text stringlengths 56 1.16k |
|---|
[2023-10-23 15:44:15,231::train::INFO] [train] Iter 572590 | loss 0.3732 | loss(rot) 0.0877 | loss(pos) 0.2297 | loss(seq) 0.0558 | grad 3.4213 | lr 0.0000 | time_forward 2.9300 | time_backward 3.8580 |
[2023-10-23 15:44:23,180::train::INFO] [train] Iter 572591 | loss 0.7269 | loss(rot) 0.6733 | loss(pos) 0.0245 | loss(seq) 0.0292 | grad 3.0574 | lr 0.0000 | time_forward 3.3240 | time_backward 4.6220 |
[2023-10-23 15:44:25,388::train::INFO] [train] Iter 572592 | loss 0.9219 | loss(rot) 0.8652 | loss(pos) 0.0560 | loss(seq) 0.0008 | grad 3.6217 | lr 0.0000 | time_forward 1.0150 | time_backward 1.1890 |
[2023-10-23 15:44:32,276::train::INFO] [train] Iter 572593 | loss 0.7286 | loss(rot) 0.0859 | loss(pos) 0.6128 | loss(seq) 0.0298 | grad 4.7717 | lr 0.0000 | time_forward 2.9920 | time_backward 3.8920 |
[2023-10-23 15:44:34,939::train::INFO] [train] Iter 572594 | loss 0.1768 | loss(rot) 0.1070 | loss(pos) 0.0662 | loss(seq) 0.0036 | grad 1.3456 | lr 0.0000 | time_forward 1.2700 | time_backward 1.3900 |
[2023-10-23 15:44:41,419::train::INFO] [train] Iter 572595 | loss 1.8708 | loss(rot) 1.6483 | loss(pos) 0.1389 | loss(seq) 0.0836 | grad 8.7862 | lr 0.0000 | time_forward 2.8100 | time_backward 3.6670 |
[2023-10-23 15:44:48,484::train::INFO] [train] Iter 572596 | loss 0.3315 | loss(rot) 0.2599 | loss(pos) 0.0297 | loss(seq) 0.0420 | grad 3.0855 | lr 0.0000 | time_forward 3.1080 | time_backward 3.9540 |
[2023-10-23 15:44:51,145::train::INFO] [train] Iter 572597 | loss 0.2209 | loss(rot) 0.0266 | loss(pos) 0.1921 | loss(seq) 0.0022 | grad 5.5289 | lr 0.0000 | time_forward 1.2790 | time_backward 1.3800 |
[2023-10-23 15:44:59,270::train::INFO] [train] Iter 572598 | loss 0.6163 | loss(rot) 0.2582 | loss(pos) 0.3114 | loss(seq) 0.0467 | grad 3.7927 | lr 0.0000 | time_forward 3.3640 | time_backward 4.7580 |
[2023-10-23 15:45:07,242::train::INFO] [train] Iter 572599 | loss 0.2212 | loss(rot) 0.1897 | loss(pos) 0.0287 | loss(seq) 0.0028 | grad 2.2247 | lr 0.0000 | time_forward 3.3290 | time_backward 4.6400 |
[2023-10-23 15:45:13,970::train::INFO] [train] Iter 572600 | loss 0.6358 | loss(rot) 0.4461 | loss(pos) 0.0571 | loss(seq) 0.1326 | grad 8.8589 | lr 0.0000 | time_forward 2.9190 | time_backward 3.8050 |
[2023-10-23 15:45:21,983::train::INFO] [train] Iter 572601 | loss 0.6516 | loss(rot) 0.3730 | loss(pos) 0.0350 | loss(seq) 0.2437 | grad 4.1034 | lr 0.0000 | time_forward 3.3210 | time_backward 4.6900 |
[2023-10-23 15:45:24,213::train::INFO] [train] Iter 572602 | loss 0.3224 | loss(rot) 0.1201 | loss(pos) 0.0187 | loss(seq) 0.1836 | grad 2.2186 | lr 0.0000 | time_forward 1.0410 | time_backward 1.1850 |
[2023-10-23 15:45:26,881::train::INFO] [train] Iter 572603 | loss 0.2625 | loss(rot) 0.1246 | loss(pos) 0.0395 | loss(seq) 0.0985 | grad 2.4682 | lr 0.0000 | time_forward 1.2860 | time_backward 1.3790 |
