text stringlengths 56 1.16k |
|---|
[2023-10-23 23:26:10,824::train::INFO] [train] Iter 576586 | loss 0.5942 | loss(rot) 0.4072 | loss(pos) 0.0345 | loss(seq) 0.1525 | grad 4.2637 | lr 0.0000 | time_forward 4.2430 | time_backward 5.8810 |
[2023-10-23 23:26:19,632::train::INFO] [train] Iter 576587 | loss 4.5965 | loss(rot) 0.0036 | loss(pos) 4.5929 | loss(seq) 0.0000 | grad 31.8062 | lr 0.0000 | time_forward 3.6800 | time_backward 5.1240 |
[2023-10-23 23:26:29,670::train::INFO] [train] Iter 576588 | loss 0.7378 | loss(rot) 0.1682 | loss(pos) 0.0950 | loss(seq) 0.4745 | grad 3.9554 | lr 0.0000 | time_forward 4.0500 | time_backward 5.9840 |
[2023-10-23 23:26:38,583::train::INFO] [train] Iter 576589 | loss 0.3445 | loss(rot) 0.2625 | loss(pos) 0.0242 | loss(seq) 0.0577 | grad 2.6635 | lr 0.0000 | time_forward 3.7910 | time_backward 5.1180 |
[2023-10-23 23:26:48,626::train::INFO] [train] Iter 576590 | loss 0.5957 | loss(rot) 0.0525 | loss(pos) 0.5057 | loss(seq) 0.0376 | grad 6.0180 | lr 0.0000 | time_forward 4.0470 | time_backward 5.9920 |
[2023-10-23 23:26:51,427::train::INFO] [train] Iter 576591 | loss 1.7103 | loss(rot) 1.5979 | loss(pos) 0.0380 | loss(seq) 0.0744 | grad 5.2955 | lr 0.0000 | time_forward 1.3370 | time_backward 1.4610 |
[2023-10-23 23:27:02,610::train::INFO] [train] Iter 576592 | loss 1.1072 | loss(rot) 0.7696 | loss(pos) 0.0541 | loss(seq) 0.2835 | grad 5.3202 | lr 0.0000 | time_forward 5.1490 | time_backward 6.0310 |
[2023-10-23 23:27:11,319::train::INFO] [train] Iter 576593 | loss 0.2554 | loss(rot) 0.1041 | loss(pos) 0.0588 | loss(seq) 0.0924 | grad 2.2216 | lr 0.0000 | time_forward 3.6480 | time_backward 5.0580 |
[2023-10-23 23:27:21,393::train::INFO] [train] Iter 576594 | loss 0.7885 | loss(rot) 0.6933 | loss(pos) 0.0282 | loss(seq) 0.0669 | grad 3.3115 | lr 0.0000 | time_forward 4.0950 | time_backward 5.9760 |
[2023-10-23 23:27:31,433::train::INFO] [train] Iter 576595 | loss 0.7122 | loss(rot) 0.0618 | loss(pos) 0.5449 | loss(seq) 0.1055 | grad 4.5511 | lr 0.0000 | time_forward 4.2060 | time_backward 5.8300 |
[2023-10-23 23:27:34,212::train::INFO] [train] Iter 576596 | loss 0.4559 | loss(rot) 0.1474 | loss(pos) 0.0708 | loss(seq) 0.2377 | grad 3.2433 | lr 0.0000 | time_forward 1.3370 | time_backward 1.4380 |
[2023-10-23 23:27:41,501::train::INFO] [train] Iter 576597 | loss 0.2281 | loss(rot) 0.1975 | loss(pos) 0.0304 | loss(seq) 0.0002 | grad 2.8260 | lr 0.0000 | time_forward 3.1860 | time_backward 4.1000 |
[2023-10-23 23:27:44,581::train::INFO] [train] Iter 576598 | loss 0.5121 | loss(rot) 0.2972 | loss(pos) 0.0247 | loss(seq) 0.1902 | grad 3.4179 | lr 0.0000 | time_forward 1.5390 | time_backward 1.5350 |
[2023-10-23 23:27:47,439::train::INFO] [train] Iter 576599 | loss 0.3018 | loss(rot) 0.2427 | loss(pos) 0.0591 | loss(seq) 0.0000 | grad 2.6359 | lr 0.0000 | time_forward 1.3330 | time_backward 1.4790 |
