text
stringlengths 56
1.16k
|
|---|
[2023-09-01 18:10:39,030::train::INFO] [train] Iter 00895 | loss 1.6929 | loss(rot) 0.0390 | loss(pos) 1.6444 | loss(seq) 0.0095 | grad 4.0328 | lr 0.0010 | time_forward 1.5220 | time_backward 2.0030 |
[2023-09-01 18:10:41,762::train::INFO] [train] Iter 00896 | loss 2.2537 | loss(rot) 1.5183 | loss(pos) 0.2642 | loss(seq) 0.4711 | grad 3.3761 | lr 0.0010 | time_forward 1.2770 | time_backward 1.4510 |
[2023-09-01 18:10:50,490::train::INFO] [train] Iter 00897 | loss 2.2914 | loss(rot) 0.9862 | loss(pos) 0.8901 | loss(seq) 0.4150 | grad 4.2177 | lr 0.0010 | time_forward 3.7480 | time_backward 4.9760 |
[2023-09-01 18:10:59,467::train::INFO] [train] Iter 00898 | loss 1.9135 | loss(rot) 0.8801 | loss(pos) 0.9666 | loss(seq) 0.0668 | grad 4.0512 | lr 0.0010 | time_forward 3.8820 | time_backward 5.0920 |
[2023-09-01 18:11:07,961::train::INFO] [train] Iter 00899 | loss 5.2466 | loss(rot) 0.0118 | loss(pos) 5.2340 | loss(seq) 0.0008 | grad 9.6911 | lr 0.0010 | time_forward 3.7650 | time_backward 4.7260 |
[2023-09-01 18:11:17,554::train::INFO] [train] Iter 00900 | loss 1.5135 | loss(rot) 0.7917 | loss(pos) 0.4289 | loss(seq) 0.2929 | grad 4.0123 | lr 0.0010 | time_forward 3.8570 | time_backward 5.7340 |
[2023-09-01 18:11:25,301::train::INFO] [train] Iter 00901 | loss 3.6157 | loss(rot) 2.8653 | loss(pos) 0.3026 | loss(seq) 0.4478 | grad 3.7294 | lr 0.0010 | time_forward 3.3810 | time_backward 4.3620 |
[2023-09-01 18:11:34,054::train::INFO] [train] Iter 00902 | loss 2.2075 | loss(rot) 1.8898 | loss(pos) 0.2674 | loss(seq) 0.0503 | grad 3.0956 | lr 0.0010 | time_forward 3.7170 | time_backward 5.0330 |
[2023-09-01 18:11:36,804::train::INFO] [train] Iter 00903 | loss 2.2167 | loss(rot) 0.5637 | loss(pos) 1.4098 | loss(seq) 0.2431 | grad 5.5180 | lr 0.0010 | time_forward 1.3030 | time_backward 1.4440 |
[2023-09-01 18:11:44,793::train::INFO] [train] Iter 00904 | loss 3.0196 | loss(rot) 2.2553 | loss(pos) 0.2516 | loss(seq) 0.5127 | grad 5.0021 | lr 0.0010 | time_forward 3.4490 | time_backward 4.5370 |
[2023-09-01 18:11:53,166::train::INFO] [train] Iter 00905 | loss 1.4775 | loss(rot) 0.4779 | loss(pos) 0.9399 | loss(seq) 0.0597 | grad 4.4391 | lr 0.0010 | time_forward 3.5860 | time_backward 4.7830 |
[2023-09-01 18:12:00,618::train::INFO] [train] Iter 00906 | loss 2.0559 | loss(rot) 0.2472 | loss(pos) 1.5445 | loss(seq) 0.2642 | grad 5.7859 | lr 0.0010 | time_forward 3.2160 | time_backward 4.2330 |
[2023-09-01 18:12:03,375::train::INFO] [train] Iter 00907 | loss 2.7225 | loss(rot) 1.9379 | loss(pos) 0.4227 | loss(seq) 0.3619 | grad 3.5553 | lr 0.0010 | time_forward 1.3000 | time_backward 1.4540 |
[2023-09-01 18:12:04,170::train::INFO] [train] Iter 00908 | loss 3.2585 | loss(rot) 2.5575 | loss(pos) 0.5726 | loss(seq) 0.1284 | grad 7.5934 | lr 0.0010 | time_forward 0.4310 | time_backward 0.3320 |
