text stringlengths 56 1.16k |
|---|
[2023-10-22 23:00:26,706::train::INFO] [train] Iter 563000 | loss 0.7286 | loss(rot) 0.3682 | loss(pos) 0.1480 | loss(seq) 0.2124 | grad 5.1371 | lr 0.0000 | time_forward 3.1320 | time_backward 4.0440 |
[2023-10-22 23:01:11,270::train::INFO] [val] Iter 563000 | loss 1.1618 | loss(rot) 0.6886 | loss(pos) 0.2928 | loss(seq) 0.1804 |
[2023-10-22 23:01:18,148::train::INFO] [train] Iter 563001 | loss 0.3943 | loss(rot) 0.1099 | loss(pos) 0.0168 | loss(seq) 0.2676 | grad 2.5634 | lr 0.0000 | time_forward 2.8840 | time_backward 3.6570 |
[2023-10-22 23:01:25,904::train::INFO] [train] Iter 563002 | loss 0.9914 | loss(rot) 0.6621 | loss(pos) 0.1612 | loss(seq) 0.1681 | grad 4.4056 | lr 0.0000 | time_forward 3.2990 | time_backward 4.4530 |
[2023-10-22 23:01:28,498::train::INFO] [train] Iter 563003 | loss 0.1579 | loss(rot) 0.1206 | loss(pos) 0.0320 | loss(seq) 0.0053 | grad 3.1416 | lr 0.0000 | time_forward 1.2250 | time_backward 1.3660 |
[2023-10-22 23:01:36,195::train::INFO] [train] Iter 563004 | loss 0.8412 | loss(rot) 0.0516 | loss(pos) 0.5486 | loss(seq) 0.2410 | grad 4.1665 | lr 0.0000 | time_forward 3.1860 | time_backward 4.4960 |
[2023-10-22 23:01:43,981::train::INFO] [train] Iter 563005 | loss 0.6090 | loss(rot) 0.2961 | loss(pos) 0.0322 | loss(seq) 0.2807 | grad 3.0797 | lr 0.0000 | time_forward 3.2890 | time_backward 4.4930 |
[2023-10-22 23:01:50,277::train::INFO] [train] Iter 563006 | loss 0.2601 | loss(rot) 0.2284 | loss(pos) 0.0304 | loss(seq) 0.0013 | grad 2.6743 | lr 0.0000 | time_forward 2.7250 | time_backward 3.5680 |
[2023-10-22 23:01:58,702::train::INFO] [train] Iter 563007 | loss 1.6622 | loss(rot) 1.0508 | loss(pos) 0.1430 | loss(seq) 0.4684 | grad 3.0220 | lr 0.0000 | time_forward 3.8350 | time_backward 4.5860 |
[2023-10-22 23:02:05,517::train::INFO] [train] Iter 563008 | loss 0.4580 | loss(rot) 0.2643 | loss(pos) 0.0152 | loss(seq) 0.1785 | grad 3.0883 | lr 0.0000 | time_forward 2.9750 | time_backward 3.8380 |
[2023-10-22 23:02:12,082::train::INFO] [train] Iter 563009 | loss 1.2711 | loss(rot) 0.7167 | loss(pos) 0.0983 | loss(seq) 0.4561 | grad 2.9571 | lr 0.0000 | time_forward 2.8560 | time_backward 3.7050 |
[2023-10-22 23:02:18,934::train::INFO] [train] Iter 563010 | loss 0.1320 | loss(rot) 0.0979 | loss(pos) 0.0176 | loss(seq) 0.0165 | grad 2.3607 | lr 0.0000 | time_forward 2.9630 | time_backward 3.8850 |
[2023-10-22 23:02:25,848::train::INFO] [train] Iter 563011 | loss 0.4881 | loss(rot) 0.0687 | loss(pos) 0.2042 | loss(seq) 0.2152 | grad 4.2069 | lr 0.0000 | time_forward 2.9860 | time_backward 3.9250 |
[2023-10-22 23:02:28,498::train::INFO] [train] Iter 563012 | loss 1.3016 | loss(rot) 0.6994 | loss(pos) 0.0636 | loss(seq) 0.5387 | grad 18.2895 | lr 0.0000 | time_forward 1.2470 | time_backward 1.3990 |
