text stringlengths 56 1.16k |
|---|
[2023-10-24 13:30:42,729::train::INFO] [train] Iter 583679 | loss 0.4645 | loss(rot) 0.1404 | loss(pos) 0.2411 | loss(seq) 0.0830 | grad 6.6059 | lr 0.0000 | time_forward 3.8300 | time_backward 5.2030 |
[2023-10-24 13:30:52,656::train::INFO] [train] Iter 583680 | loss 0.9139 | loss(rot) 0.5438 | loss(pos) 0.1323 | loss(seq) 0.2379 | grad 3.3545 | lr 0.0000 | time_forward 4.0170 | time_backward 5.9070 |
[2023-10-24 13:30:59,630::train::INFO] [train] Iter 583681 | loss 0.4320 | loss(rot) 0.3732 | loss(pos) 0.0413 | loss(seq) 0.0175 | grad 16.2749 | lr 0.0000 | time_forward 2.9880 | time_backward 3.9830 |
[2023-10-24 13:31:09,492::train::INFO] [train] Iter 583682 | loss 0.2250 | loss(rot) 0.1236 | loss(pos) 0.0607 | loss(seq) 0.0407 | grad 1.8552 | lr 0.0000 | time_forward 4.0030 | time_backward 5.8570 |
[2023-10-24 13:31:12,273::train::INFO] [train] Iter 583683 | loss 0.3303 | loss(rot) 0.0187 | loss(pos) 0.3059 | loss(seq) 0.0057 | grad 6.1679 | lr 0.0000 | time_forward 1.3180 | time_backward 1.4600 |
[2023-10-24 13:31:22,172::train::INFO] [train] Iter 583684 | loss 0.4360 | loss(rot) 0.3437 | loss(pos) 0.0316 | loss(seq) 0.0607 | grad 3.8585 | lr 0.0000 | time_forward 4.0190 | time_backward 5.8410 |
[2023-10-24 13:31:32,067::train::INFO] [train] Iter 583685 | loss 0.2210 | loss(rot) 0.0677 | loss(pos) 0.0226 | loss(seq) 0.1308 | grad 1.8240 | lr 0.0000 | time_forward 4.0020 | time_backward 5.8890 |
[2023-10-24 13:31:41,239::train::INFO] [train] Iter 583686 | loss 0.1693 | loss(rot) 0.1268 | loss(pos) 0.0129 | loss(seq) 0.0296 | grad 1.5931 | lr 0.0000 | time_forward 3.8870 | time_backward 5.2820 |
[2023-10-24 13:31:51,279::train::INFO] [train] Iter 583687 | loss 0.3648 | loss(rot) 0.1352 | loss(pos) 0.0262 | loss(seq) 0.2035 | grad 2.2983 | lr 0.0000 | time_forward 4.1260 | time_backward 5.9110 |
[2023-10-24 13:31:53,983::train::INFO] [train] Iter 583688 | loss 0.3515 | loss(rot) 0.0279 | loss(pos) 0.0262 | loss(seq) 0.2974 | grad 2.0413 | lr 0.0000 | time_forward 1.2570 | time_backward 1.4440 |
[2023-10-24 13:31:56,880::train::INFO] [train] Iter 583689 | loss 0.2748 | loss(rot) 0.0544 | loss(pos) 0.0243 | loss(seq) 0.1961 | grad 1.9281 | lr 0.0000 | time_forward 1.3620 | time_backward 1.5060 |
[2023-10-24 13:31:59,731::train::INFO] [train] Iter 583690 | loss 0.8123 | loss(rot) 0.1089 | loss(pos) 0.6020 | loss(seq) 0.1013 | grad 4.8872 | lr 0.0000 | time_forward 1.3520 | time_backward 1.4830 |
[2023-10-24 13:32:09,706::train::INFO] [train] Iter 583691 | loss 0.5138 | loss(rot) 0.2788 | loss(pos) 0.0410 | loss(seq) 0.1940 | grad 3.3569 | lr 0.0000 | time_forward 4.0630 | time_backward 5.8870 |
[2023-10-24 13:32:13,054::train::INFO] [train] Iter 583692 | loss 1.6883 | loss(rot) 1.0520 | loss(pos) 0.2428 | loss(seq) 0.3935 | grad 8.1649 | lr 0.0000 | time_forward 1.4940 | time_backward 1.8510 |
