text stringlengths 56 1.16k |
|---|
[2023-10-23 23:38:09,927::train::INFO] [train] Iter 576686 | loss 1.3414 | loss(rot) 0.0058 | loss(pos) 1.3353 | loss(seq) 0.0003 | grad 11.5710 | lr 0.0000 | time_forward 4.0800 | time_backward 5.8530 |
[2023-10-23 23:38:12,753::train::INFO] [train] Iter 576687 | loss 0.7937 | loss(rot) 0.7547 | loss(pos) 0.0337 | loss(seq) 0.0053 | grad 15.0482 | lr 0.0000 | time_forward 1.3310 | time_backward 1.4910 |
[2023-10-23 23:38:15,605::train::INFO] [train] Iter 576688 | loss 0.4360 | loss(rot) 0.1810 | loss(pos) 0.0295 | loss(seq) 0.2254 | grad 3.2864 | lr 0.0000 | time_forward 1.3410 | time_backward 1.4830 |
[2023-10-23 23:38:29,603::train::INFO] [train] Iter 576689 | loss 0.3691 | loss(rot) 0.1044 | loss(pos) 0.0491 | loss(seq) 0.2156 | grad 2.5615 | lr 0.0000 | time_forward 6.1850 | time_backward 7.8100 |
[2023-10-23 23:38:32,402::train::INFO] [train] Iter 576690 | loss 0.5636 | loss(rot) 0.0818 | loss(pos) 0.4484 | loss(seq) 0.0334 | grad 8.1603 | lr 0.0000 | time_forward 1.3290 | time_backward 1.4660 |
[2023-10-23 23:38:43,426::train::INFO] [train] Iter 576691 | loss 0.2456 | loss(rot) 0.1421 | loss(pos) 0.0241 | loss(seq) 0.0795 | grad 2.0654 | lr 0.0000 | time_forward 5.8410 | time_backward 5.1750 |
[2023-10-23 23:38:52,984::train::INFO] [train] Iter 576692 | loss 1.0209 | loss(rot) 0.5784 | loss(pos) 0.0987 | loss(seq) 0.3437 | grad 4.3648 | lr 0.0000 | time_forward 3.8490 | time_backward 5.7060 |
[2023-10-23 23:39:02,053::train::INFO] [train] Iter 576693 | loss 0.4297 | loss(rot) 0.1389 | loss(pos) 0.0608 | loss(seq) 0.2300 | grad 2.7536 | lr 0.0000 | time_forward 3.8170 | time_backward 5.2480 |
[2023-10-23 23:39:04,807::train::INFO] [train] Iter 576694 | loss 0.5771 | loss(rot) 0.3114 | loss(pos) 0.0383 | loss(seq) 0.2274 | grad 1.9053 | lr 0.0000 | time_forward 1.3240 | time_backward 1.4260 |
[2023-10-23 23:39:12,065::train::INFO] [train] Iter 576695 | loss 1.0513 | loss(rot) 0.9838 | loss(pos) 0.0619 | loss(seq) 0.0056 | grad 64.8165 | lr 0.0000 | time_forward 3.0910 | time_backward 4.1330 |
[2023-10-23 23:39:14,771::train::INFO] [train] Iter 576696 | loss 1.1707 | loss(rot) 1.1315 | loss(pos) 0.0391 | loss(seq) 0.0000 | grad 5.0663 | lr 0.0000 | time_forward 1.2600 | time_backward 1.4440 |
[2023-10-23 23:39:24,326::train::INFO] [train] Iter 576697 | loss 0.2653 | loss(rot) 0.2182 | loss(pos) 0.0386 | loss(seq) 0.0085 | grad 2.7151 | lr 0.0000 | time_forward 3.8930 | time_backward 5.6440 |
[2023-10-23 23:39:34,148::train::INFO] [train] Iter 576698 | loss 0.7901 | loss(rot) 0.3529 | loss(pos) 0.1217 | loss(seq) 0.3155 | grad 2.9334 | lr 0.0000 | time_forward 3.9900 | time_backward 5.8280 |
[2023-10-23 23:39:36,932::train::INFO] [train] Iter 576699 | loss 0.6423 | loss(rot) 0.4286 | loss(pos) 0.0254 | loss(seq) 0.1883 | grad 3.5705 | lr 0.0000 | time_forward 1.2920 | time_backward 1.4880 |
