text stringlengths 56 1.16k |
|---|
[2023-10-23 00:02:32,981::train::INFO] [train] Iter 563599 | loss 1.1716 | loss(rot) 0.4219 | loss(pos) 0.1879 | loss(seq) 0.5618 | grad 5.6477 | lr 0.0000 | time_forward 1.3340 | time_backward 1.4800 |
[2023-10-23 00:02:41,250::train::INFO] [train] Iter 563600 | loss 1.0383 | loss(rot) 0.2013 | loss(pos) 0.5927 | loss(seq) 0.2443 | grad 5.0172 | lr 0.0000 | time_forward 3.5660 | time_backward 4.7010 |
[2023-10-23 00:02:43,992::train::INFO] [train] Iter 563601 | loss 0.5746 | loss(rot) 0.2528 | loss(pos) 0.0584 | loss(seq) 0.2634 | grad 4.1671 | lr 0.0000 | time_forward 1.3220 | time_backward 1.4170 |
[2023-10-23 00:02:52,304::train::INFO] [train] Iter 563602 | loss 0.6043 | loss(rot) 0.3152 | loss(pos) 0.0390 | loss(seq) 0.2502 | grad 3.7676 | lr 0.0000 | time_forward 3.4520 | time_backward 4.8570 |
[2023-10-23 00:03:00,746::train::INFO] [train] Iter 563603 | loss 0.8244 | loss(rot) 0.7788 | loss(pos) 0.0448 | loss(seq) 0.0008 | grad 9.6283 | lr 0.0000 | time_forward 3.4850 | time_backward 4.9540 |
[2023-10-23 00:03:08,682::train::INFO] [train] Iter 563604 | loss 1.9573 | loss(rot) 1.7143 | loss(pos) 0.0334 | loss(seq) 0.2097 | grad 23.6463 | lr 0.0000 | time_forward 3.5540 | time_backward 4.3790 |
[2023-10-23 00:03:15,969::train::INFO] [train] Iter 563605 | loss 0.2145 | loss(rot) 0.0734 | loss(pos) 0.1060 | loss(seq) 0.0351 | grad 3.5072 | lr 0.0000 | time_forward 3.0950 | time_backward 4.1880 |
[2023-10-23 00:03:24,438::train::INFO] [train] Iter 563606 | loss 0.9372 | loss(rot) 0.8556 | loss(pos) 0.0765 | loss(seq) 0.0052 | grad 5.0880 | lr 0.0000 | time_forward 3.3870 | time_backward 5.0790 |
[2023-10-23 00:03:31,526::train::INFO] [train] Iter 563607 | loss 0.2995 | loss(rot) 0.1300 | loss(pos) 0.0455 | loss(seq) 0.1240 | grad 2.7021 | lr 0.0000 | time_forward 3.0620 | time_backward 4.0230 |
[2023-10-23 00:03:38,471::train::INFO] [train] Iter 563608 | loss 0.6829 | loss(rot) 0.2863 | loss(pos) 0.3945 | loss(seq) 0.0021 | grad 4.9265 | lr 0.0000 | time_forward 2.9340 | time_backward 4.0070 |
[2023-10-23 00:03:41,223::train::INFO] [train] Iter 563609 | loss 0.8383 | loss(rot) 0.6157 | loss(pos) 0.0278 | loss(seq) 0.1948 | grad 14.9748 | lr 0.0000 | time_forward 1.3120 | time_backward 1.4370 |
[2023-10-23 00:03:49,357::train::INFO] [train] Iter 563610 | loss 0.9129 | loss(rot) 0.5228 | loss(pos) 0.0251 | loss(seq) 0.3650 | grad 29.5662 | lr 0.0000 | time_forward 3.7600 | time_backward 4.3710 |
[2023-10-23 00:03:56,289::train::INFO] [train] Iter 563611 | loss 0.2857 | loss(rot) 0.0636 | loss(pos) 0.1166 | loss(seq) 0.1056 | grad 2.6609 | lr 0.0000 | time_forward 2.8870 | time_backward 3.7580 |
[2023-10-23 00:04:04,659::train::INFO] [train] Iter 563612 | loss 0.2981 | loss(rot) 0.1340 | loss(pos) 0.0324 | loss(seq) 0.1317 | grad 2.3489 | lr 0.0000 | time_forward 3.4640 | time_backward 4.9030 |
