281167) | Lr: 0.0034026315079488997
[2022-06-18 06:14:42,525] Test: Loss: 1.011 | Acc: 75.228 (37614/50000)
[2022-06-18 06:14:42,525] Epoch: 458
[2022-06-18 06:25:42,571] Train: Loss: 0.864 | Acc: 78.749 (1008910/1281167) | Lr: 0.0031145322110330566
[2022-06-18 06:26:23,567] Test: Loss: 1.013 | Acc: 75.354 (37677/50000)
[2022-06-18 06:26:23,567] Epoch: 459
[2022-06-18 06:37:10,759] Train: Loss: 0.861 | Acc: 78.775 (1009235/1281167) | Lr: 0.0028391507136290403
[2022-06-18 06:37:51,658] Test: Loss: 1.008 | Acc: 75.354 (37677/50000)
[2022-06-18 06:37:51,659] Epoch: 460
[2022-06-18 06:48:45,158] Train: Loss: 0.859 | Acc: 78.784 (1009359/1281167) | Lr: 0.0025764988121637525
[2022-06-18 06:49:27,388] Test: Loss: 1.009 | Acc: 75.416 (37708/50000)
[2022-06-18 06:49:27,389] Saving..
[2022-06-18 06:49:27,492] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 06:49:27,492] Epoch: 461
[2022-06-18 07:00:22,367] Train: Loss: 0.859 | Acc: 78.862 (1010355/1281167) | Lr: 0.0023265877577704455
[2022-06-18 07:01:02,544] Test: Loss: 1.005 | Acc: 75.560 (37780/50000)
[2022-06-18 07:01:02,544] Saving..
[2022-06-18 07:01:02,641] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 07:01:02,641] Epoch: 462
[2022-06-18 07:12:05,542] Train: Loss: 0.850 | Acc: 79.012 (1012278/1281167) | Lr: 0.002089428255806885
[2022-06-18 07:12:45,788] Test: Loss: 1.008 | Acc: 75.516 (37758/50000)
[2022-06-18 07:12:45,789] Epoch: 463
[2022-06-18 07:23:37,328] Train: Loss: 0.847 | Acc: 79.134 (1013844/1281167) | Lr: 0.0018650304653968564
[2022-06-18 07:24:17,956] Test: Loss: 1.009 | Acc: 75.590 (37795/50000)
[2022-06-18 07:24:17,956] Saving..
[2022-06-18 07:24:18,028] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 07:24:18,028] Epoch: 464
[2022-06-18 07:35:23,244] Train: Loss: 0.848 | Acc: 79.116 (1013613/1281167) | Lr: 0.00165340399899482
[2022-06-18 07:36:02,986] Test: Loss: 1.002 | Acc: 75.520 (37760/50000)
[2022-06-18 07:36:02,987] Epoch: 465
[2022-06-18 07:46:51,128] Train: Loss: 0.845 | Acc: 79.194 (1014602/1281167) | Lr: 0.0014545579219743146
[2022-06-18 07:47:31,692] Test: Loss: 1.004 | Acc: 75.650 (37825/50000)
[2022-06-18 07:47:31,693] Saving..
[2022-06-18 07:47:31,796] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 07:47:31,796] Epoch: 466
[2022-06-18 07:58:29,331] Train: Loss: 0.842 | Acc: 79.235 (1015131/1281167) | Lr: 0.001268500752239375
[2022-06-18 07:59:09,291] Test: Loss: 1.004 | Acc: 75.692 (37846/50000)
[2022-06-18 07:59:09,292] Saving..
[2022-06-18 07:59:09,361] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 07:59:09,361] Epoch: 467
[2022-06-18 08:10:02,773] Train: Loss: 0.840 | Acc: 79.291 (1015855/1281167) | Lr: 0.001095240459859929
[2022-06-18 08:10:41,596] Test: Loss: 1.000 | Acc: 75.568 (37784/50000)
[2022-06-18 08:10:41,597] Epoch: 468
[2022-06-18 08:21:40,064] Train: Loss: 0.836 | Acc: 79.414 (1017427/1281167) | Lr: 0.000934784466730276
[2022-06-18 08:22:20,769] Test: Loss: 0.998 | Acc: 75.696 (37848/50000)
[2022-06-18 08:22:20,769] Saving..