[2023-10-23 15:45:34,060::train::INFO] [train] Iter 572604 | loss 0.3481 | loss(rot) 0.1405 | loss(pos) 0.0122 | loss(seq) 0.1954 | grad 2.5744 | lr 0.0000 | time_forward 3.1600 | time_backward 4.0160 |
[2023-10-23 15:45:37,174::train::INFO] [train] Iter 572605 | loss 0.8938 | loss(rot) 0.6268 | loss(pos) 0.0787 | loss(seq) 0.1884 | grad 3.4053 | lr 0.0000 | time_forward 1.4000 | time_backward 1.7120 |
[2023-10-23 15:45:39,447::train::INFO] [train] Iter 572606 | loss 0.2418 | loss(rot) 0.1939 | loss(pos) 0.0480 | loss(seq) 0.0000 | grad 3.2633 | lr 0.0000 | time_forward 1.0290 | time_backward 1.2290 |
[2023-10-23 15:45:46,522::train::INFO] [train] Iter 572607 | loss 0.7871 | loss(rot) 0.3849 | loss(pos) 0.3483 | loss(seq) 0.0538 | grad 7.0970 | lr 0.0000 | time_forward 3.0390 | time_backward 4.0310 |
[2023-10-23 15:45:54,595::train::INFO] [train] Iter 572608 | loss 0.9580 | loss(rot) 0.8677 | loss(pos) 0.0299 | loss(seq) 0.0604 | grad 2.5977 | lr 0.0000 | time_forward 3.3490 | time_backward 4.7220 |
[2023-10-23 15:45:57,325::train::INFO] [train] Iter 572609 | loss 0.2952 | loss(rot) 0.0616 | loss(pos) 0.0093 | loss(seq) 0.2243 | grad 1.6348 | lr 0.0000 | time_forward 1.3010 | time_backward 1.4260 |
[2023-10-23 15:46:05,410::train::INFO] [train] Iter 572610 | loss 0.2420 | loss(rot) 0.1715 | loss(pos) 0.0292 | loss(seq) 0.0414 | grad 2.9450 | lr 0.0000 | time_forward 3.3650 | time_backward 4.6980 |
[2023-10-23 15:46:13,375::train::INFO] [train] Iter 572611 | loss 0.5135 | loss(rot) 0.3886 | loss(pos) 0.0180 | loss(seq) 0.1068 | grad 52.8390 | lr 0.0000 | time_forward 3.2810 | time_backward 4.6800 |
[2023-10-23 15:46:20,699::train::INFO] [train] Iter 572612 | loss 0.4824 | loss(rot) 0.4443 | loss(pos) 0.0369 | loss(seq) 0.0012 | grad 2.9092 | lr 0.0000 | time_forward 3.2080 | time_backward 4.1130 |
[2023-10-23 15:46:28,096::train::INFO] [train] Iter 572613 | loss 1.0705 | loss(rot) 0.9099 | loss(pos) 0.0337 | loss(seq) 0.1269 | grad 3.3707 | lr 0.0000 | time_forward 3.2440 | time_backward 4.1500 |
[2023-10-23 15:46:35,505::train::INFO] [train] Iter 572614 | loss 0.8447 | loss(rot) 0.7896 | loss(pos) 0.0546 | loss(seq) 0.0005 | grad 30.2257 | lr 0.0000 | time_forward 3.2200 | time_backward 4.1860 |
[2023-10-23 15:46:42,595::train::INFO] [train] Iter 572615 | loss 0.7819 | loss(rot) 0.2894 | loss(pos) 0.2651 | loss(seq) 0.2273 | grad 3.4361 | lr 0.0000 | time_forward 3.1310 | time_backward 3.9550 |
[2023-10-23 15:46:49,681::train::INFO] [train] Iter 572616 | loss 0.3154 | loss(rot) 0.0455 | loss(pos) 0.2644 | loss(seq) 0.0055 | grad 5.7906 | lr 0.0000 | time_forward 3.0370 | time_backward 4.0450 |
[2023-10-23 15:46:52,391::train::INFO] [train] Iter 572617 | loss 1.2295 | loss(rot) 0.3411 | loss(pos) 0.8866 | loss(seq) 0.0017 | grad 9.5012 | lr 0.0000 | time_forward 1.3130 | time_backward 1.3940 |
[2023-10-23 15:46:55,123::train::INFO] [train] Iter 572618 | loss 0.2636 | loss(rot) 0.1712 | loss(pos) 0.0183 | loss(seq) 0.0741 | grad 2.0120 | lr 0.0000 | time_forward 1.3090 | time_backward 1.4150 |
