text
stringlengths 56
1.16k
|
|---|
[2023-10-25 20:04:26,716::train::INFO] [train] Iter 599763 | loss 0.7509 | loss(rot) 0.3875 | loss(pos) 0.1968 | loss(seq) 0.1666 | grad 4.4034 | lr 0.0000 | time_forward 4.0610 | time_backward 5.9280 |
[2023-10-25 20:04:36,789::train::INFO] [train] Iter 599764 | loss 1.9155 | loss(rot) 1.5314 | loss(pos) 0.1256 | loss(seq) 0.2584 | grad 28.7263 | lr 0.0000 | time_forward 4.2300 | time_backward 5.8390 |
[2023-10-25 20:04:39,585::train::INFO] [train] Iter 599765 | loss 0.6741 | loss(rot) 0.5117 | loss(pos) 0.0160 | loss(seq) 0.1463 | grad 1.7104 | lr 0.0000 | time_forward 1.3340 | time_backward 1.4590 |
[2023-10-25 20:04:49,648::train::INFO] [train] Iter 599766 | loss 0.9078 | loss(rot) 0.4788 | loss(pos) 0.2830 | loss(seq) 0.1460 | grad 5.2510 | lr 0.0000 | time_forward 4.0980 | time_backward 5.9620 |
[2023-10-25 20:04:51,953::train::INFO] [train] Iter 599767 | loss 0.2946 | loss(rot) 0.0728 | loss(pos) 0.1871 | loss(seq) 0.0348 | grad 7.4634 | lr 0.0000 | time_forward 1.0580 | time_backward 1.2430 |
[2023-10-25 20:05:01,376::train::INFO] [train] Iter 599768 | loss 0.1980 | loss(rot) 0.0959 | loss(pos) 0.0224 | loss(seq) 0.0796 | grad 1.9370 | lr 0.0000 | time_forward 3.9860 | time_backward 5.4330 |
[2023-10-25 20:05:11,623::train::INFO] [train] Iter 599769 | loss 1.0460 | loss(rot) 1.0002 | loss(pos) 0.0458 | loss(seq) 0.0000 | grad 8.3196 | lr 0.0000 | time_forward 4.2840 | time_backward 5.9600 |
[2023-10-25 20:05:20,141::train::INFO] [train] Iter 599770 | loss 0.5042 | loss(rot) 0.4594 | loss(pos) 0.0448 | loss(seq) 0.0000 | grad 1.7163 | lr 0.0000 | time_forward 3.5960 | time_backward 4.9200 |
[2023-10-25 20:05:22,856::train::INFO] [train] Iter 599771 | loss 0.7159 | loss(rot) 0.3673 | loss(pos) 0.0679 | loss(seq) 0.2807 | grad 5.0291 | lr 0.0000 | time_forward 1.2680 | time_backward 1.4440 |
[2023-10-25 20:05:33,008::train::INFO] [train] Iter 599772 | loss 0.2719 | loss(rot) 0.0776 | loss(pos) 0.0497 | loss(seq) 0.1447 | grad 2.1893 | lr 0.0000 | time_forward 4.1380 | time_backward 5.9850 |
[2023-10-25 20:05:42,325::train::INFO] [train] Iter 599773 | loss 0.7693 | loss(rot) 0.4530 | loss(pos) 0.0495 | loss(seq) 0.2668 | grad 4.1067 | lr 0.0000 | time_forward 3.8970 | time_backward 5.4160 |
[2023-10-25 20:05:52,445::train::INFO] [train] Iter 599774 | loss 0.8324 | loss(rot) 0.7997 | loss(pos) 0.0164 | loss(seq) 0.0162 | grad 34.1713 | lr 0.0000 | time_forward 4.1450 | time_backward 5.9730 |
[2023-10-25 20:06:01,272::train::INFO] [train] Iter 599775 | loss 0.2721 | loss(rot) 0.1040 | loss(pos) 0.0393 | loss(seq) 0.1288 | grad 2.8133 | lr 0.0000 | time_forward 3.6800 | time_backward 5.1430 |
[2023-10-25 20:06:11,402::train::INFO] [train] Iter 599776 | loss 0.2362 | loss(rot) 0.0322 | loss(pos) 0.1864 | loss(seq) 0.0176 | grad 5.1259 | lr 0.0000 | time_forward 4.0990 | time_backward 6.0270 |