[2023-10-23 00:04:12,701::train::INFO] [train] Iter 563613 | loss 0.9235 | loss(rot) 0.6277 | loss(pos) 0.1967 | loss(seq) 0.0991 | grad 5.5119 | lr 0.0000 | time_forward 3.3750 | time_backward 4.6630 |
[2023-10-23 00:04:19,539::train::INFO] [train] Iter 563614 | loss 1.0581 | loss(rot) 0.4050 | loss(pos) 0.6106 | loss(seq) 0.0425 | grad 8.3259 | lr 0.0000 | time_forward 2.9590 | time_backward 3.8770 |
[2023-10-23 00:04:27,702::train::INFO] [train] Iter 563615 | loss 1.1278 | loss(rot) 0.5382 | loss(pos) 0.1210 | loss(seq) 0.4686 | grad 3.8722 | lr 0.0000 | time_forward 3.3540 | time_backward 4.8050 |
[2023-10-23 00:04:29,912::train::INFO] [train] Iter 563616 | loss 0.1540 | loss(rot) 0.1120 | loss(pos) 0.0279 | loss(seq) 0.0141 | grad 1.6831 | lr 0.0000 | time_forward 1.0120 | time_backward 1.1950 |
[2023-10-23 00:04:32,526::train::INFO] [train] Iter 563617 | loss 0.2674 | loss(rot) 0.1558 | loss(pos) 0.0278 | loss(seq) 0.0838 | grad 2.2380 | lr 0.0000 | time_forward 1.2410 | time_backward 1.3700 |
[2023-10-23 00:04:38,465::train::INFO] [train] Iter 563618 | loss 0.8137 | loss(rot) 0.5523 | loss(pos) 0.0297 | loss(seq) 0.2317 | grad 21.1625 | lr 0.0000 | time_forward 2.5820 | time_backward 3.3530 |
[2023-10-23 00:04:40,881::train::INFO] [train] Iter 563619 | loss 1.6216 | loss(rot) 1.4355 | loss(pos) 0.0600 | loss(seq) 0.1261 | grad 4.7630 | lr 0.0000 | time_forward 1.1730 | time_backward 1.2400 |
[2023-10-23 00:04:47,097::train::INFO] [train] Iter 563620 | loss 0.7574 | loss(rot) 0.4921 | loss(pos) 0.0362 | loss(seq) 0.2291 | grad 4.2384 | lr 0.0000 | time_forward 2.7010 | time_backward 3.5010 |
[2023-10-23 00:04:49,732::train::INFO] [train] Iter 563621 | loss 1.1754 | loss(rot) 1.1481 | loss(pos) 0.0226 | loss(seq) 0.0047 | grad 14.2750 | lr 0.0000 | time_forward 1.2590 | time_backward 1.3730 |
[2023-10-23 00:04:57,781::train::INFO] [train] Iter 563622 | loss 1.5998 | loss(rot) 1.5116 | loss(pos) 0.0284 | loss(seq) 0.0597 | grad 6.5371 | lr 0.0000 | time_forward 3.3590 | time_backward 4.6880 |
[2023-10-23 00:05:12,171::train::INFO] [train] Iter 563623 | loss 2.7325 | loss(rot) 2.6930 | loss(pos) 0.0395 | loss(seq) 0.0000 | grad 6.3858 | lr 0.0000 | time_forward 5.0870 | time_backward 9.3000 |
[2023-10-23 00:05:21,995::train::INFO] [train] Iter 563624 | loss 0.3427 | loss(rot) 0.0658 | loss(pos) 0.0378 | loss(seq) 0.2390 | grad 2.2017 | lr 0.0000 | time_forward 5.7870 | time_backward 4.0340 |
[2023-10-23 00:05:26,094::train::INFO] [train] Iter 563625 | loss 0.8222 | loss(rot) 0.2608 | loss(pos) 0.0509 | loss(seq) 0.5105 | grad 3.6187 | lr 0.0000 | time_forward 1.8810 | time_backward 2.2150 |
[2023-10-23 00:05:40,691::train::INFO] [train] Iter 563626 | loss 0.8515 | loss(rot) 0.5941 | loss(pos) 0.1036 | loss(seq) 0.1538 | grad 5.4596 | lr 0.0000 | time_forward 10.0250 | time_backward 4.1510 |
[2023-10-23 00:05:47,479::train::INFO] [train] Iter 563627 | loss 0.4312 | loss(rot) 0.1755 | loss(pos) 0.0873 | loss(seq) 0.1684 | grad 2.4412 | lr 0.0000 | time_forward 2.9410 | time_backward 3.8440 |
