text
stringlengths 56
1.16k
|
|---|
[2023-10-25 15:45:16,352::train::INFO] [train] Iter 597465 | loss 0.8688 | loss(rot) 0.0278 | loss(pos) 0.8398 | loss(seq) 0.0011 | grad 14.1915 | lr 0.0000 | time_forward 2.9390 | time_backward 4.1080 |
[2023-10-25 15:45:24,808::train::INFO] [train] Iter 597466 | loss 1.2533 | loss(rot) 1.1722 | loss(pos) 0.0315 | loss(seq) 0.0495 | grad 3.9369 | lr 0.0000 | time_forward 3.5390 | time_backward 4.9150 |
[2023-10-25 15:45:31,697::train::INFO] [train] Iter 597467 | loss 2.0346 | loss(rot) 2.0107 | loss(pos) 0.0209 | loss(seq) 0.0031 | grad 4.6930 | lr 0.0000 | time_forward 2.9430 | time_backward 3.9430 |
[2023-10-25 15:45:40,052::train::INFO] [train] Iter 597468 | loss 1.9706 | loss(rot) 0.2076 | loss(pos) 1.7623 | loss(seq) 0.0008 | grad 16.1348 | lr 0.0000 | time_forward 3.4580 | time_backward 4.8940 |
[2023-10-25 15:45:47,437::train::INFO] [train] Iter 597469 | loss 1.3654 | loss(rot) 1.1841 | loss(pos) 0.0329 | loss(seq) 0.1483 | grad 72.5936 | lr 0.0000 | time_forward 3.2250 | time_backward 4.1570 |
[2023-10-25 15:45:54,509::train::INFO] [train] Iter 597470 | loss 2.5497 | loss(rot) 2.1806 | loss(pos) 0.1423 | loss(seq) 0.2268 | grad 3.6241 | lr 0.0000 | time_forward 3.0570 | time_backward 4.0110 |
[2023-10-25 15:45:57,196::train::INFO] [train] Iter 597471 | loss 0.6463 | loss(rot) 0.6255 | loss(pos) 0.0162 | loss(seq) 0.0047 | grad 3.2410 | lr 0.0000 | time_forward 1.2730 | time_backward 1.4110 |
[2023-10-25 15:45:59,966::train::INFO] [train] Iter 597472 | loss 1.0899 | loss(rot) 0.4926 | loss(pos) 0.1380 | loss(seq) 0.4593 | grad 4.3452 | lr 0.0000 | time_forward 1.3370 | time_backward 1.4300 |
[2023-10-25 15:46:02,720::train::INFO] [train] Iter 597473 | loss 0.3256 | loss(rot) 0.0743 | loss(pos) 0.0230 | loss(seq) 0.2283 | grad 2.3481 | lr 0.0000 | time_forward 1.3270 | time_backward 1.4240 |
[2023-10-25 15:46:10,994::train::INFO] [train] Iter 597474 | loss 0.6737 | loss(rot) 0.1098 | loss(pos) 0.1922 | loss(seq) 0.3718 | grad 5.3401 | lr 0.0000 | time_forward 3.4570 | time_backward 4.8130 |
[2023-10-25 15:46:19,247::train::INFO] [train] Iter 597475 | loss 0.4082 | loss(rot) 0.0814 | loss(pos) 0.3202 | loss(seq) 0.0066 | grad 2.5887 | lr 0.0000 | time_forward 3.5450 | time_backward 4.7060 |
[2023-10-25 15:46:21,941::train::INFO] [train] Iter 597476 | loss 0.6459 | loss(rot) 0.2189 | loss(pos) 0.0590 | loss(seq) 0.3680 | grad 2.3691 | lr 0.0000 | time_forward 1.2810 | time_backward 1.4100 |
[2023-10-25 15:46:29,281::train::INFO] [train] Iter 597477 | loss 0.3165 | loss(rot) 0.0827 | loss(pos) 0.1894 | loss(seq) 0.0444 | grad 3.3063 | lr 0.0000 | time_forward 3.2010 | time_backward 4.1350 |
[2023-10-25 15:46:37,440::train::INFO] [train] Iter 597478 | loss 0.5250 | loss(rot) 0.1934 | loss(pos) 0.0138 | loss(seq) 0.3178 | grad 2.3927 | lr 0.0000 | time_forward 3.3940 | time_backward 4.7620 |
