text stringlengths 56 1.16k |
|---|
[2023-10-24 01:47:51,276::train::INFO] [train] Iter 577785 | loss 0.1607 | loss(rot) 0.0637 | loss(pos) 0.0145 | loss(seq) 0.0824 | grad 1.6294 | lr 0.0000 | time_forward 3.7400 | time_backward 5.0200 |
[2023-10-24 01:47:59,687::train::INFO] [train] Iter 577786 | loss 0.2358 | loss(rot) 0.0491 | loss(pos) 0.0299 | loss(seq) 0.1568 | grad 1.9610 | lr 0.0000 | time_forward 3.5550 | time_backward 4.8530 |
[2023-10-24 01:48:07,601::train::INFO] [train] Iter 577787 | loss 0.8492 | loss(rot) 0.6339 | loss(pos) 0.0357 | loss(seq) 0.1796 | grad 7.8842 | lr 0.0000 | time_forward 3.2990 | time_backward 4.6120 |
[2023-10-24 01:48:10,356::train::INFO] [train] Iter 577788 | loss 0.7695 | loss(rot) 0.0602 | loss(pos) 0.5524 | loss(seq) 0.1570 | grad 6.8716 | lr 0.0000 | time_forward 1.3210 | time_backward 1.4310 |
[2023-10-24 01:48:19,295::train::INFO] [train] Iter 577789 | loss 0.2196 | loss(rot) 0.1772 | loss(pos) 0.0110 | loss(seq) 0.0314 | grad 7.8536 | lr 0.0000 | time_forward 3.7870 | time_backward 5.1500 |
[2023-10-24 01:48:29,015::train::INFO] [train] Iter 577790 | loss 0.3751 | loss(rot) 0.0572 | loss(pos) 0.3124 | loss(seq) 0.0055 | grad 8.0743 | lr 0.0000 | time_forward 3.9850 | time_backward 5.7320 |
[2023-10-24 01:48:31,624::train::INFO] [train] Iter 577791 | loss 0.9888 | loss(rot) 0.5018 | loss(pos) 0.4072 | loss(seq) 0.0799 | grad 7.6778 | lr 0.0000 | time_forward 1.2200 | time_backward 1.3850 |
[2023-10-24 01:48:40,250::train::INFO] [train] Iter 577792 | loss 0.7704 | loss(rot) 0.2233 | loss(pos) 0.5168 | loss(seq) 0.0303 | grad 6.0737 | lr 0.0000 | time_forward 3.6790 | time_backward 4.9440 |
[2023-10-24 01:48:50,064::train::INFO] [train] Iter 577793 | loss 0.5491 | loss(rot) 0.4852 | loss(pos) 0.0176 | loss(seq) 0.0463 | grad 4.6350 | lr 0.0000 | time_forward 4.0150 | time_backward 5.7960 |
[2023-10-24 01:48:52,406::train::INFO] [train] Iter 577794 | loss 0.3865 | loss(rot) 0.1436 | loss(pos) 0.1883 | loss(seq) 0.0546 | grad 2.4615 | lr 0.0000 | time_forward 1.0650 | time_backward 1.2740 |
[2023-10-24 01:48:55,189::train::INFO] [train] Iter 577795 | loss 0.5356 | loss(rot) 0.0697 | loss(pos) 0.4566 | loss(seq) 0.0093 | grad 4.8533 | lr 0.0000 | time_forward 1.3240 | time_backward 1.4550 |
[2023-10-24 01:49:04,104::train::INFO] [train] Iter 577796 | loss 0.7699 | loss(rot) 0.5177 | loss(pos) 0.0407 | loss(seq) 0.2115 | grad 5.8626 | lr 0.0000 | time_forward 3.7760 | time_backward 5.0990 |
[2023-10-24 01:49:11,946::train::INFO] [train] Iter 577797 | loss 0.6286 | loss(rot) 0.2940 | loss(pos) 0.1368 | loss(seq) 0.1978 | grad 3.0069 | lr 0.0000 | time_forward 3.3350 | time_backward 4.5040 |
[2023-10-24 01:49:14,472::train::INFO] [train] Iter 577798 | loss 1.2745 | loss(rot) 0.6305 | loss(pos) 0.2437 | loss(seq) 0.4002 | grad 4.8540 | lr 0.0000 | time_forward 1.2150 | time_backward 1.3070 |