[2023-09-01 18:12:14,235::train::INFO] [train] Iter 00909 | loss 2.5595 | loss(rot) 0.0138 | loss(pos) 2.5430 | loss(seq) 0.0027 | grad 7.3068 | lr 0.0010 | time_forward 4.2300 | time_backward 5.8330 |
[2023-09-01 18:12:21,731::train::INFO] [train] Iter 00910 | loss 3.8625 | loss(rot) 0.0354 | loss(pos) 3.8228 | loss(seq) 0.0044 | grad 9.7492 | lr 0.0010 | time_forward 3.1120 | time_backward 4.3800 |
[2023-09-01 18:12:24,431::train::INFO] [train] Iter 00911 | loss 1.7828 | loss(rot) 0.9277 | loss(pos) 0.7542 | loss(seq) 0.1009 | grad 4.3518 | lr 0.0010 | time_forward 1.2570 | time_backward 1.4400 |
[2023-09-01 18:12:29,341::train::INFO] [train] Iter 00912 | loss 1.6878 | loss(rot) 0.7823 | loss(pos) 0.6033 | loss(seq) 0.3022 | grad 6.0897 | lr 0.0010 | time_forward 2.1710 | time_backward 2.7360 |
[2023-09-01 18:12:38,686::train::INFO] [train] Iter 00913 | loss 3.3186 | loss(rot) 2.7346 | loss(pos) 0.5579 | loss(seq) 0.0261 | grad 5.9664 | lr 0.0010 | time_forward 3.7010 | time_backward 5.6400 |
[2023-09-01 18:12:41,551::train::INFO] [train] Iter 00914 | loss 3.7075 | loss(rot) 3.2198 | loss(pos) 0.4866 | loss(seq) 0.0011 | grad 6.6549 | lr 0.0010 | time_forward 1.3240 | time_backward 1.4620 |
[2023-09-01 18:12:50,465::train::INFO] [train] Iter 00915 | loss 3.3801 | loss(rot) 0.2188 | loss(pos) 2.6143 | loss(seq) 0.5470 | grad 7.4711 | lr 0.0010 | time_forward 3.7960 | time_backward 5.0680 |
[2023-09-01 18:13:00,689::train::INFO] [train] Iter 00916 | loss 3.3713 | loss(rot) 3.1176 | loss(pos) 0.2177 | loss(seq) 0.0360 | grad 2.6343 | lr 0.0010 | time_forward 4.1680 | time_backward 6.0540 |
[2023-09-01 18:13:03,998::train::INFO] [train] Iter 00917 | loss 3.4619 | loss(rot) 2.8467 | loss(pos) 0.6151 | loss(seq) 0.0001 | grad 3.8349 | lr 0.0010 | time_forward 1.4870 | time_backward 1.8190 |
[2023-09-01 18:13:14,067::train::INFO] [train] Iter 00918 | loss 2.8091 | loss(rot) 1.1322 | loss(pos) 1.0265 | loss(seq) 0.6503 | grad 3.9954 | lr 0.0010 | time_forward 4.1110 | time_backward 5.9390 |
[2023-09-01 18:13:24,141::train::INFO] [train] Iter 00919 | loss 2.0742 | loss(rot) 0.5689 | loss(pos) 1.4176 | loss(seq) 0.0876 | grad 3.9617 | lr 0.0010 | time_forward 4.1480 | time_backward 5.9140 |
[2023-09-01 18:13:33,557::train::INFO] [train] Iter 00920 | loss 3.4592 | loss(rot) 2.0924 | loss(pos) 0.9361 | loss(seq) 0.4307 | grad 4.1936 | lr 0.0010 | time_forward 3.8990 | time_backward 5.5080 |
[2023-09-01 18:13:42,330::train::INFO] [train] Iter 00921 | loss 3.2105 | loss(rot) 1.2010 | loss(pos) 1.4540 | loss(seq) 0.5555 | grad 6.1878 | lr 0.0010 | time_forward 3.6400 | time_backward 5.1300 |
[2023-09-01 18:13:50,465::train::INFO] [train] Iter 00922 | loss 3.5546 | loss(rot) 2.2091 | loss(pos) 0.8581 | loss(seq) 0.4874 | grad 4.2681 | lr 0.0010 | time_forward 3.3090 | time_backward 4.8230 |