[2023-10-24 13:32:16,483::train::INFO] [train] Iter 583693 | loss 0.8358 | loss(rot) 0.4105 | loss(pos) 0.3369 | loss(seq) 0.0885 | grad 3.2769 | lr 0.0000 | time_forward 1.5230 | time_backward 1.9040 |
[2023-10-24 13:32:24,706::train::INFO] [train] Iter 583694 | loss 0.8949 | loss(rot) 0.1197 | loss(pos) 0.7711 | loss(seq) 0.0041 | grad 7.1572 | lr 0.0000 | time_forward 3.3910 | time_backward 4.8290 |
[2023-10-24 13:32:35,127::train::INFO] [train] Iter 583695 | loss 1.1279 | loss(rot) 0.9226 | loss(pos) 0.0346 | loss(seq) 0.1707 | grad 4.6708 | lr 0.0000 | time_forward 4.1850 | time_backward 6.2320 |
[2023-10-24 13:32:45,423::train::INFO] [train] Iter 583696 | loss 0.3445 | loss(rot) 0.0970 | loss(pos) 0.0245 | loss(seq) 0.2230 | grad 2.2866 | lr 0.0000 | time_forward 4.2330 | time_backward 6.0610 |
[2023-10-24 13:32:48,222::train::INFO] [train] Iter 583697 | loss 0.9922 | loss(rot) 0.5330 | loss(pos) 0.0491 | loss(seq) 0.4101 | grad 4.3189 | lr 0.0000 | time_forward 1.3190 | time_backward 1.4770 |
[2023-10-24 13:32:57,084::train::INFO] [train] Iter 583698 | loss 0.6214 | loss(rot) 0.0741 | loss(pos) 0.2300 | loss(seq) 0.3173 | grad 3.6454 | lr 0.0000 | time_forward 3.7420 | time_backward 5.1170 |
[2023-10-24 13:33:07,546::train::INFO] [train] Iter 583699 | loss 2.1430 | loss(rot) 1.6328 | loss(pos) 0.0941 | loss(seq) 0.4161 | grad 4.9558 | lr 0.0000 | time_forward 4.4550 | time_backward 6.0040 |
[2023-10-24 13:33:16,425::train::INFO] [train] Iter 583700 | loss 0.6879 | loss(rot) 0.1840 | loss(pos) 0.2812 | loss(seq) 0.2227 | grad 3.5548 | lr 0.0000 | time_forward 3.7670 | time_backward 5.1080 |
[2023-10-24 13:33:19,207::train::INFO] [train] Iter 583701 | loss 0.4657 | loss(rot) 0.1350 | loss(pos) 0.0482 | loss(seq) 0.2824 | grad 2.6995 | lr 0.0000 | time_forward 1.3320 | time_backward 1.4480 |
[2023-10-24 13:33:29,191::train::INFO] [train] Iter 583702 | loss 0.3546 | loss(rot) 0.2651 | loss(pos) 0.0893 | loss(seq) 0.0002 | grad 3.0349 | lr 0.0000 | time_forward 4.0460 | time_backward 5.9000 |
[2023-10-24 13:33:39,276::train::INFO] [train] Iter 583703 | loss 0.4357 | loss(rot) 0.1122 | loss(pos) 0.1818 | loss(seq) 0.1417 | grad 3.4072 | lr 0.0000 | time_forward 4.0150 | time_backward 6.0670 |
[2023-10-24 13:33:47,912::train::INFO] [train] Iter 583704 | loss 0.5906 | loss(rot) 0.2540 | loss(pos) 0.0381 | loss(seq) 0.2985 | grad 5.7558 | lr 0.0000 | time_forward 3.6950 | time_backward 4.9380 |
[2023-10-24 13:33:58,489::train::INFO] [train] Iter 583705 | loss 0.5784 | loss(rot) 0.0208 | loss(pos) 0.5564 | loss(seq) 0.0012 | grad 9.6545 | lr 0.0000 | time_forward 4.4940 | time_backward 6.0800 |
[2023-10-24 13:34:07,940::train::INFO] [train] Iter 583706 | loss 0.3849 | loss(rot) 0.0992 | loss(pos) 0.0336 | loss(seq) 0.2522 | grad 2.6627 | lr 0.0000 | time_forward 4.2030 | time_backward 5.2450 |
[2023-10-24 13:34:17,080::train::INFO] [train] Iter 583707 | loss 0.3329 | loss(rot) 0.0341 | loss(pos) 0.1594 | loss(seq) 0.1394 | grad 2.9543 | lr 0.0000 | time_forward 3.7100 | time_backward 5.4270 |
