Datasets:
YueHuLab
/
AlphaPanda_checkpoints

Dataset card Data Studio Files Community
1
text
stringlengths
56
1.16k
[2023-09-01 19:37:40,377::train::INFO] [train] Iter 01594 | loss 3.0007 | loss(rot) 2.7996 | loss(pos) 0.1322 | loss(seq) 0.0688 | grad 2.7058 | lr 0.0010 | time_forward 1.2320 | time_backward 1.4530
[2023-09-01 19:37:49,056::train::INFO] [train] Iter 01595 | loss 2.4806 | loss(rot) 2.2538 | loss(pos) 0.1738 | loss(seq) 0.0530 | grad 3.1487 | lr 0.0010 | time_forward 3.7620 | time_backward 4.9130
[2023-09-01 19:37:57,063::train::INFO] [train] Iter 01596 | loss 3.6412 | loss(rot) 3.3842 | loss(pos) 0.2538 | loss(seq) 0.0031 | grad 3.7288 | lr 0.0010 | time_forward 3.4700 | time_backward 4.5330
[2023-09-01 19:38:07,174::train::INFO] [train] Iter 01597 | loss 3.1316 | loss(rot) 2.9385 | loss(pos) 0.1842 | loss(seq) 0.0089 | grad 2.9996 | lr 0.0010 | time_forward 4.0100 | time_backward 6.0980
[2023-09-01 19:38:09,831::train::INFO] [train] Iter 01598 | loss 3.7155 | loss(rot) 3.1255 | loss(pos) 0.2544 | loss(seq) 0.3357 | grad 3.6297 | lr 0.0010 | time_forward 1.2290 | time_backward 1.4250
[2023-09-01 19:38:17,664::train::INFO] [train] Iter 01599 | loss 3.4716 | loss(rot) 2.7280 | loss(pos) 0.3410 | loss(seq) 0.4025 | grad 3.9873 | lr 0.0010 | time_forward 3.2700 | time_backward 4.5180
[2023-09-01 19:38:27,403::train::INFO] [train] Iter 01600 | loss 1.9464 | loss(rot) 0.6786 | loss(pos) 0.8023 | loss(seq) 0.4655 | grad 4.3443 | lr 0.0010 | time_forward 4.0540 | time_backward 5.6820
[2023-09-01 19:38:35,854::train::INFO] [train] Iter 01601 | loss 1.5106 | loss(rot) 0.1248 | loss(pos) 1.3652 | loss(seq) 0.0207 | grad 4.9119 | lr 0.0010 | time_forward 3.5870 | time_backward 4.8590
[2023-09-01 19:38:43,777::train::INFO] [train] Iter 01602 | loss 2.3542 | loss(rot) 1.6197 | loss(pos) 0.3279 | loss(seq) 0.4066 | grad 3.2008 | lr 0.0010 | time_forward 3.3360 | time_backward 4.5830
[2023-09-01 19:38:52,058::train::INFO] [train] Iter 01603 | loss 2.4515 | loss(rot) 1.0811 | loss(pos) 0.9587 | loss(seq) 0.4117 | grad 5.2843 | lr 0.0010 | time_forward 3.4520 | time_backward 4.8250
[2023-09-01 19:39:01,607::train::INFO] [train] Iter 01604 | loss 1.3008 | loss(rot) 0.1128 | loss(pos) 1.1690 | loss(seq) 0.0191 | grad 6.0286 | lr 0.0010 | time_forward 3.9350 | time_backward 5.6110
[2023-09-01 19:39:08,829::train::INFO] [train] Iter 01605 | loss 1.4407 | loss(rot) 0.6046 | loss(pos) 0.7889 | loss(seq) 0.0472 | grad 3.8678 | lr 0.0010 | time_forward 3.0370 | time_backward 4.1740