[2023-10-25 20:06:19,860::train::INFO] [train] Iter 599777 | loss 0.6375 | loss(rot) 0.5773 | loss(pos) 0.0117 | loss(seq) 0.0485 | grad 7.4290 | lr 0.0000 | time_forward 3.5680 | time_backward 4.8870 |
[2023-10-25 20:06:30,124::train::INFO] [train] Iter 599778 | loss 0.5069 | loss(rot) 0.3075 | loss(pos) 0.0731 | loss(seq) 0.1264 | grad 10.9029 | lr 0.0000 | time_forward 4.1280 | time_backward 6.1330 |
[2023-10-25 20:06:39,709::train::INFO] [train] Iter 599779 | loss 0.2940 | loss(rot) 0.1140 | loss(pos) 0.1294 | loss(seq) 0.0505 | grad 2.6406 | lr 0.0000 | time_forward 4.1120 | time_backward 5.4700 |
[2023-10-25 20:06:49,797::train::INFO] [train] Iter 599780 | loss 1.2197 | loss(rot) 1.1361 | loss(pos) 0.0836 | loss(seq) 0.0000 | grad 3.9469 | lr 0.0000 | time_forward 4.1150 | time_backward 5.9690 |
[2023-10-25 20:06:59,941::train::INFO] [train] Iter 599781 | loss 1.2028 | loss(rot) 0.3748 | loss(pos) 0.5367 | loss(seq) 0.2913 | grad 2.9833 | lr 0.0000 | time_forward 4.1880 | time_backward 5.9540 |
[2023-10-25 20:07:08,012::train::INFO] [train] Iter 599782 | loss 0.4553 | loss(rot) 0.1903 | loss(pos) 0.0732 | loss(seq) 0.1918 | grad 4.1744 | lr 0.0000 | time_forward 3.3950 | time_backward 4.6730 |
[2023-10-25 20:07:18,005::train::INFO] [train] Iter 599783 | loss 1.6912 | loss(rot) 1.6231 | loss(pos) 0.0681 | loss(seq) 0.0000 | grad 4.2850 | lr 0.0000 | time_forward 4.0410 | time_backward 5.9490 |
[2023-10-25 20:07:19,908::train::INFO] [train] Iter 599784 | loss 2.6100 | loss(rot) 1.7837 | loss(pos) 0.3879 | loss(seq) 0.4384 | grad 4.9123 | lr 0.0000 | time_forward 0.8600 | time_backward 1.0400 |
[2023-10-25 20:07:30,246::train::INFO] [train] Iter 599785 | loss 0.6891 | loss(rot) 0.3397 | loss(pos) 0.0584 | loss(seq) 0.2910 | grad 3.5479 | lr 0.0000 | time_forward 4.2640 | time_backward 6.0700 |
[2023-10-25 20:07:33,097::train::INFO] [train] Iter 599786 | loss 0.3843 | loss(rot) 0.3618 | loss(pos) 0.0211 | loss(seq) 0.0014 | grad 25.1358 | lr 0.0000 | time_forward 1.3480 | time_backward 1.4990 |
[2023-10-25 20:07:42,015::train::INFO] [train] Iter 599787 | loss 0.4187 | loss(rot) 0.3993 | loss(pos) 0.0164 | loss(seq) 0.0029 | grad 3.0882 | lr 0.0000 | time_forward 3.7600 | time_backward 5.1560 |
[2023-10-25 20:07:50,228::train::INFO] [train] Iter 599788 | loss 0.6512 | loss(rot) 0.0711 | loss(pos) 0.0612 | loss(seq) 0.5189 | grad 2.8929 | lr 0.0000 | time_forward 3.3900 | time_backward 4.8190 |
[2023-10-25 20:08:00,740::train::INFO] [train] Iter 599789 | loss 0.2657 | loss(rot) 0.0455 | loss(pos) 0.2121 | loss(seq) 0.0081 | grad 4.2968 | lr 0.0000 | time_forward 4.2830 | time_backward 6.2260 |
[2023-10-25 20:08:10,986::train::INFO] [train] Iter 599790 | loss 0.8954 | loss(rot) 0.5235 | loss(pos) 0.0958 | loss(seq) 0.2761 | grad 4.0054 | lr 0.0000 | time_forward 4.2260 | time_backward 6.0170 |
