text stringlengths 56 1.16k |
|---|
[2023-10-22 23:20:46,930::train::INFO] [train] Iter 563199 | loss 1.9104 | loss(rot) 1.4661 | loss(pos) 0.1797 | loss(seq) 0.2647 | grad 6.2488 | lr 0.0000 | time_forward 5.0400 | time_backward 4.3100 |
[2023-10-22 23:20:57,491::train::INFO] [train] Iter 563200 | loss 0.1604 | loss(rot) 0.1275 | loss(pos) 0.0253 | loss(seq) 0.0077 | grad 2.1072 | lr 0.0000 | time_forward 5.0950 | time_backward 5.4630 |
[2023-10-22 23:21:01,172::train::INFO] [train] Iter 563201 | loss 1.0035 | loss(rot) 0.3599 | loss(pos) 0.2001 | loss(seq) 0.4436 | grad 2.5636 | lr 0.0000 | time_forward 1.8770 | time_backward 1.8000 |
[2023-10-22 23:21:08,671::train::INFO] [train] Iter 563202 | loss 1.7210 | loss(rot) 1.1375 | loss(pos) 0.0978 | loss(seq) 0.4857 | grad 6.5036 | lr 0.0000 | time_forward 3.5920 | time_backward 3.9040 |
[2023-10-22 23:21:17,319::train::INFO] [train] Iter 563203 | loss 0.8762 | loss(rot) 0.6652 | loss(pos) 0.0123 | loss(seq) 0.1987 | grad 3.7815 | lr 0.0000 | time_forward 4.2730 | time_backward 4.3710 |
[2023-10-22 23:21:25,420::train::INFO] [train] Iter 563204 | loss 0.8845 | loss(rot) 0.7965 | loss(pos) 0.0439 | loss(seq) 0.0442 | grad 2.9128 | lr 0.0000 | time_forward 3.5350 | time_backward 4.5630 |
[2023-10-22 23:21:32,335::train::INFO] [train] Iter 563205 | loss 0.4739 | loss(rot) 0.0216 | loss(pos) 0.4499 | loss(seq) 0.0024 | grad 13.6997 | lr 0.0000 | time_forward 2.9990 | time_backward 3.9120 |
[2023-10-22 23:21:35,055::train::INFO] [train] Iter 563206 | loss 0.4965 | loss(rot) 0.2741 | loss(pos) 0.0135 | loss(seq) 0.2089 | grad 2.5633 | lr 0.0000 | time_forward 1.2980 | time_backward 1.4180 |
[2023-10-22 23:21:42,082::train::INFO] [train] Iter 563207 | loss 0.4955 | loss(rot) 0.2060 | loss(pos) 0.0566 | loss(seq) 0.2329 | grad 5.3204 | lr 0.0000 | time_forward 3.1220 | time_backward 3.9010 |
[2023-10-22 23:21:44,873::train::INFO] [train] Iter 563208 | loss 0.7220 | loss(rot) 0.0887 | loss(pos) 0.1873 | loss(seq) 0.4460 | grad 2.9899 | lr 0.0000 | time_forward 1.2590 | time_backward 1.4170 |
[2023-10-22 23:21:52,608::train::INFO] [train] Iter 563209 | loss 0.2827 | loss(rot) 0.0745 | loss(pos) 0.0251 | loss(seq) 0.1831 | grad 2.2014 | lr 0.0000 | time_forward 3.1350 | time_backward 4.5780 |
[2023-10-22 23:21:59,184::train::INFO] [train] Iter 563210 | loss 0.2711 | loss(rot) 0.1918 | loss(pos) 0.0220 | loss(seq) 0.0573 | grad 11.7418 | lr 0.0000 | time_forward 2.8710 | time_backward 3.7020 |
[2023-10-22 23:22:05,948::train::INFO] [train] Iter 563211 | loss 0.4647 | loss(rot) 0.1535 | loss(pos) 0.0221 | loss(seq) 0.2891 | grad 2.9443 | lr 0.0000 | time_forward 2.9570 | time_backward 3.8040 |
[2023-10-22 23:22:13,136::train::INFO] [train] Iter 563212 | loss 0.4837 | loss(rot) 0.1201 | loss(pos) 0.1654 | loss(seq) 0.1982 | grad 4.3969 | lr 0.0000 | time_forward 3.1260 | time_backward 4.0600 |
