text stringlengths 56 1.16k |
|---|
[2023-10-23 11:36:04,027::train::INFO] [train] Iter 570292 | loss 1.5884 | loss(rot) 1.2061 | loss(pos) 0.0598 | loss(seq) 0.3225 | grad 4.2484 | lr 0.0000 | time_forward 3.3610 | time_backward 4.7760 |
[2023-10-23 11:36:12,043::train::INFO] [train] Iter 570293 | loss 0.6195 | loss(rot) 0.5051 | loss(pos) 0.0271 | loss(seq) 0.0874 | grad 11.7056 | lr 0.0000 | time_forward 3.3570 | time_backward 4.6560 |
[2023-10-23 11:36:14,441::train::INFO] [train] Iter 570294 | loss 0.1584 | loss(rot) 0.1075 | loss(pos) 0.0221 | loss(seq) 0.0288 | grad 2.7018 | lr 0.0000 | time_forward 1.1690 | time_backward 1.2260 |
[2023-10-23 11:36:17,184::train::INFO] [train] Iter 570295 | loss 0.8503 | loss(rot) 0.4591 | loss(pos) 0.3541 | loss(seq) 0.0371 | grad 4.4577 | lr 0.0000 | time_forward 1.3040 | time_backward 1.4360 |
[2023-10-23 11:36:22,586::train::INFO] [train] Iter 570296 | loss 0.4606 | loss(rot) 0.0493 | loss(pos) 0.4040 | loss(seq) 0.0072 | grad 3.9648 | lr 0.0000 | time_forward 2.3160 | time_backward 3.0670 |
[2023-10-23 11:36:29,364::train::INFO] [train] Iter 570297 | loss 1.4325 | loss(rot) 1.1070 | loss(pos) 0.0347 | loss(seq) 0.2908 | grad 4.8400 | lr 0.0000 | time_forward 2.9990 | time_backward 3.7690 |
[2023-10-23 11:36:37,448::train::INFO] [train] Iter 570298 | loss 0.7238 | loss(rot) 0.0554 | loss(pos) 0.4879 | loss(seq) 0.1805 | grad 5.1676 | lr 0.0000 | time_forward 3.2900 | time_backward 4.7900 |
[2023-10-23 11:36:40,111::train::INFO] [train] Iter 570299 | loss 0.4401 | loss(rot) 0.0454 | loss(pos) 0.1221 | loss(seq) 0.2726 | grad 3.2536 | lr 0.0000 | time_forward 1.2570 | time_backward 1.4030 |
[2023-10-23 11:36:42,817::train::INFO] [train] Iter 570300 | loss 0.2589 | loss(rot) 0.1966 | loss(pos) 0.0133 | loss(seq) 0.0490 | grad 32.6755 | lr 0.0000 | time_forward 1.2790 | time_backward 1.4020 |
[2023-10-23 11:36:50,344::train::INFO] [train] Iter 570301 | loss 0.3414 | loss(rot) 0.1352 | loss(pos) 0.1389 | loss(seq) 0.0673 | grad 2.8032 | lr 0.0000 | time_forward 3.3450 | time_backward 4.1790 |
[2023-10-23 11:36:57,641::train::INFO] [train] Iter 570302 | loss 0.2186 | loss(rot) 0.1659 | loss(pos) 0.0240 | loss(seq) 0.0287 | grad 2.7307 | lr 0.0000 | time_forward 3.1730 | time_backward 4.1200 |
[2023-10-23 11:37:05,699::train::INFO] [train] Iter 570303 | loss 0.8853 | loss(rot) 0.5482 | loss(pos) 0.0891 | loss(seq) 0.2479 | grad 3.3258 | lr 0.0000 | time_forward 3.2220 | time_backward 4.8340 |
[2023-10-23 11:37:13,119::train::INFO] [train] Iter 570304 | loss 0.3119 | loss(rot) 0.1610 | loss(pos) 0.0251 | loss(seq) 0.1257 | grad 7.5615 | lr 0.0000 | time_forward 3.2110 | time_backward 4.2050 |
[2023-10-23 11:37:15,348::train::INFO] [train] Iter 570305 | loss 0.3125 | loss(rot) 0.0809 | loss(pos) 0.0262 | loss(seq) 0.2054 | grad 2.2751 | lr 0.0000 | time_forward 1.0300 | time_backward 1.1960 |
