text stringlengths 56 1.16k |
|---|
[2023-09-02 22:25:07,004::train::INFO] [train] Iter 14881 | loss 1.1452 | loss(rot) 0.7905 | loss(pos) 0.2277 | loss(seq) 0.1271 | grad 7.1871 | lr 0.0010 | time_forward 4.1350 | time_backward 5.0820 |
[2023-09-02 22:25:17,244::train::INFO] [train] Iter 14882 | loss 2.1408 | loss(rot) 1.0684 | loss(pos) 0.5036 | loss(seq) 0.5688 | grad 5.2344 | lr 0.0010 | time_forward 4.1830 | time_backward 6.0530 |
[2023-09-02 22:25:27,846::train::INFO] [train] Iter 14883 | loss 0.8194 | loss(rot) 0.1888 | loss(pos) 0.5690 | loss(seq) 0.0616 | grad 4.1506 | lr 0.0010 | time_forward 4.4030 | time_backward 6.1950 |
[2023-09-02 22:25:38,278::train::INFO] [train] Iter 14884 | loss 1.2739 | loss(rot) 0.8883 | loss(pos) 0.1196 | loss(seq) 0.2660 | grad 5.2379 | lr 0.0010 | time_forward 4.1940 | time_backward 6.2170 |
[2023-09-02 22:25:40,456::train::INFO] [train] Iter 14885 | loss 3.2598 | loss(rot) 0.0780 | loss(pos) 3.1761 | loss(seq) 0.0056 | grad 12.1092 | lr 0.0010 | time_forward 1.0370 | time_backward 1.1370 |
[2023-09-02 22:25:51,056::train::INFO] [train] Iter 14886 | loss 1.1115 | loss(rot) 0.0333 | loss(pos) 1.0715 | loss(seq) 0.0067 | grad 3.8211 | lr 0.0010 | time_forward 4.3330 | time_backward 6.2640 |
[2023-09-02 22:25:59,789::train::INFO] [train] Iter 14887 | loss 2.6124 | loss(rot) 1.2114 | loss(pos) 0.8577 | loss(seq) 0.5433 | grad 7.3112 | lr 0.0010 | time_forward 3.7010 | time_backward 5.0280 |
[2023-09-02 22:26:10,248::train::INFO] [train] Iter 14888 | loss 1.6509 | loss(rot) 1.5878 | loss(pos) 0.0561 | loss(seq) 0.0070 | grad 5.1076 | lr 0.0010 | time_forward 4.2010 | time_backward 6.2540 |
[2023-09-02 22:26:22,578::train::INFO] [train] Iter 14889 | loss 1.6090 | loss(rot) 1.3294 | loss(pos) 0.2645 | loss(seq) 0.0151 | grad 3.9159 | lr 0.0010 | time_forward 6.1470 | time_backward 6.1800 |
[2023-09-02 22:26:33,789::train::INFO] [train] Iter 14890 | loss 1.4018 | loss(rot) 1.2850 | loss(pos) 0.1004 | loss(seq) 0.0165 | grad 13.0458 | lr 0.0010 | time_forward 4.9160 | time_backward 6.2910 |
[2023-09-02 22:26:44,827::train::INFO] [train] Iter 14891 | loss 1.8473 | loss(rot) 0.0305 | loss(pos) 1.8116 | loss(seq) 0.0052 | grad 5.6955 | lr 0.0010 | time_forward 4.8590 | time_backward 6.1750 |
[2023-09-02 22:26:52,681::train::INFO] [train] Iter 14892 | loss 2.0787 | loss(rot) 1.9394 | loss(pos) 0.1255 | loss(seq) 0.0137 | grad 5.7447 | lr 0.0010 | time_forward 3.2620 | time_backward 4.5880 |
[2023-09-02 22:27:01,965::train::INFO] [train] Iter 14893 | loss 1.5883 | loss(rot) 0.8175 | loss(pos) 0.1847 | loss(seq) 0.5862 | grad 3.9762 | lr 0.0010 | time_forward 3.7240 | time_backward 5.5570 |
[2023-09-02 22:27:04,743::train::INFO] [train] Iter 14894 | loss 1.4576 | loss(rot) 0.6659 | loss(pos) 0.2541 | loss(seq) 0.5376 | grad 3.7026 | lr 0.0010 | time_forward 1.2900 | time_backward 1.4840 |
