text stringlengths 56 1.16k |
|---|
[2023-10-25 09:21:41,199::train::INFO] [train] Iter 594268 | loss 0.7082 | loss(rot) 0.1885 | loss(pos) 0.0891 | loss(seq) 0.4306 | grad 3.8781 | lr 0.0000 | time_forward 4.2720 | time_backward 5.9280 |
[2023-10-25 09:21:48,696::train::INFO] [train] Iter 594269 | loss 0.2957 | loss(rot) 0.1549 | loss(pos) 0.0189 | loss(seq) 0.1219 | grad 3.3297 | lr 0.0000 | time_forward 3.1580 | time_backward 4.3360 |
[2023-10-25 09:21:58,634::train::INFO] [train] Iter 594270 | loss 0.3144 | loss(rot) 0.0783 | loss(pos) 0.1472 | loss(seq) 0.0889 | grad 3.0400 | lr 0.0000 | time_forward 4.0610 | time_backward 5.8730 |
[2023-10-25 09:22:01,394::train::INFO] [train] Iter 594271 | loss 0.1791 | loss(rot) 0.0344 | loss(pos) 0.0469 | loss(seq) 0.0977 | grad 1.6355 | lr 0.0000 | time_forward 1.2810 | time_backward 1.4750 |
[2023-10-25 09:22:04,316::train::INFO] [train] Iter 594272 | loss 0.1472 | loss(rot) 0.1084 | loss(pos) 0.0215 | loss(seq) 0.0173 | grad 1.8285 | lr 0.0000 | time_forward 1.4460 | time_backward 1.4740 |
[2023-10-25 09:22:07,890::train::INFO] [train] Iter 594273 | loss 1.1897 | loss(rot) 1.1346 | loss(pos) 0.0229 | loss(seq) 0.0322 | grad 7.9747 | lr 0.0000 | time_forward 1.6380 | time_backward 1.9320 |
[2023-10-25 09:22:17,798::train::INFO] [train] Iter 594274 | loss 2.0153 | loss(rot) 1.6816 | loss(pos) 0.1341 | loss(seq) 0.1995 | grad 18.2030 | lr 0.0000 | time_forward 4.0640 | time_backward 5.8400 |
[2023-10-25 09:22:20,594::train::INFO] [train] Iter 594275 | loss 0.7686 | loss(rot) 0.3333 | loss(pos) 0.0497 | loss(seq) 0.3856 | grad 3.0201 | lr 0.0000 | time_forward 1.3440 | time_backward 1.4390 |
[2023-10-25 09:22:23,396::train::INFO] [train] Iter 594276 | loss 0.2431 | loss(rot) 0.1045 | loss(pos) 0.0313 | loss(seq) 0.1073 | grad 2.2412 | lr 0.0000 | time_forward 1.3530 | time_backward 1.4460 |
[2023-10-25 09:22:33,321::train::INFO] [train] Iter 594277 | loss 0.2351 | loss(rot) 0.1039 | loss(pos) 0.0818 | loss(seq) 0.0493 | grad 2.5981 | lr 0.0000 | time_forward 4.0610 | time_backward 5.8620 |
[2023-10-25 09:22:36,974::train::INFO] [train] Iter 594278 | loss 1.4107 | loss(rot) 0.6299 | loss(pos) 0.3355 | loss(seq) 0.4453 | grad 3.6794 | lr 0.0000 | time_forward 1.6820 | time_backward 1.9670 |
[2023-10-25 09:22:49,450::train::INFO] [train] Iter 594279 | loss 0.9517 | loss(rot) 0.3631 | loss(pos) 0.3547 | loss(seq) 0.2340 | grad 2.9400 | lr 0.0000 | time_forward 6.3940 | time_backward 6.0690 |
[2023-10-25 09:22:59,404::train::INFO] [train] Iter 594280 | loss 0.1651 | loss(rot) 0.1290 | loss(pos) 0.0295 | loss(seq) 0.0065 | grad 2.0584 | lr 0.0000 | time_forward 4.0830 | time_backward 5.8670 |
[2023-10-25 09:23:07,699::train::INFO] [train] Iter 594281 | loss 0.5119 | loss(rot) 0.1157 | loss(pos) 0.0926 | loss(seq) 0.3036 | grad 4.1332 | lr 0.0000 | time_forward 3.5010 | time_backward 4.7920 |