[2023-10-23 11:37:22,669::train::INFO] [train] Iter 570306 | loss 2.0169 | loss(rot) 1.4362 | loss(pos) 0.2280 | loss(seq) 0.3526 | grad 3.4437 | lr 0.0000 | time_forward 3.1880 | time_backward 4.1290 |
[2023-10-23 11:37:25,172::train::INFO] [train] Iter 570307 | loss 1.4172 | loss(rot) 0.8467 | loss(pos) 0.2095 | loss(seq) 0.3610 | grad 4.9936 | lr 0.0000 | time_forward 1.1440 | time_backward 1.3570 |
[2023-10-23 11:37:33,320::train::INFO] [train] Iter 570308 | loss 0.2927 | loss(rot) 0.0886 | loss(pos) 0.0212 | loss(seq) 0.1829 | grad 1.4433 | lr 0.0000 | time_forward 3.4190 | time_backward 4.7250 |
[2023-10-23 11:37:41,314::train::INFO] [train] Iter 570309 | loss 0.7550 | loss(rot) 0.4868 | loss(pos) 0.0442 | loss(seq) 0.2240 | grad 4.3427 | lr 0.0000 | time_forward 3.2810 | time_backward 4.7100 |
[2023-10-23 11:37:48,894::train::INFO] [train] Iter 570310 | loss 0.2701 | loss(rot) 0.0949 | loss(pos) 0.0361 | loss(seq) 0.1390 | grad 2.8712 | lr 0.0000 | time_forward 3.2620 | time_backward 4.3140 |
[2023-10-23 11:37:57,318::train::INFO] [train] Iter 570311 | loss 0.9467 | loss(rot) 0.5140 | loss(pos) 0.0917 | loss(seq) 0.3410 | grad 4.1490 | lr 0.0000 | time_forward 3.4700 | time_backward 4.9520 |
[2023-10-23 11:38:04,130::train::INFO] [train] Iter 570312 | loss 0.4019 | loss(rot) 0.0796 | loss(pos) 0.1439 | loss(seq) 0.1784 | grad 5.0659 | lr 0.0000 | time_forward 2.9410 | time_backward 3.8670 |
[2023-10-23 11:38:12,267::train::INFO] [train] Iter 570313 | loss 1.2168 | loss(rot) 1.0382 | loss(pos) 0.0482 | loss(seq) 0.1305 | grad 4.1891 | lr 0.0000 | time_forward 3.4680 | time_backward 4.6670 |
[2023-10-23 11:38:19,740::train::INFO] [train] Iter 570314 | loss 0.4694 | loss(rot) 0.1622 | loss(pos) 0.1066 | loss(seq) 0.2007 | grad 3.2492 | lr 0.0000 | time_forward 3.5880 | time_backward 3.8820 |
[2023-10-23 11:38:25,373::train::INFO] [train] Iter 570315 | loss 0.7028 | loss(rot) 0.2786 | loss(pos) 0.2808 | loss(seq) 0.1434 | grad 5.3952 | lr 0.0000 | time_forward 2.4600 | time_backward 3.1690 |
[2023-10-23 11:38:33,471::train::INFO] [train] Iter 570316 | loss 0.9794 | loss(rot) 0.8677 | loss(pos) 0.0817 | loss(seq) 0.0300 | grad 5.2193 | lr 0.0000 | time_forward 3.3670 | time_backward 4.7280 |
[2023-10-23 11:38:41,686::train::INFO] [train] Iter 570317 | loss 1.0504 | loss(rot) 0.4881 | loss(pos) 0.1426 | loss(seq) 0.4196 | grad 14.1839 | lr 0.0000 | time_forward 3.5230 | time_backward 4.6890 |
[2023-10-23 11:38:47,982::train::INFO] [train] Iter 570318 | loss 0.9964 | loss(rot) 0.5665 | loss(pos) 0.1089 | loss(seq) 0.3209 | grad 4.0413 | lr 0.0000 | time_forward 2.7540 | time_backward 3.5390 |
[2023-10-23 11:38:50,634::train::INFO] [train] Iter 570319 | loss 0.6862 | loss(rot) 0.6562 | loss(pos) 0.0153 | loss(seq) 0.0147 | grad 5.1544 | lr 0.0000 | time_forward 1.2780 | time_backward 1.3720 |
[2023-10-23 11:38:58,839::train::INFO] [train] Iter 570320 | loss 0.5301 | loss(rot) 0.2555 | loss(pos) 0.0438 | loss(seq) 0.2307 | grad 2.7262 | lr 0.0000 | time_forward 3.3730 | time_backward 4.8290 |