[2023-10-23 15:47:03,063::train::INFO] [train] Iter 572619 | loss 0.4820 | loss(rot) 0.3566 | loss(pos) 0.0246 | loss(seq) 0.1008 | grad 5.0407 | lr 0.0000 | time_forward 3.3010 | time_backward 4.6340 |
[2023-10-23 15:47:10,947::train::INFO] [train] Iter 572620 | loss 0.5539 | loss(rot) 0.0156 | loss(pos) 0.5322 | loss(seq) 0.0060 | grad 4.8647 | lr 0.0000 | time_forward 3.2660 | time_backward 4.6150 |
[2023-10-23 15:47:13,624::train::INFO] [train] Iter 572621 | loss 1.6039 | loss(rot) 1.4952 | loss(pos) 0.0240 | loss(seq) 0.0846 | grad 4.8321 | lr 0.0000 | time_forward 1.2780 | time_backward 1.3960 |
[2023-10-23 15:47:19,184::train::INFO] [train] Iter 572622 | loss 0.2795 | loss(rot) 0.0370 | loss(pos) 0.1707 | loss(seq) 0.0718 | grad 5.8794 | lr 0.0000 | time_forward 2.4480 | time_backward 3.1090 |
[2023-10-23 15:47:26,540::train::INFO] [train] Iter 572623 | loss 0.4276 | loss(rot) 0.0803 | loss(pos) 0.0377 | loss(seq) 0.3096 | grad 2.8756 | lr 0.0000 | time_forward 3.2180 | time_backward 4.1340 |
[2023-10-23 15:47:33,329::train::INFO] [train] Iter 572624 | loss 0.4790 | loss(rot) 0.4481 | loss(pos) 0.0274 | loss(seq) 0.0035 | grad 2.5951 | lr 0.0000 | time_forward 2.9380 | time_backward 3.8470 |
[2023-10-23 15:47:40,606::train::INFO] [train] Iter 572625 | loss 0.7058 | loss(rot) 0.2180 | loss(pos) 0.0700 | loss(seq) 0.4178 | grad 3.0674 | lr 0.0000 | time_forward 3.1720 | time_backward 4.1020 |
[2023-10-23 15:47:47,171::train::INFO] [train] Iter 572626 | loss 0.9410 | loss(rot) 0.7959 | loss(pos) 0.0383 | loss(seq) 0.1067 | grad 6.5633 | lr 0.0000 | time_forward 2.8080 | time_backward 3.7550 |
[2023-10-23 15:47:54,223::train::INFO] [train] Iter 572627 | loss 0.6414 | loss(rot) 0.4003 | loss(pos) 0.0350 | loss(seq) 0.2062 | grad 4.3235 | lr 0.0000 | time_forward 3.0250 | time_backward 4.0240 |
[2023-10-23 15:48:00,997::train::INFO] [train] Iter 572628 | loss 0.3706 | loss(rot) 0.2099 | loss(pos) 0.0940 | loss(seq) 0.0666 | grad 2.7192 | lr 0.0000 | time_forward 2.9360 | time_backward 3.8350 |
[2023-10-23 15:48:07,777::train::INFO] [train] Iter 572629 | loss 1.6165 | loss(rot) 1.1886 | loss(pos) 0.1709 | loss(seq) 0.2570 | grad 6.5318 | lr 0.0000 | time_forward 2.8820 | time_backward 3.8940 |
[2023-10-23 15:48:14,461::train::INFO] [train] Iter 572630 | loss 1.3648 | loss(rot) 0.7181 | loss(pos) 0.2155 | loss(seq) 0.4312 | grad 5.0048 | lr 0.0000 | time_forward 2.8850 | time_backward 3.7970 |
[2023-10-23 15:48:17,224::train::INFO] [train] Iter 572631 | loss 0.3321 | loss(rot) 0.2939 | loss(pos) 0.0166 | loss(seq) 0.0216 | grad 6.6142 | lr 0.0000 | time_forward 1.3270 | time_backward 1.4330 |
[2023-10-23 15:48:19,975::train::INFO] [train] Iter 572632 | loss 0.5620 | loss(rot) 0.3904 | loss(pos) 0.0347 | loss(seq) 0.1368 | grad 12.0828 | lr 0.0000 | time_forward 1.3110 | time_backward 1.4250 |
