text stringlengths 56 1.16k |
|---|
[2023-10-24 07:16:18,891::train::INFO] [train] Iter 580582 | loss 0.3675 | loss(rot) 0.1724 | loss(pos) 0.1436 | loss(seq) 0.0515 | grad 4.8111 | lr 0.0000 | time_forward 3.8590 | time_backward 5.6290 |
[2023-10-24 07:16:28,184::train::INFO] [train] Iter 580583 | loss 1.6195 | loss(rot) 1.4977 | loss(pos) 0.0406 | loss(seq) 0.0812 | grad 3.6261 | lr 0.0000 | time_forward 3.8090 | time_backward 5.4810 |
[2023-10-24 07:16:30,900::train::INFO] [train] Iter 580584 | loss 0.4000 | loss(rot) 0.1766 | loss(pos) 0.0195 | loss(seq) 0.2039 | grad 2.3732 | lr 0.0000 | time_forward 1.3080 | time_backward 1.4050 |
[2023-10-24 07:16:33,731::train::INFO] [train] Iter 580585 | loss 0.5586 | loss(rot) 0.5365 | loss(pos) 0.0189 | loss(seq) 0.0032 | grad 65.0499 | lr 0.0000 | time_forward 1.3650 | time_backward 1.4640 |
[2023-10-24 07:16:41,629::train::INFO] [train] Iter 580586 | loss 0.6600 | loss(rot) 0.1374 | loss(pos) 0.1264 | loss(seq) 0.3962 | grad 2.9550 | lr 0.0000 | time_forward 3.3970 | time_backward 4.4980 |
[2023-10-24 07:16:50,359::train::INFO] [train] Iter 580587 | loss 0.6570 | loss(rot) 0.2502 | loss(pos) 0.3900 | loss(seq) 0.0168 | grad 4.8780 | lr 0.0000 | time_forward 3.6980 | time_backward 5.0290 |
[2023-10-24 07:16:53,031::train::INFO] [train] Iter 580588 | loss 2.5653 | loss(rot) 1.8798 | loss(pos) 0.3417 | loss(seq) 0.3439 | grad 4.7516 | lr 0.0000 | time_forward 1.2150 | time_backward 1.4530 |
[2023-10-24 07:16:55,836::train::INFO] [train] Iter 580589 | loss 1.4174 | loss(rot) 1.3280 | loss(pos) 0.0533 | loss(seq) 0.0362 | grad 2.6975 | lr 0.0000 | time_forward 1.2580 | time_backward 1.5450 |
[2023-10-24 07:17:03,852::train::INFO] [train] Iter 580590 | loss 0.7625 | loss(rot) 0.7345 | loss(pos) 0.0121 | loss(seq) 0.0160 | grad 52.1973 | lr 0.0000 | time_forward 3.4050 | time_backward 4.6080 |
[2023-10-24 07:17:13,357::train::INFO] [train] Iter 580591 | loss 0.2336 | loss(rot) 0.1790 | loss(pos) 0.0146 | loss(seq) 0.0400 | grad 2.8092 | lr 0.0000 | time_forward 3.8590 | time_backward 5.6420 |
[2023-10-24 07:17:16,172::train::INFO] [train] Iter 580592 | loss 1.2670 | loss(rot) 0.5192 | loss(pos) 0.0768 | loss(seq) 0.6711 | grad 4.4954 | lr 0.0000 | time_forward 1.3560 | time_backward 1.4560 |
[2023-10-24 07:17:24,584::train::INFO] [train] Iter 580593 | loss 0.5735 | loss(rot) 0.1789 | loss(pos) 0.1139 | loss(seq) 0.2807 | grad 3.7470 | lr 0.0000 | time_forward 3.6120 | time_backward 4.7970 |
[2023-10-24 07:17:32,279::train::INFO] [train] Iter 580594 | loss 0.2066 | loss(rot) 0.1829 | loss(pos) 0.0190 | loss(seq) 0.0047 | grad 5.0119 | lr 0.0000 | time_forward 3.2360 | time_backward 4.4560 |
[2023-10-24 07:17:41,709::train::INFO] [train] Iter 580595 | loss 0.5059 | loss(rot) 0.2343 | loss(pos) 0.0788 | loss(seq) 0.1928 | grad 3.0157 | lr 0.0000 | time_forward 3.8540 | time_backward 5.5740 |