[2023-10-22 23:02:35,733::train::INFO] [train] Iter 563013 | loss 0.8225 | loss(rot) 0.7660 | loss(pos) 0.0242 | loss(seq) 0.0323 | grad 9.1438 | lr 0.0000 | time_forward 3.1870 | time_backward 4.0450 |
[2023-10-22 23:02:42,924::train::INFO] [train] Iter 563014 | loss 0.5352 | loss(rot) 0.1303 | loss(pos) 0.0826 | loss(seq) 0.3223 | grad 2.7220 | lr 0.0000 | time_forward 3.1210 | time_backward 4.0670 |
[2023-10-22 23:02:50,694::train::INFO] [train] Iter 563015 | loss 0.2678 | loss(rot) 0.2252 | loss(pos) 0.0128 | loss(seq) 0.0297 | grad 2.1566 | lr 0.0000 | time_forward 3.2280 | time_backward 4.5380 |
[2023-10-22 23:02:53,743::train::INFO] [train] Iter 563016 | loss 1.6726 | loss(rot) 0.7473 | loss(pos) 0.5140 | loss(seq) 0.4113 | grad 4.4285 | lr 0.0000 | time_forward 1.3860 | time_backward 1.6590 |
[2023-10-22 23:02:59,023::train::INFO] [train] Iter 563017 | loss 1.1283 | loss(rot) 0.6970 | loss(pos) 0.1751 | loss(seq) 0.2562 | grad 3.7818 | lr 0.0000 | time_forward 2.2660 | time_backward 3.0030 |
[2023-10-22 23:03:05,923::train::INFO] [train] Iter 563018 | loss 1.4388 | loss(rot) 1.0057 | loss(pos) 0.0787 | loss(seq) 0.3544 | grad 2.9029 | lr 0.0000 | time_forward 2.9920 | time_backward 3.8960 |
[2023-10-22 23:03:13,722::train::INFO] [train] Iter 563019 | loss 0.2415 | loss(rot) 0.0623 | loss(pos) 0.1648 | loss(seq) 0.0145 | grad 6.6488 | lr 0.0000 | time_forward 3.2860 | time_backward 4.5090 |
[2023-10-22 23:03:16,377::train::INFO] [train] Iter 563020 | loss 2.2142 | loss(rot) 2.1069 | loss(pos) 0.1002 | loss(seq) 0.0071 | grad 14.2323 | lr 0.0000 | time_forward 1.2490 | time_backward 1.4030 |
[2023-10-22 23:03:24,160::train::INFO] [train] Iter 563021 | loss 0.8340 | loss(rot) 0.8099 | loss(pos) 0.0225 | loss(seq) 0.0016 | grad 22.5652 | lr 0.0000 | time_forward 3.1980 | time_backward 4.5660 |
[2023-10-22 23:03:31,319::train::INFO] [train] Iter 563022 | loss 0.6043 | loss(rot) 0.0419 | loss(pos) 0.5566 | loss(seq) 0.0057 | grad 5.5639 | lr 0.0000 | time_forward 3.1280 | time_backward 4.0260 |
[2023-10-22 23:03:38,582::train::INFO] [train] Iter 563023 | loss 0.3749 | loss(rot) 0.1274 | loss(pos) 0.1414 | loss(seq) 0.1061 | grad 3.8700 | lr 0.0000 | time_forward 3.2230 | time_backward 4.0370 |
[2023-10-22 23:03:41,547::train::INFO] [train] Iter 563024 | loss 0.8696 | loss(rot) 0.6125 | loss(pos) 0.0513 | loss(seq) 0.2058 | grad 3.5205 | lr 0.0000 | time_forward 1.3740 | time_backward 1.5870 |
[2023-10-22 23:03:43,702::train::INFO] [train] Iter 563025 | loss 0.2599 | loss(rot) 0.0988 | loss(pos) 0.0286 | loss(seq) 0.1326 | grad 2.4905 | lr 0.0000 | time_forward 1.0030 | time_backward 1.1430 |
[2023-10-22 23:03:45,854::train::INFO] [train] Iter 563026 | loss 0.7464 | loss(rot) 0.6267 | loss(pos) 0.0956 | loss(seq) 0.0240 | grad 4.4208 | lr 0.0000 | time_forward 0.9920 | time_backward 1.1480 |
[2023-10-22 23:03:53,552::train::INFO] [train] Iter 563027 | loss 0.2144 | loss(rot) 0.0594 | loss(pos) 0.0901 | loss(seq) 0.0648 | grad 2.3173 | lr 0.0000 | time_forward 3.2080 | time_backward 4.4880 |
