text stringlengths 56 1.16k |
|---|
[2023-10-24 03:10:20,947::train::INFO] [train] Iter 578484 | loss 1.8520 | loss(rot) 1.3457 | loss(pos) 0.1368 | loss(seq) 0.3695 | grad 3.6503 | lr 0.0000 | time_forward 4.0020 | time_backward 5.6790 |
[2023-10-24 03:10:29,345::train::INFO] [train] Iter 578485 | loss 0.7838 | loss(rot) 0.4747 | loss(pos) 0.2924 | loss(seq) 0.0167 | grad 4.6190 | lr 0.0000 | time_forward 3.5440 | time_backward 4.8510 |
[2023-10-24 03:10:37,483::train::INFO] [train] Iter 578486 | loss 2.3777 | loss(rot) 1.6249 | loss(pos) 0.2607 | loss(seq) 0.4922 | grad 4.7420 | lr 0.0000 | time_forward 3.5380 | time_backward 4.5970 |
[2023-10-24 03:10:45,525::train::INFO] [train] Iter 578487 | loss 2.2314 | loss(rot) 1.4214 | loss(pos) 0.2743 | loss(seq) 0.5357 | grad 4.7489 | lr 0.0000 | time_forward 3.3790 | time_backward 4.6590 |
[2023-10-24 03:10:53,929::train::INFO] [train] Iter 578488 | loss 0.9053 | loss(rot) 0.3740 | loss(pos) 0.1351 | loss(seq) 0.3962 | grad 5.9920 | lr 0.0000 | time_forward 3.3990 | time_backward 5.0010 |
[2023-10-24 03:11:02,291::train::INFO] [train] Iter 578489 | loss 0.6785 | loss(rot) 0.2507 | loss(pos) 0.0568 | loss(seq) 0.3710 | grad 3.7316 | lr 0.0000 | time_forward 3.5660 | time_backward 4.7940 |
[2023-10-24 03:11:12,026::train::INFO] [train] Iter 578490 | loss 0.6506 | loss(rot) 0.0431 | loss(pos) 0.6022 | loss(seq) 0.0052 | grad 8.1402 | lr 0.0000 | time_forward 3.9860 | time_backward 5.7450 |
[2023-10-24 03:11:20,320::train::INFO] [train] Iter 578491 | loss 0.2035 | loss(rot) 0.1182 | loss(pos) 0.0199 | loss(seq) 0.0654 | grad 2.4606 | lr 0.0000 | time_forward 3.5810 | time_backward 4.7100 |
[2023-10-24 03:11:23,530::train::INFO] [train] Iter 578492 | loss 0.6502 | loss(rot) 0.2133 | loss(pos) 0.1906 | loss(seq) 0.2464 | grad 2.6317 | lr 0.0000 | time_forward 1.4380 | time_backward 1.7700 |
[2023-10-24 03:11:26,360::train::INFO] [train] Iter 578493 | loss 0.4453 | loss(rot) 0.0077 | loss(pos) 0.4343 | loss(seq) 0.0033 | grad 5.8927 | lr 0.0000 | time_forward 1.3310 | time_backward 1.4820 |
[2023-10-24 03:11:29,155::train::INFO] [train] Iter 578494 | loss 1.9219 | loss(rot) 1.5902 | loss(pos) 0.2119 | loss(seq) 0.1197 | grad 4.1097 | lr 0.0000 | time_forward 1.3330 | time_backward 1.4580 |
[2023-10-24 03:11:38,964::train::INFO] [train] Iter 578495 | loss 1.8083 | loss(rot) 1.3581 | loss(pos) 0.1542 | loss(seq) 0.2960 | grad 5.5598 | lr 0.0000 | time_forward 3.9910 | time_backward 5.8150 |
[2023-10-24 03:11:46,493::train::INFO] [train] Iter 578496 | loss 2.2103 | loss(rot) 1.7297 | loss(pos) 0.0638 | loss(seq) 0.4169 | grad 10.3127 | lr 0.0000 | time_forward 3.1660 | time_backward 4.3600 |
[2023-10-24 03:11:49,278::train::INFO] [train] Iter 578497 | loss 0.5016 | loss(rot) 0.2772 | loss(pos) 0.0360 | loss(seq) 0.1884 | grad 5.5873 | lr 0.0000 | time_forward 1.3470 | time_backward 1.4330 |
