text stringlengths 56 1.16k |
|---|
[2023-10-22 17:43:34,261::train::INFO] [train] Iter 560202 | loss 1.0687 | loss(rot) 0.0084 | loss(pos) 1.0589 | loss(seq) 0.0013 | grad 15.3752 | lr 0.0000 | time_forward 1.0530 | time_backward 1.2290 |
[2023-10-22 17:43:38,065::train::INFO] [train] Iter 560203 | loss 1.3720 | loss(rot) 0.1720 | loss(pos) 0.8726 | loss(seq) 0.3274 | grad 5.1920 | lr 0.0000 | time_forward 1.6360 | time_backward 2.1530 |
[2023-10-22 17:43:41,225::train::INFO] [train] Iter 560204 | loss 0.9367 | loss(rot) 0.0158 | loss(pos) 0.9199 | loss(seq) 0.0010 | grad 13.2919 | lr 0.0000 | time_forward 1.3910 | time_backward 1.7560 |
[2023-10-22 17:43:50,319::train::INFO] [train] Iter 560205 | loss 0.1723 | loss(rot) 0.0836 | loss(pos) 0.0162 | loss(seq) 0.0724 | grad 1.9416 | lr 0.0000 | time_forward 4.0390 | time_backward 5.0510 |
[2023-10-22 17:43:57,412::train::INFO] [train] Iter 560206 | loss 1.0900 | loss(rot) 0.5149 | loss(pos) 0.1889 | loss(seq) 0.3862 | grad 3.8828 | lr 0.0000 | time_forward 2.8470 | time_backward 4.2440 |
[2023-10-22 17:44:08,248::train::INFO] [train] Iter 560207 | loss 0.3785 | loss(rot) 0.1283 | loss(pos) 0.0702 | loss(seq) 0.1801 | grad 2.3907 | lr 0.0000 | time_forward 4.4850 | time_backward 6.3350 |
[2023-10-22 17:44:10,506::train::INFO] [train] Iter 560208 | loss 0.9063 | loss(rot) 0.8150 | loss(pos) 0.0463 | loss(seq) 0.0450 | grad 3.6451 | lr 0.0000 | time_forward 1.0570 | time_backward 1.1970 |
[2023-10-22 17:44:18,513::train::INFO] [train] Iter 560209 | loss 0.3991 | loss(rot) 0.0653 | loss(pos) 0.3033 | loss(seq) 0.0304 | grad 6.5429 | lr 0.0000 | time_forward 3.4190 | time_backward 4.5850 |
[2023-10-22 17:44:27,691::train::INFO] [train] Iter 560210 | loss 0.6571 | loss(rot) 0.4802 | loss(pos) 0.0339 | loss(seq) 0.1430 | grad 2.2852 | lr 0.0000 | time_forward 3.7690 | time_backward 5.4050 |
[2023-10-22 17:44:36,644::train::INFO] [train] Iter 560211 | loss 0.7451 | loss(rot) 0.1433 | loss(pos) 0.6002 | loss(seq) 0.0016 | grad 7.9966 | lr 0.0000 | time_forward 3.6950 | time_backward 5.2540 |
[2023-10-22 17:44:45,586::train::INFO] [train] Iter 560212 | loss 1.2703 | loss(rot) 0.5193 | loss(pos) 0.3961 | loss(seq) 0.3548 | grad 4.3820 | lr 0.0000 | time_forward 3.6790 | time_backward 5.2600 |
[2023-10-22 17:44:55,371::train::INFO] [train] Iter 560213 | loss 0.7486 | loss(rot) 0.2863 | loss(pos) 0.2837 | loss(seq) 0.1786 | grad 4.1893 | lr 0.0000 | time_forward 3.9230 | time_backward 5.8580 |
[2023-10-22 17:44:58,180::train::INFO] [train] Iter 560214 | loss 2.5920 | loss(rot) 2.5660 | loss(pos) 0.0183 | loss(seq) 0.0077 | grad 4.9849 | lr 0.0000 | time_forward 1.3230 | time_backward 1.4830 |
[2023-10-22 17:45:08,296::train::INFO] [train] Iter 560215 | loss 0.7931 | loss(rot) 0.4869 | loss(pos) 0.0751 | loss(seq) 0.2311 | grad 3.5522 | lr 0.0000 | time_forward 4.0700 | time_backward 6.0120 |
