text stringlengths 56 1.16k |
|---|
[2023-10-23 02:13:21,999::train::INFO] [train] Iter 564898 | loss 0.3380 | loss(rot) 0.1711 | loss(pos) 0.0122 | loss(seq) 0.1546 | grad 2.2313 | lr 0.0000 | time_forward 1.3250 | time_backward 1.4660 |
[2023-10-23 02:13:29,228::train::INFO] [train] Iter 564899 | loss 2.2806 | loss(rot) 0.0021 | loss(pos) 2.2785 | loss(seq) 0.0000 | grad 24.1726 | lr 0.0000 | time_forward 3.1310 | time_backward 4.0810 |
[2023-10-23 02:13:35,896::train::INFO] [train] Iter 564900 | loss 1.8565 | loss(rot) 1.7259 | loss(pos) 0.0271 | loss(seq) 0.1035 | grad 4.5748 | lr 0.0000 | time_forward 2.8770 | time_backward 3.7870 |
[2023-10-23 02:13:43,116::train::INFO] [train] Iter 564901 | loss 1.8264 | loss(rot) 0.0233 | loss(pos) 1.8029 | loss(seq) 0.0002 | grad 15.3720 | lr 0.0000 | time_forward 3.1480 | time_backward 4.0690 |
[2023-10-23 02:13:51,007::train::INFO] [train] Iter 564902 | loss 0.7038 | loss(rot) 0.6555 | loss(pos) 0.0202 | loss(seq) 0.0281 | grad 57.0460 | lr 0.0000 | time_forward 3.2650 | time_backward 4.6220 |
[2023-10-23 02:13:54,119::train::INFO] [train] Iter 564903 | loss 0.6884 | loss(rot) 0.3531 | loss(pos) 0.0492 | loss(seq) 0.2862 | grad 5.3193 | lr 0.0000 | time_forward 1.4150 | time_backward 1.6940 |
[2023-10-23 02:13:56,807::train::INFO] [train] Iter 564904 | loss 1.1772 | loss(rot) 1.0185 | loss(pos) 0.0421 | loss(seq) 0.1167 | grad 4.6444 | lr 0.0000 | time_forward 1.2760 | time_backward 1.4000 |
[2023-10-23 02:14:02,993::train::INFO] [train] Iter 564905 | loss 0.5446 | loss(rot) 0.1136 | loss(pos) 0.0663 | loss(seq) 0.3647 | grad 3.6837 | lr 0.0000 | time_forward 2.7040 | time_backward 3.4660 |
[2023-10-23 02:14:09,967::train::INFO] [train] Iter 564906 | loss 0.1722 | loss(rot) 0.0542 | loss(pos) 0.0235 | loss(seq) 0.0945 | grad 2.4235 | lr 0.0000 | time_forward 3.0510 | time_backward 3.9210 |
[2023-10-23 02:14:16,413::train::INFO] [train] Iter 564907 | loss 0.2964 | loss(rot) 0.0383 | loss(pos) 0.2462 | loss(seq) 0.0119 | grad 4.4562 | lr 0.0000 | time_forward 2.7680 | time_backward 3.6760 |
[2023-10-23 02:14:22,883::train::INFO] [train] Iter 564908 | loss 0.4574 | loss(rot) 0.1370 | loss(pos) 0.0669 | loss(seq) 0.2535 | grad 3.4129 | lr 0.0000 | time_forward 2.7690 | time_backward 3.6980 |
[2023-10-23 02:14:30,887::train::INFO] [train] Iter 564909 | loss 0.1711 | loss(rot) 0.0453 | loss(pos) 0.0601 | loss(seq) 0.0656 | grad 2.5160 | lr 0.0000 | time_forward 3.2860 | time_backward 4.7150 |
[2023-10-23 02:14:38,868::train::INFO] [train] Iter 564910 | loss 1.2546 | loss(rot) 1.2021 | loss(pos) 0.0515 | loss(seq) 0.0010 | grad 11.7660 | lr 0.0000 | time_forward 3.3180 | time_backward 4.6590 |
[2023-10-23 02:14:46,231::train::INFO] [train] Iter 564911 | loss 0.2972 | loss(rot) 0.1051 | loss(pos) 0.1708 | loss(seq) 0.0214 | grad 5.6948 | lr 0.0000 | time_forward 3.2330 | time_backward 4.1260 |
