text stringlengths 56 1.16k |
|---|
[2023-10-24 20:18:21,319::train::INFO] [train] Iter 587375 | loss 0.6289 | loss(rot) 0.3055 | loss(pos) 0.0760 | loss(seq) 0.2474 | grad 2.8418 | lr 0.0000 | time_forward 1.4570 | time_backward 1.8200 |
[2023-10-24 20:18:27,833::train::INFO] [train] Iter 587376 | loss 0.3306 | loss(rot) 0.1027 | loss(pos) 0.0488 | loss(seq) 0.1791 | grad 1.8839 | lr 0.0000 | time_forward 2.7730 | time_backward 3.7270 |
[2023-10-24 20:18:30,644::train::INFO] [train] Iter 587377 | loss 1.2584 | loss(rot) 0.0085 | loss(pos) 1.2493 | loss(seq) 0.0006 | grad 17.8504 | lr 0.0000 | time_forward 1.3410 | time_backward 1.4670 |
[2023-10-24 20:18:36,859::train::INFO] [train] Iter 587378 | loss 1.1595 | loss(rot) 1.1320 | loss(pos) 0.0266 | loss(seq) 0.0008 | grad 14.4982 | lr 0.0000 | time_forward 2.6390 | time_backward 3.5720 |
[2023-10-24 20:18:39,621::train::INFO] [train] Iter 587379 | loss 1.8359 | loss(rot) 1.1966 | loss(pos) 0.1940 | loss(seq) 0.4453 | grad 4.0806 | lr 0.0000 | time_forward 1.3070 | time_backward 1.4440 |
[2023-10-24 20:18:47,119::train::INFO] [train] Iter 587380 | loss 0.3952 | loss(rot) 0.3577 | loss(pos) 0.0313 | loss(seq) 0.0062 | grad 3.5057 | lr 0.0000 | time_forward 3.1630 | time_backward 4.3070 |
[2023-10-24 20:18:53,473::train::INFO] [train] Iter 587381 | loss 0.1134 | loss(rot) 0.0940 | loss(pos) 0.0186 | loss(seq) 0.0008 | grad 2.6865 | lr 0.0000 | time_forward 2.7110 | time_backward 3.6400 |
[2023-10-24 20:19:02,404::train::INFO] [train] Iter 587382 | loss 0.4999 | loss(rot) 0.3202 | loss(pos) 0.0122 | loss(seq) 0.1675 | grad 3.3376 | lr 0.0000 | time_forward 3.6570 | time_backward 5.2710 |
[2023-10-24 20:19:10,507::train::INFO] [train] Iter 587383 | loss 0.2775 | loss(rot) 0.1470 | loss(pos) 0.0219 | loss(seq) 0.1085 | grad 23.8397 | lr 0.0000 | time_forward 3.4010 | time_backward 4.7000 |
[2023-10-24 20:19:19,543::train::INFO] [train] Iter 587384 | loss 1.2841 | loss(rot) 1.2033 | loss(pos) 0.0468 | loss(seq) 0.0339 | grad 3.3936 | lr 0.0000 | time_forward 3.7160 | time_backward 5.3160 |
[2023-10-24 20:19:28,023::train::INFO] [train] Iter 587385 | loss 0.9760 | loss(rot) 0.0801 | loss(pos) 0.8923 | loss(seq) 0.0036 | grad 5.5358 | lr 0.0000 | time_forward 3.6040 | time_backward 4.8730 |
[2023-10-24 20:19:37,123::train::INFO] [train] Iter 587386 | loss 0.5883 | loss(rot) 0.5430 | loss(pos) 0.0284 | loss(seq) 0.0168 | grad 4.6918 | lr 0.0000 | time_forward 3.7370 | time_backward 5.3590 |
[2023-10-24 20:19:46,180::train::INFO] [train] Iter 587387 | loss 0.4790 | loss(rot) 0.4297 | loss(pos) 0.0425 | loss(seq) 0.0067 | grad 2.5393 | lr 0.0000 | time_forward 3.7190 | time_backward 5.3350 |
[2023-10-24 20:19:55,921::train::INFO] [train] Iter 587388 | loss 0.7597 | loss(rot) 0.0815 | loss(pos) 0.6636 | loss(seq) 0.0147 | grad 5.0616 | lr 0.0000 | time_forward 3.7530 | time_backward 5.9810 |