[2023-10-24 13:34:26,678::train::INFO] [train] Iter 583708 | loss 0.3604 | loss(rot) 0.1494 | loss(pos) 0.0185 | loss(seq) 0.1925 | grad 2.6527 | lr 0.0000 | time_forward 4.2660 | time_backward 5.3290 |
[2023-10-24 13:34:37,321::train::INFO] [train] Iter 583709 | loss 0.1963 | loss(rot) 0.1657 | loss(pos) 0.0254 | loss(seq) 0.0053 | grad 2.1952 | lr 0.0000 | time_forward 4.3310 | time_backward 6.3090 |
[2023-10-24 13:34:40,206::train::INFO] [train] Iter 583710 | loss 0.2308 | loss(rot) 0.0511 | loss(pos) 0.0252 | loss(seq) 0.1544 | grad 2.3806 | lr 0.0000 | time_forward 1.3960 | time_backward 1.4850 |
[2023-10-24 13:34:48,836::train::INFO] [train] Iter 583711 | loss 0.5163 | loss(rot) 0.1587 | loss(pos) 0.0835 | loss(seq) 0.2740 | grad 3.0165 | lr 0.0000 | time_forward 3.7740 | time_backward 4.8040 |
[2023-10-24 13:34:51,680::train::INFO] [train] Iter 583712 | loss 0.2998 | loss(rot) 0.2640 | loss(pos) 0.0180 | loss(seq) 0.0177 | grad 2.5151 | lr 0.0000 | time_forward 1.3400 | time_backward 1.5010 |
[2023-10-24 13:34:54,572::train::INFO] [train] Iter 583713 | loss 1.0299 | loss(rot) 0.9900 | loss(pos) 0.0369 | loss(seq) 0.0030 | grad 5.5199 | lr 0.0000 | time_forward 1.3980 | time_backward 1.4890 |
[2023-10-24 13:35:03,688::train::INFO] [train] Iter 583714 | loss 0.2704 | loss(rot) 0.0869 | loss(pos) 0.1554 | loss(seq) 0.0281 | grad 4.9999 | lr 0.0000 | time_forward 3.7630 | time_backward 5.3510 |
[2023-10-24 13:35:14,149::train::INFO] [train] Iter 583715 | loss 1.7648 | loss(rot) 1.7205 | loss(pos) 0.0350 | loss(seq) 0.0093 | grad 3.7891 | lr 0.0000 | time_forward 4.4160 | time_backward 6.0420 |
[2023-10-24 13:35:23,817::train::INFO] [train] Iter 583716 | loss 1.5526 | loss(rot) 1.5210 | loss(pos) 0.0313 | loss(seq) 0.0002 | grad 7.6955 | lr 0.0000 | time_forward 4.2260 | time_backward 5.4370 |
[2023-10-24 13:35:34,132::train::INFO] [train] Iter 583717 | loss 0.8126 | loss(rot) 0.4466 | loss(pos) 0.1008 | loss(seq) 0.2652 | grad 3.7289 | lr 0.0000 | time_forward 4.2580 | time_backward 6.0540 |
[2023-10-24 13:35:43,093::train::INFO] [train] Iter 583718 | loss 1.8575 | loss(rot) 1.8156 | loss(pos) 0.0373 | loss(seq) 0.0046 | grad 8.8904 | lr 0.0000 | time_forward 3.9530 | time_backward 5.0050 |
[2023-10-24 13:35:53,719::train::INFO] [train] Iter 583719 | loss 0.6514 | loss(rot) 0.2189 | loss(pos) 0.0469 | loss(seq) 0.3856 | grad 3.4963 | lr 0.0000 | time_forward 4.1670 | time_backward 6.4550 |
[2023-10-24 13:35:57,087::train::INFO] [train] Iter 583720 | loss 1.2683 | loss(rot) 0.2932 | loss(pos) 0.9319 | loss(seq) 0.0432 | grad 5.2839 | lr 0.0000 | time_forward 1.4830 | time_backward 1.8820 |
[2023-10-24 13:35:59,360::train::INFO] [train] Iter 583721 | loss 0.5313 | loss(rot) 0.0241 | loss(pos) 0.5053 | loss(seq) 0.0019 | grad 8.2639 | lr 0.0000 | time_forward 1.0420 | time_backward 1.2190 |