[2023-10-25 15:46:39,630::train::INFO] [train] Iter 597479 | loss 0.6520 | loss(rot) 0.4643 | loss(pos) 0.0738 | loss(seq) 0.1139 | grad 3.1203 | lr 0.0000 | time_forward 1.0020 | time_backward 1.1850 |
[2023-10-25 15:46:47,245::train::INFO] [train] Iter 597480 | loss 1.6045 | loss(rot) 1.5289 | loss(pos) 0.0754 | loss(seq) 0.0001 | grad 4.1506 | lr 0.0000 | time_forward 3.2740 | time_backward 4.3380 |
[2023-10-25 15:46:53,670::train::INFO] [train] Iter 597481 | loss 0.5367 | loss(rot) 0.2776 | loss(pos) 0.0137 | loss(seq) 0.2454 | grad 4.0131 | lr 0.0000 | time_forward 2.7920 | time_backward 3.6300 |
[2023-10-25 15:46:56,372::train::INFO] [train] Iter 597482 | loss 0.3065 | loss(rot) 0.0945 | loss(pos) 0.0646 | loss(seq) 0.1474 | grad 3.2099 | lr 0.0000 | time_forward 1.2730 | time_backward 1.4270 |
[2023-10-25 15:46:59,592::train::INFO] [train] Iter 597483 | loss 1.5275 | loss(rot) 1.4288 | loss(pos) 0.0987 | loss(seq) 0.0000 | grad 3.4299 | lr 0.0000 | time_forward 1.4680 | time_backward 1.7490 |
[2023-10-25 15:47:05,305::train::INFO] [train] Iter 597484 | loss 0.3448 | loss(rot) 0.1332 | loss(pos) 0.0226 | loss(seq) 0.1890 | grad 2.9052 | lr 0.0000 | time_forward 2.3840 | time_backward 3.3160 |
[2023-10-25 15:47:12,632::train::INFO] [train] Iter 597485 | loss 1.9098 | loss(rot) 1.8586 | loss(pos) 0.0444 | loss(seq) 0.0068 | grad 7.0445 | lr 0.0000 | time_forward 3.1830 | time_backward 4.1320 |
[2023-10-25 15:47:20,708::train::INFO] [train] Iter 597486 | loss 1.4897 | loss(rot) 0.5237 | loss(pos) 0.5952 | loss(seq) 0.3708 | grad 3.9612 | lr 0.0000 | time_forward 3.3500 | time_backward 4.7230 |
[2023-10-25 15:47:27,720::train::INFO] [train] Iter 597487 | loss 1.4473 | loss(rot) 0.0087 | loss(pos) 1.4372 | loss(seq) 0.0014 | grad 10.4116 | lr 0.0000 | time_forward 3.0070 | time_backward 4.0020 |
[2023-10-25 15:47:30,908::train::INFO] [train] Iter 597488 | loss 1.1624 | loss(rot) 1.1080 | loss(pos) 0.0477 | loss(seq) 0.0067 | grad 33.0217 | lr 0.0000 | time_forward 1.4210 | time_backward 1.7630 |
[2023-10-25 15:47:38,579::train::INFO] [train] Iter 597489 | loss 0.4498 | loss(rot) 0.0388 | loss(pos) 0.4097 | loss(seq) 0.0013 | grad 6.3016 | lr 0.0000 | time_forward 3.3520 | time_backward 4.3050 |
[2023-10-25 15:47:40,766::train::INFO] [train] Iter 597490 | loss 0.4369 | loss(rot) 0.1487 | loss(pos) 0.0869 | loss(seq) 0.2012 | grad 2.6725 | lr 0.0000 | time_forward 1.0100 | time_backward 1.1730 |
[2023-10-25 15:47:47,639::train::INFO] [train] Iter 597491 | loss 0.4146 | loss(rot) 0.1019 | loss(pos) 0.2979 | loss(seq) 0.0148 | grad 6.0298 | lr 0.0000 | time_forward 2.9450 | time_backward 3.9100 |
[2023-10-25 15:47:55,845::train::INFO] [train] Iter 597492 | loss 0.8962 | loss(rot) 0.8385 | loss(pos) 0.0575 | loss(seq) 0.0002 | grad 3.0289 | lr 0.0000 | time_forward 3.5360 | time_backward 4.6660 |
[2023-10-25 15:48:04,129::train::INFO] [train] Iter 597493 | loss 0.4876 | loss(rot) 0.1594 | loss(pos) 0.1687 | loss(seq) 0.1595 | grad 2.6840 | lr 0.0000 | time_forward 3.5730 | time_backward 4.7080 |
