text
stringlengths
56
1.16k
[2023-09-01 21:26:54,091::train::INFO] [train] Iter 02493 | loss 3.0267 | loss(rot) 2.8028 | loss(pos) 0.2069 | loss(seq) 0.0170 | grad 4.1972 | lr 0.0010 | time_forward 1.2090 | time_backward 1.2950
[2023-09-01 21:27:04,296::train::INFO] [train] Iter 02494 | loss 1.8361 | loss(rot) 0.7703 | loss(pos) 0.5055 | loss(seq) 0.5603 | grad 3.2568 | lr 0.0010 | time_forward 4.0310 | time_backward 6.1370
[2023-09-01 21:27:14,718::train::INFO] [train] Iter 02495 | loss 3.3927 | loss(rot) 3.1535 | loss(pos) 0.2379 | loss(seq) 0.0013 | grad 3.8719 | lr 0.0010 | time_forward 4.1900 | time_backward 6.2270
[2023-09-01 21:27:17,469::train::INFO] [train] Iter 02496 | loss 1.9461 | loss(rot) 1.2198 | loss(pos) 0.2534 | loss(seq) 0.4728 | grad 5.0605 | lr 0.0010 | time_forward 1.2740 | time_backward 1.4750
[2023-09-01 21:27:26,751::train::INFO] [train] Iter 02497 | loss 1.7956 | loss(rot) 1.2133 | loss(pos) 0.3730 | loss(seq) 0.2094 | grad 3.9134 | lr 0.0010 | time_forward 3.8760 | time_backward 5.4010
[2023-09-01 21:27:35,905::train::INFO] [train] Iter 02498 | loss 2.3872 | loss(rot) 1.2796 | loss(pos) 0.4407 | loss(seq) 0.6668 | grad 2.3726 | lr 0.0010 | time_forward 3.6270 | time_backward 5.5230
[2023-09-01 21:27:45,253::train::INFO] [train] Iter 02499 | loss 3.2214 | loss(rot) 3.0198 | loss(pos) 0.1882 | loss(seq) 0.0134 | grad 2.7508 | lr 0.0010 | time_forward 4.3960 | time_backward 4.9490
[2023-09-01 21:27:47,941::train::INFO] [train] Iter 02500 | loss 3.1534 | loss(rot) 2.5662 | loss(pos) 0.2166 | loss(seq) 0.3705 | grad 3.5435 | lr 0.0010 | time_forward 1.2570 | time_backward 1.4270
[2023-09-01 21:27:55,713::train::INFO] [train] Iter 02501 | loss 2.4531 | loss(rot) 0.1332 | loss(pos) 2.3199 | loss(seq) 0.0000 | grad 4.6034 | lr 0.0010 | time_forward 3.2630 | time_backward 4.5060
[2023-09-01 21:28:04,943::train::INFO] [train] Iter 02502 | loss 3.0602 | loss(rot) 2.7140 | loss(pos) 0.3462 | loss(seq) 0.0000 | grad 4.2104 | lr 0.0010 | time_forward 3.8890 | time_backward 5.3360
[2023-09-01 21:28:14,875::train::INFO] [train] Iter 02503 | loss 2.6832 | loss(rot) 2.3587 | loss(pos) 0.2645 | loss(seq) 0.0601 | grad 5.0647 | lr 0.0010 | time_forward 4.0070 | time_backward 5.9210
[2023-09-01 21:28:25,228::train::INFO] [train] Iter 02504 | loss 2.8367 | loss(rot) 2.6165 | loss(pos) 0.1960 | loss(seq) 0.0243 | grad 3.9878 | lr 0.0010 | time_forward 4.3870 | time_backward 5.9630
[2023-09-01 21:28:35,207::train::INFO] [train] Iter 02505 | loss 2.5486 | loss(rot) 1.8330 | loss(pos) 0.2535 | loss(seq) 0.4621 | grad 2.8464 | lr 0.0010 | time_forward 4.1590 | time_backward 5.8050
[2023-09-01 21:28:45,254::train::INFO] [train] Iter 02506 | loss 3.0661 | loss(rot) 2.7796 | loss(pos) 0.2802 | loss(seq) 0.0063 | grad 4.8631 | lr 0.0010 | time_forward 4.2190 | time_backward 5.8240