[2023-10-22 23:03:59,540::train::INFO] [train] Iter 563028 | loss 0.7738 | loss(rot) 0.7189 | loss(pos) 0.0192 | loss(seq) 0.0357 | grad 1.8443 | lr 0.0000 | time_forward 2.6250 | time_backward 3.3590 |
[2023-10-22 23:04:06,653::train::INFO] [train] Iter 563029 | loss 0.1880 | loss(rot) 0.0651 | loss(pos) 0.0681 | loss(seq) 0.0549 | grad 2.1381 | lr 0.0000 | time_forward 3.1000 | time_backward 4.0100 |
[2023-10-22 23:04:13,187::train::INFO] [train] Iter 563030 | loss 1.0334 | loss(rot) 0.9561 | loss(pos) 0.0289 | loss(seq) 0.0483 | grad 4.8793 | lr 0.0000 | time_forward 2.8630 | time_backward 3.6670 |
[2023-10-22 23:04:20,079::train::INFO] [train] Iter 563031 | loss 0.2836 | loss(rot) 0.0780 | loss(pos) 0.0165 | loss(seq) 0.1891 | grad 2.6198 | lr 0.0000 | time_forward 2.9960 | time_backward 3.8930 |
[2023-10-22 23:04:27,871::train::INFO] [train] Iter 563032 | loss 0.3365 | loss(rot) 0.0736 | loss(pos) 0.2538 | loss(seq) 0.0091 | grad 4.3161 | lr 0.0000 | time_forward 3.2130 | time_backward 4.5760 |
[2023-10-22 23:04:35,614::train::INFO] [train] Iter 563033 | loss 0.2127 | loss(rot) 0.1852 | loss(pos) 0.0275 | loss(seq) 0.0000 | grad 2.3428 | lr 0.0000 | time_forward 3.2200 | time_backward 4.5200 |
[2023-10-22 23:04:38,293::train::INFO] [train] Iter 563034 | loss 0.4475 | loss(rot) 0.4160 | loss(pos) 0.0303 | loss(seq) 0.0011 | grad 4.5379 | lr 0.0000 | time_forward 1.2540 | time_backward 1.4210 |
[2023-10-22 23:04:46,018::train::INFO] [train] Iter 563035 | loss 1.0613 | loss(rot) 1.0027 | loss(pos) 0.0585 | loss(seq) 0.0000 | grad 3.1160 | lr 0.0000 | time_forward 3.1970 | time_backward 4.5070 |
[2023-10-22 23:04:53,717::train::INFO] [train] Iter 563036 | loss 0.3491 | loss(rot) 0.0398 | loss(pos) 0.3042 | loss(seq) 0.0051 | grad 4.4766 | lr 0.0000 | time_forward 3.1770 | time_backward 4.5190 |
[2023-10-22 23:05:00,524::train::INFO] [train] Iter 563037 | loss 0.7032 | loss(rot) 0.2457 | loss(pos) 0.0462 | loss(seq) 0.4113 | grad 3.4827 | lr 0.0000 | time_forward 2.9500 | time_backward 3.8530 |
[2023-10-22 23:05:06,889::train::INFO] [train] Iter 563038 | loss 0.4280 | loss(rot) 0.1198 | loss(pos) 0.0234 | loss(seq) 0.2848 | grad 2.1456 | lr 0.0000 | time_forward 2.6960 | time_backward 3.6660 |
[2023-10-22 23:05:09,075::train::INFO] [train] Iter 563039 | loss 0.7410 | loss(rot) 0.0542 | loss(pos) 0.6841 | loss(seq) 0.0028 | grad 11.1031 | lr 0.0000 | time_forward 1.0000 | time_backward 1.1830 |
[2023-10-22 23:05:16,735::train::INFO] [train] Iter 563040 | loss 1.2523 | loss(rot) 0.8043 | loss(pos) 0.1478 | loss(seq) 0.3001 | grad 4.0903 | lr 0.0000 | time_forward 3.4030 | time_backward 4.2550 |
[2023-10-22 23:05:19,347::train::INFO] [train] Iter 563041 | loss 0.1910 | loss(rot) 0.1676 | loss(pos) 0.0117 | loss(seq) 0.0117 | grad 3.2952 | lr 0.0000 | time_forward 1.2390 | time_backward 1.3690 |