[2023-09-02 22:27:13,269::train::INFO] [train] Iter 14895 | loss 2.3824 | loss(rot) 1.6759 | loss(pos) 0.1426 | loss(seq) 0.5639 | grad 5.6852 | lr 0.0010 | time_forward 3.5620 | time_backward 4.9600 |
[2023-09-02 22:27:25,095::train::INFO] [train] Iter 14896 | loss 3.4342 | loss(rot) 0.0288 | loss(pos) 3.4054 | loss(seq) 0.0000 | grad 7.7419 | lr 0.0010 | time_forward 5.4230 | time_backward 6.4000 |
[2023-09-02 22:27:36,038::train::INFO] [train] Iter 14897 | loss 1.4342 | loss(rot) 0.2666 | loss(pos) 0.7409 | loss(seq) 0.4267 | grad 4.8837 | lr 0.0010 | time_forward 4.5490 | time_backward 6.3890 |
[2023-09-02 22:27:45,288::train::INFO] [train] Iter 14898 | loss 1.1632 | loss(rot) 0.3181 | loss(pos) 0.4458 | loss(seq) 0.3994 | grad 3.7630 | lr 0.0010 | time_forward 3.8830 | time_backward 5.3640 |
[2023-09-02 22:27:54,061::train::INFO] [train] Iter 14899 | loss 1.2953 | loss(rot) 1.1169 | loss(pos) 0.1772 | loss(seq) 0.0012 | grad 5.0411 | lr 0.0010 | time_forward 3.5540 | time_backward 5.2150 |
[2023-09-02 22:27:56,680::train::INFO] [train] Iter 14900 | loss 1.5441 | loss(rot) 0.7402 | loss(pos) 0.3053 | loss(seq) 0.4986 | grad 3.5667 | lr 0.0010 | time_forward 1.2280 | time_backward 1.3870 |
[2023-09-02 22:27:59,592::train::INFO] [train] Iter 14901 | loss 1.6825 | loss(rot) 1.0905 | loss(pos) 0.1583 | loss(seq) 0.4337 | grad 5.4696 | lr 0.0010 | time_forward 1.3670 | time_backward 1.5410 |
[2023-09-02 22:28:10,287::train::INFO] [train] Iter 14902 | loss 1.3638 | loss(rot) 1.1753 | loss(pos) 0.1885 | loss(seq) 0.0000 | grad 4.1068 | lr 0.0010 | time_forward 4.2790 | time_backward 6.4120 |
[2023-09-02 22:28:20,875::train::INFO] [train] Iter 14903 | loss 1.5830 | loss(rot) 1.2019 | loss(pos) 0.1392 | loss(seq) 0.2419 | grad 4.7488 | lr 0.0010 | time_forward 4.1810 | time_backward 6.4030 |
[2023-09-02 22:28:23,692::train::INFO] [train] Iter 14904 | loss 1.5976 | loss(rot) 1.3947 | loss(pos) 0.1800 | loss(seq) 0.0229 | grad 4.4362 | lr 0.0010 | time_forward 1.3130 | time_backward 1.5000 |
[2023-09-02 22:28:33,409::train::INFO] [train] Iter 14905 | loss 1.9544 | loss(rot) 1.7722 | loss(pos) 0.1286 | loss(seq) 0.0536 | grad 8.1102 | lr 0.0010 | time_forward 4.1120 | time_backward 5.6000 |
[2023-09-02 22:28:44,071::train::INFO] [train] Iter 14906 | loss 2.1079 | loss(rot) 1.3559 | loss(pos) 0.2028 | loss(seq) 0.5492 | grad 4.7262 | lr 0.0010 | time_forward 4.3440 | time_backward 6.3140 |
[2023-09-02 22:28:54,533::train::INFO] [train] Iter 14907 | loss 0.9783 | loss(rot) 0.2653 | loss(pos) 0.2476 | loss(seq) 0.4654 | grad 4.5259 | lr 0.0010 | time_forward 4.2730 | time_backward 6.1840 |
[2023-09-02 22:29:06,015::train::INFO] [train] Iter 14908 | loss 2.9847 | loss(rot) 2.8368 | loss(pos) 0.1466 | loss(seq) 0.0013 | grad 3.9196 | lr 0.0010 | time_forward 4.7270 | time_backward 6.7500 |
[2023-09-02 22:29:14,903::train::INFO] [train] Iter 14909 | loss 1.4341 | loss(rot) 0.7699 | loss(pos) 0.1075 | loss(seq) 0.5568 | grad 5.8736 | lr 0.0010 | time_forward 3.7170 | time_backward 5.1680 |