[2023-10-24 03:11:57,774::train::INFO] [train] Iter 578498 | loss 0.1323 | loss(rot) 0.1081 | loss(pos) 0.0231 | loss(seq) 0.0011 | grad 2.2275 | lr 0.0000 | time_forward 3.5350 | time_backward 4.9580 |
[2023-10-24 03:12:06,770::train::INFO] [train] Iter 578499 | loss 0.4584 | loss(rot) 0.0328 | loss(pos) 0.4185 | loss(seq) 0.0071 | grad 4.5710 | lr 0.0000 | time_forward 3.5190 | time_backward 5.4740 |
[2023-10-24 03:12:16,874::train::INFO] [train] Iter 578500 | loss 0.5914 | loss(rot) 0.5704 | loss(pos) 0.0209 | loss(seq) 0.0000 | grad 4.6198 | lr 0.0000 | time_forward 4.1690 | time_backward 5.9300 |
[2023-10-24 03:12:19,593::train::INFO] [train] Iter 578501 | loss 2.2953 | loss(rot) 2.1854 | loss(pos) 0.0579 | loss(seq) 0.0519 | grad 3.2049 | lr 0.0000 | time_forward 1.3060 | time_backward 1.4100 |
[2023-10-24 03:12:27,975::train::INFO] [train] Iter 578502 | loss 1.9889 | loss(rot) 1.9480 | loss(pos) 0.0409 | loss(seq) 0.0000 | grad 33.5853 | lr 0.0000 | time_forward 3.4810 | time_backward 4.8970 |
[2023-10-24 03:12:35,341::train::INFO] [train] Iter 578503 | loss 0.6835 | loss(rot) 0.2382 | loss(pos) 0.4163 | loss(seq) 0.0290 | grad 5.8220 | lr 0.0000 | time_forward 3.1510 | time_backward 4.2120 |
[2023-10-24 03:12:41,857::train::INFO] [train] Iter 578504 | loss 1.5295 | loss(rot) 1.5195 | loss(pos) 0.0097 | loss(seq) 0.0003 | grad 4.2609 | lr 0.0000 | time_forward 2.7990 | time_backward 3.7140 |
[2023-10-24 03:12:44,413::train::INFO] [train] Iter 578505 | loss 0.4260 | loss(rot) 0.0614 | loss(pos) 0.3480 | loss(seq) 0.0166 | grad 7.6388 | lr 0.0000 | time_forward 1.1880 | time_backward 1.3660 |
[2023-10-24 03:12:52,938::train::INFO] [train] Iter 578506 | loss 0.4059 | loss(rot) 0.1306 | loss(pos) 0.2062 | loss(seq) 0.0692 | grad 3.3417 | lr 0.0000 | time_forward 3.5680 | time_backward 4.9540 |
[2023-10-24 03:12:55,674::train::INFO] [train] Iter 578507 | loss 0.2468 | loss(rot) 0.0955 | loss(pos) 0.0865 | loss(seq) 0.0649 | grad 2.7857 | lr 0.0000 | time_forward 1.3070 | time_backward 1.4260 |
[2023-10-24 03:12:58,446::train::INFO] [train] Iter 578508 | loss 0.5510 | loss(rot) 0.4477 | loss(pos) 0.0334 | loss(seq) 0.0699 | grad 3.3089 | lr 0.0000 | time_forward 1.2930 | time_backward 1.4450 |
[2023-10-24 03:13:07,251::train::INFO] [train] Iter 578509 | loss 0.3803 | loss(rot) 0.0960 | loss(pos) 0.0448 | loss(seq) 0.2395 | grad 2.9364 | lr 0.0000 | time_forward 3.7260 | time_backward 5.0750 |
[2023-10-24 03:13:14,729::train::INFO] [train] Iter 578510 | loss 1.8788 | loss(rot) 1.2359 | loss(pos) 0.1873 | loss(seq) 0.4557 | grad 6.4746 | lr 0.0000 | time_forward 3.2060 | time_backward 4.2690 |
[2023-10-24 03:13:24,546::train::INFO] [train] Iter 578511 | loss 0.2608 | loss(rot) 0.1024 | loss(pos) 0.0350 | loss(seq) 0.1234 | grad 1.6467 | lr 0.0000 | time_forward 4.0200 | time_backward 5.7940 |
[2023-10-24 03:13:33,158::train::INFO] [train] Iter 578512 | loss 0.7745 | loss(rot) 0.7100 | loss(pos) 0.0315 | loss(seq) 0.0330 | grad 4.6323 | lr 0.0000 | time_forward 3.6440 | time_backward 4.9650 |