[2023-09-01 21:28:47,894::train::INFO] [train] Iter 02507 | loss 2.6267 | loss(rot) 2.4624 | loss(pos) 0.1643 | loss(seq) 0.0000 | grad 3.9103 | lr 0.0010 | time_forward 1.2400 | time_backward 1.3960
[2023-09-01 21:28:58,022::train::INFO] [train] Iter 02508 | loss 2.8815 | loss(rot) 2.5903 | loss(pos) 0.2049 | loss(seq) 0.0863 | grad 3.2411 | lr 0.0010 | time_forward 4.2650 | time_backward 5.8600
[2023-09-01 21:29:06,458::train::INFO] [train] Iter 02509 | loss 1.6253 | loss(rot) 0.6701 | loss(pos) 0.5993 | loss(seq) 0.3558 | grad 4.0826 | lr 0.0010 | time_forward 3.5530 | time_backward 4.8810
[2023-09-01 21:29:15,707::train::INFO] [train] Iter 02510 | loss 2.0597 | loss(rot) 1.2219 | loss(pos) 0.3489 | loss(seq) 0.4889 | grad 4.5918 | lr 0.0010 | time_forward 3.9760 | time_backward 5.2690
[2023-09-01 21:29:18,775::train::INFO] [train] Iter 02511 | loss 1.7346 | loss(rot) 0.9613 | loss(pos) 0.3582 | loss(seq) 0.4151 | grad 3.5762 | lr 0.0010 | time_forward 1.4270 | time_backward 1.6370
[2023-09-01 21:29:27,166::train::INFO] [train] Iter 02512 | loss 2.3237 | loss(rot) 1.8289 | loss(pos) 0.3267 | loss(seq) 0.1680 | grad 4.6710 | lr 0.0010 | time_forward 3.5390 | time_backward 4.8500
[2023-09-01 21:29:36,289::train::INFO] [train] Iter 02513 | loss 1.7677 | loss(rot) 0.2935 | loss(pos) 1.4427 | loss(seq) 0.0316 | grad 4.2571 | lr 0.0010 | time_forward 3.8400 | time_backward 5.2780
[2023-09-01 21:29:46,247::train::INFO] [train] Iter 02514 | loss 2.9038 | loss(rot) 2.5978 | loss(pos) 0.3026 | loss(seq) 0.0034 | grad 4.6741 | lr 0.0010 | time_forward 3.9810 | time_backward 5.9730
[2023-09-01 21:29:55,035::train::INFO] [train] Iter 02515 | loss 2.0507 | loss(rot) 1.1476 | loss(pos) 0.2193 | loss(seq) 0.6838 | grad 4.6553 | lr 0.0010 | time_forward 3.6520 | time_backward 5.1210
[2023-09-01 21:29:57,771::train::INFO] [train] Iter 02516 | loss 3.2327 | loss(rot) 3.0098 | loss(pos) 0.1646 | loss(seq) 0.0584 | grad 3.4452 | lr 0.0010 | time_forward 1.2470 | time_backward 1.4850
[2023-09-01 21:30:05,894::train::INFO] [train] Iter 02517 | loss 2.9674 | loss(rot) 2.2104 | loss(pos) 0.2988 | loss(seq) 0.4583 | grad 3.5492 | lr 0.0010 | time_forward 3.4500 | time_backward 4.6690
[2023-09-01 21:30:16,043::train::INFO] [train] Iter 02518 | loss 2.6223 | loss(rot) 1.9275 | loss(pos) 0.2532 | loss(seq) 0.4417 | grad 3.4551 | lr 0.0010 | time_forward 4.1280 | time_backward 6.0170
[2023-09-01 21:30:23,987::train::INFO] [train] Iter 02519 | loss 1.9826 | loss(rot) 1.3317 | loss(pos) 0.2227 | loss(seq) 0.4282 | grad 4.5961 | lr 0.0010 | time_forward 3.3190 | time_backward 4.6220
[2023-09-01 21:30:31,565::train::INFO] [train] Iter 02520 | loss 3.1342 | loss(rot) 2.5271 | loss(pos) 0.1746 | loss(seq) 0.4324 | grad 3.8393 | lr 0.0010 | time_forward 3.2770 | time_backward 4.2980