[2023-10-23 23:27:58,872::train::INFO] [train] Iter 576600 | loss 1.1786 | loss(rot) 0.1281 | loss(pos) 1.0443 | loss(seq) 0.0061 | grad 5.4504 | lr 0.0000 | time_forward 4.8600 | time_backward 6.5690 |
[2023-10-23 23:28:10,033::train::INFO] [train] Iter 576601 | loss 0.4157 | loss(rot) 0.0213 | loss(pos) 0.3920 | loss(seq) 0.0025 | grad 5.2013 | lr 0.0000 | time_forward 4.7130 | time_backward 6.4440 |
[2023-10-23 23:28:21,753::train::INFO] [train] Iter 576602 | loss 0.5195 | loss(rot) 0.4632 | loss(pos) 0.0563 | loss(seq) 0.0000 | grad 1.9151 | lr 0.0000 | time_forward 5.2190 | time_backward 6.4980 |
[2023-10-23 23:28:32,955::train::INFO] [train] Iter 576603 | loss 0.4960 | loss(rot) 0.4443 | loss(pos) 0.0272 | loss(seq) 0.0245 | grad 7.7472 | lr 0.0000 | time_forward 4.5280 | time_backward 6.6710 |
[2023-10-23 23:28:36,123::train::INFO] [train] Iter 576604 | loss 0.3306 | loss(rot) 0.0376 | loss(pos) 0.0219 | loss(seq) 0.2710 | grad 1.7452 | lr 0.0000 | time_forward 1.4960 | time_backward 1.6660 |
[2023-10-23 23:28:39,049::train::INFO] [train] Iter 576605 | loss 0.9089 | loss(rot) 0.4465 | loss(pos) 0.4229 | loss(seq) 0.0394 | grad 5.6642 | lr 0.0000 | time_forward 1.3830 | time_backward 1.5400 |
[2023-10-23 23:28:41,699::train::INFO] [train] Iter 576606 | loss 0.4676 | loss(rot) 0.0620 | loss(pos) 0.3869 | loss(seq) 0.0187 | grad 6.3491 | lr 0.0000 | time_forward 1.1150 | time_backward 1.5330 |
[2023-10-23 23:28:50,875::train::INFO] [train] Iter 576607 | loss 0.4265 | loss(rot) 0.1316 | loss(pos) 0.0530 | loss(seq) 0.2419 | grad 3.2944 | lr 0.0000 | time_forward 4.1270 | time_backward 5.0450 |
[2023-10-23 23:28:59,229::train::INFO] [train] Iter 576608 | loss 0.7831 | loss(rot) 0.5316 | loss(pos) 0.1846 | loss(seq) 0.0669 | grad 4.6605 | lr 0.0000 | time_forward 3.8010 | time_backward 4.5500 |
[2023-10-23 23:29:01,446::train::INFO] [train] Iter 576609 | loss 0.1004 | loss(rot) 0.0552 | loss(pos) 0.0260 | loss(seq) 0.0192 | grad 1.5458 | lr 0.0000 | time_forward 1.0220 | time_backward 1.1920 |
[2023-10-23 23:29:11,454::train::INFO] [train] Iter 576610 | loss 0.6550 | loss(rot) 0.2092 | loss(pos) 0.3084 | loss(seq) 0.1374 | grad 4.7994 | lr 0.0000 | time_forward 4.1020 | time_backward 5.9030 |
[2023-10-23 23:29:20,664::train::INFO] [train] Iter 576611 | loss 1.2604 | loss(rot) 0.8235 | loss(pos) 0.1246 | loss(seq) 0.3123 | grad 6.8337 | lr 0.0000 | time_forward 3.9140 | time_backward 5.2930 |
[2023-10-23 23:29:23,411::train::INFO] [train] Iter 576612 | loss 0.9881 | loss(rot) 0.4921 | loss(pos) 0.2350 | loss(seq) 0.2610 | grad 4.5095 | lr 0.0000 | time_forward 1.3110 | time_backward 1.4330 |
[2023-10-23 23:29:26,164::train::INFO] [train] Iter 576613 | loss 0.2815 | loss(rot) 0.1674 | loss(pos) 0.0218 | loss(seq) 0.0922 | grad 3.2580 | lr 0.0000 | time_forward 1.3630 | time_backward 1.3860 |
[2023-10-23 23:29:36,108::train::INFO] [train] Iter 576614 | loss 0.4803 | loss(rot) 0.4350 | loss(pos) 0.0305 | loss(seq) 0.0148 | grad 9.1591 | lr 0.0000 | time_forward 4.0860 | time_backward 5.8540 |