[2023-10-24 03:13:36,499::train::INFO] [train] Iter 578513 | loss 0.7563 | loss(rot) 0.7366 | loss(pos) 0.0196 | loss(seq) 0.0000 | grad 32.4247 | lr 0.0000 | time_forward 1.4940 | time_backward 1.8450 |
[2023-10-24 03:13:44,299::train::INFO] [train] Iter 578514 | loss 0.8152 | loss(rot) 0.3845 | loss(pos) 0.4250 | loss(seq) 0.0057 | grad 9.7279 | lr 0.0000 | time_forward 3.2870 | time_backward 4.5090 |
[2023-10-24 03:13:47,033::train::INFO] [train] Iter 578515 | loss 0.2009 | loss(rot) 0.0327 | loss(pos) 0.0292 | loss(seq) 0.1391 | grad 1.2781 | lr 0.0000 | time_forward 1.3240 | time_backward 1.4080 |
[2023-10-24 03:13:49,954::train::INFO] [train] Iter 578516 | loss 0.2797 | loss(rot) 0.1105 | loss(pos) 0.1003 | loss(seq) 0.0689 | grad 3.0802 | lr 0.0000 | time_forward 1.3470 | time_backward 1.5710 |
[2023-10-24 03:13:52,819::train::INFO] [train] Iter 578517 | loss 0.1335 | loss(rot) 0.0871 | loss(pos) 0.0197 | loss(seq) 0.0267 | grad 2.1564 | lr 0.0000 | time_forward 1.4010 | time_backward 1.4600 |
[2023-10-24 03:14:02,561::train::INFO] [train] Iter 578518 | loss 0.2905 | loss(rot) 0.0434 | loss(pos) 0.2315 | loss(seq) 0.0156 | grad 6.7712 | lr 0.0000 | time_forward 3.9650 | time_backward 5.7730 |
[2023-10-24 03:14:12,741::train::INFO] [train] Iter 578519 | loss 0.3976 | loss(rot) 0.0661 | loss(pos) 0.3165 | loss(seq) 0.0150 | grad 5.4652 | lr 0.0000 | time_forward 4.0010 | time_backward 6.1750 |
[2023-10-24 03:14:22,474::train::INFO] [train] Iter 578520 | loss 2.2323 | loss(rot) 2.2034 | loss(pos) 0.0244 | loss(seq) 0.0045 | grad 9.3771 | lr 0.0000 | time_forward 4.0440 | time_backward 5.6870 |
[2023-10-24 03:14:25,224::train::INFO] [train] Iter 578521 | loss 0.5754 | loss(rot) 0.5132 | loss(pos) 0.0213 | loss(seq) 0.0410 | grad 3.1794 | lr 0.0000 | time_forward 1.3050 | time_backward 1.4410 |
[2023-10-24 03:14:34,810::train::INFO] [train] Iter 578522 | loss 0.3370 | loss(rot) 0.2705 | loss(pos) 0.0628 | loss(seq) 0.0038 | grad 3.2876 | lr 0.0000 | time_forward 3.9220 | time_backward 5.6620 |
[2023-10-24 03:14:43,625::train::INFO] [train] Iter 578523 | loss 0.8932 | loss(rot) 0.7142 | loss(pos) 0.0787 | loss(seq) 0.1003 | grad 18.8018 | lr 0.0000 | time_forward 3.7380 | time_backward 5.0730 |
[2023-10-24 03:14:46,367::train::INFO] [train] Iter 578524 | loss 1.1214 | loss(rot) 1.0975 | loss(pos) 0.0212 | loss(seq) 0.0026 | grad 3.5729 | lr 0.0000 | time_forward 1.2980 | time_backward 1.4410 |
[2023-10-24 03:14:49,774::train::INFO] [train] Iter 578525 | loss 0.6079 | loss(rot) 0.5809 | loss(pos) 0.0270 | loss(seq) 0.0000 | grad 2.8815 | lr 0.0000 | time_forward 1.4850 | time_backward 1.8820 |
[2023-10-24 03:14:59,208::train::INFO] [train] Iter 578526 | loss 0.2613 | loss(rot) 0.0411 | loss(pos) 0.1843 | loss(seq) 0.0360 | grad 5.1429 | lr 0.0000 | time_forward 3.8420 | time_backward 5.5880 |