[2023-10-23 11:39:01,631::train::INFO] [train] Iter 570321 | loss 0.8530 | loss(rot) 0.5851 | loss(pos) 0.0196 | loss(seq) 0.2483 | grad 19.9468 | lr 0.0000 | time_forward 1.3490 | time_backward 1.4390 |
[2023-10-23 11:39:08,857::train::INFO] [train] Iter 570322 | loss 0.4532 | loss(rot) 0.0759 | loss(pos) 0.0738 | loss(seq) 0.3035 | grad 3.0167 | lr 0.0000 | time_forward 3.1550 | time_backward 4.0670 |
[2023-10-23 11:39:15,600::train::INFO] [train] Iter 570323 | loss 0.9948 | loss(rot) 0.9634 | loss(pos) 0.0234 | loss(seq) 0.0080 | grad 92.4924 | lr 0.0000 | time_forward 2.8390 | time_backward 3.9020 |
[2023-10-23 11:39:22,058::train::INFO] [train] Iter 570324 | loss 0.1321 | loss(rot) 0.0909 | loss(pos) 0.0238 | loss(seq) 0.0174 | grad 2.2864 | lr 0.0000 | time_forward 2.7680 | time_backward 3.6860 |
[2023-10-23 11:39:30,273::train::INFO] [train] Iter 570325 | loss 0.5899 | loss(rot) 0.1297 | loss(pos) 0.2105 | loss(seq) 0.2496 | grad 4.6920 | lr 0.0000 | time_forward 3.4400 | time_backward 4.7710 |
[2023-10-23 11:39:33,027::train::INFO] [train] Iter 570326 | loss 0.3016 | loss(rot) 0.0596 | loss(pos) 0.0145 | loss(seq) 0.2276 | grad 1.8599 | lr 0.0000 | time_forward 1.3360 | time_backward 1.4150 |
[2023-10-23 11:39:41,020::train::INFO] [train] Iter 570327 | loss 0.6997 | loss(rot) 0.3398 | loss(pos) 0.0811 | loss(seq) 0.2788 | grad 3.7758 | lr 0.0000 | time_forward 3.0360 | time_backward 4.9530 |
[2023-10-23 11:39:52,038::train::INFO] [train] Iter 570328 | loss 0.6561 | loss(rot) 0.1267 | loss(pos) 0.4813 | loss(seq) 0.0481 | grad 4.2624 | lr 0.0000 | time_forward 6.8020 | time_backward 4.2120 |
[2023-10-23 11:39:59,354::train::INFO] [train] Iter 570329 | loss 1.2616 | loss(rot) 0.7302 | loss(pos) 0.0416 | loss(seq) 0.4898 | grad 5.2647 | lr 0.0000 | time_forward 3.0880 | time_backward 4.2260 |
[2023-10-23 11:40:02,176::train::INFO] [train] Iter 570330 | loss 1.2139 | loss(rot) 1.1713 | loss(pos) 0.0094 | loss(seq) 0.0332 | grad 123.9648 | lr 0.0000 | time_forward 1.1970 | time_backward 1.6210 |
[2023-10-23 11:40:10,537::train::INFO] [train] Iter 570331 | loss 0.3779 | loss(rot) 0.1698 | loss(pos) 0.1283 | loss(seq) 0.0798 | grad 2.4335 | lr 0.0000 | time_forward 3.7030 | time_backward 4.6550 |
[2023-10-23 11:40:13,391::train::INFO] [train] Iter 570332 | loss 3.0851 | loss(rot) 2.3442 | loss(pos) 0.4333 | loss(seq) 0.3076 | grad 6.3482 | lr 0.0000 | time_forward 1.2830 | time_backward 1.5680 |
[2023-10-23 11:40:21,434::train::INFO] [train] Iter 570333 | loss 1.6187 | loss(rot) 0.9395 | loss(pos) 0.2208 | loss(seq) 0.4584 | grad 6.2392 | lr 0.0000 | time_forward 3.2730 | time_backward 4.7590 |
[2023-10-23 11:40:29,245::train::INFO] [train] Iter 570334 | loss 0.3820 | loss(rot) 0.0985 | loss(pos) 0.0361 | loss(seq) 0.2474 | grad 3.1620 | lr 0.0000 | time_forward 3.2550 | time_backward 4.5510 |