[2023-10-23 00:05:54,462::train::INFO] [train] Iter 563628 | loss 0.6492 | loss(rot) 0.0456 | loss(pos) 0.5879 | loss(seq) 0.0156 | grad 10.5997 | lr 0.0000 | time_forward 3.0200 | time_backward 3.9590 |
[2023-10-23 00:06:00,916::train::INFO] [train] Iter 563629 | loss 0.1668 | loss(rot) 0.1092 | loss(pos) 0.0197 | loss(seq) 0.0379 | grad 1.3498 | lr 0.0000 | time_forward 2.7690 | time_backward 3.6820 |
[2023-10-23 00:06:03,593::train::INFO] [train] Iter 563630 | loss 1.0812 | loss(rot) 0.8632 | loss(pos) 0.1532 | loss(seq) 0.0648 | grad 3.4479 | lr 0.0000 | time_forward 1.2590 | time_backward 1.4150 |
[2023-10-23 00:06:10,518::train::INFO] [train] Iter 563631 | loss 1.1369 | loss(rot) 0.4981 | loss(pos) 0.3179 | loss(seq) 0.3209 | grad 5.0039 | lr 0.0000 | time_forward 3.0070 | time_backward 3.9150 |
[2023-10-23 00:06:17,449::train::INFO] [train] Iter 563632 | loss 0.2739 | loss(rot) 0.0976 | loss(pos) 0.0548 | loss(seq) 0.1215 | grad 2.7979 | lr 0.0000 | time_forward 2.9810 | time_backward 3.9480 |
[2023-10-23 00:06:20,578::train::INFO] [train] Iter 563633 | loss 1.2273 | loss(rot) 0.9563 | loss(pos) 0.0363 | loss(seq) 0.2347 | grad 3.7799 | lr 0.0000 | time_forward 1.3800 | time_backward 1.7450 |
[2023-10-23 00:06:28,694::train::INFO] [train] Iter 563634 | loss 0.5949 | loss(rot) 0.2859 | loss(pos) 0.1256 | loss(seq) 0.1834 | grad 2.6291 | lr 0.0000 | time_forward 3.3920 | time_backward 4.7120 |
[2023-10-23 00:06:31,377::train::INFO] [train] Iter 563635 | loss 0.8779 | loss(rot) 0.4305 | loss(pos) 0.1664 | loss(seq) 0.2810 | grad 6.0145 | lr 0.0000 | time_forward 1.2770 | time_backward 1.4030 |
[2023-10-23 00:06:37,961::train::INFO] [train] Iter 563636 | loss 1.3575 | loss(rot) 0.9274 | loss(pos) 0.0488 | loss(seq) 0.3813 | grad 4.9643 | lr 0.0000 | time_forward 2.8180 | time_backward 3.7640 |
[2023-10-23 00:06:46,016::train::INFO] [train] Iter 563637 | loss 0.5551 | loss(rot) 0.0671 | loss(pos) 0.4735 | loss(seq) 0.0145 | grad 9.7837 | lr 0.0000 | time_forward 3.3480 | time_backward 4.7040 |
[2023-10-23 00:06:51,878::train::INFO] [train] Iter 563638 | loss 1.7785 | loss(rot) 1.7628 | loss(pos) 0.0150 | loss(seq) 0.0007 | grad 6.0381 | lr 0.0000 | time_forward 2.5650 | time_backward 3.2920 |
[2023-10-23 00:06:59,371::train::INFO] [train] Iter 563639 | loss 0.4005 | loss(rot) 0.1452 | loss(pos) 0.0203 | loss(seq) 0.2350 | grad 2.1302 | lr 0.0000 | time_forward 3.2400 | time_backward 4.2510 |
[2023-10-23 00:07:02,011::train::INFO] [train] Iter 563640 | loss 1.7114 | loss(rot) 1.5759 | loss(pos) 0.0607 | loss(seq) 0.0748 | grad 3.6911 | lr 0.0000 | time_forward 1.2260 | time_backward 1.4100 |
[2023-10-23 00:07:04,715::train::INFO] [train] Iter 563641 | loss 1.3497 | loss(rot) 0.0090 | loss(pos) 1.3404 | loss(seq) 0.0004 | grad 21.9023 | lr 0.0000 | time_forward 1.2880 | time_backward 1.4140 |
