text stringlengths 56 1.16k |
|---|
[2023-10-24 04:56:36,460::train::INFO] [train] Iter 579383 | loss 0.5668 | loss(rot) 0.2647 | loss(pos) 0.0365 | loss(seq) 0.2656 | grad 2.6445 | lr 0.0000 | time_forward 3.8270 | time_backward 5.7840 |
[2023-10-24 04:56:39,250::train::INFO] [train] Iter 579384 | loss 0.1930 | loss(rot) 0.0788 | loss(pos) 0.0221 | loss(seq) 0.0921 | grad 1.9546 | lr 0.0000 | time_forward 1.3190 | time_backward 1.4680 |
[2023-10-24 04:56:48,805::train::INFO] [train] Iter 579385 | loss 0.5136 | loss(rot) 0.4596 | loss(pos) 0.0280 | loss(seq) 0.0261 | grad 5.6276 | lr 0.0000 | time_forward 3.9190 | time_backward 5.6340 |
[2023-10-24 04:56:58,625::train::INFO] [train] Iter 579386 | loss 0.3167 | loss(rot) 0.0806 | loss(pos) 0.1878 | loss(seq) 0.0483 | grad 3.0633 | lr 0.0000 | time_forward 3.9370 | time_backward 5.8800 |
[2023-10-24 04:57:08,367::train::INFO] [train] Iter 579387 | loss 1.5842 | loss(rot) 1.2030 | loss(pos) 0.1017 | loss(seq) 0.2795 | grad 3.9362 | lr 0.0000 | time_forward 3.9330 | time_backward 5.8060 |
[2023-10-24 04:57:16,465::train::INFO] [train] Iter 579388 | loss 2.3105 | loss(rot) 1.3009 | loss(pos) 0.6546 | loss(seq) 0.3549 | grad 6.8838 | lr 0.0000 | time_forward 3.3760 | time_backward 4.7180 |
[2023-10-24 04:57:19,289::train::INFO] [train] Iter 579389 | loss 0.6431 | loss(rot) 0.2273 | loss(pos) 0.0114 | loss(seq) 0.4045 | grad 2.8379 | lr 0.0000 | time_forward 1.3450 | time_backward 1.4760 |
[2023-10-24 04:57:28,956::train::INFO] [train] Iter 579390 | loss 0.9478 | loss(rot) 0.0947 | loss(pos) 0.8508 | loss(seq) 0.0023 | grad 7.7352 | lr 0.0000 | time_forward 4.0390 | time_backward 5.5860 |
[2023-10-24 04:57:31,271::train::INFO] [train] Iter 579391 | loss 0.9151 | loss(rot) 0.8887 | loss(pos) 0.0263 | loss(seq) 0.0001 | grad 44.9916 | lr 0.0000 | time_forward 1.0480 | time_backward 1.2640 |
[2023-10-24 04:57:39,227::train::INFO] [train] Iter 579392 | loss 1.4854 | loss(rot) 1.4602 | loss(pos) 0.0180 | loss(seq) 0.0072 | grad 4.5572 | lr 0.0000 | time_forward 3.3690 | time_backward 4.5830 |
[2023-10-24 04:57:42,054::train::INFO] [train] Iter 579393 | loss 0.4765 | loss(rot) 0.2315 | loss(pos) 0.0482 | loss(seq) 0.1968 | grad 2.4238 | lr 0.0000 | time_forward 1.3020 | time_backward 1.5210 |
[2023-10-24 04:57:51,756::train::INFO] [train] Iter 579394 | loss 1.2772 | loss(rot) 0.7373 | loss(pos) 0.1272 | loss(seq) 0.4127 | grad 4.0408 | lr 0.0000 | time_forward 3.9120 | time_backward 5.7870 |
[2023-10-24 04:57:54,450::train::INFO] [train] Iter 579395 | loss 2.6935 | loss(rot) 2.0414 | loss(pos) 0.1328 | loss(seq) 0.5193 | grad 5.1398 | lr 0.0000 | time_forward 1.2440 | time_backward 1.4470 |
[2023-10-24 04:58:02,979::train::INFO] [train] Iter 579396 | loss 0.8600 | loss(rot) 0.8378 | loss(pos) 0.0204 | loss(seq) 0.0017 | grad 3.6822 | lr 0.0000 | time_forward 3.8320 | time_backward 4.6940 |