[2023-09-01 21:30:41,372::train::INFO] [train] Iter 02521 | loss 2.2484 | loss(rot) 1.3793 | loss(pos) 0.2933 | loss(seq) 0.5758 | grad 3.5303 | lr 0.0010 | time_forward 4.0550 | time_backward 5.7480
[2023-09-01 21:30:49,891::train::INFO] [train] Iter 02522 | loss 2.7442 | loss(rot) 1.8719 | loss(pos) 0.4146 | loss(seq) 0.4576 | grad 4.4327 | lr 0.0010 | time_forward 3.5370 | time_backward 4.9800
[2023-09-01 21:30:58,599::train::INFO] [train] Iter 02523 | loss 1.5760 | loss(rot) 0.7251 | loss(pos) 0.6881 | loss(seq) 0.1628 | grad 5.5698 | lr 0.0010 | time_forward 3.8130 | time_backward 4.8910
[2023-09-01 21:31:01,274::train::INFO] [train] Iter 02524 | loss 2.9690 | loss(rot) 2.3220 | loss(pos) 0.2693 | loss(seq) 0.3777 | grad 4.5835 | lr 0.0010 | time_forward 1.2480 | time_backward 1.4230
[2023-09-01 21:31:10,198::train::INFO] [train] Iter 02525 | loss 1.4430 | loss(rot) 0.6993 | loss(pos) 0.4307 | loss(seq) 0.3129 | grad 4.2630 | lr 0.0010 | time_forward 3.7220 | time_backward 5.1990
[2023-09-01 21:31:12,943::train::INFO] [train] Iter 02526 | loss 2.3057 | loss(rot) 1.3656 | loss(pos) 0.2876 | loss(seq) 0.6526 | grad 4.9438 | lr 0.0010 | time_forward 1.2530 | time_backward 1.4190
[2023-09-01 21:31:16,001::train::INFO] [train] Iter 02527 | loss 3.2415 | loss(rot) 2.7258 | loss(pos) 0.5124 | loss(seq) 0.0033 | grad 6.1259 | lr 0.0010 | time_forward 1.5800 | time_backward 1.4360
[2023-09-01 21:31:24,071::train::INFO] [train] Iter 02528 | loss 2.1617 | loss(rot) 1.6428 | loss(pos) 0.1575 | loss(seq) 0.3614 | grad 4.8984 | lr 0.0010 | time_forward 3.4650 | time_backward 4.6010
[2023-09-01 21:31:32,555::train::INFO] [train] Iter 02529 | loss 2.4512 | loss(rot) 1.8111 | loss(pos) 0.2726 | loss(seq) 0.3674 | grad 4.8446 | lr 0.0010 | time_forward 3.5470 | time_backward 4.9330
[2023-09-01 21:31:40,085::train::INFO] [train] Iter 02530 | loss 2.1854 | loss(rot) 1.8853 | loss(pos) 0.2840 | loss(seq) 0.0160 | grad 5.9134 | lr 0.0010 | time_forward 3.1290 | time_backward 4.3970
[2023-09-01 21:31:48,618::train::INFO] [train] Iter 02531 | loss 1.5932 | loss(rot) 0.3870 | loss(pos) 1.2043 | loss(seq) 0.0020 | grad 6.2399 | lr 0.0010 | time_forward 3.5830 | time_backward 4.9470
[2023-09-01 21:31:58,755::train::INFO] [train] Iter 02532 | loss 3.0338 | loss(rot) 2.6780 | loss(pos) 0.3558 | loss(seq) 0.0000 | grad 4.0273 | lr 0.0010 | time_forward 4.2910 | time_backward 5.8420
[2023-09-01 21:32:01,641::train::INFO] [train] Iter 02533 | loss 3.0159 | loss(rot) 2.1006 | loss(pos) 0.2952 | loss(seq) 0.6201 | grad 2.8203 | lr 0.0010 | time_forward 1.3800 | time_backward 1.5030
[2023-09-01 21:32:04,513::train::INFO] [train] Iter 02534 | loss 2.4483 | loss(rot) 1.0491 | loss(pos) 0.7781 | loss(seq) 0.6211 | grad 5.5456 | lr 0.0010 | time_forward 1.3850 | time_backward 1.4830
[2023-09-01 21:32:13,444::train::INFO] [train] Iter 02535 | loss 2.5710 | loss(rot) 0.9826 | loss(pos) 1.0758 | loss(seq) 0.5125 | grad 4.3553 | lr 0.0010 | time_forward 3.7660 | time_backward 5.1610