[2023-10-24 13:36:09,369::train::INFO] [train] Iter 583722 | loss 0.5203 | loss(rot) 0.2380 | loss(pos) 0.0260 | loss(seq) 0.2562 | grad 4.3952 | lr 0.0000 | time_forward 4.1320 | time_backward 5.8730 |
[2023-10-24 13:36:18,074::train::INFO] [train] Iter 583723 | loss 0.6079 | loss(rot) 0.3876 | loss(pos) 0.0378 | loss(seq) 0.1825 | grad 4.5323 | lr 0.0000 | time_forward 3.5260 | time_backward 5.1660 |
[2023-10-24 13:36:21,114::train::INFO] [train] Iter 583724 | loss 0.8097 | loss(rot) 0.7804 | loss(pos) 0.0292 | loss(seq) 0.0000 | grad 2.9350 | lr 0.0000 | time_forward 1.3830 | time_backward 1.6530 |
[2023-10-24 13:36:24,207::train::INFO] [train] Iter 583725 | loss 1.2952 | loss(rot) 1.2156 | loss(pos) 0.0290 | loss(seq) 0.0506 | grad 10.5424 | lr 0.0000 | time_forward 1.5090 | time_backward 1.5480 |
[2023-10-24 13:36:27,092::train::INFO] [train] Iter 583726 | loss 0.6126 | loss(rot) 0.4915 | loss(pos) 0.0298 | loss(seq) 0.0914 | grad 3.2290 | lr 0.0000 | time_forward 1.3480 | time_backward 1.4560 |
[2023-10-24 13:36:36,721::train::INFO] [train] Iter 583727 | loss 0.2918 | loss(rot) 0.2793 | loss(pos) 0.0125 | loss(seq) 0.0000 | grad 3.7540 | lr 0.0000 | time_forward 3.7680 | time_backward 5.8320 |
[2023-10-24 13:36:45,600::train::INFO] [train] Iter 583728 | loss 0.8179 | loss(rot) 0.7046 | loss(pos) 0.0603 | loss(seq) 0.0531 | grad 17.4782 | lr 0.0000 | time_forward 3.9550 | time_backward 4.9210 |
[2023-10-24 13:36:55,936::train::INFO] [train] Iter 583729 | loss 0.8146 | loss(rot) 0.1718 | loss(pos) 0.0489 | loss(seq) 0.5939 | grad 3.3050 | lr 0.0000 | time_forward 4.1540 | time_backward 6.1780 |
[2023-10-24 13:37:06,440::train::INFO] [train] Iter 583730 | loss 0.5419 | loss(rot) 0.5204 | loss(pos) 0.0214 | loss(seq) 0.0002 | grad 2.2114 | lr 0.0000 | time_forward 4.2290 | time_backward 6.2720 |
[2023-10-24 13:37:09,195::train::INFO] [train] Iter 583731 | loss 1.1649 | loss(rot) 0.2342 | loss(pos) 0.5303 | loss(seq) 0.4005 | grad 5.0981 | lr 0.0000 | time_forward 1.2640 | time_backward 1.4870 |
[2023-10-24 13:37:17,763::train::INFO] [train] Iter 583732 | loss 0.8693 | loss(rot) 0.6483 | loss(pos) 0.0261 | loss(seq) 0.1950 | grad 5.2149 | lr 0.0000 | time_forward 3.6310 | time_backward 4.9020 |
[2023-10-24 13:37:20,665::train::INFO] [train] Iter 583733 | loss 0.3841 | loss(rot) 0.3504 | loss(pos) 0.0320 | loss(seq) 0.0017 | grad 9.1993 | lr 0.0000 | time_forward 1.3410 | time_backward 1.5570 |
[2023-10-24 13:37:31,504::train::INFO] [train] Iter 583734 | loss 0.5593 | loss(rot) 0.3032 | loss(pos) 0.0594 | loss(seq) 0.1968 | grad 3.9062 | lr 0.0000 | time_forward 4.6140 | time_backward 6.1880 |
[2023-10-24 13:37:34,465::train::INFO] [train] Iter 583735 | loss 0.2503 | loss(rot) 0.0358 | loss(pos) 0.0176 | loss(seq) 0.1969 | grad 1.4281 | lr 0.0000 | time_forward 1.4490 | time_backward 1.5090 |