[2023-10-24 04:58:12,496::train::INFO] [train] Iter 579397 | loss 0.7980 | loss(rot) 0.7330 | loss(pos) 0.0345 | loss(seq) 0.0306 | grad 3.6434 | lr 0.0000 | time_forward 3.8840 | time_backward 5.6300 |
[2023-10-24 04:58:21,867::train::INFO] [train] Iter 579398 | loss 0.5237 | loss(rot) 0.3508 | loss(pos) 0.0407 | loss(seq) 0.1322 | grad 3.0543 | lr 0.0000 | time_forward 3.8340 | time_backward 5.5340 |
[2023-10-24 04:58:24,727::train::INFO] [train] Iter 579399 | loss 0.2010 | loss(rot) 0.1816 | loss(pos) 0.0194 | loss(seq) 0.0000 | grad 4.1994 | lr 0.0000 | time_forward 1.3090 | time_backward 1.5480 |
[2023-10-24 04:58:34,207::train::INFO] [train] Iter 579400 | loss 0.1518 | loss(rot) 0.0629 | loss(pos) 0.0501 | loss(seq) 0.0388 | grad 2.8685 | lr 0.0000 | time_forward 3.8030 | time_backward 5.6730 |
[2023-10-24 04:58:40,765::train::INFO] [train] Iter 579401 | loss 0.3361 | loss(rot) 0.0283 | loss(pos) 0.3044 | loss(seq) 0.0033 | grad 5.4469 | lr 0.0000 | time_forward 2.8010 | time_backward 3.7540 |
[2023-10-24 04:58:50,307::train::INFO] [train] Iter 579402 | loss 0.2319 | loss(rot) 0.0811 | loss(pos) 0.1361 | loss(seq) 0.0147 | grad 2.6369 | lr 0.0000 | time_forward 3.8180 | time_backward 5.7210 |
[2023-10-24 04:58:59,229::train::INFO] [train] Iter 579403 | loss 3.6666 | loss(rot) 0.0594 | loss(pos) 3.6072 | loss(seq) 0.0000 | grad 31.3791 | lr 0.0000 | time_forward 3.6480 | time_backward 5.2720 |
[2023-10-24 04:59:02,018::train::INFO] [train] Iter 579404 | loss 0.4527 | loss(rot) 0.1798 | loss(pos) 0.0649 | loss(seq) 0.2081 | grad 2.8880 | lr 0.0000 | time_forward 1.3140 | time_backward 1.4720 |
[2023-10-24 04:59:08,373::train::INFO] [train] Iter 579405 | loss 2.5320 | loss(rot) 0.0317 | loss(pos) 2.5004 | loss(seq) 0.0000 | grad 23.4227 | lr 0.0000 | time_forward 2.6720 | time_backward 3.6560 |
[2023-10-24 04:59:15,430::train::INFO] [train] Iter 579406 | loss 1.7704 | loss(rot) 1.2002 | loss(pos) 0.0234 | loss(seq) 0.5467 | grad 11.2129 | lr 0.0000 | time_forward 2.9870 | time_backward 4.0570 |
[2023-10-24 04:59:18,193::train::INFO] [train] Iter 579407 | loss 0.1204 | loss(rot) 0.0794 | loss(pos) 0.0273 | loss(seq) 0.0136 | grad 1.4603 | lr 0.0000 | time_forward 1.2980 | time_backward 1.4610 |
[2023-10-24 04:59:21,027::train::INFO] [train] Iter 579408 | loss 0.1257 | loss(rot) 0.0914 | loss(pos) 0.0343 | loss(seq) 0.0000 | grad 1.7317 | lr 0.0000 | time_forward 1.3910 | time_backward 1.4410 |
[2023-10-24 04:59:30,747::train::INFO] [train] Iter 579409 | loss 0.3156 | loss(rot) 0.1140 | loss(pos) 0.0170 | loss(seq) 0.1846 | grad 2.6132 | lr 0.0000 | time_forward 4.0010 | time_backward 5.7150 |
[2023-10-24 04:59:39,647::train::INFO] [train] Iter 579410 | loss 0.2134 | loss(rot) 0.0589 | loss(pos) 0.0151 | loss(seq) 0.1395 | grad 1.8722 | lr 0.0000 | time_forward 3.7950 | time_backward 5.1010 |
[2023-10-24 04:59:48,122::train::INFO] [train] Iter 579411 | loss 0.3198 | loss(rot) 0.0573 | loss(pos) 0.2405 | loss(seq) 0.0220 | grad 6.2827 | lr 0.0000 | time_forward 3.6050 | time_backward 4.8670 |
