text stringlengths 56 1.16k |
|---|
[2023-10-24 19:21:35,448::train::INFO] [train] Iter 586876 | loss 0.6813 | loss(rot) 0.2568 | loss(pos) 0.0451 | loss(seq) 0.3795 | grad 3.1398 | lr 0.0000 | time_forward 3.1300 | time_backward 4.0640 |
[2023-10-24 19:21:43,276::train::INFO] [train] Iter 586877 | loss 0.2575 | loss(rot) 0.2094 | loss(pos) 0.0481 | loss(seq) 0.0000 | grad 2.1603 | lr 0.0000 | time_forward 3.3520 | time_backward 4.4510 |
[2023-10-24 19:21:50,788::train::INFO] [train] Iter 586878 | loss 0.1912 | loss(rot) 0.1651 | loss(pos) 0.0240 | loss(seq) 0.0021 | grad 3.1210 | lr 0.0000 | time_forward 3.2200 | time_backward 4.2880 |
[2023-10-24 19:21:57,209::train::INFO] [train] Iter 586879 | loss 1.4480 | loss(rot) 1.0527 | loss(pos) 0.1313 | loss(seq) 0.2640 | grad 28.0877 | lr 0.0000 | time_forward 2.8020 | time_backward 3.6150 |
[2023-10-24 19:22:00,044::train::INFO] [train] Iter 586880 | loss 0.5258 | loss(rot) 0.1482 | loss(pos) 0.0410 | loss(seq) 0.3366 | grad 2.1367 | lr 0.0000 | time_forward 1.3410 | time_backward 1.4900 |
[2023-10-24 19:22:08,440::train::INFO] [train] Iter 586881 | loss 0.5238 | loss(rot) 0.3592 | loss(pos) 0.0708 | loss(seq) 0.0937 | grad 2.6710 | lr 0.0000 | time_forward 3.6760 | time_backward 4.7180 |
[2023-10-24 19:22:10,789::train::INFO] [train] Iter 586882 | loss 0.6801 | loss(rot) 0.4492 | loss(pos) 0.0595 | loss(seq) 0.1714 | grad 3.0044 | lr 0.0000 | time_forward 1.0840 | time_backward 1.2620 |
[2023-10-24 19:22:17,945::train::INFO] [train] Iter 586883 | loss 1.6651 | loss(rot) 0.6795 | loss(pos) 0.5119 | loss(seq) 0.4737 | grad 5.0300 | lr 0.0000 | time_forward 3.0830 | time_backward 4.0580 |
[2023-10-24 19:22:20,712::train::INFO] [train] Iter 586884 | loss 1.0846 | loss(rot) 0.8317 | loss(pos) 0.0494 | loss(seq) 0.2036 | grad 3.5183 | lr 0.0000 | time_forward 1.3260 | time_backward 1.4370 |
[2023-10-24 19:22:28,216::train::INFO] [train] Iter 586885 | loss 0.3716 | loss(rot) 0.1074 | loss(pos) 0.1314 | loss(seq) 0.1328 | grad 3.0520 | lr 0.0000 | time_forward 3.2180 | time_backward 4.2610 |
[2023-10-24 19:22:36,638::train::INFO] [train] Iter 586886 | loss 0.1720 | loss(rot) 0.0998 | loss(pos) 0.0301 | loss(seq) 0.0422 | grad 1.7606 | lr 0.0000 | time_forward 3.5200 | time_backward 4.8990 |
[2023-10-24 19:22:43,128::train::INFO] [train] Iter 586887 | loss 1.4708 | loss(rot) 0.8889 | loss(pos) 0.4238 | loss(seq) 0.1582 | grad 9.0398 | lr 0.0000 | time_forward 2.8280 | time_backward 3.6590 |
[2023-10-24 19:22:51,472::train::INFO] [train] Iter 586888 | loss 0.3478 | loss(rot) 0.1320 | loss(pos) 0.0714 | loss(seq) 0.1444 | grad 2.2171 | lr 0.0000 | time_forward 3.4890 | time_backward 4.8510 |
[2023-10-24 19:22:59,841::train::INFO] [train] Iter 586889 | loss 0.3134 | loss(rot) 0.2896 | loss(pos) 0.0212 | loss(seq) 0.0027 | grad 20.0864 | lr 0.0000 | time_forward 3.4700 | time_backward 4.8960 |