[2023-10-23 11:40:35,355::train::INFO] [train] Iter 570335 | loss 0.3097 | loss(rot) 0.1154 | loss(pos) 0.0248 | loss(seq) 0.1696 | grad 1.5904 | lr 0.0000 | time_forward 2.5890 | time_backward 3.5190 |
[2023-10-23 11:40:43,625::train::INFO] [train] Iter 570336 | loss 0.9379 | loss(rot) 0.2843 | loss(pos) 0.3415 | loss(seq) 0.3121 | grad 4.8583 | lr 0.0000 | time_forward 3.4280 | time_backward 4.8380 |
[2023-10-23 11:40:52,373::train::INFO] [train] Iter 570337 | loss 0.7891 | loss(rot) 0.1070 | loss(pos) 0.1092 | loss(seq) 0.5729 | grad 2.9911 | lr 0.0000 | time_forward 3.3790 | time_backward 5.3660 |
[2023-10-23 11:41:01,921::train::INFO] [train] Iter 570338 | loss 1.6436 | loss(rot) 0.0184 | loss(pos) 1.6246 | loss(seq) 0.0007 | grad 19.1107 | lr 0.0000 | time_forward 4.4380 | time_backward 5.1070 |
[2023-10-23 11:41:04,929::train::INFO] [train] Iter 570339 | loss 0.2174 | loss(rot) 0.1665 | loss(pos) 0.0509 | loss(seq) 0.0000 | grad 2.5698 | lr 0.0000 | time_forward 1.3740 | time_backward 1.6310 |
[2023-10-23 11:41:12,615::train::INFO] [train] Iter 570340 | loss 0.3947 | loss(rot) 0.1345 | loss(pos) 0.0268 | loss(seq) 0.2334 | grad 2.8518 | lr 0.0000 | time_forward 3.2390 | time_backward 4.4440 |
[2023-10-23 11:41:15,477::train::INFO] [train] Iter 570341 | loss 0.2262 | loss(rot) 0.0727 | loss(pos) 0.1501 | loss(seq) 0.0034 | grad 4.0974 | lr 0.0000 | time_forward 1.3720 | time_backward 1.4860 |
[2023-10-23 11:41:18,501::train::INFO] [train] Iter 570342 | loss 0.3608 | loss(rot) 0.2117 | loss(pos) 0.0980 | loss(seq) 0.0511 | grad 3.5336 | lr 0.0000 | time_forward 1.4950 | time_backward 1.5120 |
[2023-10-23 11:41:21,574::train::INFO] [train] Iter 570343 | loss 0.7454 | loss(rot) 0.4985 | loss(pos) 0.0589 | loss(seq) 0.1880 | grad 3.5063 | lr 0.0000 | time_forward 1.4330 | time_backward 1.6220 |
[2023-10-23 11:41:24,454::train::INFO] [train] Iter 570344 | loss 0.6070 | loss(rot) 0.3366 | loss(pos) 0.1069 | loss(seq) 0.1635 | grad 4.5455 | lr 0.0000 | time_forward 1.3870 | time_backward 1.4790 |
[2023-10-23 11:41:27,513::train::INFO] [train] Iter 570345 | loss 0.2562 | loss(rot) 0.2200 | loss(pos) 0.0306 | loss(seq) 0.0055 | grad 2.5755 | lr 0.0000 | time_forward 1.2870 | time_backward 1.7700 |
[2023-10-23 11:41:30,448::train::INFO] [train] Iter 570346 | loss 1.3779 | loss(rot) 1.3508 | loss(pos) 0.0185 | loss(seq) 0.0085 | grad 3.2448 | lr 0.0000 | time_forward 1.4220 | time_backward 1.5090 |
[2023-10-23 11:41:43,937::train::INFO] [train] Iter 570347 | loss 0.7195 | loss(rot) 0.1560 | loss(pos) 0.2806 | loss(seq) 0.2829 | grad 3.5041 | lr 0.0000 | time_forward 7.3570 | time_backward 6.1160 |
[2023-10-23 11:41:54,252::train::INFO] [train] Iter 570348 | loss 1.6985 | loss(rot) 1.5269 | loss(pos) 0.0383 | loss(seq) 0.1334 | grad 3.4784 | lr 0.0000 | time_forward 5.5960 | time_backward 4.7150 |