[2023-09-01 18:13:57,869::train::INFO] [train] Iter 00923 | loss 2.5689 | loss(rot) 0.0526 | loss(pos) 2.5114 | loss(seq) 0.0049 | grad 4.6144 | lr 0.0010 | time_forward 3.1910 | time_backward 4.2100 |
[2023-09-01 18:14:00,632::train::INFO] [train] Iter 00924 | loss 2.4575 | loss(rot) 1.3266 | loss(pos) 0.7024 | loss(seq) 0.4284 | grad 4.0616 | lr 0.0010 | time_forward 1.3080 | time_backward 1.4510 |
[2023-09-01 18:14:10,628::train::INFO] [train] Iter 00925 | loss 2.7144 | loss(rot) 1.5246 | loss(pos) 0.6645 | loss(seq) 0.5253 | grad 4.4312 | lr 0.0010 | time_forward 4.1930 | time_backward 5.8010 |
[2023-09-01 18:14:13,362::train::INFO] [train] Iter 00926 | loss 1.2342 | loss(rot) 0.1970 | loss(pos) 1.0150 | loss(seq) 0.0222 | grad 4.8428 | lr 0.0010 | time_forward 1.3030 | time_backward 1.4280 |
[2023-09-01 18:14:23,414::train::INFO] [train] Iter 00927 | loss 3.2933 | loss(rot) 2.1847 | loss(pos) 0.6303 | loss(seq) 0.4783 | grad 4.9189 | lr 0.0010 | time_forward 4.2110 | time_backward 5.8370 |
[2023-09-01 18:14:33,505::train::INFO] [train] Iter 00928 | loss 3.8157 | loss(rot) 2.5929 | loss(pos) 1.0109 | loss(seq) 0.2119 | grad 9.0182 | lr 0.0010 | time_forward 4.1520 | time_backward 5.9350 |
[2023-09-01 18:14:36,244::train::INFO] [train] Iter 00929 | loss 3.4857 | loss(rot) 2.4130 | loss(pos) 0.5319 | loss(seq) 0.5408 | grad 4.1395 | lr 0.0010 | time_forward 1.3510 | time_backward 1.3840 |
[2023-09-01 18:14:39,023::train::INFO] [train] Iter 00930 | loss 1.8469 | loss(rot) 0.4694 | loss(pos) 1.1817 | loss(seq) 0.1958 | grad 10.2807 | lr 0.0010 | time_forward 1.3100 | time_backward 1.4520 |
[2023-09-01 18:14:47,667::train::INFO] [train] Iter 00931 | loss 4.2617 | loss(rot) 3.3052 | loss(pos) 0.9565 | loss(seq) 0.0000 | grad 5.6227 | lr 0.0010 | time_forward 3.5580 | time_backward 5.0820 |
[2023-09-01 18:14:57,509::train::INFO] [train] Iter 00932 | loss 3.5438 | loss(rot) 2.7151 | loss(pos) 0.6171 | loss(seq) 0.2116 | grad 5.6456 | lr 0.0010 | time_forward 3.9760 | time_backward 5.8630 |
[2023-09-01 18:15:07,230::train::INFO] [train] Iter 00933 | loss 3.3978 | loss(rot) 2.9144 | loss(pos) 0.4832 | loss(seq) 0.0003 | grad 5.9561 | lr 0.0010 | time_forward 3.8630 | time_backward 5.8540 |
[2023-09-01 18:15:10,523::train::INFO] [train] Iter 00934 | loss 3.4940 | loss(rot) 2.6163 | loss(pos) 0.4990 | loss(seq) 0.3788 | grad 3.9598 | lr 0.0010 | time_forward 1.7870 | time_backward 1.5020 |
[2023-09-01 18:15:13,434::train::INFO] [train] Iter 00935 | loss 2.9581 | loss(rot) 2.2122 | loss(pos) 0.3946 | loss(seq) 0.3513 | grad 4.0981 | lr 0.0010 | time_forward 1.4060 | time_backward 1.5010 |
[2023-09-01 18:15:21,599::train::INFO] [train] Iter 00936 | loss 1.9585 | loss(rot) 0.6876 | loss(pos) 1.0001 | loss(seq) 0.2708 | grad 3.6786 | lr 0.0010 | time_forward 3.3010 | time_backward 4.8610 |
[2023-09-01 18:15:31,240::train::INFO] [train] Iter 00937 | loss 2.1706 | loss(rot) 0.8153 | loss(pos) 1.0944 | loss(seq) 0.2609 | grad 4.5922 | lr 0.0010 | time_forward 3.7100 | time_backward 5.9280 |