[2023-10-24 19:23:02,550::train::INFO] [train] Iter 586890 | loss 1.7999 | loss(rot) 1.4544 | loss(pos) 0.0934 | loss(seq) 0.2521 | grad 5.2552 | lr 0.0000 | time_forward 1.2590 | time_backward 1.4470 |
[2023-10-24 19:23:10,476::train::INFO] [train] Iter 586891 | loss 0.4031 | loss(rot) 0.2041 | loss(pos) 0.0351 | loss(seq) 0.1640 | grad 3.1998 | lr 0.0000 | time_forward 3.4480 | time_backward 4.4640 |
[2023-10-24 19:23:18,374::train::INFO] [train] Iter 586892 | loss 0.5510 | loss(rot) 0.0714 | loss(pos) 0.0725 | loss(seq) 0.4070 | grad 2.8668 | lr 0.0000 | time_forward 3.4260 | time_backward 4.4690 |
[2023-10-24 19:23:21,090::train::INFO] [train] Iter 586893 | loss 0.6337 | loss(rot) 0.1447 | loss(pos) 0.0913 | loss(seq) 0.3976 | grad 2.7619 | lr 0.0000 | time_forward 1.2460 | time_backward 1.4660 |
[2023-10-24 19:23:29,529::train::INFO] [train] Iter 586894 | loss 2.0538 | loss(rot) 1.1019 | loss(pos) 0.3774 | loss(seq) 0.5745 | grad 7.0425 | lr 0.0000 | time_forward 3.5190 | time_backward 4.9170 |
[2023-10-24 19:23:37,922::train::INFO] [train] Iter 586895 | loss 0.2649 | loss(rot) 0.0815 | loss(pos) 0.0312 | loss(seq) 0.1521 | grad 2.0216 | lr 0.0000 | time_forward 3.4940 | time_backward 4.8970 |
[2023-10-24 19:23:40,654::train::INFO] [train] Iter 586896 | loss 0.5913 | loss(rot) 0.2759 | loss(pos) 0.0767 | loss(seq) 0.2387 | grad 3.3598 | lr 0.0000 | time_forward 1.3180 | time_backward 1.4100 |
[2023-10-24 19:23:47,099::train::INFO] [train] Iter 586897 | loss 0.1220 | loss(rot) 0.0885 | loss(pos) 0.0333 | loss(seq) 0.0002 | grad 1.5652 | lr 0.0000 | time_forward 2.7880 | time_backward 3.6330 |
[2023-10-24 19:23:49,832::train::INFO] [train] Iter 586898 | loss 1.7247 | loss(rot) 0.1675 | loss(pos) 1.5568 | loss(seq) 0.0004 | grad 13.2980 | lr 0.0000 | time_forward 1.3050 | time_backward 1.4250 |
[2023-10-24 19:23:52,637::train::INFO] [train] Iter 586899 | loss 0.8500 | loss(rot) 0.0161 | loss(pos) 0.8327 | loss(seq) 0.0012 | grad 13.2185 | lr 0.0000 | time_forward 1.3580 | time_backward 1.4440 |
[2023-10-24 19:23:55,923::train::INFO] [train] Iter 586900 | loss 1.0366 | loss(rot) 0.5305 | loss(pos) 0.1827 | loss(seq) 0.3233 | grad 3.2588 | lr 0.0000 | time_forward 1.4720 | time_backward 1.8110 |
[2023-10-24 19:24:02,925::train::INFO] [train] Iter 586901 | loss 1.6013 | loss(rot) 1.1683 | loss(pos) 0.1388 | loss(seq) 0.2943 | grad 5.7839 | lr 0.0000 | time_forward 2.9920 | time_backward 4.0070 |
[2023-10-24 19:24:06,101::train::INFO] [train] Iter 586902 | loss 1.0037 | loss(rot) 0.6766 | loss(pos) 0.0772 | loss(seq) 0.2499 | grad 3.2531 | lr 0.0000 | time_forward 1.4590 | time_backward 1.7030 |
[2023-10-24 19:24:12,993::train::INFO] [train] Iter 586903 | loss 1.2180 | loss(rot) 0.3514 | loss(pos) 0.7049 | loss(seq) 0.1616 | grad 5.2592 | lr 0.0000 | time_forward 2.9660 | time_backward 3.9110 |
[2023-10-24 19:24:15,761::train::INFO] [train] Iter 586904 | loss 0.2818 | loss(rot) 0.0609 | loss(pos) 0.2121 | loss(seq) 0.0088 | grad 5.5419 | lr 0.0000 | time_forward 1.3450 | time_backward 1.4190 |