[2023-10-23 02:14:48,921::train::INFO] [train] Iter 564912 | loss 0.4604 | loss(rot) 0.0776 | loss(pos) 0.0449 | loss(seq) 0.3379 | grad 2.5083 | lr 0.0000 | time_forward 1.2810 | time_backward 1.4070 |
[2023-10-23 02:14:56,158::train::INFO] [train] Iter 564913 | loss 0.7926 | loss(rot) 0.4803 | loss(pos) 0.1039 | loss(seq) 0.2084 | grad 2.9598 | lr 0.0000 | time_forward 3.1430 | time_backward 4.0750 |
[2023-10-23 02:15:04,272::train::INFO] [train] Iter 564914 | loss 0.1903 | loss(rot) 0.1499 | loss(pos) 0.0305 | loss(seq) 0.0099 | grad 2.4639 | lr 0.0000 | time_forward 3.3880 | time_backward 4.7230 |
[2023-10-23 02:15:06,926::train::INFO] [train] Iter 564915 | loss 1.8813 | loss(rot) 0.6441 | loss(pos) 0.9542 | loss(seq) 0.2830 | grad 11.8519 | lr 0.0000 | time_forward 1.2600 | time_backward 1.3900 |
[2023-10-23 02:15:09,642::train::INFO] [train] Iter 564916 | loss 0.6184 | loss(rot) 0.0217 | loss(pos) 0.5925 | loss(seq) 0.0041 | grad 9.9214 | lr 0.0000 | time_forward 1.3240 | time_backward 1.3900 |
[2023-10-23 02:15:16,669::train::INFO] [train] Iter 564917 | loss 0.1272 | loss(rot) 0.0651 | loss(pos) 0.0135 | loss(seq) 0.0486 | grad 2.1009 | lr 0.0000 | time_forward 3.0710 | time_backward 3.9520 |
[2023-10-23 02:15:23,736::train::INFO] [train] Iter 564918 | loss 0.2308 | loss(rot) 0.2060 | loss(pos) 0.0220 | loss(seq) 0.0028 | grad 2.8292 | lr 0.0000 | time_forward 2.9820 | time_backward 4.0740 |
[2023-10-23 02:15:26,368::train::INFO] [train] Iter 564919 | loss 0.2695 | loss(rot) 0.1423 | loss(pos) 0.0308 | loss(seq) 0.0964 | grad 2.3489 | lr 0.0000 | time_forward 1.2380 | time_backward 1.3910 |
[2023-10-23 02:15:29,149::train::INFO] [train] Iter 564920 | loss 0.3803 | loss(rot) 0.0362 | loss(pos) 0.1427 | loss(seq) 0.2014 | grad 2.8555 | lr 0.0000 | time_forward 1.3210 | time_backward 1.4550 |
[2023-10-23 02:15:31,578::train::INFO] [train] Iter 564921 | loss 1.6950 | loss(rot) 1.5857 | loss(pos) 0.0268 | loss(seq) 0.0824 | grad 25.4385 | lr 0.0000 | time_forward 1.2010 | time_backward 1.2250 |
[2023-10-23 02:15:39,574::train::INFO] [train] Iter 564922 | loss 1.8538 | loss(rot) 1.7760 | loss(pos) 0.0771 | loss(seq) 0.0006 | grad 10.9922 | lr 0.0000 | time_forward 3.4570 | time_backward 4.5180 |
[2023-10-23 02:15:47,593::train::INFO] [train] Iter 564923 | loss 0.4742 | loss(rot) 0.0210 | loss(pos) 0.4492 | loss(seq) 0.0040 | grad 8.3055 | lr 0.0000 | time_forward 3.3370 | time_backward 4.6780 |
[2023-10-23 02:15:54,549::train::INFO] [train] Iter 564924 | loss 1.8768 | loss(rot) 1.1245 | loss(pos) 0.2817 | loss(seq) 0.4706 | grad 4.2564 | lr 0.0000 | time_forward 2.9580 | time_backward 3.9950 |
[2023-10-23 02:16:02,541::train::INFO] [train] Iter 564925 | loss 0.5874 | loss(rot) 0.1572 | loss(pos) 0.3925 | loss(seq) 0.0377 | grad 3.8563 | lr 0.0000 | time_forward 3.2940 | time_backward 4.6950 |
[2023-10-23 02:16:10,522::train::INFO] [train] Iter 564926 | loss 1.1720 | loss(rot) 0.5696 | loss(pos) 0.1643 | loss(seq) 0.4381 | grad 3.8523 | lr 0.0000 | time_forward 3.3180 | time_backward 4.6590 |