[2023-10-24 07:17:51,260::train::INFO] [train] Iter 580596 | loss 0.2946 | loss(rot) 0.2639 | loss(pos) 0.0306 | loss(seq) 0.0000 | grad 2.5912 | lr 0.0000 | time_forward 3.8620 | time_backward 5.6860 |
[2023-10-24 07:17:54,097::train::INFO] [train] Iter 580597 | loss 0.0777 | loss(rot) 0.0491 | loss(pos) 0.0148 | loss(seq) 0.0138 | grad 1.5214 | lr 0.0000 | time_forward 1.3550 | time_backward 1.4780 |
[2023-10-24 07:18:02,777::train::INFO] [train] Iter 580598 | loss 0.5193 | loss(rot) 0.1919 | loss(pos) 0.0392 | loss(seq) 0.2882 | grad 4.5414 | lr 0.0000 | time_forward 3.7180 | time_backward 4.9590 |
[2023-10-24 07:18:11,799::train::INFO] [train] Iter 580599 | loss 0.3230 | loss(rot) 0.2945 | loss(pos) 0.0224 | loss(seq) 0.0060 | grad 3.0285 | lr 0.0000 | time_forward 3.7620 | time_backward 5.2570 |
[2023-10-24 07:18:20,603::train::INFO] [train] Iter 580600 | loss 0.3630 | loss(rot) 0.1097 | loss(pos) 0.0238 | loss(seq) 0.2296 | grad 1.9509 | lr 0.0000 | time_forward 3.7820 | time_backward 5.0180 |
[2023-10-24 07:18:23,375::train::INFO] [train] Iter 580601 | loss 0.5944 | loss(rot) 0.2985 | loss(pos) 0.0495 | loss(seq) 0.2464 | grad 3.8281 | lr 0.0000 | time_forward 1.3100 | time_backward 1.4590 |
[2023-10-24 07:18:32,733::train::INFO] [train] Iter 580602 | loss 0.2338 | loss(rot) 0.1426 | loss(pos) 0.0668 | loss(seq) 0.0243 | grad 2.1286 | lr 0.0000 | time_forward 3.7930 | time_backward 5.5360 |
[2023-10-24 07:18:42,097::train::INFO] [train] Iter 580603 | loss 0.3262 | loss(rot) 0.1425 | loss(pos) 0.0175 | loss(seq) 0.1662 | grad 1.9891 | lr 0.0000 | time_forward 3.8700 | time_backward 5.4910 |
[2023-10-24 07:18:50,111::train::INFO] [train] Iter 580604 | loss 0.3891 | loss(rot) 0.0365 | loss(pos) 0.3502 | loss(seq) 0.0024 | grad 7.3620 | lr 0.0000 | time_forward 3.3860 | time_backward 4.6250 |
[2023-10-24 07:18:58,263::train::INFO] [train] Iter 580605 | loss 0.6749 | loss(rot) 0.3220 | loss(pos) 0.0696 | loss(seq) 0.2833 | grad 14.0279 | lr 0.0000 | time_forward 3.4380 | time_backward 4.7100 |
[2023-10-24 07:19:07,000::train::INFO] [train] Iter 580606 | loss 1.7159 | loss(rot) 1.1960 | loss(pos) 0.1373 | loss(seq) 0.3826 | grad 6.8335 | lr 0.0000 | time_forward 3.7330 | time_backward 5.0010 |
[2023-10-24 07:19:15,910::train::INFO] [train] Iter 580607 | loss 1.2592 | loss(rot) 1.0460 | loss(pos) 0.0980 | loss(seq) 0.1152 | grad 7.5463 | lr 0.0000 | time_forward 3.8530 | time_backward 5.0530 |
[2023-10-24 07:19:23,962::train::INFO] [train] Iter 580608 | loss 0.5618 | loss(rot) 0.1567 | loss(pos) 0.0485 | loss(seq) 0.3566 | grad 3.6911 | lr 0.0000 | time_forward 3.4040 | time_backward 4.6450 |
[2023-10-24 07:19:32,744::train::INFO] [train] Iter 580609 | loss 0.8522 | loss(rot) 0.3110 | loss(pos) 0.1430 | loss(seq) 0.3982 | grad 3.5653 | lr 0.0000 | time_forward 3.6990 | time_backward 5.0810 |
[2023-10-24 07:19:40,759::train::INFO] [train] Iter 580610 | loss 0.3636 | loss(rot) 0.3448 | loss(pos) 0.0119 | loss(seq) 0.0069 | grad 4.2110 | lr 0.0000 | time_forward 3.4000 | time_backward 4.6120 |