[2023-10-25 15:48:12,556::train::INFO] [train] Iter 597494 | loss 1.0594 | loss(rot) 0.5308 | loss(pos) 0.0739 | loss(seq) 0.4547 | grad 17.9754 | lr 0.0000 | time_forward 3.6250 | time_backward 4.7990 |
[2023-10-25 15:48:19,373::train::INFO] [train] Iter 597495 | loss 1.9854 | loss(rot) 1.9645 | loss(pos) 0.0101 | loss(seq) 0.0108 | grad 4.0208 | lr 0.0000 | time_forward 3.0140 | time_backward 3.8000 |
[2023-10-25 15:48:26,882::train::INFO] [train] Iter 597496 | loss 0.9491 | loss(rot) 0.1018 | loss(pos) 0.0980 | loss(seq) 0.7492 | grad 3.0683 | lr 0.0000 | time_forward 3.2640 | time_backward 4.2420 |
[2023-10-25 15:48:35,270::train::INFO] [train] Iter 597497 | loss 1.0342 | loss(rot) 0.9959 | loss(pos) 0.0366 | loss(seq) 0.0017 | grad 14.3967 | lr 0.0000 | time_forward 3.5140 | time_backward 4.8700 |
[2023-10-25 15:48:41,487::train::INFO] [train] Iter 597498 | loss 0.5733 | loss(rot) 0.1457 | loss(pos) 0.0551 | loss(seq) 0.3725 | grad 3.8948 | lr 0.0000 | time_forward 2.6680 | time_backward 3.5460 |
[2023-10-25 15:48:49,754::train::INFO] [train] Iter 597499 | loss 0.5285 | loss(rot) 0.3222 | loss(pos) 0.0346 | loss(seq) 0.1717 | grad 3.6645 | lr 0.0000 | time_forward 3.4400 | time_backward 4.8240 |
[2023-10-25 15:48:56,775::train::INFO] [train] Iter 597500 | loss 0.6960 | loss(rot) 0.6672 | loss(pos) 0.0253 | loss(seq) 0.0035 | grad 4.1659 | lr 0.0000 | time_forward 2.9860 | time_backward 4.0330 |
[2023-10-25 15:49:04,563::train::INFO] [train] Iter 597501 | loss 4.5555 | loss(rot) 0.0019 | loss(pos) 4.5536 | loss(seq) 0.0000 | grad 35.9147 | lr 0.0000 | time_forward 3.3610 | time_backward 4.4240 |
[2023-10-25 15:49:11,108::train::INFO] [train] Iter 597502 | loss 2.2524 | loss(rot) 2.2126 | loss(pos) 0.0355 | loss(seq) 0.0043 | grad 5.3693 | lr 0.0000 | time_forward 2.8440 | time_backward 3.6980 |
[2023-10-25 15:49:18,021::train::INFO] [train] Iter 597503 | loss 0.6426 | loss(rot) 0.5658 | loss(pos) 0.0768 | loss(seq) 0.0000 | grad 27.3169 | lr 0.0000 | time_forward 2.9420 | time_backward 3.9680 |
[2023-10-25 15:49:25,421::train::INFO] [train] Iter 597504 | loss 0.2138 | loss(rot) 0.0889 | loss(pos) 0.0382 | loss(seq) 0.0867 | grad 2.5155 | lr 0.0000 | time_forward 3.2040 | time_backward 4.1930 |
[2023-10-25 15:49:28,114::train::INFO] [train] Iter 597505 | loss 0.5945 | loss(rot) 0.1117 | loss(pos) 0.0210 | loss(seq) 0.4618 | grad 2.2249 | lr 0.0000 | time_forward 1.3120 | time_backward 1.3790 |
[2023-10-25 15:49:35,938::train::INFO] [train] Iter 597506 | loss 0.6960 | loss(rot) 0.3651 | loss(pos) 0.1316 | loss(seq) 0.1993 | grad 3.6052 | lr 0.0000 | time_forward 3.3400 | time_backward 4.4820 |
[2023-10-25 15:49:44,302::train::INFO] [train] Iter 597507 | loss 0.9563 | loss(rot) 0.9060 | loss(pos) 0.0498 | loss(seq) 0.0004 | grad 4.4115 | lr 0.0000 | time_forward 3.6270 | time_backward 4.7330 |