[2023-10-25 09:23:17,707::train::INFO] [train] Iter 594282 | loss 0.4428 | loss(rot) 0.2339 | loss(pos) 0.0827 | loss(seq) 0.1263 | grad 4.2516 | lr 0.0000 | time_forward 4.1980 | time_backward 5.8070 |
[2023-10-25 09:23:27,759::train::INFO] [train] Iter 594283 | loss 0.7149 | loss(rot) 0.4105 | loss(pos) 0.0383 | loss(seq) 0.2661 | grad 6.8690 | lr 0.0000 | time_forward 4.0650 | time_backward 5.9830 |
[2023-10-25 09:23:30,401::train::INFO] [train] Iter 594284 | loss 0.6225 | loss(rot) 0.0726 | loss(pos) 0.5307 | loss(seq) 0.0192 | grad 6.8361 | lr 0.0000 | time_forward 1.2280 | time_backward 1.4110 |
[2023-10-25 09:23:40,390::train::INFO] [train] Iter 594285 | loss 0.9711 | loss(rot) 0.5111 | loss(pos) 0.0751 | loss(seq) 0.3849 | grad 2.6438 | lr 0.0000 | time_forward 4.0440 | time_backward 5.9420 |
[2023-10-25 09:23:49,378::train::INFO] [train] Iter 594286 | loss 0.8398 | loss(rot) 0.5128 | loss(pos) 0.0352 | loss(seq) 0.2918 | grad 5.5809 | lr 0.0000 | time_forward 3.8030 | time_backward 5.1820 |
[2023-10-25 09:24:02,472::train::INFO] [train] Iter 594287 | loss 1.6545 | loss(rot) 0.0143 | loss(pos) 1.6401 | loss(seq) 0.0001 | grad 18.9962 | lr 0.0000 | time_forward 8.0060 | time_backward 5.0840 |
[2023-10-25 09:24:10,590::train::INFO] [train] Iter 594288 | loss 1.3624 | loss(rot) 0.6453 | loss(pos) 0.3023 | loss(seq) 0.4148 | grad 5.2413 | lr 0.0000 | time_forward 3.4160 | time_backward 4.7000 |
[2023-10-25 09:24:12,855::train::INFO] [train] Iter 594289 | loss 0.4309 | loss(rot) 0.1498 | loss(pos) 0.2316 | loss(seq) 0.0494 | grad 3.7161 | lr 0.0000 | time_forward 1.0270 | time_backward 1.2350 |
[2023-10-25 09:24:22,968::train::INFO] [train] Iter 594290 | loss 0.1586 | loss(rot) 0.0380 | loss(pos) 0.0790 | loss(seq) 0.0416 | grad 2.5383 | lr 0.0000 | time_forward 4.0370 | time_backward 6.0720 |
[2023-10-25 09:24:32,528::train::INFO] [train] Iter 594291 | loss 0.7174 | loss(rot) 0.2340 | loss(pos) 0.0427 | loss(seq) 0.4407 | grad 3.4572 | lr 0.0000 | time_forward 4.4950 | time_backward 5.0620 |
[2023-10-25 09:24:41,362::train::INFO] [train] Iter 594292 | loss 0.3978 | loss(rot) 0.0080 | loss(pos) 0.3896 | loss(seq) 0.0002 | grad 6.9057 | lr 0.0000 | time_forward 3.6650 | time_backward 5.1660 |
[2023-10-25 09:24:44,701::train::INFO] [train] Iter 594293 | loss 0.2228 | loss(rot) 0.1597 | loss(pos) 0.0197 | loss(seq) 0.0434 | grad 2.1373 | lr 0.0000 | time_forward 1.5070 | time_backward 1.8280 |
[2023-10-25 09:24:47,513::train::INFO] [train] Iter 594294 | loss 0.3839 | loss(rot) 0.0843 | loss(pos) 0.1205 | loss(seq) 0.1791 | grad 2.2373 | lr 0.0000 | time_forward 1.3160 | time_backward 1.4800 |
[2023-10-25 09:24:57,167::train::INFO] [train] Iter 594295 | loss 0.9391 | loss(rot) 0.1562 | loss(pos) 0.7733 | loss(seq) 0.0096 | grad 8.6052 | lr 0.0000 | time_forward 4.5150 | time_backward 5.1030 |
[2023-10-25 09:25:08,026::train::INFO] [train] Iter 594296 | loss 0.4372 | loss(rot) 0.0777 | loss(pos) 0.3507 | loss(seq) 0.0087 | grad 5.5370 | lr 0.0000 | time_forward 4.8550 | time_backward 6.0000 |
