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FinalLoss nan Loss nan Accuracy 0.0000. #4

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mtli77 opened this issue Aug 16, 2021 · 2 comments
Open

FinalLoss nan Loss nan Accuracy 0.0000. #4

mtli77 opened this issue Aug 16, 2021 · 2 comments

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@mtli77
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mtli77 commented Aug 16, 2021

Hi, @liuzhengzhe
Thanks for sharing the code.
Considering my experiments environment:
cudatoolkit 10.1.243, pytorch 1.4.0, V10.0.130, python 3.7
But during the training phase, after about one thousand iterations, the accuracy was reported to be 0.000.
So, what is wrong with it?

[2021-08-16 13:41:51,361 INFO train.py line 404 34387] Epoch: [1/50][10/11134] Data 0.000 (0.092) Batch 0.455 (0.590) Remain 91:10:10 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.8626 FinalLoss 4.4894 Loss 6.3520 Accuracy 0.2712.
[2021-08-16 13:41:55,951 INFO train.py line 404 34387] Epoch: [1/50][20/11134] Data 0.000 (0.046) Batch 0.458 (0.524) Remain 81:04:28 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3041 FinalLoss 2.5996 Loss 3.9037 Accuracy 0.0000.
[2021-08-16 13:42:00,536 INFO train.py line 404 34387] Epoch: [1/50][30/11134] Data 0.000 (0.031) Batch 0.464 (0.502) Remain 77:41:01 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.4762 FinalLoss 2.7216 Loss 4.1978 Accuracy 0.1985.
[2021-08-16 13:42:05,016 INFO train.py line 404 34387] Epoch: [1/50][40/11134] Data 0.000 (0.023) Batch 0.443 (0.489) Remain 75:34:39 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.9213 FinalLoss 2.9439 Loss 4.8653 Accuracy 0.0022.
[2021-08-16 13:42:09,354 INFO train.py line 404 34387] Epoch: [1/50][50/11134] Data 0.000 (0.019) Batch 0.430 (0.478) Remain 73:52:39 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3698 FinalLoss 2.2716 Loss 3.6415 Accuracy 0.3121.
[2021-08-16 13:42:13,693 INFO train.py line 404 34387] Epoch: [1/50][60/11134] Data 0.000 (0.016) Batch 0.434 (0.470) Remain 72:44:41 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.1811 FinalLoss 1.5288 Loss 2.7098 Accuracy 0.2534.
[2021-08-16 13:42:18,093 INFO train.py line 404 34387] Epoch: [1/50][70/11134] Data 0.000 (0.014) Batch 0.436 (0.466) Remain 72:04:18 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3603 FinalLoss 2.2345 Loss 3.5948 Accuracy 0.1033.
[2021-08-16 13:42:22,459 INFO train.py line 404 34387] Epoch: [1/50][80/11134] Data 0.001 (0.012) Batch 0.443 (0.462) Remain 71:29:57 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 0.9852 FinalLoss 1.8379 Loss 2.8231 Accuracy 0.1762.
[2021-08-16 13:42:26,894 INFO train.py line 404 34387] Epoch: [1/50][90/11134] Data 0.000 (0.011) Batch 0.442 (0.460) Remain 71:10:22 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.2670 FinalLoss 1.7410 Loss 3.0080 Accuracy 0.5093.
[2021-08-16 13:42:31,337 INFO train.py line 404 34387] Epoch: [1/50][100/11134] Data 0.000 (0.010) Batch 0.448 (0.459) Remain 70:55:24 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.1950 FinalLoss 2.5355 Loss 3.7305 Accuracy 0.5194.
[2021-08-16 13:42:35,810 INFO train.py line 404 34387] Epoch: [1/50][110/11134] Data 0.000 (0.009) Batch 0.445 (0.458) Remain 70:45:42 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.1078 FinalLoss 2.5912 Loss 4.6991 Accuracy 0.0602.
[2021-08-16 13:42:40,195 INFO train.py line 404 34387] Epoch: [1/50][120/11134] Data 0.000 (0.008) Batch 0.439 (0.456) Remain 70:30:48 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.7995 FinalLoss 3.8212 Loss 5.6207 Accuracy 0.0527.
[2021-08-16 13:42:44,666 INFO train.py line 404 34387] Epoch: [1/50][130/11134] Data 0.001 (0.008) Batch 0.475 (0.455) Remain 70:24:16 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.9591 FinalLoss 2.8220 Loss 4.7811 Accuracy 0.0123.
[2021-08-16 13:42:49,175 INFO train.py line 404 34387] Epoch: [1/50][140/11134] Data 0.000 (0.007) Batch 0.453 (0.455) Remain 70:21:13 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.6016 FinalLoss 2.4701 Loss 4.0716 Accuracy 0.4707.
[2021-08-16 13:42:53,635 INFO train.py line 404 34387] Epoch: [1/50][150/11134] Data 0.000 (0.007) Batch 0.441 (0.454) Remain 70:15:34 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.5330 FinalLoss 2.6253 Loss 4.1583 Accuracy 0.0816.
[2021-08-16 13:42:58,093 INFO train.py line 404 34387] Epoch: [1/50][160/11134] Data 0.000 (0.006) Batch 0.435 (0.454) Remain 70:10:25 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.5738 FinalLoss 3.1311 Loss 4.7048 Accuracy 0.2208.
[2021-08-16 13:43:02,481 INFO train.py line 404 34387] Epoch: [1/50][170/11134] Data 0.000 (0.006) Batch 0.432 (0.453) Remain 70:02:08 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.2956 FinalLoss 2.3680 Loss 3.6636 Accuracy 0.1467.
[2021-08-16 13:43:06,854 INFO train.py line 404 34387] Epoch: [1/50][180/11134] Data 0.000 (0.006) Batch 0.438 (0.452) Remain 69:53:57 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.1471 FinalLoss 2.6863 Loss 4.8334 Accuracy 0.3574.