[2023-10-23 00:07:07,722::train::INFO] [train] Iter 563642 | loss 0.2229 | loss(rot) 0.0412 | loss(pos) 0.1767 | loss(seq) 0.0050 | grad 4.9441 | lr 0.0000 | time_forward 1.3140 | time_backward 1.4120 |
[2023-10-23 00:07:14,576::train::INFO] [train] Iter 563643 | loss 0.9642 | loss(rot) 0.4949 | loss(pos) 0.0515 | loss(seq) 0.4178 | grad 76.7460 | lr 0.0000 | time_forward 2.9660 | time_backward 3.8650 |
[2023-10-23 00:07:22,735::train::INFO] [train] Iter 563644 | loss 0.2600 | loss(rot) 0.1536 | loss(pos) 0.0200 | loss(seq) 0.0864 | grad 1.8032 | lr 0.0000 | time_forward 3.3540 | time_backward 4.8020 |
[2023-10-23 00:07:30,731::train::INFO] [train] Iter 563645 | loss 1.4387 | loss(rot) 0.0041 | loss(pos) 1.4343 | loss(seq) 0.0003 | grad 6.5683 | lr 0.0000 | time_forward 3.3340 | time_backward 4.6590 |
[2023-10-23 00:07:37,599::train::INFO] [train] Iter 563646 | loss 2.9784 | loss(rot) 0.0067 | loss(pos) 2.9717 | loss(seq) 0.0000 | grad 13.8521 | lr 0.0000 | time_forward 2.9560 | time_backward 3.9080 |
[2023-10-23 00:07:40,331::train::INFO] [train] Iter 563647 | loss 0.3515 | loss(rot) 0.2997 | loss(pos) 0.0214 | loss(seq) 0.0303 | grad 2.9164 | lr 0.0000 | time_forward 1.3040 | time_backward 1.4250 |
[2023-10-23 00:07:48,797::train::INFO] [train] Iter 563648 | loss 0.7859 | loss(rot) 0.7496 | loss(pos) 0.0362 | loss(seq) 0.0000 | grad 2.9689 | lr 0.0000 | time_forward 3.5100 | time_backward 4.5330 |
[2023-10-23 00:07:55,609::train::INFO] [train] Iter 563649 | loss 1.0037 | loss(rot) 0.9448 | loss(pos) 0.0210 | loss(seq) 0.0378 | grad 2.3904 | lr 0.0000 | time_forward 2.9680 | time_backward 3.8410 |
[2023-10-23 00:08:02,359::train::INFO] [train] Iter 563650 | loss 1.2469 | loss(rot) 0.8430 | loss(pos) 0.1291 | loss(seq) 0.2748 | grad 5.3192 | lr 0.0000 | time_forward 2.9690 | time_backward 3.7780 |
[2023-10-23 00:08:05,038::train::INFO] [train] Iter 563651 | loss 0.5441 | loss(rot) 0.0175 | loss(pos) 0.5245 | loss(seq) 0.0021 | grad 6.6510 | lr 0.0000 | time_forward 1.2690 | time_backward 1.4080 |
[2023-10-23 00:08:11,869::train::INFO] [train] Iter 563652 | loss 0.1277 | loss(rot) 0.0350 | loss(pos) 0.0842 | loss(seq) 0.0085 | grad 3.1007 | lr 0.0000 | time_forward 2.9520 | time_backward 3.8750 |
[2023-10-23 00:08:18,652::train::INFO] [train] Iter 563653 | loss 0.9233 | loss(rot) 0.6626 | loss(pos) 0.0434 | loss(seq) 0.2173 | grad 5.6051 | lr 0.0000 | time_forward 2.9510 | time_backward 3.8300 |
[2023-10-23 00:08:26,623::train::INFO] [train] Iter 563654 | loss 0.8262 | loss(rot) 0.7058 | loss(pos) 0.1109 | loss(seq) 0.0095 | grad 6.0114 | lr 0.0000 | time_forward 3.2880 | time_backward 4.6790 |
[2023-10-23 00:08:32,827::train::INFO] [train] Iter 563655 | loss 1.8131 | loss(rot) 1.0502 | loss(pos) 0.1141 | loss(seq) 0.6488 | grad 11.4528 | lr 0.0000 | time_forward 2.7040 | time_backward 3.4970 |