[2023-10-24 01:49:17,280::train::INFO] [train] Iter 577799 | loss 0.1466 | loss(rot) 0.1010 | loss(pos) 0.0278 | loss(seq) 0.0178 | grad 1.8529 | lr 0.0000 | time_forward 1.3290 | time_backward 1.4640 |
[2023-10-24 01:49:27,057::train::INFO] [train] Iter 577800 | loss 0.3211 | loss(rot) 0.2508 | loss(pos) 0.0260 | loss(seq) 0.0443 | grad 3.0530 | lr 0.0000 | time_forward 4.0090 | time_backward 5.7280 |
[2023-10-24 01:49:36,014::train::INFO] [train] Iter 577801 | loss 0.7493 | loss(rot) 0.0468 | loss(pos) 0.0689 | loss(seq) 0.6336 | grad 2.8446 | lr 0.0000 | time_forward 3.8560 | time_backward 5.0990 |
[2023-10-24 01:49:45,663::train::INFO] [train] Iter 577802 | loss 0.4548 | loss(rot) 0.2207 | loss(pos) 0.1393 | loss(seq) 0.0948 | grad 3.4616 | lr 0.0000 | time_forward 3.9400 | time_backward 5.7050 |
[2023-10-24 01:49:48,350::train::INFO] [train] Iter 577803 | loss 0.3631 | loss(rot) 0.1819 | loss(pos) 0.0424 | loss(seq) 0.1389 | grad 2.7329 | lr 0.0000 | time_forward 1.2410 | time_backward 1.4440 |
[2023-10-24 01:49:57,993::train::INFO] [train] Iter 577804 | loss 0.4550 | loss(rot) 0.2609 | loss(pos) 0.0535 | loss(seq) 0.1406 | grad 3.5322 | lr 0.0000 | time_forward 4.0880 | time_backward 5.5510 |
[2023-10-24 01:50:00,774::train::INFO] [train] Iter 577805 | loss 0.1907 | loss(rot) 0.0378 | loss(pos) 0.1287 | loss(seq) 0.0243 | grad 3.5248 | lr 0.0000 | time_forward 1.3110 | time_backward 1.4680 |
[2023-10-24 01:50:09,695::train::INFO] [train] Iter 577806 | loss 1.3540 | loss(rot) 1.0628 | loss(pos) 0.0758 | loss(seq) 0.2154 | grad 3.1615 | lr 0.0000 | time_forward 3.7430 | time_backward 5.1320 |
[2023-10-24 01:50:19,471::train::INFO] [train] Iter 577807 | loss 0.9250 | loss(rot) 0.0149 | loss(pos) 0.9079 | loss(seq) 0.0022 | grad 10.7547 | lr 0.0000 | time_forward 3.9280 | time_backward 5.8440 |
[2023-10-24 01:50:27,391::train::INFO] [train] Iter 577808 | loss 1.6479 | loss(rot) 0.9864 | loss(pos) 0.0741 | loss(seq) 0.5874 | grad 12.3530 | lr 0.0000 | time_forward 3.3460 | time_backward 4.5710 |
[2023-10-24 01:50:36,009::train::INFO] [train] Iter 577809 | loss 0.5199 | loss(rot) 0.4108 | loss(pos) 0.0242 | loss(seq) 0.0849 | grad 2.1703 | lr 0.0000 | time_forward 3.6150 | time_backward 5.0000 |
[2023-10-24 01:50:45,755::train::INFO] [train] Iter 577810 | loss 0.1390 | loss(rot) 0.1013 | loss(pos) 0.0378 | loss(seq) 0.0000 | grad 2.1540 | lr 0.0000 | time_forward 3.9730 | time_backward 5.7700 |
[2023-10-24 01:50:53,948::train::INFO] [train] Iter 577811 | loss 0.1767 | loss(rot) 0.0546 | loss(pos) 0.0131 | loss(seq) 0.1091 | grad 1.6953 | lr 0.0000 | time_forward 3.4920 | time_backward 4.6980 |
[2023-10-24 01:51:03,598::train::INFO] [train] Iter 577812 | loss 0.2433 | loss(rot) 0.1280 | loss(pos) 0.0208 | loss(seq) 0.0944 | grad 5.4369 | lr 0.0000 | time_forward 3.9210 | time_backward 5.7260 |
[2023-10-24 01:51:06,457::train::INFO] [train] Iter 577813 | loss 0.4201 | loss(rot) 0.1472 | loss(pos) 0.2577 | loss(seq) 0.0151 | grad 6.3444 | lr 0.0000 | time_forward 1.3000 | time_backward 1.5560 |