[2023-10-23 02:16:18,634::train::INFO] [train] Iter 564927 | loss 1.5770 | loss(rot) 0.9564 | loss(pos) 0.1096 | loss(seq) 0.5109 | grad 3.7611 | lr 0.0000 | time_forward 3.3740 | time_backward 4.7320 |
[2023-10-23 02:16:26,720::train::INFO] [train] Iter 564928 | loss 1.1402 | loss(rot) 0.7871 | loss(pos) 0.0940 | loss(seq) 0.2590 | grad 6.4586 | lr 0.0000 | time_forward 3.3170 | time_backward 4.7650 |
[2023-10-23 02:16:34,176::train::INFO] [train] Iter 564929 | loss 1.7661 | loss(rot) 1.5544 | loss(pos) 0.0565 | loss(seq) 0.1552 | grad 6.5549 | lr 0.0000 | time_forward 3.2620 | time_backward 4.1910 |
[2023-10-23 02:16:40,007::train::INFO] [train] Iter 564930 | loss 0.8389 | loss(rot) 0.2747 | loss(pos) 0.0525 | loss(seq) 0.5117 | grad 3.9535 | lr 0.0000 | time_forward 2.6060 | time_backward 3.2220 |
[2023-10-23 02:16:46,566::train::INFO] [train] Iter 564931 | loss 0.3143 | loss(rot) 0.1378 | loss(pos) 0.0732 | loss(seq) 0.1033 | grad 3.5967 | lr 0.0000 | time_forward 2.8060 | time_backward 3.7490 |
[2023-10-23 02:16:48,770::train::INFO] [train] Iter 564932 | loss 0.2591 | loss(rot) 0.0659 | loss(pos) 0.0512 | loss(seq) 0.1420 | grad 2.3275 | lr 0.0000 | time_forward 1.0370 | time_backward 1.1640 |
[2023-10-23 02:16:51,383::train::INFO] [train] Iter 564933 | loss 1.3608 | loss(rot) 0.8849 | loss(pos) 0.0855 | loss(seq) 0.3904 | grad 12.1425 | lr 0.0000 | time_forward 1.2430 | time_backward 1.3680 |
[2023-10-23 02:16:58,289::train::INFO] [train] Iter 564934 | loss 0.6191 | loss(rot) 0.4206 | loss(pos) 0.0326 | loss(seq) 0.1660 | grad 3.7585 | lr 0.0000 | time_forward 3.0030 | time_backward 3.9000 |
[2023-10-23 02:17:06,330::train::INFO] [train] Iter 564935 | loss 0.6395 | loss(rot) 0.3807 | loss(pos) 0.0608 | loss(seq) 0.1980 | grad 3.9246 | lr 0.0000 | time_forward 3.3850 | time_backward 4.6530 |
[2023-10-23 02:17:14,254::train::INFO] [train] Iter 564936 | loss 0.1761 | loss(rot) 0.1530 | loss(pos) 0.0231 | loss(seq) 0.0001 | grad 2.1816 | lr 0.0000 | time_forward 3.2690 | time_backward 4.6510 |
[2023-10-23 02:17:21,195::train::INFO] [train] Iter 564937 | loss 0.3801 | loss(rot) 0.2155 | loss(pos) 0.0276 | loss(seq) 0.1370 | grad 2.7107 | lr 0.0000 | time_forward 3.0220 | time_backward 3.9160 |
[2023-10-23 02:17:23,842::train::INFO] [train] Iter 564938 | loss 0.4305 | loss(rot) 0.1251 | loss(pos) 0.0401 | loss(seq) 0.2653 | grad 2.4022 | lr 0.0000 | time_forward 1.2590 | time_backward 1.3840 |
[2023-10-23 02:17:30,840::train::INFO] [train] Iter 564939 | loss 0.8183 | loss(rot) 0.4895 | loss(pos) 0.0852 | loss(seq) 0.2437 | grad 5.2103 | lr 0.0000 | time_forward 3.0470 | time_backward 3.9360 |
[2023-10-23 02:17:33,504::train::INFO] [train] Iter 564940 | loss 0.3061 | loss(rot) 0.1535 | loss(pos) 0.0196 | loss(seq) 0.1330 | grad 2.3176 | lr 0.0000 | time_forward 1.2760 | time_backward 1.3850 |