[2023-10-24 04:59:51,452::train::INFO] [train] Iter 579412 | loss 0.9869 | loss(rot) 0.0406 | loss(pos) 0.6243 | loss(seq) 0.3219 | grad 5.0019 | lr 0.0000 | time_forward 1.4830 | time_backward 1.8450 |
[2023-10-24 04:59:54,180::train::INFO] [train] Iter 579413 | loss 0.2815 | loss(rot) 0.1015 | loss(pos) 0.0145 | loss(seq) 0.1656 | grad 2.0821 | lr 0.0000 | time_forward 1.2770 | time_backward 1.4480 |
[2023-10-24 04:59:57,580::train::INFO] [train] Iter 579414 | loss 0.6023 | loss(rot) 0.3267 | loss(pos) 0.0484 | loss(seq) 0.2272 | grad 2.4174 | lr 0.0000 | time_forward 1.5410 | time_backward 1.8560 |
[2023-10-24 05:00:05,457::train::INFO] [train] Iter 579415 | loss 0.5792 | loss(rot) 0.5276 | loss(pos) 0.0265 | loss(seq) 0.0251 | grad 4.4379 | lr 0.0000 | time_forward 3.2940 | time_backward 4.5800 |
[2023-10-24 05:00:15,187::train::INFO] [train] Iter 579416 | loss 0.5489 | loss(rot) 0.2050 | loss(pos) 0.3341 | loss(seq) 0.0098 | grad 8.2365 | lr 0.0000 | time_forward 3.9070 | time_backward 5.8190 |
[2023-10-24 05:00:24,116::train::INFO] [train] Iter 579417 | loss 0.4645 | loss(rot) 0.2903 | loss(pos) 0.0467 | loss(seq) 0.1275 | grad 2.3876 | lr 0.0000 | time_forward 3.7930 | time_backward 5.1330 |
[2023-10-24 05:00:26,883::train::INFO] [train] Iter 579418 | loss 0.7038 | loss(rot) 0.5657 | loss(pos) 0.0144 | loss(seq) 0.1236 | grad 2.8360 | lr 0.0000 | time_forward 1.3090 | time_backward 1.4550 |
[2023-10-24 05:00:29,676::train::INFO] [train] Iter 579419 | loss 0.1852 | loss(rot) 0.1643 | loss(pos) 0.0207 | loss(seq) 0.0002 | grad 2.3171 | lr 0.0000 | time_forward 1.3110 | time_backward 1.4530 |
[2023-10-24 05:00:39,883::train::INFO] [train] Iter 579420 | loss 0.6998 | loss(rot) 0.2403 | loss(pos) 0.2666 | loss(seq) 0.1930 | grad 5.0100 | lr 0.0000 | time_forward 4.2230 | time_backward 5.9810 |
[2023-10-24 05:00:48,485::train::INFO] [train] Iter 579421 | loss 1.1834 | loss(rot) 0.5518 | loss(pos) 0.2954 | loss(seq) 0.3362 | grad 3.5129 | lr 0.0000 | time_forward 3.6560 | time_backward 4.9420 |
[2023-10-24 05:00:56,413::train::INFO] [train] Iter 579422 | loss 2.5701 | loss(rot) 1.7081 | loss(pos) 0.4062 | loss(seq) 0.4559 | grad 5.1330 | lr 0.0000 | time_forward 3.3060 | time_backward 4.6190 |
[2023-10-24 05:00:59,075::train::INFO] [train] Iter 579423 | loss 0.4526 | loss(rot) 0.0866 | loss(pos) 0.3402 | loss(seq) 0.0258 | grad 3.7023 | lr 0.0000 | time_forward 1.2480 | time_backward 1.4110 |
[2023-10-24 05:01:07,344::train::INFO] [train] Iter 579424 | loss 1.7411 | loss(rot) 1.3509 | loss(pos) 0.1022 | loss(seq) 0.2880 | grad 3.5930 | lr 0.0000 | time_forward 3.5120 | time_backward 4.7120 |
[2023-10-24 05:01:17,577::train::INFO] [train] Iter 579425 | loss 0.6286 | loss(rot) 0.2390 | loss(pos) 0.1826 | loss(seq) 0.2070 | grad 3.4712 | lr 0.0000 | time_forward 4.0200 | time_backward 6.2090 |