[2023-10-23 23:29:44,431::train::INFO] [train] Iter 576615 | loss 0.5310 | loss(rot) 0.1307 | loss(pos) 0.0709 | loss(seq) 0.3293 | grad 2.7259 | lr 0.0000 | time_forward 3.4890 | time_backward 4.8300 |
[2023-10-23 23:29:53,115::train::INFO] [train] Iter 576616 | loss 2.1965 | loss(rot) 1.6670 | loss(pos) 0.1456 | loss(seq) 0.3839 | grad 4.1475 | lr 0.0000 | time_forward 3.6470 | time_backward 5.0340 |
[2023-10-23 23:30:02,244::train::INFO] [train] Iter 576617 | loss 0.1990 | loss(rot) 0.1644 | loss(pos) 0.0315 | loss(seq) 0.0032 | grad 2.7651 | lr 0.0000 | time_forward 3.8290 | time_backward 5.2960 |
[2023-10-23 23:30:10,565::train::INFO] [train] Iter 576618 | loss 2.0892 | loss(rot) 0.1534 | loss(pos) 1.9351 | loss(seq) 0.0007 | grad 14.8346 | lr 0.0000 | time_forward 3.5210 | time_backward 4.7970 |
[2023-10-23 23:30:20,445::train::INFO] [train] Iter 576619 | loss 0.7680 | loss(rot) 0.3463 | loss(pos) 0.0345 | loss(seq) 0.3873 | grad 2.9646 | lr 0.0000 | time_forward 3.9830 | time_backward 5.8940 |
[2023-10-23 23:30:23,258::train::INFO] [train] Iter 576620 | loss 0.1362 | loss(rot) 0.0475 | loss(pos) 0.0489 | loss(seq) 0.0398 | grad 1.8441 | lr 0.0000 | time_forward 1.3560 | time_backward 1.4540 |
[2023-10-23 23:30:33,269::train::INFO] [train] Iter 576621 | loss 1.5851 | loss(rot) 0.9171 | loss(pos) 0.2053 | loss(seq) 0.4628 | grad 2.9643 | lr 0.0000 | time_forward 4.0910 | time_backward 5.9160 |
[2023-10-23 23:30:43,198::train::INFO] [train] Iter 576622 | loss 0.5363 | loss(rot) 0.4236 | loss(pos) 0.0224 | loss(seq) 0.0903 | grad 2.9142 | lr 0.0000 | time_forward 4.0450 | time_backward 5.8810 |
[2023-10-23 23:30:51,216::train::INFO] [train] Iter 576623 | loss 0.6426 | loss(rot) 0.1558 | loss(pos) 0.4695 | loss(seq) 0.0172 | grad 6.5357 | lr 0.0000 | time_forward 3.3960 | time_backward 4.6180 |
[2023-10-23 23:31:01,072::train::INFO] [train] Iter 576624 | loss 0.8701 | loss(rot) 0.6318 | loss(pos) 0.1008 | loss(seq) 0.1375 | grad 5.8132 | lr 0.0000 | time_forward 3.9990 | time_backward 5.8540 |
[2023-10-23 23:31:11,759::train::INFO] [train] Iter 576625 | loss 0.3445 | loss(rot) 0.0824 | loss(pos) 0.0269 | loss(seq) 0.2351 | grad 1.8452 | lr 0.0000 | time_forward 4.7190 | time_backward 5.9650 |
[2023-10-23 23:31:21,695::train::INFO] [train] Iter 576626 | loss 1.5249 | loss(rot) 1.1044 | loss(pos) 0.0552 | loss(seq) 0.3653 | grad 5.4017 | lr 0.0000 | time_forward 4.0340 | time_backward 5.8980 |
[2023-10-23 23:31:30,063::train::INFO] [train] Iter 576627 | loss 2.1266 | loss(rot) 1.4964 | loss(pos) 0.1117 | loss(seq) 0.5185 | grad 13.4088 | lr 0.0000 | time_forward 3.5280 | time_backward 4.8370 |
[2023-10-23 23:31:39,323::train::INFO] [train] Iter 576628 | loss 0.2496 | loss(rot) 0.1739 | loss(pos) 0.0725 | loss(seq) 0.0031 | grad 5.5751 | lr 0.0000 | time_forward 3.8770 | time_backward 5.3810 |