[2023-10-25 09:25:11,105::train::INFO] [train] Iter 594297 | loss 0.3129 | loss(rot) 0.1140 | loss(pos) 0.0219 | loss(seq) 0.1770 | grad 1.8849 | lr 0.0000 | time_forward 1.3430 | time_backward 1.7320 |
[2023-10-25 09:25:20,364::train::INFO] [train] Iter 594298 | loss 0.1875 | loss(rot) 0.0512 | loss(pos) 0.0232 | loss(seq) 0.1131 | grad 1.9423 | lr 0.0000 | time_forward 3.9750 | time_backward 5.2570 |
[2023-10-25 09:25:23,202::train::INFO] [train] Iter 594299 | loss 0.4771 | loss(rot) 0.3577 | loss(pos) 0.0255 | loss(seq) 0.0939 | grad 4.6060 | lr 0.0000 | time_forward 1.3180 | time_backward 1.5170 |
[2023-10-25 09:25:33,305::train::INFO] [train] Iter 594300 | loss 0.4864 | loss(rot) 0.3070 | loss(pos) 0.0098 | loss(seq) 0.1697 | grad 3.4663 | lr 0.0000 | time_forward 4.0800 | time_backward 6.0190 |
[2023-10-25 09:25:43,323::train::INFO] [train] Iter 594301 | loss 0.6730 | loss(rot) 0.0167 | loss(pos) 0.6517 | loss(seq) 0.0047 | grad 7.0223 | lr 0.0000 | time_forward 5.9860 | time_backward 4.0290 |
[2023-10-25 09:25:46,860::train::INFO] [train] Iter 594302 | loss 1.5483 | loss(rot) 1.1583 | loss(pos) 0.0604 | loss(seq) 0.3296 | grad 6.2591 | lr 0.0000 | time_forward 1.4990 | time_backward 2.0350 |
[2023-10-25 09:25:50,699::train::INFO] [train] Iter 594303 | loss 2.3641 | loss(rot) 2.0346 | loss(pos) 0.0853 | loss(seq) 0.2441 | grad 5.4583 | lr 0.0000 | time_forward 2.1850 | time_backward 1.6510 |
[2023-10-25 09:26:03,244::train::INFO] [train] Iter 594304 | loss 0.6939 | loss(rot) 0.6655 | loss(pos) 0.0264 | loss(seq) 0.0019 | grad 3.1200 | lr 0.0000 | time_forward 7.2140 | time_backward 5.3260 |
[2023-10-25 09:26:13,449::train::INFO] [train] Iter 594305 | loss 2.3007 | loss(rot) 2.1813 | loss(pos) 0.1063 | loss(seq) 0.0131 | grad 3.3210 | lr 0.0000 | time_forward 4.1890 | time_backward 6.0120 |
[2023-10-25 09:26:23,825::train::INFO] [train] Iter 594306 | loss 1.0500 | loss(rot) 0.0069 | loss(pos) 1.0424 | loss(seq) 0.0007 | grad 17.1050 | lr 0.0000 | time_forward 4.3340 | time_backward 6.0390 |
[2023-10-25 09:26:33,118::train::INFO] [train] Iter 594307 | loss 0.6051 | loss(rot) 0.0471 | loss(pos) 0.4951 | loss(seq) 0.0629 | grad 10.6513 | lr 0.0000 | time_forward 3.9900 | time_backward 5.3000 |
[2023-10-25 09:26:43,872::train::INFO] [train] Iter 594308 | loss 1.5274 | loss(rot) 0.6416 | loss(pos) 0.4600 | loss(seq) 0.4258 | grad 7.1632 | lr 0.0000 | time_forward 6.4160 | time_backward 4.3360 |
[2023-10-25 09:26:46,797::train::INFO] [train] Iter 594309 | loss 0.1650 | loss(rot) 0.1308 | loss(pos) 0.0185 | loss(seq) 0.0157 | grad 2.5026 | lr 0.0000 | time_forward 1.3520 | time_backward 1.5700 |
[2023-10-25 09:27:00,227::train::INFO] [train] Iter 594310 | loss 2.4251 | loss(rot) 1.6716 | loss(pos) 0.2620 | loss(seq) 0.4914 | grad 4.6165 | lr 0.0000 | time_forward 7.4890 | time_backward 5.9370 |