[2023-10-22 23:22:21,222::train::INFO] [train] Iter 563213 | loss 0.8974 | loss(rot) 0.5536 | loss(pos) 0.0451 | loss(seq) 0.2987 | grad 4.9112 | lr 0.0000 | time_forward 3.4820 | time_backward 4.6000 |
[2023-10-22 23:22:23,928::train::INFO] [train] Iter 563214 | loss 0.9678 | loss(rot) 0.0099 | loss(pos) 0.9573 | loss(seq) 0.0007 | grad 13.4972 | lr 0.0000 | time_forward 1.2510 | time_backward 1.4510 |
[2023-10-22 23:22:26,578::train::INFO] [train] Iter 563215 | loss 0.9112 | loss(rot) 0.6797 | loss(pos) 0.0431 | loss(seq) 0.1885 | grad 4.4129 | lr 0.0000 | time_forward 1.2490 | time_backward 1.3820 |
[2023-10-22 23:22:34,563::train::INFO] [train] Iter 563216 | loss 0.4905 | loss(rot) 0.2773 | loss(pos) 0.0655 | loss(seq) 0.1477 | grad 3.7175 | lr 0.0000 | time_forward 3.3040 | time_backward 4.6770 |
[2023-10-22 23:22:41,822::train::INFO] [train] Iter 563217 | loss 0.1782 | loss(rot) 0.0291 | loss(pos) 0.1263 | loss(seq) 0.0228 | grad 2.7657 | lr 0.0000 | time_forward 3.1930 | time_backward 4.0630 |
[2023-10-22 23:22:51,283::train::INFO] [train] Iter 563218 | loss 0.5682 | loss(rot) 0.4637 | loss(pos) 0.0335 | loss(seq) 0.0710 | grad 2.9226 | lr 0.0000 | time_forward 3.8790 | time_backward 5.5790 |
[2023-10-22 23:22:58,172::train::INFO] [train] Iter 563219 | loss 0.0714 | loss(rot) 0.0468 | loss(pos) 0.0145 | loss(seq) 0.0101 | grad 1.7554 | lr 0.0000 | time_forward 2.9650 | time_backward 3.9100 |
[2023-10-22 23:23:01,095::train::INFO] [train] Iter 563220 | loss 1.6611 | loss(rot) 0.9501 | loss(pos) 0.0998 | loss(seq) 0.6112 | grad 3.5607 | lr 0.0000 | time_forward 1.5140 | time_backward 1.4050 |
[2023-10-22 23:23:08,166::train::INFO] [train] Iter 563221 | loss 0.8949 | loss(rot) 0.2631 | loss(pos) 0.2612 | loss(seq) 0.3706 | grad 5.5625 | lr 0.0000 | time_forward 3.2010 | time_backward 3.8600 |
[2023-10-22 23:23:16,045::train::INFO] [train] Iter 563222 | loss 0.9502 | loss(rot) 0.4820 | loss(pos) 0.1707 | loss(seq) 0.2975 | grad 4.4504 | lr 0.0000 | time_forward 3.2680 | time_backward 4.6080 |
[2023-10-22 23:23:23,913::train::INFO] [train] Iter 563223 | loss 0.8233 | loss(rot) 0.8052 | loss(pos) 0.0160 | loss(seq) 0.0021 | grad 28.5089 | lr 0.0000 | time_forward 3.3030 | time_backward 4.5610 |
[2023-10-22 23:23:31,229::train::INFO] [train] Iter 563224 | loss 1.9340 | loss(rot) 1.6603 | loss(pos) 0.0664 | loss(seq) 0.2073 | grad 5.0977 | lr 0.0000 | time_forward 3.1920 | time_backward 4.1220 |
[2023-10-22 23:23:39,318::train::INFO] [train] Iter 563225 | loss 0.4606 | loss(rot) 0.2027 | loss(pos) 0.0443 | loss(seq) 0.2136 | grad 3.0122 | lr 0.0000 | time_forward 3.3460 | time_backward 4.7400 |
[2023-10-22 23:23:46,281::train::INFO] [train] Iter 563226 | loss 1.7310 | loss(rot) 0.9976 | loss(pos) 0.2804 | loss(seq) 0.4529 | grad 5.7066 | lr 0.0000 | time_forward 3.0540 | time_backward 3.9050 |
[2023-10-22 23:23:54,197::train::INFO] [train] Iter 563227 | loss 1.1811 | loss(rot) 0.6230 | loss(pos) 0.1511 | loss(seq) 0.4070 | grad 3.6560 | lr 0.0000 | time_forward 3.2510 | time_backward 4.6620 |