[2023-10-25 15:49:52,040::train::INFO] [train] Iter 597508 | loss 0.9326 | loss(rot) 0.7477 | loss(pos) 0.0402 | loss(seq) 0.1446 | grad 3.0471 | lr 0.0000 | time_forward 3.3720 | time_backward 4.3620 |
[2023-10-25 15:49:54,699::train::INFO] [train] Iter 597509 | loss 0.7280 | loss(rot) 0.6551 | loss(pos) 0.0594 | loss(seq) 0.0135 | grad 5.3600 | lr 0.0000 | time_forward 1.2820 | time_backward 1.3750 |
[2023-10-25 15:49:57,448::train::INFO] [train] Iter 597510 | loss 0.6560 | loss(rot) 0.4541 | loss(pos) 0.1197 | loss(seq) 0.0822 | grad 3.7623 | lr 0.0000 | time_forward 1.3250 | time_backward 1.4210 |
[2023-10-25 15:50:05,190::train::INFO] [train] Iter 597511 | loss 0.5697 | loss(rot) 0.5400 | loss(pos) 0.0180 | loss(seq) 0.0118 | grad 3.1342 | lr 0.0000 | time_forward 3.3590 | time_backward 4.3790 |
[2023-10-25 15:50:13,610::train::INFO] [train] Iter 597512 | loss 0.8037 | loss(rot) 0.7503 | loss(pos) 0.0375 | loss(seq) 0.0159 | grad 4.9278 | lr 0.0000 | time_forward 3.5180 | time_backward 4.9000 |
[2023-10-25 15:50:20,603::train::INFO] [train] Iter 597513 | loss 1.6725 | loss(rot) 1.5862 | loss(pos) 0.0281 | loss(seq) 0.0581 | grad 82.3799 | lr 0.0000 | time_forward 2.9980 | time_backward 3.9910 |
[2023-10-25 15:50:27,751::train::INFO] [train] Iter 597514 | loss 0.6477 | loss(rot) 0.1542 | loss(pos) 0.0873 | loss(seq) 0.4062 | grad 3.6654 | lr 0.0000 | time_forward 3.0820 | time_backward 4.0630 |
[2023-10-25 15:50:35,214::train::INFO] [train] Iter 597515 | loss 0.5141 | loss(rot) 0.2247 | loss(pos) 0.1434 | loss(seq) 0.1459 | grad 3.4638 | lr 0.0000 | time_forward 3.2120 | time_backward 4.2480 |
[2023-10-25 15:50:37,946::train::INFO] [train] Iter 597516 | loss 1.2654 | loss(rot) 0.4073 | loss(pos) 0.4258 | loss(seq) 0.4324 | grad 3.8360 | lr 0.0000 | time_forward 1.3250 | time_backward 1.4050 |
[2023-10-25 15:50:40,720::train::INFO] [train] Iter 597517 | loss 0.1339 | loss(rot) 0.0843 | loss(pos) 0.0302 | loss(seq) 0.0194 | grad 2.2079 | lr 0.0000 | time_forward 1.3640 | time_backward 1.4070 |
[2023-10-25 15:50:43,476::train::INFO] [train] Iter 597518 | loss 0.6238 | loss(rot) 0.5816 | loss(pos) 0.0290 | loss(seq) 0.0132 | grad 3.6495 | lr 0.0000 | time_forward 1.3080 | time_backward 1.4150 |
[2023-10-25 15:50:51,349::train::INFO] [train] Iter 597519 | loss 0.7432 | loss(rot) 0.3041 | loss(pos) 0.2727 | loss(seq) 0.1664 | grad 2.7257 | lr 0.0000 | time_forward 3.3900 | time_backward 4.4790 |
[2023-10-25 15:50:57,932::train::INFO] [train] Iter 597520 | loss 0.6581 | loss(rot) 0.0994 | loss(pos) 0.0443 | loss(seq) 0.5144 | grad 2.9675 | lr 0.0000 | time_forward 2.8840 | time_backward 3.6970 |
[2023-10-25 15:51:00,687::train::INFO] [train] Iter 597521 | loss 0.7811 | loss(rot) 0.7085 | loss(pos) 0.0400 | loss(seq) 0.0326 | grad 5.6600 | lr 0.0000 | time_forward 1.2860 | time_backward 1.4650 |