[2023-10-24 19:24:24,084::train::INFO] [train] Iter 586905 | loss 0.1958 | loss(rot) 0.1521 | loss(pos) 0.0429 | loss(seq) 0.0008 | grad 1.8142 | lr 0.0000 | time_forward 3.4710 | time_backward 4.8250 |
[2023-10-24 19:24:31,347::train::INFO] [train] Iter 586906 | loss 1.0806 | loss(rot) 0.1433 | loss(pos) 0.9278 | loss(seq) 0.0095 | grad 8.6222 | lr 0.0000 | time_forward 3.1470 | time_backward 4.1130 |
[2023-10-24 19:24:39,550::train::INFO] [train] Iter 586907 | loss 0.5076 | loss(rot) 0.2764 | loss(pos) 0.0638 | loss(seq) 0.1675 | grad 2.8932 | lr 0.0000 | time_forward 3.3810 | time_backward 4.8190 |
[2023-10-24 19:24:47,264::train::INFO] [train] Iter 586908 | loss 0.7543 | loss(rot) 0.4771 | loss(pos) 0.0720 | loss(seq) 0.2052 | grad 2.8502 | lr 0.0000 | time_forward 3.3100 | time_backward 4.4010 |
[2023-10-24 19:25:04,068::train::INFO] [train] Iter 586909 | loss 0.4059 | loss(rot) 0.3715 | loss(pos) 0.0303 | loss(seq) 0.0040 | grad 2.7447 | lr 0.0000 | time_forward 3.5050 | time_backward 13.2960 |
[2023-10-24 19:25:28,920::train::INFO] [train] Iter 586910 | loss 0.1471 | loss(rot) 0.1102 | loss(pos) 0.0239 | loss(seq) 0.0130 | grad 1.6338 | lr 0.0000 | time_forward 16.6860 | time_backward 8.1640 |
[2023-10-24 19:25:31,773::train::INFO] [train] Iter 586911 | loss 1.1679 | loss(rot) 0.8235 | loss(pos) 0.0305 | loss(seq) 0.3139 | grad 9.7956 | lr 0.0000 | time_forward 1.3280 | time_backward 1.5220 |
[2023-10-24 19:25:40,329::train::INFO] [train] Iter 586912 | loss 2.0676 | loss(rot) 2.0275 | loss(pos) 0.0356 | loss(seq) 0.0045 | grad 5.9117 | lr 0.0000 | time_forward 3.6550 | time_backward 4.8970 |
[2023-10-24 19:25:45,837::train::INFO] [train] Iter 586913 | loss 0.6775 | loss(rot) 0.3177 | loss(pos) 0.3565 | loss(seq) 0.0033 | grad 8.1454 | lr 0.0000 | time_forward 2.3580 | time_backward 3.1470 |
[2023-10-24 19:25:48,283::train::INFO] [train] Iter 586914 | loss 1.9535 | loss(rot) 0.0037 | loss(pos) 1.9498 | loss(seq) 0.0000 | grad 44.7252 | lr 0.0000 | time_forward 1.1960 | time_backward 1.2380 |
[2023-10-24 19:25:51,016::train::INFO] [train] Iter 586915 | loss 0.2953 | loss(rot) 0.0884 | loss(pos) 0.1434 | loss(seq) 0.0635 | grad 4.7671 | lr 0.0000 | time_forward 1.3240 | time_backward 1.4060 |
[2023-10-24 19:25:58,254::train::INFO] [train] Iter 586916 | loss 0.5855 | loss(rot) 0.3179 | loss(pos) 0.0754 | loss(seq) 0.1922 | grad 3.7329 | lr 0.0000 | time_forward 3.1990 | time_backward 4.0350 |
[2023-10-24 19:26:00,962::train::INFO] [train] Iter 586917 | loss 0.5701 | loss(rot) 0.3439 | loss(pos) 0.0318 | loss(seq) 0.1943 | grad 3.2947 | lr 0.0000 | time_forward 1.3190 | time_backward 1.3850 |
[2023-10-24 19:26:03,695::train::INFO] [train] Iter 586918 | loss 0.6309 | loss(rot) 0.0419 | loss(pos) 0.5807 | loss(seq) 0.0084 | grad 5.9967 | lr 0.0000 | time_forward 1.3400 | time_backward 1.3900 |