[2023-09-01 21:32:21,695::train::INFO] [train] Iter 02536 | loss 2.5772 | loss(rot) 2.1847 | loss(pos) 0.1454 | loss(seq) 0.2471 | grad 2.7125 | lr 0.0010 | time_forward 3.4510 | time_backward 4.7980
[2023-09-01 21:32:24,373::train::INFO] [train] Iter 02537 | loss 3.0245 | loss(rot) 0.2216 | loss(pos) 2.8010 | loss(seq) 0.0019 | grad 6.6521 | lr 0.0010 | time_forward 1.2410 | time_backward 1.4330
[2023-09-01 21:32:32,642::train::INFO] [train] Iter 02538 | loss 1.1161 | loss(rot) 0.2747 | loss(pos) 0.7954 | loss(seq) 0.0460 | grad 4.1246 | lr 0.0010 | time_forward 3.4210 | time_backward 4.8440
[2023-09-01 21:32:40,955::train::INFO] [train] Iter 02539 | loss 1.2039 | loss(rot) 0.1736 | loss(pos) 0.9616 | loss(seq) 0.0688 | grad 5.0669 | lr 0.0010 | time_forward 3.5750 | time_backward 4.7350
[2023-09-01 21:32:48,491::train::INFO] [train] Iter 02540 | loss 2.6068 | loss(rot) 1.9289 | loss(pos) 0.3215 | loss(seq) 0.3564 | grad 3.4200 | lr 0.0010 | time_forward 3.1660 | time_backward 4.3660
[2023-09-01 21:32:57,698::train::INFO] [train] Iter 02541 | loss 2.8931 | loss(rot) 2.8000 | loss(pos) 0.0928 | loss(seq) 0.0003 | grad 2.4176 | lr 0.0010 | time_forward 3.8840 | time_backward 5.3200
[2023-09-01 21:33:07,844::train::INFO] [train] Iter 02542 | loss 1.8210 | loss(rot) 1.5111 | loss(pos) 0.2975 | loss(seq) 0.0125 | grad 4.4600 | lr 0.0010 | time_forward 4.0700 | time_backward 6.0730
[2023-09-01 21:33:17,674::train::INFO] [train] Iter 02543 | loss 2.8473 | loss(rot) 1.9799 | loss(pos) 0.6488 | loss(seq) 0.2186 | grad 5.0897 | lr 0.0010 | time_forward 4.0600 | time_backward 5.7670
[2023-09-01 21:33:27,935::train::INFO] [train] Iter 02544 | loss 1.9672 | loss(rot) 0.3035 | loss(pos) 1.5038 | loss(seq) 0.1599 | grad 7.2285 | lr 0.0010 | time_forward 4.2150 | time_backward 6.0430
[2023-09-01 21:33:35,372::train::INFO] [train] Iter 02545 | loss 2.2343 | loss(rot) 1.6861 | loss(pos) 0.1206 | loss(seq) 0.4276 | grad 4.0178 | lr 0.0010 | time_forward 3.1290 | time_backward 4.3040
[2023-09-01 21:33:38,177::train::INFO] [train] Iter 02546 | loss 2.9921 | loss(rot) 2.6143 | loss(pos) 0.2780 | loss(seq) 0.0998 | grad 4.8235 | lr 0.0010 | time_forward 1.3750 | time_backward 1.4280
[2023-09-01 21:33:48,261::train::INFO] [train] Iter 02547 | loss 1.4665 | loss(rot) 0.3499 | loss(pos) 1.0708 | loss(seq) 0.0459 | grad 5.4781 | lr 0.0010 | time_forward 4.0980 | time_backward 5.9830
[2023-09-01 21:33:56,915::train::INFO] [train] Iter 02548 | loss 2.0016 | loss(rot) 1.0482 | loss(pos) 0.5020 | loss(seq) 0.4514 | grad 5.4158 | lr 0.0010 | time_forward 3.6820 | time_backward 4.9680
[2023-09-01 21:34:03,647::train::INFO] [train] Iter 02549 | loss 1.7907 | loss(rot) 0.7318 | loss(pos) 0.5321 | loss(seq) 0.5269 | grad 5.1278 | lr 0.0010 | time_forward 2.8880 | time_backward 3.8420