[2023-09-01 18:15:34,029::train::INFO] [train] Iter 00938 | loss 2.7765 | loss(rot) 1.5718 | loss(pos) 0.4591 | loss(seq) 0.7456 | grad 5.2216 | lr 0.0010 | time_forward 1.3140 | time_backward 1.4720 |
[2023-09-01 18:15:43,083::train::INFO] [train] Iter 00939 | loss 3.6798 | loss(rot) 2.7671 | loss(pos) 0.6403 | loss(seq) 0.2725 | grad 4.0674 | lr 0.0010 | time_forward 3.6980 | time_backward 5.3520 |
[2023-09-01 18:15:53,103::train::INFO] [train] Iter 00940 | loss 2.0762 | loss(rot) 0.3531 | loss(pos) 1.6518 | loss(seq) 0.0714 | grad 4.2990 | lr 0.0010 | time_forward 3.9810 | time_backward 6.0350 |
[2023-09-01 18:16:00,111::train::INFO] [train] Iter 00941 | loss 2.1585 | loss(rot) 1.2800 | loss(pos) 0.4726 | loss(seq) 0.4059 | grad 3.2492 | lr 0.0010 | time_forward 2.8530 | time_backward 4.1520 |
[2023-09-01 18:16:09,657::train::INFO] [train] Iter 00942 | loss 2.9409 | loss(rot) 1.1434 | loss(pos) 1.3234 | loss(seq) 0.4741 | grad 5.4331 | lr 0.0010 | time_forward 4.2240 | time_backward 5.3180 |
[2023-09-01 18:16:19,555::train::INFO] [train] Iter 00943 | loss 2.4991 | loss(rot) 1.5505 | loss(pos) 0.4701 | loss(seq) 0.4784 | grad 3.8153 | lr 0.0010 | time_forward 4.0390 | time_backward 5.8570 |
[2023-09-01 18:16:28,841::train::INFO] [train] Iter 00944 | loss 2.3236 | loss(rot) 0.5110 | loss(pos) 1.7478 | loss(seq) 0.0648 | grad 5.2286 | lr 0.0010 | time_forward 4.0580 | time_backward 5.2250 |
[2023-09-01 18:16:39,102::train::INFO] [train] Iter 00945 | loss 3.3058 | loss(rot) 2.8639 | loss(pos) 0.4072 | loss(seq) 0.0346 | grad 3.7112 | lr 0.0010 | time_forward 4.2620 | time_backward 5.9960 |
[2023-09-01 18:16:48,403::train::INFO] [train] Iter 00946 | loss 3.6162 | loss(rot) 3.0753 | loss(pos) 0.5378 | loss(seq) 0.0031 | grad 5.5658 | lr 0.0010 | time_forward 3.9520 | time_backward 5.3450 |
[2023-09-01 18:16:56,969::train::INFO] [train] Iter 00947 | loss 2.7634 | loss(rot) 1.8136 | loss(pos) 0.3757 | loss(seq) 0.5741 | grad 3.0715 | lr 0.0010 | time_forward 3.6230 | time_backward 4.9390 |
[2023-09-01 18:17:05,970::train::INFO] [train] Iter 00948 | loss 3.0133 | loss(rot) 1.7032 | loss(pos) 0.8198 | loss(seq) 0.4903 | grad 6.3643 | lr 0.0010 | time_forward 3.8640 | time_backward 5.1340 |
[2023-09-01 18:17:14,842::train::INFO] [train] Iter 00949 | loss 3.4506 | loss(rot) 3.1506 | loss(pos) 0.2956 | loss(seq) 0.0044 | grad 3.7026 | lr 0.0010 | time_forward 3.8010 | time_backward 5.0680 |
[2023-09-01 18:17:25,065::train::INFO] [train] Iter 00950 | loss 2.8074 | loss(rot) 1.5164 | loss(pos) 0.7958 | loss(seq) 0.4951 | grad 4.2084 | lr 0.0010 | time_forward 4.3240 | time_backward 5.8960 |
[2023-09-01 18:17:33,000::train::INFO] [train] Iter 00951 | loss 1.9602 | loss(rot) 0.7034 | loss(pos) 0.6340 | loss(seq) 0.6228 | grad 4.6475 | lr 0.0010 | time_forward 3.5050 | time_backward 4.4260 |
