text stringlengths 56 1.16k |
|---|
[2023-10-23 11:57:49,076::train::INFO] [train] Iter 570492 | loss 0.2462 | loss(rot) 0.0754 | loss(pos) 0.0379 | loss(seq) 0.1329 | grad 2.4521 | lr 0.0000 | time_forward 3.5050 | time_backward 4.8750 |
[2023-10-23 11:57:54,178::train::INFO] [train] Iter 570493 | loss 0.7954 | loss(rot) 0.6656 | loss(pos) 0.0140 | loss(seq) 0.1158 | grad 14.8908 | lr 0.0000 | time_forward 2.1720 | time_backward 2.9270 |
[2023-10-23 11:57:56,910::train::INFO] [train] Iter 570494 | loss 0.4090 | loss(rot) 0.0461 | loss(pos) 0.0350 | loss(seq) 0.3279 | grad 2.5131 | lr 0.0000 | time_forward 1.2850 | time_backward 1.4430 |
[2023-10-23 11:58:03,861::train::INFO] [train] Iter 570495 | loss 0.1631 | loss(rot) 0.0811 | loss(pos) 0.0222 | loss(seq) 0.0599 | grad 2.2689 | lr 0.0000 | time_forward 2.9200 | time_backward 4.0290 |
[2023-10-23 11:58:10,846::train::INFO] [train] Iter 570496 | loss 0.2329 | loss(rot) 0.1282 | loss(pos) 0.0273 | loss(seq) 0.0774 | grad 2.6408 | lr 0.0000 | time_forward 2.9340 | time_backward 4.0480 |
[2023-10-23 11:58:18,072::train::INFO] [train] Iter 570497 | loss 0.4139 | loss(rot) 0.1797 | loss(pos) 0.0450 | loss(seq) 0.1891 | grad 2.4285 | lr 0.0000 | time_forward 3.1570 | time_backward 4.0660 |
[2023-10-23 11:58:26,437::train::INFO] [train] Iter 570498 | loss 0.2107 | loss(rot) 0.0320 | loss(pos) 0.1422 | loss(seq) 0.0364 | grad 4.1137 | lr 0.0000 | time_forward 3.4830 | time_backward 4.8780 |
[2023-10-23 11:58:29,211::train::INFO] [train] Iter 570499 | loss 1.6861 | loss(rot) 1.0188 | loss(pos) 0.2988 | loss(seq) 0.3685 | grad 6.9812 | lr 0.0000 | time_forward 1.3100 | time_backward 1.4600 |
[2023-10-23 11:58:32,031::train::INFO] [train] Iter 570500 | loss 0.5950 | loss(rot) 0.2469 | loss(pos) 0.1168 | loss(seq) 0.2313 | grad 3.4189 | lr 0.0000 | time_forward 1.3050 | time_backward 1.5120 |
[2023-10-23 11:58:40,368::train::INFO] [train] Iter 570501 | loss 0.2782 | loss(rot) 0.0656 | loss(pos) 0.0390 | loss(seq) 0.1736 | grad 1.5056 | lr 0.0000 | time_forward 3.5030 | time_backward 4.8300 |
[2023-10-23 11:58:46,848::train::INFO] [train] Iter 570502 | loss 0.3168 | loss(rot) 0.2699 | loss(pos) 0.0202 | loss(seq) 0.0266 | grad 13.5251 | lr 0.0000 | time_forward 2.8240 | time_backward 3.6520 |
[2023-10-23 11:58:55,026::train::INFO] [train] Iter 570503 | loss 1.6057 | loss(rot) 0.0530 | loss(pos) 1.5501 | loss(seq) 0.0026 | grad 7.7822 | lr 0.0000 | time_forward 3.4780 | time_backward 4.6970 |
[2023-10-23 11:59:03,294::train::INFO] [train] Iter 570504 | loss 0.6310 | loss(rot) 0.2965 | loss(pos) 0.0682 | loss(seq) 0.2663 | grad 3.1895 | lr 0.0000 | time_forward 3.4310 | time_backward 4.8340 |