[2023-09-01 21:34:06,366::train::INFO] [train] Iter 02550 | loss 3.2242 | loss(rot) 1.9617 | loss(pos) 0.7481 | loss(seq) 0.5143 | grad 4.0874 | lr 0.0010 | time_forward 1.2740 | time_backward 1.4410
[2023-09-01 21:34:12,545::train::INFO] [train] Iter 02551 | loss 3.0239 | loss(rot) 2.2780 | loss(pos) 0.3656 | loss(seq) 0.3803 | grad 5.3467 | lr 0.0010 | time_forward 2.7040 | time_backward 3.4720
[2023-09-01 21:34:15,299::train::INFO] [train] Iter 02552 | loss 2.3256 | loss(rot) 0.2163 | loss(pos) 1.6322 | loss(seq) 0.4772 | grad 7.8669 | lr 0.0010 | time_forward 1.2730 | time_backward 1.4780
[2023-09-01 21:34:18,209::train::INFO] [train] Iter 02553 | loss 1.3216 | loss(rot) 0.2720 | loss(pos) 0.9956 | loss(seq) 0.0540 | grad 5.4407 | lr 0.0010 | time_forward 1.4210 | time_backward 1.4850
[2023-09-01 21:34:27,381::train::INFO] [train] Iter 02554 | loss 2.7453 | loss(rot) 2.2294 | loss(pos) 0.5070 | loss(seq) 0.0089 | grad 5.7669 | lr 0.0010 | time_forward 3.8430 | time_backward 5.3240
[2023-09-01 21:34:30,110::train::INFO] [train] Iter 02555 | loss 1.8937 | loss(rot) 1.1771 | loss(pos) 0.2200 | loss(seq) 0.4966 | grad 3.9089 | lr 0.0010 | time_forward 1.3340 | time_backward 1.3920
[2023-09-01 21:34:37,276::train::INFO] [train] Iter 02556 | loss 2.5139 | loss(rot) 2.3833 | loss(pos) 0.1182 | loss(seq) 0.0124 | grad 4.0004 | lr 0.0010 | time_forward 3.1820 | time_backward 3.9800
[2023-09-01 21:34:39,959::train::INFO] [train] Iter 02557 | loss 1.1169 | loss(rot) 0.4438 | loss(pos) 0.3982 | loss(seq) 0.2749 | grad 4.4988 | lr 0.0010 | time_forward 1.3030 | time_backward 1.3770
[2023-09-01 21:34:42,717::train::INFO] [train] Iter 02558 | loss 3.4326 | loss(rot) 3.3112 | loss(pos) 0.1193 | loss(seq) 0.0021 | grad 2.8063 | lr 0.0010 | time_forward 1.3250 | time_backward 1.4310
[2023-09-01 21:34:53,403::train::INFO] [train] Iter 02559 | loss 1.6190 | loss(rot) 0.7078 | loss(pos) 0.3989 | loss(seq) 0.5123 | grad 3.1884 | lr 0.0010 | time_forward 4.6440 | time_backward 6.0390
[2023-09-01 21:34:56,148::train::INFO] [train] Iter 02560 | loss 3.6311 | loss(rot) 2.8884 | loss(pos) 0.2999 | loss(seq) 0.4429 | grad 4.8887 | lr 0.0010 | time_forward 1.2900 | time_backward 1.4510
[2023-09-01 21:34:57,914::train::INFO] [train] Iter 02561 | loss 0.9215 | loss(rot) 0.3451 | loss(pos) 0.4716 | loss(seq) 0.1048 | grad 4.6093 | lr 0.0010 | time_forward 0.8510 | time_backward 0.9110
[2023-09-01 21:35:06,848::train::INFO] [train] Iter 02562 | loss 2.3561 | loss(rot) 1.6057 | loss(pos) 0.2198 | loss(seq) 0.5307 | grad 3.3850 | lr 0.0010 | time_forward 3.7840 | time_backward 5.1460
[2023-09-01 21:35:17,202::train::INFO] [train] Iter 02563 | loss 3.3159 | loss(rot) 3.0226 | loss(pos) 0.2912 | loss(seq) 0.0021 | grad 5.5225 | lr 0.0010 | time_forward 4.4350 | time_backward 5.9150