[2023-10-24 05:01:27,135::train::INFO] [train] Iter 579426 | loss 0.7605 | loss(rot) 0.2151 | loss(pos) 0.3196 | loss(seq) 0.2258 | grad 2.3054 | lr 0.0000 | time_forward 4.0710 | time_backward 5.4830 |
[2023-10-24 05:01:35,222::train::INFO] [train] Iter 579427 | loss 0.8638 | loss(rot) 0.1930 | loss(pos) 0.4386 | loss(seq) 0.2322 | grad 5.8504 | lr 0.0000 | time_forward 3.2990 | time_backward 4.7840 |
[2023-10-24 05:01:44,385::train::INFO] [train] Iter 579428 | loss 0.3711 | loss(rot) 0.1673 | loss(pos) 0.0482 | loss(seq) 0.1556 | grad 2.6518 | lr 0.0000 | time_forward 3.8210 | time_backward 5.3390 |
[2023-10-24 05:01:54,413::train::INFO] [train] Iter 579429 | loss 0.4274 | loss(rot) 0.3855 | loss(pos) 0.0419 | loss(seq) 0.0000 | grad 2.8426 | lr 0.0000 | time_forward 4.3010 | time_backward 5.7240 |
[2023-10-24 05:02:03,928::train::INFO] [train] Iter 579430 | loss 0.4735 | loss(rot) 0.2375 | loss(pos) 0.0726 | loss(seq) 0.1635 | grad 2.2083 | lr 0.0000 | time_forward 3.8580 | time_backward 5.6530 |
[2023-10-24 05:02:06,767::train::INFO] [train] Iter 579431 | loss 0.5411 | loss(rot) 0.2418 | loss(pos) 0.0988 | loss(seq) 0.2005 | grad 4.2587 | lr 0.0000 | time_forward 1.3950 | time_backward 1.4420 |
[2023-10-24 05:02:14,629::train::INFO] [train] Iter 579432 | loss 2.4348 | loss(rot) 1.9591 | loss(pos) 0.1472 | loss(seq) 0.3285 | grad 5.4491 | lr 0.0000 | time_forward 3.3510 | time_backward 4.5080 |
[2023-10-24 05:02:21,971::train::INFO] [train] Iter 579433 | loss 0.5584 | loss(rot) 0.1266 | loss(pos) 0.0256 | loss(seq) 0.4062 | grad 3.1702 | lr 0.0000 | time_forward 3.1210 | time_backward 4.2170 |
[2023-10-24 05:02:31,589::train::INFO] [train] Iter 579434 | loss 4.5460 | loss(rot) 0.0445 | loss(pos) 4.5015 | loss(seq) 0.0000 | grad 30.8088 | lr 0.0000 | time_forward 3.9480 | time_backward 5.6670 |
[2023-10-24 05:02:34,380::train::INFO] [train] Iter 579435 | loss 0.4932 | loss(rot) 0.3027 | loss(pos) 0.0489 | loss(seq) 0.1416 | grad 7.6361 | lr 0.0000 | time_forward 1.2880 | time_backward 1.5010 |
[2023-10-24 05:02:43,552::train::INFO] [train] Iter 579436 | loss 1.8193 | loss(rot) 0.0147 | loss(pos) 1.8030 | loss(seq) 0.0016 | grad 12.3966 | lr 0.0000 | time_forward 3.7690 | time_backward 5.3990 |
[2023-10-24 05:02:52,704::train::INFO] [train] Iter 579437 | loss 1.6157 | loss(rot) 1.3110 | loss(pos) 0.0693 | loss(seq) 0.2354 | grad 13.2855 | lr 0.0000 | time_forward 3.9660 | time_backward 5.1830 |
[2023-10-24 05:02:55,569::train::INFO] [train] Iter 579438 | loss 0.8307 | loss(rot) 0.6710 | loss(pos) 0.0294 | loss(seq) 0.1303 | grad 13.2961 | lr 0.0000 | time_forward 1.3010 | time_backward 1.5610 |
[2023-10-24 05:03:02,134::train::INFO] [train] Iter 579439 | loss 1.6676 | loss(rot) 0.9898 | loss(pos) 0.1195 | loss(seq) 0.5584 | grad 4.0825 | lr 0.0000 | time_forward 2.8480 | time_backward 3.7130 |