[2023-10-25 20:08:16,639::train::INFO] [train] Iter 599791 | loss 0.6782 | loss(rot) 0.4511 | loss(pos) 0.0921 | loss(seq) 0.1349 | grad 3.9890 | lr 0.0000 | time_forward 2.4040 | time_backward 3.2460 |
[2023-10-25 20:08:24,661::train::INFO] [train] Iter 599792 | loss 0.5839 | loss(rot) 0.0097 | loss(pos) 0.5739 | loss(seq) 0.0003 | grad 12.4493 | lr 0.0000 | time_forward 3.3700 | time_backward 4.6500 |
[2023-10-25 20:08:27,521::train::INFO] [train] Iter 599793 | loss 1.7500 | loss(rot) 1.2390 | loss(pos) 0.0774 | loss(seq) 0.4336 | grad 3.6078 | lr 0.0000 | time_forward 1.2910 | time_backward 1.5660 |
[2023-10-25 20:08:36,324::train::INFO] [train] Iter 599794 | loss 0.7410 | loss(rot) 0.3631 | loss(pos) 0.0516 | loss(seq) 0.3263 | grad 4.8993 | lr 0.0000 | time_forward 3.6810 | time_backward 5.0960 |
[2023-10-25 20:08:39,103::train::INFO] [train] Iter 599795 | loss 1.3448 | loss(rot) 1.3206 | loss(pos) 0.0206 | loss(seq) 0.0036 | grad 15.5405 | lr 0.0000 | time_forward 1.3340 | time_backward 1.4420 |
[2023-10-25 20:08:49,292::train::INFO] [train] Iter 599796 | loss 1.6568 | loss(rot) 1.4256 | loss(pos) 0.0718 | loss(seq) 0.1594 | grad 3.4666 | lr 0.0000 | time_forward 4.3060 | time_backward 5.8780 |
[2023-10-25 20:08:58,023::train::INFO] [train] Iter 599797 | loss 0.8997 | loss(rot) 0.3423 | loss(pos) 0.5515 | loss(seq) 0.0058 | grad 9.4026 | lr 0.0000 | time_forward 3.6840 | time_backward 5.0430 |
[2023-10-25 20:09:02,993::train::INFO] [train] Iter 599798 | loss 2.3010 | loss(rot) 2.1968 | loss(pos) 0.0530 | loss(seq) 0.0511 | grad 9.7781 | lr 0.0000 | time_forward 2.2120 | time_backward 2.7550 |
[2023-10-25 20:09:11,471::train::INFO] [train] Iter 599799 | loss 0.0883 | loss(rot) 0.0725 | loss(pos) 0.0077 | loss(seq) 0.0081 | grad 1.8214 | lr 0.0000 | time_forward 3.5210 | time_backward 4.9550 |
[2023-10-25 20:09:13,853::train::INFO] [train] Iter 599800 | loss 0.2180 | loss(rot) 0.1543 | loss(pos) 0.0637 | loss(seq) 0.0000 | grad 2.0674 | lr 0.0000 | time_forward 1.0630 | time_backward 1.2380 |
[2023-10-25 20:09:20,583::train::INFO] [train] Iter 599801 | loss 1.7672 | loss(rot) 1.6467 | loss(pos) 0.0498 | loss(seq) 0.0708 | grad 4.6791 | lr 0.0000 | time_forward 2.8940 | time_backward 3.8330 |
[2023-10-25 20:09:30,820::train::INFO] [train] Iter 599802 | loss 0.8385 | loss(rot) 0.2511 | loss(pos) 0.4536 | loss(seq) 0.1338 | grad 3.9382 | lr 0.0000 | time_forward 4.3590 | time_backward 5.8740 |
[2023-10-25 20:09:40,726::train::INFO] [train] Iter 599803 | loss 0.4993 | loss(rot) 0.1853 | loss(pos) 0.2926 | loss(seq) 0.0214 | grad 3.1884 | lr 0.0000 | time_forward 4.1760 | time_backward 5.7270 |
[2023-10-25 20:09:48,999::train::INFO] [train] Iter 599804 | loss 1.1602 | loss(rot) 0.7184 | loss(pos) 0.0293 | loss(seq) 0.4125 | grad 5.3582 | lr 0.0000 | time_forward 3.4740 | time_backward 4.7970 |
[2023-10-25 20:09:57,276::train::INFO] [train] Iter 599805 | loss 0.3039 | loss(rot) 0.0243 | loss(pos) 0.2773 | loss(seq) 0.0023 | grad 5.6377 | lr 0.0000 | time_forward 3.4920 | time_backward 4.7820 |