[2023-10-24 03:15:07,654::train::INFO] [train] Iter 578527 | loss 2.2412 | loss(rot) 2.1819 | loss(pos) 0.0590 | loss(seq) 0.0003 | grad 13.2511 | lr 0.0000 | time_forward 3.5500 | time_backward 4.8840 |
[2023-10-24 03:15:15,768::train::INFO] [train] Iter 578528 | loss 0.6147 | loss(rot) 0.2120 | loss(pos) 0.0982 | loss(seq) 0.3045 | grad 2.8467 | lr 0.0000 | time_forward 3.4190 | time_backward 4.6910 |
[2023-10-24 03:15:22,796::train::INFO] [train] Iter 578529 | loss 0.2352 | loss(rot) 0.0480 | loss(pos) 0.1759 | loss(seq) 0.0113 | grad 3.3458 | lr 0.0000 | time_forward 2.9290 | time_backward 4.0960 |
[2023-10-24 03:15:31,807::train::INFO] [train] Iter 578530 | loss 0.6788 | loss(rot) 0.2354 | loss(pos) 0.0549 | loss(seq) 0.3885 | grad 2.8490 | lr 0.0000 | time_forward 3.7950 | time_backward 5.2120 |
[2023-10-24 03:15:34,788::train::INFO] [train] Iter 578531 | loss 0.6701 | loss(rot) 0.5909 | loss(pos) 0.0187 | loss(seq) 0.0605 | grad 6.6709 | lr 0.0000 | time_forward 1.3700 | time_backward 1.6080 |
[2023-10-24 03:15:43,075::train::INFO] [train] Iter 578532 | loss 1.0314 | loss(rot) 0.6984 | loss(pos) 0.0406 | loss(seq) 0.2924 | grad 3.0343 | lr 0.0000 | time_forward 3.4620 | time_backward 4.8230 |
[2023-10-24 03:15:45,396::train::INFO] [train] Iter 578533 | loss 1.1984 | loss(rot) 0.7494 | loss(pos) 0.0664 | loss(seq) 0.3826 | grad 3.9834 | lr 0.0000 | time_forward 1.0650 | time_backward 1.2520 |
[2023-10-24 03:15:53,981::train::INFO] [train] Iter 578534 | loss 0.2114 | loss(rot) 0.1276 | loss(pos) 0.0226 | loss(seq) 0.0612 | grad 2.0642 | lr 0.0000 | time_forward 3.6150 | time_backward 4.9660 |
[2023-10-24 03:15:56,713::train::INFO] [train] Iter 578535 | loss 0.6387 | loss(rot) 0.6067 | loss(pos) 0.0228 | loss(seq) 0.0092 | grad 3.5909 | lr 0.0000 | time_forward 1.2550 | time_backward 1.4730 |
[2023-10-24 03:16:06,408::train::INFO] [train] Iter 578536 | loss 0.4190 | loss(rot) 0.1786 | loss(pos) 0.0241 | loss(seq) 0.2163 | grad 3.3970 | lr 0.0000 | time_forward 3.9110 | time_backward 5.7810 |
[2023-10-24 03:16:15,297::train::INFO] [train] Iter 578537 | loss 0.2687 | loss(rot) 0.1480 | loss(pos) 0.0856 | loss(seq) 0.0351 | grad 2.9234 | lr 0.0000 | time_forward 3.7840 | time_backward 5.1010 |
[2023-10-24 03:16:23,398::train::INFO] [train] Iter 578538 | loss 1.4033 | loss(rot) 0.5765 | loss(pos) 0.3367 | loss(seq) 0.4900 | grad 6.3158 | lr 0.0000 | time_forward 3.4220 | time_backward 4.6770 |
[2023-10-24 03:16:26,180::train::INFO] [train] Iter 578539 | loss 0.3180 | loss(rot) 0.1029 | loss(pos) 0.1015 | loss(seq) 0.1136 | grad 2.9382 | lr 0.0000 | time_forward 1.2970 | time_backward 1.4810 |
[2023-10-24 03:16:29,652::train::INFO] [train] Iter 578540 | loss 1.4179 | loss(rot) 1.0476 | loss(pos) 0.0669 | loss(seq) 0.3035 | grad 24.9169 | lr 0.0000 | time_forward 1.5670 | time_backward 1.9000 |