[2022-06-18 08:22:20,840] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 08:22:20,840] Epoch: 469
[2022-06-18 08:33:13,877] Train: Loss: 0.834 | Acc: 79.402 (1017273/1281167) | Lr: 0.0007871396462511146
[2022-06-18 08:33:54,750] Test: Loss: 1.003 | Acc: 75.622 (37811/50000)
[2022-06-18 08:33:54,750] Epoch: 470
[2022-06-18 08:44:46,819] Train: Loss: 0.832 | Acc: 79.466 (1018094/1281167) | Lr: 0.0006523123230351489
[2022-06-18 08:45:27,194] Test: Loss: 0.998 | Acc: 75.756 (37878/50000)
[2022-06-18 08:45:27,195] Saving..
[2022-06-18 08:45:27,315] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 08:45:27,315] Epoch: 471
[2022-06-18 08:56:20,528] Train: Loss: 0.832 | Acc: 79.511 (1018668/1281167) | Lr: 0.0005303082726361429
[2022-06-18 08:57:00,664] Test: Loss: 1.000 | Acc: 75.694 (37847/50000)
[2022-06-18 08:57:00,664] Epoch: 472
[2022-06-18 09:07:50,914] Train: Loss: 0.831 | Acc: 79.567 (1019386/1281167) | Lr: 0.00042113272130162266
[2022-06-18 09:08:36,873] Test: Loss: 0.996 | Acc: 75.714 (37857/50000)
[2022-06-18 09:08:36,873] Epoch: 473
[2022-06-18 09:19:39,703] Train: Loss: 0.830 | Acc: 79.548 (1019147/1281167) | Lr: 0.0003247903457487596
[2022-06-18 09:20:20,562] Test: Loss: 0.998 | Acc: 75.672 (37836/50000)
[2022-06-18 09:20:20,563] Epoch: 474
[2022-06-18 09:31:24,025] Train: Loss: 0.828 | Acc: 79.596 (1019756/1281167) | Lr: 0.0002412852729643332
[2022-06-18 09:32:04,678] Test: Loss: 0.997 | Acc: 75.782 (37891/50000)
[2022-06-18 09:32:04,679] Saving..
[2022-06-18 09:32:04,927] * Saved checkpoint to ./results/14133147/FENet_imagenet.t7
[2022-06-18 09:32:04,928] Epoch: 475
[2022-06-18 09:43:07,183] Train: Loss: 0.829 | Acc: 79.568 (1019398/1281167) | Lr: 0.00017062108002767639
[2022-06-18 09:43:50,238] Test: Loss: 1.000 | Acc: 75.734 (37867/50000)
[2022-06-18 09:43:50,238] Epoch: 476
[2022-06-18 09:54:43,056] Train: Loss: 0.824 | Acc: 79.690 (1020963/1281167) | Lr: 0.00011280079395769976
[2022-06-18 09:55:22,930] Test: Loss: 0.998 | Acc: 75.768 (37884/50000)
[2022-06-18 09:55:22,931] Epoch: 477
[2022-06-18 10:06:28,004] Train: Loss: 0.827 | Acc: 79.669 (1020695/1281167) | Lr: 6.782689158296475e-05
[2022-06-18 10:07:16,254] Test: Loss: 0.999 | Acc: 75.666 (37833/50000)
[2022-06-18 10:07:16,255] Epoch: 478
[2022-06-18 10:18:04,949] Train: Loss: 0.827 | Acc: 79.619 (1020050/1281167) | Lr: 3.5701299435769944e-05
[2022-06-18 10:18:46,373] Test: Loss: 0.995 | Acc: 75.744 (37872/50000)
[2022-06-18 10:18:46,373] Epoch: 479
[2022-06-18 10:29:38,606] Train: Loss: 0.826 | Acc: 79.676 (1020782/1281167) | Lr: 1.642539366955192e-05
[2022-06-18 10:30:18,450] Test: Loss: 0.996 | Acc: 75.704 (37852/50000)