[2023-10-23 11:41:56,541::train::INFO] [train] Iter 570349 | loss 1.0584 | loss(rot) 0.5451 | loss(pos) 0.1128 | loss(seq) 0.4005 | grad 3.6015 | lr 0.0000 | time_forward 1.0270 | time_backward 1.2570 |
[2023-10-23 11:42:03,230::train::INFO] [train] Iter 570350 | loss 0.6614 | loss(rot) 0.3387 | loss(pos) 0.0239 | loss(seq) 0.2988 | grad 4.4410 | lr 0.0000 | time_forward 2.8430 | time_backward 3.8420 |
[2023-10-23 11:42:10,897::train::INFO] [train] Iter 570351 | loss 1.1847 | loss(rot) 1.1524 | loss(pos) 0.0322 | loss(seq) 0.0000 | grad 3.8707 | lr 0.0000 | time_forward 3.1410 | time_backward 4.5220 |
[2023-10-23 11:42:13,626::train::INFO] [train] Iter 570352 | loss 0.8774 | loss(rot) 0.4498 | loss(pos) 0.2297 | loss(seq) 0.1979 | grad 3.6126 | lr 0.0000 | time_forward 1.2890 | time_backward 1.4370 |
[2023-10-23 11:42:16,436::train::INFO] [train] Iter 570353 | loss 0.0634 | loss(rot) 0.0377 | loss(pos) 0.0197 | loss(seq) 0.0059 | grad 1.2977 | lr 0.0000 | time_forward 1.3120 | time_backward 1.4940 |
[2023-10-23 11:42:24,097::train::INFO] [train] Iter 570354 | loss 0.8025 | loss(rot) 0.6137 | loss(pos) 0.0374 | loss(seq) 0.1514 | grad 5.9003 | lr 0.0000 | time_forward 3.3590 | time_backward 4.2790 |
[2023-10-23 11:42:31,719::train::INFO] [train] Iter 570355 | loss 1.5663 | loss(rot) 1.3088 | loss(pos) 0.0532 | loss(seq) 0.2043 | grad 7.3226 | lr 0.0000 | time_forward 3.3650 | time_backward 4.2540 |
[2023-10-23 11:42:34,810::train::INFO] [train] Iter 570356 | loss 0.2515 | loss(rot) 0.1504 | loss(pos) 0.0201 | loss(seq) 0.0810 | grad 1.8161 | lr 0.0000 | time_forward 1.4220 | time_backward 1.6660 |
[2023-10-23 11:42:37,172::train::INFO] [train] Iter 570357 | loss 0.9977 | loss(rot) 0.8817 | loss(pos) 0.0375 | loss(seq) 0.0785 | grad 4.6008 | lr 0.0000 | time_forward 1.1190 | time_backward 1.2390 |
[2023-10-23 11:42:39,903::train::INFO] [train] Iter 570358 | loss 0.7541 | loss(rot) 0.7335 | loss(pos) 0.0189 | loss(seq) 0.0017 | grad 4.4210 | lr 0.0000 | time_forward 1.3230 | time_backward 1.3900 |
[2023-10-23 11:42:42,811::train::INFO] [train] Iter 570359 | loss 0.8655 | loss(rot) 0.1198 | loss(pos) 0.7147 | loss(seq) 0.0310 | grad 6.8063 | lr 0.0000 | time_forward 1.4240 | time_backward 1.4790 |
[2023-10-23 11:42:49,997::train::INFO] [train] Iter 570360 | loss 0.5002 | loss(rot) 0.1598 | loss(pos) 0.0573 | loss(seq) 0.2831 | grad 3.7818 | lr 0.0000 | time_forward 3.1780 | time_backward 4.0050 |
[2023-10-23 11:42:58,540::train::INFO] [train] Iter 570361 | loss 0.5401 | loss(rot) 0.2306 | loss(pos) 0.0736 | loss(seq) 0.2359 | grad 3.8949 | lr 0.0000 | time_forward 3.5560 | time_backward 4.9840 |
[2023-10-23 11:43:06,058::train::INFO] [train] Iter 570362 | loss 0.1524 | loss(rot) 0.1440 | loss(pos) 0.0083 | loss(seq) 0.0001 | grad 1.7492 | lr 0.0000 | time_forward 3.3180 | time_backward 4.1960 |
[2023-10-23 11:43:14,548::train::INFO] [train] Iter 570363 | loss 1.1964 | loss(rot) 1.1740 | loss(pos) 0.0223 | loss(seq) 0.0001 | grad 2.6847 | lr 0.0000 | time_forward 3.5840 | time_backward 4.9040 |