[2023-09-02 22:29:25,972::train::INFO] [train] Iter 14910 | loss 1.2150 | loss(rot) 0.3491 | loss(pos) 0.8229 | loss(seq) 0.0430 | grad 5.6214 | lr 0.0010 | time_forward 5.0310 | time_backward 6.0330 |
[2023-09-02 22:29:34,901::train::INFO] [train] Iter 14911 | loss 1.2310 | loss(rot) 0.7620 | loss(pos) 0.2026 | loss(seq) 0.2665 | grad 5.1596 | lr 0.0010 | time_forward 3.7810 | time_backward 5.1450 |
[2023-09-02 22:29:38,373::train::INFO] [train] Iter 14912 | loss 1.9819 | loss(rot) 1.5573 | loss(pos) 0.4246 | loss(seq) 0.0000 | grad 7.0415 | lr 0.0010 | time_forward 1.5230 | time_backward 1.9440 |
[2023-09-02 22:29:47,913::train::INFO] [train] Iter 14913 | loss 2.3588 | loss(rot) 2.2485 | loss(pos) 0.1033 | loss(seq) 0.0070 | grad 3.4142 | lr 0.0010 | time_forward 4.0240 | time_backward 5.5120 |
[2023-09-02 22:29:50,517::train::INFO] [train] Iter 14914 | loss 0.5806 | loss(rot) 0.1365 | loss(pos) 0.3974 | loss(seq) 0.0468 | grad 3.0441 | lr 0.0010 | time_forward 1.2140 | time_backward 1.3860 |
[2023-09-02 22:30:00,551::train::INFO] [train] Iter 14915 | loss 2.3162 | loss(rot) 0.0221 | loss(pos) 2.2935 | loss(seq) 0.0006 | grad 8.1527 | lr 0.0010 | time_forward 4.2470 | time_backward 5.7840 |
[2023-09-02 22:30:09,946::train::INFO] [train] Iter 14916 | loss 0.6378 | loss(rot) 0.4900 | loss(pos) 0.1478 | loss(seq) 0.0000 | grad 3.7829 | lr 0.0010 | time_forward 3.9020 | time_backward 5.4890 |
[2023-09-02 22:30:17,918::train::INFO] [train] Iter 14917 | loss 2.3537 | loss(rot) 1.6986 | loss(pos) 0.1859 | loss(seq) 0.4693 | grad 8.0556 | lr 0.0010 | time_forward 3.3960 | time_backward 4.5710 |
[2023-09-02 22:30:25,469::train::INFO] [train] Iter 14918 | loss 1.0941 | loss(rot) 0.9566 | loss(pos) 0.1040 | loss(seq) 0.0335 | grad 5.3984 | lr 0.0010 | time_forward 3.1500 | time_backward 4.3970 |
[2023-09-02 22:30:34,515::train::INFO] [train] Iter 14919 | loss 1.3096 | loss(rot) 0.3898 | loss(pos) 0.2678 | loss(seq) 0.6519 | grad 4.9800 | lr 0.0010 | time_forward 3.8640 | time_backward 5.1780 |
[2023-09-02 22:30:37,316::train::INFO] [train] Iter 14920 | loss 1.2367 | loss(rot) 0.2797 | loss(pos) 0.5162 | loss(seq) 0.4408 | grad 3.3161 | lr 0.0010 | time_forward 1.2880 | time_backward 1.5100 |
[2023-09-02 22:30:45,934::train::INFO] [train] Iter 14921 | loss 2.4585 | loss(rot) 1.9725 | loss(pos) 0.1897 | loss(seq) 0.2963 | grad 7.9062 | lr 0.0010 | time_forward 3.5600 | time_backward 5.0540 |
[2023-09-02 22:30:54,627::train::INFO] [train] Iter 14922 | loss 0.9891 | loss(rot) 0.3497 | loss(pos) 0.3583 | loss(seq) 0.2811 | grad 3.8598 | lr 0.0010 | time_forward 3.6150 | time_backward 5.0690 |
[2023-09-02 22:30:57,394::train::INFO] [train] Iter 14923 | loss 1.3579 | loss(rot) 0.0169 | loss(pos) 1.3392 | loss(seq) 0.0018 | grad 6.8201 | lr 0.0010 | time_forward 1.2980 | time_backward 1.4650 |