[2023-10-24 19:26:11,841::train::INFO] [train] Iter 586919 | loss 0.9240 | loss(rot) 0.0912 | loss(pos) 0.8143 | loss(seq) 0.0184 | grad 8.5427 | lr 0.0000 | time_forward 3.3650 | time_backward 4.7780 |
[2023-10-24 19:26:14,579::train::INFO] [train] Iter 586920 | loss 2.0994 | loss(rot) 2.0497 | loss(pos) 0.0494 | loss(seq) 0.0003 | grad 6.8629 | lr 0.0000 | time_forward 1.3040 | time_backward 1.4300 |
[2023-10-24 19:26:22,503::train::INFO] [train] Iter 586921 | loss 1.1923 | loss(rot) 1.1103 | loss(pos) 0.0302 | loss(seq) 0.0518 | grad 4.5828 | lr 0.0000 | time_forward 3.4470 | time_backward 4.4740 |
[2023-10-24 19:26:30,451::train::INFO] [train] Iter 586922 | loss 0.9874 | loss(rot) 0.7000 | loss(pos) 0.0530 | loss(seq) 0.2345 | grad 3.6993 | lr 0.0000 | time_forward 3.3080 | time_backward 4.6370 |
[2023-10-24 19:26:38,503::train::INFO] [train] Iter 586923 | loss 0.5762 | loss(rot) 0.5304 | loss(pos) 0.0381 | loss(seq) 0.0077 | grad 26.4891 | lr 0.0000 | time_forward 3.3210 | time_backward 4.7270 |
[2023-10-24 19:26:46,558::train::INFO] [train] Iter 586924 | loss 0.3075 | loss(rot) 0.0323 | loss(pos) 0.2661 | loss(seq) 0.0091 | grad 3.7989 | lr 0.0000 | time_forward 3.2730 | time_backward 4.7790 |
[2023-10-24 19:26:53,903::train::INFO] [train] Iter 586925 | loss 0.8526 | loss(rot) 0.3972 | loss(pos) 0.0293 | loss(seq) 0.4261 | grad 3.3193 | lr 0.0000 | time_forward 3.1630 | time_backward 4.1790 |
[2023-10-24 19:26:56,799::train::INFO] [train] Iter 586926 | loss 0.6440 | loss(rot) 0.4291 | loss(pos) 0.1736 | loss(seq) 0.0413 | grad 7.9157 | lr 0.0000 | time_forward 1.3040 | time_backward 1.5880 |
[2023-10-24 19:27:03,634::train::INFO] [train] Iter 586927 | loss 1.8441 | loss(rot) 1.1576 | loss(pos) 0.0956 | loss(seq) 0.5909 | grad 5.8314 | lr 0.0000 | time_forward 2.8980 | time_backward 3.9340 |
[2023-10-24 19:27:06,200::train::INFO] [train] Iter 586928 | loss 0.6796 | loss(rot) 0.2564 | loss(pos) 0.0742 | loss(seq) 0.3491 | grad 3.0034 | lr 0.0000 | time_forward 1.2070 | time_backward 1.3550 |
[2023-10-24 19:27:09,007::train::INFO] [train] Iter 586929 | loss 0.2930 | loss(rot) 0.0974 | loss(pos) 0.0507 | loss(seq) 0.1449 | grad 2.6122 | lr 0.0000 | time_forward 1.3080 | time_backward 1.4880 |
[2023-10-24 19:27:16,395::train::INFO] [train] Iter 586930 | loss 0.0953 | loss(rot) 0.0654 | loss(pos) 0.0297 | loss(seq) 0.0002 | grad 1.2307 | lr 0.0000 | time_forward 3.1880 | time_backward 4.1970 |
[2023-10-24 19:27:23,723::train::INFO] [train] Iter 586931 | loss 1.4394 | loss(rot) 1.2671 | loss(pos) 0.0336 | loss(seq) 0.1387 | grad 6.5122 | lr 0.0000 | time_forward 3.1810 | time_backward 4.1430 |
[2023-10-24 19:27:30,514::train::INFO] [train] Iter 586932 | loss 0.8294 | loss(rot) 0.7943 | loss(pos) 0.0350 | loss(seq) 0.0002 | grad 4.2683 | lr 0.0000 | time_forward 2.9470 | time_backward 3.8410 |