[2023-09-01 19:39:16,887::train::INFO] [train] Iter 01606 | loss 3.6341 | loss(rot) 3.0161 | loss(pos) 0.6169 | loss(seq) 0.0011 | grad 11.0836 | lr 0.0010 | time_forward 3.3850 | time_backward 4.6700
[2023-09-01 19:39:24,840::train::INFO] [train] Iter 01607 | loss 2.4939 | loss(rot) 1.0255 | loss(pos) 1.0815 | loss(seq) 0.3869 | grad 5.2117 | lr 0.0010 | time_forward 3.1970 | time_backward 4.7530
[2023-09-01 19:39:34,167::train::INFO] [train] Iter 01608 | loss 3.8470 | loss(rot) 2.4788 | loss(pos) 1.0286 | loss(seq) 0.3396 | grad 11.3537 | lr 0.0010 | time_forward 3.8950 | time_backward 5.4280
[2023-09-01 19:39:43,704::train::INFO] [train] Iter 01609 | loss 2.3227 | loss(rot) 0.9214 | loss(pos) 0.9411 | loss(seq) 0.4602 | grad 6.7270 | lr 0.0010 | time_forward 3.6940 | time_backward 5.8390
[2023-09-01 19:39:46,756::train::INFO] [train] Iter 01610 | loss 1.4940 | loss(rot) 0.0947 | loss(pos) 1.3849 | loss(seq) 0.0143 | grad 5.0038 | lr 0.0010 | time_forward 1.4040 | time_backward 1.6370
[2023-09-01 19:39:53,045::train::INFO] [train] Iter 01611 | loss 2.2226 | loss(rot) 0.8325 | loss(pos) 1.1428 | loss(seq) 0.2473 | grad 4.7879 | lr 0.0010 | time_forward 2.5870 | time_backward 3.6990
[2023-09-01 19:39:55,776::train::INFO] [train] Iter 01612 | loss 2.8470 | loss(rot) 2.1414 | loss(pos) 0.3110 | loss(seq) 0.3947 | grad 4.9190 | lr 0.0010 | time_forward 1.2780 | time_backward 1.4500
[2023-09-01 19:39:58,930::train::INFO] [train] Iter 01613 | loss 3.1983 | loss(rot) 2.7488 | loss(pos) 0.3061 | loss(seq) 0.1434 | grad 2.4510 | lr 0.0010 | time_forward 1.4160 | time_backward 1.7130
[2023-09-01 19:40:06,661::train::INFO] [train] Iter 01614 | loss 2.0062 | loss(rot) 0.8895 | loss(pos) 0.7141 | loss(seq) 0.4026 | grad 3.7505 | lr 0.0010 | time_forward 3.2150 | time_backward 4.5130
[2023-09-01 19:40:10,286::train::INFO] [train] Iter 01615 | loss 1.3304 | loss(rot) 0.4714 | loss(pos) 0.6442 | loss(seq) 0.2148 | grad 3.6279 | lr 0.0010 | time_forward 2.1980 | time_backward 1.4240
[2023-09-01 19:40:13,053::train::INFO] [train] Iter 01616 | loss 3.3973 | loss(rot) 2.8948 | loss(pos) 0.2374 | loss(seq) 0.2651 | grad 2.7009 | lr 0.0010 | time_forward 1.3300 | time_backward 1.4330
[2023-09-01 19:40:21,395::train::INFO] [train] Iter 01617 | loss 1.8999 | loss(rot) 0.4754 | loss(pos) 1.3687 | loss(seq) 0.0558 | grad 4.3173 | lr 0.0010 | time_forward 3.4890 | time_backward 4.8050
[2023-09-01 19:40:24,123::train::INFO] [train] Iter 01618 | loss 3.1923 | loss(rot) 2.7575 | loss(pos) 0.3274 | loss(seq) 0.1074 | grad 2.7676 | lr 0.0010 | time_forward 1.2880 | time_backward 1.4360