[2023-09-01 21:35:25,325::train::INFO] [train] Iter 02564 | loss 1.6150 | loss(rot) 0.2778 | loss(pos) 0.8728 | loss(seq) 0.4644 | grad 4.7745 | lr 0.0010 | time_forward 3.4840 | time_backward 4.6350
[2023-09-01 21:35:34,601::train::INFO] [train] Iter 02565 | loss 2.5795 | loss(rot) 2.3778 | loss(pos) 0.1987 | loss(seq) 0.0030 | grad 4.2469 | lr 0.0010 | time_forward 3.7850 | time_backward 5.4870
[2023-09-01 21:35:43,787::train::INFO] [train] Iter 02566 | loss 1.6002 | loss(rot) 0.6172 | loss(pos) 0.5639 | loss(seq) 0.4191 | grad 3.1523 | lr 0.0010 | time_forward 3.8610 | time_backward 5.3210
[2023-09-01 21:35:54,448::train::INFO] [train] Iter 02567 | loss 3.0605 | loss(rot) 2.8462 | loss(pos) 0.1968 | loss(seq) 0.0175 | grad 3.4763 | lr 0.0010 | time_forward 4.2650 | time_backward 6.3930
[2023-09-01 21:36:04,584::train::INFO] [train] Iter 02568 | loss 3.3115 | loss(rot) 2.7635 | loss(pos) 0.5411 | loss(seq) 0.0069 | grad 5.9560 | lr 0.0010 | time_forward 4.0150 | time_backward 6.1180
[2023-09-01 21:36:14,611::train::INFO] [train] Iter 02569 | loss 3.3884 | loss(rot) 3.0670 | loss(pos) 0.3123 | loss(seq) 0.0091 | grad 4.2142 | lr 0.0010 | time_forward 4.0770 | time_backward 5.9460
[2023-09-01 21:36:17,292::train::INFO] [train] Iter 02570 | loss 2.8999 | loss(rot) 2.5554 | loss(pos) 0.2113 | loss(seq) 0.1332 | grad 4.3408 | lr 0.0010 | time_forward 1.2270 | time_backward 1.4500
[2023-09-01 21:36:25,784::train::INFO] [train] Iter 02571 | loss 3.5503 | loss(rot) 0.4773 | loss(pos) 3.0715 | loss(seq) 0.0015 | grad 9.0462 | lr 0.0010 | time_forward 3.6330 | time_backward 4.8570
[2023-09-01 21:36:33,334::train::INFO] [train] Iter 02572 | loss 2.2277 | loss(rot) 1.2549 | loss(pos) 0.5400 | loss(seq) 0.4328 | grad 4.9329 | lr 0.0010 | time_forward 3.2010 | time_backward 4.3450
[2023-09-01 21:36:36,112::train::INFO] [train] Iter 02573 | loss 3.3362 | loss(rot) 2.6334 | loss(pos) 0.7028 | loss(seq) 0.0000 | grad 4.1076 | lr 0.0010 | time_forward 1.2510 | time_backward 1.5250
[2023-09-01 21:36:44,290::train::INFO] [train] Iter 02574 | loss 1.5043 | loss(rot) 0.8152 | loss(pos) 0.5925 | loss(seq) 0.0966 | grad 6.5809 | lr 0.0010 | time_forward 3.5060 | time_backward 4.6680
[2023-09-01 21:36:52,746::train::INFO] [train] Iter 02575 | loss 2.5136 | loss(rot) 0.0465 | loss(pos) 2.4636 | loss(seq) 0.0035 | grad 9.1285 | lr 0.0010 | time_forward 3.6290 | time_backward 4.8220
[2023-09-01 21:37:02,823::train::INFO] [train] Iter 02576 | loss 1.5846 | loss(rot) 0.7410 | loss(pos) 0.5359 | loss(seq) 0.3077 | grad 3.8169 | lr 0.0010 | time_forward 3.9810 | time_backward 6.0920
[2023-09-01 21:37:05,582::train::INFO] [train] Iter 02577 | loss 1.7517 | loss(rot) 0.6933 | loss(pos) 0.7269 | loss(seq) 0.3315 | grad 6.6939 | lr 0.0010 | time_forward 1.2470 | time_backward 1.5080
[2023-09-01 21:37:15,682::train::INFO] [train] Iter 02578 | loss 2.0224 | loss(rot) 1.1536 | loss(pos) 0.4817 | loss(seq) 0.3870 | grad 4.1521 | lr 0.0010 | time_forward 4.2410 | time_backward 5.8550