[2023-10-24 01:51:09,194::train::INFO] [train] Iter 577814 | loss 0.6849 | loss(rot) 0.4317 | loss(pos) 0.1287 | loss(seq) 0.1244 | grad 4.1995 | lr 0.0000 | time_forward 1.3340 | time_backward 1.4010 |
[2023-10-24 01:51:11,943::train::INFO] [train] Iter 577815 | loss 0.6586 | loss(rot) 0.2748 | loss(pos) 0.1112 | loss(seq) 0.2726 | grad 4.3479 | lr 0.0000 | time_forward 1.3360 | time_backward 1.4090 |
[2023-10-24 01:51:21,906::train::INFO] [train] Iter 577816 | loss 0.7227 | loss(rot) 0.3528 | loss(pos) 0.1231 | loss(seq) 0.2469 | grad 3.1616 | lr 0.0000 | time_forward 3.9220 | time_backward 6.0060 |
[2023-10-24 01:51:28,738::train::INFO] [train] Iter 577817 | loss 1.7065 | loss(rot) 1.3138 | loss(pos) 0.0555 | loss(seq) 0.3371 | grad 5.2460 | lr 0.0000 | time_forward 2.9480 | time_backward 3.8810 |
[2023-10-24 01:51:36,917::train::INFO] [train] Iter 577818 | loss 0.3983 | loss(rot) 0.1228 | loss(pos) 0.0495 | loss(seq) 0.2260 | grad 3.2198 | lr 0.0000 | time_forward 3.4530 | time_backward 4.7220 |
[2023-10-24 01:51:39,667::train::INFO] [train] Iter 577819 | loss 1.2662 | loss(rot) 1.2278 | loss(pos) 0.0384 | loss(seq) 0.0000 | grad 17.8998 | lr 0.0000 | time_forward 1.3250 | time_backward 1.4220 |
[2023-10-24 01:51:42,550::train::INFO] [train] Iter 577820 | loss 0.6349 | loss(rot) 0.0096 | loss(pos) 0.6251 | loss(seq) 0.0002 | grad 10.3492 | lr 0.0000 | time_forward 1.3120 | time_backward 1.5350 |
[2023-10-24 01:51:51,218::train::INFO] [train] Iter 577821 | loss 0.6535 | loss(rot) 0.3388 | loss(pos) 0.0252 | loss(seq) 0.2895 | grad 8.0961 | lr 0.0000 | time_forward 3.5970 | time_backward 5.0670 |
[2023-10-24 01:51:59,793::train::INFO] [train] Iter 577822 | loss 1.1078 | loss(rot) 1.0443 | loss(pos) 0.0365 | loss(seq) 0.0269 | grad 10.2002 | lr 0.0000 | time_forward 3.6430 | time_backward 4.9280 |
[2023-10-24 01:52:02,304::train::INFO] [train] Iter 577823 | loss 0.9370 | loss(rot) 0.0123 | loss(pos) 0.9233 | loss(seq) 0.0014 | grad 22.0723 | lr 0.0000 | time_forward 1.2030 | time_backward 1.3050 |
[2023-10-24 01:52:09,811::train::INFO] [train] Iter 577824 | loss 1.1595 | loss(rot) 1.1296 | loss(pos) 0.0153 | loss(seq) 0.0147 | grad 6.6562 | lr 0.0000 | time_forward 3.1890 | time_backward 4.3150 |
[2023-10-24 01:52:17,984::train::INFO] [train] Iter 577825 | loss 0.2627 | loss(rot) 0.1517 | loss(pos) 0.0797 | loss(seq) 0.0313 | grad 3.6372 | lr 0.0000 | time_forward 3.4280 | time_backward 4.7410 |
[2023-10-24 01:52:20,327::train::INFO] [train] Iter 577826 | loss 0.5298 | loss(rot) 0.1260 | loss(pos) 0.0898 | loss(seq) 0.3141 | grad 3.6953 | lr 0.0000 | time_forward 1.0750 | time_backward 1.2660 |
[2023-10-24 01:52:23,079::train::INFO] [train] Iter 577827 | loss 1.3890 | loss(rot) 1.2243 | loss(pos) 0.0547 | loss(seq) 0.1101 | grad 9.8710 | lr 0.0000 | time_forward 1.2660 | time_backward 1.4820 |