[2023-10-24 20:19:58,712::train::INFO] [train] Iter 587389 | loss 0.8638 | loss(rot) 0.8277 | loss(pos) 0.0351 | loss(seq) 0.0010 | grad 3.7535 | lr 0.0000 | time_forward 1.3280 | time_backward 1.4610 |
[2023-10-24 20:20:08,644::train::INFO] [train] Iter 587390 | loss 0.4717 | loss(rot) 0.4509 | loss(pos) 0.0180 | loss(seq) 0.0027 | grad 4.2012 | lr 0.0000 | time_forward 4.0910 | time_backward 5.8120 |
[2023-10-24 20:20:17,707::train::INFO] [train] Iter 587391 | loss 0.4026 | loss(rot) 0.2576 | loss(pos) 0.0434 | loss(seq) 0.1017 | grad 2.9750 | lr 0.0000 | time_forward 3.8540 | time_backward 5.2070 |
[2023-10-24 20:20:26,874::train::INFO] [train] Iter 587392 | loss 1.6682 | loss(rot) 1.0265 | loss(pos) 0.1149 | loss(seq) 0.5268 | grad 4.7104 | lr 0.0000 | time_forward 3.8890 | time_backward 5.2740 |
[2023-10-24 20:20:36,742::train::INFO] [train] Iter 587393 | loss 1.2461 | loss(rot) 1.0483 | loss(pos) 0.0685 | loss(seq) 0.1293 | grad 7.6880 | lr 0.0000 | time_forward 4.0450 | time_backward 5.8190 |
[2023-10-24 20:20:45,321::train::INFO] [train] Iter 587394 | loss 0.3344 | loss(rot) 0.2055 | loss(pos) 0.0180 | loss(seq) 0.1109 | grad 1.8416 | lr 0.0000 | time_forward 3.6170 | time_backward 4.9580 |
[2023-10-24 20:20:48,107::train::INFO] [train] Iter 587395 | loss 0.2192 | loss(rot) 0.0622 | loss(pos) 0.1283 | loss(seq) 0.0287 | grad 2.0044 | lr 0.0000 | time_forward 1.3140 | time_backward 1.4690 |
[2023-10-24 20:20:50,949::train::INFO] [train] Iter 587396 | loss 0.3884 | loss(rot) 0.2069 | loss(pos) 0.0633 | loss(seq) 0.1182 | grad 4.0406 | lr 0.0000 | time_forward 1.3690 | time_backward 1.4710 |
[2023-10-24 20:21:00,768::train::INFO] [train] Iter 587397 | loss 0.2224 | loss(rot) 0.0721 | loss(pos) 0.1404 | loss(seq) 0.0100 | grad 3.1662 | lr 0.0000 | time_forward 4.0890 | time_backward 5.7260 |
[2023-10-24 20:21:08,044::train::INFO] [train] Iter 587398 | loss 0.0970 | loss(rot) 0.0608 | loss(pos) 0.0340 | loss(seq) 0.0022 | grad 1.6434 | lr 0.0000 | time_forward 3.1580 | time_backward 4.1150 |
[2023-10-24 20:21:20,047::train::INFO] [train] Iter 587399 | loss 0.5103 | loss(rot) 0.2795 | loss(pos) 0.0288 | loss(seq) 0.2020 | grad 3.3679 | lr 0.0000 | time_forward 5.0370 | time_backward 6.9480 |
[2023-10-24 20:21:22,816::train::INFO] [train] Iter 587400 | loss 0.4443 | loss(rot) 0.2071 | loss(pos) 0.0417 | loss(seq) 0.1954 | grad 2.4623 | lr 0.0000 | time_forward 1.2680 | time_backward 1.4960 |
[2023-10-24 20:21:33,382::train::INFO] [train] Iter 587401 | loss 0.4449 | loss(rot) 0.4011 | loss(pos) 0.0327 | loss(seq) 0.0112 | grad 2.9265 | lr 0.0000 | time_forward 4.6400 | time_backward 5.9060 |
[2023-10-24 20:21:43,531::train::INFO] [train] Iter 587402 | loss 0.7656 | loss(rot) 0.3978 | loss(pos) 0.0325 | loss(seq) 0.3354 | grad 4.5303 | lr 0.0000 | time_forward 3.9430 | time_backward 6.1990 |
[2023-10-24 20:21:47,102::train::INFO] [train] Iter 587403 | loss 0.3978 | loss(rot) 0.1481 | loss(pos) 0.1162 | loss(seq) 0.1335 | grad 3.7543 | lr 0.0000 | time_forward 1.6830 | time_backward 1.8850 |