[2023-10-24 07:19:49,396::train::INFO] [train] Iter 580611 | loss 0.8751 | loss(rot) 0.0693 | loss(pos) 0.8032 | loss(seq) 0.0026 | grad 7.4490 | lr 0.0000 | time_forward 3.6660 | time_backward 4.9670 |
[2023-10-24 07:19:59,103::train::INFO] [train] Iter 580612 | loss 1.3888 | loss(rot) 1.3148 | loss(pos) 0.0416 | loss(seq) 0.0324 | grad 5.7693 | lr 0.0000 | time_forward 4.1800 | time_backward 5.5240 |
[2023-10-24 07:20:01,948::train::INFO] [train] Iter 580613 | loss 0.3301 | loss(rot) 0.1765 | loss(pos) 0.0156 | loss(seq) 0.1380 | grad 2.6860 | lr 0.0000 | time_forward 1.3690 | time_backward 1.4730 |
[2023-10-24 07:20:10,197::train::INFO] [train] Iter 580614 | loss 0.5352 | loss(rot) 0.0766 | loss(pos) 0.1414 | loss(seq) 0.3172 | grad 3.6542 | lr 0.0000 | time_forward 3.6210 | time_backward 4.6250 |
[2023-10-24 07:20:19,497::train::INFO] [train] Iter 580615 | loss 0.5228 | loss(rot) 0.1008 | loss(pos) 0.3097 | loss(seq) 0.1123 | grad 3.2039 | lr 0.0000 | time_forward 3.9870 | time_backward 5.3100 |
[2023-10-24 07:20:21,719::train::INFO] [train] Iter 580616 | loss 1.2213 | loss(rot) 0.9267 | loss(pos) 0.0709 | loss(seq) 0.2236 | grad 33.9472 | lr 0.0000 | time_forward 1.0180 | time_backward 1.2010 |
[2023-10-24 07:20:29,969::train::INFO] [train] Iter 580617 | loss 0.3826 | loss(rot) 0.0733 | loss(pos) 0.0498 | loss(seq) 0.2594 | grad 2.8175 | lr 0.0000 | time_forward 3.4100 | time_backward 4.8360 |
[2023-10-24 07:20:32,876::train::INFO] [train] Iter 580618 | loss 0.4615 | loss(rot) 0.1021 | loss(pos) 0.0296 | loss(seq) 0.3298 | grad 2.3613 | lr 0.0000 | time_forward 1.3750 | time_backward 1.5280 |
[2023-10-24 07:20:42,461::train::INFO] [train] Iter 580619 | loss 0.5914 | loss(rot) 0.0370 | loss(pos) 0.1600 | loss(seq) 0.3945 | grad 5.1573 | lr 0.0000 | time_forward 3.8780 | time_backward 5.6680 |
[2023-10-24 07:20:51,752::train::INFO] [train] Iter 580620 | loss 0.5195 | loss(rot) 0.1302 | loss(pos) 0.2392 | loss(seq) 0.1500 | grad 3.2152 | lr 0.0000 | time_forward 3.7750 | time_backward 5.5130 |
[2023-10-24 07:21:01,346::train::INFO] [train] Iter 580621 | loss 0.5741 | loss(rot) 0.0863 | loss(pos) 0.2781 | loss(seq) 0.2097 | grad 7.1472 | lr 0.0000 | time_forward 4.1380 | time_backward 5.4520 |
[2023-10-24 07:21:09,427::train::INFO] [train] Iter 580622 | loss 0.6637 | loss(rot) 0.3690 | loss(pos) 0.2866 | loss(seq) 0.0081 | grad 4.5506 | lr 0.0000 | time_forward 3.4960 | time_backward 4.5820 |
[2023-10-24 07:21:18,557::train::INFO] [train] Iter 580623 | loss 0.2387 | loss(rot) 0.1145 | loss(pos) 0.0359 | loss(seq) 0.0883 | grad 1.8254 | lr 0.0000 | time_forward 3.7230 | time_backward 5.4040 |
[2023-10-24 07:21:27,319::train::INFO] [train] Iter 580624 | loss 0.5744 | loss(rot) 0.5306 | loss(pos) 0.0262 | loss(seq) 0.0176 | grad 2.8983 | lr 0.0000 | time_forward 3.5260 | time_backward 5.2320 |