[2023-10-23 02:17:41,498::train::INFO] [train] Iter 564941 | loss 0.9922 | loss(rot) 0.2477 | loss(pos) 0.5402 | loss(seq) 0.2043 | grad 6.3397 | lr 0.0000 | time_forward 3.3230 | time_backward 4.6670 |
[2023-10-23 02:17:44,187::train::INFO] [train] Iter 564942 | loss 0.3864 | loss(rot) 0.2661 | loss(pos) 0.0790 | loss(seq) 0.0414 | grad 2.7735 | lr 0.0000 | time_forward 1.2970 | time_backward 1.3890 |
[2023-10-23 02:17:50,118::train::INFO] [train] Iter 564943 | loss 1.2199 | loss(rot) 0.0465 | loss(pos) 1.0163 | loss(seq) 0.1571 | grad 6.5150 | lr 0.0000 | time_forward 2.5660 | time_backward 3.3610 |
[2023-10-23 02:17:58,114::train::INFO] [train] Iter 564944 | loss 0.4217 | loss(rot) 0.2035 | loss(pos) 0.0367 | loss(seq) 0.1816 | grad 2.4711 | lr 0.0000 | time_forward 3.2160 | time_backward 4.7760 |
[2023-10-23 02:18:00,829::train::INFO] [train] Iter 564945 | loss 0.2156 | loss(rot) 0.1927 | loss(pos) 0.0212 | loss(seq) 0.0017 | grad 2.8269 | lr 0.0000 | time_forward 1.3250 | time_backward 1.3870 |
[2023-10-23 02:18:08,875::train::INFO] [train] Iter 564946 | loss 0.5851 | loss(rot) 0.1343 | loss(pos) 0.0288 | loss(seq) 0.4220 | grad 3.6642 | lr 0.0000 | time_forward 3.3650 | time_backward 4.6790 |
[2023-10-23 02:18:11,535::train::INFO] [train] Iter 564947 | loss 0.7726 | loss(rot) 0.5289 | loss(pos) 0.0441 | loss(seq) 0.1996 | grad 2.7850 | lr 0.0000 | time_forward 1.2940 | time_backward 1.3620 |
[2023-10-23 02:18:14,650::train::INFO] [train] Iter 564948 | loss 1.2717 | loss(rot) 0.7151 | loss(pos) 0.2104 | loss(seq) 0.3463 | grad 3.3685 | lr 0.0000 | time_forward 1.4460 | time_backward 1.6670 |
[2023-10-23 02:18:20,934::train::INFO] [train] Iter 564949 | loss 0.3386 | loss(rot) 0.0608 | loss(pos) 0.0564 | loss(seq) 0.2214 | grad 2.7823 | lr 0.0000 | time_forward 2.6420 | time_backward 3.6380 |
[2023-10-23 02:18:29,201::train::INFO] [train] Iter 564950 | loss 0.3201 | loss(rot) 0.1077 | loss(pos) 0.0293 | loss(seq) 0.1831 | grad 1.9424 | lr 0.0000 | time_forward 3.4180 | time_backward 4.8320 |
[2023-10-23 02:18:37,403::train::INFO] [train] Iter 564951 | loss 0.6960 | loss(rot) 0.5796 | loss(pos) 0.0208 | loss(seq) 0.0956 | grad 2.0366 | lr 0.0000 | time_forward 3.4420 | time_backward 4.7570 |
[2023-10-23 02:18:40,132::train::INFO] [train] Iter 564952 | loss 1.5571 | loss(rot) 0.0158 | loss(pos) 1.5374 | loss(seq) 0.0039 | grad 13.3939 | lr 0.0000 | time_forward 1.3300 | time_backward 1.3950 |
[2023-10-23 02:18:46,769::train::INFO] [train] Iter 564953 | loss 0.6767 | loss(rot) 0.0559 | loss(pos) 0.4811 | loss(seq) 0.1398 | grad 8.0826 | lr 0.0000 | time_forward 2.8720 | time_backward 3.7600 |
[2023-10-23 02:18:54,192::train::INFO] [train] Iter 564954 | loss 1.3278 | loss(rot) 1.2942 | loss(pos) 0.0319 | loss(seq) 0.0017 | grad 2.9248 | lr 0.0000 | time_forward 3.2310 | time_backward 4.1900 |