[2023-10-23 15:48:22,736::train::INFO] [train] Iter 572633 | loss 0.8656 | loss(rot) 0.0516 | loss(pos) 0.8083 | loss(seq) 0.0056 | grad 10.2833 | lr 0.0000 | time_forward 1.3230 | time_backward 1.4340 |
[2023-10-23 15:48:30,957::train::INFO] [train] Iter 572634 | loss 2.2864 | loss(rot) 1.6487 | loss(pos) 0.2149 | loss(seq) 0.4228 | grad 4.9461 | lr 0.0000 | time_forward 3.4500 | time_backward 4.7510 |
[2023-10-23 15:48:33,677::train::INFO] [train] Iter 572635 | loss 0.4559 | loss(rot) 0.1024 | loss(pos) 0.0165 | loss(seq) 0.3370 | grad 2.0800 | lr 0.0000 | time_forward 1.2820 | time_backward 1.4350 |
[2023-10-23 15:48:41,548::train::INFO] [train] Iter 572636 | loss 0.3021 | loss(rot) 0.2610 | loss(pos) 0.0342 | loss(seq) 0.0069 | grad 1.9977 | lr 0.0000 | time_forward 3.2790 | time_backward 4.5890 |
[2023-10-23 15:48:48,831::train::INFO] [train] Iter 572637 | loss 0.2907 | loss(rot) 0.0484 | loss(pos) 0.2271 | loss(seq) 0.0152 | grad 3.7778 | lr 0.0000 | time_forward 3.1210 | time_backward 4.1600 |
[2023-10-23 15:48:51,579::train::INFO] [train] Iter 572638 | loss 0.3227 | loss(rot) 0.1113 | loss(pos) 0.0668 | loss(seq) 0.1446 | grad 3.1954 | lr 0.0000 | time_forward 1.3100 | time_backward 1.4340 |
[2023-10-23 15:48:54,375::train::INFO] [train] Iter 572639 | loss 0.4977 | loss(rot) 0.0172 | loss(pos) 0.4790 | loss(seq) 0.0015 | grad 6.2008 | lr 0.0000 | time_forward 1.3350 | time_backward 1.4580 |
[2023-10-23 15:49:01,063::train::INFO] [train] Iter 572640 | loss 0.3786 | loss(rot) 0.1803 | loss(pos) 0.0437 | loss(seq) 0.1546 | grad 3.5082 | lr 0.0000 | time_forward 2.9020 | time_backward 3.7820 |
[2023-10-23 15:49:09,167::train::INFO] [train] Iter 572641 | loss 0.6431 | loss(rot) 0.6002 | loss(pos) 0.0117 | loss(seq) 0.0311 | grad 3.4209 | lr 0.0000 | time_forward 3.3560 | time_backward 4.7450 |
[2023-10-23 15:49:16,578::train::INFO] [train] Iter 572642 | loss 0.5358 | loss(rot) 0.2981 | loss(pos) 0.1988 | loss(seq) 0.0389 | grad 4.8805 | lr 0.0000 | time_forward 3.2130 | time_backward 4.1940 |
[2023-10-23 15:49:23,453::train::INFO] [train] Iter 572643 | loss 1.5329 | loss(rot) 1.0123 | loss(pos) 0.1241 | loss(seq) 0.3965 | grad 5.0204 | lr 0.0000 | time_forward 2.9880 | time_backward 3.8830 |
[2023-10-23 15:49:26,353::train::INFO] [train] Iter 572644 | loss 1.0368 | loss(rot) 0.5834 | loss(pos) 0.0935 | loss(seq) 0.3598 | grad 2.9109 | lr 0.0000 | time_forward 1.3410 | time_backward 1.5570 |
[2023-10-23 15:49:35,260::train::INFO] [train] Iter 572645 | loss 1.0186 | loss(rot) 0.6082 | loss(pos) 0.0862 | loss(seq) 0.3242 | grad 5.8008 | lr 0.0000 | time_forward 3.6620 | time_backward 5.2400 |
[2023-10-23 15:49:43,909::train::INFO] [train] Iter 572646 | loss 0.5412 | loss(rot) 0.1189 | loss(pos) 0.0584 | loss(seq) 0.3639 | grad 2.7967 | lr 0.0000 | time_forward 3.6420 | time_backward 5.0040 |