[2023-09-02 22:31:07,796::train::INFO] [train] Iter 14924 | loss 1.5693 | loss(rot) 0.8140 | loss(pos) 0.1412 | loss(seq) 0.6141 | grad 5.3374 | lr 0.0010 | time_forward 4.3820 | time_backward 6.0150 |
[2023-09-02 22:31:10,879::train::INFO] [train] Iter 14925 | loss 1.7645 | loss(rot) 1.5950 | loss(pos) 0.1695 | loss(seq) 0.0000 | grad 7.6944 | lr 0.0010 | time_forward 1.3590 | time_backward 1.7180 |
[2023-09-02 22:31:19,658::train::INFO] [train] Iter 14926 | loss 1.2916 | loss(rot) 0.7288 | loss(pos) 0.2105 | loss(seq) 0.3522 | grad 14.9266 | lr 0.0010 | time_forward 3.6770 | time_backward 5.0970 |
[2023-09-02 22:31:28,305::train::INFO] [train] Iter 14927 | loss 1.0835 | loss(rot) 0.2431 | loss(pos) 0.3529 | loss(seq) 0.4875 | grad 4.5071 | lr 0.0010 | time_forward 3.6130 | time_backward 5.0320 |
[2023-09-02 22:31:36,758::train::INFO] [train] Iter 14928 | loss 2.0383 | loss(rot) 1.8688 | loss(pos) 0.1049 | loss(seq) 0.0646 | grad 6.6088 | lr 0.0010 | time_forward 3.4540 | time_backward 4.9950 |
[2023-09-02 22:31:45,359::train::INFO] [train] Iter 14929 | loss 2.1565 | loss(rot) 0.6284 | loss(pos) 1.5126 | loss(seq) 0.0155 | grad 7.2203 | lr 0.0010 | time_forward 3.5540 | time_backward 5.0430 |
[2023-09-02 22:31:52,899::train::INFO] [train] Iter 14930 | loss 2.0566 | loss(rot) 1.7191 | loss(pos) 0.1190 | loss(seq) 0.2185 | grad 3.6857 | lr 0.0010 | time_forward 3.1140 | time_backward 4.4230 |
[2023-09-02 22:32:03,583::train::INFO] [train] Iter 14931 | loss 1.3586 | loss(rot) 0.5610 | loss(pos) 0.5221 | loss(seq) 0.2755 | grad 3.9221 | lr 0.0010 | time_forward 4.2420 | time_backward 6.4380 |
[2023-09-02 22:32:14,056::train::INFO] [train] Iter 14932 | loss 1.9578 | loss(rot) 0.8954 | loss(pos) 0.4989 | loss(seq) 0.5635 | grad 5.5685 | lr 0.0010 | time_forward 4.3120 | time_backward 6.1580 |
[2023-09-02 22:32:21,180::train::INFO] [train] Iter 14933 | loss 1.7727 | loss(rot) 0.9669 | loss(pos) 0.5017 | loss(seq) 0.3041 | grad 5.3000 | lr 0.0010 | time_forward 3.0670 | time_backward 4.0530 |
[2023-09-02 22:32:30,626::train::INFO] [train] Iter 14934 | loss 1.3217 | loss(rot) 0.3552 | loss(pos) 0.7130 | loss(seq) 0.2535 | grad 8.0609 | lr 0.0010 | time_forward 4.0180 | time_backward 5.4230 |
[2023-09-02 22:32:41,185::train::INFO] [train] Iter 14935 | loss 1.5765 | loss(rot) 0.1804 | loss(pos) 1.0034 | loss(seq) 0.3927 | grad 8.8256 | lr 0.0010 | time_forward 4.2620 | time_backward 6.2940 |
[2023-09-02 22:32:44,064::train::INFO] [train] Iter 14936 | loss 0.7711 | loss(rot) 0.1486 | loss(pos) 0.5959 | loss(seq) 0.0265 | grad 5.4670 | lr 0.0010 | time_forward 1.3140 | time_backward 1.5620 |
[2023-09-02 22:32:53,926::train::INFO] [train] Iter 14937 | loss 1.2115 | loss(rot) 0.6196 | loss(pos) 0.1512 | loss(seq) 0.4407 | grad 8.6893 | lr 0.0010 | time_forward 4.1180 | time_backward 5.7400 |