[2023-10-22 23:24:02,003::train::INFO] [train] Iter 563228 | loss 2.6064 | loss(rot) 0.1306 | loss(pos) 2.4750 | loss(seq) 0.0008 | grad 12.2282 | lr 0.0000 | time_forward 3.2350 | time_backward 4.5670 |
[2023-10-22 23:24:09,886::train::INFO] [train] Iter 563229 | loss 0.4302 | loss(rot) 0.1265 | loss(pos) 0.2517 | loss(seq) 0.0521 | grad 4.3421 | lr 0.0000 | time_forward 3.3010 | time_backward 4.5790 |
[2023-10-22 23:24:12,326::train::INFO] [train] Iter 563230 | loss 0.3724 | loss(rot) 0.0561 | loss(pos) 0.2723 | loss(seq) 0.0439 | grad 5.1364 | lr 0.0000 | time_forward 1.1670 | time_backward 1.2700 |
[2023-10-22 23:24:18,727::train::INFO] [train] Iter 563231 | loss 1.2237 | loss(rot) 1.1221 | loss(pos) 0.0347 | loss(seq) 0.0669 | grad 6.6857 | lr 0.0000 | time_forward 2.7250 | time_backward 3.6720 |
[2023-10-22 23:24:25,593::train::INFO] [train] Iter 563232 | loss 0.4217 | loss(rot) 0.0883 | loss(pos) 0.0618 | loss(seq) 0.2716 | grad 3.1474 | lr 0.0000 | time_forward 3.0160 | time_backward 3.8470 |
[2023-10-22 23:24:33,471::train::INFO] [train] Iter 563233 | loss 1.0893 | loss(rot) 0.7896 | loss(pos) 0.0545 | loss(seq) 0.2453 | grad 5.8379 | lr 0.0000 | time_forward 3.2890 | time_backward 4.5850 |
[2023-10-22 23:24:39,961::train::INFO] [train] Iter 563234 | loss 0.1283 | loss(rot) 0.0883 | loss(pos) 0.0394 | loss(seq) 0.0007 | grad 1.9776 | lr 0.0000 | time_forward 2.7920 | time_backward 3.6950 |
[2023-10-22 23:24:47,817::train::INFO] [train] Iter 563235 | loss 0.6006 | loss(rot) 0.5471 | loss(pos) 0.0486 | loss(seq) 0.0050 | grad 4.7524 | lr 0.0000 | time_forward 3.3130 | time_backward 4.5400 |
[2023-10-22 23:24:50,069::train::INFO] [train] Iter 563236 | loss 1.0552 | loss(rot) 0.6570 | loss(pos) 0.1399 | loss(seq) 0.2583 | grad 4.0900 | lr 0.0000 | time_forward 1.0130 | time_backward 1.2360 |
[2023-10-22 23:24:58,046::train::INFO] [train] Iter 563237 | loss 1.1115 | loss(rot) 0.6646 | loss(pos) 0.2233 | loss(seq) 0.2237 | grad 4.2174 | lr 0.0000 | time_forward 3.6590 | time_backward 4.3150 |
[2023-10-22 23:25:04,641::train::INFO] [train] Iter 563238 | loss 0.5024 | loss(rot) 0.0940 | loss(pos) 0.0980 | loss(seq) 0.3105 | grad 3.1495 | lr 0.0000 | time_forward 2.8520 | time_backward 3.7410 |
[2023-10-22 23:25:12,014::train::INFO] [train] Iter 563239 | loss 0.4900 | loss(rot) 0.0796 | loss(pos) 0.0485 | loss(seq) 0.3619 | grad 3.3650 | lr 0.0000 | time_forward 3.1350 | time_backward 4.2350 |
[2023-10-22 23:25:19,002::train::INFO] [train] Iter 563240 | loss 1.3554 | loss(rot) 0.8814 | loss(pos) 0.0810 | loss(seq) 0.3930 | grad 4.1238 | lr 0.0000 | time_forward 3.0260 | time_backward 3.9580 |
[2023-10-22 23:25:25,966::train::INFO] [train] Iter 563241 | loss 0.6734 | loss(rot) 0.3869 | loss(pos) 0.0452 | loss(seq) 0.2414 | grad 3.6628 | lr 0.0000 | time_forward 3.0140 | time_backward 3.9470 |