[2023-10-24 03:16:37,861::train::INFO] [train] Iter 578541 | loss 0.2079 | loss(rot) 0.1849 | loss(pos) 0.0229 | loss(seq) 0.0000 | grad 2.5663 | lr 0.0000 | time_forward 3.4560 | time_backward 4.7490 |
[2023-10-24 03:16:40,632::train::INFO] [train] Iter 578542 | loss 0.9422 | loss(rot) 0.8590 | loss(pos) 0.0138 | loss(seq) 0.0693 | grad 5.8417 | lr 0.0000 | time_forward 1.3230 | time_backward 1.4350 |
[2023-10-24 03:16:48,056::train::INFO] [train] Iter 578543 | loss 0.6003 | loss(rot) 0.4718 | loss(pos) 0.0407 | loss(seq) 0.0877 | grad 72.7884 | lr 0.0000 | time_forward 3.0970 | time_backward 4.3240 |
[2023-10-24 03:16:56,185::train::INFO] [train] Iter 578544 | loss 0.5518 | loss(rot) 0.4229 | loss(pos) 0.0201 | loss(seq) 0.1089 | grad 21.6386 | lr 0.0000 | time_forward 3.4100 | time_backward 4.7160 |
[2023-10-24 03:16:58,823::train::INFO] [train] Iter 578545 | loss 0.6238 | loss(rot) 0.2307 | loss(pos) 0.0256 | loss(seq) 0.3675 | grad 2.7967 | lr 0.0000 | time_forward 1.2380 | time_backward 1.3970 |
[2023-10-24 03:17:07,248::train::INFO] [train] Iter 578546 | loss 1.2186 | loss(rot) 1.0728 | loss(pos) 0.0504 | loss(seq) 0.0955 | grad 54.5449 | lr 0.0000 | time_forward 3.4860 | time_backward 4.8960 |
[2023-10-24 03:17:15,223::train::INFO] [train] Iter 578547 | loss 0.7128 | loss(rot) 0.3574 | loss(pos) 0.0418 | loss(seq) 0.3137 | grad 3.7065 | lr 0.0000 | time_forward 3.4340 | time_backward 4.5370 |
[2023-10-24 03:17:24,946::train::INFO] [train] Iter 578548 | loss 0.5093 | loss(rot) 0.1982 | loss(pos) 0.0611 | loss(seq) 0.2501 | grad 2.3180 | lr 0.0000 | time_forward 3.9200 | time_backward 5.8000 |
[2023-10-24 03:17:34,680::train::INFO] [train] Iter 578549 | loss 0.7059 | loss(rot) 0.6639 | loss(pos) 0.0418 | loss(seq) 0.0002 | grad 3.8649 | lr 0.0000 | time_forward 4.1710 | time_backward 5.5600 |
[2023-10-24 03:17:44,212::train::INFO] [train] Iter 578550 | loss 0.4716 | loss(rot) 0.4398 | loss(pos) 0.0302 | loss(seq) 0.0016 | grad 3.0450 | lr 0.0000 | time_forward 3.9330 | time_backward 5.5960 |
[2023-10-24 03:17:46,942::train::INFO] [train] Iter 578551 | loss 0.2455 | loss(rot) 0.1921 | loss(pos) 0.0344 | loss(seq) 0.0190 | grad 7.7142 | lr 0.0000 | time_forward 1.3150 | time_backward 1.4130 |
[2023-10-24 03:17:55,897::train::INFO] [train] Iter 578552 | loss 0.1434 | loss(rot) 0.0635 | loss(pos) 0.0364 | loss(seq) 0.0435 | grad 1.5983 | lr 0.0000 | time_forward 3.7950 | time_backward 5.1570 |
[2023-10-24 03:17:58,675::train::INFO] [train] Iter 578553 | loss 0.1352 | loss(rot) 0.0580 | loss(pos) 0.0159 | loss(seq) 0.0613 | grad 1.3606 | lr 0.0000 | time_forward 1.3060 | time_backward 1.4690 |
[2023-10-24 03:18:00,989::train::INFO] [train] Iter 578554 | loss 0.1692 | loss(rot) 0.0464 | loss(pos) 0.1066 | loss(seq) 0.0162 | grad 3.9277 | lr 0.0000 | time_forward 1.0640 | time_backward 1.2120 |
[2023-10-24 03:18:03,714::train::INFO] [train] Iter 578555 | loss 0.5546 | loss(rot) 0.5195 | loss(pos) 0.0325 | loss(seq) 0.0026 | grad 1.9010 | lr 0.0000 | time_forward 1.2720 | time_backward 1.4490 |