[2023-10-22 23:05:21,976::train::INFO] [train] Iter 563042 | loss 0.3134 | loss(rot) 0.0590 | loss(pos) 0.0229 | loss(seq) 0.2315 | grad 1.8328 | lr 0.0000 | time_forward 1.2680 | time_backward 1.3580 |
[2023-10-22 23:05:28,934::train::INFO] [train] Iter 563043 | loss 0.4926 | loss(rot) 0.1988 | loss(pos) 0.0288 | loss(seq) 0.2650 | grad 4.1208 | lr 0.0000 | time_forward 3.0460 | time_backward 3.9090 |
[2023-10-22 23:05:31,531::train::INFO] [train] Iter 563044 | loss 2.8704 | loss(rot) 0.0591 | loss(pos) 2.8109 | loss(seq) 0.0004 | grad 11.9344 | lr 0.0000 | time_forward 1.2150 | time_backward 1.3790 |
[2023-10-22 23:05:38,611::train::INFO] [train] Iter 563045 | loss 0.9529 | loss(rot) 0.5882 | loss(pos) 0.0252 | loss(seq) 0.3395 | grad 3.8253 | lr 0.0000 | time_forward 3.1030 | time_backward 3.9640 |
[2023-10-22 23:05:43,260::train::INFO] [train] Iter 563046 | loss 0.2407 | loss(rot) 0.0919 | loss(pos) 0.0979 | loss(seq) 0.0509 | grad 3.0730 | lr 0.0000 | time_forward 2.0310 | time_backward 2.6160 |
[2023-10-22 23:05:49,942::train::INFO] [train] Iter 563047 | loss 0.3449 | loss(rot) 0.1918 | loss(pos) 0.1390 | loss(seq) 0.0141 | grad 5.3647 | lr 0.0000 | time_forward 2.8880 | time_backward 3.7910 |
[2023-10-22 23:05:56,206::train::INFO] [train] Iter 563048 | loss 0.2105 | loss(rot) 0.1362 | loss(pos) 0.0176 | loss(seq) 0.0567 | grad 2.2644 | lr 0.0000 | time_forward 2.6810 | time_backward 3.5800 |
[2023-10-22 23:06:03,898::train::INFO] [train] Iter 563049 | loss 0.8299 | loss(rot) 0.8101 | loss(pos) 0.0136 | loss(seq) 0.0062 | grad 3.2941 | lr 0.0000 | time_forward 3.1760 | time_backward 4.5120 |
[2023-10-22 23:06:10,916::train::INFO] [train] Iter 563050 | loss 0.2016 | loss(rot) 0.1112 | loss(pos) 0.0234 | loss(seq) 0.0670 | grad 1.9749 | lr 0.0000 | time_forward 3.0760 | time_backward 3.9380 |
[2023-10-22 23:06:13,525::train::INFO] [train] Iter 563051 | loss 0.4385 | loss(rot) 0.2786 | loss(pos) 0.0199 | loss(seq) 0.1401 | grad 3.1961 | lr 0.0000 | time_forward 1.1960 | time_backward 1.4100 |
[2023-10-22 23:06:18,962::train::INFO] [train] Iter 563052 | loss 0.6863 | loss(rot) 0.4138 | loss(pos) 0.0688 | loss(seq) 0.2037 | grad 1.9711 | lr 0.0000 | time_forward 2.3930 | time_backward 3.0400 |
[2023-10-22 23:06:21,664::train::INFO] [train] Iter 563053 | loss 0.2574 | loss(rot) 0.0956 | loss(pos) 0.1166 | loss(seq) 0.0452 | grad 3.0677 | lr 0.0000 | time_forward 1.2640 | time_backward 1.4350 |
[2023-10-22 23:06:28,600::train::INFO] [train] Iter 563054 | loss 1.9575 | loss(rot) 1.8592 | loss(pos) 0.0819 | loss(seq) 0.0164 | grad 4.8154 | lr 0.0000 | time_forward 2.9470 | time_backward 3.9850 |
[2023-10-22 23:06:31,228::train::INFO] [train] Iter 563055 | loss 0.9580 | loss(rot) 0.7022 | loss(pos) 0.0860 | loss(seq) 0.1698 | grad 3.7535 | lr 0.0000 | time_forward 1.2530 | time_backward 1.3710 |