[2023-10-23 02:18:56,970::train::INFO] [train] Iter 564955 | loss 0.4846 | loss(rot) 0.1278 | loss(pos) 0.2972 | loss(seq) 0.0597 | grad 2.7483 | lr 0.0000 | time_forward 1.2730 | time_backward 1.5010 |
[2023-10-23 02:19:04,248::train::INFO] [train] Iter 564956 | loss 0.7882 | loss(rot) 0.4794 | loss(pos) 0.0208 | loss(seq) 0.2880 | grad 68.3631 | lr 0.0000 | time_forward 3.1120 | time_backward 4.1470 |
[2023-10-23 02:19:12,305::train::INFO] [train] Iter 564957 | loss 0.9405 | loss(rot) 0.7092 | loss(pos) 0.1106 | loss(seq) 0.1206 | grad 4.0969 | lr 0.0000 | time_forward 3.3810 | time_backward 4.6720 |
[2023-10-23 02:19:15,076::train::INFO] [train] Iter 564958 | loss 0.3181 | loss(rot) 0.1009 | loss(pos) 0.1891 | loss(seq) 0.0280 | grad 3.5741 | lr 0.0000 | time_forward 1.3020 | time_backward 1.4660 |
[2023-10-23 02:19:22,128::train::INFO] [train] Iter 564959 | loss 0.4423 | loss(rot) 0.1222 | loss(pos) 0.0283 | loss(seq) 0.2918 | grad 3.3197 | lr 0.0000 | time_forward 3.0410 | time_backward 3.9930 |
[2023-10-23 02:19:29,496::train::INFO] [train] Iter 564960 | loss 2.3444 | loss(rot) 2.0625 | loss(pos) 0.1213 | loss(seq) 0.1607 | grad 5.4628 | lr 0.0000 | time_forward 3.1960 | time_backward 4.1690 |
[2023-10-23 02:19:32,181::train::INFO] [train] Iter 564961 | loss 0.8820 | loss(rot) 0.6616 | loss(pos) 0.0384 | loss(seq) 0.1819 | grad 19.3725 | lr 0.0000 | time_forward 1.2720 | time_backward 1.4080 |
[2023-10-23 02:19:34,873::train::INFO] [train] Iter 564962 | loss 1.1221 | loss(rot) 0.6875 | loss(pos) 0.0736 | loss(seq) 0.3611 | grad 3.3643 | lr 0.0000 | time_forward 1.3010 | time_backward 1.3860 |
[2023-10-23 02:19:42,782::train::INFO] [train] Iter 564963 | loss 0.6809 | loss(rot) 0.0236 | loss(pos) 0.6558 | loss(seq) 0.0015 | grad 10.8205 | lr 0.0000 | time_forward 3.3120 | time_backward 4.5940 |
[2023-10-23 02:19:51,084::train::INFO] [train] Iter 564964 | loss 2.3175 | loss(rot) 1.9740 | loss(pos) 0.1964 | loss(seq) 0.1471 | grad 9.1166 | lr 0.0000 | time_forward 3.5490 | time_backward 4.7490 |
[2023-10-23 02:19:57,826::train::INFO] [train] Iter 564965 | loss 0.4004 | loss(rot) 0.0629 | loss(pos) 0.0626 | loss(seq) 0.2749 | grad 2.8993 | lr 0.0000 | time_forward 2.9720 | time_backward 3.7670 |
[2023-10-23 02:20:00,567::train::INFO] [train] Iter 564966 | loss 0.1875 | loss(rot) 0.1243 | loss(pos) 0.0184 | loss(seq) 0.0448 | grad 3.0249 | lr 0.0000 | time_forward 1.2670 | time_backward 1.4710 |
[2023-10-23 02:20:08,582::train::INFO] [train] Iter 564967 | loss 1.7068 | loss(rot) 1.1724 | loss(pos) 0.0708 | loss(seq) 0.4636 | grad 4.7394 | lr 0.0000 | time_forward 3.2990 | time_backward 4.7130 |
[2023-10-23 02:20:16,428::train::INFO] [train] Iter 564968 | loss 1.0735 | loss(rot) 0.8824 | loss(pos) 0.0228 | loss(seq) 0.1683 | grad 3.2732 | lr 0.0000 | time_forward 3.2530 | time_backward 4.5900 |
[2023-10-23 02:20:23,710::train::INFO] [train] Iter 564969 | loss 1.3397 | loss(rot) 1.1746 | loss(pos) 0.0806 | loss(seq) 0.0845 | grad 4.3348 | lr 0.0000 | time_forward 3.1510 | time_backward 4.1280 |