[2023-10-22 23:25:33,073::train::INFO] [train] Iter 563242 | loss 0.9838 | loss(rot) 0.9340 | loss(pos) 0.0498 | loss(seq) 0.0000 | grad 20.1093 | lr 0.0000 | time_forward 2.8670 | time_backward 4.2360 |
[2023-10-22 23:25:42,094::train::INFO] [train] Iter 563243 | loss 1.0187 | loss(rot) 0.6200 | loss(pos) 0.1528 | loss(seq) 0.2460 | grad 3.3269 | lr 0.0000 | time_forward 3.7160 | time_backward 5.3020 |
[2023-10-22 23:25:49,620::train::INFO] [train] Iter 563244 | loss 0.2855 | loss(rot) 0.2583 | loss(pos) 0.0246 | loss(seq) 0.0027 | grad 2.9641 | lr 0.0000 | time_forward 3.2450 | time_backward 4.2770 |
[2023-10-22 23:25:58,703::train::INFO] [train] Iter 563245 | loss 0.2406 | loss(rot) 0.2202 | loss(pos) 0.0203 | loss(seq) 0.0002 | grad 3.1247 | lr 0.0000 | time_forward 3.6990 | time_backward 5.3810 |
[2023-10-22 23:26:06,661::train::INFO] [train] Iter 563246 | loss 0.2692 | loss(rot) 0.0913 | loss(pos) 0.1194 | loss(seq) 0.0584 | grad 2.9033 | lr 0.0000 | time_forward 3.4070 | time_backward 4.5480 |
[2023-10-22 23:26:15,671::train::INFO] [train] Iter 563247 | loss 0.7028 | loss(rot) 0.6346 | loss(pos) 0.0271 | loss(seq) 0.0411 | grad 10.3796 | lr 0.0000 | time_forward 3.7250 | time_backward 5.2810 |
[2023-10-22 23:26:24,528::train::INFO] [train] Iter 563248 | loss 0.3522 | loss(rot) 0.2969 | loss(pos) 0.0552 | loss(seq) 0.0000 | grad 4.1002 | lr 0.0000 | time_forward 3.8070 | time_backward 5.0470 |
[2023-10-22 23:26:33,535::train::INFO] [train] Iter 563249 | loss 0.3792 | loss(rot) 0.1123 | loss(pos) 0.0149 | loss(seq) 0.2520 | grad 1.9725 | lr 0.0000 | time_forward 3.6220 | time_backward 5.3830 |
[2023-10-22 23:26:42,769::train::INFO] [train] Iter 563250 | loss 0.4994 | loss(rot) 0.4643 | loss(pos) 0.0282 | loss(seq) 0.0069 | grad 3.7558 | lr 0.0000 | time_forward 3.7940 | time_backward 5.4370 |
[2023-10-22 23:26:45,674::train::INFO] [train] Iter 563251 | loss 0.3596 | loss(rot) 0.0800 | loss(pos) 0.2015 | loss(seq) 0.0780 | grad 2.9391 | lr 0.0000 | time_forward 1.3130 | time_backward 1.5880 |
[2023-10-22 23:26:53,840::train::INFO] [train] Iter 563252 | loss 0.3202 | loss(rot) 0.0464 | loss(pos) 0.1030 | loss(seq) 0.1708 | grad 4.2065 | lr 0.0000 | time_forward 3.4030 | time_backward 4.7590 |
[2023-10-22 23:27:02,624::train::INFO] [train] Iter 563253 | loss 1.3298 | loss(rot) 0.6067 | loss(pos) 0.2832 | loss(seq) 0.4400 | grad 7.4395 | lr 0.0000 | time_forward 3.5590 | time_backward 5.2220 |
[2023-10-22 23:27:10,887::train::INFO] [train] Iter 563254 | loss 0.9370 | loss(rot) 0.5268 | loss(pos) 0.0586 | loss(seq) 0.3517 | grad 3.5304 | lr 0.0000 | time_forward 3.5560 | time_backward 4.7040 |
[2023-10-22 23:27:14,511::train::INFO] [train] Iter 563255 | loss 0.4628 | loss(rot) 0.1448 | loss(pos) 0.1245 | loss(seq) 0.1935 | grad 2.8062 | lr 0.0000 | time_forward 1.5690 | time_backward 2.0520 |