[2021-08-16 13:43:11,240 INFO train.py line 404 34387] Epoch: [1/50][190/11134] Data 0.000 (0.005) Batch 0.436 (0.451) Remain 69:47:14 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3994 FinalLoss 2.0155 Loss 3.4149 Accuracy 0.5467.
[2021-08-16 13:43:15,626 INFO train.py line 404 34387] Epoch: [1/50][200/11134] Data 0.000 (0.005) Batch 0.438 (0.451) Remain 69:41:13 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3985 FinalLoss 2.2922 Loss 3.6907 Accuracy 0.2464.
[2021-08-16 13:43:19,972 INFO train.py line 404 34387] Epoch: [1/50][210/11134] Data 0.000 (0.005) Batch 0.437 (0.450) Remain 69:33:59 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.5389 FinalLoss 2.9367 Loss 4.4756 Accuracy 0.0000.
[2021-08-16 13:43:24,377 INFO train.py line 404 34387] Epoch: [1/50][220/11134] Data 0.000 (0.005) Batch 0.446 (0.450) Remain 69:29:54 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.4376 FinalLoss 2.4369 Loss 3.8745 Accuracy 0.3331.
[2021-08-16 13:43:28,776 INFO train.py line 404 34387] Epoch: [1/50][230/11134] Data 0.000 (0.004) Batch 0.437 (0.449) Remain 69:25:52 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.7511 FinalLoss 1.5723 Loss 3.3234 Accuracy 0.0901.
[2021-08-16 13:43:33,207 INFO train.py line 404 34387] Epoch: [1/50][240/11134] Data 0.000 (0.004) Batch 0.441 (0.449) Remain 69:23:29 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.8288 FinalLoss 2.7171 Loss 4.5458 Accuracy 0.0000.
[2021-08-16 13:43:37,615 INFO train.py line 404 34387] Epoch: [1/50][250/11134] Data 0.000 (0.004) Batch 0.441 (0.449) Remain 69:20:23 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.6246 FinalLoss 3.0870 Loss 4.7117 Accuracy 0.2876.
[2021-08-16 13:43:42,015 INFO train.py line 404 34387] Epoch: [1/50][260/11134] Data 0.000 (0.004) Batch 0.460 (0.448) Remain 69:17:13 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3587 FinalLoss 2.1987 Loss 3.5575 Accuracy 0.1534.
[2021-08-16 13:43:46,408 INFO train.py line 404 34387] Epoch: [1/50][270/11134] Data 0.000 (0.004) Batch 0.433 (0.448) Remain 69:14:05 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.6425 FinalLoss 3.3426 Loss 5.9851 Accuracy 0.0000.
[2021-08-16 13:43:50,811 INFO train.py line 404 34387] Epoch: [1/50][280/11134] Data 0.000 (0.004) Batch 0.433 (0.448) Remain 69:11:28 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.7713 FinalLoss 2.5098 Loss 4.2811 Accuracy 0.0022.
[2021-08-16 13:43:55,164 INFO train.py line 404 34387] Epoch: [1/50][290/11134] Data 0.000 (0.004) Batch 0.434 (0.447) Remain 69:07:26 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.1183 FinalLoss 1.7739 Loss 2.8922 Accuracy 0.1032.
[2021-08-16 13:43:59,541 INFO train.py line 404 34387] Epoch: [1/50][300/11134] Data 0.000 (0.004) Batch 0.436 (0.447) Remain 69:04:26 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.2018 FinalLoss 1.7992 Loss 3.0011 Accuracy 0.1387.
[2021-08-16 13:44:03,899 INFO train.py line 404 34387] Epoch: [1/50][310/11134] Data 0.000 (0.003) Batch 0.433 (0.447) Remain 69:01:01 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.0422 FinalLoss 1.8174 Loss 2.8596 Accuracy 0.0000.
[2021-08-16 13:44:08,321 INFO train.py line 404 34387] Epoch: [1/50][320/11134] Data 0.000 (0.003) Batch 0.443 (0.446) Remain 68:59:41 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.1704 FinalLoss 1.6980 Loss 2.8684 Accuracy 0.4856.
[2021-08-16 13:44:12,769 INFO train.py line 404 34387] Epoch: [1/50][330/11134] Data 0.000 (0.003) Batch 0.442 (0.446) Remain 68:59:09 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.7535 FinalLoss 3.3933 Loss 6.1469 Accuracy 0.0000.
[2021-08-16 13:44:17,215 INFO train.py line 404 34387] Epoch: [1/50][340/11134] Data 0.000 (0.003) Batch 0.439 (0.446) Remain 68:58:35 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.1200 FinalLoss 2.5649 Loss 3.6848 Accuracy 0.5482.
[2021-08-16 13:44:21,606 INFO train.py line 404 34387] Epoch: [1/50][350/11134] Data 0.000 (0.003) Batch 0.436 (0.446) Remain 68:56:37 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.9767 FinalLoss 2.8143 Loss 4.7911 Accuracy 0.1695.
[2021-08-16 13:44:26,038 INFO train.py line 404 34387] Epoch: [1/50][360/11134] Data 0.001 (0.003) Batch 0.444 (0.446) Remain 68:55:46 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.6400 FinalLoss 2.2408 Loss 3.8808 Accuracy 0.0429.
[2021-08-16 13:44:30,430 INFO train.py line 404 34387] Epoch: [1/50][370/11134] Data 0.000 (0.003) Batch 0.431 (0.446) Remain 68:53:59 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.4424 FinalLoss 2.5104 Loss 3.9528 Accuracy 0.2123.
[2021-08-16 13:44:34,865 INFO train.py line 404 34387] Epoch: [1/50][380/11134] Data 0.000 (0.003) Batch 0.443 (0.446) Remain 68:53:20 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3785 FinalLoss 2.4542 Loss 3.8327 Accuracy 0.4446.