[2023-10-23 15:49:51,231::train::INFO] [train] Iter 572647 | loss 0.5025 | loss(rot) 0.4746 | loss(pos) 0.0275 | loss(seq) 0.0004 | grad 4.4240 | lr 0.0000 | time_forward 3.2180 | time_backward 4.1020 |
[2023-10-23 15:49:53,922::train::INFO] [train] Iter 572648 | loss 0.3549 | loss(rot) 0.2973 | loss(pos) 0.0216 | loss(seq) 0.0360 | grad 9.7941 | lr 0.0000 | time_forward 1.2760 | time_backward 1.4110 |
[2023-10-23 15:50:00,524::train::INFO] [train] Iter 572649 | loss 0.4395 | loss(rot) 0.4196 | loss(pos) 0.0177 | loss(seq) 0.0022 | grad 2.6493 | lr 0.0000 | time_forward 2.8770 | time_backward 3.7220 |
[2023-10-23 15:50:03,178::train::INFO] [train] Iter 572650 | loss 2.5934 | loss(rot) 1.6532 | loss(pos) 0.4335 | loss(seq) 0.5067 | grad 5.4839 | lr 0.0000 | time_forward 1.2770 | time_backward 1.3740 |
[2023-10-23 15:50:10,036::train::INFO] [train] Iter 572651 | loss 0.4047 | loss(rot) 0.0403 | loss(pos) 0.2005 | loss(seq) 0.1639 | grad 4.2869 | lr 0.0000 | time_forward 2.9450 | time_backward 3.8930 |
[2023-10-23 15:50:18,674::train::INFO] [train] Iter 572652 | loss 0.3320 | loss(rot) 0.0856 | loss(pos) 0.0564 | loss(seq) 0.1899 | grad 2.2853 | lr 0.0000 | time_forward 3.4770 | time_backward 5.1580 |
[2023-10-23 15:50:25,542::train::INFO] [train] Iter 572653 | loss 0.3283 | loss(rot) 0.2889 | loss(pos) 0.0218 | loss(seq) 0.0177 | grad 2.9370 | lr 0.0000 | time_forward 3.0490 | time_backward 3.8160 |
[2023-10-23 15:50:33,688::train::INFO] [train] Iter 572654 | loss 0.3784 | loss(rot) 0.0854 | loss(pos) 0.0498 | loss(seq) 0.2432 | grad 2.2585 | lr 0.0000 | time_forward 3.4220 | time_backward 4.7210 |
[2023-10-23 15:50:41,523::train::INFO] [train] Iter 572655 | loss 0.4560 | loss(rot) 0.1817 | loss(pos) 0.0451 | loss(seq) 0.2292 | grad 3.2705 | lr 0.0000 | time_forward 3.2410 | time_backward 4.5920 |
[2023-10-23 15:50:49,413::train::INFO] [train] Iter 572656 | loss 0.2546 | loss(rot) 0.0665 | loss(pos) 0.1669 | loss(seq) 0.0212 | grad 4.0833 | lr 0.0000 | time_forward 3.3410 | time_backward 4.5460 |
[2023-10-23 15:50:56,337::train::INFO] [train] Iter 572657 | loss 0.8824 | loss(rot) 0.6525 | loss(pos) 0.0231 | loss(seq) 0.2069 | grad 3.1385 | lr 0.0000 | time_forward 2.6300 | time_backward 4.2900 |
[2023-10-23 15:50:59,314::train::INFO] [train] Iter 572658 | loss 0.9821 | loss(rot) 0.1870 | loss(pos) 0.2912 | loss(seq) 0.5038 | grad 5.7430 | lr 0.0000 | time_forward 1.4810 | time_backward 1.4940 |
[2023-10-23 15:51:02,330::train::INFO] [train] Iter 572659 | loss 0.9971 | loss(rot) 0.9700 | loss(pos) 0.0200 | loss(seq) 0.0070 | grad 4.7569 | lr 0.0000 | time_forward 1.5790 | time_backward 1.4330 |
[2023-10-23 15:51:04,998::train::INFO] [train] Iter 572660 | loss 1.5015 | loss(rot) 0.6462 | loss(pos) 0.4375 | loss(seq) 0.4178 | grad 8.7717 | lr 0.0000 | time_forward 1.2760 | time_backward 1.3900 |
[2023-10-23 15:51:11,090::train::INFO] [train] Iter 572661 | loss 0.1422 | loss(rot) 0.1200 | loss(pos) 0.0209 | loss(seq) 0.0013 | grad 1.7027 | lr 0.0000 | time_forward 2.6300 | time_backward 3.4590 |