[2023-10-24 07:21:35,304::train::INFO] [train] Iter 580625 | loss 0.2817 | loss(rot) 0.1854 | loss(pos) 0.0175 | loss(seq) 0.0789 | grad 2.8198 | lr 0.0000 | time_forward 3.3680 | time_backward 4.6130 |
[2023-10-24 07:21:44,735::train::INFO] [train] Iter 580626 | loss 1.1432 | loss(rot) 0.8461 | loss(pos) 0.1004 | loss(seq) 0.1967 | grad 3.7127 | lr 0.0000 | time_forward 4.0450 | time_backward 5.3830 |
[2023-10-24 07:21:54,273::train::INFO] [train] Iter 580627 | loss 0.5555 | loss(rot) 0.3341 | loss(pos) 0.0561 | loss(seq) 0.1653 | grad 3.9349 | lr 0.0000 | time_forward 4.0500 | time_backward 5.4850 |
[2023-10-24 07:22:02,303::train::INFO] [train] Iter 580628 | loss 1.1422 | loss(rot) 0.5014 | loss(pos) 0.0599 | loss(seq) 0.5810 | grad 5.5346 | lr 0.0000 | time_forward 3.3760 | time_backward 4.6500 |
[2023-10-24 07:22:05,003::train::INFO] [train] Iter 580629 | loss 0.5948 | loss(rot) 0.1600 | loss(pos) 0.1214 | loss(seq) 0.3134 | grad 4.0720 | lr 0.0000 | time_forward 1.2780 | time_backward 1.4200 |
[2023-10-24 07:22:14,704::train::INFO] [train] Iter 580630 | loss 1.0765 | loss(rot) 1.0311 | loss(pos) 0.0428 | loss(seq) 0.0027 | grad 3.5252 | lr 0.0000 | time_forward 4.2990 | time_backward 5.3980 |
[2023-10-24 07:22:23,080::train::INFO] [train] Iter 580631 | loss 1.5743 | loss(rot) 1.2773 | loss(pos) 0.0545 | loss(seq) 0.2425 | grad 4.9459 | lr 0.0000 | time_forward 3.5070 | time_backward 4.8650 |
[2023-10-24 07:22:32,511::train::INFO] [train] Iter 580632 | loss 0.3119 | loss(rot) 0.1285 | loss(pos) 0.0701 | loss(seq) 0.1133 | grad 2.9664 | lr 0.0000 | time_forward 3.8260 | time_backward 5.6030 |
[2023-10-24 07:22:35,265::train::INFO] [train] Iter 580633 | loss 5.3640 | loss(rot) 0.6067 | loss(pos) 4.7573 | loss(seq) 0.0000 | grad 21.3610 | lr 0.0000 | time_forward 1.3400 | time_backward 1.4100 |
[2023-10-24 07:22:38,084::train::INFO] [train] Iter 580634 | loss 0.4233 | loss(rot) 0.3184 | loss(pos) 0.0231 | loss(seq) 0.0818 | grad 3.9107 | lr 0.0000 | time_forward 1.3340 | time_backward 1.4410 |
[2023-10-24 07:22:45,869::train::INFO] [train] Iter 580635 | loss 1.3008 | loss(rot) 0.4545 | loss(pos) 0.8290 | loss(seq) 0.0173 | grad 8.8694 | lr 0.0000 | time_forward 3.3000 | time_backward 4.4820 |
[2023-10-24 07:22:48,729::train::INFO] [train] Iter 580636 | loss 0.5495 | loss(rot) 0.3022 | loss(pos) 0.0300 | loss(seq) 0.2173 | grad 3.5230 | lr 0.0000 | time_forward 1.3630 | time_backward 1.4930 |
[2023-10-24 07:22:58,182::train::INFO] [train] Iter 580637 | loss 0.2400 | loss(rot) 0.1675 | loss(pos) 0.0503 | loss(seq) 0.0222 | grad 2.1886 | lr 0.0000 | time_forward 3.8230 | time_backward 5.5940 |
[2023-10-24 07:23:00,460::train::INFO] [train] Iter 580638 | loss 0.8007 | loss(rot) 0.4754 | loss(pos) 0.0441 | loss(seq) 0.2812 | grad 3.9921 | lr 0.0000 | time_forward 1.0470 | time_backward 1.2280 |