[2023-10-24 19:27:35,578::train::INFO] [train] Iter 586933 | loss 0.4963 | loss(rot) 0.1578 | loss(pos) 0.1100 | loss(seq) 0.2285 | grad 3.1715 | lr 0.0000 | time_forward 2.2530 | time_backward 2.8090 |
[2023-10-24 19:27:43,398::train::INFO] [train] Iter 586934 | loss 0.5676 | loss(rot) 0.5030 | loss(pos) 0.0598 | loss(seq) 0.0048 | grad 2.9028 | lr 0.0000 | time_forward 3.2670 | time_backward 4.5500 |
[2023-10-24 19:27:50,265::train::INFO] [train] Iter 586935 | loss 0.1259 | loss(rot) 0.0780 | loss(pos) 0.0479 | loss(seq) 0.0000 | grad 2.0889 | lr 0.0000 | time_forward 2.9600 | time_backward 3.9030 |
[2023-10-24 19:27:56,826::train::INFO] [train] Iter 586936 | loss 0.2443 | loss(rot) 0.0258 | loss(pos) 0.0146 | loss(seq) 0.2039 | grad 1.4080 | lr 0.0000 | time_forward 2.8520 | time_backward 3.7070 |
[2023-10-24 19:28:04,046::train::INFO] [train] Iter 586937 | loss 0.7759 | loss(rot) 0.7471 | loss(pos) 0.0283 | loss(seq) 0.0005 | grad 7.9256 | lr 0.0000 | time_forward 3.1140 | time_backward 4.1020 |
[2023-10-24 19:28:06,704::train::INFO] [train] Iter 586938 | loss 0.6076 | loss(rot) 0.0599 | loss(pos) 0.5410 | loss(seq) 0.0067 | grad 8.9243 | lr 0.0000 | time_forward 1.2560 | time_backward 1.3990 |
[2023-10-24 19:28:13,436::train::INFO] [train] Iter 586939 | loss 0.1275 | loss(rot) 0.0945 | loss(pos) 0.0322 | loss(seq) 0.0008 | grad 2.3030 | lr 0.0000 | time_forward 2.9080 | time_backward 3.7980 |
[2023-10-24 19:28:16,140::train::INFO] [train] Iter 586940 | loss 0.4579 | loss(rot) 0.0384 | loss(pos) 0.4110 | loss(seq) 0.0085 | grad 8.9679 | lr 0.0000 | time_forward 1.2870 | time_backward 1.4150 |
[2023-10-24 19:28:22,713::train::INFO] [train] Iter 586941 | loss 0.9617 | loss(rot) 0.3979 | loss(pos) 0.1908 | loss(seq) 0.3729 | grad 3.6610 | lr 0.0000 | time_forward 2.8370 | time_backward 3.7330 |
[2023-10-24 19:28:25,211::train::INFO] [train] Iter 586942 | loss 0.2246 | loss(rot) 0.1749 | loss(pos) 0.0374 | loss(seq) 0.0123 | grad 2.8481 | lr 0.0000 | time_forward 1.2120 | time_backward 1.2830 |
[2023-10-24 19:28:30,346::train::INFO] [train] Iter 586943 | loss 0.1859 | loss(rot) 0.0625 | loss(pos) 0.0252 | loss(seq) 0.0982 | grad 1.5115 | lr 0.0000 | time_forward 2.2640 | time_backward 2.8670 |
[2023-10-24 19:28:37,658::train::INFO] [train] Iter 586944 | loss 0.1802 | loss(rot) 0.1396 | loss(pos) 0.0272 | loss(seq) 0.0134 | grad 2.1239 | lr 0.0000 | time_forward 3.1940 | time_backward 4.1150 |
[2023-10-24 19:28:45,609::train::INFO] [train] Iter 586945 | loss 0.8151 | loss(rot) 0.5913 | loss(pos) 0.0166 | loss(seq) 0.2071 | grad 4.0405 | lr 0.0000 | time_forward 3.3110 | time_backward 4.6380 |
[2023-10-24 19:28:48,268::train::INFO] [train] Iter 586946 | loss 0.6547 | loss(rot) 0.6016 | loss(pos) 0.0147 | loss(seq) 0.0384 | grad 6.2096 | lr 0.0000 | time_forward 1.2610 | time_backward 1.3940 |
[2023-10-24 19:28:55,754::train::INFO] [train] Iter 586947 | loss 0.5649 | loss(rot) 0.0387 | loss(pos) 0.5194 | loss(seq) 0.0068 | grad 11.8759 | lr 0.0000 | time_forward 3.2410 | time_backward 4.2220 |