[2023-10-22 23:06:37,867::train::INFO] [train] Iter 563056 | loss 0.2448 | loss(rot) 0.0921 | loss(pos) 0.0189 | loss(seq) 0.1339 | grad 2.7486 | lr 0.0000 | time_forward 2.8890 | time_backward 3.7470 |
[2023-10-22 23:06:45,028::train::INFO] [train] Iter 563057 | loss 0.4680 | loss(rot) 0.2113 | loss(pos) 0.0116 | loss(seq) 0.2452 | grad 28.1319 | lr 0.0000 | time_forward 3.1320 | time_backward 4.0250 |
[2023-10-22 23:06:47,205::train::INFO] [train] Iter 563058 | loss 0.5651 | loss(rot) 0.3757 | loss(pos) 0.0150 | loss(seq) 0.1744 | grad 2.7113 | lr 0.0000 | time_forward 1.0000 | time_backward 1.1730 |
[2023-10-22 23:06:54,965::train::INFO] [train] Iter 563059 | loss 0.8675 | loss(rot) 0.5798 | loss(pos) 0.0410 | loss(seq) 0.2466 | grad 5.0345 | lr 0.0000 | time_forward 3.2230 | time_backward 4.5340 |
[2023-10-22 23:06:57,406::train::INFO] [train] Iter 563060 | loss 1.4076 | loss(rot) 0.9641 | loss(pos) 0.0338 | loss(seq) 0.4097 | grad 5.9492 | lr 0.0000 | time_forward 1.1400 | time_backward 1.2970 |
[2023-10-22 23:07:04,268::train::INFO] [train] Iter 563061 | loss 0.1596 | loss(rot) 0.0759 | loss(pos) 0.0160 | loss(seq) 0.0677 | grad 1.8345 | lr 0.0000 | time_forward 2.9960 | time_backward 3.8620 |
[2023-10-22 23:07:10,853::train::INFO] [train] Iter 563062 | loss 0.5085 | loss(rot) 0.2620 | loss(pos) 0.0415 | loss(seq) 0.2050 | grad 6.3117 | lr 0.0000 | time_forward 2.8530 | time_backward 3.7280 |
[2023-10-22 23:07:13,861::train::INFO] [train] Iter 563063 | loss 0.8428 | loss(rot) 0.5279 | loss(pos) 0.0771 | loss(seq) 0.2378 | grad 3.7577 | lr 0.0000 | time_forward 1.4010 | time_backward 1.6040 |
[2023-10-22 23:07:16,500::train::INFO] [train] Iter 563064 | loss 0.6911 | loss(rot) 0.4777 | loss(pos) 0.0533 | loss(seq) 0.1602 | grad 3.8066 | lr 0.0000 | time_forward 1.2530 | time_backward 1.3740 |
[2023-10-22 23:07:22,923::train::INFO] [train] Iter 563065 | loss 1.7743 | loss(rot) 1.7469 | loss(pos) 0.0274 | loss(seq) 0.0000 | grad 7.5573 | lr 0.0000 | time_forward 2.7450 | time_backward 3.6680 |
[2023-10-22 23:07:29,391::train::INFO] [train] Iter 563066 | loss 1.9046 | loss(rot) 0.9133 | loss(pos) 0.2690 | loss(seq) 0.7223 | grad 5.0200 | lr 0.0000 | time_forward 2.7520 | time_backward 3.7120 |
[2023-10-22 23:07:37,258::train::INFO] [train] Iter 563067 | loss 0.7453 | loss(rot) 0.6853 | loss(pos) 0.0600 | loss(seq) 0.0000 | grad 5.7247 | lr 0.0000 | time_forward 3.5010 | time_backward 4.3620 |
[2023-10-22 23:07:45,003::train::INFO] [train] Iter 563068 | loss 1.6516 | loss(rot) 1.1613 | loss(pos) 0.0769 | loss(seq) 0.4134 | grad 3.4291 | lr 0.0000 | time_forward 3.2340 | time_backward 4.5090 |
[2023-10-22 23:07:47,614::train::INFO] [train] Iter 563069 | loss 0.1606 | loss(rot) 0.0507 | loss(pos) 0.0356 | loss(seq) 0.0743 | grad 1.9453 | lr 0.0000 | time_forward 1.2450 | time_backward 1.3620 |
[2023-10-22 23:07:50,273::train::INFO] [train] Iter 563070 | loss 0.1739 | loss(rot) 0.1257 | loss(pos) 0.0186 | loss(seq) 0.0297 | grad 1.4444 | lr 0.0000 | time_forward 1.2840 | time_backward 1.3720 |