[2023-10-24 13:37:44,838::train::INFO] [train] Iter 583736 | loss 1.3251 | loss(rot) 0.6961 | loss(pos) 0.0656 | loss(seq) 0.5633 | grad 6.3351 | lr 0.0000 | time_forward 4.3110 | time_backward 6.0590 |
[2023-10-24 13:37:48,007::train::INFO] [train] Iter 583737 | loss 0.2371 | loss(rot) 0.1946 | loss(pos) 0.0147 | loss(seq) 0.0278 | grad 3.1710 | lr 0.0000 | time_forward 1.4130 | time_backward 1.7530 |
[2023-10-24 13:37:57,709::train::INFO] [train] Iter 583738 | loss 0.1593 | loss(rot) 0.0821 | loss(pos) 0.0221 | loss(seq) 0.0551 | grad 1.9340 | lr 0.0000 | time_forward 4.1610 | time_backward 5.5380 |
[2023-10-24 13:38:08,630::train::INFO] [train] Iter 583739 | loss 0.6835 | loss(rot) 0.2236 | loss(pos) 0.4280 | loss(seq) 0.0319 | grad 4.5171 | lr 0.0000 | time_forward 4.7030 | time_backward 6.2150 |
[2023-10-24 13:38:11,481::train::INFO] [train] Iter 583740 | loss 0.3621 | loss(rot) 0.3349 | loss(pos) 0.0268 | loss(seq) 0.0003 | grad 2.8245 | lr 0.0000 | time_forward 1.3340 | time_backward 1.5140 |
[2023-10-24 13:38:18,862::train::INFO] [train] Iter 583741 | loss 0.2655 | loss(rot) 0.2415 | loss(pos) 0.0219 | loss(seq) 0.0022 | grad 3.8893 | lr 0.0000 | time_forward 3.1180 | time_backward 4.2160 |
[2023-10-24 13:38:28,903::train::INFO] [train] Iter 583742 | loss 0.2930 | loss(rot) 0.0835 | loss(pos) 0.1406 | loss(seq) 0.0689 | grad 4.0775 | lr 0.0000 | time_forward 4.2510 | time_backward 5.7880 |
[2023-10-24 13:38:31,804::train::INFO] [train] Iter 583743 | loss 0.1484 | loss(rot) 0.1082 | loss(pos) 0.0211 | loss(seq) 0.0191 | grad 2.3194 | lr 0.0000 | time_forward 1.3960 | time_backward 1.5010 |
[2023-10-24 13:38:35,052::train::INFO] [train] Iter 583744 | loss 2.6230 | loss(rot) 2.2373 | loss(pos) 0.1064 | loss(seq) 0.2793 | grad 5.4418 | lr 0.0000 | time_forward 1.5460 | time_backward 1.6980 |
[2023-10-24 13:38:44,289::train::INFO] [train] Iter 583745 | loss 0.3097 | loss(rot) 0.0439 | loss(pos) 0.2642 | loss(seq) 0.0016 | grad 5.7054 | lr 0.0000 | time_forward 4.0040 | time_backward 5.2150 |
[2023-10-24 13:38:53,854::train::INFO] [train] Iter 583746 | loss 0.5409 | loss(rot) 0.1352 | loss(pos) 0.1481 | loss(seq) 0.2576 | grad 4.4012 | lr 0.0000 | time_forward 3.9840 | time_backward 5.5620 |
[2023-10-24 13:39:04,603::train::INFO] [train] Iter 583747 | loss 1.3659 | loss(rot) 1.2704 | loss(pos) 0.0858 | loss(seq) 0.0097 | grad 4.4607 | lr 0.0000 | time_forward 4.7050 | time_backward 6.0410 |
[2023-10-24 13:39:14,136::train::INFO] [train] Iter 583748 | loss 1.2763 | loss(rot) 0.9404 | loss(pos) 0.0475 | loss(seq) 0.2884 | grad 3.2380 | lr 0.0000 | time_forward 4.0090 | time_backward 5.5190 |
[2023-10-24 13:39:23,724::train::INFO] [train] Iter 583749 | loss 0.2037 | loss(rot) 0.1882 | loss(pos) 0.0154 | loss(seq) 0.0001 | grad 2.2100 | lr 0.0000 | time_forward 4.1140 | time_backward 5.4710 |
[2023-10-24 13:39:30,680::train::INFO] [train] Iter 583750 | loss 2.1494 | loss(rot) 1.5898 | loss(pos) 0.1584 | loss(seq) 0.4013 | grad 6.3987 | lr 0.0000 | time_forward 3.0260 | time_backward 3.9270 |