[2023-10-24 19:28:58,473::train::INFO] [train] Iter 586948 | loss 0.1776 | loss(rot) 0.0856 | loss(pos) 0.0666 | loss(seq) 0.0253 | grad 2.3588 | lr 0.0000 | time_forward 1.3180 | time_backward 1.3970 |
[2023-10-24 19:29:05,097::train::INFO] [train] Iter 586949 | loss 0.1529 | loss(rot) 0.0797 | loss(pos) 0.0476 | loss(seq) 0.0256 | grad 2.7772 | lr 0.0000 | time_forward 2.8630 | time_backward 3.7580 |
[2023-10-24 19:29:11,884::train::INFO] [train] Iter 586950 | loss 1.6764 | loss(rot) 1.5945 | loss(pos) 0.0353 | loss(seq) 0.0466 | grad 5.8944 | lr 0.0000 | time_forward 2.9580 | time_backward 3.8260 |
[2023-10-24 19:29:14,115::train::INFO] [train] Iter 586951 | loss 0.8490 | loss(rot) 0.1375 | loss(pos) 0.0313 | loss(seq) 0.6802 | grad 3.4579 | lr 0.0000 | time_forward 1.0380 | time_backward 1.1890 |
[2023-10-24 19:29:21,159::train::INFO] [train] Iter 586952 | loss 0.5257 | loss(rot) 0.0340 | loss(pos) 0.4863 | loss(seq) 0.0053 | grad 6.9177 | lr 0.0000 | time_forward 3.0450 | time_backward 3.9960 |
[2023-10-24 19:29:23,826::train::INFO] [train] Iter 586953 | loss 0.7526 | loss(rot) 0.7240 | loss(pos) 0.0279 | loss(seq) 0.0006 | grad 5.3815 | lr 0.0000 | time_forward 1.2270 | time_backward 1.4370 |
[2023-10-24 19:29:31,835::train::INFO] [train] Iter 586954 | loss 1.7361 | loss(rot) 1.3542 | loss(pos) 0.0375 | loss(seq) 0.3444 | grad 3.9812 | lr 0.0000 | time_forward 3.3610 | time_backward 4.6450 |
[2023-10-24 19:29:38,669::train::INFO] [train] Iter 586955 | loss 0.6354 | loss(rot) 0.5832 | loss(pos) 0.0516 | loss(seq) 0.0006 | grad 5.3635 | lr 0.0000 | time_forward 2.9600 | time_backward 3.8700 |
[2023-10-24 19:29:45,348::train::INFO] [train] Iter 586956 | loss 0.1932 | loss(rot) 0.0487 | loss(pos) 0.0280 | loss(seq) 0.1165 | grad 1.9717 | lr 0.0000 | time_forward 2.8850 | time_backward 3.7910 |
[2023-10-24 19:29:53,527::train::INFO] [train] Iter 586957 | loss 0.6833 | loss(rot) 0.0156 | loss(pos) 0.6617 | loss(seq) 0.0059 | grad 7.9590 | lr 0.0000 | time_forward 3.4310 | time_backward 4.7460 |
[2023-10-24 19:30:01,732::train::INFO] [train] Iter 586958 | loss 0.4193 | loss(rot) 0.1835 | loss(pos) 0.0380 | loss(seq) 0.1979 | grad 2.2819 | lr 0.0000 | time_forward 3.4170 | time_backward 4.7840 |
[2023-10-24 19:30:09,169::train::INFO] [train] Iter 586959 | loss 0.7050 | loss(rot) 0.3012 | loss(pos) 0.1343 | loss(seq) 0.2696 | grad 3.4061 | lr 0.0000 | time_forward 3.2880 | time_backward 4.1460 |
[2023-10-24 19:30:11,881::train::INFO] [train] Iter 586960 | loss 1.4375 | loss(rot) 1.2536 | loss(pos) 0.0839 | loss(seq) 0.0999 | grad 3.8004 | lr 0.0000 | time_forward 1.3140 | time_backward 1.3950 |
[2023-10-24 19:30:19,973::train::INFO] [train] Iter 586961 | loss 0.2667 | loss(rot) 0.2347 | loss(pos) 0.0231 | loss(seq) 0.0089 | grad 2.5978 | lr 0.0000 | time_forward 3.3910 | time_backward 4.6800 |