[2023-10-25 20:10:07,174::train::INFO] [train] Iter 599806 | loss 0.6754 | loss(rot) 0.0363 | loss(pos) 0.6226 | loss(seq) 0.0165 | grad 11.4208 | lr 0.0000 | time_forward 3.9800 | time_backward 5.9140 |
[2023-10-25 20:10:17,114::train::INFO] [train] Iter 599807 | loss 0.5850 | loss(rot) 0.5228 | loss(pos) 0.0581 | loss(seq) 0.0041 | grad 17.8269 | lr 0.0000 | time_forward 4.0180 | time_backward 5.9190 |
[2023-10-25 20:10:27,057::train::INFO] [train] Iter 599808 | loss 0.6542 | loss(rot) 0.5232 | loss(pos) 0.0213 | loss(seq) 0.1097 | grad 3.8845 | lr 0.0000 | time_forward 4.0170 | time_backward 5.9230 |
[2023-10-25 20:10:38,044::train::INFO] [train] Iter 599809 | loss 2.8591 | loss(rot) 2.4991 | loss(pos) 0.2145 | loss(seq) 0.1455 | grad 3.6982 | lr 0.0000 | time_forward 4.5020 | time_backward 6.4810 |
[2023-10-25 20:10:47,952::train::INFO] [train] Iter 599810 | loss 0.9842 | loss(rot) 0.8723 | loss(pos) 0.0407 | loss(seq) 0.0712 | grad 3.2827 | lr 0.0000 | time_forward 3.9830 | time_backward 5.9220 |
[2023-10-25 20:10:57,088::train::INFO] [train] Iter 599811 | loss 0.4031 | loss(rot) 0.3247 | loss(pos) 0.0552 | loss(seq) 0.0232 | grad 2.6726 | lr 0.0000 | time_forward 3.8270 | time_backward 5.3060 |
[2023-10-25 20:11:07,125::train::INFO] [train] Iter 599812 | loss 0.9891 | loss(rot) 0.3919 | loss(pos) 0.2398 | loss(seq) 0.3574 | grad 4.5492 | lr 0.0000 | time_forward 4.1040 | time_backward 5.9290 |
[2023-10-25 20:11:17,057::train::INFO] [train] Iter 599813 | loss 1.3051 | loss(rot) 0.5778 | loss(pos) 0.3580 | loss(seq) 0.3693 | grad 3.3894 | lr 0.0000 | time_forward 4.2040 | time_backward 5.7260 |
[2023-10-25 20:11:27,046::train::INFO] [train] Iter 599814 | loss 0.9941 | loss(rot) 0.6176 | loss(pos) 0.0164 | loss(seq) 0.3601 | grad 3.1330 | lr 0.0000 | time_forward 4.0610 | time_backward 5.9240 |
[2023-10-25 20:11:36,183::train::INFO] [train] Iter 599815 | loss 0.3780 | loss(rot) 0.2771 | loss(pos) 0.0145 | loss(seq) 0.0865 | grad 2.9597 | lr 0.0000 | time_forward 3.8690 | time_backward 5.2650 |
[2023-10-25 20:11:46,141::train::INFO] [train] Iter 599816 | loss 0.5734 | loss(rot) 0.1214 | loss(pos) 0.4458 | loss(seq) 0.0061 | grad 6.5060 | lr 0.0000 | time_forward 4.0640 | time_backward 5.8900 |
[2023-10-25 20:11:54,544::train::INFO] [train] Iter 599817 | loss 0.3713 | loss(rot) 0.3054 | loss(pos) 0.0194 | loss(seq) 0.0465 | grad 2.8061 | lr 0.0000 | time_forward 3.5440 | time_backward 4.8560 |
[2023-10-25 20:12:02,063::train::INFO] [train] Iter 599818 | loss 1.8803 | loss(rot) 1.3952 | loss(pos) 0.0588 | loss(seq) 0.4263 | grad 5.4640 | lr 0.0000 | time_forward 3.1950 | time_backward 4.3210 |
[2023-10-25 20:12:04,864::train::INFO] [train] Iter 599819 | loss 0.7357 | loss(rot) 0.5737 | loss(pos) 0.0672 | loss(seq) 0.0949 | grad 3.1213 | lr 0.0000 | time_forward 1.3320 | time_backward 1.4660 |