[2023-10-23 02:20:31,176::train::INFO] [train] Iter 564970 | loss 0.8087 | loss(rot) 0.7729 | loss(pos) 0.0173 | loss(seq) 0.0185 | grad 5.0743 | lr 0.0000 | time_forward 3.2320 | time_backward 4.2300 |
[2023-10-23 02:20:38,542::train::INFO] [train] Iter 564971 | loss 0.4159 | loss(rot) 0.0728 | loss(pos) 0.1050 | loss(seq) 0.2381 | grad 3.0863 | lr 0.0000 | time_forward 3.1900 | time_backward 4.1740 |
[2023-10-23 02:20:45,840::train::INFO] [train] Iter 564972 | loss 1.0797 | loss(rot) 0.7864 | loss(pos) 0.0948 | loss(seq) 0.1985 | grad 5.0724 | lr 0.0000 | time_forward 3.1960 | time_backward 4.0980 |
[2023-10-23 02:20:52,146::train::INFO] [train] Iter 564973 | loss 0.3047 | loss(rot) 0.1189 | loss(pos) 0.0319 | loss(seq) 0.1539 | grad 2.0600 | lr 0.0000 | time_forward 2.7970 | time_backward 3.5060 |
[2023-10-23 02:20:54,706::train::INFO] [train] Iter 564974 | loss 0.6811 | loss(rot) 0.2040 | loss(pos) 0.0641 | loss(seq) 0.4130 | grad 4.0664 | lr 0.0000 | time_forward 1.2360 | time_backward 1.3200 |
[2023-10-23 02:21:02,091::train::INFO] [train] Iter 564975 | loss 0.4802 | loss(rot) 0.0897 | loss(pos) 0.3852 | loss(seq) 0.0053 | grad 7.2218 | lr 0.0000 | time_forward 3.1860 | time_backward 4.1960 |
[2023-10-23 02:21:09,046::train::INFO] [train] Iter 564976 | loss 0.6770 | loss(rot) 0.2916 | loss(pos) 0.0374 | loss(seq) 0.3479 | grad 3.0933 | lr 0.0000 | time_forward 3.0240 | time_backward 3.9270 |
[2023-10-23 02:21:17,258::train::INFO] [train] Iter 564977 | loss 0.2768 | loss(rot) 0.1942 | loss(pos) 0.0205 | loss(seq) 0.0621 | grad 5.8853 | lr 0.0000 | time_forward 3.2480 | time_backward 4.9620 |
[2023-10-23 02:21:18,874::train::INFO] [train] Iter 564978 | loss 0.2334 | loss(rot) 0.1122 | loss(pos) 0.0242 | loss(seq) 0.0971 | grad 2.5220 | lr 0.0000 | time_forward 0.7660 | time_backward 0.8470 |
[2023-10-23 02:21:26,159::train::INFO] [train] Iter 564979 | loss 0.6251 | loss(rot) 0.5831 | loss(pos) 0.0162 | loss(seq) 0.0258 | grad 2.9174 | lr 0.0000 | time_forward 3.1010 | time_backward 4.1800 |
[2023-10-23 02:21:28,496::train::INFO] [train] Iter 564980 | loss 0.2860 | loss(rot) 0.1231 | loss(pos) 0.1244 | loss(seq) 0.0385 | grad 4.6435 | lr 0.0000 | time_forward 1.0710 | time_backward 1.2640 |
[2023-10-23 02:21:35,339::train::INFO] [train] Iter 564981 | loss 1.8486 | loss(rot) 1.8239 | loss(pos) 0.0246 | loss(seq) 0.0000 | grad 4.4863 | lr 0.0000 | time_forward 2.9650 | time_backward 3.8740 |
[2023-10-23 02:21:41,934::train::INFO] [train] Iter 564982 | loss 0.3735 | loss(rot) 0.3084 | loss(pos) 0.0252 | loss(seq) 0.0399 | grad 8.8286 | lr 0.0000 | time_forward 2.8380 | time_backward 3.7540 |
[2023-10-23 02:21:44,599::train::INFO] [train] Iter 564983 | loss 0.4416 | loss(rot) 0.3089 | loss(pos) 0.0287 | loss(seq) 0.1040 | grad 2.4671 | lr 0.0000 | time_forward 1.2730 | time_backward 1.3880 |