[2023-10-22 23:07:56,895::train::INFO] [train] Iter 563071 | loss 0.3693 | loss(rot) 0.1172 | loss(pos) 0.0297 | loss(seq) 0.2224 | grad 3.0682 | lr 0.0000 | time_forward 2.8800 | time_backward 3.7210 |
[2023-10-22 23:08:03,696::train::INFO] [train] Iter 563072 | loss 0.5854 | loss(rot) 0.3454 | loss(pos) 0.0518 | loss(seq) 0.1881 | grad 8.8087 | lr 0.0000 | time_forward 2.9530 | time_backward 3.8440 |
[2023-10-22 23:08:11,427::train::INFO] [train] Iter 563073 | loss 0.9774 | loss(rot) 0.5390 | loss(pos) 0.1272 | loss(seq) 0.3113 | grad 2.8789 | lr 0.0000 | time_forward 3.2030 | time_backward 4.5250 |
[2023-10-22 23:08:19,243::train::INFO] [train] Iter 563074 | loss 1.1979 | loss(rot) 0.7670 | loss(pos) 0.1259 | loss(seq) 0.3051 | grad 4.1729 | lr 0.0000 | time_forward 3.2290 | time_backward 4.5830 |
[2023-10-22 23:08:26,083::train::INFO] [train] Iter 563075 | loss 0.4489 | loss(rot) 0.2791 | loss(pos) 0.0262 | loss(seq) 0.1437 | grad 2.6378 | lr 0.0000 | time_forward 2.9490 | time_backward 3.8870 |
[2023-10-22 23:08:28,527::train::INFO] [train] Iter 563076 | loss 0.3501 | loss(rot) 0.0868 | loss(pos) 0.2146 | loss(seq) 0.0488 | grad 3.3193 | lr 0.0000 | time_forward 1.1700 | time_backward 1.2700 |
[2023-10-22 23:08:35,128::train::INFO] [train] Iter 563077 | loss 0.5632 | loss(rot) 0.0145 | loss(pos) 0.5472 | loss(seq) 0.0014 | grad 9.0751 | lr 0.0000 | time_forward 2.8500 | time_backward 3.7490 |
[2023-10-22 23:08:42,239::train::INFO] [train] Iter 563078 | loss 0.3800 | loss(rot) 0.3313 | loss(pos) 0.0487 | loss(seq) 0.0000 | grad 8.1571 | lr 0.0000 | time_forward 3.1140 | time_backward 3.9940 |
[2023-10-22 23:08:49,983::train::INFO] [train] Iter 563079 | loss 0.8212 | loss(rot) 0.4872 | loss(pos) 0.0476 | loss(seq) 0.2863 | grad 3.7336 | lr 0.0000 | time_forward 3.2270 | time_backward 4.5130 |
[2023-10-22 23:08:56,584::train::INFO] [train] Iter 563080 | loss 0.8836 | loss(rot) 0.8218 | loss(pos) 0.0617 | loss(seq) 0.0001 | grad 16.0440 | lr 0.0000 | time_forward 2.8580 | time_backward 3.7390 |
[2023-10-22 23:08:59,324::train::INFO] [train] Iter 563081 | loss 0.2107 | loss(rot) 0.1287 | loss(pos) 0.0206 | loss(seq) 0.0615 | grad 9.4937 | lr 0.0000 | time_forward 1.2750 | time_backward 1.4620 |
[2023-10-22 23:09:05,810::train::INFO] [train] Iter 563082 | loss 0.5133 | loss(rot) 0.0238 | loss(pos) 0.4830 | loss(seq) 0.0065 | grad 6.5253 | lr 0.0000 | time_forward 2.8180 | time_backward 3.6640 |
[2023-10-22 23:09:13,542::train::INFO] [train] Iter 563083 | loss 0.4296 | loss(rot) 0.1541 | loss(pos) 0.0606 | loss(seq) 0.2149 | grad 2.8389 | lr 0.0000 | time_forward 3.1750 | time_backward 4.5540 |
[2023-10-22 23:09:15,712::train::INFO] [train] Iter 563084 | loss 0.4914 | loss(rot) 0.2793 | loss(pos) 0.1521 | loss(seq) 0.0600 | grad 2.7528 | lr 0.0000 | time_forward 0.9950 | time_backward 1.1720 |