[2023-10-22 23:27:24,431::train::INFO] [train] Iter 563256 | loss 0.4988 | loss(rot) 0.0681 | loss(pos) 0.4290 | loss(seq) 0.0017 | grad 7.7884 | lr 0.0000 | time_forward 4.2140 | time_backward 5.6890 |
[2023-10-22 23:27:33,528::train::INFO] [train] Iter 563257 | loss 0.3327 | loss(rot) 0.2215 | loss(pos) 0.0384 | loss(seq) 0.0728 | grad 2.6456 | lr 0.0000 | time_forward 4.0360 | time_backward 5.0580 |
[2023-10-22 23:27:41,508::train::INFO] [train] Iter 563258 | loss 2.6954 | loss(rot) 2.4839 | loss(pos) 0.1246 | loss(seq) 0.0869 | grad 4.6704 | lr 0.0000 | time_forward 3.2570 | time_backward 4.7200 |
[2023-10-22 23:27:48,074::train::INFO] [train] Iter 563259 | loss 0.2841 | loss(rot) 0.1011 | loss(pos) 0.0908 | loss(seq) 0.0922 | grad 3.6747 | lr 0.0000 | time_forward 2.8320 | time_backward 3.7300 |
[2023-10-22 23:27:56,094::train::INFO] [train] Iter 563260 | loss 0.3252 | loss(rot) 0.0374 | loss(pos) 0.2812 | loss(seq) 0.0065 | grad 4.8772 | lr 0.0000 | time_forward 3.3550 | time_backward 4.6610 |
[2023-10-22 23:28:03,465::train::INFO] [train] Iter 563261 | loss 0.8226 | loss(rot) 0.1301 | loss(pos) 0.1610 | loss(seq) 0.5315 | grad 3.8437 | lr 0.0000 | time_forward 3.2010 | time_backward 4.1650 |
[2023-10-22 23:28:11,873::train::INFO] [train] Iter 563262 | loss 0.3978 | loss(rot) 0.1568 | loss(pos) 0.0238 | loss(seq) 0.2171 | grad 2.4256 | lr 0.0000 | time_forward 3.7630 | time_backward 4.6430 |
[2023-10-22 23:28:20,866::train::INFO] [train] Iter 563263 | loss 0.8738 | loss(rot) 0.0374 | loss(pos) 0.4582 | loss(seq) 0.3783 | grad 4.4361 | lr 0.0000 | time_forward 3.7820 | time_backward 5.2070 |
[2023-10-22 23:28:29,098::train::INFO] [train] Iter 563264 | loss 0.8352 | loss(rot) 0.2145 | loss(pos) 0.2789 | loss(seq) 0.3418 | grad 4.6767 | lr 0.0000 | time_forward 3.3820 | time_backward 4.8470 |
[2023-10-22 23:28:35,989::train::INFO] [train] Iter 563265 | loss 0.3815 | loss(rot) 0.1438 | loss(pos) 0.0285 | loss(seq) 0.2092 | grad 2.7801 | lr 0.0000 | time_forward 2.9410 | time_backward 3.9470 |
[2023-10-22 23:28:43,902::train::INFO] [train] Iter 563266 | loss 0.4298 | loss(rot) 0.1413 | loss(pos) 0.2804 | loss(seq) 0.0081 | grad 3.8912 | lr 0.0000 | time_forward 3.4040 | time_backward 4.5070 |
[2023-10-22 23:28:52,963::train::INFO] [train] Iter 563267 | loss 0.1379 | loss(rot) 0.0982 | loss(pos) 0.0397 | loss(seq) 0.0000 | grad 2.0331 | lr 0.0000 | time_forward 3.7340 | time_backward 5.3240 |
[2023-10-22 23:29:00,616::train::INFO] [train] Iter 563268 | loss 0.1046 | loss(rot) 0.0739 | loss(pos) 0.0136 | loss(seq) 0.0172 | grad 2.0976 | lr 0.0000 | time_forward 3.2740 | time_backward 4.3750 |
[2023-10-22 23:29:07,940::train::INFO] [train] Iter 563269 | loss 0.9917 | loss(rot) 0.0116 | loss(pos) 0.9795 | loss(seq) 0.0005 | grad 16.6498 | lr 0.0000 | time_forward 3.1340 | time_backward 4.1870 |
[2023-10-22 23:29:10,767::train::INFO] [train] Iter 563270 | loss 0.5282 | loss(rot) 0.4648 | loss(pos) 0.0629 | loss(seq) 0.0005 | grad 25.8459 | lr 0.0000 | time_forward 1.3320 | time_backward 1.4910 |