[2023-09-01 19:40:26,874::train::INFO] [train] Iter 01619 | loss 3.0155 | loss(rot) 2.0448 | loss(pos) 0.3556 | loss(seq) 0.6151 | grad 3.7702 | lr 0.0010 | time_forward 1.3310 | time_backward 1.4170
[2023-09-01 19:40:34,762::train::INFO] [train] Iter 01620 | loss 3.1319 | loss(rot) 0.4432 | loss(pos) 2.6826 | loss(seq) 0.0061 | grad 5.6319 | lr 0.0010 | time_forward 3.3290 | time_backward 4.5550
[2023-09-01 19:40:43,203::train::INFO] [train] Iter 01621 | loss 3.3982 | loss(rot) 2.9239 | loss(pos) 0.3230 | loss(seq) 0.1513 | grad 4.3070 | lr 0.0010 | time_forward 3.5050 | time_backward 4.9320
[2023-09-01 19:40:49,410::train::INFO] [train] Iter 01622 | loss 2.9023 | loss(rot) 1.9987 | loss(pos) 0.3833 | loss(seq) 0.5203 | grad 4.7584 | lr 0.0010 | time_forward 2.5780 | time_backward 3.6250
[2023-09-01 19:40:57,091::train::INFO] [train] Iter 01623 | loss 2.2746 | loss(rot) 1.2246 | loss(pos) 0.5274 | loss(seq) 0.5226 | grad 3.6941 | lr 0.0010 | time_forward 3.2980 | time_backward 4.3790
[2023-09-01 19:41:04,542::train::INFO] [train] Iter 01624 | loss 2.9015 | loss(rot) 1.8283 | loss(pos) 0.4753 | loss(seq) 0.5979 | grad 3.7751 | lr 0.0010 | time_forward 3.1860 | time_backward 4.2610
[2023-09-01 19:41:13,803::train::INFO] [train] Iter 01625 | loss 1.2489 | loss(rot) 0.5526 | loss(pos) 0.5638 | loss(seq) 0.1325 | grad 3.3884 | lr 0.0010 | time_forward 3.8370 | time_backward 5.4210
[2023-09-01 19:41:22,734::train::INFO] [train] Iter 01626 | loss 1.7817 | loss(rot) 0.4835 | loss(pos) 1.2343 | loss(seq) 0.0639 | grad 4.6888 | lr 0.0010 | time_forward 3.6680 | time_backward 5.2590
[2023-09-01 19:41:31,084::train::INFO] [train] Iter 01627 | loss 2.8462 | loss(rot) 2.2290 | loss(pos) 0.2063 | loss(seq) 0.4108 | grad 3.8583 | lr 0.0010 | time_forward 3.4780 | time_backward 4.8560
[2023-09-01 19:41:33,799::train::INFO] [train] Iter 01628 | loss 1.8210 | loss(rot) 0.3924 | loss(pos) 1.3705 | loss(seq) 0.0581 | grad 6.9631 | lr 0.0010 | time_forward 1.2780 | time_backward 1.4330
[2023-09-01 19:41:41,286::train::INFO] [train] Iter 01629 | loss 3.0761 | loss(rot) 1.9776 | loss(pos) 0.5947 | loss(seq) 0.5039 | grad 4.0383 | lr 0.0010 | time_forward 3.1460 | time_backward 4.3380
[2023-09-01 19:41:49,151::train::INFO] [train] Iter 01630 | loss 3.3399 | loss(rot) 2.7160 | loss(pos) 0.3648 | loss(seq) 0.2591 | grad 3.9034 | lr 0.0010 | time_forward 3.1990 | time_backward 4.6620
[2023-09-01 19:41:57,012::train::INFO] [train] Iter 01631 | loss 2.2181 | loss(rot) 1.5771 | loss(pos) 0.2157 | loss(seq) 0.4253 | grad 3.6884 | lr 0.0010 | time_forward 3.3190 | time_backward 4.5390