[2023-10-22 17:45:19,132::train::INFO] [train] Iter 560216 | loss 0.3798 | loss(rot) 0.1501 | loss(pos) 0.0401 | loss(seq) 0.1897 | grad 2.4556 | lr 0.0000 | time_forward 4.2610 | time_backward 6.5710 |
[2023-10-22 17:45:27,986::train::INFO] [train] Iter 560217 | loss 0.7780 | loss(rot) 0.1414 | loss(pos) 0.0480 | loss(seq) 0.5885 | grad 3.8672 | lr 0.0000 | time_forward 4.0740 | time_backward 4.7750 |
[2023-10-22 17:45:30,482::train::INFO] [train] Iter 560218 | loss 0.7625 | loss(rot) 0.0672 | loss(pos) 0.6496 | loss(seq) 0.0457 | grad 8.4324 | lr 0.0000 | time_forward 1.2270 | time_backward 1.2650 |
[2023-10-22 17:45:38,299::train::INFO] [train] Iter 560219 | loss 0.8427 | loss(rot) 0.4406 | loss(pos) 0.0862 | loss(seq) 0.3160 | grad 27.3341 | lr 0.0000 | time_forward 3.4510 | time_backward 4.3630 |
[2023-10-22 17:45:47,179::train::INFO] [train] Iter 560220 | loss 0.9204 | loss(rot) 0.8425 | loss(pos) 0.0432 | loss(seq) 0.0347 | grad 6.9752 | lr 0.0000 | time_forward 3.8430 | time_backward 5.0340 |
[2023-10-22 17:45:56,139::train::INFO] [train] Iter 560221 | loss 0.5001 | loss(rot) 0.0508 | loss(pos) 0.4437 | loss(seq) 0.0056 | grad 6.9184 | lr 0.0000 | time_forward 3.5970 | time_backward 5.3600 |
[2023-10-22 17:46:04,925::train::INFO] [train] Iter 560222 | loss 0.1215 | loss(rot) 0.1028 | loss(pos) 0.0186 | loss(seq) 0.0001 | grad 2.9160 | lr 0.0000 | time_forward 3.5600 | time_backward 5.2220 |
[2023-10-22 17:46:08,039::train::INFO] [train] Iter 560223 | loss 0.5969 | loss(rot) 0.3934 | loss(pos) 0.0810 | loss(seq) 0.1226 | grad 3.9111 | lr 0.0000 | time_forward 1.4750 | time_backward 1.6360 |
[2023-10-22 17:46:11,147::train::INFO] [train] Iter 560224 | loss 0.6550 | loss(rot) 0.0686 | loss(pos) 0.5660 | loss(seq) 0.0204 | grad 6.8385 | lr 0.0000 | time_forward 1.4300 | time_backward 1.6730 |
[2023-10-22 17:46:21,845::train::INFO] [train] Iter 560225 | loss 0.2826 | loss(rot) 0.0943 | loss(pos) 0.0272 | loss(seq) 0.1612 | grad 2.6223 | lr 0.0000 | time_forward 4.3320 | time_backward 6.3620 |
[2023-10-22 17:46:31,650::train::INFO] [train] Iter 560226 | loss 0.4246 | loss(rot) 0.2113 | loss(pos) 0.0277 | loss(seq) 0.1856 | grad 2.6944 | lr 0.0000 | time_forward 4.1400 | time_backward 5.6610 |
[2023-10-22 17:46:41,638::train::INFO] [train] Iter 560227 | loss 0.9539 | loss(rot) 0.5521 | loss(pos) 0.0963 | loss(seq) 0.3055 | grad 4.5447 | lr 0.0000 | time_forward 4.3450 | time_backward 5.6400 |
[2023-10-22 17:46:50,011::train::INFO] [train] Iter 560228 | loss 1.3303 | loss(rot) 0.7916 | loss(pos) 0.1501 | loss(seq) 0.3886 | grad 3.9421 | lr 0.0000 | time_forward 3.5500 | time_backward 4.8190 |
[2023-10-22 17:46:57,836::train::INFO] [train] Iter 560229 | loss 0.9334 | loss(rot) 0.2839 | loss(pos) 0.0955 | loss(seq) 0.5540 | grad 4.0368 | lr 0.0000 | time_forward 3.3350 | time_backward 4.4860 |
[2023-10-22 17:47:00,515::train::INFO] [train] Iter 560230 | loss 0.2791 | loss(rot) 0.2067 | loss(pos) 0.0724 | loss(seq) 0.0000 | grad 1.6685 | lr 0.0000 | time_forward 1.2010 | time_backward 1.4740 |