[2023-10-24 20:21:57,893::train::INFO] [train] Iter 587404 | loss 0.4015 | loss(rot) 0.1709 | loss(pos) 0.0757 | loss(seq) 0.1548 | grad 3.3458 | lr 0.0000 | time_forward 4.2430 | time_backward 6.5440 |
[2023-10-24 20:22:06,737::train::INFO] [train] Iter 587405 | loss 0.6590 | loss(rot) 0.3318 | loss(pos) 0.0280 | loss(seq) 0.2992 | grad 4.5164 | lr 0.0000 | time_forward 3.7580 | time_backward 5.0830 |
[2023-10-24 20:22:16,704::train::INFO] [train] Iter 587406 | loss 1.1812 | loss(rot) 0.8246 | loss(pos) 0.1653 | loss(seq) 0.1912 | grad 3.3989 | lr 0.0000 | time_forward 4.1660 | time_backward 5.7970 |
[2023-10-24 20:22:26,679::train::INFO] [train] Iter 587407 | loss 0.8822 | loss(rot) 0.1448 | loss(pos) 0.4558 | loss(seq) 0.2815 | grad 5.9764 | lr 0.0000 | time_forward 4.0100 | time_backward 5.9610 |
[2023-10-24 20:22:36,930::train::INFO] [train] Iter 587408 | loss 0.5908 | loss(rot) 0.2752 | loss(pos) 0.3085 | loss(seq) 0.0071 | grad 4.1460 | lr 0.0000 | time_forward 4.2200 | time_backward 6.0270 |
[2023-10-24 20:22:46,062::train::INFO] [train] Iter 587409 | loss 0.4568 | loss(rot) 0.2623 | loss(pos) 0.0314 | loss(seq) 0.1632 | grad 3.0465 | lr 0.0000 | time_forward 3.8290 | time_backward 5.2980 |
[2023-10-24 20:22:55,104::train::INFO] [train] Iter 587410 | loss 0.4369 | loss(rot) 0.1813 | loss(pos) 0.0405 | loss(seq) 0.2152 | grad 3.7345 | lr 0.0000 | time_forward 3.8350 | time_backward 5.2030 |
[2023-10-24 20:23:05,412::train::INFO] [train] Iter 587411 | loss 0.5339 | loss(rot) 0.0611 | loss(pos) 0.4634 | loss(seq) 0.0094 | grad 4.0815 | lr 0.0000 | time_forward 4.2990 | time_backward 6.0050 |
[2023-10-24 20:23:15,744::train::INFO] [train] Iter 587412 | loss 0.7149 | loss(rot) 0.4117 | loss(pos) 0.0461 | loss(seq) 0.2572 | grad 4.3310 | lr 0.0000 | time_forward 4.0950 | time_backward 6.2330 |
[2023-10-24 20:23:18,742::train::INFO] [train] Iter 587413 | loss 0.5724 | loss(rot) 0.4976 | loss(pos) 0.0428 | loss(seq) 0.0320 | grad 6.7475 | lr 0.0000 | time_forward 1.3750 | time_backward 1.6200 |
[2023-10-24 20:23:28,981::train::INFO] [train] Iter 587414 | loss 0.7115 | loss(rot) 0.1108 | loss(pos) 0.5974 | loss(seq) 0.0033 | grad 7.7067 | lr 0.0000 | time_forward 4.1090 | time_backward 6.1110 |
[2023-10-24 20:23:39,220::train::INFO] [train] Iter 587415 | loss 1.4489 | loss(rot) 1.1363 | loss(pos) 0.0736 | loss(seq) 0.2390 | grad 2.9812 | lr 0.0000 | time_forward 4.1170 | time_backward 6.1190 |
[2023-10-24 20:23:41,547::train::INFO] [train] Iter 587416 | loss 0.4193 | loss(rot) 0.2029 | loss(pos) 0.0340 | loss(seq) 0.1824 | grad 2.5641 | lr 0.0000 | time_forward 1.0650 | time_backward 1.2580 |
[2023-10-24 20:23:44,434::train::INFO] [train] Iter 587417 | loss 0.3047 | loss(rot) 0.2039 | loss(pos) 0.0350 | loss(seq) 0.0658 | grad 2.7905 | lr 0.0000 | time_forward 1.3830 | time_backward 1.5000 |