[2023-10-23 00:08:40,037::train::INFO] [train] Iter 563656 | loss 0.5967 | loss(rot) 0.1325 | loss(pos) 0.0630 | loss(seq) 0.4012 | grad 3.2055 | lr 0.0000 | time_forward 3.1420 | time_backward 4.0650 |
[2023-10-23 00:08:47,472::train::INFO] [train] Iter 563657 | loss 0.1257 | loss(rot) 0.0898 | loss(pos) 0.0318 | loss(seq) 0.0041 | grad 1.7775 | lr 0.0000 | time_forward 3.2180 | time_backward 4.2130 |
[2023-10-23 00:08:49,681::train::INFO] [train] Iter 563658 | loss 0.1474 | loss(rot) 0.0882 | loss(pos) 0.0257 | loss(seq) 0.0335 | grad 2.4253 | lr 0.0000 | time_forward 1.0290 | time_backward 1.1770 |
[2023-10-23 00:08:56,915::train::INFO] [train] Iter 563659 | loss 0.6573 | loss(rot) 0.1837 | loss(pos) 0.0894 | loss(seq) 0.3842 | grad 4.3848 | lr 0.0000 | time_forward 3.0380 | time_backward 4.1940 |
[2023-10-23 00:09:03,759::train::INFO] [train] Iter 563660 | loss 0.4631 | loss(rot) 0.0389 | loss(pos) 0.4188 | loss(seq) 0.0055 | grad 4.9065 | lr 0.0000 | time_forward 2.9930 | time_backward 3.8470 |
[2023-10-23 00:09:06,508::train::INFO] [train] Iter 563661 | loss 0.3225 | loss(rot) 0.0326 | loss(pos) 0.0381 | loss(seq) 0.2519 | grad 3.2079 | lr 0.0000 | time_forward 1.2980 | time_backward 1.4490 |
[2023-10-23 00:09:09,319::train::INFO] [train] Iter 563662 | loss 0.5373 | loss(rot) 0.2318 | loss(pos) 0.0342 | loss(seq) 0.2714 | grad 2.5962 | lr 0.0000 | time_forward 1.3260 | time_backward 1.4270 |
[2023-10-23 00:09:16,631::train::INFO] [train] Iter 563663 | loss 0.5245 | loss(rot) 0.1761 | loss(pos) 0.0366 | loss(seq) 0.3118 | grad 11.8469 | lr 0.0000 | time_forward 3.1040 | time_backward 4.2050 |
[2023-10-23 00:09:24,612::train::INFO] [train] Iter 563664 | loss 0.7179 | loss(rot) 0.2274 | loss(pos) 0.1849 | loss(seq) 0.3057 | grad 4.3282 | lr 0.0000 | time_forward 3.4780 | time_backward 4.4990 |
[2023-10-23 00:09:32,761::train::INFO] [train] Iter 563665 | loss 0.2795 | loss(rot) 0.1843 | loss(pos) 0.0429 | loss(seq) 0.0523 | grad 1.6705 | lr 0.0000 | time_forward 3.3780 | time_backward 4.7680 |
[2023-10-23 00:09:40,722::train::INFO] [train] Iter 563666 | loss 3.0489 | loss(rot) 0.0059 | loss(pos) 3.0423 | loss(seq) 0.0007 | grad 21.1383 | lr 0.0000 | time_forward 3.2810 | time_backward 4.6770 |
[2023-10-23 00:09:48,688::train::INFO] [train] Iter 563667 | loss 1.4912 | loss(rot) 0.8611 | loss(pos) 0.1046 | loss(seq) 0.5254 | grad 4.2833 | lr 0.0000 | time_forward 3.2920 | time_backward 4.6700 |
[2023-10-23 00:09:56,637::train::INFO] [train] Iter 563668 | loss 0.3461 | loss(rot) 0.0751 | loss(pos) 0.2536 | loss(seq) 0.0174 | grad 4.0019 | lr 0.0000 | time_forward 3.3120 | time_backward 4.6330 |
[2023-10-23 00:10:02,607::train::INFO] [train] Iter 563669 | loss 0.3543 | loss(rot) 0.1593 | loss(pos) 0.0460 | loss(seq) 0.1490 | grad 2.6372 | lr 0.0000 | time_forward 2.5470 | time_backward 3.4200 |
[2023-10-23 00:10:05,398::train::INFO] [train] Iter 563670 | loss 0.4758 | loss(rot) 0.1368 | loss(pos) 0.0291 | loss(seq) 0.3099 | grad 2.5600 | lr 0.0000 | time_forward 1.3570 | time_backward 1.4300 |