[2023-10-22 17:47:03,630::train::INFO] [train] Iter 560231 | loss 1.0225 | loss(rot) 0.1482 | loss(pos) 0.2945 | loss(seq) 0.5798 | grad 5.2592 | lr 0.0000 | time_forward 1.4550 | time_backward 1.6550 |
[2023-10-22 17:47:10,205::train::INFO] [train] Iter 560232 | loss 1.1737 | loss(rot) 0.5622 | loss(pos) 0.1035 | loss(seq) 0.5080 | grad 3.4581 | lr 0.0000 | time_forward 2.7520 | time_backward 3.8200 |
[2023-10-22 17:47:19,342::train::INFO] [train] Iter 560233 | loss 1.9219 | loss(rot) 1.3238 | loss(pos) 0.2067 | loss(seq) 0.3915 | grad 3.7772 | lr 0.0000 | time_forward 3.9340 | time_backward 5.2000 |
[2023-10-22 17:47:22,654::train::INFO] [train] Iter 560234 | loss 2.7439 | loss(rot) 2.1034 | loss(pos) 0.1411 | loss(seq) 0.4995 | grad 10.3621 | lr 0.0000 | time_forward 1.4910 | time_backward 1.8170 |
[2023-10-22 17:47:31,671::train::INFO] [train] Iter 560235 | loss 0.2388 | loss(rot) 0.1515 | loss(pos) 0.0284 | loss(seq) 0.0589 | grad 2.2216 | lr 0.0000 | time_forward 3.9820 | time_backward 5.0300 |
[2023-10-22 17:47:42,283::train::INFO] [train] Iter 560236 | loss 0.3935 | loss(rot) 0.0607 | loss(pos) 0.2524 | loss(seq) 0.0803 | grad 4.0764 | lr 0.0000 | time_forward 4.1460 | time_backward 6.4630 |
[2023-10-22 17:47:51,148::train::INFO] [train] Iter 560237 | loss 0.2074 | loss(rot) 0.0503 | loss(pos) 0.1075 | loss(seq) 0.0496 | grad 2.2316 | lr 0.0000 | time_forward 4.0670 | time_backward 4.7930 |
[2023-10-22 17:47:59,351::train::INFO] [train] Iter 560238 | loss 0.6240 | loss(rot) 0.5923 | loss(pos) 0.0316 | loss(seq) 0.0000 | grad 2.5809 | lr 0.0000 | time_forward 3.5080 | time_backward 4.6930 |
[2023-10-22 17:48:08,024::train::INFO] [train] Iter 560239 | loss 1.4899 | loss(rot) 0.0079 | loss(pos) 1.4808 | loss(seq) 0.0011 | grad 11.9084 | lr 0.0000 | time_forward 3.6270 | time_backward 5.0420 |
[2023-10-22 17:48:11,004::train::INFO] [train] Iter 560240 | loss 0.3160 | loss(rot) 0.2460 | loss(pos) 0.0700 | loss(seq) 0.0000 | grad 3.9340 | lr 0.0000 | time_forward 1.3770 | time_backward 1.6000 |
[2023-10-22 17:48:20,920::train::INFO] [train] Iter 560241 | loss 1.3798 | loss(rot) 0.7223 | loss(pos) 0.2476 | loss(seq) 0.4099 | grad 4.5218 | lr 0.0000 | time_forward 3.9330 | time_backward 5.9550 |
[2023-10-22 17:48:30,471::train::INFO] [train] Iter 560242 | loss 0.4830 | loss(rot) 0.0913 | loss(pos) 0.3662 | loss(seq) 0.0256 | grad 2.5881 | lr 0.0000 | time_forward 4.1510 | time_backward 5.3960 |
[2023-10-22 17:48:33,423::train::INFO] [train] Iter 560243 | loss 1.1887 | loss(rot) 1.1625 | loss(pos) 0.0245 | loss(seq) 0.0016 | grad 5.3905 | lr 0.0000 | time_forward 1.4250 | time_backward 1.5230 |
[2023-10-22 17:48:42,035::train::INFO] [train] Iter 560244 | loss 0.2676 | loss(rot) 0.0469 | loss(pos) 0.0434 | loss(seq) 0.1773 | grad 2.2169 | lr 0.0000 | time_forward 3.6740 | time_backward 4.9340 |