[2023-10-24 20:23:47,414::train::INFO] [train] Iter 587418 | loss 0.6606 | loss(rot) 0.5596 | loss(pos) 0.0230 | loss(seq) 0.0780 | grad 25.9983 | lr 0.0000 | time_forward 1.5000 | time_backward 1.4770 |
[2023-10-24 20:23:50,349::train::INFO] [train] Iter 587419 | loss 1.1436 | loss(rot) 0.9493 | loss(pos) 0.0401 | loss(seq) 0.1542 | grad 4.5387 | lr 0.0000 | time_forward 1.4300 | time_backward 1.5020 |
[2023-10-24 20:23:58,642::train::INFO] [train] Iter 587420 | loss 0.9796 | loss(rot) 0.4165 | loss(pos) 0.2181 | loss(seq) 0.3450 | grad 5.2443 | lr 0.0000 | time_forward 3.5460 | time_backward 4.7440 |
[2023-10-24 20:24:09,157::train::INFO] [train] Iter 587421 | loss 2.1427 | loss(rot) 1.6387 | loss(pos) 0.1125 | loss(seq) 0.3915 | grad 3.5799 | lr 0.0000 | time_forward 4.3340 | time_backward 6.1680 |
[2023-10-24 20:24:17,455::train::INFO] [train] Iter 587422 | loss 1.7917 | loss(rot) 1.7315 | loss(pos) 0.0533 | loss(seq) 0.0068 | grad 8.1339 | lr 0.0000 | time_forward 3.4600 | time_backward 4.8350 |
[2023-10-24 20:24:25,115::train::INFO] [train] Iter 587423 | loss 0.6752 | loss(rot) 0.2950 | loss(pos) 0.2614 | loss(seq) 0.1189 | grad 4.9447 | lr 0.0000 | time_forward 3.2570 | time_backward 4.4000 |
[2023-10-24 20:24:35,300::train::INFO] [train] Iter 587424 | loss 0.9631 | loss(rot) 0.7378 | loss(pos) 0.0280 | loss(seq) 0.1974 | grad 5.6684 | lr 0.0000 | time_forward 4.1710 | time_backward 6.0110 |
[2023-10-24 20:24:43,792::train::INFO] [train] Iter 587425 | loss 0.3544 | loss(rot) 0.2152 | loss(pos) 0.0535 | loss(seq) 0.0857 | grad 4.0273 | lr 0.0000 | time_forward 3.5620 | time_backward 4.9260 |
[2023-10-24 20:24:46,123::train::INFO] [train] Iter 587426 | loss 0.4416 | loss(rot) 0.1072 | loss(pos) 0.1628 | loss(seq) 0.1716 | grad 3.7772 | lr 0.0000 | time_forward 1.0990 | time_backward 1.2280 |
[2023-10-24 20:24:56,413::train::INFO] [train] Iter 587427 | loss 1.3893 | loss(rot) 0.7304 | loss(pos) 0.0824 | loss(seq) 0.5765 | grad 3.9305 | lr 0.0000 | time_forward 4.0820 | time_backward 6.1360 |
[2023-10-24 20:25:05,902::train::INFO] [train] Iter 587428 | loss 0.8639 | loss(rot) 0.8067 | loss(pos) 0.0489 | loss(seq) 0.0083 | grad 6.3229 | lr 0.0000 | time_forward 3.9460 | time_backward 5.5400 |
[2023-10-24 20:25:16,000::train::INFO] [train] Iter 587429 | loss 0.5023 | loss(rot) 0.4421 | loss(pos) 0.0396 | loss(seq) 0.0205 | grad 4.0962 | lr 0.0000 | time_forward 4.0600 | time_backward 6.0340 |
[2023-10-24 20:25:18,831::train::INFO] [train] Iter 587430 | loss 0.7222 | loss(rot) 0.5409 | loss(pos) 0.0245 | loss(seq) 0.1569 | grad 4.6779 | lr 0.0000 | time_forward 1.3500 | time_backward 1.4780 |
[2023-10-24 20:25:28,559::train::INFO] [train] Iter 587431 | loss 0.2033 | loss(rot) 0.1203 | loss(pos) 0.0195 | loss(seq) 0.0635 | grad 2.1516 | lr 0.0000 | time_forward 3.9350 | time_backward 5.7890 |