[2023-10-24 13:39:39,303::train::INFO] [train] Iter 583751 | loss 0.9840 | loss(rot) 0.5945 | loss(pos) 0.3360 | loss(seq) 0.0536 | grad 5.2682 | lr 0.0000 | time_forward 3.6550 | time_backward 4.9650 |
[2023-10-24 13:39:48,420::train::INFO] [train] Iter 583752 | loss 0.9879 | loss(rot) 0.7687 | loss(pos) 0.0475 | loss(seq) 0.1717 | grad 3.4214 | lr 0.0000 | time_forward 3.7930 | time_backward 5.3210 |
[2023-10-24 13:39:57,435::train::INFO] [train] Iter 583753 | loss 0.3695 | loss(rot) 0.3468 | loss(pos) 0.0226 | loss(seq) 0.0000 | grad 3.6138 | lr 0.0000 | time_forward 3.8140 | time_backward 5.1970 |
[2023-10-24 13:40:07,740::train::INFO] [train] Iter 583754 | loss 0.2715 | loss(rot) 0.1503 | loss(pos) 0.0302 | loss(seq) 0.0910 | grad 2.7711 | lr 0.0000 | time_forward 4.2940 | time_backward 6.0080 |
[2023-10-24 13:40:17,662::train::INFO] [train] Iter 583755 | loss 0.2234 | loss(rot) 0.0677 | loss(pos) 0.0907 | loss(seq) 0.0650 | grad 4.6231 | lr 0.0000 | time_forward 4.1440 | time_backward 5.7750 |
[2023-10-24 13:40:27,449::train::INFO] [train] Iter 583756 | loss 0.7297 | loss(rot) 0.3210 | loss(pos) 0.0512 | loss(seq) 0.3575 | grad 5.6085 | lr 0.0000 | time_forward 4.0450 | time_backward 5.7390 |
[2023-10-24 13:40:38,009::train::INFO] [train] Iter 583757 | loss 0.7664 | loss(rot) 0.6731 | loss(pos) 0.0117 | loss(seq) 0.0817 | grad 3.8733 | lr 0.0000 | time_forward 4.3060 | time_backward 6.2510 |
[2023-10-24 13:40:48,309::train::INFO] [train] Iter 583758 | loss 0.9227 | loss(rot) 0.5596 | loss(pos) 0.0939 | loss(seq) 0.2691 | grad 3.4208 | lr 0.0000 | time_forward 4.3720 | time_backward 5.9240 |
[2023-10-24 13:40:58,597::train::INFO] [train] Iter 583759 | loss 0.4510 | loss(rot) 0.4250 | loss(pos) 0.0160 | loss(seq) 0.0099 | grad 2.5739 | lr 0.0000 | time_forward 4.2150 | time_backward 6.0700 |
[2023-10-24 13:41:07,967::train::INFO] [train] Iter 583760 | loss 0.3034 | loss(rot) 0.0327 | loss(pos) 0.2666 | loss(seq) 0.0041 | grad 7.7231 | lr 0.0000 | time_forward 3.6850 | time_backward 5.6780 |
[2023-10-24 13:41:18,244::train::INFO] [train] Iter 583761 | loss 0.9388 | loss(rot) 0.9246 | loss(pos) 0.0141 | loss(seq) 0.0000 | grad 24.2722 | lr 0.0000 | time_forward 4.3960 | time_backward 5.8780 |
[2023-10-24 13:41:27,147::train::INFO] [train] Iter 583762 | loss 0.6305 | loss(rot) 0.2931 | loss(pos) 0.1866 | loss(seq) 0.1508 | grad 2.7965 | lr 0.0000 | time_forward 3.9730 | time_backward 4.9280 |
[2023-10-24 13:41:37,728::train::INFO] [train] Iter 583763 | loss 1.5136 | loss(rot) 0.8536 | loss(pos) 0.3568 | loss(seq) 0.3032 | grad 4.2808 | lr 0.0000 | time_forward 4.4120 | time_backward 6.1650 |
[2023-10-24 13:41:47,821::train::INFO] [train] Iter 583764 | loss 0.9102 | loss(rot) 0.6222 | loss(pos) 0.0577 | loss(seq) 0.2302 | grad 2.9745 | lr 0.0000 | time_forward 4.4100 | time_backward 5.6800 |