[2023-10-22 17:48:45,643::train::INFO] [train] Iter 560245 | loss 1.2269 | loss(rot) 0.7931 | loss(pos) 0.0706 | loss(seq) 0.3632 | grad 7.5276 | lr 0.0000 | time_forward 1.6120 | time_backward 1.9920 |
[2023-10-22 17:48:54,349::train::INFO] [train] Iter 560246 | loss 0.3579 | loss(rot) 0.3363 | loss(pos) 0.0200 | loss(seq) 0.0015 | grad 2.5383 | lr 0.0000 | time_forward 3.7350 | time_backward 4.9560 |
[2023-10-22 17:49:02,821::train::INFO] [train] Iter 560247 | loss 2.9649 | loss(rot) 0.0058 | loss(pos) 2.9584 | loss(seq) 0.0007 | grad 22.3735 | lr 0.0000 | time_forward 3.6470 | time_backward 4.8220 |
[2023-10-22 17:49:10,750::train::INFO] [train] Iter 560248 | loss 0.5046 | loss(rot) 0.1439 | loss(pos) 0.2786 | loss(seq) 0.0820 | grad 2.9998 | lr 0.0000 | time_forward 3.3870 | time_backward 4.5380 |
[2023-10-22 17:49:20,300::train::INFO] [train] Iter 560249 | loss 0.5263 | loss(rot) 0.2494 | loss(pos) 0.0974 | loss(seq) 0.1796 | grad 3.3167 | lr 0.0000 | time_forward 3.8690 | time_backward 5.6790 |
[2023-10-22 17:49:29,666::train::INFO] [train] Iter 560250 | loss 1.6685 | loss(rot) 1.0589 | loss(pos) 0.0775 | loss(seq) 0.5320 | grad 5.1572 | lr 0.0000 | time_forward 3.8210 | time_backward 5.5420 |
[2023-10-22 17:49:32,575::train::INFO] [train] Iter 560251 | loss 0.4522 | loss(rot) 0.0959 | loss(pos) 0.2572 | loss(seq) 0.0992 | grad 8.1619 | lr 0.0000 | time_forward 1.3910 | time_backward 1.5140 |
[2023-10-22 17:49:35,544::train::INFO] [train] Iter 560252 | loss 0.1922 | loss(rot) 0.0817 | loss(pos) 0.0243 | loss(seq) 0.0862 | grad 2.1809 | lr 0.0000 | time_forward 1.4360 | time_backward 1.5020 |
[2023-10-22 17:49:44,370::train::INFO] [train] Iter 560253 | loss 0.5047 | loss(rot) 0.0695 | loss(pos) 0.2622 | loss(seq) 0.1729 | grad 5.3368 | lr 0.0000 | time_forward 3.7250 | time_backward 5.0970 |
[2023-10-22 17:49:52,885::train::INFO] [train] Iter 560254 | loss 0.1634 | loss(rot) 0.0406 | loss(pos) 0.0661 | loss(seq) 0.0566 | grad 2.7485 | lr 0.0000 | time_forward 3.7580 | time_backward 4.7550 |
[2023-10-22 17:50:02,270::train::INFO] [train] Iter 560255 | loss 1.1868 | loss(rot) 1.1484 | loss(pos) 0.0378 | loss(seq) 0.0006 | grad 3.6639 | lr 0.0000 | time_forward 3.9190 | time_backward 5.4620 |
[2023-10-22 17:50:10,210::train::INFO] [train] Iter 560256 | loss 0.7023 | loss(rot) 0.6664 | loss(pos) 0.0283 | loss(seq) 0.0076 | grad 3.1133 | lr 0.0000 | time_forward 3.4060 | time_backward 4.5310 |
[2023-10-22 17:50:18,534::train::INFO] [train] Iter 560257 | loss 0.5786 | loss(rot) 0.1442 | loss(pos) 0.2751 | loss(seq) 0.1593 | grad 3.6600 | lr 0.0000 | time_forward 3.5160 | time_backward 4.8050 |
[2023-10-22 17:50:21,232::train::INFO] [train] Iter 560258 | loss 0.4208 | loss(rot) 0.2052 | loss(pos) 0.0278 | loss(seq) 0.1878 | grad 3.4993 | lr 0.0000 | time_forward 1.2910 | time_backward 1.4050 |