[2023-10-24 05:03:09,611::train::INFO] [train] Iter 579440 | loss 0.2627 | loss(rot) 0.2208 | loss(pos) 0.0419 | loss(seq) 0.0000 | grad 4.0600 | lr 0.0000 | time_forward 3.1680 | time_backward 4.3050 |
[2023-10-24 05:03:19,508::train::INFO] [train] Iter 579441 | loss 1.7115 | loss(rot) 1.0003 | loss(pos) 0.2584 | loss(seq) 0.4528 | grad 4.5008 | lr 0.0000 | time_forward 4.0840 | time_backward 5.8100 |
[2023-10-24 05:03:24,349::train::INFO] [train] Iter 579442 | loss 0.4747 | loss(rot) 0.0940 | loss(pos) 0.0338 | loss(seq) 0.3470 | grad 2.3761 | lr 0.0000 | time_forward 2.2050 | time_backward 2.6320 |
[2023-10-24 05:03:26,916::train::INFO] [train] Iter 579443 | loss 0.4019 | loss(rot) 0.2004 | loss(pos) 0.0748 | loss(seq) 0.1267 | grad 2.9039 | lr 0.0000 | time_forward 1.1380 | time_backward 1.4260 |
[2023-10-24 05:03:36,841::train::INFO] [train] Iter 579444 | loss 0.2618 | loss(rot) 0.0901 | loss(pos) 0.1314 | loss(seq) 0.0404 | grad 4.0648 | lr 0.0000 | time_forward 4.1180 | time_backward 5.8040 |
[2023-10-24 05:03:45,107::train::INFO] [train] Iter 579445 | loss 0.1581 | loss(rot) 0.0854 | loss(pos) 0.0112 | loss(seq) 0.0615 | grad 1.6584 | lr 0.0000 | time_forward 3.5540 | time_backward 4.7080 |
[2023-10-24 05:03:53,657::train::INFO] [train] Iter 579446 | loss 0.6236 | loss(rot) 0.3263 | loss(pos) 0.0259 | loss(seq) 0.2714 | grad 31.6301 | lr 0.0000 | time_forward 3.6240 | time_backward 4.9230 |
[2023-10-24 05:03:56,482::train::INFO] [train] Iter 579447 | loss 1.0319 | loss(rot) 0.9469 | loss(pos) 0.0552 | loss(seq) 0.0298 | grad 3.6243 | lr 0.0000 | time_forward 1.3400 | time_backward 1.4820 |
[2023-10-24 05:04:05,348::train::INFO] [train] Iter 579448 | loss 1.1232 | loss(rot) 0.1374 | loss(pos) 0.7828 | loss(seq) 0.2030 | grad 8.9072 | lr 0.0000 | time_forward 3.7580 | time_backward 5.1040 |
[2023-10-24 05:04:14,211::train::INFO] [train] Iter 579449 | loss 0.1330 | loss(rot) 0.1020 | loss(pos) 0.0306 | loss(seq) 0.0004 | grad 1.5174 | lr 0.0000 | time_forward 3.7280 | time_backward 5.1310 |
[2023-10-24 05:04:17,013::train::INFO] [train] Iter 579450 | loss 1.6491 | loss(rot) 1.1539 | loss(pos) 0.1374 | loss(seq) 0.3579 | grad 4.5182 | lr 0.0000 | time_forward 1.3300 | time_backward 1.4680 |
[2023-10-24 05:04:26,663::train::INFO] [train] Iter 579451 | loss 0.3874 | loss(rot) 0.0735 | loss(pos) 0.3091 | loss(seq) 0.0047 | grad 4.8541 | lr 0.0000 | time_forward 3.9940 | time_backward 5.6540 |
[2023-10-24 05:04:36,382::train::INFO] [train] Iter 579452 | loss 0.8036 | loss(rot) 0.2543 | loss(pos) 0.2261 | loss(seq) 0.3232 | grad 2.6008 | lr 0.0000 | time_forward 4.1210 | time_backward 5.5940 |
[2023-10-24 05:04:46,145::train::INFO] [train] Iter 579453 | loss 0.7476 | loss(rot) 0.2666 | loss(pos) 0.0984 | loss(seq) 0.3826 | grad 2.3120 | lr 0.0000 | time_forward 3.9780 | time_backward 5.7830 |
[2023-10-24 05:04:55,871::train::INFO] [train] Iter 579454 | loss 0.8605 | loss(rot) 0.3549 | loss(pos) 0.2125 | loss(seq) 0.2931 | grad 4.8887 | lr 0.0000 | time_forward 3.9860 | time_backward 5.7370 |