[2023-10-23 23:39:45,506::train::INFO] [train] Iter 576700 | loss 1.6113 | loss(rot) 1.3567 | loss(pos) 0.0656 | loss(seq) 0.1890 | grad 6.8402 | lr 0.0000 | time_forward 3.4360 | time_backward 5.1350 |
[2023-10-23 23:39:56,084::train::INFO] [train] Iter 576701 | loss 0.8558 | loss(rot) 0.4430 | loss(pos) 0.1069 | loss(seq) 0.3058 | grad 3.8202 | lr 0.0000 | time_forward 4.5850 | time_backward 5.9890 |
[2023-10-23 23:40:04,355::train::INFO] [train] Iter 576702 | loss 0.3528 | loss(rot) 0.1745 | loss(pos) 0.0900 | loss(seq) 0.0884 | grad 2.9111 | lr 0.0000 | time_forward 3.4820 | time_backward 4.7870 |
[2023-10-23 23:40:11,377::train::INFO] [train] Iter 576703 | loss 0.2737 | loss(rot) 0.0318 | loss(pos) 0.1592 | loss(seq) 0.0827 | grad 4.4759 | lr 0.0000 | time_forward 3.0040 | time_backward 4.0140 |
[2023-10-23 23:40:19,579::train::INFO] [train] Iter 576704 | loss 1.2483 | loss(rot) 0.8948 | loss(pos) 0.2428 | loss(seq) 0.1107 | grad 7.6579 | lr 0.0000 | time_forward 3.4930 | time_backward 4.7060 |
[2023-10-23 23:40:30,297::train::INFO] [train] Iter 576705 | loss 0.6130 | loss(rot) 0.0767 | loss(pos) 0.3083 | loss(seq) 0.2280 | grad 4.2923 | lr 0.0000 | time_forward 3.9960 | time_backward 6.7170 |
[2023-10-23 23:40:39,517::train::INFO] [train] Iter 576706 | loss 0.8180 | loss(rot) 0.6372 | loss(pos) 0.0388 | loss(seq) 0.1421 | grad 3.5482 | lr 0.0000 | time_forward 3.9290 | time_backward 5.2870 |
[2023-10-23 23:40:46,808::train::INFO] [train] Iter 576707 | loss 1.9021 | loss(rot) 0.8838 | loss(pos) 0.4647 | loss(seq) 0.5536 | grad 6.5230 | lr 0.0000 | time_forward 3.0600 | time_backward 4.2260 |
[2023-10-23 23:40:56,615::train::INFO] [train] Iter 576708 | loss 0.9679 | loss(rot) 0.5713 | loss(pos) 0.0267 | loss(seq) 0.3699 | grad 4.1708 | lr 0.0000 | time_forward 3.9700 | time_backward 5.8330 |
[2023-10-23 23:41:03,967::train::INFO] [train] Iter 576709 | loss 0.4938 | loss(rot) 0.2368 | loss(pos) 0.0429 | loss(seq) 0.2141 | grad 3.1761 | lr 0.0000 | time_forward 2.8330 | time_backward 4.5160 |
[2023-10-23 23:41:12,565::train::INFO] [train] Iter 576710 | loss 0.3089 | loss(rot) 0.1665 | loss(pos) 0.0235 | loss(seq) 0.1189 | grad 2.9321 | lr 0.0000 | time_forward 3.6660 | time_backward 4.9150 |
[2023-10-23 23:41:15,287::train::INFO] [train] Iter 576711 | loss 0.6151 | loss(rot) 0.2580 | loss(pos) 0.3374 | loss(seq) 0.0197 | grad 5.2439 | lr 0.0000 | time_forward 1.2960 | time_backward 1.4220 |
[2023-10-23 23:41:18,023::train::INFO] [train] Iter 576712 | loss 0.5384 | loss(rot) 0.1948 | loss(pos) 0.0520 | loss(seq) 0.2916 | grad 2.5021 | lr 0.0000 | time_forward 1.3470 | time_backward 1.3860 |
[2023-10-23 23:41:20,830::train::INFO] [train] Iter 576713 | loss 0.9769 | loss(rot) 0.7538 | loss(pos) 0.0253 | loss(seq) 0.1977 | grad 9.4757 | lr 0.0000 | time_forward 1.3150 | time_backward 1.4890 |
[2023-10-23 23:41:23,485::train::INFO] [train] Iter 576714 | loss 0.3464 | loss(rot) 0.1326 | loss(pos) 0.0237 | loss(seq) 0.1902 | grad 2.5961 | lr 0.0000 | time_forward 1.2280 | time_backward 1.4240 |