[2023-10-22 17:50:29,900::train::INFO] [train] Iter 560259 | loss 0.2055 | loss(rot) 0.1607 | loss(pos) 0.0444 | loss(seq) 0.0004 | grad 2.5101 | lr 0.0000 | time_forward 3.7610 | time_backward 4.9030 |
[2023-10-22 17:50:32,895::train::INFO] [train] Iter 560260 | loss 0.2737 | loss(rot) 0.1259 | loss(pos) 0.1127 | loss(seq) 0.0351 | grad 3.9557 | lr 0.0000 | time_forward 1.4120 | time_backward 1.5790 |
[2023-10-22 17:50:36,017::train::INFO] [train] Iter 560261 | loss 0.1203 | loss(rot) 0.1002 | loss(pos) 0.0181 | loss(seq) 0.0021 | grad 2.0418 | lr 0.0000 | time_forward 1.5250 | time_backward 1.5940 |
[2023-10-22 17:50:40,940::train::INFO] [train] Iter 560262 | loss 1.3724 | loss(rot) 0.8496 | loss(pos) 0.2461 | loss(seq) 0.2767 | grad 5.1028 | lr 0.0000 | time_forward 2.1880 | time_backward 2.7320 |
[2023-10-22 17:50:43,242::train::INFO] [train] Iter 560263 | loss 0.2723 | loss(rot) 0.0356 | loss(pos) 0.2339 | loss(seq) 0.0028 | grad 6.8887 | lr 0.0000 | time_forward 1.0220 | time_backward 1.2760 |
[2023-10-22 17:50:46,888::train::INFO] [train] Iter 560264 | loss 0.6158 | loss(rot) 0.5623 | loss(pos) 0.0534 | loss(seq) 0.0000 | grad 5.4019 | lr 0.0000 | time_forward 1.6830 | time_backward 1.9220 |
[2023-10-22 17:50:56,403::train::INFO] [train] Iter 560265 | loss 0.8733 | loss(rot) 0.4982 | loss(pos) 0.3696 | loss(seq) 0.0055 | grad 5.1665 | lr 0.0000 | time_forward 3.8750 | time_backward 5.6360 |
[2023-10-22 17:50:59,492::train::INFO] [train] Iter 560266 | loss 0.4659 | loss(rot) 0.0612 | loss(pos) 0.0613 | loss(seq) 0.3434 | grad 3.3922 | lr 0.0000 | time_forward 1.4030 | time_backward 1.6830 |
[2023-10-22 17:51:07,860::train::INFO] [train] Iter 560267 | loss 0.4484 | loss(rot) 0.0944 | loss(pos) 0.0612 | loss(seq) 0.2928 | grad 2.4893 | lr 0.0000 | time_forward 3.5410 | time_backward 4.8140 |
[2023-10-22 17:51:15,780::train::INFO] [train] Iter 560268 | loss 0.8690 | loss(rot) 0.8464 | loss(pos) 0.0225 | loss(seq) 0.0000 | grad 3.5875 | lr 0.0000 | time_forward 3.4280 | time_backward 4.4880 |
[2023-10-22 17:51:23,855::train::INFO] [train] Iter 560269 | loss 0.6576 | loss(rot) 0.1788 | loss(pos) 0.0883 | loss(seq) 0.3906 | grad 2.9124 | lr 0.0000 | time_forward 3.4370 | time_backward 4.6350 |
[2023-10-22 17:51:30,288::train::INFO] [train] Iter 560270 | loss 0.1864 | loss(rot) 0.1454 | loss(pos) 0.0144 | loss(seq) 0.0266 | grad 1.5500 | lr 0.0000 | time_forward 2.7680 | time_backward 3.6610 |
[2023-10-22 17:51:40,141::train::INFO] [train] Iter 560271 | loss 0.6735 | loss(rot) 0.0347 | loss(pos) 0.6315 | loss(seq) 0.0073 | grad 4.7321 | lr 0.0000 | time_forward 3.8910 | time_backward 5.9600 |
[2023-10-22 17:51:48,797::train::INFO] [train] Iter 560272 | loss 0.2369 | loss(rot) 0.1691 | loss(pos) 0.0129 | loss(seq) 0.0549 | grad 2.5211 | lr 0.0000 | time_forward 3.7060 | time_backward 4.9460 |
[2023-10-22 17:51:51,891::train::INFO] [train] Iter 560273 | loss 0.4846 | loss(rot) 0.2553 | loss(pos) 0.0216 | loss(seq) 0.2078 | grad 3.5720 | lr 0.0000 | time_forward 1.4200 | time_backward 1.6700 |