[2023-10-22 23:09:23,474::train::INFO] [train] Iter 563085 | loss 0.8482 | loss(rot) 0.3765 | loss(pos) 0.0445 | loss(seq) 0.4272 | grad 3.0189 | lr 0.0000 | time_forward 3.2430 | time_backward 4.5160 |
[2023-10-22 23:09:26,464::train::INFO] [train] Iter 563086 | loss 1.2119 | loss(rot) 0.7822 | loss(pos) 0.1043 | loss(seq) 0.3254 | grad 6.9856 | lr 0.0000 | time_forward 1.3750 | time_backward 1.6120 |
[2023-10-22 23:09:33,316::train::INFO] [train] Iter 563087 | loss 0.6869 | loss(rot) 0.1031 | loss(pos) 0.3066 | loss(seq) 0.2772 | grad 5.2176 | lr 0.0000 | time_forward 2.9760 | time_backward 3.8650 |
[2023-10-22 23:09:40,361::train::INFO] [train] Iter 563088 | loss 0.3690 | loss(rot) 0.2526 | loss(pos) 0.0283 | loss(seq) 0.0881 | grad 3.0839 | lr 0.0000 | time_forward 3.0500 | time_backward 3.9910 |
[2023-10-22 23:09:43,011::train::INFO] [train] Iter 563089 | loss 1.6315 | loss(rot) 1.4473 | loss(pos) 0.0348 | loss(seq) 0.1493 | grad 10.6129 | lr 0.0000 | time_forward 1.2680 | time_backward 1.3790 |
[2023-10-22 23:09:49,069::train::INFO] [train] Iter 563090 | loss 0.4736 | loss(rot) 0.0781 | loss(pos) 0.1015 | loss(seq) 0.2940 | grad 3.0130 | lr 0.0000 | time_forward 2.6460 | time_backward 3.3910 |
[2023-10-22 23:09:51,776::train::INFO] [train] Iter 563091 | loss 0.2255 | loss(rot) 0.1368 | loss(pos) 0.0252 | loss(seq) 0.0634 | grad 2.3641 | lr 0.0000 | time_forward 1.2720 | time_backward 1.4260 |
[2023-10-22 23:09:58,633::train::INFO] [train] Iter 563092 | loss 0.1987 | loss(rot) 0.0502 | loss(pos) 0.1120 | loss(seq) 0.0365 | grad 4.1936 | lr 0.0000 | time_forward 2.8970 | time_backward 3.9570 |
[2023-10-22 23:10:06,631::train::INFO] [train] Iter 563093 | loss 1.7804 | loss(rot) 1.6747 | loss(pos) 0.0589 | loss(seq) 0.0468 | grad 3.8343 | lr 0.0000 | time_forward 3.3260 | time_backward 4.6680 |
[2023-10-22 23:10:14,048::train::INFO] [train] Iter 563094 | loss 1.5013 | loss(rot) 1.4757 | loss(pos) 0.0256 | loss(seq) 0.0000 | grad 7.5610 | lr 0.0000 | time_forward 3.2550 | time_backward 4.1590 |
[2023-10-22 23:10:21,862::train::INFO] [train] Iter 563095 | loss 1.2943 | loss(rot) 0.7223 | loss(pos) 0.2050 | loss(seq) 0.3670 | grad 3.9833 | lr 0.0000 | time_forward 3.1860 | time_backward 4.6240 |
[2023-10-22 23:10:29,748::train::INFO] [train] Iter 563096 | loss 0.7006 | loss(rot) 0.4430 | loss(pos) 0.1035 | loss(seq) 0.1541 | grad 2.7628 | lr 0.0000 | time_forward 3.3590 | time_backward 4.5250 |
[2023-10-22 23:10:32,181::train::INFO] [train] Iter 563097 | loss 1.6468 | loss(rot) 1.5952 | loss(pos) 0.0486 | loss(seq) 0.0030 | grad 4.4560 | lr 0.0000 | time_forward 1.1570 | time_backward 1.2720 |
[2023-10-22 23:10:38,579::train::INFO] [train] Iter 563098 | loss 0.4849 | loss(rot) 0.0205 | loss(pos) 0.4610 | loss(seq) 0.0035 | grad 3.9670 | lr 0.0000 | time_forward 2.7200 | time_backward 3.6730 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.