[2023-10-23 23:41:29,039::train::INFO] [train] Iter 576715 | loss 0.2959 | loss(rot) 0.0969 | loss(pos) 0.1504 | loss(seq) 0.0486 | grad 3.3018 | lr 0.0000 | time_forward 2.4520 | time_backward 3.0970 |
[2023-10-23 23:41:38,112::train::INFO] [train] Iter 576716 | loss 0.7292 | loss(rot) 0.4802 | loss(pos) 0.0390 | loss(seq) 0.2100 | grad 2.7949 | lr 0.0000 | time_forward 3.3290 | time_backward 5.7410 |
[2023-10-23 23:41:46,770::train::INFO] [train] Iter 576717 | loss 1.4121 | loss(rot) 1.3673 | loss(pos) 0.0446 | loss(seq) 0.0002 | grad 4.4731 | lr 0.0000 | time_forward 3.5940 | time_backward 5.0620 |
[2023-10-23 23:41:55,960::train::INFO] [train] Iter 576718 | loss 0.9497 | loss(rot) 0.1002 | loss(pos) 0.7682 | loss(seq) 0.0813 | grad 7.3400 | lr 0.0000 | time_forward 3.8660 | time_backward 5.3210 |
[2023-10-23 23:41:58,831::train::INFO] [train] Iter 576719 | loss 0.2418 | loss(rot) 0.1342 | loss(pos) 0.0229 | loss(seq) 0.0847 | grad 3.0549 | lr 0.0000 | time_forward 1.3120 | time_backward 1.5550 |
[2023-10-23 23:42:01,900::train::INFO] [train] Iter 576720 | loss 0.2345 | loss(rot) 0.0729 | loss(pos) 0.0308 | loss(seq) 0.1307 | grad 1.7916 | lr 0.0000 | time_forward 1.3430 | time_backward 1.7020 |
[2023-10-23 23:42:06,177::train::INFO] [train] Iter 576721 | loss 1.7273 | loss(rot) 1.6492 | loss(pos) 0.0778 | loss(seq) 0.0003 | grad 4.4802 | lr 0.0000 | time_forward 1.9230 | time_backward 2.3500 |
[2023-10-23 23:42:14,643::train::INFO] [train] Iter 576722 | loss 0.2852 | loss(rot) 0.2588 | loss(pos) 0.0263 | loss(seq) 0.0000 | grad 6.5609 | lr 0.0000 | time_forward 3.5150 | time_backward 4.9470 |
[2023-10-23 23:42:24,671::train::INFO] [train] Iter 576723 | loss 1.1161 | loss(rot) 0.7715 | loss(pos) 0.1799 | loss(seq) 0.1647 | grad 5.5944 | lr 0.0000 | time_forward 4.0680 | time_backward 5.9550 |
[2023-10-23 23:42:32,245::train::INFO] [train] Iter 576724 | loss 1.1322 | loss(rot) 0.7648 | loss(pos) 0.0569 | loss(seq) 0.3105 | grad 4.7674 | lr 0.0000 | time_forward 3.2420 | time_backward 4.3290 |
[2023-10-23 23:42:35,147::train::INFO] [train] Iter 576725 | loss 1.3864 | loss(rot) 0.0792 | loss(pos) 0.2491 | loss(seq) 1.0581 | grad 8.7887 | lr 0.0000 | time_forward 1.3300 | time_backward 1.5700 |
[2023-10-23 23:42:43,724::train::INFO] [train] Iter 576726 | loss 0.6047 | loss(rot) 0.3060 | loss(pos) 0.0381 | loss(seq) 0.2606 | grad 3.2746 | lr 0.0000 | time_forward 3.6990 | time_backward 4.8450 |
[2023-10-23 23:42:51,891::train::INFO] [train] Iter 576727 | loss 1.0243 | loss(rot) 0.8273 | loss(pos) 0.0295 | loss(seq) 0.1675 | grad 5.7141 | lr 0.0000 | time_forward 3.4940 | time_backward 4.6700 |
[2023-10-23 23:43:02,748::train::INFO] [train] Iter 576728 | loss 1.2946 | loss(rot) 0.7646 | loss(pos) 0.1628 | loss(seq) 0.3671 | grad 4.4709 | lr 0.0000 | time_forward 4.0210 | time_backward 6.8330 |