[2023-09-01 21:37:24,137::train::INFO] [train] Iter 02579 | loss 1.7638 | loss(rot) 0.2217 | loss(pos) 1.5308 | loss(seq) 0.0113 | grad 5.3756 | lr 0.0010 | time_forward 3.5680 | time_backward 4.8830
[2023-09-01 21:37:34,167::train::INFO] [train] Iter 02580 | loss 2.5703 | loss(rot) 2.2340 | loss(pos) 0.3363 | loss(seq) 0.0000 | grad 4.5661 | lr 0.0010 | time_forward 4.0040 | time_backward 6.0240
[2023-09-01 21:37:36,370::train::INFO] [train] Iter 02581 | loss 1.8197 | loss(rot) 0.8298 | loss(pos) 0.6986 | loss(seq) 0.2914 | grad 6.4919 | lr 0.0010 | time_forward 0.9830 | time_backward 1.2170
[2023-09-01 21:37:46,452::train::INFO] [train] Iter 02582 | loss 1.5806 | loss(rot) 0.6998 | loss(pos) 0.6694 | loss(seq) 0.2113 | grad 5.4347 | lr 0.0010 | time_forward 4.1390 | time_backward 5.9370
[2023-09-01 21:37:55,337::train::INFO] [train] Iter 02583 | loss 1.6253 | loss(rot) 0.4182 | loss(pos) 0.7340 | loss(seq) 0.4731 | grad 5.8519 | lr 0.0010 | time_forward 3.9030 | time_backward 4.9800
[2023-09-01 21:38:05,402::train::INFO] [train] Iter 02584 | loss 2.5918 | loss(rot) 2.3241 | loss(pos) 0.2176 | loss(seq) 0.0500 | grad 5.2546 | lr 0.0010 | time_forward 3.9750 | time_backward 6.0860
[2023-09-01 21:38:14,834::train::INFO] [train] Iter 02585 | loss 3.0517 | loss(rot) 2.6430 | loss(pos) 0.2563 | loss(seq) 0.1525 | grad 4.3226 | lr 0.0010 | time_forward 4.1560 | time_backward 5.2710
[2023-09-01 21:38:17,531::train::INFO] [train] Iter 02586 | loss 2.8878 | loss(rot) 2.5882 | loss(pos) 0.2168 | loss(seq) 0.0828 | grad 3.3982 | lr 0.0010 | time_forward 1.2370 | time_backward 1.4570
[2023-09-01 21:38:26,303::train::INFO] [train] Iter 02587 | loss 2.6505 | loss(rot) 0.0681 | loss(pos) 2.5808 | loss(seq) 0.0015 | grad 10.2268 | lr 0.0010 | time_forward 3.7510 | time_backward 5.0180
[2023-09-01 21:38:34,608::train::INFO] [train] Iter 02588 | loss 1.8227 | loss(rot) 1.0030 | loss(pos) 0.4525 | loss(seq) 0.3672 | grad 4.0132 | lr 0.0010 | time_forward 3.4490 | time_backward 4.8520
[2023-09-01 21:38:46,033::train::INFO] [train] Iter 02589 | loss 1.9781 | loss(rot) 1.1346 | loss(pos) 0.5979 | loss(seq) 0.2456 | grad 5.1298 | lr 0.0010 | time_forward 5.1560 | time_backward 6.2660
[2023-09-01 21:38:48,311::train::INFO] [train] Iter 02590 | loss 3.1957 | loss(rot) 3.0253 | loss(pos) 0.1673 | loss(seq) 0.0031 | grad 3.6448 | lr 0.0010 | time_forward 1.0570 | time_backward 1.2170
[2023-09-01 21:38:51,102::train::INFO] [train] Iter 02591 | loss 2.1811 | loss(rot) 1.6921 | loss(pos) 0.2593 | loss(seq) 0.2296 | grad 4.0127 | lr 0.0010 | time_forward 1.2890 | time_backward 1.4980
[2023-09-01 21:38:58,996::train::INFO] [train] Iter 02592 | loss 0.6845 | loss(rot) 0.1961 | loss(pos) 0.4523 | loss(seq) 0.0361 | grad 4.1993 | lr 0.0010 | time_forward 3.3540 | time_backward 4.5370