[2023-10-24 01:52:25,811::train::INFO] [train] Iter 577828 | loss 0.3227 | loss(rot) 0.0633 | loss(pos) 0.2475 | loss(seq) 0.0120 | grad 3.7117 | lr 0.0000 | time_forward 1.2840 | time_backward 1.4460 |
[2023-10-24 01:52:35,483::train::INFO] [train] Iter 577829 | loss 0.7071 | loss(rot) 0.4768 | loss(pos) 0.0706 | loss(seq) 0.1597 | grad 3.0258 | lr 0.0000 | time_forward 3.9310 | time_backward 5.7360 |
[2023-10-24 01:52:43,627::train::INFO] [train] Iter 577830 | loss 0.3279 | loss(rot) 0.1229 | loss(pos) 0.0331 | loss(seq) 0.1719 | grad 2.2001 | lr 0.0000 | time_forward 3.4730 | time_backward 4.6670 |
[2023-10-24 01:52:53,216::train::INFO] [train] Iter 577831 | loss 0.9762 | loss(rot) 0.0107 | loss(pos) 0.9644 | loss(seq) 0.0012 | grad 6.3090 | lr 0.0000 | time_forward 3.9130 | time_backward 5.6740 |
[2023-10-24 01:53:02,934::train::INFO] [train] Iter 577832 | loss 1.7188 | loss(rot) 1.0713 | loss(pos) 0.2366 | loss(seq) 0.4109 | grad 3.0616 | lr 0.0000 | time_forward 4.0940 | time_backward 5.6220 |
[2023-10-24 01:53:06,274::train::INFO] [train] Iter 577833 | loss 0.9689 | loss(rot) 0.3846 | loss(pos) 0.0718 | loss(seq) 0.5125 | grad 3.5205 | lr 0.0000 | time_forward 1.4970 | time_backward 1.8400 |
[2023-10-24 01:53:14,783::train::INFO] [train] Iter 577834 | loss 1.0873 | loss(rot) 0.7765 | loss(pos) 0.0628 | loss(seq) 0.2480 | grad 3.6553 | lr 0.0000 | time_forward 3.6110 | time_backward 4.8820 |
[2023-10-24 01:53:21,786::train::INFO] [train] Iter 577835 | loss 1.3630 | loss(rot) 1.1814 | loss(pos) 0.0390 | loss(seq) 0.1425 | grad 13.3827 | lr 0.0000 | time_forward 2.9640 | time_backward 4.0360 |
[2023-10-24 01:53:25,015::train::INFO] [train] Iter 577836 | loss 0.7286 | loss(rot) 0.4804 | loss(pos) 0.0658 | loss(seq) 0.1824 | grad 3.8101 | lr 0.0000 | time_forward 1.4840 | time_backward 1.7420 |
[2023-10-24 01:53:33,562::train::INFO] [train] Iter 577837 | loss 0.5317 | loss(rot) 0.1919 | loss(pos) 0.1789 | loss(seq) 0.1608 | grad 3.7739 | lr 0.0000 | time_forward 3.6550 | time_backward 4.8780 |
[2023-10-24 01:53:42,472::train::INFO] [train] Iter 577838 | loss 1.3376 | loss(rot) 0.7624 | loss(pos) 0.0371 | loss(seq) 0.5380 | grad 4.0608 | lr 0.0000 | time_forward 3.7880 | time_backward 5.1190 |
[2023-10-24 01:53:49,365::train::INFO] [train] Iter 577839 | loss 0.5793 | loss(rot) 0.2537 | loss(pos) 0.0599 | loss(seq) 0.2658 | grad 3.7532 | lr 0.0000 | time_forward 2.9490 | time_backward 3.9420 |
[2023-10-24 01:53:59,189::train::INFO] [train] Iter 577840 | loss 1.2662 | loss(rot) 0.6468 | loss(pos) 0.6074 | loss(seq) 0.0121 | grad 6.2252 | lr 0.0000 | time_forward 3.9180 | time_backward 5.9030 |
[2023-10-24 01:54:07,827::train::INFO] [train] Iter 577841 | loss 0.6626 | loss(rot) 0.4513 | loss(pos) 0.0428 | loss(seq) 0.1685 | grad 3.3249 | lr 0.0000 | time_forward 3.6780 | time_backward 4.9570 |