[2023-10-23 23:43:11,127::train::INFO] [train] Iter 576729 | loss 2.3315 | loss(rot) 1.7588 | loss(pos) 0.1006 | loss(seq) 0.4721 | grad 4.9126 | lr 0.0000 | time_forward 3.4880 | time_backward 4.8880 |
[2023-10-23 23:43:13,377::train::INFO] [train] Iter 576730 | loss 0.7576 | loss(rot) 0.6338 | loss(pos) 0.0252 | loss(seq) 0.0987 | grad 4.0223 | lr 0.0000 | time_forward 1.0540 | time_backward 1.1920 |
[2023-10-23 23:43:21,692::train::INFO] [train] Iter 576731 | loss 1.3216 | loss(rot) 0.8056 | loss(pos) 0.2295 | loss(seq) 0.2865 | grad 16.3810 | lr 0.0000 | time_forward 3.5190 | time_backward 4.7920 |
[2023-10-23 23:43:32,314::train::INFO] [train] Iter 576732 | loss 3.7768 | loss(rot) 0.0365 | loss(pos) 3.7403 | loss(seq) 0.0000 | grad 17.6933 | lr 0.0000 | time_forward 4.0930 | time_backward 6.5260 |
[2023-10-23 23:43:42,026::train::INFO] [train] Iter 576733 | loss 1.0290 | loss(rot) 0.0492 | loss(pos) 0.9642 | loss(seq) 0.0156 | grad 12.4382 | lr 0.0000 | time_forward 4.0720 | time_backward 5.6360 |
[2023-10-23 23:43:49,585::train::INFO] [train] Iter 576734 | loss 1.1240 | loss(rot) 0.5366 | loss(pos) 0.3795 | loss(seq) 0.2079 | grad 7.2488 | lr 0.0000 | time_forward 2.9760 | time_backward 4.5800 |
[2023-10-23 23:43:59,454::train::INFO] [train] Iter 576735 | loss 1.0424 | loss(rot) 0.9953 | loss(pos) 0.0471 | loss(seq) 0.0000 | grad 6.6578 | lr 0.0000 | time_forward 4.0710 | time_backward 5.7960 |
[2023-10-23 23:44:02,798::train::INFO] [train] Iter 576736 | loss 1.0258 | loss(rot) 0.7779 | loss(pos) 0.0322 | loss(seq) 0.2157 | grad 2.7737 | lr 0.0000 | time_forward 1.4880 | time_backward 1.8520 |
[2023-10-23 23:44:11,666::train::INFO] [train] Iter 576737 | loss 1.0189 | loss(rot) 0.4449 | loss(pos) 0.1361 | loss(seq) 0.4379 | grad 4.2613 | lr 0.0000 | time_forward 3.7790 | time_backward 5.0860 |
[2023-10-23 23:44:14,402::train::INFO] [train] Iter 576738 | loss 1.2564 | loss(rot) 0.8858 | loss(pos) 0.0255 | loss(seq) 0.3451 | grad 5.1966 | lr 0.0000 | time_forward 1.2620 | time_backward 1.4720 |
[2023-10-23 23:44:21,567::train::INFO] [train] Iter 576739 | loss 1.8318 | loss(rot) 1.6720 | loss(pos) 0.0451 | loss(seq) 0.1146 | grad 7.8268 | lr 0.0000 | time_forward 3.0410 | time_backward 4.1200 |
[2023-10-23 23:44:29,687::train::INFO] [train] Iter 576740 | loss 1.1415 | loss(rot) 1.1041 | loss(pos) 0.0327 | loss(seq) 0.0047 | grad 8.1659 | lr 0.0000 | time_forward 3.4430 | time_backward 4.6750 |
[2023-10-23 23:44:38,299::train::INFO] [train] Iter 576741 | loss 0.4334 | loss(rot) 0.2165 | loss(pos) 0.0478 | loss(seq) 0.1691 | grad 3.5922 | lr 0.0000 | time_forward 3.5720 | time_backward 5.0370 |
[2023-10-23 23:44:48,205::train::INFO] [train] Iter 576742 | loss 0.5353 | loss(rot) 0.2092 | loss(pos) 0.0200 | loss(seq) 0.3060 | grad 2.5534 | lr 0.0000 | time_forward 5.6450 | time_backward 4.2570 |