[2023-10-24 20:25:39,808::train::INFO] [train] Iter 587432 | loss 0.1865 | loss(rot) 0.0519 | loss(pos) 0.1140 | loss(seq) 0.0207 | grad 3.4133 | lr 0.0000 | time_forward 4.6110 | time_backward 6.6350 |
[2023-10-24 20:25:49,291::train::INFO] [train] Iter 587433 | loss 0.7710 | loss(rot) 0.1091 | loss(pos) 0.0682 | loss(seq) 0.5937 | grad 2.7029 | lr 0.0000 | time_forward 3.9370 | time_backward 5.5420 |
[2023-10-24 20:25:59,551::train::INFO] [train] Iter 587434 | loss 0.2225 | loss(rot) 0.0201 | loss(pos) 0.1980 | loss(seq) 0.0044 | grad 3.9782 | lr 0.0000 | time_forward 4.3800 | time_backward 5.8760 |
[2023-10-24 20:26:09,798::train::INFO] [train] Iter 587435 | loss 0.4604 | loss(rot) 0.1518 | loss(pos) 0.0420 | loss(seq) 0.2666 | grad 2.7565 | lr 0.0000 | time_forward 4.2730 | time_backward 5.9720 |
[2023-10-24 20:26:18,568::train::INFO] [train] Iter 587436 | loss 0.3990 | loss(rot) 0.0876 | loss(pos) 0.2659 | loss(seq) 0.0455 | grad 8.4309 | lr 0.0000 | time_forward 3.6820 | time_backward 5.0840 |
[2023-10-24 20:26:27,336::train::INFO] [train] Iter 587437 | loss 0.9951 | loss(rot) 0.5711 | loss(pos) 0.0517 | loss(seq) 0.3723 | grad 4.4643 | lr 0.0000 | time_forward 3.7340 | time_backward 5.0310 |
[2023-10-24 20:26:41,580::train::INFO] [train] Iter 587438 | loss 1.3552 | loss(rot) 1.3109 | loss(pos) 0.0363 | loss(seq) 0.0080 | grad 30.9628 | lr 0.0000 | time_forward 5.0130 | time_backward 9.2290 |
[2023-10-24 20:26:46,786::train::INFO] [train] Iter 587439 | loss 0.3228 | loss(rot) 0.1114 | loss(pos) 0.0472 | loss(seq) 0.1642 | grad 2.5659 | lr 0.0000 | time_forward 3.7550 | time_backward 1.4470 |
[2023-10-24 20:26:58,063::train::INFO] [train] Iter 587440 | loss 0.8762 | loss(rot) 0.6316 | loss(pos) 0.1102 | loss(seq) 0.1344 | grad 3.5879 | lr 0.0000 | time_forward 5.9000 | time_backward 5.3510 |
[2023-10-24 20:27:07,074::train::INFO] [train] Iter 587441 | loss 0.4531 | loss(rot) 0.0801 | loss(pos) 0.3464 | loss(seq) 0.0267 | grad 4.5761 | lr 0.0000 | time_forward 3.6720 | time_backward 5.3350 |
[2023-10-24 20:27:09,864::train::INFO] [train] Iter 587442 | loss 1.0471 | loss(rot) 0.5075 | loss(pos) 0.2282 | loss(seq) 0.3114 | grad 4.6919 | lr 0.0000 | time_forward 1.2560 | time_backward 1.5320 |
[2023-10-24 20:27:17,948::train::INFO] [train] Iter 587443 | loss 1.1033 | loss(rot) 0.8397 | loss(pos) 0.0271 | loss(seq) 0.2365 | grad 5.3444 | lr 0.0000 | time_forward 3.6170 | time_backward 4.4630 |
[2023-10-24 20:27:20,749::train::INFO] [train] Iter 587444 | loss 0.2831 | loss(rot) 0.0662 | loss(pos) 0.0281 | loss(seq) 0.1888 | grad 2.2715 | lr 0.0000 | time_forward 1.3580 | time_backward 1.4400 |
[2023-10-24 20:27:28,965::train::INFO] [train] Iter 587445 | loss 0.6003 | loss(rot) 0.2984 | loss(pos) 0.0436 | loss(seq) 0.2583 | grad 2.8697 | lr 0.0000 | time_forward 3.2370 | time_backward 4.9750 |
[2023-10-24 20:27:31,280::train::INFO] [train] Iter 587446 | loss 0.3377 | loss(rot) 0.1205 | loss(pos) 0.1205 | loss(seq) 0.0966 | grad 4.0364 | lr 0.0000 | time_forward 1.0900 | time_backward 1.2220 |