[2023-10-24 05:04:58,676::train::INFO] [train] Iter 579455 | loss 0.5199 | loss(rot) 0.0915 | loss(pos) 0.1865 | loss(seq) 0.2420 | grad 4.7036 | lr 0.0000 | time_forward 1.3330 | time_backward 1.4680 |
[2023-10-24 05:05:08,342::train::INFO] [train] Iter 579456 | loss 1.9271 | loss(rot) 1.4146 | loss(pos) 0.0655 | loss(seq) 0.4471 | grad 11.5802 | lr 0.0000 | time_forward 4.0260 | time_backward 5.6370 |
[2023-10-24 05:05:16,769::train::INFO] [train] Iter 579457 | loss 0.1576 | loss(rot) 0.1426 | loss(pos) 0.0145 | loss(seq) 0.0004 | grad 2.1899 | lr 0.0000 | time_forward 3.5320 | time_backward 4.8910 |
[2023-10-24 05:05:24,864::train::INFO] [train] Iter 579458 | loss 1.0510 | loss(rot) 0.4587 | loss(pos) 0.1957 | loss(seq) 0.3966 | grad 4.0999 | lr 0.0000 | time_forward 3.2680 | time_backward 4.8240 |
[2023-10-24 05:05:27,597::train::INFO] [train] Iter 579459 | loss 0.4449 | loss(rot) 0.1522 | loss(pos) 0.0360 | loss(seq) 0.2567 | grad 2.6014 | lr 0.0000 | time_forward 1.3080 | time_backward 1.4210 |
[2023-10-24 05:05:33,390::train::INFO] [train] Iter 579460 | loss 0.5004 | loss(rot) 0.2876 | loss(pos) 0.0428 | loss(seq) 0.1700 | grad 4.5233 | lr 0.0000 | time_forward 2.5300 | time_backward 3.2600 |
[2023-10-24 05:05:42,930::train::INFO] [train] Iter 579461 | loss 1.8612 | loss(rot) 1.2998 | loss(pos) 0.1601 | loss(seq) 0.4013 | grad 3.4668 | lr 0.0000 | time_forward 3.8970 | time_backward 5.6250 |
[2023-10-24 05:05:50,617::train::INFO] [train] Iter 579462 | loss 0.2557 | loss(rot) 0.0529 | loss(pos) 0.1846 | loss(seq) 0.0182 | grad 3.5917 | lr 0.0000 | time_forward 3.2590 | time_backward 4.4240 |
[2023-10-24 05:06:00,063::train::INFO] [train] Iter 579463 | loss 0.1983 | loss(rot) 0.0297 | loss(pos) 0.0900 | loss(seq) 0.0786 | grad 2.7762 | lr 0.0000 | time_forward 3.8100 | time_backward 5.6330 |
[2023-10-24 05:06:02,983::train::INFO] [train] Iter 579464 | loss 1.4078 | loss(rot) 1.3719 | loss(pos) 0.0359 | loss(seq) 0.0000 | grad 5.1133 | lr 0.0000 | time_forward 1.3060 | time_backward 1.6110 |
[2023-10-24 05:06:05,726::train::INFO] [train] Iter 579465 | loss 1.6945 | loss(rot) 0.9790 | loss(pos) 0.0595 | loss(seq) 0.6560 | grad 4.9626 | lr 0.0000 | time_forward 1.3070 | time_backward 1.4320 |
[2023-10-24 05:06:13,903::train::INFO] [train] Iter 579466 | loss 0.7056 | loss(rot) 0.6475 | loss(pos) 0.0188 | loss(seq) 0.0393 | grad 4.4071 | lr 0.0000 | time_forward 3.5140 | time_backward 4.6550 |
[2023-10-24 05:06:16,617::train::INFO] [train] Iter 579467 | loss 0.6007 | loss(rot) 0.2988 | loss(pos) 0.0258 | loss(seq) 0.2761 | grad 4.5182 | lr 0.0000 | time_forward 1.2880 | time_backward 1.4220 |
[2023-10-24 05:06:24,778::train::INFO] [train] Iter 579468 | loss 0.2117 | loss(rot) 0.0843 | loss(pos) 0.1065 | loss(seq) 0.0209 | grad 4.1141 | lr 0.0000 | time_forward 3.4810 | time_backward 4.6770 |