[2021-08-16 13:44:39,257 INFO train.py line 404 34387] Epoch: [1/50][390/11134] Data 0.000 (0.003) Batch 0.433 (0.446) Remain 68:51:42 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.0629 FinalLoss 2.1120 Loss 4.1750 Accuracy 0.5002.
[2021-08-16 13:44:43,713 INFO train.py line 404 34387] Epoch: [1/50][400/11134] Data 0.000 (0.003) Batch 0.441 (0.446) Remain 68:51:37 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.1650 FinalLoss 1.7254 Loss 2.8904 Accuracy 0.5221.
[2021-08-16 13:44:48,091 INFO train.py line 404 34387] Epoch: [1/50][410/11134] Data 0.000 (0.003) Batch 0.435 (0.445) Remain 68:49:47 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.9285 FinalLoss 3.1403 Loss 5.0688 Accuracy 0.0542.
[2021-08-16 13:44:52,500 INFO train.py line 404 34387] Epoch: [1/50][420/11134] Data 0.001 (0.003) Batch 0.460 (0.445) Remain 68:48:43 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.2889 FinalLoss 2.1098 Loss 3.3987 Accuracy 0.1998.
[2021-08-16 13:44:56,946 INFO train.py line 404 34387] Epoch: [1/50][430/11134] Data 0.000 (0.003) Batch 0.450 (0.445) Remain 68:48:29 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.4326 FinalLoss 2.5013 Loss 3.9339 Accuracy 0.2415.
[2021-08-16 13:45:01,427 INFO train.py line 404 34387] Epoch: [1/50][440/11134] Data 0.000 (0.003) Batch 0.450 (0.445) Remain 68:49:00 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.6745 FinalLoss 1.9876 Loss 3.6620 Accuracy 0.2809.
[2021-08-16 13:45:05,915 INFO train.py line 404 34387] Epoch: [1/50][450/11134] Data 0.000 (0.003) Batch 0.439 (0.445) Remain 68:49:37 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.6759 FinalLoss 3.5198 Loss 5.1958 Accuracy 0.0000.
[2021-08-16 13:45:10,309 INFO train.py line 404 34387] Epoch: [1/50][460/11134] Data 0.000 (0.002) Batch 0.437 (0.445) Remain 68:48:20 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 0.9369 FinalLoss 2.8517 Loss 3.7887 Accuracy 0.0000.
[2021-08-16 13:45:14,812 INFO train.py line 404 34387] Epoch: [1/50][470/11134] Data 0.001 (0.002) Batch 0.443 (0.445) Remain 68:49:15 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.4094 FinalLoss 2.1544 Loss 3.5638 Accuracy 0.2066.
[2021-08-16 13:45:19,244 INFO train.py line 404 34387] Epoch: [1/50][480/11134] Data 0.000 (0.002) Batch 0.446 (0.445) Remain 68:48:44 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.0260 FinalLoss 1.9798 Loss 3.0058 Accuracy 0.2649.
[2021-08-16 13:45:23,700 INFO train.py line 404 34387] Epoch: [1/50][490/11134] Data 0.000 (0.002) Batch 0.444 (0.445) Remain 68:48:42 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3884 FinalLoss 0.7465 Loss 2.1349 Accuracy 1.0000.
[2021-08-16 13:45:28,108 INFO train.py line 404 34387] Epoch: [1/50][500/11134] Data 0.000 (0.002) Batch 0.446 (0.445) Remain 68:47:47 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 0.4746 FinalLoss 1.3997 Loss 1.8743 Accuracy 0.2699.
[2021-08-16 13:45:32,535 INFO train.py line 404 34387] Epoch: [1/50][510/11134] Data 0.000 (0.002) Batch 0.443 (0.445) Remain 68:47:14 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.5177 FinalLoss 2.1365 Loss 3.6543 Accuracy 0.0000.
[2021-08-16 13:45:36,947 INFO train.py line 404 34387] Epoch: [1/50][520/11134] Data 0.000 (0.002) Batch 0.443 (0.445) Remain 68:46:27 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.2228 FinalLoss 2.0238 Loss 3.2466 Accuracy 0.3617.
[2021-08-16 13:45:41,384 INFO train.py line 404 34387] Epoch: [1/50][530/11134] Data 0.000 (0.002) Batch 0.438 (0.445) Remain 68:46:07 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 0.8642 FinalLoss 1.7713 Loss 2.6355 Accuracy 0.0000.
[2021-08-16 13:45:45,802 INFO train.py line 404 34387] Epoch: [1/50][540/11134] Data 0.000 (0.002) Batch 0.464 (0.445) Remain 68:45:28 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.5904 FinalLoss 2.2726 Loss 3.8630 Accuracy 0.1168.
[2021-08-16 13:45:50,265 INFO train.py line 404 34387] Epoch: [1/50][550/11134] Data 0.000 (0.002) Batch 0.444 (0.445) Remain 68:45:36 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.7165 FinalLoss 2.8632 Loss 4.5796 Accuracy 0.1717.
[2021-08-16 13:45:54,718 INFO train.py line 404 34387] Epoch: [1/50][560/11134] Data 0.000 (0.002) Batch 0.443 (0.445) Remain 68:45:33 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.6187 FinalLoss 2.3799 Loss 3.9986 Accuracy 0.4661.
[2021-08-16 13:45:59,170 INFO train.py line 404 34387] Epoch: [1/50][570/11134] Data 0.000 (0.002) Batch 0.441 (0.445) Remain 68:45:31 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 0.9729 FinalLoss 1.9634 Loss 2.9363 Accuracy 0.2288.
[2021-08-16 13:46:03,579 INFO train.py line 404 34387] Epoch: [1/50][580/11134] Data 0.000 (0.002) Batch 0.437 (0.445) Remain 68:44:46 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3900 FinalLoss 0.8930 Loss 2.2830 Accuracy 1.0000.