[2023-10-25 20:12:08,372::train::INFO] [train] Iter 599820 | loss 0.7913 | loss(rot) 0.7525 | loss(pos) 0.0387 | loss(seq) 0.0001 | grad 2.5083 | lr 0.0000 | time_forward 1.5590 | time_backward 1.9470 |
[2023-10-25 20:12:18,405::train::INFO] [train] Iter 599821 | loss 1.7823 | loss(rot) 1.1574 | loss(pos) 0.1936 | loss(seq) 0.4312 | grad 4.1247 | lr 0.0000 | time_forward 4.0110 | time_backward 6.0180 |
[2023-10-25 20:12:21,280::train::INFO] [train] Iter 599822 | loss 0.3283 | loss(rot) 0.1346 | loss(pos) 0.0804 | loss(seq) 0.1133 | grad 2.9752 | lr 0.0000 | time_forward 1.3370 | time_backward 1.5240 |
[2023-10-25 20:12:28,631::train::INFO] [train] Iter 599823 | loss 0.2274 | loss(rot) 0.0770 | loss(pos) 0.0208 | loss(seq) 0.1295 | grad 2.0848 | lr 0.0000 | time_forward 3.1470 | time_backward 4.2010 |
[2023-10-25 20:12:38,713::train::INFO] [train] Iter 599824 | loss 0.4562 | loss(rot) 0.3975 | loss(pos) 0.0261 | loss(seq) 0.0325 | grad 3.3382 | lr 0.0000 | time_forward 4.0690 | time_backward 6.0100 |
[2023-10-25 20:12:41,607::train::INFO] [train] Iter 599825 | loss 0.5487 | loss(rot) 0.3228 | loss(pos) 0.0323 | loss(seq) 0.1936 | grad 3.1317 | lr 0.0000 | time_forward 1.4000 | time_backward 1.4900 |
[2023-10-25 20:12:44,519::train::INFO] [train] Iter 599826 | loss 0.2502 | loss(rot) 0.2047 | loss(pos) 0.0239 | loss(seq) 0.0215 | grad 2.4438 | lr 0.0000 | time_forward 1.3830 | time_backward 1.5260 |
[2023-10-25 20:12:53,451::train::INFO] [train] Iter 599827 | loss 0.1844 | loss(rot) 0.0382 | loss(pos) 0.0936 | loss(seq) 0.0525 | grad 3.3004 | lr 0.0000 | time_forward 3.7710 | time_backward 5.1580 |
[2023-10-25 20:13:02,847::train::INFO] [train] Iter 599828 | loss 0.2514 | loss(rot) 0.0755 | loss(pos) 0.0520 | loss(seq) 0.1239 | grad 2.3684 | lr 0.0000 | time_forward 4.0290 | time_backward 5.3640 |
[2023-10-25 20:13:05,674::train::INFO] [train] Iter 599829 | loss 0.4344 | loss(rot) 0.2073 | loss(pos) 0.0135 | loss(seq) 0.2136 | grad 3.2730 | lr 0.0000 | time_forward 1.3450 | time_backward 1.4780 |
[2023-10-25 20:13:08,593::train::INFO] [train] Iter 599830 | loss 0.8945 | loss(rot) 0.0306 | loss(pos) 0.8561 | loss(seq) 0.0078 | grad 17.4341 | lr 0.0000 | time_forward 1.3850 | time_backward 1.4990 |
[2023-10-25 20:13:16,776::train::INFO] [train] Iter 599831 | loss 1.3675 | loss(rot) 0.8606 | loss(pos) 0.0355 | loss(seq) 0.4714 | grad 4.7824 | lr 0.0000 | time_forward 3.4690 | time_backward 4.7100 |
[2023-10-25 20:13:19,050::train::INFO] [train] Iter 599832 | loss 0.2370 | loss(rot) 0.0883 | loss(pos) 0.0251 | loss(seq) 0.1236 | grad 2.0976 | lr 0.0000 | time_forward 1.0530 | time_backward 1.2180 |
[2023-10-25 20:13:29,127::train::INFO] [train] Iter 599833 | loss 1.4674 | loss(rot) 1.1490 | loss(pos) 0.0353 | loss(seq) 0.2831 | grad 3.9142 | lr 0.0000 | time_forward 4.1270 | time_backward 5.9460 |