[2023-10-25 09:27:03,334::train::INFO] [train] Iter 594311 | loss 0.4150 | loss(rot) 0.1056 | loss(pos) 0.1001 | loss(seq) 0.2093 | grad 3.7048 | lr 0.0000 | time_forward 1.8370 | time_backward 1.2660 |
[2023-10-25 09:27:13,586::train::INFO] [train] Iter 594312 | loss 0.3944 | loss(rot) 0.3473 | loss(pos) 0.0471 | loss(seq) 0.0000 | grad 16.5548 | lr 0.0000 | time_forward 4.2370 | time_backward 6.0120 |
[2023-10-25 09:27:22,788::train::INFO] [train] Iter 594313 | loss 1.7299 | loss(rot) 1.6848 | loss(pos) 0.0445 | loss(seq) 0.0007 | grad 9.4504 | lr 0.0000 | time_forward 3.9110 | time_backward 5.2880 |
[2023-10-25 09:27:25,052::train::INFO] [train] Iter 594314 | loss 0.5481 | loss(rot) 0.0783 | loss(pos) 0.2878 | loss(seq) 0.1821 | grad 5.0366 | lr 0.0000 | time_forward 1.0330 | time_backward 1.2290 |
[2023-10-25 09:27:32,870::train::INFO] [train] Iter 594315 | loss 0.2548 | loss(rot) 0.0791 | loss(pos) 0.0172 | loss(seq) 0.1585 | grad 2.1831 | lr 0.0000 | time_forward 3.2830 | time_backward 4.5320 |
[2023-10-25 09:27:40,340::train::INFO] [train] Iter 594316 | loss 0.6669 | loss(rot) 0.3181 | loss(pos) 0.0546 | loss(seq) 0.2942 | grad 3.8918 | lr 0.0000 | time_forward 3.1450 | time_backward 4.3210 |
[2023-10-25 09:27:43,086::train::INFO] [train] Iter 594317 | loss 1.8800 | loss(rot) 1.5839 | loss(pos) 0.0839 | loss(seq) 0.2122 | grad 6.0042 | lr 0.0000 | time_forward 1.3010 | time_backward 1.4420 |
[2023-10-25 09:27:46,491::train::INFO] [train] Iter 594318 | loss 1.2124 | loss(rot) 0.8018 | loss(pos) 0.0628 | loss(seq) 0.3479 | grad 4.2282 | lr 0.0000 | time_forward 1.5450 | time_backward 1.8570 |
[2023-10-25 09:27:54,827::train::INFO] [train] Iter 594319 | loss 0.4350 | loss(rot) 0.1800 | loss(pos) 0.1137 | loss(seq) 0.1414 | grad 2.9977 | lr 0.0000 | time_forward 3.5180 | time_backward 4.8140 |
[2023-10-25 09:28:03,145::train::INFO] [train] Iter 594320 | loss 2.2517 | loss(rot) 1.6288 | loss(pos) 0.1854 | loss(seq) 0.4374 | grad 6.0643 | lr 0.0000 | time_forward 3.4930 | time_backward 4.8110 |
[2023-10-25 09:28:05,933::train::INFO] [train] Iter 594321 | loss 0.2198 | loss(rot) 0.1476 | loss(pos) 0.0148 | loss(seq) 0.0574 | grad 1.5891 | lr 0.0000 | time_forward 1.3340 | time_backward 1.4510 |
[2023-10-25 09:28:08,762::train::INFO] [train] Iter 594322 | loss 0.8031 | loss(rot) 0.2365 | loss(pos) 0.1573 | loss(seq) 0.4093 | grad 5.1838 | lr 0.0000 | time_forward 1.3610 | time_backward 1.4330 |
[2023-10-25 09:28:18,140::train::INFO] [train] Iter 594323 | loss 0.3940 | loss(rot) 0.1687 | loss(pos) 0.1096 | loss(seq) 0.1158 | grad 4.0578 | lr 0.0000 | time_forward 3.9970 | time_backward 5.3250 |
[2023-10-25 09:28:23,873::train::INFO] [train] Iter 594324 | loss 0.2368 | loss(rot) 0.1928 | loss(pos) 0.0440 | loss(seq) 0.0000 | grad 3.1620 | lr 0.0000 | time_forward 2.5020 | time_backward 3.2280 |