[2023-10-23 15:51:18,011::train::INFO] [train] Iter 572662 | loss 0.5346 | loss(rot) 0.4090 | loss(pos) 0.0182 | loss(seq) 0.1074 | grad 3.3354 | lr 0.0000 | time_forward 3.0100 | time_backward 3.9080 |
[2023-10-23 15:51:24,061::train::INFO] [train] Iter 572663 | loss 0.4437 | loss(rot) 0.1361 | loss(pos) 0.0263 | loss(seq) 0.2814 | grad 2.2376 | lr 0.0000 | time_forward 2.6270 | time_backward 3.4200 |
[2023-10-23 15:51:31,248::train::INFO] [train] Iter 572664 | loss 0.5124 | loss(rot) 0.0096 | loss(pos) 0.2714 | loss(seq) 0.2314 | grad 8.8181 | lr 0.0000 | time_forward 3.1490 | time_backward 4.0350 |
[2023-10-23 15:51:33,452::train::INFO] [train] Iter 572665 | loss 0.4253 | loss(rot) 0.0342 | loss(pos) 0.3883 | loss(seq) 0.0028 | grad 5.2449 | lr 0.0000 | time_forward 0.9950 | time_backward 1.2060 |
[2023-10-23 15:51:36,461::train::INFO] [train] Iter 572666 | loss 1.0057 | loss(rot) 0.3701 | loss(pos) 0.6260 | loss(seq) 0.0096 | grad 5.9178 | lr 0.0000 | time_forward 1.3590 | time_backward 1.6480 |
[2023-10-23 15:51:44,281::train::INFO] [train] Iter 572667 | loss 0.2612 | loss(rot) 0.2149 | loss(pos) 0.0448 | loss(seq) 0.0015 | grad 12.2225 | lr 0.0000 | time_forward 3.2620 | time_backward 4.5540 |
[2023-10-23 15:51:50,459::train::INFO] [train] Iter 572668 | loss 0.9698 | loss(rot) 0.6553 | loss(pos) 0.0733 | loss(seq) 0.2412 | grad 5.3612 | lr 0.0000 | time_forward 2.6330 | time_backward 3.5350 |
[2023-10-23 15:51:58,193::train::INFO] [train] Iter 572669 | loss 0.7621 | loss(rot) 0.2349 | loss(pos) 0.3637 | loss(seq) 0.1635 | grad 6.1009 | lr 0.0000 | time_forward 3.2640 | time_backward 4.4660 |
[2023-10-23 15:52:04,750::train::INFO] [train] Iter 572670 | loss 2.0426 | loss(rot) 1.5832 | loss(pos) 0.0535 | loss(seq) 0.4059 | grad 8.0700 | lr 0.0000 | time_forward 2.8590 | time_backward 3.6940 |
[2023-10-23 15:52:07,327::train::INFO] [train] Iter 572671 | loss 0.6484 | loss(rot) 0.5521 | loss(pos) 0.0962 | loss(seq) 0.0000 | grad 18.8023 | lr 0.0000 | time_forward 1.2250 | time_backward 1.3500 |
[2023-10-23 15:52:09,937::train::INFO] [train] Iter 572672 | loss 1.7390 | loss(rot) 1.0820 | loss(pos) 0.1230 | loss(seq) 0.5340 | grad 5.5728 | lr 0.0000 | time_forward 1.2610 | time_backward 1.3450 |
[2023-10-23 15:52:16,036::train::INFO] [train] Iter 572673 | loss 0.8292 | loss(rot) 0.1900 | loss(pos) 0.6270 | loss(seq) 0.0122 | grad 5.5396 | lr 0.0000 | time_forward 2.6670 | time_backward 3.4290 |
[2023-10-23 15:52:23,756::train::INFO] [train] Iter 572674 | loss 1.3868 | loss(rot) 0.9718 | loss(pos) 0.0765 | loss(seq) 0.3384 | grad 23.4318 | lr 0.0000 | time_forward 3.3690 | time_backward 4.3480 |
[2023-10-23 15:52:30,422::train::INFO] [train] Iter 572675 | loss 1.2745 | loss(rot) 0.9634 | loss(pos) 0.0338 | loss(seq) 0.2774 | grad 6.6766 | lr 0.0000 | time_forward 2.9190 | time_backward 3.7440 |