[2023-10-24 03:18:06,065::train::INFO] [train] Iter 578556 | loss 0.5338 | loss(rot) 0.2332 | loss(pos) 0.0344 | loss(seq) 0.2662 | grad 2.6854 | lr 0.0000 | time_forward 1.0760 | time_backward 1.2410 |
[2023-10-24 03:18:15,023::train::INFO] [train] Iter 578557 | loss 0.2308 | loss(rot) 0.1893 | loss(pos) 0.0405 | loss(seq) 0.0010 | grad 2.6989 | lr 0.0000 | time_forward 3.8090 | time_backward 5.1330 |
[2023-10-24 03:18:22,406::train::INFO] [train] Iter 578558 | loss 0.3136 | loss(rot) 0.0796 | loss(pos) 0.0431 | loss(seq) 0.1909 | grad 2.4752 | lr 0.0000 | time_forward 3.0980 | time_backward 4.2750 |
[2023-10-24 03:18:32,131::train::INFO] [train] Iter 578559 | loss 0.8979 | loss(rot) 0.1154 | loss(pos) 0.1263 | loss(seq) 0.6562 | grad 4.1163 | lr 0.0000 | time_forward 4.0080 | time_backward 5.7130 |
[2023-10-24 03:18:42,114::train::INFO] [train] Iter 578560 | loss 0.3742 | loss(rot) 0.1850 | loss(pos) 0.0327 | loss(seq) 0.1565 | grad 2.5309 | lr 0.0000 | time_forward 4.1060 | time_backward 5.8750 |
[2023-10-24 03:18:44,870::train::INFO] [train] Iter 578561 | loss 1.5244 | loss(rot) 1.2401 | loss(pos) 0.1284 | loss(seq) 0.1559 | grad 6.1269 | lr 0.0000 | time_forward 1.3080 | time_backward 1.4440 |
[2023-10-24 03:18:48,219::train::INFO] [train] Iter 578562 | loss 1.6284 | loss(rot) 1.4667 | loss(pos) 0.0590 | loss(seq) 0.1026 | grad 10.3011 | lr 0.0000 | time_forward 1.4930 | time_backward 1.8520 |
[2023-10-24 03:18:50,863::train::INFO] [train] Iter 578563 | loss 0.3922 | loss(rot) 0.0687 | loss(pos) 0.2995 | loss(seq) 0.0240 | grad 3.1406 | lr 0.0000 | time_forward 1.2100 | time_backward 1.4320 |
[2023-10-24 03:18:59,732::train::INFO] [train] Iter 578564 | loss 1.3177 | loss(rot) 0.4792 | loss(pos) 0.2312 | loss(seq) 0.6073 | grad 3.3633 | lr 0.0000 | time_forward 3.6550 | time_backward 5.1770 |
[2023-10-24 03:19:09,827::train::INFO] [train] Iter 578565 | loss 1.1142 | loss(rot) 0.3941 | loss(pos) 0.5517 | loss(seq) 0.1684 | grad 6.4481 | lr 0.0000 | time_forward 4.2950 | time_backward 5.7960 |
[2023-10-24 03:19:18,374::train::INFO] [train] Iter 578566 | loss 0.5846 | loss(rot) 0.3900 | loss(pos) 0.0196 | loss(seq) 0.1750 | grad 3.7091 | lr 0.0000 | time_forward 3.6580 | time_backward 4.8850 |
[2023-10-24 03:19:28,109::train::INFO] [train] Iter 578567 | loss 0.7695 | loss(rot) 0.2247 | loss(pos) 0.3958 | loss(seq) 0.1490 | grad 3.6887 | lr 0.0000 | time_forward 3.9890 | time_backward 5.7440 |
[2023-10-24 03:19:36,527::train::INFO] [train] Iter 578568 | loss 0.7125 | loss(rot) 0.0314 | loss(pos) 0.3824 | loss(seq) 0.2987 | grad 7.5051 | lr 0.0000 | time_forward 3.5380 | time_backward 4.8770 |
[2023-10-24 03:19:46,297::train::INFO] [train] Iter 578569 | loss 1.7825 | loss(rot) 1.3734 | loss(pos) 0.0761 | loss(seq) 0.3330 | grad 3.7553 | lr 0.0000 | time_forward 3.9370 | time_backward 5.8300 |