[2023-10-25 09:28:26,693::train::INFO] [train] Iter 594325 | loss 0.7514 | loss(rot) 0.3135 | loss(pos) 0.4277 | loss(seq) 0.0102 | grad 12.5112 | lr 0.0000 | time_forward 1.3110 | time_backward 1.5060 |
[2023-10-25 09:28:37,375::train::INFO] [train] Iter 594326 | loss 0.3810 | loss(rot) 0.0516 | loss(pos) 0.0098 | loss(seq) 0.3196 | grad 1.5899 | lr 0.0000 | time_forward 5.5930 | time_backward 5.0720 |
[2023-10-25 09:28:44,243::train::INFO] [train] Iter 594327 | loss 0.2724 | loss(rot) 0.1414 | loss(pos) 0.0147 | loss(seq) 0.1164 | grad 2.6617 | lr 0.0000 | time_forward 2.9310 | time_backward 3.9340 |
[2023-10-25 09:28:55,606::train::INFO] [train] Iter 594328 | loss 0.8129 | loss(rot) 0.7424 | loss(pos) 0.0704 | loss(seq) 0.0000 | grad 28.2993 | lr 0.0000 | time_forward 6.3690 | time_backward 4.9910 |
[2023-10-25 09:29:03,441::train::INFO] [train] Iter 594329 | loss 1.4130 | loss(rot) 0.6612 | loss(pos) 0.3175 | loss(seq) 0.4343 | grad 3.9692 | lr 0.0000 | time_forward 3.2850 | time_backward 4.5370 |
[2023-10-25 09:29:13,096::train::INFO] [train] Iter 594330 | loss 0.3766 | loss(rot) 0.1353 | loss(pos) 0.2056 | loss(seq) 0.0356 | grad 3.3788 | lr 0.0000 | time_forward 3.9260 | time_backward 5.7250 |
[2023-10-25 09:29:22,673::train::INFO] [train] Iter 594331 | loss 1.5869 | loss(rot) 0.8969 | loss(pos) 0.1286 | loss(seq) 0.5615 | grad 7.8341 | lr 0.0000 | time_forward 3.8680 | time_backward 5.7060 |
[2023-10-25 09:29:25,469::train::INFO] [train] Iter 594332 | loss 0.2603 | loss(rot) 0.2357 | loss(pos) 0.0195 | loss(seq) 0.0052 | grad 3.0020 | lr 0.0000 | time_forward 1.3180 | time_backward 1.4760 |
[2023-10-25 09:29:33,605::train::INFO] [train] Iter 594333 | loss 0.4764 | loss(rot) 0.1066 | loss(pos) 0.0206 | loss(seq) 0.3492 | grad 3.3508 | lr 0.0000 | time_forward 3.4390 | time_backward 4.6930 |
[2023-10-25 09:29:41,441::train::INFO] [train] Iter 594334 | loss 0.2335 | loss(rot) 0.0793 | loss(pos) 0.0902 | loss(seq) 0.0640 | grad 3.9440 | lr 0.0000 | time_forward 3.2720 | time_backward 4.5610 |
[2023-10-25 09:29:49,690::train::INFO] [train] Iter 594335 | loss 0.3461 | loss(rot) 0.1186 | loss(pos) 0.0404 | loss(seq) 0.1871 | grad 2.7706 | lr 0.0000 | time_forward 3.4920 | time_backward 4.7530 |
[2023-10-25 09:29:51,942::train::INFO] [train] Iter 594336 | loss 0.5006 | loss(rot) 0.2761 | loss(pos) 0.0395 | loss(seq) 0.1850 | grad 2.1383 | lr 0.0000 | time_forward 1.0330 | time_backward 1.2170 |
[2023-10-25 09:30:00,534::train::INFO] [train] Iter 594337 | loss 0.7732 | loss(rot) 0.1920 | loss(pos) 0.3210 | loss(seq) 0.2603 | grad 3.0714 | lr 0.0000 | time_forward 3.6320 | time_backward 4.9570 |
[2023-10-25 09:30:03,268::train::INFO] [train] Iter 594338 | loss 0.1860 | loss(rot) 0.0523 | loss(pos) 0.1238 | loss(seq) 0.0100 | grad 3.8107 | lr 0.0000 | time_forward 1.3060 | time_backward 1.4240 |
[2023-10-25 09:30:12,410::train::INFO] [train] Iter 594339 | loss 1.5613 | loss(rot) 1.4681 | loss(pos) 0.0635 | loss(seq) 0.0297 | grad 4.8984 | lr 0.0000 | time_forward 3.8800 | time_backward 5.2590 |