[2023-10-23 15:52:37,728::train::INFO] [train] Iter 572676 | loss 0.7751 | loss(rot) 0.1241 | loss(pos) 0.6386 | loss(seq) 0.0124 | grad 4.8470 | lr 0.0000 | time_forward 3.1960 | time_backward 4.1070 |
[2023-10-23 15:52:45,573::train::INFO] [train] Iter 572677 | loss 0.2905 | loss(rot) 0.0748 | loss(pos) 0.2031 | loss(seq) 0.0126 | grad 4.2312 | lr 0.0000 | time_forward 3.2730 | time_backward 4.5690 |
[2023-10-23 15:52:52,724::train::INFO] [train] Iter 572678 | loss 0.3762 | loss(rot) 0.0850 | loss(pos) 0.0729 | loss(seq) 0.2183 | grad 2.8115 | lr 0.0000 | time_forward 3.0990 | time_backward 4.0490 |
[2023-10-23 15:52:55,340::train::INFO] [train] Iter 572679 | loss 0.3468 | loss(rot) 0.1193 | loss(pos) 0.0237 | loss(seq) 0.2037 | grad 2.1661 | lr 0.0000 | time_forward 1.2490 | time_backward 1.3650 |
[2023-10-23 15:53:03,157::train::INFO] [train] Iter 572680 | loss 0.4405 | loss(rot) 0.1902 | loss(pos) 0.1656 | loss(seq) 0.0847 | grad 3.9923 | lr 0.0000 | time_forward 3.2290 | time_backward 4.5630 |
[2023-10-23 15:53:09,800::train::INFO] [train] Iter 572681 | loss 0.5507 | loss(rot) 0.2792 | loss(pos) 0.0238 | loss(seq) 0.2477 | grad 8.4581 | lr 0.0000 | time_forward 2.9040 | time_backward 3.7370 |
[2023-10-23 15:53:17,648::train::INFO] [train] Iter 572682 | loss 0.3426 | loss(rot) 0.2345 | loss(pos) 0.0310 | loss(seq) 0.0771 | grad 2.7120 | lr 0.0000 | time_forward 3.2400 | time_backward 4.6050 |
[2023-10-23 15:53:24,162::train::INFO] [train] Iter 572683 | loss 0.3761 | loss(rot) 0.3500 | loss(pos) 0.0255 | loss(seq) 0.0006 | grad 3.3917 | lr 0.0000 | time_forward 2.7950 | time_backward 3.7160 |
[2023-10-23 15:53:26,834::train::INFO] [train] Iter 572684 | loss 0.6208 | loss(rot) 0.0973 | loss(pos) 0.0868 | loss(seq) 0.4367 | grad 3.6082 | lr 0.0000 | time_forward 1.2560 | time_backward 1.4120 |
[2023-10-23 15:53:34,116::train::INFO] [train] Iter 572685 | loss 0.4562 | loss(rot) 0.1352 | loss(pos) 0.2214 | loss(seq) 0.0996 | grad 3.8250 | lr 0.0000 | time_forward 3.1660 | time_backward 4.1120 |
[2023-10-23 15:53:40,574::train::INFO] [train] Iter 572686 | loss 0.8553 | loss(rot) 0.0346 | loss(pos) 0.5292 | loss(seq) 0.2915 | grad 10.2624 | lr 0.0000 | time_forward 2.8100 | time_backward 3.6450 |
[2023-10-23 15:53:47,811::train::INFO] [train] Iter 572687 | loss 0.6211 | loss(rot) 0.0848 | loss(pos) 0.5239 | loss(seq) 0.0124 | grad 10.1774 | lr 0.0000 | time_forward 3.1340 | time_backward 4.1000 |
[2023-10-23 15:53:50,424::train::INFO] [train] Iter 572688 | loss 0.5973 | loss(rot) 0.1044 | loss(pos) 0.3927 | loss(seq) 0.1002 | grad 3.6850 | lr 0.0000 | time_forward 1.2460 | time_backward 1.3640 |
[2023-10-23 15:53:53,492::train::INFO] [train] Iter 572689 | loss 0.9450 | loss(rot) 0.8976 | loss(pos) 0.0276 | loss(seq) 0.0198 | grad 2.5737 | lr 0.0000 | time_forward 1.3890 | time_backward 1.6610 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.