[2023-10-24 20:27:34,140::train::INFO] [train] Iter 587447 | loss 0.3994 | loss(rot) 0.2173 | loss(pos) 0.0189 | loss(seq) 0.1633 | grad 2.4269 | lr 0.0000 | time_forward 1.3680 | time_backward 1.4900 |
[2023-10-24 20:27:37,020::train::INFO] [train] Iter 587448 | loss 0.4414 | loss(rot) 0.2080 | loss(pos) 0.0808 | loss(seq) 0.1526 | grad 2.3258 | lr 0.0000 | time_forward 1.4120 | time_backward 1.4640 |
[2023-10-24 20:27:39,824::train::INFO] [train] Iter 587449 | loss 0.5894 | loss(rot) 0.2281 | loss(pos) 0.2220 | loss(seq) 0.1392 | grad 3.9939 | lr 0.0000 | time_forward 1.3810 | time_backward 1.4210 |
[2023-10-24 20:27:48,704::train::INFO] [train] Iter 587450 | loss 1.6722 | loss(rot) 1.6256 | loss(pos) 0.0456 | loss(seq) 0.0011 | grad 3.7495 | lr 0.0000 | time_forward 3.8160 | time_backward 5.0590 |
[2023-10-24 20:27:56,904::train::INFO] [train] Iter 587451 | loss 2.7849 | loss(rot) 1.7163 | loss(pos) 1.0413 | loss(seq) 0.0273 | grad 15.2634 | lr 0.0000 | time_forward 3.5140 | time_backward 4.6830 |
[2023-10-24 20:28:05,791::train::INFO] [train] Iter 587452 | loss 0.2220 | loss(rot) 0.1092 | loss(pos) 0.0276 | loss(seq) 0.0852 | grad 2.6970 | lr 0.0000 | time_forward 3.6380 | time_backward 5.2450 |
[2023-10-24 20:28:08,655::train::INFO] [train] Iter 587453 | loss 0.1662 | loss(rot) 0.0789 | loss(pos) 0.0314 | loss(seq) 0.0559 | grad 1.5695 | lr 0.0000 | time_forward 1.2420 | time_backward 1.6190 |
[2023-10-24 20:28:18,216::train::INFO] [train] Iter 587454 | loss 0.5843 | loss(rot) 0.0313 | loss(pos) 0.0371 | loss(seq) 0.5159 | grad 2.9616 | lr 0.0000 | time_forward 4.1370 | time_backward 5.4200 |
[2023-10-24 20:28:20,979::train::INFO] [train] Iter 587455 | loss 0.2635 | loss(rot) 0.1811 | loss(pos) 0.0129 | loss(seq) 0.0695 | grad 1.9636 | lr 0.0000 | time_forward 1.3390 | time_backward 1.4210 |
[2023-10-24 20:28:23,814::train::INFO] [train] Iter 587456 | loss 0.3194 | loss(rot) 0.1501 | loss(pos) 0.0414 | loss(seq) 0.1279 | grad 2.8201 | lr 0.0000 | time_forward 1.3680 | time_backward 1.4640 |
[2023-10-24 20:28:26,595::train::INFO] [train] Iter 587457 | loss 4.2006 | loss(rot) 0.0047 | loss(pos) 4.1959 | loss(seq) 0.0000 | grad 28.8716 | lr 0.0000 | time_forward 1.3290 | time_backward 1.4080 |
[2023-10-24 20:28:34,389::train::INFO] [train] Iter 587458 | loss 0.4166 | loss(rot) 0.0386 | loss(pos) 0.3616 | loss(seq) 0.0164 | grad 4.3714 | lr 0.0000 | time_forward 3.1180 | time_backward 4.6740 |
[2023-10-24 20:28:42,396::train::INFO] [train] Iter 587459 | loss 1.8077 | loss(rot) 1.7554 | loss(pos) 0.0510 | loss(seq) 0.0012 | grad 4.8710 | lr 0.0000 | time_forward 3.3920 | time_backward 4.6110 |
[2023-10-24 20:28:50,027::train::INFO] [train] Iter 587460 | loss 0.6704 | loss(rot) 0.0515 | loss(pos) 0.2450 | loss(seq) 0.3739 | grad 4.3942 | lr 0.0000 | time_forward 3.1660 | time_backward 4.4630 |