[2023-10-23 02:21:52,492::train::INFO] [train] Iter 564984 | loss 0.4221 | loss(rot) 0.3285 | loss(pos) 0.0201 | loss(seq) 0.0735 | grad 6.8127 | lr 0.0000 | time_forward 3.2810 | time_backward 4.5920 |
[2023-10-23 02:21:58,723::train::INFO] [train] Iter 564985 | loss 0.4298 | loss(rot) 0.2739 | loss(pos) 0.0221 | loss(seq) 0.1339 | grad 4.0326 | lr 0.0000 | time_forward 2.7040 | time_backward 3.5240 |
[2023-10-23 02:22:01,425::train::INFO] [train] Iter 564986 | loss 0.4562 | loss(rot) 0.0777 | loss(pos) 0.0449 | loss(seq) 0.3337 | grad 2.8172 | lr 0.0000 | time_forward 1.2920 | time_backward 1.4070 |
[2023-10-23 02:22:08,889::train::INFO] [train] Iter 564987 | loss 0.6571 | loss(rot) 0.6235 | loss(pos) 0.0240 | loss(seq) 0.0095 | grad 2.2942 | lr 0.0000 | time_forward 3.2540 | time_backward 4.2060 |
[2023-10-23 02:22:16,933::train::INFO] [train] Iter 564988 | loss 1.0129 | loss(rot) 0.3808 | loss(pos) 0.6056 | loss(seq) 0.0266 | grad 6.3633 | lr 0.0000 | time_forward 3.3040 | time_backward 4.7370 |
[2023-10-23 02:22:24,478::train::INFO] [train] Iter 564989 | loss 0.4183 | loss(rot) 0.1588 | loss(pos) 0.0237 | loss(seq) 0.2359 | grad 3.6377 | lr 0.0000 | time_forward 3.2220 | time_backward 4.3200 |
[2023-10-23 02:22:32,637::train::INFO] [train] Iter 564990 | loss 0.9098 | loss(rot) 0.1309 | loss(pos) 0.2626 | loss(seq) 0.5163 | grad 4.1278 | lr 0.0000 | time_forward 3.4820 | time_backward 4.6740 |
[2023-10-23 02:22:35,313::train::INFO] [train] Iter 564991 | loss 0.3159 | loss(rot) 0.0580 | loss(pos) 0.2376 | loss(seq) 0.0202 | grad 4.3866 | lr 0.0000 | time_forward 1.2790 | time_backward 1.3940 |
[2023-10-23 02:22:41,932::train::INFO] [train] Iter 564992 | loss 0.8358 | loss(rot) 0.3370 | loss(pos) 0.1554 | loss(seq) 0.3434 | grad 4.2734 | lr 0.0000 | time_forward 2.8790 | time_backward 3.7370 |
[2023-10-23 02:22:49,784::train::INFO] [train] Iter 564993 | loss 0.6278 | loss(rot) 0.1533 | loss(pos) 0.2617 | loss(seq) 0.2128 | grad 4.0716 | lr 0.0000 | time_forward 3.3770 | time_backward 4.4710 |
[2023-10-23 02:22:57,832::train::INFO] [train] Iter 564994 | loss 1.0312 | loss(rot) 0.4385 | loss(pos) 0.3536 | loss(seq) 0.2392 | grad 4.4339 | lr 0.0000 | time_forward 3.4330 | time_backward 4.6120 |
[2023-10-23 02:23:00,488::train::INFO] [train] Iter 564995 | loss 0.4878 | loss(rot) 0.4685 | loss(pos) 0.0185 | loss(seq) 0.0008 | grad 12.1643 | lr 0.0000 | time_forward 1.2700 | time_backward 1.3830 |
[2023-10-23 02:23:08,416::train::INFO] [train] Iter 564996 | loss 0.4179 | loss(rot) 0.1949 | loss(pos) 0.0755 | loss(seq) 0.1475 | grad 3.0327 | lr 0.0000 | time_forward 3.4860 | time_backward 4.4370 |
[2023-10-23 02:23:16,610::train::INFO] [train] Iter 564997 | loss 0.7505 | loss(rot) 0.4248 | loss(pos) 0.0366 | loss(seq) 0.2891 | grad 3.3692 | lr 0.0000 | time_forward 3.5570 | time_backward 4.6340 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.