[2023-10-23 00:10:11,753::train::INFO] [train] Iter 563671 | loss 0.0966 | loss(rot) 0.0664 | loss(pos) 0.0185 | loss(seq) 0.0118 | grad 2.0516 | lr 0.0000 | time_forward 2.8130 | time_backward 3.5390 |
[2023-10-23 00:10:14,466::train::INFO] [train] Iter 563672 | loss 1.2848 | loss(rot) 0.7223 | loss(pos) 0.0851 | loss(seq) 0.4774 | grad 5.9513 | lr 0.0000 | time_forward 1.3040 | time_backward 1.4060 |
[2023-10-23 00:10:22,673::train::INFO] [train] Iter 563673 | loss 0.8273 | loss(rot) 0.2870 | loss(pos) 0.0896 | loss(seq) 0.4507 | grad 3.1827 | lr 0.0000 | time_forward 3.3460 | time_backward 4.8590 |
[2023-10-23 00:10:24,919::train::INFO] [train] Iter 563674 | loss 0.6250 | loss(rot) 0.2149 | loss(pos) 0.0389 | loss(seq) 0.3711 | grad 2.4492 | lr 0.0000 | time_forward 1.0410 | time_backward 1.2010 |
[2023-10-23 00:10:27,631::train::INFO] [train] Iter 563675 | loss 0.2232 | loss(rot) 0.0754 | loss(pos) 0.0333 | loss(seq) 0.1145 | grad 2.8386 | lr 0.0000 | time_forward 1.3290 | time_backward 1.3810 |
[2023-10-23 00:10:30,340::train::INFO] [train] Iter 563676 | loss 1.1028 | loss(rot) 0.5625 | loss(pos) 0.1919 | loss(seq) 0.3484 | grad 3.6275 | lr 0.0000 | time_forward 1.3210 | time_backward 1.3840 |
[2023-10-23 00:10:37,784::train::INFO] [train] Iter 563677 | loss 0.0803 | loss(rot) 0.0592 | loss(pos) 0.0161 | loss(seq) 0.0050 | grad 1.5591 | lr 0.0000 | time_forward 3.2620 | time_backward 4.1790 |
[2023-10-23 00:10:46,032::train::INFO] [train] Iter 563678 | loss 0.2876 | loss(rot) 0.0570 | loss(pos) 0.0363 | loss(seq) 0.1943 | grad 2.1493 | lr 0.0000 | time_forward 3.5120 | time_backward 4.7330 |
[2023-10-23 00:10:53,340::train::INFO] [train] Iter 563679 | loss 0.1301 | loss(rot) 0.1168 | loss(pos) 0.0130 | loss(seq) 0.0003 | grad 2.6755 | lr 0.0000 | time_forward 3.1310 | time_backward 4.1730 |
[2023-10-23 00:11:01,647::train::INFO] [train] Iter 563680 | loss 0.1810 | loss(rot) 0.0782 | loss(pos) 0.0134 | loss(seq) 0.0894 | grad 1.2352 | lr 0.0000 | time_forward 3.4650 | time_backward 4.8380 |
[2023-10-23 00:11:03,712::train::INFO] [train] Iter 563681 | loss 1.7703 | loss(rot) 1.4799 | loss(pos) 0.1098 | loss(seq) 0.1807 | grad 7.3381 | lr 0.0000 | time_forward 0.9720 | time_backward 1.0910 |
[2023-10-23 00:11:11,245::train::INFO] [train] Iter 563682 | loss 0.3900 | loss(rot) 0.0671 | loss(pos) 0.0669 | loss(seq) 0.2560 | grad 2.8324 | lr 0.0000 | time_forward 3.3230 | time_backward 4.1990 |
[2023-10-23 00:11:13,426::train::INFO] [train] Iter 563683 | loss 1.2737 | loss(rot) 0.8034 | loss(pos) 0.0900 | loss(seq) 0.3803 | grad 11.5167 | lr 0.0000 | time_forward 1.0140 | time_backward 1.1630 |
[2023-10-23 00:11:16,060::train::INFO] [train] Iter 563684 | loss 1.3076 | loss(rot) 1.2521 | loss(pos) 0.0548 | loss(seq) 0.0007 | grad 3.0426 | lr 0.0000 | time_forward 1.2550 | time_backward 1.3750 |