[2023-10-24 20:29:00,164::train::INFO] [train] Iter 587461 | loss 0.3815 | loss(rot) 0.1296 | loss(pos) 0.0577 | loss(seq) 0.1942 | grad 2.6874 | lr 0.0000 | time_forward 4.2100 | time_backward 5.9220 |
[2023-10-24 20:29:10,269::train::INFO] [train] Iter 587462 | loss 1.4068 | loss(rot) 0.7176 | loss(pos) 0.2911 | loss(seq) 0.3981 | grad 2.6069 | lr 0.0000 | time_forward 4.2710 | time_backward 5.8300 |
[2023-10-24 20:29:21,930::train::INFO] [train] Iter 587463 | loss 0.5731 | loss(rot) 0.1577 | loss(pos) 0.0945 | loss(seq) 0.3209 | grad 3.9326 | lr 0.0000 | time_forward 5.7140 | time_backward 5.9450 |
[2023-10-24 20:29:31,814::train::INFO] [train] Iter 587464 | loss 1.2224 | loss(rot) 0.6025 | loss(pos) 0.5087 | loss(seq) 0.1112 | grad 3.2389 | lr 0.0000 | time_forward 5.5700 | time_backward 4.3100 |
[2023-10-24 20:29:34,702::train::INFO] [train] Iter 587465 | loss 2.1476 | loss(rot) 1.7992 | loss(pos) 0.0547 | loss(seq) 0.2938 | grad 26.3438 | lr 0.0000 | time_forward 1.3900 | time_backward 1.4940 |
[2023-10-24 20:29:43,908::train::INFO] [train] Iter 587466 | loss 0.5947 | loss(rot) 0.3266 | loss(pos) 0.0557 | loss(seq) 0.2123 | grad 6.0514 | lr 0.0000 | time_forward 4.2700 | time_backward 4.9320 |
[2023-10-24 20:29:46,884::train::INFO] [train] Iter 587467 | loss 0.5179 | loss(rot) 0.3225 | loss(pos) 0.0790 | loss(seq) 0.1164 | grad 3.4068 | lr 0.0000 | time_forward 1.4680 | time_backward 1.5060 |
[2023-10-24 20:29:49,237::train::INFO] [train] Iter 587468 | loss 0.4838 | loss(rot) 0.1162 | loss(pos) 0.0300 | loss(seq) 0.3376 | grad 2.9233 | lr 0.0000 | time_forward 1.0480 | time_backward 1.2850 |
[2023-10-24 20:29:59,651::train::INFO] [train] Iter 587469 | loss 0.8130 | loss(rot) 0.1754 | loss(pos) 0.6314 | loss(seq) 0.0062 | grad 12.1668 | lr 0.0000 | time_forward 4.9800 | time_backward 5.4300 |
[2023-10-24 20:30:09,914::train::INFO] [train] Iter 587470 | loss 0.6163 | loss(rot) 0.1031 | loss(pos) 0.0461 | loss(seq) 0.4672 | grad 3.8760 | lr 0.0000 | time_forward 4.0230 | time_backward 6.2370 |
[2023-10-24 20:30:20,588::train::INFO] [train] Iter 587471 | loss 0.9212 | loss(rot) 0.8896 | loss(pos) 0.0280 | loss(seq) 0.0036 | grad 2.8034 | lr 0.0000 | time_forward 4.5470 | time_backward 6.1230 |
[2023-10-24 20:30:30,585::train::INFO] [train] Iter 587472 | loss 0.4147 | loss(rot) 0.2418 | loss(pos) 0.0210 | loss(seq) 0.1518 | grad 6.3138 | lr 0.0000 | time_forward 4.1380 | time_backward 5.8550 |
[2023-10-24 20:30:40,142::train::INFO] [train] Iter 587473 | loss 0.4005 | loss(rot) 0.0812 | loss(pos) 0.0784 | loss(seq) 0.2409 | grad 3.1337 | lr 0.0000 | time_forward 4.1850 | time_backward 5.3690 |
[2023-10-24 20:30:43,260::train::INFO] [train] Iter 587474 | loss 0.4062 | loss(rot) 0.0963 | loss(pos) 0.0372 | loss(seq) 0.2727 | grad 2.5378 | lr 0.0000 | time_forward 1.5780 | time_backward 1.5360 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.