[2023-10-23 23:44:50,784::train::INFO] [train] Iter 576743 | loss 0.6017 | loss(rot) 0.2161 | loss(pos) 0.0271 | loss(seq) 0.3586 | grad 3.0293 | lr 0.0000 | time_forward 1.1790 | time_backward 1.3970 |
[2023-10-23 23:44:58,050::train::INFO] [train] Iter 576744 | loss 1.0765 | loss(rot) 0.5619 | loss(pos) 0.0367 | loss(seq) 0.4779 | grad 4.4971 | lr 0.0000 | time_forward 2.7800 | time_backward 4.4820 |
[2023-10-23 23:45:06,959::train::INFO] [train] Iter 576745 | loss 0.4313 | loss(rot) 0.1859 | loss(pos) 0.2275 | loss(seq) 0.0179 | grad 3.2556 | lr 0.0000 | time_forward 3.6840 | time_backward 5.2230 |
[2023-10-23 23:45:15,080::train::INFO] [train] Iter 576746 | loss 1.4481 | loss(rot) 0.5386 | loss(pos) 0.4048 | loss(seq) 0.5047 | grad 3.5445 | lr 0.0000 | time_forward 3.3260 | time_backward 4.7920 |
[2023-10-23 23:45:25,303::train::INFO] [train] Iter 576747 | loss 2.2524 | loss(rot) 2.0879 | loss(pos) 0.1411 | loss(seq) 0.0235 | grad 11.1056 | lr 0.0000 | time_forward 4.7870 | time_backward 5.4330 |
[2023-10-23 23:45:28,029::train::INFO] [train] Iter 576748 | loss 0.3679 | loss(rot) 0.0552 | loss(pos) 0.0471 | loss(seq) 0.2656 | grad 2.3807 | lr 0.0000 | time_forward 1.3170 | time_backward 1.4050 |
[2023-10-23 23:45:30,782::train::INFO] [train] Iter 576749 | loss 0.8858 | loss(rot) 0.5969 | loss(pos) 0.2400 | loss(seq) 0.0489 | grad 3.6555 | lr 0.0000 | time_forward 1.3000 | time_backward 1.4190 |
[2023-10-23 23:45:33,552::train::INFO] [train] Iter 576750 | loss 0.8713 | loss(rot) 0.1036 | loss(pos) 0.5920 | loss(seq) 0.1758 | grad 4.5353 | lr 0.0000 | time_forward 1.3680 | time_backward 1.3990 |
[2023-10-23 23:45:41,703::train::INFO] [train] Iter 576751 | loss 0.2303 | loss(rot) 0.0667 | loss(pos) 0.0150 | loss(seq) 0.1487 | grad 1.8052 | lr 0.0000 | time_forward 3.5330 | time_backward 4.5900 |
[2023-10-23 23:45:49,148::train::INFO] [train] Iter 576752 | loss 0.5473 | loss(rot) 0.0973 | loss(pos) 0.1740 | loss(seq) 0.2760 | grad 3.8906 | lr 0.0000 | time_forward 3.2230 | time_backward 4.2190 |
[2023-10-23 23:45:56,139::train::INFO] [train] Iter 576753 | loss 0.0718 | loss(rot) 0.0440 | loss(pos) 0.0239 | loss(seq) 0.0039 | grad 1.5414 | lr 0.0000 | time_forward 2.9870 | time_backward 4.0010 |
[2023-10-23 23:46:03,269::train::INFO] [train] Iter 576754 | loss 0.7722 | loss(rot) 0.5430 | loss(pos) 0.0425 | loss(seq) 0.1868 | grad 17.5184 | lr 0.0000 | time_forward 2.9920 | time_backward 4.1350 |
[2023-10-23 23:46:06,027::train::INFO] [train] Iter 576755 | loss 0.9327 | loss(rot) 0.5157 | loss(pos) 0.3974 | loss(seq) 0.0196 | grad 6.4863 | lr 0.0000 | time_forward 1.2560 | time_backward 1.5000 |
[2023-10-23 23:46:14,670::train::INFO] [train] Iter 576756 | loss 3.8279 | loss(rot) 0.0043 | loss(pos) 3.8236 | loss(seq) 0.0000 | grad 21.9970 | lr 0.0000 | time_forward 3.3410 | time_backward 5.2980 |
[2023-10-23 23:46:22,766::train::INFO] [train] Iter 576757 | loss 1.2582 | loss(rot) 0.3365 | loss(pos) 0.9158 | loss(seq) 0.0059 | grad 5.7545 | lr 0.0000 | time_forward 3.4290 | time_backward 4.6640 |