[2023-10-25 15:51:07,899::train::INFO] [train] Iter 597522 | loss 0.5285 | loss(rot) 0.3772 | loss(pos) 0.0427 | loss(seq) 0.1086 | grad 39.4809 | lr 0.0000 | time_forward 3.0640 | time_backward 4.1450 |
[2023-10-25 15:51:10,603::train::INFO] [train] Iter 597523 | loss 0.3617 | loss(rot) 0.3395 | loss(pos) 0.0220 | loss(seq) 0.0001 | grad 76.0314 | lr 0.0000 | time_forward 1.2800 | time_backward 1.4210 |
[2023-10-25 15:51:18,012::train::INFO] [train] Iter 597524 | loss 1.0910 | loss(rot) 0.0261 | loss(pos) 1.0613 | loss(seq) 0.0035 | grad 9.2738 | lr 0.0000 | time_forward 3.1930 | time_backward 4.2130 |
[2023-10-25 15:51:25,702::train::INFO] [train] Iter 597525 | loss 0.9780 | loss(rot) 0.2762 | loss(pos) 0.1644 | loss(seq) 0.5374 | grad 4.7517 | lr 0.0000 | time_forward 3.2990 | time_backward 4.3880 |
[2023-10-25 15:51:33,214::train::INFO] [train] Iter 597526 | loss 0.2966 | loss(rot) 0.1217 | loss(pos) 0.0189 | loss(seq) 0.1561 | grad 2.4972 | lr 0.0000 | time_forward 3.2260 | time_backward 4.2810 |
[2023-10-25 15:51:41,610::train::INFO] [train] Iter 597527 | loss 1.3182 | loss(rot) 0.8025 | loss(pos) 0.1733 | loss(seq) 0.3424 | grad 4.0835 | lr 0.0000 | time_forward 3.6330 | time_backward 4.7610 |
[2023-10-25 15:51:44,805::train::INFO] [train] Iter 597528 | loss 2.1081 | loss(rot) 1.8610 | loss(pos) 0.1375 | loss(seq) 0.1095 | grad 45.4876 | lr 0.0000 | time_forward 1.4670 | time_backward 1.7240 |
[2023-10-25 15:51:52,447::train::INFO] [train] Iter 597529 | loss 0.1107 | loss(rot) 0.0860 | loss(pos) 0.0109 | loss(seq) 0.0138 | grad 1.5282 | lr 0.0000 | time_forward 3.2660 | time_backward 4.3600 |
[2023-10-25 15:52:01,053::train::INFO] [train] Iter 597530 | loss 1.5259 | loss(rot) 0.9762 | loss(pos) 0.0589 | loss(seq) 0.4908 | grad 5.1016 | lr 0.0000 | time_forward 3.5480 | time_backward 5.0550 |
[2023-10-25 15:52:09,551::train::INFO] [train] Iter 597531 | loss 0.4493 | loss(rot) 0.2006 | loss(pos) 0.2434 | loss(seq) 0.0053 | grad 3.2238 | lr 0.0000 | time_forward 3.5600 | time_backward 4.9350 |
[2023-10-25 15:52:17,078::train::INFO] [train] Iter 597532 | loss 0.4743 | loss(rot) 0.2006 | loss(pos) 0.2332 | loss(seq) 0.0405 | grad 4.9783 | lr 0.0000 | time_forward 3.2620 | time_backward 4.2610 |
[2023-10-25 15:52:24,401::train::INFO] [train] Iter 597533 | loss 1.1403 | loss(rot) 1.0015 | loss(pos) 0.0314 | loss(seq) 0.1074 | grad 45.6909 | lr 0.0000 | time_forward 3.1720 | time_backward 4.1480 |
[2023-10-25 15:52:27,112::train::INFO] [train] Iter 597534 | loss 0.2542 | loss(rot) 0.2274 | loss(pos) 0.0245 | loss(seq) 0.0023 | grad 1.9043 | lr 0.0000 | time_forward 1.2920 | time_backward 1.4160 |
[2023-10-25 15:52:35,577::train::INFO] [train] Iter 597535 | loss 0.9423 | loss(rot) 0.5001 | loss(pos) 0.4027 | loss(seq) 0.0395 | grad 5.8631 | lr 0.0000 | time_forward 3.5460 | time_backward 4.9160 |
[2023-10-25 15:52:43,944::train::INFO] [train] Iter 597536 | loss 0.6407 | loss(rot) 0.2038 | loss(pos) 0.2863 | loss(seq) 0.1505 | grad 5.2424 | lr 0.0000 | time_forward 3.6290 | time_backward 4.7350 |