[2021-08-16 13:46:08,037 INFO train.py line 404 34387] Epoch: [1/50][590/11134] Data 0.000 (0.002) Batch 0.444 (0.445) Remain 68:44:49 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 0.4730 FinalLoss 1.6217 Loss 2.0947 Accuracy 0.9400.
[2021-08-16 13:46:12,481 INFO train.py line 404 34387] Epoch: [1/50][600/11134] Data 0.000 (0.002) Batch 0.442 (0.445) Remain 68:44:38 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3871 FinalLoss 2.7187 Loss 4.1059 Accuracy 0.2726.
[2021-08-16 13:46:16,926 INFO train.py line 404 34387] Epoch: [1/50][610/11134] Data 0.000 (0.002) Batch 0.442 (0.445) Remain 68:44:29 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.4289 FinalLoss 2.3520 Loss 3.7809 Accuracy 0.0724.
[2021-08-16 13:46:21,374 INFO train.py line 404 34387] Epoch: [1/50][620/11134] Data 0.000 (0.002) Batch 0.447 (0.445) Remain 68:44:23 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.2056 FinalLoss 1.9841 Loss 3.1896 Accuracy 0.4603.
[2021-08-16 13:46:25,869 INFO train.py line 404 34387] Epoch: [1/50][630/11134] Data 0.000 (0.002) Batch 0.452 (0.445) Remain 68:44:58 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.1753 FinalLoss 2.1837 Loss 3.3590 Accuracy 0.0000.
[2021-08-16 13:46:30,370 INFO train.py line 404 34387] Epoch: [1/50][640/11134] Data 0.000 (0.002) Batch 0.446 (0.445) Remain 68:45:37 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.4884 FinalLoss 2.3822 Loss 3.8706 Accuracy 0.4814.
[2021-08-16 13:46:34,829 INFO train.py line 404 34387] Epoch: [1/50][650/11134] Data 0.000 (0.002) Batch 0.445 (0.445) Remain 68:45:39 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.4525 FinalLoss 3.9008 Loss 5.3533 Accuracy 0.1980.
[2021-08-16 13:46:39,266 INFO train.py line 404 34387] Epoch: [1/50][660/11134] Data 0.000 (0.002) Batch 0.444 (0.445) Remain 68:45:21 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.3269 FinalLoss 2.4849 Loss 4.8118 Accuracy 0.0247.
[2021-08-16 13:46:43,717 INFO train.py line 404 34387] Epoch: [1/50][670/11134] Data 0.000 (0.002) Batch 0.442 (0.445) Remain 68:45:17 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.5046 FinalLoss 2.1646 Loss 3.6692 Accuracy 0.0000.
[2021-08-16 13:46:48,165 INFO train.py line 404 34387] Epoch: [1/50][680/11134] Data 0.000 (0.002) Batch 0.446 (0.445) Remain 68:45:09 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.5442 FinalLoss 2.4619 Loss 4.0061 Accuracy 0.0000.
[2021-08-16 13:46:52,611 INFO train.py line 404 34387] Epoch: [1/50][690/11134] Data 0.000 (0.002) Batch 0.453 (0.445) Remain 68:45:01 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3234 FinalLoss 2.7271 Loss 4.0506 Accuracy 0.3641.
[2021-08-16 13:46:57,065 INFO train.py line 404 34387] Epoch: [1/50][700/11134] Data 0.000 (0.002) Batch 0.443 (0.445) Remain 68:44:58 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3752 FinalLoss 2.3810 Loss 3.7562 Accuracy 0.2526.
[2021-08-16 13:47:01,508 INFO train.py line 404 34387] Epoch: [1/50][710/11134] Data 0.000 (0.002) Batch 0.438 (0.445) Remain 68:44:47 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.5383 FinalLoss 2.2835 Loss 3.8218 Accuracy 0.4996.
[2021-08-16 13:47:05,984 INFO train.py line 404 34387] Epoch: [1/50][720/11134] Data 0.000 (0.002) Batch 0.445 (0.445) Remain 68:45:02 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 0.4711 FinalLoss 2.2784 Loss 2.7495 Accuracy 0.2799.
[2021-08-16 13:47:10,423 INFO train.py line 404 34387] Epoch: [1/50][730/11134] Data 0.000 (0.002) Batch 0.441 (0.445) Remain 68:44:48 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.2853 FinalLoss 1.7163 Loss 3.0016 Accuracy 0.4092.
[2021-08-16 13:47:14,895 INFO train.py line 404 34387] Epoch: [1/50][740/11134] Data 0.000 (0.002) Batch 0.450 (0.445) Remain 68:44:59 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.8706 FinalLoss 2.6512 Loss 4.5218 Accuracy 0.0810.
[2021-08-16 13:47:19,356 INFO train.py line 404 34387] Epoch: [1/50][750/11134] Data 0.000 (0.002) Batch 0.439 (0.445) Remain 68:45:01 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.2029 FinalLoss 2.0406 Loss 3.2435 Accuracy 0.1246.
[2021-08-16 13:47:23,814 INFO train.py line 404 34387] Epoch: [1/50][760/11134] Data 0.000 (0.002) Batch 0.445 (0.445) Remain 68:45:01 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.6191 FinalLoss 2.6377 Loss 4.2568 Accuracy 0.3866.
[2021-08-16 13:47:28,271 INFO train.py line 404 34387] Epoch: [1/50][770/11134] Data 0.000 (0.002) Batch 0.444 (0.445) Remain 68:45:01 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 0.6191 FinalLoss 1.9460 Loss 2.5651 Accuracy 0.0000.
[2021-08-16 13:47:32,716 INFO train.py line 404 34387] Epoch: [1/50][780/11134] Data 0.000 (0.002) Batch 0.446 (0.445) Remain 68:44:51 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3176 FinalLoss 2.0726 Loss 3.3901 Accuracy 0.3394.