[2023-09-02 22:33:04,469::train::INFO] [train] Iter 14938 | loss 2.1961 | loss(rot) 1.1498 | loss(pos) 0.4891 | loss(seq) 0.5573 | grad 3.5581 | lr 0.0010 | time_forward 4.4220 | time_backward 6.1180 |
[2023-09-02 22:33:07,387::train::INFO] [train] Iter 14939 | loss 3.0666 | loss(rot) 0.0315 | loss(pos) 3.0332 | loss(seq) 0.0019 | grad 11.7895 | lr 0.0010 | time_forward 1.3010 | time_backward 1.5990 |
[2023-09-02 22:33:17,950::train::INFO] [train] Iter 14940 | loss 1.4246 | loss(rot) 0.3910 | loss(pos) 0.7248 | loss(seq) 0.3087 | grad 4.1929 | lr 0.0010 | time_forward 4.2570 | time_backward 6.3010 |
[2023-09-02 22:33:27,628::train::INFO] [train] Iter 14941 | loss 0.7755 | loss(rot) 0.0587 | loss(pos) 0.6997 | loss(seq) 0.0171 | grad 3.7633 | lr 0.0010 | time_forward 4.0970 | time_backward 5.5770 |
[2023-09-02 22:33:38,204::train::INFO] [train] Iter 14942 | loss 2.3038 | loss(rot) 1.6246 | loss(pos) 0.2552 | loss(seq) 0.4241 | grad 8.5163 | lr 0.0010 | time_forward 4.2740 | time_backward 6.2980 |
[2023-09-02 22:33:48,733::train::INFO] [train] Iter 14943 | loss 2.5852 | loss(rot) 2.0239 | loss(pos) 0.3245 | loss(seq) 0.2368 | grad 5.1766 | lr 0.0010 | time_forward 4.2610 | time_backward 6.2640 |
[2023-09-02 22:33:59,091::train::INFO] [train] Iter 14944 | loss 1.4229 | loss(rot) 0.2768 | loss(pos) 0.5215 | loss(seq) 0.6246 | grad 3.0931 | lr 0.0010 | time_forward 4.3170 | time_backward 6.0380 |
[2023-09-02 22:34:06,085::train::INFO] [train] Iter 14945 | loss 1.3997 | loss(rot) 0.8323 | loss(pos) 0.2926 | loss(seq) 0.2748 | grad 6.0164 | lr 0.0010 | time_forward 2.8810 | time_backward 4.1090 |
[2023-09-02 22:34:08,479::train::INFO] [train] Iter 14946 | loss 1.0822 | loss(rot) 0.2105 | loss(pos) 0.7121 | loss(seq) 0.1596 | grad 5.7645 | lr 0.0010 | time_forward 1.1080 | time_backward 1.2810 |
[2023-09-02 22:34:11,412::train::INFO] [train] Iter 14947 | loss 1.3820 | loss(rot) 0.1455 | loss(pos) 1.2190 | loss(seq) 0.0175 | grad 4.1130 | lr 0.0010 | time_forward 1.3530 | time_backward 1.5770 |
[2023-09-02 22:34:21,996::train::INFO] [train] Iter 14948 | loss 0.7389 | loss(rot) 0.1278 | loss(pos) 0.3451 | loss(seq) 0.2661 | grad 3.4405 | lr 0.0010 | time_forward 4.3670 | time_backward 6.2130 |
[2023-09-02 22:34:25,020::train::INFO] [train] Iter 14949 | loss 1.8795 | loss(rot) 1.1718 | loss(pos) 0.1801 | loss(seq) 0.5276 | grad 5.3973 | lr 0.0010 | time_forward 1.3530 | time_backward 1.6650 |
[2023-09-02 22:34:35,693::train::INFO] [train] Iter 14950 | loss 1.4841 | loss(rot) 0.5844 | loss(pos) 0.4637 | loss(seq) 0.4361 | grad 4.0706 | lr 0.0010 | time_forward 4.3710 | time_backward 6.2980 |
[2023-09-02 22:34:44,602::train::INFO] [train] Iter 14951 | loss 1.3099 | loss(rot) 0.7868 | loss(pos) 0.1086 | loss(seq) 0.4145 | grad 9.1882 | lr 0.0010 | time_forward 3.7370 | time_backward 5.1590 |
[2023-09-02 22:34:55,070::train::INFO] [train] Iter 14952 | loss 2.3301 | loss(rot) 1.1453 | loss(pos) 0.6583 | loss(seq) 0.5265 | grad 4.7643 | lr 0.0010 | time_forward 4.2810 | time_backward 6.1840 |