[2023-10-24 07:23:09,253::train::INFO] [train] Iter 580639 | loss 0.2658 | loss(rot) 0.0584 | loss(pos) 0.1956 | loss(seq) 0.0118 | grad 5.6469 | lr 0.0000 | time_forward 3.6070 | time_backward 5.1720 |
[2023-10-24 07:23:18,679::train::INFO] [train] Iter 580640 | loss 1.3361 | loss(rot) 1.1693 | loss(pos) 0.0620 | loss(seq) 0.1048 | grad 5.2394 | lr 0.0000 | time_forward 3.8770 | time_backward 5.5450 |
[2023-10-24 07:23:28,198::train::INFO] [train] Iter 580641 | loss 0.2495 | loss(rot) 0.1479 | loss(pos) 0.0169 | loss(seq) 0.0847 | grad 1.9342 | lr 0.0000 | time_forward 3.8850 | time_backward 5.6310 |
[2023-10-24 07:23:37,521::train::INFO] [train] Iter 580642 | loss 0.3743 | loss(rot) 0.1731 | loss(pos) 0.0287 | loss(seq) 0.1725 | grad 2.2917 | lr 0.0000 | time_forward 3.8320 | time_backward 5.4880 |
[2023-10-24 07:23:45,525::train::INFO] [train] Iter 580643 | loss 0.3569 | loss(rot) 0.2276 | loss(pos) 0.0379 | loss(seq) 0.0914 | grad 6.9094 | lr 0.0000 | time_forward 3.4210 | time_backward 4.5810 |
[2023-10-24 07:23:54,814::train::INFO] [train] Iter 580644 | loss 0.8329 | loss(rot) 0.2445 | loss(pos) 0.3477 | loss(seq) 0.2407 | grad 3.3578 | lr 0.0000 | time_forward 3.7730 | time_backward 5.5130 |
[2023-10-24 07:24:03,681::train::INFO] [train] Iter 580645 | loss 0.3521 | loss(rot) 0.1429 | loss(pos) 0.1620 | loss(seq) 0.0472 | grad 4.3381 | lr 0.0000 | time_forward 3.6930 | time_backward 5.1700 |
[2023-10-24 07:24:07,414::train::INFO] [train] Iter 580646 | loss 1.4168 | loss(rot) 0.6643 | loss(pos) 0.3610 | loss(seq) 0.3915 | grad 3.6621 | lr 0.0000 | time_forward 1.7100 | time_backward 2.0200 |
[2023-10-24 07:24:11,469::train::INFO] [train] Iter 580647 | loss 2.9370 | loss(rot) 1.7491 | loss(pos) 0.6348 | loss(seq) 0.5531 | grad 6.4477 | lr 0.0000 | time_forward 1.7390 | time_backward 2.3020 |
[2023-10-24 07:24:14,343::train::INFO] [train] Iter 580648 | loss 0.3148 | loss(rot) 0.0532 | loss(pos) 0.0375 | loss(seq) 0.2241 | grad 2.6280 | lr 0.0000 | time_forward 1.3630 | time_backward 1.4800 |
[2023-10-24 07:24:23,014::train::INFO] [train] Iter 580649 | loss 0.2224 | loss(rot) 0.1029 | loss(pos) 0.0334 | loss(seq) 0.0861 | grad 2.7054 | lr 0.0000 | time_forward 3.7600 | time_backward 4.9070 |
[2023-10-24 07:24:32,473::train::INFO] [train] Iter 580650 | loss 0.5435 | loss(rot) 0.2968 | loss(pos) 0.2162 | loss(seq) 0.0306 | grad 4.7995 | lr 0.0000 | time_forward 3.8050 | time_backward 5.6510 |
[2023-10-24 07:24:41,852::train::INFO] [train] Iter 580651 | loss 0.8753 | loss(rot) 0.4589 | loss(pos) 0.0200 | loss(seq) 0.3964 | grad 3.7963 | lr 0.0000 | time_forward 3.8140 | time_backward 5.5620 |
[2023-10-24 07:24:44,591::train::INFO] [train] Iter 580652 | loss 1.1874 | loss(rot) 0.0063 | loss(pos) 1.1802 | loss(seq) 0.0010 | grad 13.3846 | lr 0.0000 | time_forward 1.3220 | time_backward 1.4150 |
[2023-10-24 07:24:51,886::train::INFO] [train] Iter 580653 | loss 0.5075 | loss(rot) 0.0480 | loss(pos) 0.4437 | loss(seq) 0.0159 | grad 3.6619 | lr 0.0000 | time_forward 3.1100 | time_backward 4.1810 |