[2023-10-24 01:54:10,608::train::INFO] [train] Iter 577842 | loss 0.5619 | loss(rot) 0.5103 | loss(pos) 0.0309 | loss(seq) 0.0207 | grad 4.6279 | lr 0.0000 | time_forward 1.3210 | time_backward 1.4560 |
[2023-10-24 01:54:17,680::train::INFO] [train] Iter 577843 | loss 0.7167 | loss(rot) 0.2588 | loss(pos) 0.0394 | loss(seq) 0.4185 | grad 6.9703 | lr 0.0000 | time_forward 3.0060 | time_backward 4.0310 |
[2023-10-24 01:54:20,501::train::INFO] [train] Iter 577844 | loss 0.9381 | loss(rot) 0.3412 | loss(pos) 0.0720 | loss(seq) 0.5248 | grad 4.9788 | lr 0.0000 | time_forward 1.3420 | time_backward 1.4760 |
[2023-10-24 01:54:28,608::train::INFO] [train] Iter 577845 | loss 0.1293 | loss(rot) 0.0983 | loss(pos) 0.0132 | loss(seq) 0.0178 | grad 1.9697 | lr 0.0000 | time_forward 3.3780 | time_backward 4.6960 |
[2023-10-24 01:54:36,600::train::INFO] [train] Iter 577846 | loss 0.5047 | loss(rot) 0.4580 | loss(pos) 0.0467 | loss(seq) 0.0000 | grad 3.6305 | lr 0.0000 | time_forward 3.3820 | time_backward 4.6070 |
[2023-10-24 01:54:43,028::train::INFO] [train] Iter 577847 | loss 0.1810 | loss(rot) 0.0903 | loss(pos) 0.0105 | loss(seq) 0.0802 | grad 2.5500 | lr 0.0000 | time_forward 2.7000 | time_backward 3.7260 |
[2023-10-24 01:54:50,513::train::INFO] [train] Iter 577848 | loss 0.3111 | loss(rot) 0.2899 | loss(pos) 0.0208 | loss(seq) 0.0005 | grad 18.3193 | lr 0.0000 | time_forward 3.2160 | time_backward 4.2520 |
[2023-10-24 01:54:58,035::train::INFO] [train] Iter 577849 | loss 0.9439 | loss(rot) 0.5972 | loss(pos) 0.1216 | loss(seq) 0.2252 | grad 4.4509 | lr 0.0000 | time_forward 3.2100 | time_backward 4.3090 |
[2023-10-24 01:55:06,919::train::INFO] [train] Iter 577850 | loss 1.3988 | loss(rot) 0.9983 | loss(pos) 0.1183 | loss(seq) 0.2821 | grad 51.4125 | lr 0.0000 | time_forward 3.8190 | time_backward 5.0620 |
[2023-10-24 01:55:13,808::train::INFO] [train] Iter 577851 | loss 0.7924 | loss(rot) 0.4894 | loss(pos) 0.0294 | loss(seq) 0.2737 | grad 5.2556 | lr 0.0000 | time_forward 2.9330 | time_backward 3.9530 |
[2023-10-24 01:55:21,229::train::INFO] [train] Iter 577852 | loss 1.4471 | loss(rot) 1.2222 | loss(pos) 0.0413 | loss(seq) 0.1836 | grad 4.2751 | lr 0.0000 | time_forward 3.1410 | time_backward 4.2770 |
[2023-10-24 01:55:29,743::train::INFO] [train] Iter 577853 | loss 0.5007 | loss(rot) 0.2520 | loss(pos) 0.0672 | loss(seq) 0.1815 | grad 2.6780 | lr 0.0000 | time_forward 3.6090 | time_backward 4.9030 |
[2023-10-24 01:55:38,289::train::INFO] [train] Iter 577854 | loss 0.3868 | loss(rot) 0.1163 | loss(pos) 0.1091 | loss(seq) 0.1613 | grad 3.7480 | lr 0.0000 | time_forward 3.6100 | time_backward 4.9320 |
[2023-10-24 01:55:47,970::train::INFO] [train] Iter 577855 | loss 0.2361 | loss(rot) 0.0808 | loss(pos) 0.1160 | loss(seq) 0.0393 | grad 2.9407 | lr 0.0000 | time_forward 3.9130 | time_backward 5.7640 |
[2023-10-24 01:55:57,802::train::INFO] [train] Iter 577856 | loss 1.5095 | loss(rot) 1.3439 | loss(pos) 0.1073 | loss(seq) 0.0583 | grad 5.1005 | lr 0.0000 | time_forward 3.9720 | time_backward 5.8580 |