[2023-10-25 15:52:52,225::train::INFO] [train] Iter 597537 | loss 1.5139 | loss(rot) 0.0082 | loss(pos) 1.4925 | loss(seq) 0.0132 | grad 13.0975 | lr 0.0000 | time_forward 3.3820 | time_backward 4.8960 |
[2023-10-25 15:53:00,491::train::INFO] [train] Iter 597538 | loss 0.5902 | loss(rot) 0.1425 | loss(pos) 0.2100 | loss(seq) 0.2377 | grad 2.5932 | lr 0.0000 | time_forward 3.3720 | time_backward 4.8910 |
[2023-10-25 15:53:07,899::train::INFO] [train] Iter 597539 | loss 0.2933 | loss(rot) 0.0649 | loss(pos) 0.1085 | loss(seq) 0.1199 | grad 3.4939 | lr 0.0000 | time_forward 3.1550 | time_backward 4.2500 |
[2023-10-25 15:53:15,164::train::INFO] [train] Iter 597540 | loss 0.4293 | loss(rot) 0.1669 | loss(pos) 0.1006 | loss(seq) 0.1618 | grad 3.8645 | lr 0.0000 | time_forward 3.1370 | time_backward 4.1240 |
[2023-10-25 15:53:22,370::train::INFO] [train] Iter 597541 | loss 1.0087 | loss(rot) 0.7428 | loss(pos) 0.0334 | loss(seq) 0.2325 | grad 3.8093 | lr 0.0000 | time_forward 3.1010 | time_backward 4.1010 |
[2023-10-25 15:53:30,383::train::INFO] [train] Iter 597542 | loss 0.5929 | loss(rot) 0.5461 | loss(pos) 0.0467 | loss(seq) 0.0000 | grad 3.0856 | lr 0.0000 | time_forward 3.5150 | time_backward 4.4950 |
[2023-10-25 15:53:37,976::train::INFO] [train] Iter 597543 | loss 1.7953 | loss(rot) 1.2388 | loss(pos) 0.1378 | loss(seq) 0.4187 | grad 3.6242 | lr 0.0000 | time_forward 3.2370 | time_backward 4.3530 |
[2023-10-25 15:53:46,519::train::INFO] [train] Iter 597544 | loss 1.6568 | loss(rot) 1.5944 | loss(pos) 0.0622 | loss(seq) 0.0001 | grad 4.0922 | lr 0.0000 | time_forward 3.5240 | time_backward 5.0160 |
[2023-10-25 15:53:49,345::train::INFO] [train] Iter 597545 | loss 0.6383 | loss(rot) 0.0794 | loss(pos) 0.0392 | loss(seq) 0.5198 | grad 2.0578 | lr 0.0000 | time_forward 1.3440 | time_backward 1.4790 |
[2023-10-25 15:53:57,215::train::INFO] [train] Iter 597546 | loss 1.8810 | loss(rot) 1.8022 | loss(pos) 0.0586 | loss(seq) 0.0202 | grad 7.4174 | lr 0.0000 | time_forward 3.4450 | time_backward 4.4090 |
[2023-10-25 15:53:59,952::train::INFO] [train] Iter 597547 | loss 0.1689 | loss(rot) 0.1468 | loss(pos) 0.0164 | loss(seq) 0.0057 | grad 3.1167 | lr 0.0000 | time_forward 1.3190 | time_backward 1.4160 |
[2023-10-25 15:54:07,211::train::INFO] [train] Iter 597548 | loss 0.3848 | loss(rot) 0.0761 | loss(pos) 0.0757 | loss(seq) 0.2330 | grad 2.9801 | lr 0.0000 | time_forward 3.1310 | time_backward 4.1020 |
[2023-10-25 15:54:09,854::train::INFO] [train] Iter 597549 | loss 0.5018 | loss(rot) 0.1757 | loss(pos) 0.0400 | loss(seq) 0.2861 | grad 3.1269 | lr 0.0000 | time_forward 1.2270 | time_backward 1.4130 |
[2023-10-25 15:54:12,574::train::INFO] [train] Iter 597550 | loss 0.4637 | loss(rot) 0.0242 | loss(pos) 0.0512 | loss(seq) 0.3883 | grad 2.3065 | lr 0.0000 | time_forward 1.3250 | time_backward 1.3910 |