[2021-08-16 13:47:37,190 INFO train.py line 404 34387] Epoch: [1/50][790/11134] Data 0.001 (0.002) Batch 0.447 (0.445) Remain 68:45:02 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 0.8148 FinalLoss 1.8500 Loss 2.6648 Accuracy 0.7156.
[2021-08-16 13:47:41,653 INFO train.py line 404 34387] Epoch: [1/50][800/11134] Data 0.000 (0.002) Batch 0.444 (0.445) Remain 68:45:05 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.5808 FinalLoss 2.4740 Loss 4.0548 Accuracy 0.1144.
[2021-08-16 13:47:46,170 INFO train.py line 404 34387] Epoch: [1/50][810/11134] Data 0.000 (0.002) Batch 0.444 (0.445) Remain 68:45:45 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.0939 FinalLoss 2.5968 Loss 3.6907 Accuracy 0.2771.
[2021-08-16 13:47:50,627 INFO train.py line 404 34387] Epoch: [1/50][820/11134] Data 0.000 (0.002) Batch 0.444 (0.445) Remain 68:45:43 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 3.3481 FinalLoss 3.4132 Loss 6.7613 Accuracy 0.0628.
[2021-08-16 13:47:55,080 INFO train.py line 404 34387] Epoch: [1/50][830/11134] Data 0.000 (0.002) Batch 0.447 (0.445) Remain 68:45:39 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.1288 FinalLoss 1.8862 Loss 3.0151 Accuracy 0.2820.
[2021-08-16 13:47:59,542 INFO train.py line 404 34387] Epoch: [1/50][840/11134] Data 0.000 (0.002) Batch 0.451 (0.445) Remain 68:45:40 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.6030 FinalLoss 2.9935 Loss 4.5965 Accuracy 0.3344.
[2021-08-16 13:48:04,034 INFO train.py line 404 34387] Epoch: [1/50][850/11134] Data 0.000 (0.002) Batch 0.447 (0.445) Remain 68:46:01 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.7201 FinalLoss 1.9270 Loss 3.6472 Accuracy 0.5677.
[2021-08-16 13:48:08,518 INFO train.py line 404 34387] Epoch: [1/50][860/11134] Data 0.000 (0.002) Batch 0.449 (0.445) Remain 68:46:16 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3860 FinalLoss 1.9059 Loss 3.2920 Accuracy 0.4910.
[2021-08-16 13:48:13,008 INFO train.py line 404 34387] Epoch: [1/50][870/11134] Data 0.000 (0.002) Batch 0.454 (0.445) Remain 68:46:34 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 0.9220 FinalLoss 1.6483 Loss 2.5702 Accuracy 0.6863.
[2021-08-16 13:48:17,503 INFO train.py line 404 34387] Epoch: [1/50][880/11134] Data 0.000 (0.002) Batch 0.445 (0.445) Remain 68:46:55 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.8363 FinalLoss 3.2620 Loss 5.0983 Accuracy 0.0263.
[2021-08-16 13:48:21,976 INFO train.py line 404 34387] Epoch: [1/50][890/11134] Data 0.000 (0.002) Batch 0.448 (0.446) Remain 68:47:02 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3070 FinalLoss 1.6259 Loss 2.9329 Accuracy 0.1249.
[2021-08-16 13:48:26,496 INFO train.py line 404 34387] Epoch: [1/50][900/11134] Data 0.000 (0.001) Batch 0.446 (0.446) Remain 68:47:38 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.8482 FinalLoss 3.0037 Loss 4.8519 Accuracy 0.2569.
[2021-08-16 13:48:31,039 INFO train.py line 404 34387] Epoch: [1/50][910/11134] Data 0.000 (0.001) Batch 0.451 (0.446) Remain 68:48:27 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.1857 FinalLoss 2.7592 Loss 4.9449 Accuracy 0.4474.
[2021-08-16 13:48:35,590 INFO train.py line 404 34387] Epoch: [1/50][920/11134] Data 0.001 (0.001) Batch 0.456 (0.446) Remain 68:49:19 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.7413 FinalLoss 2.6087 Loss 4.3500 Accuracy 0.0585.
[2021-08-16 13:48:40,129 INFO train.py line 404 34387] Epoch: [1/50][930/11134] Data 0.000 (0.001) Batch 0.451 (0.446) Remain 68:50:03 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.7038 FinalLoss 2.5617 Loss 4.2655 Accuracy 0.0764.
[2021-08-16 13:48:44,654 INFO train.py line 404 34387] Epoch: [1/50][940/11134] Data 0.000 (0.001) Batch 0.455 (0.446) Remain 68:50:38 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.7089 FinalLoss 2.6333 Loss 4.3422 Accuracy 0.1380.
[2021-08-16 13:48:49,125 INFO train.py line 404 34387] Epoch: [1/50][950/11134] Data 0.000 (0.001) Batch 0.448 (0.446) Remain 68:50:40 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.1911 FinalLoss 1.8770 Loss 3.0681 Accuracy 0.5113.
[2021-08-16 13:48:53,641 INFO train.py line 404 34387] Epoch: [1/50][960/11134] Data 0.000 (0.001) Batch 0.453 (0.446) Remain 68:51:08 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.5177 FinalLoss 2.7894 Loss 4.3071 Accuracy 0.0097.
[2021-08-16 13:48:58,145 INFO train.py line 404 34387] Epoch: [1/50][970/11134] Data 0.001 (0.001) Batch 0.449 (0.446) Remain 68:51:29 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3997 FinalLoss 2.2693 Loss 3.6690 Accuracy 0.0000.
[2021-08-16 13:49:02,617 INFO train.py line 404 34387] Epoch: [1/50][980/11134] Data 0.000 (0.001) Batch 0.445 (0.446) Remain 68:51:31 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.1512 FinalLoss 1.7244 Loss 2.8756 Accuracy 0.5570.