[2023-10-23 00:11:22,859::train::INFO] [train] Iter 563685 | loss 0.4899 | loss(rot) 0.4519 | loss(pos) 0.0380 | loss(seq) 0.0000 | grad 16.1478 | lr 0.0000 | time_forward 2.9650 | time_backward 3.8310 |
[2023-10-23 00:11:25,337::train::INFO] [train] Iter 563686 | loss 0.8294 | loss(rot) 0.4905 | loss(pos) 0.0413 | loss(seq) 0.2976 | grad 2.4093 | lr 0.0000 | time_forward 1.2080 | time_backward 1.2670 |
[2023-10-23 00:11:33,485::train::INFO] [train] Iter 563687 | loss 0.7598 | loss(rot) 0.1789 | loss(pos) 0.1870 | loss(seq) 0.3939 | grad 2.8746 | lr 0.0000 | time_forward 3.3870 | time_backward 4.7580 |
[2023-10-23 00:11:36,175::train::INFO] [train] Iter 563688 | loss 0.6046 | loss(rot) 0.1936 | loss(pos) 0.0329 | loss(seq) 0.3781 | grad 2.5441 | lr 0.0000 | time_forward 1.2880 | time_backward 1.3990 |
[2023-10-23 00:11:44,261::train::INFO] [train] Iter 563689 | loss 1.5586 | loss(rot) 1.5042 | loss(pos) 0.0427 | loss(seq) 0.0117 | grad 3.0503 | lr 0.0000 | time_forward 3.5300 | time_backward 4.5430 |
[2023-10-23 00:11:50,964::train::INFO] [train] Iter 563690 | loss 0.5306 | loss(rot) 0.2671 | loss(pos) 0.0406 | loss(seq) 0.2228 | grad 4.4730 | lr 0.0000 | time_forward 2.8980 | time_backward 3.8010 |
[2023-10-23 00:11:54,366::train::INFO] [train] Iter 563691 | loss 1.7856 | loss(rot) 1.1838 | loss(pos) 0.1338 | loss(seq) 0.4680 | grad 3.9511 | lr 0.0000 | time_forward 1.5130 | time_backward 1.7420 |
[2023-10-23 00:11:57,003::train::INFO] [train] Iter 563692 | loss 0.8831 | loss(rot) 0.4245 | loss(pos) 0.0532 | loss(seq) 0.4055 | grad 7.8834 | lr 0.0000 | time_forward 1.2480 | time_backward 1.3850 |
[2023-10-23 00:12:05,421::train::INFO] [train] Iter 563693 | loss 0.6159 | loss(rot) 0.3862 | loss(pos) 0.0375 | loss(seq) 0.1922 | grad 2.1763 | lr 0.0000 | time_forward 3.5000 | time_backward 4.6840 |
[2023-10-23 00:12:13,733::train::INFO] [train] Iter 563694 | loss 1.6862 | loss(rot) 0.8328 | loss(pos) 0.4081 | loss(seq) 0.4452 | grad 3.1646 | lr 0.0000 | time_forward 3.3460 | time_backward 4.9620 |
[2023-10-23 00:12:19,344::train::INFO] [train] Iter 563695 | loss 0.2246 | loss(rot) 0.0974 | loss(pos) 0.0326 | loss(seq) 0.0947 | grad 1.5848 | lr 0.0000 | time_forward 2.4780 | time_backward 3.1290 |
[2023-10-23 00:12:22,014::train::INFO] [train] Iter 563696 | loss 0.2966 | loss(rot) 0.2525 | loss(pos) 0.0426 | loss(seq) 0.0015 | grad 79.2668 | lr 0.0000 | time_forward 1.2080 | time_backward 1.4580 |
[2023-10-23 00:12:29,030::train::INFO] [train] Iter 563697 | loss 0.2991 | loss(rot) 0.0593 | loss(pos) 0.2341 | loss(seq) 0.0057 | grad 3.5552 | lr 0.0000 | time_forward 3.0020 | time_backward 3.9450 |
[2023-10-23 00:12:35,798::train::INFO] [train] Iter 563698 | loss 0.8841 | loss(rot) 0.0223 | loss(pos) 0.8605 | loss(seq) 0.0013 | grad 15.4950 | lr 0.0000 | time_forward 2.8940 | time_backward 3.8710 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.