[2023-09-01 19:41:59,806::train::INFO] [train] Iter 01632 | loss 2.2578 | loss(rot) 1.7879 | loss(pos) 0.3949 | loss(seq) 0.0750 | grad 7.4993 | lr 0.0010 | time_forward 1.3230 | time_backward 1.4660
[2023-09-01 19:42:02,609::train::INFO] [train] Iter 01633 | loss 1.9919 | loss(rot) 0.4795 | loss(pos) 1.3635 | loss(seq) 0.1489 | grad 4.8578 | lr 0.0010 | time_forward 1.2930 | time_backward 1.4910
[2023-09-01 19:42:05,859::train::INFO] [train] Iter 01634 | loss 3.2222 | loss(rot) 0.2110 | loss(pos) 2.8236 | loss(seq) 0.1876 | grad 7.5185 | lr 0.0010 | time_forward 1.4340 | time_backward 1.8120
[2023-09-01 19:42:15,147::train::INFO] [train] Iter 01635 | loss 2.3987 | loss(rot) 0.8662 | loss(pos) 1.3010 | loss(seq) 0.2316 | grad 4.3628 | lr 0.0010 | time_forward 3.7990 | time_backward 5.4850
[2023-09-01 19:42:24,340::train::INFO] [train] Iter 01636 | loss 1.6666 | loss(rot) 0.5443 | loss(pos) 0.8080 | loss(seq) 0.3143 | grad 5.2941 | lr 0.0010 | time_forward 3.7500 | time_backward 5.4390
[2023-09-01 19:42:33,564::train::INFO] [train] Iter 01637 | loss 3.8848 | loss(rot) 3.4439 | loss(pos) 0.3158 | loss(seq) 0.1251 | grad 3.9029 | lr 0.0010 | time_forward 4.0000 | time_backward 5.2210
[2023-09-01 19:42:36,382::train::INFO] [train] Iter 01638 | loss 2.8363 | loss(rot) 2.0295 | loss(pos) 0.3124 | loss(seq) 0.4943 | grad 4.0212 | lr 0.0010 | time_forward 1.2740 | time_backward 1.5410
[2023-09-01 19:42:39,136::train::INFO] [train] Iter 01639 | loss 1.8277 | loss(rot) 0.9870 | loss(pos) 0.5185 | loss(seq) 0.3222 | grad 4.4741 | lr 0.0010 | time_forward 1.3050 | time_backward 1.4450
[2023-09-01 19:42:47,778::train::INFO] [train] Iter 01640 | loss 3.0710 | loss(rot) 2.0832 | loss(pos) 0.4863 | loss(seq) 0.5016 | grad 3.2099 | lr 0.0010 | time_forward 3.7210 | time_backward 4.9170
[2023-09-01 19:42:55,783::train::INFO] [train] Iter 01641 | loss 1.5186 | loss(rot) 0.2840 | loss(pos) 1.0369 | loss(seq) 0.1977 | grad 4.5037 | lr 0.0010 | time_forward 3.3860 | time_backward 4.6150
[2023-09-01 19:42:58,511::train::INFO] [train] Iter 01642 | loss 2.7632 | loss(rot) 2.2955 | loss(pos) 0.3651 | loss(seq) 0.1026 | grad 4.8176 | lr 0.0010 | time_forward 1.2830 | time_backward 1.4420
[2023-09-01 19:43:07,755::train::INFO] [train] Iter 01643 | loss 2.9820 | loss(rot) 2.2081 | loss(pos) 0.2959 | loss(seq) 0.4781 | grad 3.6972 | lr 0.0010 | time_forward 3.7210 | time_backward 5.5200
[2023-09-01 19:43:11,352::train::INFO] [train] Iter 01644 | loss 2.1021 | loss(rot) 0.5021 | loss(pos) 1.3097 | loss(seq) 0.2903 | grad 6.2637 | lr 0.0010 | time_forward 1.7370 | time_backward 1.8560