[2023-10-24 19:30:26,552::train::INFO] [train] Iter 586962 | loss 0.4146 | loss(rot) 0.0849 | loss(pos) 0.2585 | loss(seq) 0.0711 | grad 3.5534 | lr 0.0000 | time_forward 2.8690 | time_backward 3.7080 |
[2023-10-24 19:30:33,234::train::INFO] [train] Iter 586963 | loss 0.7192 | loss(rot) 0.4154 | loss(pos) 0.1049 | loss(seq) 0.1989 | grad 4.6727 | lr 0.0000 | time_forward 2.8810 | time_backward 3.7970 |
[2023-10-24 19:30:41,199::train::INFO] [train] Iter 586964 | loss 0.8124 | loss(rot) 0.5853 | loss(pos) 0.0391 | loss(seq) 0.1880 | grad 3.1009 | lr 0.0000 | time_forward 3.3420 | time_backward 4.6190 |
[2023-10-24 19:30:47,014::train::INFO] [train] Iter 586965 | loss 0.4408 | loss(rot) 0.2647 | loss(pos) 0.0241 | loss(seq) 0.1519 | grad 2.7898 | lr 0.0000 | time_forward 2.5470 | time_backward 3.2660 |
[2023-10-24 19:30:49,727::train::INFO] [train] Iter 586966 | loss 1.7042 | loss(rot) 1.1535 | loss(pos) 0.1175 | loss(seq) 0.4332 | grad 13.2293 | lr 0.0000 | time_forward 1.2930 | time_backward 1.4170 |
[2023-10-24 19:30:56,816::train::INFO] [train] Iter 586967 | loss 0.8356 | loss(rot) 0.3987 | loss(pos) 0.0365 | loss(seq) 0.4004 | grad 2.9485 | lr 0.0000 | time_forward 3.1080 | time_backward 3.9780 |
[2023-10-24 19:30:59,495::train::INFO] [train] Iter 586968 | loss 0.9664 | loss(rot) 0.7924 | loss(pos) 0.0436 | loss(seq) 0.1304 | grad 6.2900 | lr 0.0000 | time_forward 1.2820 | time_backward 1.3940 |
[2023-10-24 19:31:01,749::train::INFO] [train] Iter 586969 | loss 0.5626 | loss(rot) 0.4116 | loss(pos) 0.0385 | loss(seq) 0.1125 | grad 8.9958 | lr 0.0000 | time_forward 1.0380 | time_backward 1.1940 |
[2023-10-24 19:31:08,280::train::INFO] [train] Iter 586970 | loss 0.2132 | loss(rot) 0.0389 | loss(pos) 0.0476 | loss(seq) 0.1267 | grad 1.9723 | lr 0.0000 | time_forward 2.8240 | time_backward 3.7040 |
[2023-10-24 19:31:15,311::train::INFO] [train] Iter 586971 | loss 0.5413 | loss(rot) 0.3342 | loss(pos) 0.1977 | loss(seq) 0.0094 | grad 4.5645 | lr 0.0000 | time_forward 3.0540 | time_backward 3.9730 |
[2023-10-24 19:31:23,272::train::INFO] [train] Iter 586972 | loss 0.2787 | loss(rot) 0.1986 | loss(pos) 0.0801 | loss(seq) 0.0000 | grad 2.9620 | lr 0.0000 | time_forward 3.3160 | time_backward 4.6420 |
[2023-10-24 19:31:31,274::train::INFO] [train] Iter 586973 | loss 0.9799 | loss(rot) 0.7960 | loss(pos) 0.0488 | loss(seq) 0.1352 | grad 22.2209 | lr 0.0000 | time_forward 3.4520 | time_backward 4.5480 |
[2023-10-24 19:31:34,034::train::INFO] [train] Iter 586974 | loss 0.5992 | loss(rot) 0.2048 | loss(pos) 0.0334 | loss(seq) 0.3610 | grad 2.4870 | lr 0.0000 | time_forward 1.3280 | time_backward 1.4290 |
[2023-10-24 19:31:42,127::train::INFO] [train] Iter 586975 | loss 1.0803 | loss(rot) 1.0366 | loss(pos) 0.0420 | loss(seq) 0.0017 | grad 3.1196 | lr 0.0000 | time_forward 3.4210 | time_backward 4.6540 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.