[2023-09-01 18:17:41,976::train::INFO] [train] Iter 00952 | loss 2.6916 | loss(rot) 2.3749 | loss(pos) 0.2351 | loss(seq) 0.0816 | grad 5.2774 | lr 0.0010 | time_forward 3.9200 | time_backward 5.0520 |
[2023-09-01 18:17:50,704::train::INFO] [train] Iter 00953 | loss 2.2075 | loss(rot) 1.0544 | loss(pos) 0.8009 | loss(seq) 0.3522 | grad 4.4180 | lr 0.0010 | time_forward 3.7120 | time_backward 5.0140 |
[2023-09-01 18:17:59,420::train::INFO] [train] Iter 00954 | loss 1.6918 | loss(rot) 0.6785 | loss(pos) 0.8546 | loss(seq) 0.1587 | grad 4.7447 | lr 0.0010 | time_forward 3.6900 | time_backward 5.0220 |
[2023-09-01 18:18:08,342::train::INFO] [train] Iter 00955 | loss 1.5679 | loss(rot) 0.3699 | loss(pos) 1.1565 | loss(seq) 0.0415 | grad 4.1531 | lr 0.0010 | time_forward 3.7530 | time_backward 5.1660 |
[2023-09-01 18:18:11,311::train::INFO] [train] Iter 00956 | loss 3.3086 | loss(rot) 2.7702 | loss(pos) 0.5384 | loss(seq) 0.0000 | grad 6.1990 | lr 0.0010 | time_forward 1.4450 | time_backward 1.5200 |
[2023-09-01 18:18:14,192::train::INFO] [train] Iter 00957 | loss 3.5385 | loss(rot) 2.6957 | loss(pos) 0.5565 | loss(seq) 0.2863 | grad 5.1696 | lr 0.0010 | time_forward 1.3840 | time_backward 1.4940 |
[2023-09-01 18:18:17,216::train::INFO] [train] Iter 00958 | loss 3.2120 | loss(rot) 2.7481 | loss(pos) 0.4636 | loss(seq) 0.0003 | grad 5.9260 | lr 0.0010 | time_forward 1.5280 | time_backward 1.4930 |
[2023-09-01 18:18:20,805::train::INFO] [train] Iter 00959 | loss 2.5947 | loss(rot) 1.5668 | loss(pos) 0.5551 | loss(seq) 0.4727 | grad 2.9256 | lr 0.0010 | time_forward 1.6260 | time_backward 1.9400 |
[2023-09-01 18:18:27,341::train::INFO] [train] Iter 00960 | loss 3.1470 | loss(rot) 2.1312 | loss(pos) 0.4531 | loss(seq) 0.5627 | grad 4.3514 | lr 0.0010 | time_forward 2.6050 | time_backward 3.9280 |
[2023-09-01 18:18:37,631::train::INFO] [train] Iter 00961 | loss 1.7743 | loss(rot) 0.4140 | loss(pos) 1.1005 | loss(seq) 0.2597 | grad 3.9631 | lr 0.0010 | time_forward 4.1020 | time_backward 6.1590 |
[2023-09-01 18:18:40,540::train::INFO] [train] Iter 00962 | loss 2.5861 | loss(rot) 2.3189 | loss(pos) 0.2608 | loss(seq) 0.0065 | grad 2.8898 | lr 0.0010 | time_forward 1.3980 | time_backward 1.5070 |
[2023-09-01 18:18:44,411::train::INFO] [train] Iter 00963 | loss 3.9936 | loss(rot) 3.0657 | loss(pos) 0.4693 | loss(seq) 0.4586 | grad 4.2661 | lr 0.0010 | time_forward 2.3170 | time_backward 1.5110 |
[2023-09-01 18:18:47,408::train::INFO] [train] Iter 00964 | loss 3.1892 | loss(rot) 2.1867 | loss(pos) 0.4835 | loss(seq) 0.5191 | grad 2.6349 | lr 0.0010 | time_forward 1.4900 | time_backward 1.5030 |
[2023-09-01 18:18:50,160::train::INFO] [train] Iter 00965 | loss 2.8879 | loss(rot) 1.7020 | loss(pos) 0.8042 | loss(seq) 0.3817 | grad 3.8615 | lr 0.0010 | time_forward 1.2880 | time_backward 1.4410 |