[2023-09-01 19:43:14,242::train::INFO] [train] Iter 01645 | loss 3.0502 | loss(rot) 2.1386 | loss(pos) 0.4877 | loss(seq) 0.4240 | grad 3.6916 | lr 0.0010 | time_forward 1.3610 | time_backward 1.5260
[2023-09-01 19:43:23,930::train::INFO] [train] Iter 01646 | loss 1.3977 | loss(rot) 0.5078 | loss(pos) 0.3701 | loss(seq) 0.5198 | grad 2.3736 | lr 0.0010 | time_forward 4.2270 | time_backward 5.4390
[2023-09-01 19:43:33,127::train::INFO] [train] Iter 01647 | loss 1.9308 | loss(rot) 0.9055 | loss(pos) 0.8409 | loss(seq) 0.1844 | grad 4.5977 | lr 0.0010 | time_forward 3.7120 | time_backward 5.4810
[2023-09-01 19:43:41,758::train::INFO] [train] Iter 01648 | loss 3.0101 | loss(rot) 2.7612 | loss(pos) 0.2443 | loss(seq) 0.0046 | grad 4.0864 | lr 0.0010 | time_forward 3.5860 | time_backward 5.0370
[2023-09-01 19:43:50,077::train::INFO] [train] Iter 01649 | loss 2.8946 | loss(rot) 2.1098 | loss(pos) 0.2783 | loss(seq) 0.5065 | grad 3.0956 | lr 0.0010 | time_forward 3.7200 | time_backward 4.5960
[2023-09-01 19:43:58,855::train::INFO] [train] Iter 01650 | loss 3.0754 | loss(rot) 2.2189 | loss(pos) 0.3263 | loss(seq) 0.5302 | grad 4.1752 | lr 0.0010 | time_forward 3.5920 | time_backward 5.1820
[2023-09-01 19:44:06,544::train::INFO] [train] Iter 01651 | loss 1.4342 | loss(rot) 0.0838 | loss(pos) 1.3406 | loss(seq) 0.0098 | grad 4.3691 | lr 0.0010 | time_forward 3.3100 | time_backward 4.3750
[2023-09-01 19:44:09,447::train::INFO] [train] Iter 01652 | loss 3.4318 | loss(rot) 3.1311 | loss(pos) 0.2398 | loss(seq) 0.0610 | grad 3.5993 | lr 0.0010 | time_forward 1.4250 | time_backward 1.4750
[2023-09-01 19:44:16,399::train::INFO] [train] Iter 01653 | loss 3.5840 | loss(rot) 2.7524 | loss(pos) 0.4035 | loss(seq) 0.4281 | grad 4.3209 | lr 0.0010 | time_forward 2.9910 | time_backward 3.9580
[2023-09-01 19:44:19,112::train::INFO] [train] Iter 01654 | loss 1.4810 | loss(rot) 0.4206 | loss(pos) 1.0063 | loss(seq) 0.0542 | grad 4.7662 | lr 0.0010 | time_forward 1.2700 | time_backward 1.4390
[2023-09-01 19:44:28,509::train::INFO] [train] Iter 01655 | loss 2.1501 | loss(rot) 1.3851 | loss(pos) 0.2726 | loss(seq) 0.4924 | grad 2.8783 | lr 0.0010 | time_forward 4.2570 | time_backward 5.1360
[2023-09-01 19:44:37,097::train::INFO] [train] Iter 01656 | loss 3.4767 | loss(rot) 2.6028 | loss(pos) 0.4518 | loss(seq) 0.4222 | grad 3.6900 | lr 0.0010 | time_forward 3.4780 | time_backward 5.1070
[2023-09-01 19:44:44,419::train::INFO] [train] Iter 01657 | loss 2.0734 | loss(rot) 0.5546 | loss(pos) 1.4676 | loss(seq) 0.0512 | grad 6.7090 | lr 0.0010 | time_forward 3.2080 | time_backward 4.1110