[2023-10-22 17:52:02,844::train::INFO] [train] Iter 560274 | loss 1.3572 | loss(rot) 1.3072 | loss(pos) 0.0249 | loss(seq) 0.0250 | grad 4.0814 | lr 0.0000 | time_forward 4.5170 | time_backward 6.4310 |
[2023-10-22 17:52:12,766::train::INFO] [train] Iter 560275 | loss 0.7764 | loss(rot) 0.7269 | loss(pos) 0.0455 | loss(seq) 0.0040 | grad 3.9980 | lr 0.0000 | time_forward 4.5670 | time_backward 5.3500 |
[2023-10-22 17:52:21,931::train::INFO] [train] Iter 560276 | loss 1.1127 | loss(rot) 1.0799 | loss(pos) 0.0300 | loss(seq) 0.0028 | grad 26.9251 | lr 0.0000 | time_forward 3.8600 | time_backward 5.3010 |
[2023-10-22 17:52:30,181::train::INFO] [train] Iter 560277 | loss 0.6918 | loss(rot) 0.4054 | loss(pos) 0.1918 | loss(seq) 0.0946 | grad 2.9717 | lr 0.0000 | time_forward 3.4850 | time_backward 4.7630 |
[2023-10-22 17:52:39,495::train::INFO] [train] Iter 560278 | loss 0.1213 | loss(rot) 0.0920 | loss(pos) 0.0130 | loss(seq) 0.0163 | grad 1.7710 | lr 0.0000 | time_forward 3.7620 | time_backward 5.5490 |
[2023-10-22 17:52:49,551::train::INFO] [train] Iter 560279 | loss 0.5977 | loss(rot) 0.5559 | loss(pos) 0.0417 | loss(seq) 0.0001 | grad 3.8118 | lr 0.0000 | time_forward 3.9440 | time_backward 6.1090 |
[2023-10-22 17:52:52,110::train::INFO] [train] Iter 560280 | loss 0.7514 | loss(rot) 0.2879 | loss(pos) 0.1030 | loss(seq) 0.3605 | grad 2.6637 | lr 0.0000 | time_forward 1.1710 | time_backward 1.3840 |
[2023-10-22 17:52:55,339::train::INFO] [train] Iter 560281 | loss 0.3885 | loss(rot) 0.0964 | loss(pos) 0.0195 | loss(seq) 0.2726 | grad 2.2070 | lr 0.0000 | time_forward 1.5270 | time_backward 1.6830 |
[2023-10-22 17:53:05,979::train::INFO] [train] Iter 560282 | loss 1.0944 | loss(rot) 0.6846 | loss(pos) 0.0459 | loss(seq) 0.3639 | grad 2.5450 | lr 0.0000 | time_forward 4.4030 | time_backward 6.2320 |
[2023-10-22 17:53:14,700::train::INFO] [train] Iter 560283 | loss 1.7008 | loss(rot) 1.4989 | loss(pos) 0.0621 | loss(seq) 0.1399 | grad 3.5127 | lr 0.0000 | time_forward 3.7570 | time_backward 4.9600 |
[2023-10-22 17:53:23,809::train::INFO] [train] Iter 560284 | loss 0.3708 | loss(rot) 0.1452 | loss(pos) 0.1058 | loss(seq) 0.1199 | grad 3.2727 | lr 0.0000 | time_forward 3.7890 | time_backward 5.3170 |
[2023-10-22 17:53:31,530::train::INFO] [train] Iter 560285 | loss 2.1615 | loss(rot) 2.0388 | loss(pos) 0.0745 | loss(seq) 0.0482 | grad 5.9510 | lr 0.0000 | time_forward 3.3470 | time_backward 4.3710 |
[2023-10-22 17:53:37,379::train::INFO] [train] Iter 560286 | loss 0.2765 | loss(rot) 0.1082 | loss(pos) 0.1252 | loss(seq) 0.0430 | grad 2.7050 | lr 0.0000 | time_forward 2.4140 | time_backward 3.4320 |
[2023-10-22 17:53:45,452::train::INFO] [train] Iter 560287 | loss 0.3115 | loss(rot) 0.1344 | loss(pos) 0.1411 | loss(seq) 0.0359 | grad 4.3144 | lr 0.0000 | time_forward 3.3780 | time_backward 4.6930 |