[2023-10-25 20:13:32,554::train::INFO] [train] Iter 599834 | loss 0.8925 | loss(rot) 0.2867 | loss(pos) 0.2263 | loss(seq) 0.3795 | grad 3.3255 | lr 0.0000 | time_forward 1.4870 | time_backward 1.9380 |
[2023-10-25 20:13:42,753::train::INFO] [train] Iter 599835 | loss 0.9466 | loss(rot) 0.5756 | loss(pos) 0.1865 | loss(seq) 0.1845 | grad 1.9846 | lr 0.0000 | time_forward 4.3020 | time_backward 5.8930 |
[2023-10-25 20:13:45,519::train::INFO] [train] Iter 599836 | loss 2.9390 | loss(rot) 0.0294 | loss(pos) 2.9096 | loss(seq) 0.0000 | grad 26.0542 | lr 0.0000 | time_forward 1.3230 | time_backward 1.4250 |
[2023-10-25 20:13:48,273::train::INFO] [train] Iter 599837 | loss 0.2195 | loss(rot) 0.0373 | loss(pos) 0.1230 | loss(seq) 0.0593 | grad 2.8064 | lr 0.0000 | time_forward 1.3500 | time_backward 1.4000 |
[2023-10-25 20:13:55,897::train::INFO] [train] Iter 599838 | loss 1.8877 | loss(rot) 1.6038 | loss(pos) 0.1101 | loss(seq) 0.1738 | grad 10.2079 | lr 0.0000 | time_forward 3.2690 | time_backward 4.3510 |
[2023-10-25 20:14:06,158::train::INFO] [train] Iter 599839 | loss 0.6667 | loss(rot) 0.5991 | loss(pos) 0.0275 | loss(seq) 0.0401 | grad 2.8393 | lr 0.0000 | time_forward 4.3060 | time_backward 5.9520 |
[2023-10-25 20:14:16,424::train::INFO] [train] Iter 599840 | loss 0.2133 | loss(rot) 0.1278 | loss(pos) 0.0291 | loss(seq) 0.0564 | grad 3.0720 | lr 0.0000 | time_forward 4.2480 | time_backward 6.0130 |
[2023-10-25 20:14:24,509::train::INFO] [train] Iter 599841 | loss 0.5554 | loss(rot) 0.0353 | loss(pos) 0.5166 | loss(seq) 0.0035 | grad 5.5133 | lr 0.0000 | time_forward 3.3910 | time_backward 4.6910 |
[2023-10-25 20:14:32,981::train::INFO] [train] Iter 599842 | loss 0.9524 | loss(rot) 0.2746 | loss(pos) 0.5781 | loss(seq) 0.0997 | grad 4.5327 | lr 0.0000 | time_forward 3.6830 | time_backward 4.7850 |
[2023-10-25 20:14:42,872::train::INFO] [train] Iter 599843 | loss 0.9925 | loss(rot) 0.4192 | loss(pos) 0.2351 | loss(seq) 0.3382 | grad 3.8157 | lr 0.0000 | time_forward 4.0010 | time_backward 5.8880 |
[2023-10-25 20:14:50,896::train::INFO] [train] Iter 599844 | loss 0.8373 | loss(rot) 0.4712 | loss(pos) 0.0337 | loss(seq) 0.3325 | grad 34.0616 | lr 0.0000 | time_forward 3.3810 | time_backward 4.6390 |
[2023-10-25 20:14:58,928::train::INFO] [train] Iter 599845 | loss 0.6211 | loss(rot) 0.5853 | loss(pos) 0.0104 | loss(seq) 0.0253 | grad 4.4679 | lr 0.0000 | time_forward 3.3860 | time_backward 4.6430 |
[2023-10-25 20:15:06,907::train::INFO] [train] Iter 599846 | loss 1.9924 | loss(rot) 1.8097 | loss(pos) 0.0399 | loss(seq) 0.1429 | grad 5.3360 | lr 0.0000 | time_forward 3.3100 | time_backward 4.6660 |
[2023-10-25 20:15:16,803::train::INFO] [train] Iter 599847 | loss 1.1514 | loss(rot) 1.1299 | loss(pos) 0.0213 | loss(seq) 0.0003 | grad 20.5152 | lr 0.0000 | time_forward 4.0080 | time_backward 5.8850 |
[2023-10-25 20:15:25,961::train::INFO] [train] Iter 599848 | loss 1.6990 | loss(rot) 1.1413 | loss(pos) 0.0566 | loss(seq) 0.5010 | grad 3.6945 | lr 0.0000 | time_forward 3.8410 | time_backward 5.3120 |