[2021-08-16 13:49:07,138 INFO train.py line 404 34387] Epoch: [1/50][990/11134] Data 0.000 (0.001) Batch 0.451 (0.446) Remain 68:52:00 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.2323 FinalLoss 1.6534 Loss 2.8857 Accuracy 0.2434.
[2021-08-16 13:49:11,671 INFO train.py line 404 34387] Epoch: [1/50][1000/11134] Data 0.000 (0.001) Batch 0.452 (0.446) Remain 68:52:35 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 0.7756 FinalLoss 1.1093 Loss 1.8849 Accuracy 0.8780.
[2021-08-16 13:49:16,210 INFO train.py line 404 34387] Epoch: [1/50][1010/11134] Data 0.000 (0.001) Batch 0.455 (0.446) Remain 68:53:14 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3168 FinalLoss 2.1713 Loss 3.4881 Accuracy 0.6497.
[2021-08-16 13:49:20,713 INFO train.py line 404 34387] Epoch: [1/50][1020/11134] Data 0.000 (0.001) Batch 0.449 (0.446) Remain 68:53:31 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.2316 FinalLoss 1.6224 Loss 2.8540 Accuracy 0.2718.
[2021-08-16 13:49:25,242 INFO train.py line 404 34387] Epoch: [1/50][1030/11134] Data 0.000 (0.001) Batch 0.466 (0.446) Remain 68:54:02 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 0.7873 FinalLoss 2.1616 Loss 2.9489 Accuracy 0.3048.
[2021-08-16 13:49:29,774 INFO train.py line 404 34387] Epoch: [1/50][1040/11134] Data 0.000 (0.001) Batch 0.454 (0.446) Remain 68:54:34 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.4898 FinalLoss 2.7843 Loss 4.2741 Accuracy 0.0349.
[2021-08-16 13:49:34,280 INFO train.py line 404 34387] Epoch: [1/50][1050/11134] Data 0.000 (0.001) Batch 0.446 (0.446) Remain 68:54:52 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.1675 FinalLoss 3.6866 Loss 5.8541 Accuracy 0.2737.
[2021-08-16 13:49:38,813 INFO train.py line 404 34387] Epoch: [1/50][1060/11134] Data 0.000 (0.001) Batch 0.446 (0.447) Remain 68:55:23 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.5553 FinalLoss 2.0986 Loss 3.6538 Accuracy 0.1597.
[2021-08-16 13:49:43,323 INFO train.py line 404 34387] Epoch: [1/50][1070/11134] Data 0.000 (0.001) Batch 0.452 (0.447) Remain 68:55:41 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3722 FinalLoss 4.4256 Loss 5.7978 Accuracy 0.0085.
[2021-08-16 13:49:47,832 INFO train.py line 404 34387] Epoch: [1/50][1080/11134] Data 0.000 (0.001) Batch 0.458 (0.447) Remain 68:55:59 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.2374 FinalLoss 2.5958 Loss 3.8332 Accuracy 0.0135.
[2021-08-16 13:49:52,334 INFO train.py line 404 34387] Epoch: [1/50][1090/11134] Data 0.000 (0.001) Batch 0.455 (0.447) Remain 68:56:13 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 0.8181 FinalLoss 1.4390 Loss 2.2570 Accuracy 0.5842.
[2021-08-16 13:49:56,837 INFO train.py line 404 34387] Epoch: [1/50][1100/11134] Data 0.000 (0.001) Batch 0.442 (0.447) Remain 68:56:27 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.7727 FinalLoss 3.4040 Loss 5.1767 Accuracy 0.0110.
[2021-08-16 13:50:01,392 INFO train.py line 404 34387] Epoch: [1/50][1110/11134] Data 0.000 (0.001) Batch 0.452 (0.447) Remain 68:57:06 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.4383 FinalLoss 1.9205 Loss 3.3587 Accuracy 0.2505.
[2021-08-16 13:50:05,890 INFO train.py line 404 34387] Epoch: [1/50][1120/11134] Data 0.000 (0.001) Batch 0.448 (0.447) Remain 68:57:17 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.1951 FinalLoss 2.5721 Loss 3.7672 Accuracy 0.3405.
[2021-08-16 13:50:10,392 INFO train.py line 404 34387] Epoch: [1/50][1130/11134] Data 0.000 (0.001) Batch 0.448 (0.447) Remain 68:57:29 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.6424 FinalLoss 2.9030 Loss 4.5455 Accuracy 0.1082.
[2021-08-16 13:50:14,906 INFO train.py line 404 34387] Epoch: [1/50][1140/11134] Data 0.000 (0.001) Batch 0.456 (0.447) Remain 68:57:46 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.2740 FinalLoss 2.7361 Loss 4.0101 Accuracy 0.3646.
[2021-08-16 13:50:19,442 INFO train.py line 404 34387] Epoch: [1/50][1150/11134] Data 0.000 (0.001) Batch 0.451 (0.447) Remain 68:58:15 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.7430 FinalLoss 1.9771 Loss 3.7201 Accuracy 0.4905.
[2021-08-16 13:50:23,962 INFO train.py line 404 34387] Epoch: [1/50][1160/11134] Data 0.000 (0.001) Batch 0.455 (0.447) Remain 68:58:34 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.2242 FinalLoss 2.4018 Loss 3.6260 Accuracy 0.2414.
[2021-08-16 13:50:28,512 INFO train.py line 404 34387] Epoch: [1/50][1170/11134] Data 0.000 (0.001) Batch 0.454 (0.447) Remain 68:59:08 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 0.7080 FinalLoss 2.0300 Loss 2.7380 Accuracy 0.2529.
[2021-08-16 13:50:33,047 INFO train.py line 404 34387] Epoch: [1/50][1180/11134] Data 0.000 (0.001) Batch 0.447 (0.447) Remain 68:59:34 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 0.9407 FinalLoss 2.2272 Loss 3.1678 Accuracy 0.0000.