[2023-10-23 23:31:49,588::train::INFO] [train] Iter 576629 | loss 0.8997 | loss(rot) 0.6968 | loss(pos) 0.0321 | loss(seq) 0.1708 | grad 28.2957 | lr 0.0000 | time_forward 4.1960 | time_backward 6.0650 |
[2023-10-23 23:31:52,365::train::INFO] [train] Iter 576630 | loss 0.6548 | loss(rot) 0.0555 | loss(pos) 0.5820 | loss(seq) 0.0173 | grad 4.8783 | lr 0.0000 | time_forward 1.3360 | time_backward 1.4380 |
[2023-10-23 23:31:55,309::train::INFO] [train] Iter 576631 | loss 0.6690 | loss(rot) 0.4198 | loss(pos) 0.0945 | loss(seq) 0.1547 | grad 2.2880 | lr 0.0000 | time_forward 1.3420 | time_backward 1.5580 |
[2023-10-23 23:32:05,362::train::INFO] [train] Iter 576632 | loss 0.1935 | loss(rot) 0.1664 | loss(pos) 0.0271 | loss(seq) 0.0000 | grad 1.7450 | lr 0.0000 | time_forward 4.1340 | time_backward 5.9160 |
[2023-10-23 23:32:13,322::train::INFO] [train] Iter 576633 | loss 0.7779 | loss(rot) 0.3709 | loss(pos) 0.0267 | loss(seq) 0.3803 | grad 3.1064 | lr 0.0000 | time_forward 3.3610 | time_backward 4.5960 |
[2023-10-23 23:32:22,701::train::INFO] [train] Iter 576634 | loss 0.1242 | loss(rot) 0.0732 | loss(pos) 0.0249 | loss(seq) 0.0261 | grad 1.6601 | lr 0.0000 | time_forward 3.9880 | time_backward 5.3880 |
[2023-10-23 23:32:31,052::train::INFO] [train] Iter 576635 | loss 0.3440 | loss(rot) 0.0445 | loss(pos) 0.2627 | loss(seq) 0.0368 | grad 5.3207 | lr 0.0000 | time_forward 3.5130 | time_backward 4.8350 |
[2023-10-23 23:32:34,045::train::INFO] [train] Iter 576636 | loss 0.2641 | loss(rot) 0.0429 | loss(pos) 0.1774 | loss(seq) 0.0437 | grad 3.7316 | lr 0.0000 | time_forward 1.4120 | time_backward 1.5770 |
[2023-10-23 23:32:36,842::train::INFO] [train] Iter 576637 | loss 0.4064 | loss(rot) 0.2402 | loss(pos) 0.0180 | loss(seq) 0.1482 | grad 2.9303 | lr 0.0000 | time_forward 1.3420 | time_backward 1.4510 |
[2023-10-23 23:32:46,221::train::INFO] [train] Iter 576638 | loss 0.7760 | loss(rot) 0.1990 | loss(pos) 0.3531 | loss(seq) 0.2239 | grad 2.9544 | lr 0.0000 | time_forward 3.8810 | time_backward 5.4960 |
[2023-10-23 23:32:49,045::train::INFO] [train] Iter 576639 | loss 0.2456 | loss(rot) 0.2078 | loss(pos) 0.0329 | loss(seq) 0.0049 | grad 2.1882 | lr 0.0000 | time_forward 1.3240 | time_backward 1.4960 |
[2023-10-23 23:32:57,178::train::INFO] [train] Iter 576640 | loss 0.9928 | loss(rot) 0.9271 | loss(pos) 0.0348 | loss(seq) 0.0309 | grad 2.9185 | lr 0.0000 | time_forward 3.4540 | time_backward 4.6760 |
[2023-10-23 23:33:00,051::train::INFO] [train] Iter 576641 | loss 1.7799 | loss(rot) 1.3178 | loss(pos) 0.0609 | loss(seq) 0.4013 | grad 4.2869 | lr 0.0000 | time_forward 1.4390 | time_backward 1.4300 |
[2023-10-23 23:33:06,041::train::INFO] [train] Iter 576642 | loss 1.3151 | loss(rot) 1.3016 | loss(pos) 0.0126 | loss(seq) 0.0010 | grad 7.6787 | lr 0.0000 | time_forward 2.6060 | time_backward 3.3790 |