[2023-10-25 15:54:14,832::train::INFO] [train] Iter 597551 | loss 0.1645 | loss(rot) 0.1088 | loss(pos) 0.0546 | loss(seq) 0.0011 | grad 1.8880 | lr 0.0000 | time_forward 1.0610 | time_backward 1.1930 |
[2023-10-25 15:54:21,471::train::INFO] [train] Iter 597552 | loss 0.4505 | loss(rot) 0.4212 | loss(pos) 0.0264 | loss(seq) 0.0029 | grad 4.3366 | lr 0.0000 | time_forward 2.9020 | time_backward 3.7220 |
[2023-10-25 15:54:24,181::train::INFO] [train] Iter 597553 | loss 0.6521 | loss(rot) 0.1916 | loss(pos) 0.1577 | loss(seq) 0.3029 | grad 4.3309 | lr 0.0000 | time_forward 1.3070 | time_backward 1.4000 |
[2023-10-25 15:54:26,929::train::INFO] [train] Iter 597554 | loss 0.5207 | loss(rot) 0.0096 | loss(pos) 0.5103 | loss(seq) 0.0008 | grad 11.5471 | lr 0.0000 | time_forward 1.2880 | time_backward 1.4330 |
[2023-10-25 15:54:29,729::train::INFO] [train] Iter 597555 | loss 0.3215 | loss(rot) 0.2280 | loss(pos) 0.0198 | loss(seq) 0.0736 | grad 2.4117 | lr 0.0000 | time_forward 1.3540 | time_backward 1.4440 |
[2023-10-25 15:54:37,475::train::INFO] [train] Iter 597556 | loss 0.3750 | loss(rot) 0.1632 | loss(pos) 0.0210 | loss(seq) 0.1908 | grad 2.8362 | lr 0.0000 | time_forward 3.4000 | time_backward 4.3110 |
[2023-10-25 15:54:40,169::train::INFO] [train] Iter 597557 | loss 0.0743 | loss(rot) 0.0511 | loss(pos) 0.0178 | loss(seq) 0.0054 | grad 1.5719 | lr 0.0000 | time_forward 1.2840 | time_backward 1.4070 |
[2023-10-25 15:54:47,991::train::INFO] [train] Iter 597558 | loss 0.3392 | loss(rot) 0.1252 | loss(pos) 0.0154 | loss(seq) 0.1986 | grad 12.6130 | lr 0.0000 | time_forward 3.3440 | time_backward 4.4730 |
[2023-10-25 15:54:55,697::train::INFO] [train] Iter 597559 | loss 1.9015 | loss(rot) 0.0036 | loss(pos) 1.8979 | loss(seq) 0.0001 | grad 15.8931 | lr 0.0000 | time_forward 3.3390 | time_backward 4.3640 |
[2023-10-25 15:54:58,474::train::INFO] [train] Iter 597560 | loss 1.7790 | loss(rot) 1.6066 | loss(pos) 0.0342 | loss(seq) 0.1382 | grad 5.6635 | lr 0.0000 | time_forward 1.3220 | time_backward 1.4520 |
[2023-10-25 15:55:05,274::train::INFO] [train] Iter 597561 | loss 1.4338 | loss(rot) 0.8078 | loss(pos) 0.5255 | loss(seq) 0.1005 | grad 4.4209 | lr 0.0000 | time_forward 2.9760 | time_backward 3.8210 |
[2023-10-25 15:55:13,140::train::INFO] [train] Iter 597562 | loss 1.1716 | loss(rot) 0.6518 | loss(pos) 0.2547 | loss(seq) 0.2651 | grad 3.8592 | lr 0.0000 | time_forward 3.4340 | time_backward 4.4280 |
[2023-10-25 15:55:21,557::train::INFO] [train] Iter 597563 | loss 0.2087 | loss(rot) 0.0993 | loss(pos) 0.0196 | loss(seq) 0.0899 | grad 2.2151 | lr 0.0000 | time_forward 3.5000 | time_backward 4.9140 |
[2023-10-25 15:55:27,486::train::INFO] [train] Iter 597564 | loss 0.1527 | loss(rot) 0.1008 | loss(pos) 0.0519 | loss(seq) 0.0000 | grad 2.5530 | lr 0.0000 | time_forward 2.4510 | time_backward 3.4750 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.