[2023-09-01 18:18:58,310::train::INFO] [train] Iter 00966 | loss 1.9624 | loss(rot) 0.6205 | loss(pos) 0.9388 | loss(seq) 0.4031 | grad 5.0709 | lr 0.0010 | time_forward 3.4340 | time_backward 4.7130 |
[2023-09-01 18:19:01,050::train::INFO] [train] Iter 00967 | loss 3.0891 | loss(rot) 2.7222 | loss(pos) 0.3547 | loss(seq) 0.0122 | grad 3.4665 | lr 0.0010 | time_forward 1.2970 | time_backward 1.4390 |
[2023-09-01 18:19:09,045::train::INFO] [train] Iter 00968 | loss 2.6578 | loss(rot) 2.3315 | loss(pos) 0.3243 | loss(seq) 0.0020 | grad 3.1741 | lr 0.0010 | time_forward 3.3170 | time_backward 4.6760 |
[2023-09-01 18:19:19,325::train::INFO] [train] Iter 00969 | loss 2.9963 | loss(rot) 1.1468 | loss(pos) 1.1503 | loss(seq) 0.6992 | grad 5.9374 | lr 0.0010 | time_forward 4.4060 | time_backward 5.8700 |
[2023-09-01 18:19:28,714::train::INFO] [train] Iter 00970 | loss 3.3577 | loss(rot) 3.0872 | loss(pos) 0.2698 | loss(seq) 0.0006 | grad 3.6248 | lr 0.0010 | time_forward 3.7180 | time_backward 5.6570 |
[2023-09-01 18:19:38,628::train::INFO] [train] Iter 00971 | loss 3.2593 | loss(rot) 3.0762 | loss(pos) 0.1828 | loss(seq) 0.0003 | grad 3.1783 | lr 0.0010 | time_forward 3.9230 | time_backward 5.9880 |
[2023-09-01 18:19:41,558::train::INFO] [train] Iter 00972 | loss 2.9023 | loss(rot) 2.6324 | loss(pos) 0.2297 | loss(seq) 0.0403 | grad 3.4759 | lr 0.0010 | time_forward 1.4690 | time_backward 1.4570 |
[2023-09-01 18:19:50,245::train::INFO] [train] Iter 00973 | loss 2.1962 | loss(rot) 0.1070 | loss(pos) 2.0675 | loss(seq) 0.0217 | grad 4.4914 | lr 0.0010 | time_forward 3.7520 | time_backward 4.9310 |
[2023-09-01 18:19:52,924::train::INFO] [train] Iter 00974 | loss 2.0583 | loss(rot) 0.1330 | loss(pos) 1.9008 | loss(seq) 0.0245 | grad 5.0239 | lr 0.0010 | time_forward 1.2640 | time_backward 1.4110 |
[2023-09-01 18:19:55,752::train::INFO] [train] Iter 00975 | loss 1.9633 | loss(rot) 0.0391 | loss(pos) 1.9193 | loss(seq) 0.0050 | grad 4.8293 | lr 0.0010 | time_forward 1.3950 | time_backward 1.4310 |
[2023-09-01 18:20:06,255::train::INFO] [train] Iter 00976 | loss 3.7042 | loss(rot) 2.5471 | loss(pos) 0.7049 | loss(seq) 0.4521 | grad 6.5214 | lr 0.0010 | time_forward 4.2560 | time_backward 6.2430 |
[2023-09-01 18:20:09,079::train::INFO] [train] Iter 00977 | loss 3.4025 | loss(rot) 2.4724 | loss(pos) 0.7277 | loss(seq) 0.2024 | grad 6.4292 | lr 0.0010 | time_forward 1.3330 | time_backward 1.4870 |
[2023-09-01 18:20:18,938::train::INFO] [train] Iter 00978 | loss 3.4605 | loss(rot) 2.9076 | loss(pos) 0.5131 | loss(seq) 0.0399 | grad 6.1121 | lr 0.0010 | time_forward 4.2450 | time_backward 5.6100 |
[2023-09-01 18:20:28,529::train::INFO] [train] Iter 00979 | loss 3.8405 | loss(rot) 3.2802 | loss(pos) 0.4242 | loss(seq) 0.1362 | grad 3.5540 | lr 0.0010 | time_forward 3.9940 | time_backward 5.5940 |
[2023-09-01 18:20:36,879::train::INFO] [train] Iter 00980 | loss 3.7165 | loss(rot) 3.1448 | loss(pos) 0.1681 | loss(seq) 0.4036 | grad 2.8392 | lr 0.0010 | time_forward 3.4620 | time_backward 4.8760 |