[2023-10-23 23:33:14,170::train::INFO] [train] Iter 576643 | loss 1.5922 | loss(rot) 0.9657 | loss(pos) 0.3262 | loss(seq) 0.3003 | grad 5.6041 | lr 0.0000 | time_forward 3.4800 | time_backward 4.6340 |
[2023-10-23 23:33:17,080::train::INFO] [train] Iter 576644 | loss 0.1350 | loss(rot) 0.0812 | loss(pos) 0.0325 | loss(seq) 0.0213 | grad 1.5990 | lr 0.0000 | time_forward 1.4160 | time_backward 1.4910 |
[2023-10-23 23:33:27,241::train::INFO] [train] Iter 576645 | loss 0.0923 | loss(rot) 0.0669 | loss(pos) 0.0245 | loss(seq) 0.0009 | grad 1.4585 | lr 0.0000 | time_forward 4.1220 | time_backward 6.0360 |
[2023-10-23 23:33:37,364::train::INFO] [train] Iter 576646 | loss 0.8202 | loss(rot) 0.3991 | loss(pos) 0.3079 | loss(seq) 0.1132 | grad 3.6191 | lr 0.0000 | time_forward 4.0270 | time_backward 6.0930 |
[2023-10-23 23:33:45,808::train::INFO] [train] Iter 576647 | loss 0.9188 | loss(rot) 0.2542 | loss(pos) 0.0972 | loss(seq) 0.5674 | grad 5.1813 | lr 0.0000 | time_forward 3.5850 | time_backward 4.8560 |
[2023-10-23 23:33:48,573::train::INFO] [train] Iter 576648 | loss 0.6564 | loss(rot) 0.2090 | loss(pos) 0.1925 | loss(seq) 0.2549 | grad 4.1613 | lr 0.0000 | time_forward 1.2310 | time_backward 1.5300 |
[2023-10-23 23:33:58,587::train::INFO] [train] Iter 576649 | loss 0.5548 | loss(rot) 0.0381 | loss(pos) 0.3660 | loss(seq) 0.1507 | grad 3.1525 | lr 0.0000 | time_forward 4.2000 | time_backward 5.7880 |
[2023-10-23 23:34:07,454::train::INFO] [train] Iter 576650 | loss 0.5352 | loss(rot) 0.2335 | loss(pos) 0.0332 | loss(seq) 0.2685 | grad 2.8982 | lr 0.0000 | time_forward 3.6950 | time_backward 5.1700 |
[2023-10-23 23:34:10,202::train::INFO] [train] Iter 576651 | loss 0.1986 | loss(rot) 0.0883 | loss(pos) 0.0408 | loss(seq) 0.0695 | grad 2.0527 | lr 0.0000 | time_forward 1.3160 | time_backward 1.4280 |
[2023-10-23 23:34:13,000::train::INFO] [train] Iter 576652 | loss 0.3512 | loss(rot) 0.0528 | loss(pos) 0.2779 | loss(seq) 0.0205 | grad 7.4007 | lr 0.0000 | time_forward 1.3710 | time_backward 1.4230 |
[2023-10-23 23:34:21,913::train::INFO] [train] Iter 576653 | loss 0.3414 | loss(rot) 0.0362 | loss(pos) 0.0197 | loss(seq) 0.2854 | grad 2.7214 | lr 0.0000 | time_forward 3.8740 | time_backward 5.0370 |
[2023-10-23 23:34:32,005::train::INFO] [train] Iter 576654 | loss 0.4120 | loss(rot) 0.1170 | loss(pos) 0.1155 | loss(seq) 0.1796 | grad 2.3086 | lr 0.0000 | time_forward 4.3220 | time_backward 5.7660 |
[2023-10-23 23:34:42,081::train::INFO] [train] Iter 576655 | loss 1.4566 | loss(rot) 1.4070 | loss(pos) 0.0469 | loss(seq) 0.0027 | grad 11.6891 | lr 0.0000 | time_forward 4.0750 | time_backward 5.9990 |
[2023-10-23 23:34:51,315::train::INFO] [train] Iter 576656 | loss 0.6780 | loss(rot) 0.2045 | loss(pos) 0.1278 | loss(seq) 0.3456 | grad 3.3624 | lr 0.0000 | time_forward 3.8560 | time_backward 5.3740 |
[2023-10-23 23:35:00,532::train::INFO] [train] Iter 576657 | loss 0.4065 | loss(rot) 0.1357 | loss(pos) 0.0337 | loss(seq) 0.2371 | grad 2.4796 | lr 0.0000 | time_forward 3.9700 | time_backward 5.2430 |