[2023-10-23 11:43:21,863::train::INFO] [train] Iter 570364 | loss 0.4084 | loss(rot) 0.3133 | loss(pos) 0.0198 | loss(seq) 0.0753 | grad 2.8328 | lr 0.0000 | time_forward 3.2470 | time_backward 4.0650 |
[2023-10-23 11:43:30,401::train::INFO] [train] Iter 570365 | loss 0.3863 | loss(rot) 0.2427 | loss(pos) 0.0231 | loss(seq) 0.1204 | grad 2.9384 | lr 0.0000 | time_forward 3.5800 | time_backward 4.9550 |
[2023-10-23 11:43:38,406::train::INFO] [train] Iter 570366 | loss 0.4367 | loss(rot) 0.1091 | loss(pos) 0.0205 | loss(seq) 0.3071 | grad 2.2398 | lr 0.0000 | time_forward 3.5130 | time_backward 4.4890 |
[2023-10-23 11:43:45,689::train::INFO] [train] Iter 570367 | loss 0.6784 | loss(rot) 0.0447 | loss(pos) 0.6299 | loss(seq) 0.0039 | grad 18.5575 | lr 0.0000 | time_forward 3.1220 | time_backward 4.1580 |
[2023-10-23 11:43:52,414::train::INFO] [train] Iter 570368 | loss 0.3640 | loss(rot) 0.0943 | loss(pos) 0.1569 | loss(seq) 0.1128 | grad 3.3063 | lr 0.0000 | time_forward 2.8690 | time_backward 3.8520 |
[2023-10-23 11:43:54,736::train::INFO] [train] Iter 570369 | loss 1.2842 | loss(rot) 0.0041 | loss(pos) 1.2798 | loss(seq) 0.0003 | grad 25.7074 | lr 0.0000 | time_forward 1.0990 | time_backward 1.2200 |
[2023-10-23 11:43:58,009::train::INFO] [train] Iter 570370 | loss 1.6421 | loss(rot) 0.0420 | loss(pos) 1.5988 | loss(seq) 0.0013 | grad 6.3237 | lr 0.0000 | time_forward 1.4770 | time_backward 1.7920 |
[2023-10-23 11:44:02,337::train::INFO] [train] Iter 570371 | loss 0.3611 | loss(rot) 0.3438 | loss(pos) 0.0170 | loss(seq) 0.0003 | grad 20.0454 | lr 0.0000 | time_forward 2.1530 | time_backward 2.1690 |
[2023-10-23 11:44:05,273::train::INFO] [train] Iter 570372 | loss 0.4214 | loss(rot) 0.0863 | loss(pos) 0.0614 | loss(seq) 0.2737 | grad 3.0341 | lr 0.0000 | time_forward 1.4340 | time_backward 1.4800 |
[2023-10-23 11:44:14,367::train::INFO] [train] Iter 570373 | loss 0.8222 | loss(rot) 0.7555 | loss(pos) 0.0665 | loss(seq) 0.0002 | grad 5.5117 | lr 0.0000 | time_forward 4.8190 | time_backward 4.2720 |
[2023-10-23 11:44:23,212::train::INFO] [train] Iter 570374 | loss 1.2591 | loss(rot) 0.6064 | loss(pos) 0.1711 | loss(seq) 0.4815 | grad 3.4872 | lr 0.0000 | time_forward 3.7230 | time_backward 5.1190 |
[2023-10-23 11:44:32,097::train::INFO] [train] Iter 570375 | loss 1.0246 | loss(rot) 0.9795 | loss(pos) 0.0353 | loss(seq) 0.0098 | grad 10.7421 | lr 0.0000 | time_forward 3.5400 | time_backward 5.3420 |
[2023-10-23 11:44:35,433::train::INFO] [train] Iter 570376 | loss 0.5976 | loss(rot) 0.4233 | loss(pos) 0.0334 | loss(seq) 0.1409 | grad 2.3439 | lr 0.0000 | time_forward 1.4940 | time_backward 1.8390 |
[2023-10-23 11:44:44,214::train::INFO] [train] Iter 570377 | loss 0.5248 | loss(rot) 0.1299 | loss(pos) 0.0227 | loss(seq) 0.3721 | grad 11.5146 | lr 0.0000 | time_forward 3.6060 | time_backward 5.1590 |