[2023-09-01 18:20:39,885::train::INFO] [train] Iter 00981 | loss 2.3022 | loss(rot) 1.5045 | loss(pos) 0.6187 | loss(seq) 0.1791 | grad 3.6563 | lr 0.0010 | time_forward 1.4550 | time_backward 1.5470 |
[2023-09-01 18:20:42,553::train::INFO] [train] Iter 00982 | loss 2.7183 | loss(rot) 2.3252 | loss(pos) 0.3909 | loss(seq) 0.0022 | grad 4.0534 | lr 0.0010 | time_forward 1.2980 | time_backward 1.3670 |
[2023-09-01 18:20:53,101::train::INFO] [train] Iter 00983 | loss 2.4853 | loss(rot) 0.0427 | loss(pos) 2.4344 | loss(seq) 0.0081 | grad 3.8160 | lr 0.0010 | time_forward 4.7270 | time_backward 5.7800 |
[2023-09-01 18:21:01,385::train::INFO] [train] Iter 00984 | loss 1.4796 | loss(rot) 0.8564 | loss(pos) 0.5138 | loss(seq) 0.1094 | grad 3.4055 | lr 0.0010 | time_forward 3.3260 | time_backward 4.9280 |
[2023-09-01 18:21:11,039::train::INFO] [train] Iter 00985 | loss 1.9431 | loss(rot) 1.2231 | loss(pos) 0.2536 | loss(seq) 0.4664 | grad 2.5399 | lr 0.0010 | time_forward 3.9100 | time_backward 5.7410 |
[2023-09-01 18:21:19,358::train::INFO] [train] Iter 00986 | loss 1.8825 | loss(rot) 0.1587 | loss(pos) 1.7053 | loss(seq) 0.0186 | grad 6.1599 | lr 0.0010 | time_forward 3.6680 | time_backward 4.6330 |
[2023-09-01 18:21:28,593::train::INFO] [train] Iter 00987 | loss 3.8291 | loss(rot) 3.2914 | loss(pos) 0.5375 | loss(seq) 0.0001 | grad 3.7576 | lr 0.0010 | time_forward 3.9370 | time_backward 5.2930 |
[2023-09-01 18:21:36,500::train::INFO] [train] Iter 00988 | loss 1.2863 | loss(rot) 0.1359 | loss(pos) 1.1131 | loss(seq) 0.0373 | grad 4.0420 | lr 0.0010 | time_forward 3.4330 | time_backward 4.4700 |
[2023-09-01 18:21:46,468::train::INFO] [train] Iter 00989 | loss 1.4979 | loss(rot) 0.9968 | loss(pos) 0.1873 | loss(seq) 0.3138 | grad 3.1473 | lr 0.0010 | time_forward 4.6850 | time_backward 5.2810 |
[2023-09-01 18:21:55,086::train::INFO] [train] Iter 00990 | loss 2.8961 | loss(rot) 2.1262 | loss(pos) 0.3014 | loss(seq) 0.4685 | grad 3.5301 | lr 0.0010 | time_forward 3.6510 | time_backward 4.9630 |
[2023-09-01 18:22:03,638::train::INFO] [train] Iter 00991 | loss 2.3792 | loss(rot) 1.7202 | loss(pos) 0.1665 | loss(seq) 0.4925 | grad 2.9284 | lr 0.0010 | time_forward 3.6350 | time_backward 4.9130 |
[2023-09-01 18:22:12,179::train::INFO] [train] Iter 00992 | loss 2.8962 | loss(rot) 1.8167 | loss(pos) 0.6054 | loss(seq) 0.4741 | grad 5.2653 | lr 0.0010 | time_forward 3.5380 | time_backward 5.0000 |
[2023-09-01 18:22:21,840::train::INFO] [train] Iter 00993 | loss 3.1483 | loss(rot) 0.0387 | loss(pos) 3.1014 | loss(seq) 0.0082 | grad 3.7178 | lr 0.0010 | time_forward 3.9240 | time_backward 5.7330 |
[2023-09-01 18:22:30,436::train::INFO] [train] Iter 00994 | loss 1.1278 | loss(rot) 0.1607 | loss(pos) 0.9426 | loss(seq) 0.0244 | grad 4.0808 | lr 0.0010 | time_forward 3.4190 | time_backward 5.1700 |
Subsets and Splits