[2023-10-24 01:56:05,820::train::INFO] [train] Iter 577857 | loss 0.9073 | loss(rot) 0.6241 | loss(pos) 0.1765 | loss(seq) 0.1067 | grad 5.6654 | lr 0.0000 | time_forward 3.3850 | time_backward 4.6300 |
[2023-10-24 01:56:08,627::train::INFO] [train] Iter 577858 | loss 0.6388 | loss(rot) 0.3134 | loss(pos) 0.0769 | loss(seq) 0.2485 | grad 3.0147 | lr 0.0000 | time_forward 1.3530 | time_backward 1.4510 |
[2023-10-24 01:56:18,416::train::INFO] [train] Iter 577859 | loss 1.4619 | loss(rot) 1.0775 | loss(pos) 0.0478 | loss(seq) 0.3366 | grad 6.0785 | lr 0.0000 | time_forward 4.4700 | time_backward 5.2760 |
[2023-10-24 01:56:27,377::train::INFO] [train] Iter 577860 | loss 0.3387 | loss(rot) 0.1071 | loss(pos) 0.0464 | loss(seq) 0.1852 | grad 2.8914 | lr 0.0000 | time_forward 3.8260 | time_backward 5.1310 |
[2023-10-24 01:56:37,150::train::INFO] [train] Iter 577861 | loss 1.3167 | loss(rot) 1.2537 | loss(pos) 0.0449 | loss(seq) 0.0181 | grad 3.2449 | lr 0.0000 | time_forward 3.9700 | time_backward 5.8010 |
[2023-10-24 01:56:39,922::train::INFO] [train] Iter 577862 | loss 0.7089 | loss(rot) 0.0618 | loss(pos) 0.5919 | loss(seq) 0.0552 | grad 8.8866 | lr 0.0000 | time_forward 1.3520 | time_backward 1.4170 |
[2023-10-24 01:56:48,442::train::INFO] [train] Iter 577863 | loss 0.4244 | loss(rot) 0.2814 | loss(pos) 0.0397 | loss(seq) 0.1032 | grad 2.7901 | lr 0.0000 | time_forward 3.6100 | time_backward 4.8670 |
[2023-10-24 01:56:58,267::train::INFO] [train] Iter 577864 | loss 1.0757 | loss(rot) 0.5401 | loss(pos) 0.0746 | loss(seq) 0.4609 | grad 4.1745 | lr 0.0000 | time_forward 3.9610 | time_backward 5.8610 |
[2023-10-24 01:57:06,466::train::INFO] [train] Iter 577865 | loss 1.2580 | loss(rot) 0.5049 | loss(pos) 0.1848 | loss(seq) 0.5684 | grad 8.1213 | lr 0.0000 | time_forward 3.4670 | time_backward 4.7280 |
[2023-10-24 01:57:16,101::train::INFO] [train] Iter 577866 | loss 1.2048 | loss(rot) 0.6631 | loss(pos) 0.3393 | loss(seq) 0.2024 | grad 10.0598 | lr 0.0000 | time_forward 3.9670 | time_backward 5.6660 |
[2023-10-24 01:57:24,295::train::INFO] [train] Iter 577867 | loss 1.1914 | loss(rot) 0.4216 | loss(pos) 0.1608 | loss(seq) 0.6089 | grad 5.8693 | lr 0.0000 | time_forward 3.4020 | time_backward 4.7890 |
[2023-10-24 01:57:27,169::train::INFO] [train] Iter 577868 | loss 0.6391 | loss(rot) 0.5951 | loss(pos) 0.0144 | loss(seq) 0.0295 | grad 3.5812 | lr 0.0000 | time_forward 1.3230 | time_backward 1.5470 |
[2023-10-24 01:57:34,198::train::INFO] [train] Iter 577869 | loss 0.6518 | loss(rot) 0.0703 | loss(pos) 0.3073 | loss(seq) 0.2742 | grad 9.1612 | lr 0.0000 | time_forward 3.0130 | time_backward 4.0000 |
[2023-10-24 01:57:42,686::train::INFO] [train] Iter 577870 | loss 0.6706 | loss(rot) 0.6305 | loss(pos) 0.0265 | loss(seq) 0.0136 | grad 5.4119 | lr 0.0000 | time_forward 3.5440 | time_backward 4.9420 |