[2023-10-23 11:44:53,071::train::INFO] [train] Iter 570378 | loss 0.8434 | loss(rot) 0.6708 | loss(pos) 0.0700 | loss(seq) 0.1025 | grad 2.9314 | lr 0.0000 | time_forward 3.8720 | time_backward 4.9820 |
[2023-10-23 11:44:56,097::train::INFO] [train] Iter 570379 | loss 0.4251 | loss(rot) 0.0838 | loss(pos) 0.0425 | loss(seq) 0.2988 | grad 3.1156 | lr 0.0000 | time_forward 1.5020 | time_backward 1.5210 |
[2023-10-23 11:44:59,040::train::INFO] [train] Iter 570380 | loss 2.2636 | loss(rot) 1.3982 | loss(pos) 0.3435 | loss(seq) 0.5219 | grad 3.7900 | lr 0.0000 | time_forward 1.3520 | time_backward 1.5630 |
[2023-10-23 11:45:07,221::train::INFO] [train] Iter 570381 | loss 1.6671 | loss(rot) 1.2098 | loss(pos) 0.1315 | loss(seq) 0.3258 | grad 11.3149 | lr 0.0000 | time_forward 3.6200 | time_backward 4.5570 |
[2023-10-23 11:45:16,419::train::INFO] [train] Iter 570382 | loss 0.2045 | loss(rot) 0.1219 | loss(pos) 0.0233 | loss(seq) 0.0593 | grad 1.6586 | lr 0.0000 | time_forward 3.8490 | time_backward 5.3450 |
[2023-10-23 11:45:23,934::train::INFO] [train] Iter 570383 | loss 1.7825 | loss(rot) 1.5701 | loss(pos) 0.0539 | loss(seq) 0.1585 | grad 5.8700 | lr 0.0000 | time_forward 3.1720 | time_backward 4.3400 |
[2023-10-23 11:45:32,061::train::INFO] [train] Iter 570384 | loss 1.6671 | loss(rot) 1.1647 | loss(pos) 0.1234 | loss(seq) 0.3790 | grad 3.3022 | lr 0.0000 | time_forward 3.5250 | time_backward 4.5990 |
[2023-10-23 11:45:41,134::train::INFO] [train] Iter 570385 | loss 1.1305 | loss(rot) 1.0790 | loss(pos) 0.0511 | loss(seq) 0.0004 | grad 30.4442 | lr 0.0000 | time_forward 3.7620 | time_backward 5.3070 |
[2023-10-23 11:45:48,162::train::INFO] [train] Iter 570386 | loss 0.1906 | loss(rot) 0.0490 | loss(pos) 0.0246 | loss(seq) 0.1170 | grad 2.2621 | lr 0.0000 | time_forward 3.0680 | time_backward 3.9560 |
[2023-10-23 11:45:55,982::train::INFO] [train] Iter 570387 | loss 0.3639 | loss(rot) 0.1736 | loss(pos) 0.0357 | loss(seq) 0.1547 | grad 2.4808 | lr 0.0000 | time_forward 3.4600 | time_backward 4.3560 |
[2023-10-23 11:46:04,262::train::INFO] [train] Iter 570388 | loss 0.9645 | loss(rot) 0.8908 | loss(pos) 0.0737 | loss(seq) 0.0000 | grad 13.2080 | lr 0.0000 | time_forward 3.6820 | time_backward 4.5950 |
[2023-10-23 11:46:12,077::train::INFO] [train] Iter 570389 | loss 7.2349 | loss(rot) 0.7637 | loss(pos) 6.4712 | loss(seq) 0.0000 | grad 57.4172 | lr 0.0000 | time_forward 3.4400 | time_backward 4.3720 |
[2023-10-23 11:46:15,093::train::INFO] [train] Iter 570390 | loss 0.2160 | loss(rot) 0.1791 | loss(pos) 0.0125 | loss(seq) 0.0244 | grad 2.8887 | lr 0.0000 | time_forward 1.5020 | time_backward 1.5110 |
[2023-10-23 11:46:23,562::train::INFO] [train] Iter 570391 | loss 0.5345 | loss(rot) 0.2778 | loss(pos) 0.0842 | loss(seq) 0.1726 | grad 4.0045 | lr 0.0000 | time_forward 3.7150 | time_backward 4.7510 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.