[2023-10-25 20:15:35,946::train::INFO] [train] Iter 599849 | loss 0.7092 | loss(rot) 0.5459 | loss(pos) 0.0373 | loss(seq) 0.1261 | grad 2.8470 | lr 0.0000 | time_forward 4.0420 | time_backward 5.9390 |
[2023-10-25 20:15:38,786::train::INFO] [train] Iter 599850 | loss 0.2317 | loss(rot) 0.1867 | loss(pos) 0.0223 | loss(seq) 0.0227 | grad 8.6672 | lr 0.0000 | time_forward 1.3550 | time_backward 1.4810 |
[2023-10-25 20:15:48,898::train::INFO] [train] Iter 599851 | loss 0.5546 | loss(rot) 0.5159 | loss(pos) 0.0220 | loss(seq) 0.0166 | grad 3.8243 | lr 0.0000 | time_forward 4.2570 | time_backward 5.8300 |
[2023-10-25 20:15:58,897::train::INFO] [train] Iter 599852 | loss 0.4144 | loss(rot) 0.2981 | loss(pos) 0.0140 | loss(seq) 0.1024 | grad 3.2043 | lr 0.0000 | time_forward 4.0950 | time_backward 5.9010 |
[2023-10-25 20:16:06,997::train::INFO] [train] Iter 599853 | loss 0.7497 | loss(rot) 0.3731 | loss(pos) 0.2871 | loss(seq) 0.0895 | grad 5.1505 | lr 0.0000 | time_forward 3.3840 | time_backward 4.7130 |
[2023-10-25 20:16:17,204::train::INFO] [train] Iter 599854 | loss 0.8036 | loss(rot) 0.5426 | loss(pos) 0.0498 | loss(seq) 0.2112 | grad 2.9559 | lr 0.0000 | time_forward 4.1490 | time_backward 6.0560 |
[2023-10-25 20:16:25,394::train::INFO] [train] Iter 599855 | loss 0.4519 | loss(rot) 0.3952 | loss(pos) 0.0297 | loss(seq) 0.0270 | grad 5.1174 | lr 0.0000 | time_forward 3.4390 | time_backward 4.7470 |
[2023-10-25 20:16:28,202::train::INFO] [train] Iter 599856 | loss 0.3433 | loss(rot) 0.1585 | loss(pos) 0.0295 | loss(seq) 0.1553 | grad 2.2375 | lr 0.0000 | time_forward 1.3230 | time_backward 1.4820 |
[2023-10-25 20:16:36,706::train::INFO] [train] Iter 599857 | loss 1.1247 | loss(rot) 0.6032 | loss(pos) 0.1217 | loss(seq) 0.3998 | grad 4.8000 | lr 0.0000 | time_forward 3.5850 | time_backward 4.8930 |
[2023-10-25 20:16:45,875::train::INFO] [train] Iter 599858 | loss 1.1585 | loss(rot) 0.9482 | loss(pos) 0.0237 | loss(seq) 0.1867 | grad 4.8326 | lr 0.0000 | time_forward 3.8590 | time_backward 5.3070 |
[2023-10-25 20:16:54,638::train::INFO] [train] Iter 599859 | loss 1.0634 | loss(rot) 0.9535 | loss(pos) 0.0302 | loss(seq) 0.0796 | grad 3.5304 | lr 0.0000 | time_forward 3.7060 | time_backward 5.0550 |
[2023-10-25 20:17:04,563::train::INFO] [train] Iter 599860 | loss 0.2836 | loss(rot) 0.0388 | loss(pos) 0.2221 | loss(seq) 0.0227 | grad 3.2978 | lr 0.0000 | time_forward 4.0630 | time_backward 5.8590 |
[2023-10-25 20:17:07,392::train::INFO] [train] Iter 599861 | loss 1.0742 | loss(rot) 0.5655 | loss(pos) 0.0557 | loss(seq) 0.4530 | grad 4.2965 | lr 0.0000 | time_forward 1.2990 | time_backward 1.5260 |
[2023-10-25 20:17:15,758::train::INFO] [train] Iter 599862 | loss 0.5571 | loss(rot) 0.0981 | loss(pos) 0.4361 | loss(seq) 0.0229 | grad 11.1699 | lr 0.0000 | time_forward 3.5410 | time_backward 4.8100 |
Subsets and Splits