[2023-10-23 23:35:09,269::train::INFO] [train] Iter 576658 | loss 0.9693 | loss(rot) 0.6905 | loss(pos) 0.0750 | loss(seq) 0.2038 | grad 39.7255 | lr 0.0000 | time_forward 3.6750 | time_backward 5.0460 |
[2023-10-23 23:35:12,138::train::INFO] [train] Iter 576659 | loss 2.3352 | loss(rot) 0.1067 | loss(pos) 2.2281 | loss(seq) 0.0004 | grad 20.2729 | lr 0.0000 | time_forward 1.4220 | time_backward 1.4440 |
[2023-10-23 23:35:22,200::train::INFO] [train] Iter 576660 | loss 1.1033 | loss(rot) 1.0728 | loss(pos) 0.0181 | loss(seq) 0.0125 | grad 3.3964 | lr 0.0000 | time_forward 4.1450 | time_backward 5.9120 |
[2023-10-23 23:35:30,960::train::INFO] [train] Iter 576661 | loss 2.9097 | loss(rot) 2.4687 | loss(pos) 0.2215 | loss(seq) 0.2196 | grad 5.3249 | lr 0.0000 | time_forward 3.7190 | time_backward 5.0370 |
[2023-10-23 23:35:41,014::train::INFO] [train] Iter 576662 | loss 0.5502 | loss(rot) 0.1162 | loss(pos) 0.4123 | loss(seq) 0.0216 | grad 6.9302 | lr 0.0000 | time_forward 4.0740 | time_backward 5.9750 |
[2023-10-23 23:35:43,776::train::INFO] [train] Iter 576663 | loss 0.1828 | loss(rot) 0.0683 | loss(pos) 0.0966 | loss(seq) 0.0179 | grad 3.0861 | lr 0.0000 | time_forward 1.3270 | time_backward 1.4310 |
[2023-10-23 23:35:52,519::train::INFO] [train] Iter 576664 | loss 2.1305 | loss(rot) 1.8882 | loss(pos) 0.0615 | loss(seq) 0.1808 | grad 4.3107 | lr 0.0000 | time_forward 3.6190 | time_backward 5.0780 |
[2023-10-23 23:36:02,519::train::INFO] [train] Iter 576665 | loss 1.0442 | loss(rot) 0.6257 | loss(pos) 0.1090 | loss(seq) 0.3095 | grad 3.4774 | lr 0.0000 | time_forward 4.1130 | time_backward 5.8840 |
[2023-10-23 23:36:10,555::train::INFO] [train] Iter 576666 | loss 0.5305 | loss(rot) 0.5054 | loss(pos) 0.0222 | loss(seq) 0.0029 | grad 3.3618 | lr 0.0000 | time_forward 3.3180 | time_backward 4.7150 |
[2023-10-23 23:36:19,321::train::INFO] [train] Iter 576667 | loss 0.3060 | loss(rot) 0.0264 | loss(pos) 0.2756 | loss(seq) 0.0040 | grad 6.5311 | lr 0.0000 | time_forward 3.6680 | time_backward 5.0960 |
[2023-10-23 23:36:22,167::train::INFO] [train] Iter 576668 | loss 0.8389 | loss(rot) 0.3886 | loss(pos) 0.1183 | loss(seq) 0.3319 | grad 2.6260 | lr 0.0000 | time_forward 1.3330 | time_backward 1.5090 |
[2023-10-23 23:36:25,710::train::INFO] [train] Iter 576669 | loss 0.4504 | loss(rot) 0.2679 | loss(pos) 0.0480 | loss(seq) 0.1345 | grad 3.4707 | lr 0.0000 | time_forward 1.5420 | time_backward 1.9980 |
[2023-10-23 23:36:28,513::train::INFO] [train] Iter 576670 | loss 0.6341 | loss(rot) 0.1843 | loss(pos) 0.0475 | loss(seq) 0.4024 | grad 3.6898 | lr 0.0000 | time_forward 1.3190 | time_backward 1.4800 |
[2023-10-23 23:36:37,630::train::INFO] [train] Iter 576671 | loss 1.9140 | loss(rot) 1.8383 | loss(pos) 0.0672 | loss(seq) 0.0085 | grad 3.9379 | lr 0.0000 | time_forward 3.9310 | time_backward 5.1840 |