[2023-10-24 07:25:01,223::train::INFO] [train] Iter 580654 | loss 0.3000 | loss(rot) 0.0962 | loss(pos) 0.1344 | loss(seq) 0.0693 | grad 3.0686 | lr 0.0000 | time_forward 3.8330 | time_backward 5.5020 |
[2023-10-24 07:25:07,714::train::INFO] [train] Iter 580655 | loss 0.8135 | loss(rot) 0.0825 | loss(pos) 0.5258 | loss(seq) 0.2052 | grad 3.5323 | lr 0.0000 | time_forward 2.7470 | time_backward 3.7400 |
[2023-10-24 07:25:10,524::train::INFO] [train] Iter 580656 | loss 0.8312 | loss(rot) 0.0366 | loss(pos) 0.7936 | loss(seq) 0.0010 | grad 15.7843 | lr 0.0000 | time_forward 1.3340 | time_backward 1.4640 |
[2023-10-24 07:25:19,879::train::INFO] [train] Iter 580657 | loss 0.2630 | loss(rot) 0.0753 | loss(pos) 0.0513 | loss(seq) 0.1364 | grad 2.3123 | lr 0.0000 | time_forward 3.8730 | time_backward 5.4780 |
[2023-10-24 07:25:29,403::train::INFO] [train] Iter 580658 | loss 0.1781 | loss(rot) 0.1354 | loss(pos) 0.0100 | loss(seq) 0.0327 | grad 1.9734 | lr 0.0000 | time_forward 3.9010 | time_backward 5.6200 |
[2023-10-24 07:25:37,703::train::INFO] [train] Iter 580659 | loss 2.1434 | loss(rot) 1.3643 | loss(pos) 0.2793 | loss(seq) 0.4997 | grad 7.1289 | lr 0.0000 | time_forward 3.3920 | time_backward 4.9050 |
[2023-10-24 07:25:45,952::train::INFO] [train] Iter 580660 | loss 0.8443 | loss(rot) 0.1212 | loss(pos) 0.3786 | loss(seq) 0.3445 | grad 3.1071 | lr 0.0000 | time_forward 3.4530 | time_backward 4.7920 |
[2023-10-24 07:25:53,310::train::INFO] [train] Iter 580661 | loss 1.3904 | loss(rot) 1.3598 | loss(pos) 0.0287 | loss(seq) 0.0018 | grad 5.5032 | lr 0.0000 | time_forward 3.1760 | time_backward 4.1790 |
[2023-10-24 07:26:01,683::train::INFO] [train] Iter 580662 | loss 2.0330 | loss(rot) 1.9573 | loss(pos) 0.0297 | loss(seq) 0.0460 | grad 4.4920 | lr 0.0000 | time_forward 3.5420 | time_backward 4.8290 |
[2023-10-24 07:26:04,455::train::INFO] [train] Iter 580663 | loss 0.1231 | loss(rot) 0.0956 | loss(pos) 0.0172 | loss(seq) 0.0104 | grad 1.7279 | lr 0.0000 | time_forward 1.3090 | time_backward 1.4600 |
[2023-10-24 07:26:12,900::train::INFO] [train] Iter 580664 | loss 1.5620 | loss(rot) 1.0164 | loss(pos) 0.3183 | loss(seq) 0.2272 | grad 6.1544 | lr 0.0000 | time_forward 3.6180 | time_backward 4.7830 |
[2023-10-24 07:26:21,749::train::INFO] [train] Iter 580665 | loss 0.9869 | loss(rot) 0.7426 | loss(pos) 0.0263 | loss(seq) 0.2180 | grad 4.6070 | lr 0.0000 | time_forward 3.8440 | time_backward 5.0010 |
[2023-10-24 07:26:29,483::train::INFO] [train] Iter 580666 | loss 0.5299 | loss(rot) 0.0467 | loss(pos) 0.4735 | loss(seq) 0.0097 | grad 4.5365 | lr 0.0000 | time_forward 3.2480 | time_backward 4.4840 |
[2023-10-24 07:26:38,276::train::INFO] [train] Iter 580667 | loss 0.8499 | loss(rot) 0.4856 | loss(pos) 0.0329 | loss(seq) 0.3314 | grad 3.5005 | lr 0.0000 | time_forward 3.7150 | time_backward 5.0740 |