[2023-10-22 23:29:19,842::train::INFO] [train] Iter 563271 | loss 1.0912 | loss(rot) 0.7096 | loss(pos) 0.0591 | loss(seq) 0.3225 | grad 3.2729 | lr 0.0000 | time_forward 3.7030 | time_backward 5.3460 |
[2023-10-22 23:29:27,277::train::INFO] [train] Iter 563272 | loss 1.0698 | loss(rot) 0.0053 | loss(pos) 1.0641 | loss(seq) 0.0004 | grad 17.1500 | lr 0.0000 | time_forward 3.1710 | time_backward 4.2580 |
[2023-10-22 23:29:35,007::train::INFO] [train] Iter 563273 | loss 0.2544 | loss(rot) 0.1502 | loss(pos) 0.0466 | loss(seq) 0.0577 | grad 2.3653 | lr 0.0000 | time_forward 3.3060 | time_backward 4.4190 |
[2023-10-22 23:29:37,755::train::INFO] [train] Iter 563274 | loss 0.7240 | loss(rot) 0.7043 | loss(pos) 0.0175 | loss(seq) 0.0022 | grad 1.9122 | lr 0.0000 | time_forward 1.3330 | time_backward 1.4120 |
[2023-10-22 23:29:43,248::train::INFO] [train] Iter 563275 | loss 0.2817 | loss(rot) 0.0963 | loss(pos) 0.1182 | loss(seq) 0.0673 | grad 2.8063 | lr 0.0000 | time_forward 2.4160 | time_backward 3.0750 |
[2023-10-22 23:29:46,067::train::INFO] [train] Iter 563276 | loss 0.4036 | loss(rot) 0.3502 | loss(pos) 0.0318 | loss(seq) 0.0216 | grad 4.1833 | lr 0.0000 | time_forward 1.3280 | time_backward 1.4760 |
[2023-10-22 23:29:48,310::train::INFO] [train] Iter 563277 | loss 1.1284 | loss(rot) 0.6724 | loss(pos) 0.1702 | loss(seq) 0.2858 | grad 3.8873 | lr 0.0000 | time_forward 1.0320 | time_backward 1.2070 |
[2023-10-22 23:29:48,880::train::INFO] [train] Iter 563278 | loss 2.0133 | loss(rot) 1.3702 | loss(pos) 0.3789 | loss(seq) 0.2642 | grad 12.0933 | lr 0.0000 | time_forward 0.2450 | time_backward 0.3230 |
[2023-10-22 23:29:56,633::train::INFO] [train] Iter 563279 | loss 0.5019 | loss(rot) 0.4425 | loss(pos) 0.0239 | loss(seq) 0.0354 | grad 10.6107 | lr 0.0000 | time_forward 3.3300 | time_backward 4.4190 |
[2023-10-22 23:30:05,657::train::INFO] [train] Iter 563280 | loss 0.3815 | loss(rot) 0.0568 | loss(pos) 0.0355 | loss(seq) 0.2892 | grad 2.6475 | lr 0.0000 | time_forward 3.8040 | time_backward 5.2160 |
[2023-10-22 23:30:08,377::train::INFO] [train] Iter 563281 | loss 0.2207 | loss(rot) 0.1966 | loss(pos) 0.0239 | loss(seq) 0.0002 | grad 3.2286 | lr 0.0000 | time_forward 1.2870 | time_backward 1.4310 |
[2023-10-22 23:30:15,739::train::INFO] [train] Iter 563282 | loss 2.3025 | loss(rot) 1.4233 | loss(pos) 0.4837 | loss(seq) 0.3954 | grad 8.1803 | lr 0.0000 | time_forward 3.1420 | time_backward 4.2170 |
[2023-10-22 23:30:18,448::train::INFO] [train] Iter 563283 | loss 0.5327 | loss(rot) 0.3218 | loss(pos) 0.0296 | loss(seq) 0.1813 | grad 2.9611 | lr 0.0000 | time_forward 1.3050 | time_backward 1.4000 |
[2023-10-22 23:30:27,548::train::INFO] [train] Iter 563284 | loss 0.3218 | loss(rot) 0.1041 | loss(pos) 0.0383 | loss(seq) 0.1794 | grad 2.9881 | lr 0.0000 | time_forward 3.7610 | time_backward 5.3120 |