[2023-10-23 23:36:40,492::train::INFO] [train] Iter 576672 | loss 0.4771 | loss(rot) 0.3984 | loss(pos) 0.0787 | loss(seq) 0.0000 | grad 39.2486 | lr 0.0000 | time_forward 1.3410 | time_backward 1.5170 |
[2023-10-23 23:36:43,451::train::INFO] [train] Iter 576673 | loss 0.4648 | loss(rot) 0.1405 | loss(pos) 0.0231 | loss(seq) 0.3012 | grad 3.3865 | lr 0.0000 | time_forward 1.4440 | time_backward 1.5110 |
[2023-10-23 23:36:51,531::train::INFO] [train] Iter 576674 | loss 0.6359 | loss(rot) 0.4297 | loss(pos) 0.0615 | loss(seq) 0.1447 | grad 3.0554 | lr 0.0000 | time_forward 3.4280 | time_backward 4.6480 |
[2023-10-23 23:37:00,641::train::INFO] [train] Iter 576675 | loss 0.6158 | loss(rot) 0.0988 | loss(pos) 0.1155 | loss(seq) 0.4016 | grad 3.6436 | lr 0.0000 | time_forward 3.7920 | time_backward 5.3150 |
[2023-10-23 23:37:03,430::train::INFO] [train] Iter 576676 | loss 0.4297 | loss(rot) 0.1953 | loss(pos) 0.0566 | loss(seq) 0.1778 | grad 2.6980 | lr 0.0000 | time_forward 1.2910 | time_backward 1.4940 |
[2023-10-23 23:37:12,133::train::INFO] [train] Iter 576677 | loss 0.3155 | loss(rot) 0.1676 | loss(pos) 0.0322 | loss(seq) 0.1156 | grad 2.3847 | lr 0.0000 | time_forward 3.7230 | time_backward 4.9770 |
[2023-10-23 23:37:20,820::train::INFO] [train] Iter 576678 | loss 0.3108 | loss(rot) 0.2705 | loss(pos) 0.0122 | loss(seq) 0.0281 | grad 2.9913 | lr 0.0000 | time_forward 3.6910 | time_backward 4.9940 |
[2023-10-23 23:37:23,143::train::INFO] [train] Iter 576679 | loss 1.3635 | loss(rot) 0.9250 | loss(pos) 0.0282 | loss(seq) 0.4104 | grad 4.2165 | lr 0.0000 | time_forward 1.0760 | time_backward 1.2440 |
[2023-10-23 23:37:29,172::train::INFO] [train] Iter 576680 | loss 0.0771 | loss(rot) 0.0517 | loss(pos) 0.0128 | loss(seq) 0.0126 | grad 1.4436 | lr 0.0000 | time_forward 2.6370 | time_backward 3.3720 |
[2023-10-23 23:37:31,986::train::INFO] [train] Iter 576681 | loss 0.8707 | loss(rot) 0.8334 | loss(pos) 0.0318 | loss(seq) 0.0054 | grad 5.9490 | lr 0.0000 | time_forward 1.3270 | time_backward 1.4830 |
[2023-10-23 23:37:40,511::train::INFO] [train] Iter 576682 | loss 0.0869 | loss(rot) 0.0495 | loss(pos) 0.0145 | loss(seq) 0.0228 | grad 1.6303 | lr 0.0000 | time_forward 3.6740 | time_backward 4.8390 |
[2023-10-23 23:37:49,227::train::INFO] [train] Iter 576683 | loss 0.5454 | loss(rot) 0.1563 | loss(pos) 0.0922 | loss(seq) 0.2968 | grad 3.5729 | lr 0.0000 | time_forward 3.7270 | time_backward 4.9850 |
[2023-10-23 23:37:57,209::train::INFO] [train] Iter 576684 | loss 0.9548 | loss(rot) 0.6361 | loss(pos) 0.0234 | loss(seq) 0.2953 | grad 6.2995 | lr 0.0000 | time_forward 3.3180 | time_backward 4.6610 |
[2023-10-23 23:37:59,991::train::INFO] [train] Iter 576685 | loss 1.2552 | loss(rot) 0.9761 | loss(pos) 0.0872 | loss(seq) 0.1918 | grad 4.6199 | lr 0.0000 | time_forward 1.3220 | time_backward 1.4560 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.