[2023-10-24 13:41:50,587::train::INFO] [train] Iter 583765 | loss 0.2815 | loss(rot) 0.1179 | loss(pos) 0.0840 | loss(seq) 0.0796 | grad 3.3874 | lr 0.0000 | time_forward 1.3220 | time_backward 1.4410 |
[2023-10-24 13:41:52,973::train::INFO] [train] Iter 583766 | loss 0.3873 | loss(rot) 0.1136 | loss(pos) 0.0785 | loss(seq) 0.1952 | grad 2.8113 | lr 0.0000 | time_forward 1.0620 | time_backward 1.2950 |
[2023-10-24 13:42:03,032::train::INFO] [train] Iter 583767 | loss 2.3414 | loss(rot) 1.3614 | loss(pos) 0.2985 | loss(seq) 0.6815 | grad 3.1552 | lr 0.0000 | time_forward 4.7960 | time_backward 5.2580 |
[2023-10-24 13:42:06,633::train::INFO] [train] Iter 583768 | loss 0.3812 | loss(rot) 0.0842 | loss(pos) 0.2895 | loss(seq) 0.0075 | grad 3.6857 | lr 0.0000 | time_forward 1.5180 | time_backward 2.0800 |
[2023-10-24 13:42:15,514::train::INFO] [train] Iter 583769 | loss 0.4621 | loss(rot) 0.3214 | loss(pos) 0.0407 | loss(seq) 0.0999 | grad 3.9181 | lr 0.0000 | time_forward 3.6590 | time_backward 5.2060 |
[2023-10-24 13:42:25,108::train::INFO] [train] Iter 583770 | loss 1.6531 | loss(rot) 0.5728 | loss(pos) 1.0770 | loss(seq) 0.0033 | grad 13.2244 | lr 0.0000 | time_forward 3.7900 | time_backward 5.8010 |
[2023-10-24 13:42:28,188::train::INFO] [train] Iter 583771 | loss 0.1238 | loss(rot) 0.0964 | loss(pos) 0.0198 | loss(seq) 0.0077 | grad 2.0323 | lr 0.0000 | time_forward 1.4600 | time_backward 1.6180 |
[2023-10-24 13:42:30,821::train::INFO] [train] Iter 583772 | loss 1.1083 | loss(rot) 0.7527 | loss(pos) 0.0183 | loss(seq) 0.3373 | grad 6.0722 | lr 0.0000 | time_forward 1.1920 | time_backward 1.3990 |
[2023-10-24 13:42:38,984::train::INFO] [train] Iter 583773 | loss 0.9426 | loss(rot) 0.8583 | loss(pos) 0.0199 | loss(seq) 0.0644 | grad 6.1380 | lr 0.0000 | time_forward 3.4600 | time_backward 4.6660 |
[2023-10-24 13:42:43,243::train::INFO] [train] Iter 583774 | loss 1.7976 | loss(rot) 1.0629 | loss(pos) 0.2588 | loss(seq) 0.4759 | grad 3.6571 | lr 0.0000 | time_forward 1.9760 | time_backward 2.2800 |
[2023-10-24 13:42:54,705::train::INFO] [train] Iter 583775 | loss 1.4260 | loss(rot) 1.2216 | loss(pos) 0.0640 | loss(seq) 0.1405 | grad 6.3650 | lr 0.0000 | time_forward 4.6430 | time_backward 6.8020 |
[2023-10-24 13:43:03,829::train::INFO] [train] Iter 583776 | loss 0.9869 | loss(rot) 0.2577 | loss(pos) 0.4692 | loss(seq) 0.2600 | grad 3.8634 | lr 0.0000 | time_forward 4.2620 | time_backward 4.8580 |
[2023-10-24 13:43:06,617::train::INFO] [train] Iter 583777 | loss 0.6232 | loss(rot) 0.3476 | loss(pos) 0.0763 | loss(seq) 0.1994 | grad 5.4520 | lr 0.0000 | time_forward 1.3370 | time_backward 1.4490 |
[2023-10-24 13:43:15,177::train::INFO] [train] Iter 583778 | loss 0.4504 | loss(rot) 0.1549 | loss(pos) 0.0250 | loss(seq) 0.2706 | grad 3.2525 | lr 0.0000 | time_forward 3.6010 | time_backward 4.9310 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.