[2023-09-02 22:35:04,076::train::INFO] [train] Iter 14953 | loss 0.7901 | loss(rot) 0.0296 | loss(pos) 0.7574 | loss(seq) 0.0031 | grad 5.0688 | lr 0.0010 | time_forward 3.6550 | time_backward 5.3460 |
[2023-09-02 22:35:10,379::train::INFO] [train] Iter 14954 | loss 0.8829 | loss(rot) 0.4343 | loss(pos) 0.1869 | loss(seq) 0.2617 | grad 3.7140 | lr 0.0010 | time_forward 2.6190 | time_backward 3.6800 |
[2023-09-02 22:35:13,246::train::INFO] [train] Iter 14955 | loss 1.5174 | loss(rot) 0.5756 | loss(pos) 0.3662 | loss(seq) 0.5756 | grad 3.6104 | lr 0.0010 | time_forward 1.2950 | time_backward 1.5690 |
[2023-09-02 22:35:23,073::train::INFO] [train] Iter 14956 | loss 1.6854 | loss(rot) 1.5765 | loss(pos) 0.1068 | loss(seq) 0.0021 | grad 13.0699 | lr 0.0010 | time_forward 4.1050 | time_backward 5.7190 |
[2023-09-02 22:35:33,580::train::INFO] [train] Iter 14957 | loss 2.7641 | loss(rot) 2.3012 | loss(pos) 0.2548 | loss(seq) 0.2081 | grad 3.7448 | lr 0.0010 | time_forward 4.1970 | time_backward 6.3050 |
[2023-09-02 22:35:40,380::train::INFO] [train] Iter 14958 | loss 1.6169 | loss(rot) 0.0677 | loss(pos) 1.5463 | loss(seq) 0.0029 | grad 7.2019 | lr 0.0010 | time_forward 2.8330 | time_backward 3.9640 |
[2023-09-02 22:35:51,072::train::INFO] [train] Iter 14959 | loss 0.8633 | loss(rot) 0.0796 | loss(pos) 0.7669 | loss(seq) 0.0168 | grad 4.8219 | lr 0.0010 | time_forward 4.4200 | time_backward 6.2690 |
[2023-09-02 22:35:53,956::train::INFO] [train] Iter 14960 | loss 1.4656 | loss(rot) 0.7974 | loss(pos) 0.2027 | loss(seq) 0.4656 | grad 4.8343 | lr 0.0010 | time_forward 1.3380 | time_backward 1.5390 |
[2023-09-02 22:36:04,765::train::INFO] [train] Iter 14961 | loss 2.3974 | loss(rot) 2.2951 | loss(pos) 0.1021 | loss(seq) 0.0001 | grad 4.3402 | lr 0.0010 | time_forward 4.5350 | time_backward 6.2700 |
[2023-09-02 22:36:07,407::train::INFO] [train] Iter 14962 | loss 1.9270 | loss(rot) 1.0619 | loss(pos) 0.3179 | loss(seq) 0.5472 | grad 4.8289 | lr 0.0010 | time_forward 1.2490 | time_backward 1.3880 |
[2023-09-02 22:36:17,970::train::INFO] [train] Iter 14963 | loss 1.3600 | loss(rot) 0.3456 | loss(pos) 0.5377 | loss(seq) 0.4767 | grad 5.4867 | lr 0.0010 | time_forward 4.3260 | time_backward 6.2340 |
[2023-09-02 22:36:29,021::train::INFO] [train] Iter 14964 | loss 2.1941 | loss(rot) 1.6340 | loss(pos) 0.3414 | loss(seq) 0.2186 | grad 11.0967 | lr 0.0010 | time_forward 4.6500 | time_backward 6.3970 |
[2023-09-02 22:36:38,142::train::INFO] [train] Iter 14965 | loss 1.2460 | loss(rot) 0.7594 | loss(pos) 0.1723 | loss(seq) 0.3143 | grad 5.8305 | lr 0.0010 | time_forward 3.8890 | time_backward 5.2280 |
[2023-09-02 22:36:48,747::train::INFO] [train] Iter 14966 | loss 2.2002 | loss(rot) 1.5417 | loss(pos) 0.2248 | loss(seq) 0.4337 | grad 5.2115 | lr 0.0010 | time_forward 4.2450 | time_backward 6.3560 |