[2023-09-01 19:44:51,422::train::INFO] [train] Iter 01658 | loss 3.0303 | loss(rot) 2.6264 | loss(pos) 0.3327 | loss(seq) 0.0711 | grad 3.2591 | lr 0.0010 | time_forward 2.9840 | time_backward 4.0140
[2023-09-01 19:45:00,000::train::INFO] [train] Iter 01659 | loss 2.2115 | loss(rot) 0.7963 | loss(pos) 1.2792 | loss(seq) 0.1360 | grad 5.1989 | lr 0.0010 | time_forward 3.6350 | time_backward 4.9380
[2023-09-01 19:45:02,727::train::INFO] [train] Iter 01660 | loss 1.2669 | loss(rot) 0.1384 | loss(pos) 1.1056 | loss(seq) 0.0229 | grad 4.4783 | lr 0.0010 | time_forward 1.2970 | time_backward 1.4270
[2023-09-01 19:45:05,414::train::INFO] [train] Iter 01661 | loss 3.5552 | loss(rot) 3.2026 | loss(pos) 0.2652 | loss(seq) 0.0874 | grad 2.9988 | lr 0.0010 | time_forward 1.2250 | time_backward 1.4310
[2023-09-01 19:45:12,238::train::INFO] [train] Iter 01662 | loss 2.9835 | loss(rot) 2.3666 | loss(pos) 0.3083 | loss(seq) 0.3086 | grad 4.3481 | lr 0.0010 | time_forward 2.9450 | time_backward 3.8750
[2023-09-01 19:45:15,329::train::INFO] [train] Iter 01663 | loss 2.3789 | loss(rot) 1.0067 | loss(pos) 0.7837 | loss(seq) 0.5885 | grad 5.5673 | lr 0.0010 | time_forward 1.6540 | time_backward 1.4330
[2023-09-01 19:45:18,040::train::INFO] [train] Iter 01664 | loss 3.0276 | loss(rot) 2.6534 | loss(pos) 0.3645 | loss(seq) 0.0097 | grad 4.0964 | lr 0.0010 | time_forward 1.2670 | time_backward 1.4390
[2023-09-01 19:45:26,945::train::INFO] [train] Iter 01665 | loss 2.1236 | loss(rot) 1.3394 | loss(pos) 0.2866 | loss(seq) 0.4977 | grad 2.5569 | lr 0.0010 | time_forward 4.0000 | time_backward 4.9010
[2023-09-01 19:45:34,944::train::INFO] [train] Iter 01666 | loss 3.4167 | loss(rot) 2.7927 | loss(pos) 0.5803 | loss(seq) 0.0436 | grad 6.4231 | lr 0.0010 | time_forward 3.3340 | time_backward 4.6630
[2023-09-01 19:45:37,789::train::INFO] [train] Iter 01667 | loss 3.2240 | loss(rot) 2.9333 | loss(pos) 0.2907 | loss(seq) 0.0000 | grad 2.6735 | lr 0.0010 | time_forward 1.6010 | time_backward 1.2400
[2023-09-01 19:45:46,456::train::INFO] [train] Iter 01668 | loss 3.7941 | loss(rot) 3.1562 | loss(pos) 0.6326 | loss(seq) 0.0052 | grad 5.5764 | lr 0.0010 | time_forward 3.5190 | time_backward 5.1450
[2023-09-01 19:45:54,342::train::INFO] [train] Iter 01669 | loss 1.2829 | loss(rot) 0.4104 | loss(pos) 0.8283 | loss(seq) 0.0442 | grad 4.0940 | lr 0.0010 | time_forward 3.3750 | time_backward 4.5070
[2023-09-01 19:45:57,206::train::INFO] [train] Iter 01670 | loss 1.9238 | loss(rot) 0.7284 | loss(pos) 0.3973 | loss(seq) 0.7982 | grad 4.9852 | lr 0.0010 | time_forward 1.2940 | time_backward 1.5670