[2023-10-24 01:57:45,617::train::INFO] [train] Iter 577871 | loss 1.9668 | loss(rot) 1.5226 | loss(pos) 0.1019 | loss(seq) 0.3423 | grad 24.2146 | lr 0.0000 | time_forward 1.3560 | time_backward 1.5710 |
[2023-10-24 01:57:48,575::train::INFO] [train] Iter 577872 | loss 0.2882 | loss(rot) 0.0379 | loss(pos) 0.0978 | loss(seq) 0.1525 | grad 4.0857 | lr 0.0000 | time_forward 1.3980 | time_backward 1.5560 |
[2023-10-24 01:57:51,608::train::INFO] [train] Iter 577873 | loss 0.5656 | loss(rot) 0.2999 | loss(pos) 0.0939 | loss(seq) 0.1717 | grad 3.8692 | lr 0.0000 | time_forward 1.3460 | time_backward 1.6420 |
[2023-10-24 01:57:57,277::train::INFO] [train] Iter 577874 | loss 0.3558 | loss(rot) 0.0550 | loss(pos) 0.0189 | loss(seq) 0.2820 | grad 2.0046 | lr 0.0000 | time_forward 2.4040 | time_backward 3.2480 |
[2023-10-24 01:58:07,159::train::INFO] [train] Iter 577875 | loss 0.3099 | loss(rot) 0.2788 | loss(pos) 0.0309 | loss(seq) 0.0002 | grad 2.0693 | lr 0.0000 | time_forward 4.1110 | time_backward 5.7680 |
[2023-10-24 01:58:15,588::train::INFO] [train] Iter 577876 | loss 0.1654 | loss(rot) 0.1346 | loss(pos) 0.0291 | loss(seq) 0.0017 | grad 3.1454 | lr 0.0000 | time_forward 3.5460 | time_backward 4.8790 |
[2023-10-24 01:58:25,188::train::INFO] [train] Iter 577877 | loss 0.7456 | loss(rot) 0.4581 | loss(pos) 0.0434 | loss(seq) 0.2440 | grad 3.6828 | lr 0.0000 | time_forward 3.9230 | time_backward 5.6740 |
[2023-10-24 01:58:31,744::train::INFO] [train] Iter 577878 | loss 0.5466 | loss(rot) 0.0800 | loss(pos) 0.1292 | loss(seq) 0.3374 | grad 4.7512 | lr 0.0000 | time_forward 2.8670 | time_backward 3.6860 |
[2023-10-24 01:58:41,325::train::INFO] [train] Iter 577879 | loss 1.0461 | loss(rot) 1.0331 | loss(pos) 0.0125 | loss(seq) 0.0005 | grad 5.9779 | lr 0.0000 | time_forward 3.8560 | time_backward 5.7220 |
[2023-10-24 01:58:49,884::train::INFO] [train] Iter 577880 | loss 0.3257 | loss(rot) 0.2802 | loss(pos) 0.0393 | loss(seq) 0.0061 | grad 2.2277 | lr 0.0000 | time_forward 3.5640 | time_backward 4.9920 |
[2023-10-24 01:58:58,773::train::INFO] [train] Iter 577881 | loss 0.2421 | loss(rot) 0.0747 | loss(pos) 0.0417 | loss(seq) 0.1256 | grad 2.8146 | lr 0.0000 | time_forward 3.7300 | time_backward 5.1560 |
[2023-10-24 01:59:01,522::train::INFO] [train] Iter 577882 | loss 0.6722 | loss(rot) 0.1938 | loss(pos) 0.1688 | loss(seq) 0.3097 | grad 3.7394 | lr 0.0000 | time_forward 1.3240 | time_backward 1.4220 |
[2023-10-24 01:59:09,754::train::INFO] [train] Iter 577883 | loss 0.8334 | loss(rot) 0.5647 | loss(pos) 0.2250 | loss(seq) 0.0438 | grad 4.5501 | lr 0.0000 | time_forward 3.4640 | time_backward 4.7240 |
[2023-10-24 01:59:12,520::train::INFO] [train] Iter 577884 | loss 1.0376 | loss(rot) 0.9990 | loss(pos) 0.0370 | loss(seq) 0.0016 | grad 4.3857 | lr 0.0000 | time_forward 1.3250 | time_backward 1.4370 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.