[2023-10-22 17:53:48,348::train::INFO] [train] Iter 560288 | loss 0.3395 | loss(rot) 0.0140 | loss(pos) 0.3226 | loss(seq) 0.0030 | grad 6.0245 | lr 0.0000 | time_forward 1.3820 | time_backward 1.5100 |
[2023-10-22 17:53:51,330::train::INFO] [train] Iter 560289 | loss 1.7808 | loss(rot) 1.7082 | loss(pos) 0.0342 | loss(seq) 0.0384 | grad 8.0313 | lr 0.0000 | time_forward 1.4180 | time_backward 1.5600 |
[2023-10-22 17:54:01,210::train::INFO] [train] Iter 560290 | loss 0.7791 | loss(rot) 0.4243 | loss(pos) 0.1558 | loss(seq) 0.1989 | grad 3.1844 | lr 0.0000 | time_forward 4.3090 | time_backward 5.5680 |
[2023-10-22 17:54:04,293::train::INFO] [train] Iter 560291 | loss 2.1210 | loss(rot) 2.0404 | loss(pos) 0.0399 | loss(seq) 0.0407 | grad 6.4858 | lr 0.0000 | time_forward 1.4620 | time_backward 1.6180 |
[2023-10-22 17:54:11,438::train::INFO] [train] Iter 560292 | loss 0.4557 | loss(rot) 0.1431 | loss(pos) 0.0167 | loss(seq) 0.2960 | grad 2.7541 | lr 0.0000 | time_forward 3.1480 | time_backward 3.9930 |
[2023-10-22 17:54:19,771::train::INFO] [train] Iter 560293 | loss 0.5341 | loss(rot) 0.1975 | loss(pos) 0.0349 | loss(seq) 0.3017 | grad 4.1226 | lr 0.0000 | time_forward 3.6060 | time_backward 4.7240 |
[2023-10-22 17:54:22,554::train::INFO] [train] Iter 560294 | loss 0.2318 | loss(rot) 0.0527 | loss(pos) 0.1727 | loss(seq) 0.0064 | grad 3.0874 | lr 0.0000 | time_forward 1.3240 | time_backward 1.4560 |
[2023-10-22 17:54:30,960::train::INFO] [train] Iter 560295 | loss 0.2738 | loss(rot) 0.0465 | loss(pos) 0.0150 | loss(seq) 0.2123 | grad 1.9686 | lr 0.0000 | time_forward 3.5350 | time_backward 4.8380 |
[2023-10-22 17:54:39,929::train::INFO] [train] Iter 560296 | loss 0.6367 | loss(rot) 0.5150 | loss(pos) 0.0446 | loss(seq) 0.0771 | grad 8.1332 | lr 0.0000 | time_forward 3.6490 | time_backward 5.3150 |
[2023-10-22 17:54:42,807::train::INFO] [train] Iter 560297 | loss 0.9930 | loss(rot) 0.5598 | loss(pos) 0.0749 | loss(seq) 0.3583 | grad 3.7274 | lr 0.0000 | time_forward 1.3330 | time_backward 1.5420 |
[2023-10-22 17:54:52,271::train::INFO] [train] Iter 560298 | loss 0.2731 | loss(rot) 0.2586 | loss(pos) 0.0145 | loss(seq) 0.0000 | grad 3.3995 | lr 0.0000 | time_forward 3.9090 | time_backward 5.5530 |
[2023-10-22 17:55:02,493::train::INFO] [train] Iter 560299 | loss 0.5840 | loss(rot) 0.2766 | loss(pos) 0.1556 | loss(seq) 0.1518 | grad 2.4547 | lr 0.0000 | time_forward 4.2000 | time_backward 6.0190 |
[2023-10-22 17:55:06,470::train::INFO] [train] Iter 560300 | loss 1.2888 | loss(rot) 0.4661 | loss(pos) 0.3861 | loss(seq) 0.4366 | grad 3.6063 | lr 0.0000 | time_forward 1.8690 | time_backward 2.1030 |
[2023-10-22 17:55:16,099::train::INFO] [train] Iter 560301 | loss 0.3665 | loss(rot) 0.1694 | loss(pos) 0.0415 | loss(seq) 0.1556 | grad 2.6641 | lr 0.0000 | time_forward 3.7730 | time_backward 5.8420 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.