[2023-10-23 23:46:30,669::train::INFO] [train] Iter 576758 | loss 0.6345 | loss(rot) 0.4558 | loss(pos) 0.0436 | loss(seq) 0.1350 | grad 3.2931 | lr 0.0000 | time_forward 3.2740 | time_backward 4.6240 |
[2023-10-23 23:46:38,696::train::INFO] [train] Iter 576759 | loss 0.3082 | loss(rot) 0.2896 | loss(pos) 0.0183 | loss(seq) 0.0003 | grad 1.6928 | lr 0.0000 | time_forward 3.4250 | time_backward 4.5990 |
[2023-10-23 23:46:45,438::train::INFO] [train] Iter 576760 | loss 1.5110 | loss(rot) 1.2130 | loss(pos) 0.0397 | loss(seq) 0.2583 | grad 3.8060 | lr 0.0000 | time_forward 2.8810 | time_backward 3.8570 |
[2023-10-23 23:46:52,602::train::INFO] [train] Iter 576761 | loss 0.2636 | loss(rot) 0.2181 | loss(pos) 0.0454 | loss(seq) 0.0001 | grad 2.7016 | lr 0.0000 | time_forward 3.0360 | time_backward 4.1260 |
[2023-10-23 23:47:01,028::train::INFO] [train] Iter 576762 | loss 0.4702 | loss(rot) 0.0272 | loss(pos) 0.4403 | loss(seq) 0.0028 | grad 6.8044 | lr 0.0000 | time_forward 3.4640 | time_backward 4.9580 |
[2023-10-23 23:47:06,416::train::INFO] [train] Iter 576763 | loss 0.4030 | loss(rot) 0.0705 | loss(pos) 0.0972 | loss(seq) 0.2354 | grad 3.1843 | lr 0.0000 | time_forward 2.3060 | time_backward 3.0800 |
[2023-10-23 23:47:14,367::train::INFO] [train] Iter 576764 | loss 1.1827 | loss(rot) 1.0877 | loss(pos) 0.0238 | loss(seq) 0.0712 | grad 22.3052 | lr 0.0000 | time_forward 3.2340 | time_backward 4.7050 |
[2023-10-23 23:47:22,807::train::INFO] [train] Iter 576765 | loss 0.1613 | loss(rot) 0.0404 | loss(pos) 0.1033 | loss(seq) 0.0176 | grad 5.8320 | lr 0.0000 | time_forward 3.8350 | time_backward 4.6010 |
[2023-10-23 23:47:30,155::train::INFO] [train] Iter 576766 | loss 0.5851 | loss(rot) 0.3350 | loss(pos) 0.0260 | loss(seq) 0.2241 | grad 2.4053 | lr 0.0000 | time_forward 2.9640 | time_backward 4.3800 |
[2023-10-23 23:47:32,322::train::INFO] [train] Iter 576767 | loss 0.3592 | loss(rot) 0.2152 | loss(pos) 0.0572 | loss(seq) 0.0868 | grad 27.8277 | lr 0.0000 | time_forward 0.9890 | time_backward 1.1750 |
[2023-10-23 23:47:40,513::train::INFO] [train] Iter 576768 | loss 0.6577 | loss(rot) 0.2502 | loss(pos) 0.3310 | loss(seq) 0.0764 | grad 3.0359 | lr 0.0000 | time_forward 3.3460 | time_backward 4.8400 |
[2023-10-23 23:47:48,845::train::INFO] [train] Iter 576769 | loss 0.4880 | loss(rot) 0.0380 | loss(pos) 0.2218 | loss(seq) 0.2283 | grad 5.9150 | lr 0.0000 | time_forward 3.6010 | time_backward 4.7280 |
[2023-10-23 23:47:57,709::train::INFO] [train] Iter 576770 | loss 0.5162 | loss(rot) 0.3071 | loss(pos) 0.0454 | loss(seq) 0.1638 | grad 3.5337 | lr 0.0000 | time_forward 3.7720 | time_backward 5.0890 |
[2023-10-23 23:48:06,547::train::INFO] [train] Iter 576771 | loss 0.8161 | loss(rot) 0.1475 | loss(pos) 0.1279 | loss(seq) 0.5407 | grad 6.0553 | lr 0.0000 | time_forward 3.6500 | time_backward 5.1850 |