[2023-09-02 22:36:57,990::train::INFO] [train] Iter 14967 | loss 1.2144 | loss(rot) 0.9736 | loss(pos) 0.1471 | loss(seq) 0.0937 | grad 11.7864 | lr 0.0010 | time_forward 3.9790 | time_backward 5.2610 |
[2023-09-02 22:37:08,808::train::INFO] [train] Iter 14968 | loss 0.8476 | loss(rot) 0.6589 | loss(pos) 0.1212 | loss(seq) 0.0674 | grad 5.0932 | lr 0.0010 | time_forward 4.2880 | time_backward 6.5260 |
[2023-09-02 22:37:17,371::train::INFO] [train] Iter 14969 | loss 1.7271 | loss(rot) 1.5495 | loss(pos) 0.1776 | loss(seq) 0.0000 | grad 10.6990 | lr 0.0010 | time_forward 3.5820 | time_backward 4.9770 |
[2023-09-02 22:37:27,933::train::INFO] [train] Iter 14970 | loss 1.7333 | loss(rot) 0.8741 | loss(pos) 0.2215 | loss(seq) 0.6377 | grad 4.5091 | lr 0.0010 | time_forward 4.2630 | time_backward 6.2960 |
[2023-09-02 22:37:30,783::train::INFO] [train] Iter 14971 | loss 0.7105 | loss(rot) 0.1266 | loss(pos) 0.3919 | loss(seq) 0.1920 | grad 3.6200 | lr 0.0010 | time_forward 1.3260 | time_backward 1.5190 |
[2023-09-02 22:37:41,325::train::INFO] [train] Iter 14972 | loss 1.3294 | loss(rot) 0.4678 | loss(pos) 0.5596 | loss(seq) 0.3020 | grad 5.5047 | lr 0.0010 | time_forward 4.2110 | time_backward 6.3280 |
[2023-09-02 22:37:44,170::train::INFO] [train] Iter 14973 | loss 1.4297 | loss(rot) 0.8196 | loss(pos) 0.1229 | loss(seq) 0.4872 | grad 4.3196 | lr 0.0010 | time_forward 1.2940 | time_backward 1.5470 |
[2023-09-02 22:37:53,040::train::INFO] [train] Iter 14974 | loss 2.1258 | loss(rot) 1.9530 | loss(pos) 0.0812 | loss(seq) 0.0916 | grad 5.9793 | lr 0.0010 | time_forward 3.7670 | time_backward 5.0990 |
[2023-09-02 22:37:55,863::train::INFO] [train] Iter 14975 | loss 1.8308 | loss(rot) 1.0848 | loss(pos) 0.2166 | loss(seq) 0.5294 | grad 3.6254 | lr 0.0010 | time_forward 1.3060 | time_backward 1.5130 |
[2023-09-02 22:38:06,642::train::INFO] [train] Iter 14976 | loss 1.7822 | loss(rot) 0.8243 | loss(pos) 0.3271 | loss(seq) 0.6308 | grad 4.2981 | lr 0.0010 | time_forward 4.3470 | time_backward 6.4280 |
[2023-09-02 22:38:16,359::train::INFO] [train] Iter 14977 | loss 1.1485 | loss(rot) 0.1389 | loss(pos) 0.9776 | loss(seq) 0.0320 | grad 6.6604 | lr 0.0010 | time_forward 4.0700 | time_backward 5.6420 |
[2023-09-02 22:38:26,936::train::INFO] [train] Iter 14978 | loss 2.0421 | loss(rot) 1.6479 | loss(pos) 0.3939 | loss(seq) 0.0003 | grad 4.9086 | lr 0.0010 | time_forward 4.2570 | time_backward 6.3180 |
[2023-09-02 22:38:35,794::train::INFO] [train] Iter 14979 | loss 0.7037 | loss(rot) 0.1533 | loss(pos) 0.3149 | loss(seq) 0.2356 | grad 2.6781 | lr 0.0010 | time_forward 3.7020 | time_backward 5.1510 |
[2023-09-02 22:38:46,070::train::INFO] [train] Iter 14980 | loss 1.4316 | loss(rot) 0.6544 | loss(pos) 0.1854 | loss(seq) 0.5917 | grad 3.8622 | lr 0.0010 | time_forward 4.4590 | time_backward 5.7930 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.