[2023-10-25 09:30:22,427::train::INFO] [train] Iter 594340 | loss 1.4369 | loss(rot) 1.3394 | loss(pos) 0.0682 | loss(seq) 0.0293 | grad 3.9562 | lr 0.0000 | time_forward 4.2760 | time_backward 5.7350 |
[2023-10-25 09:30:31,410::train::INFO] [train] Iter 594341 | loss 0.4063 | loss(rot) 0.3451 | loss(pos) 0.0420 | loss(seq) 0.0192 | grad 3.9090 | lr 0.0000 | time_forward 3.7870 | time_backward 5.1930 |
[2023-10-25 09:30:39,611::train::INFO] [train] Iter 594342 | loss 2.4147 | loss(rot) 2.1179 | loss(pos) 0.0519 | loss(seq) 0.2448 | grad 4.9735 | lr 0.0000 | time_forward 3.3370 | time_backward 4.8610 |
[2023-10-25 09:30:48,522::train::INFO] [train] Iter 594343 | loss 0.5715 | loss(rot) 0.0140 | loss(pos) 0.5562 | loss(seq) 0.0014 | grad 8.9142 | lr 0.0000 | time_forward 3.7700 | time_backward 5.1370 |
[2023-10-25 09:30:58,421::train::INFO] [train] Iter 594344 | loss 0.6390 | loss(rot) 0.0262 | loss(pos) 0.3689 | loss(seq) 0.2439 | grad 10.2425 | lr 0.0000 | time_forward 3.9380 | time_backward 5.9590 |
[2023-10-25 09:31:07,089::train::INFO] [train] Iter 594345 | loss 1.6221 | loss(rot) 0.8078 | loss(pos) 0.3120 | loss(seq) 0.5023 | grad 9.8077 | lr 0.0000 | time_forward 3.6000 | time_backward 5.0640 |
[2023-10-25 09:31:17,042::train::INFO] [train] Iter 594346 | loss 1.1381 | loss(rot) 1.0507 | loss(pos) 0.0501 | loss(seq) 0.0372 | grad 4.6060 | lr 0.0000 | time_forward 4.0300 | time_backward 5.9200 |
[2023-10-25 09:31:26,111::train::INFO] [train] Iter 594347 | loss 0.8110 | loss(rot) 0.0324 | loss(pos) 0.7747 | loss(seq) 0.0039 | grad 10.2804 | lr 0.0000 | time_forward 3.8040 | time_backward 5.2630 |
[2023-10-25 09:31:34,771::train::INFO] [train] Iter 594348 | loss 1.0805 | loss(rot) 0.4922 | loss(pos) 0.0372 | loss(seq) 0.5511 | grad 5.3015 | lr 0.0000 | time_forward 3.6130 | time_backward 5.0440 |
[2023-10-25 09:31:42,347::train::INFO] [train] Iter 594349 | loss 0.7483 | loss(rot) 0.1536 | loss(pos) 0.1401 | loss(seq) 0.4546 | grad 4.0074 | lr 0.0000 | time_forward 3.2730 | time_backward 4.2990 |
[2023-10-25 09:31:52,263::train::INFO] [train] Iter 594350 | loss 0.7173 | loss(rot) 0.4908 | loss(pos) 0.1338 | loss(seq) 0.0926 | grad 2.8965 | lr 0.0000 | time_forward 4.0030 | time_backward 5.9100 |
[2023-10-25 09:32:00,567::train::INFO] [train] Iter 594351 | loss 2.5699 | loss(rot) 2.0656 | loss(pos) 0.2275 | loss(seq) 0.2768 | grad 5.5358 | lr 0.0000 | time_forward 3.4650 | time_backward 4.8370 |
[2023-10-25 09:32:02,903::train::INFO] [train] Iter 594352 | loss 0.2226 | loss(rot) 0.0334 | loss(pos) 0.1792 | loss(seq) 0.0100 | grad 3.9107 | lr 0.0000 | time_forward 1.0720 | time_backward 1.2600 |
[2023-10-25 09:32:12,086::train::INFO] [train] Iter 594353 | loss 2.1923 | loss(rot) 2.1359 | loss(pos) 0.0558 | loss(seq) 0.0006 | grad 4.3551 | lr 0.0000 | time_forward 3.8880 | time_backward 5.2930 |