[2023-10-24 05:06:32,883::train::INFO] [train] Iter 579469 | loss 0.6042 | loss(rot) 0.0404 | loss(pos) 0.5613 | loss(seq) 0.0026 | grad 7.2981 | lr 0.0000 | time_forward 3.4210 | time_backward 4.6810 |
[2023-10-24 05:06:35,604::train::INFO] [train] Iter 579470 | loss 0.4284 | loss(rot) 0.1217 | loss(pos) 0.1768 | loss(seq) 0.1298 | grad 4.3819 | lr 0.0000 | time_forward 1.2940 | time_backward 1.4240 |
[2023-10-24 05:06:42,200::train::INFO] [train] Iter 579471 | loss 0.2990 | loss(rot) 0.1309 | loss(pos) 0.0639 | loss(seq) 0.1042 | grad 3.0903 | lr 0.0000 | time_forward 2.8810 | time_backward 3.7110 |
[2023-10-24 05:06:50,650::train::INFO] [train] Iter 579472 | loss 0.3318 | loss(rot) 0.2884 | loss(pos) 0.0432 | loss(seq) 0.0001 | grad 3.5000 | lr 0.0000 | time_forward 3.5980 | time_backward 4.8490 |
[2023-10-24 05:06:53,489::train::INFO] [train] Iter 579473 | loss 0.2139 | loss(rot) 0.0850 | loss(pos) 0.0861 | loss(seq) 0.0428 | grad 2.8575 | lr 0.0000 | time_forward 1.3510 | time_backward 1.4840 |
[2023-10-24 05:07:03,185::train::INFO] [train] Iter 579474 | loss 0.3605 | loss(rot) 0.1305 | loss(pos) 0.0358 | loss(seq) 0.1943 | grad 2.2204 | lr 0.0000 | time_forward 4.0010 | time_backward 5.6910 |
[2023-10-24 05:07:05,730::train::INFO] [train] Iter 579475 | loss 1.9997 | loss(rot) 1.6116 | loss(pos) 0.0861 | loss(seq) 0.3020 | grad 6.0157 | lr 0.0000 | time_forward 1.2220 | time_backward 1.3190 |
[2023-10-24 05:07:13,917::train::INFO] [train] Iter 579476 | loss 1.7603 | loss(rot) 1.6446 | loss(pos) 0.0330 | loss(seq) 0.0826 | grad 45.5278 | lr 0.0000 | time_forward 3.4630 | time_backward 4.7000 |
[2023-10-24 05:07:22,121::train::INFO] [train] Iter 579477 | loss 2.2418 | loss(rot) 2.1646 | loss(pos) 0.0291 | loss(seq) 0.0481 | grad 24.4724 | lr 0.0000 | time_forward 3.4830 | time_backward 4.7170 |
[2023-10-24 05:07:31,994::train::INFO] [train] Iter 579478 | loss 0.8300 | loss(rot) 0.2757 | loss(pos) 0.2111 | loss(seq) 0.3432 | grad 2.4898 | lr 0.0000 | time_forward 4.2380 | time_backward 5.6320 |
[2023-10-24 05:07:38,296::train::INFO] [train] Iter 579479 | loss 0.4457 | loss(rot) 0.1957 | loss(pos) 0.0189 | loss(seq) 0.2311 | grad 3.8380 | lr 0.0000 | time_forward 2.5890 | time_backward 3.7100 |
[2023-10-24 05:07:46,200::train::INFO] [train] Iter 579480 | loss 1.9278 | loss(rot) 1.3785 | loss(pos) 0.1674 | loss(seq) 0.3819 | grad 3.2460 | lr 0.0000 | time_forward 3.3140 | time_backward 4.5720 |
[2023-10-24 05:07:53,703::train::INFO] [train] Iter 579481 | loss 0.8873 | loss(rot) 0.3166 | loss(pos) 0.2521 | loss(seq) 0.3186 | grad 4.0737 | lr 0.0000 | time_forward 3.2310 | time_backward 4.2690 |
[2023-10-24 05:08:01,119::train::INFO] [train] Iter 579482 | loss 0.4785 | loss(rot) 0.2676 | loss(pos) 0.1203 | loss(seq) 0.0906 | grad 4.6650 | lr 0.0000 | time_forward 3.1770 | time_backward 4.2360 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.