[2023-10-24 03:19:54,884::train::INFO] [train] Iter 578570 | loss 1.5166 | loss(rot) 0.9474 | loss(pos) 0.1398 | loss(seq) 0.4293 | grad 8.9307 | lr 0.0000 | time_forward 3.5550 | time_backward 5.0280 |
[2023-10-24 03:20:03,974::train::INFO] [train] Iter 578571 | loss 0.3318 | loss(rot) 0.1403 | loss(pos) 0.0386 | loss(seq) 0.1529 | grad 2.2866 | lr 0.0000 | time_forward 3.7950 | time_backward 5.2920 |
[2023-10-24 03:20:13,672::train::INFO] [train] Iter 578572 | loss 0.8164 | loss(rot) 0.7422 | loss(pos) 0.0523 | loss(seq) 0.0219 | grad 8.1531 | lr 0.0000 | time_forward 3.9400 | time_backward 5.7550 |
[2023-10-24 03:20:16,636::train::INFO] [train] Iter 578573 | loss 0.2696 | loss(rot) 0.1422 | loss(pos) 0.0215 | loss(seq) 0.1059 | grad 2.4944 | lr 0.0000 | time_forward 1.3290 | time_backward 1.6310 |
[2023-10-24 03:20:26,309::train::INFO] [train] Iter 578574 | loss 0.4483 | loss(rot) 0.1297 | loss(pos) 0.3002 | loss(seq) 0.0184 | grad 3.6143 | lr 0.0000 | time_forward 3.8920 | time_backward 5.7770 |
[2023-10-24 03:20:29,009::train::INFO] [train] Iter 578575 | loss 0.7170 | loss(rot) 0.0633 | loss(pos) 0.4600 | loss(seq) 0.1937 | grad 5.6868 | lr 0.0000 | time_forward 1.2780 | time_backward 1.4190 |
[2023-10-24 03:20:38,781::train::INFO] [train] Iter 578576 | loss 0.3463 | loss(rot) 0.0982 | loss(pos) 0.1205 | loss(seq) 0.1276 | grad 3.5386 | lr 0.0000 | time_forward 4.0290 | time_backward 5.7410 |
[2023-10-24 03:20:48,353::train::INFO] [train] Iter 578577 | loss 0.5133 | loss(rot) 0.3199 | loss(pos) 0.1158 | loss(seq) 0.0776 | grad 3.2915 | lr 0.0000 | time_forward 3.9010 | time_backward 5.6680 |
[2023-10-24 03:20:57,523::train::INFO] [train] Iter 578578 | loss 0.5179 | loss(rot) 0.0897 | loss(pos) 0.2053 | loss(seq) 0.2229 | grad 4.6828 | lr 0.0000 | time_forward 3.7590 | time_backward 5.4080 |
[2023-10-24 03:21:00,264::train::INFO] [train] Iter 578579 | loss 0.5040 | loss(rot) 0.3522 | loss(pos) 0.0213 | loss(seq) 0.1305 | grad 2.5266 | lr 0.0000 | time_forward 1.3110 | time_backward 1.4260 |
[2023-10-24 03:21:03,069::train::INFO] [train] Iter 578580 | loss 0.1750 | loss(rot) 0.1448 | loss(pos) 0.0288 | loss(seq) 0.0014 | grad 1.9338 | lr 0.0000 | time_forward 1.3490 | time_backward 1.4510 |
[2023-10-24 03:21:05,320::train::INFO] [train] Iter 578581 | loss 0.5057 | loss(rot) 0.4811 | loss(pos) 0.0179 | loss(seq) 0.0066 | grad 4.2439 | lr 0.0000 | time_forward 1.0320 | time_backward 1.1870 |
[2023-10-24 03:21:08,083::train::INFO] [train] Iter 578582 | loss 1.3962 | loss(rot) 1.3382 | loss(pos) 0.0416 | loss(seq) 0.0165 | grad 10.2925 | lr 0.0000 | time_forward 1.3390 | time_backward 1.4190 |
[2023-10-24 03:21:16,681::train::INFO] [train] Iter 578583 | loss 0.8180 | loss(rot) 0.2937 | loss(pos) 0.1453 | loss(seq) 0.3790 | grad 3.0062 | lr 0.0000 | time_forward 3.6270 | time_backward 4.9680 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.