[2023-10-24 07:26:47,555::train::INFO] [train] Iter 580668 | loss 0.7844 | loss(rot) 0.4761 | loss(pos) 0.0566 | loss(seq) 0.2518 | grad 2.8653 | lr 0.0000 | time_forward 3.9230 | time_backward 5.3520 |
[2023-10-24 07:26:55,294::train::INFO] [train] Iter 580669 | loss 1.3941 | loss(rot) 1.2000 | loss(pos) 0.0823 | loss(seq) 0.1118 | grad 3.4491 | lr 0.0000 | time_forward 3.2480 | time_backward 4.4870 |
[2023-10-24 07:27:04,137::train::INFO] [train] Iter 580670 | loss 0.9874 | loss(rot) 0.0986 | loss(pos) 0.8879 | loss(seq) 0.0009 | grad 8.5061 | lr 0.0000 | time_forward 3.7850 | time_backward 5.0560 |
[2023-10-24 07:27:13,648::train::INFO] [train] Iter 580671 | loss 2.0600 | loss(rot) 0.0304 | loss(pos) 2.0293 | loss(seq) 0.0004 | grad 13.5260 | lr 0.0000 | time_forward 3.8630 | time_backward 5.6450 |
[2023-10-24 07:27:16,468::train::INFO] [train] Iter 580672 | loss 0.2248 | loss(rot) 0.0160 | loss(pos) 0.0132 | loss(seq) 0.1957 | grad 1.2286 | lr 0.0000 | time_forward 1.3450 | time_backward 1.4720 |
[2023-10-24 07:27:25,365::train::INFO] [train] Iter 580673 | loss 1.4532 | loss(rot) 0.8360 | loss(pos) 0.2915 | loss(seq) 0.3258 | grad 3.8488 | lr 0.0000 | time_forward 3.9610 | time_backward 4.8790 |
[2023-10-24 07:27:33,503::train::INFO] [train] Iter 580674 | loss 0.3760 | loss(rot) 0.1392 | loss(pos) 0.1776 | loss(seq) 0.0592 | grad 4.0196 | lr 0.0000 | time_forward 3.4520 | time_backward 4.6820 |
[2023-10-24 07:27:42,972::train::INFO] [train] Iter 580675 | loss 0.5328 | loss(rot) 0.4694 | loss(pos) 0.0219 | loss(seq) 0.0415 | grad 2.0427 | lr 0.0000 | time_forward 3.8480 | time_backward 5.6180 |
[2023-10-24 07:27:45,725::train::INFO] [train] Iter 580676 | loss 1.7413 | loss(rot) 1.5702 | loss(pos) 0.0540 | loss(seq) 0.1172 | grad 2.8652 | lr 0.0000 | time_forward 1.3110 | time_backward 1.4390 |
[2023-10-24 07:27:54,991::train::INFO] [train] Iter 580677 | loss 0.7937 | loss(rot) 0.0180 | loss(pos) 0.7709 | loss(seq) 0.0048 | grad 6.4992 | lr 0.0000 | time_forward 3.8190 | time_backward 5.4420 |
[2023-10-24 07:28:04,300::train::INFO] [train] Iter 580678 | loss 0.4499 | loss(rot) 0.0962 | loss(pos) 0.0444 | loss(seq) 0.3093 | grad 2.1437 | lr 0.0000 | time_forward 3.8050 | time_backward 5.5020 |
[2023-10-24 07:28:13,545::train::INFO] [train] Iter 580679 | loss 1.6583 | loss(rot) 0.0937 | loss(pos) 1.5601 | loss(seq) 0.0046 | grad 11.3636 | lr 0.0000 | time_forward 3.7730 | time_backward 5.4680 |
[2023-10-24 07:28:15,823::train::INFO] [train] Iter 580680 | loss 0.2743 | loss(rot) 0.0352 | loss(pos) 0.0198 | loss(seq) 0.2192 | grad 2.3052 | lr 0.0000 | time_forward 1.0600 | time_backward 1.2150 |
[2023-10-24 07:28:25,121::train::INFO] [train] Iter 580681 | loss 0.3422 | loss(rot) 0.0861 | loss(pos) 0.0227 | loss(seq) 0.2333 | grad 2.2223 | lr 0.0000 | time_forward 3.7810 | time_backward 5.5140 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.