[2021-08-16 13:50:37,556 INFO train.py line 404 34387] Epoch: [1/50][1190/11134] Data 0.000 (0.001) Batch 0.450 (0.447) Remain 68:59:47 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.2407 FinalLoss 2.0048 Loss 3.2456 Accuracy 0.5249.
[2021-08-16 13:50:42,020 INFO train.py line 404 34387] Epoch: [1/50][1200/11134] Data 0.000 (0.001) Batch 0.442 (0.447) Remain 68:59:39 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.7475 FinalLoss 3.4837 Loss 6.2313 Accuracy 0.1117.
[2021-08-16 13:50:46,498 INFO train.py line 404 34387] Epoch: [1/50][1210/11134] Data 0.000 (0.001) Batch 0.447 (0.447) Remain 68:59:38 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.1380 FinalLoss 2.3439 Loss 3.4819 Accuracy 0.4917.
[2021-08-16 13:50:50,997 INFO train.py line 404 34387] Epoch: [1/50][1220/11134] Data 0.000 (0.001) Batch 0.455 (0.447) Remain 68:59:46 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.8836 FinalLoss 3.3178 Loss 6.2014 Accuracy 0.1806.
[2021-08-16 13:50:55,461 INFO train.py line 404 34387] Epoch: [1/50][1230/11134] Data 0.000 (0.001) Batch 0.439 (0.447) Remain 68:59:38 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.3367 FinalLoss 2.2429 Loss 3.5796 Accuracy 0.5170.
[2021-08-16 13:50:59,926 INFO train.py line 404 34387] Epoch: [1/50][1240/11134] Data 0.000 (0.001) Batch 0.451 (0.447) Remain 68:59:30 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.8952 FinalLoss 3.9005 Loss 6.7957 Accuracy 0.1113.
[2021-08-16 13:51:04,434 INFO train.py line 404 34387] Epoch: [1/50][1250/11134] Data 0.000 (0.001) Batch 0.450 (0.447) Remain 68:59:42 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.0987 FinalLoss 3.0169 Loss 4.1155 Accuracy 0.0000.
[2021-08-16 13:51:08,929 INFO train.py line 404 34387] Epoch: [1/50][1260/11134] Data 0.000 (0.001) Batch 0.447 (0.447) Remain 68:59:48 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 3.1764 FinalLoss 3.2835 Loss 6.4600 Accuracy 0.0000.
[2021-08-16 13:51:13,456 INFO train.py line 404 34387] Epoch: [1/50][1270/11134] Data 0.001 (0.001) Batch 0.458 (0.447) Remain 69:00:08 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.6864 FinalLoss 2.3854 Loss 4.0718 Accuracy 0.1933.
[2021-08-16 13:51:18,014 INFO train.py line 404 34387] Epoch: [1/50][1280/11134] Data 0.000 (0.001) Batch 0.470 (0.447) Remain 69:00:40 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.5360 FinalLoss 1.6260 Loss 3.1620 Accuracy 0.7094.
[2021-08-16 13:51:22,532 INFO train.py line 404 34387] Epoch: [1/50][1290/11134] Data 0.000 (0.001) Batch 0.452 (0.447) Remain 69:00:55 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.0630 FinalLoss 1.5333 Loss 2.5963 Accuracy 0.6547.
[2021-08-16 13:51:27,030 INFO train.py line 404 34387] Epoch: [1/50][1300/11134] Data 0.000 (0.001) Batch 0.453 (0.447) Remain 69:01:01 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.1016 FinalLoss 1.8319 Loss 2.9335 Accuracy 0.5452.
[2021-08-16 13:51:31,542 INFO train.py line 404 34387] Epoch: [1/50][1310/11134] Data 0.000 (0.001) Batch 0.447 (0.447) Remain 69:01:13 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.6814 FinalLoss 3.1562 Loss 4.8376 Accuracy 0.2167.
[2021-08-16 13:51:36,082 INFO train.py line 404 34387] Epoch: [1/50][1320/11134] Data 0.000 (0.001) Batch 0.450 (0.447) Remain 69:01:37 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.4985 FinalLoss 2.2382 Loss 3.7367 Accuracy 0.3413.
[2021-08-16 13:51:40,600 INFO train.py line 404 34387] Epoch: [1/50][1330/11134] Data 0.000 (0.001) Batch 0.448 (0.447) Remain 69:01:50 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.0348 FinalLoss 3.5870 Loss 5.6218 Accuracy 0.0000.
[2021-08-16 13:51:45,139 INFO train.py line 404 34387] Epoch: [1/50][1340/11134] Data 0.000 (0.001) Batch 0.454 (0.448) Remain 69:02:12 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.2739 FinalLoss 1.8914 Loss 3.1653 Accuracy 0.4705.
[2021-08-16 13:51:49,674 INFO train.py line 404 34387] Epoch: [1/50][1350/11134] Data 0.001 (0.001) Batch 0.449 (0.448) Remain 69:02:33 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.1109 FinalLoss 1.9550 Loss 3.0659 Accuracy 0.3048.
[2021-08-16 13:51:54,206 INFO train.py line 404 34387] Epoch: [1/50][1360/11134] Data 0.001 (0.001) Batch 0.454 (0.448) Remain 69:02:51 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.6413 FinalLoss 2.3996 Loss 4.0409 Accuracy 0.1414.
[2021-08-16 13:51:58,754 INFO train.py line 404 34387] Epoch: [1/50][1370/11134] Data 0.001 (0.001) Batch 0.464 (0.448) Remain 69:03:16 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.2508 FinalLoss 2.2694 Loss 3.5202 Accuracy 0.0000.
[2021-08-16 13:52:03,205 INFO train.py line 404 34387] Epoch: [1/50][1380/11134] Data 0.001 (0.001) Batch 0.441 (0.448) Remain 69:03:01 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 0.9516 FinalLoss 0.8739 Loss 1.8255 Accuracy 0.9915.