[2023-10-25 09:32:20,330::train::INFO] [train] Iter 594354 | loss 1.0973 | loss(rot) 0.9168 | loss(pos) 0.0257 | loss(seq) 0.1548 | grad 3.3209 | lr 0.0000 | time_forward 3.4510 | time_backward 4.7890 |
[2023-10-25 09:32:30,283::train::INFO] [train] Iter 594355 | loss 1.2256 | loss(rot) 1.1987 | loss(pos) 0.0218 | loss(seq) 0.0050 | grad 3.8348 | lr 0.0000 | time_forward 4.0230 | time_backward 5.9280 |
[2023-10-25 09:32:39,168::train::INFO] [train] Iter 594356 | loss 0.5074 | loss(rot) 0.3646 | loss(pos) 0.0163 | loss(seq) 0.1266 | grad 21.0538 | lr 0.0000 | time_forward 3.6780 | time_backward 5.2030 |
[2023-10-25 09:32:49,348::train::INFO] [train] Iter 594357 | loss 0.8697 | loss(rot) 0.5973 | loss(pos) 0.0375 | loss(seq) 0.2350 | grad 6.9686 | lr 0.0000 | time_forward 4.1150 | time_backward 6.0620 |
[2023-10-25 09:32:58,488::train::INFO] [train] Iter 594358 | loss 0.2384 | loss(rot) 0.1814 | loss(pos) 0.0265 | loss(seq) 0.0305 | grad 1.7204 | lr 0.0000 | time_forward 3.9000 | time_backward 5.2370 |
[2023-10-25 09:33:08,467::train::INFO] [train] Iter 594359 | loss 1.1260 | loss(rot) 0.8913 | loss(pos) 0.0440 | loss(seq) 0.1907 | grad 2.3962 | lr 0.0000 | time_forward 4.2060 | time_backward 5.7690 |
[2023-10-25 09:33:17,586::train::INFO] [train] Iter 594360 | loss 1.5604 | loss(rot) 0.4052 | loss(pos) 1.1488 | loss(seq) 0.0064 | grad 13.2552 | lr 0.0000 | time_forward 3.8910 | time_backward 5.2260 |
[2023-10-25 09:33:26,592::train::INFO] [train] Iter 594361 | loss 0.6067 | loss(rot) 0.3755 | loss(pos) 0.1989 | loss(seq) 0.0323 | grad 4.4702 | lr 0.0000 | time_forward 3.7960 | time_backward 5.2070 |
[2023-10-25 09:33:36,615::train::INFO] [train] Iter 594362 | loss 0.7993 | loss(rot) 0.6830 | loss(pos) 0.0811 | loss(seq) 0.0352 | grad 3.5743 | lr 0.0000 | time_forward 4.1960 | time_backward 5.8230 |
[2023-10-25 09:33:45,288::train::INFO] [train] Iter 594363 | loss 0.3212 | loss(rot) 0.1459 | loss(pos) 0.0256 | loss(seq) 0.1498 | grad 2.5392 | lr 0.0000 | time_forward 3.5810 | time_backward 5.0890 |
[2023-10-25 09:33:48,111::train::INFO] [train] Iter 594364 | loss 0.5924 | loss(rot) 0.4989 | loss(pos) 0.0266 | loss(seq) 0.0670 | grad 2.1095 | lr 0.0000 | time_forward 1.2940 | time_backward 1.5260 |
[2023-10-25 09:33:57,331::train::INFO] [train] Iter 594365 | loss 1.6993 | loss(rot) 1.4180 | loss(pos) 0.0221 | loss(seq) 0.2592 | grad 5.0416 | lr 0.0000 | time_forward 3.8530 | time_backward 5.3650 |
[2023-10-25 09:34:07,224::train::INFO] [train] Iter 594366 | loss 1.6281 | loss(rot) 1.5446 | loss(pos) 0.0671 | loss(seq) 0.0165 | grad 3.6536 | lr 0.0000 | time_forward 4.0120 | time_backward 5.8770 |
[2023-10-25 09:34:13,106::train::INFO] [train] Iter 594367 | loss 0.7356 | loss(rot) 0.7031 | loss(pos) 0.0320 | loss(seq) 0.0004 | grad 5.2123 | lr 0.0000 | time_forward 2.4900 | time_backward 3.3890 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.