[2023-10-22 23:30:30,254::train::INFO] [train] Iter 563285 | loss 0.4942 | loss(rot) 0.2873 | loss(pos) 0.0223 | loss(seq) 0.1846 | grad 3.1028 | lr 0.0000 | time_forward 1.3070 | time_backward 1.3970 |
[2023-10-22 23:30:39,398::train::INFO] [train] Iter 563286 | loss 0.8378 | loss(rot) 0.7406 | loss(pos) 0.0223 | loss(seq) 0.0749 | grad 87.3835 | lr 0.0000 | time_forward 3.7730 | time_backward 5.3510 |
[2023-10-22 23:30:46,508::train::INFO] [train] Iter 563287 | loss 0.5577 | loss(rot) 0.1608 | loss(pos) 0.1866 | loss(seq) 0.2103 | grad 2.6896 | lr 0.0000 | time_forward 2.9640 | time_backward 4.1430 |
[2023-10-22 23:30:49,193::train::INFO] [train] Iter 563288 | loss 0.5922 | loss(rot) 0.2994 | loss(pos) 0.1177 | loss(seq) 0.1751 | grad 3.0032 | lr 0.0000 | time_forward 1.2730 | time_backward 1.4080 |
[2023-10-22 23:30:56,363::train::INFO] [train] Iter 563289 | loss 0.9505 | loss(rot) 0.8949 | loss(pos) 0.0217 | loss(seq) 0.0339 | grad 9.0361 | lr 0.0000 | time_forward 3.0790 | time_backward 4.0890 |
[2023-10-22 23:31:04,136::train::INFO] [train] Iter 563290 | loss 0.3989 | loss(rot) 0.0850 | loss(pos) 0.2840 | loss(seq) 0.0299 | grad 5.3287 | lr 0.0000 | time_forward 3.4180 | time_backward 4.3520 |
[2023-10-22 23:31:11,460::train::INFO] [train] Iter 563291 | loss 0.3575 | loss(rot) 0.1622 | loss(pos) 0.1340 | loss(seq) 0.0612 | grad 3.2968 | lr 0.0000 | time_forward 3.1610 | time_backward 4.1590 |
[2023-10-22 23:31:14,192::train::INFO] [train] Iter 563292 | loss 0.1917 | loss(rot) 0.0302 | loss(pos) 0.1580 | loss(seq) 0.0034 | grad 2.9323 | lr 0.0000 | time_forward 1.2520 | time_backward 1.4010 |
[2023-10-22 23:31:22,375::train::INFO] [train] Iter 563293 | loss 0.6507 | loss(rot) 0.4852 | loss(pos) 0.0421 | loss(seq) 0.1234 | grad 3.5640 | lr 0.0000 | time_forward 3.4230 | time_backward 4.7470 |
[2023-10-22 23:31:25,199::train::INFO] [train] Iter 563294 | loss 0.3048 | loss(rot) 0.2228 | loss(pos) 0.0820 | loss(seq) 0.0000 | grad 3.1947 | lr 0.0000 | time_forward 1.2850 | time_backward 1.5360 |
[2023-10-22 23:31:28,341::train::INFO] [train] Iter 563295 | loss 0.4376 | loss(rot) 0.2686 | loss(pos) 0.0599 | loss(seq) 0.1091 | grad 2.4819 | lr 0.0000 | time_forward 1.4280 | time_backward 1.7100 |
[2023-10-22 23:31:35,205::train::INFO] [train] Iter 563296 | loss 1.5600 | loss(rot) 0.9387 | loss(pos) 0.2822 | loss(seq) 0.3391 | grad 3.9844 | lr 0.0000 | time_forward 2.9710 | time_backward 3.8890 |
[2023-10-22 23:31:37,929::train::INFO] [train] Iter 563297 | loss 0.4711 | loss(rot) 0.3515 | loss(pos) 0.0205 | loss(seq) 0.0991 | grad 7.2408 | lr 0.0000 | time_forward 1.2720 | time_backward 1.4410 |
[2023-10-22 23:31:40,351::train::INFO] [train] Iter 563298 | loss 2.4528 | loss(rot) 0.0020 | loss(pos) 2.4508 | loss(seq) 0.0000 | grad 14.7871 | lr 0.0000 | time_forward 1.1500 | time_backward 1.2690 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.