[2021-08-16 13:52:07,681 INFO train.py line 404 34387] Epoch: [1/50][1390/11134] Data 0.000 (0.001) Batch 0.442 (0.448) Remain 69:02:56 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.2609 FinalLoss 2.7621 Loss 4.0229 Accuracy 0.0000.
[2021-08-16 13:52:12,153 INFO train.py line 404 34387] Epoch: [1/50][1400/11134] Data 0.000 (0.001) Batch 0.443 (0.448) Remain 69:02:50 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.2209 FinalLoss nan Loss nan Accuracy 0.0000.
[2021-08-16 13:52:16,615 INFO train.py line 404 34387] Epoch: [1/50][1410/11134] Data 0.000 (0.001) Batch 0.448 (0.448) Remain 69:02:40 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.5167 FinalLoss nan Loss nan Accuracy 0.0000.
[2021-08-16 13:52:21,079 INFO train.py line 404 34387] Epoch: [1/50][1420/11134] Data 0.000 (0.001) Batch 0.445 (0.448) Remain 69:02:31 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.9679 FinalLoss nan Loss nan Accuracy 0.0000.
[2021-08-16 13:52:25,519 INFO train.py line 404 34387] Epoch: [1/50][1430/11134] Data 0.001 (0.001) Batch 0.439 (0.448) Remain 69:02:12 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.8107 FinalLoss nan Loss nan Accuracy 0.0000.
[2021-08-16 13:52:29,990 INFO train.py line 404 34387] Epoch: [1/50][1440/11134] Data 0.000 (0.001) Batch 0.440 (0.448) Remain 69:02:06 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.0621 FinalLoss nan Loss nan Accuracy 0.0000.
[2021-08-16 13:52:34,436 INFO train.py line 404 34387] Epoch: [1/50][1450/11134] Data 0.000 (0.001) Batch 0.443 (0.448) Remain 69:01:50 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.0649 FinalLoss nan Loss nan Accuracy 0.0000.
[2021-08-16 13:52:38,914 INFO train.py line 404 34387] Epoch: [1/50][1460/11134] Data 0.000 (0.001) Batch 0.453 (0.448) Remain 69:01:46 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.2776 FinalLoss nan Loss nan Accuracy 0.0000.
[2021-08-16 13:52:43,406 INFO train.py line 404 34387] Epoch: [1/50][1470/11134] Data 0.000 (0.001) Batch 0.449 (0.448) Remain 69:01:48 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.8996 FinalLoss nan Loss nan Accuracy 0.0000.
[2021-08-16 13:52:47,848 INFO train.py line 404 34387] Epoch: [1/50][1480/11134] Data 0.000 (0.001) Batch 0.437 (0.448) Remain 69:01:31 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 3.2841 FinalLoss nan Loss nan Accuracy 0.0000.
[2021-08-16 13:52:52,246 INFO train.py line 404 34387] Epoch: [1/50][1490/11134] Data 0.000 (0.001) Batch 0.441 (0.448) Remain 69:00:58 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.7820 FinalLoss nan Loss nan Accuracy 0.0000.
[2021-08-16 13:52:56,765 INFO train.py line 404 34387] Epoch: [1/50][1500/11134] Data 0.000 (0.001) Batch 0.449 (0.448) Remain 69:01:09 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.3175 FinalLoss nan Loss nan Accuracy 0.0000.
[2021-08-16 13:53:01,234 INFO train.py line 404 34387] Epoch: [1/50][1510/11134] Data 0.000 (0.001) Batch 0.441 (0.448) Remain 69:01:02 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.2982 FinalLoss nan Loss nan Accuracy 0.0000.
[2021-08-16 13:53:05,686 INFO train.py line 404 34387] Epoch: [1/50][1520/11134] Data 0.000 (0.001) Batch 0.442 (0.448) Remain 69:00:50 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.1052 FinalLoss nan Loss nan Accuracy 0.0000.
[2021-08-16 13:53:10,159 INFO train.py line 404 34387] Epoch: [1/50][1530/11134] Data 0.000 (0.001) Batch 0.460 (0.448) Remain 69:00:44 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.6419 FinalLoss nan Loss nan Accuracy 0.0000.
[2021-08-16 13:53:14,590 INFO train.py line 404 34387] Epoch: [1/50][1540/11134] Data 0.000 (0.001) Batch 0.438 (0.447) Remain 69:00:24 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.2729 FinalLoss nan Loss nan Accuracy 0.0000.
[2021-08-16 13:53:19,104 INFO train.py line 404 34387] Epoch: [1/50][1550/11134] Data 0.000 (0.001) Batch 0.456 (0.448) Remain 69:00:33 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.7142 FinalLoss nan Loss nan Accuracy 0.0000.
[2021-08-16 13:53:23,665 INFO train.py line 404 34387] Epoch: [1/50][1560/11134] Data 0.000 (0.001) Batch 0.446 (0.448) Remain 69:00:59 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.2909 FinalLoss nan Loss nan Accuracy 0.0000.
[2021-08-16 13:53:28,190 INFO train.py line 404 34387] Epoch: [1/50][1570/11134] Data 0.000 (0.001) Batch 0.443 (0.448) Remain 69:01:13 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 1.9410 FinalLoss nan Loss nan Accuracy 0.0000.
[2021-08-16 13:53:32,631 INFO train.py line 404 34387] Epoch: [1/50][1580/11134] Data 0.000 (0.001) Batch 0.439 (0.448) Remain 69:00:56 MainLoss 0.0000 AuxLoss 0.0000 RegLoss 2.6594 FinalLoss nan Loss nan Accuracy 0.0000.
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liuzhengzhe commented Aug 17, 2021 via email

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liuzhengzhe commented Aug 18, 2021 via email

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