[2023-09-01 19:46:00,947::train::INFO] [train] Iter 01671 | loss 3.1713 | loss(rot) 2.0965 | loss(pos) 0.4868 | loss(seq) 0.5880 | grad 3.6229 | lr 0.0010 | time_forward 1.7360 | time_backward 2.0010
[2023-09-01 19:46:08,455::train::INFO] [train] Iter 01672 | loss 1.4762 | loss(rot) 0.6239 | loss(pos) 0.5042 | loss(seq) 0.3480 | grad 4.8094 | lr 0.0010 | time_forward 3.1440 | time_backward 4.3610
[2023-09-01 19:46:16,384::train::INFO] [train] Iter 01673 | loss 2.7958 | loss(rot) 1.9021 | loss(pos) 0.1928 | loss(seq) 0.7009 | grad 3.4918 | lr 0.0010 | time_forward 3.3110 | time_backward 4.6130
[2023-09-01 19:46:18,786::train::INFO] [train] Iter 01674 | loss 1.9517 | loss(rot) 0.1365 | loss(pos) 1.3286 | loss(seq) 0.4865 | grad 8.4292 | lr 0.0010 | time_forward 1.1200 | time_backward 1.2780
[2023-09-01 19:46:26,403::train::INFO] [train] Iter 01675 | loss 3.5276 | loss(rot) 3.1413 | loss(pos) 0.1756 | loss(seq) 0.2106 | grad 3.3632 | lr 0.0010 | time_forward 3.1050 | time_backward 4.5080
[2023-09-01 19:46:29,133::train::INFO] [train] Iter 01676 | loss 3.1091 | loss(rot) 2.6174 | loss(pos) 0.2016 | loss(seq) 0.2900 | grad 2.6632 | lr 0.0010 | time_forward 1.2530 | time_backward 1.4750
[2023-09-01 19:46:31,889::train::INFO] [train] Iter 01677 | loss 2.9932 | loss(rot) 0.0145 | loss(pos) 2.9756 | loss(seq) 0.0031 | grad 5.2230 | lr 0.0010 | time_forward 1.2770 | time_backward 1.4520
[2023-09-01 19:46:39,613::train::INFO] [train] Iter 01678 | loss 3.2811 | loss(rot) 0.0129 | loss(pos) 3.2664 | loss(seq) 0.0018 | grad 6.7903 | lr 0.0010 | time_forward 3.2760 | time_backward 4.4440
[2023-09-01 19:46:48,287::train::INFO] [train] Iter 01679 | loss 2.6471 | loss(rot) 1.7687 | loss(pos) 0.4718 | loss(seq) 0.4066 | grad 3.1060 | lr 0.0010 | time_forward 3.5650 | time_backward 5.1060
[2023-09-01 19:46:56,329::train::INFO] [train] Iter 01680 | loss 2.4525 | loss(rot) 0.0063 | loss(pos) 2.4454 | loss(seq) 0.0008 | grad 3.2083 | lr 0.0010 | time_forward 3.4160 | time_backward 4.6220
[2023-09-01 19:47:05,199::train::INFO] [train] Iter 01681 | loss 1.6830 | loss(rot) 0.9710 | loss(pos) 0.4006 | loss(seq) 0.3114 | grad 2.9851 | lr 0.0010 | time_forward 3.6290 | time_backward 5.2380
[2023-09-01 19:47:14,556::train::INFO] [train] Iter 01682 | loss 1.3403 | loss(rot) 0.4581 | loss(pos) 0.6841 | loss(seq) 0.1981 | grad 2.5056 | lr 0.0010 | time_forward 4.1890 | time_backward 5.1610
[2023-09-01 19:47:23,247::train::INFO] [train] Iter 01683 | loss 2.7712 | loss(rot) 2.5220 | loss(pos) 0.2479 | loss(seq) 0.0012 | grad 4.0285 | lr 0.0010 | time_forward 3.6280 | time_backward 5.0600
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