[2023-10-23 23:48:14,497::train::INFO] [train] Iter 576772 | loss 0.2472 | loss(rot) 0.2101 | loss(pos) 0.0370 | loss(seq) 0.0000 | grad 2.9364 | lr 0.0000 | time_forward 3.4900 | time_backward 4.4580 |
[2023-10-23 23:48:22,580::train::INFO] [train] Iter 576773 | loss 0.2530 | loss(rot) 0.2250 | loss(pos) 0.0278 | loss(seq) 0.0002 | grad 2.6877 | lr 0.0000 | time_forward 3.4830 | time_backward 4.5970 |
[2023-10-23 23:48:29,768::train::INFO] [train] Iter 576774 | loss 0.4666 | loss(rot) 0.4353 | loss(pos) 0.0229 | loss(seq) 0.0085 | grad 3.8670 | lr 0.0000 | time_forward 3.1160 | time_backward 4.0680 |
[2023-10-23 23:48:32,551::train::INFO] [train] Iter 576775 | loss 0.1642 | loss(rot) 0.1181 | loss(pos) 0.0180 | loss(seq) 0.0281 | grad 1.7269 | lr 0.0000 | time_forward 1.2860 | time_backward 1.4950 |
[2023-10-23 23:48:40,573::train::INFO] [train] Iter 576776 | loss 2.1026 | loss(rot) 0.0031 | loss(pos) 2.0995 | loss(seq) 0.0000 | grad 29.5956 | lr 0.0000 | time_forward 3.3000 | time_backward 4.7170 |
[2023-10-23 23:48:48,452::train::INFO] [train] Iter 576777 | loss 1.1276 | loss(rot) 1.0595 | loss(pos) 0.0330 | loss(seq) 0.0350 | grad 15.7184 | lr 0.0000 | time_forward 3.2510 | time_backward 4.6260 |
[2023-10-23 23:48:51,281::train::INFO] [train] Iter 576778 | loss 0.6649 | loss(rot) 0.0174 | loss(pos) 0.6466 | loss(seq) 0.0009 | grad 9.8314 | lr 0.0000 | time_forward 1.2550 | time_backward 1.5700 |
[2023-10-23 23:48:59,162::train::INFO] [train] Iter 576779 | loss 0.3055 | loss(rot) 0.2738 | loss(pos) 0.0316 | loss(seq) 0.0001 | grad 2.5881 | lr 0.0000 | time_forward 3.1950 | time_backward 4.6830 |
[2023-10-23 23:49:05,715::train::INFO] [train] Iter 576780 | loss 1.8875 | loss(rot) 1.7851 | loss(pos) 0.1024 | loss(seq) 0.0000 | grad 3.8964 | lr 0.0000 | time_forward 2.8340 | time_backward 3.7160 |
[2023-10-23 23:49:13,559::train::INFO] [train] Iter 576781 | loss 0.4289 | loss(rot) 0.1279 | loss(pos) 0.0864 | loss(seq) 0.2147 | grad 2.6962 | lr 0.0000 | time_forward 3.1920 | time_backward 4.6500 |
[2023-10-23 23:49:20,694::train::INFO] [train] Iter 576782 | loss 1.6021 | loss(rot) 1.1782 | loss(pos) 0.1347 | loss(seq) 0.2892 | grad 3.8707 | lr 0.0000 | time_forward 3.0620 | time_backward 4.0690 |
[2023-10-23 23:49:27,099::train::INFO] [train] Iter 576783 | loss 1.8981 | loss(rot) 1.2053 | loss(pos) 0.1227 | loss(seq) 0.5701 | grad 5.4932 | lr 0.0000 | time_forward 2.6660 | time_backward 3.7360 |
[2023-10-23 23:49:35,642::train::INFO] [train] Iter 576784 | loss 0.5238 | loss(rot) 0.2137 | loss(pos) 0.1454 | loss(seq) 0.1647 | grad 4.1800 | lr 0.0000 | time_forward 3.8020 | time_backward 4.7370 |
[2023-10-23 23:49:42,489::train::INFO] [train] Iter 576785 | loss 0.3064 | loss(rot) 0.0603 | loss(pos) 0.0196 | loss(seq) 0.2265 | grad 2.1427 | lr 0.0000 | time_forward 2.9300 | time_backward 3.9150 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.