[2023-10-23 11:59:10,858::train::INFO] [train] Iter 570505 | loss 0.4619 | loss(rot) 0.3348 | loss(pos) 0.0221 | loss(seq) 0.1051 | grad 3.1570 | lr 0.0000 | time_forward 3.3050 | time_backward 4.2570 |
[2023-10-23 11:59:13,612::train::INFO] [train] Iter 570506 | loss 0.1884 | loss(rot) 0.0329 | loss(pos) 0.0434 | loss(seq) 0.1121 | grad 2.3715 | lr 0.0000 | time_forward 1.3120 | time_backward 1.4380 |
[2023-10-23 11:59:20,353::train::INFO] [train] Iter 570507 | loss 1.1315 | loss(rot) 0.0371 | loss(pos) 1.0871 | loss(seq) 0.0073 | grad 9.9956 | lr 0.0000 | time_forward 2.8920 | time_backward 3.8460 |
[2023-10-23 11:59:26,499::train::INFO] [train] Iter 570508 | loss 0.0998 | loss(rot) 0.0738 | loss(pos) 0.0208 | loss(seq) 0.0052 | grad 2.0253 | lr 0.0000 | time_forward 2.6570 | time_backward 3.4870 |
[2023-10-23 11:59:28,786::train::INFO] [train] Iter 570509 | loss 0.5479 | loss(rot) 0.1922 | loss(pos) 0.0303 | loss(seq) 0.3253 | grad 3.2704 | lr 0.0000 | time_forward 1.0540 | time_backward 1.2290 |
[2023-10-23 11:59:36,873::train::INFO] [train] Iter 570510 | loss 0.4113 | loss(rot) 0.0699 | loss(pos) 0.2704 | loss(seq) 0.0709 | grad 3.0482 | lr 0.0000 | time_forward 3.5150 | time_backward 4.5690 |
[2023-10-23 11:59:43,905::train::INFO] [train] Iter 570511 | loss 0.4211 | loss(rot) 0.1409 | loss(pos) 0.0876 | loss(seq) 0.1926 | grad 3.3142 | lr 0.0000 | time_forward 3.0180 | time_backward 4.0110 |
[2023-10-23 11:59:51,285::train::INFO] [train] Iter 570512 | loss 0.3408 | loss(rot) 0.3169 | loss(pos) 0.0200 | loss(seq) 0.0039 | grad 3.6760 | lr 0.0000 | time_forward 3.1710 | time_backward 4.2060 |
[2023-10-23 11:59:53,904::train::INFO] [train] Iter 570513 | loss 0.5915 | loss(rot) 0.2875 | loss(pos) 0.0488 | loss(seq) 0.2552 | grad 2.9747 | lr 0.0000 | time_forward 1.1970 | time_backward 1.4190 |
[2023-10-23 12:00:01,202::train::INFO] [train] Iter 570514 | loss 1.0741 | loss(rot) 0.9499 | loss(pos) 0.0254 | loss(seq) 0.0987 | grad 4.3849 | lr 0.0000 | time_forward 3.1780 | time_backward 4.1020 |
[2023-10-23 12:00:08,955::train::INFO] [train] Iter 570515 | loss 0.3425 | loss(rot) 0.1714 | loss(pos) 0.0212 | loss(seq) 0.1499 | grad 5.5905 | lr 0.0000 | time_forward 3.3390 | time_backward 4.4110 |
[2023-10-23 12:00:16,117::train::INFO] [train] Iter 570516 | loss 1.1796 | loss(rot) 0.9838 | loss(pos) 0.0703 | loss(seq) 0.1254 | grad 5.0358 | lr 0.0000 | time_forward 3.0850 | time_backward 4.0740 |
[2023-10-23 12:00:23,326::train::INFO] [train] Iter 570517 | loss 0.2760 | loss(rot) 0.1541 | loss(pos) 0.0162 | loss(seq) 0.1057 | grad 2.3453 | lr 0.0000 | time_forward 3.0680 | time_backward 4.1370 |
[2023-10-23 12:00:30,160::train::INFO] [train] Iter 570518 | loss 2.1798 | loss(rot) 1.9191 | loss(pos) 0.0292 | loss(seq) 0.2315 | grad 4.3208 | lr 0.0000 | time_forward 2.8700 | time_backward 3.9610 |
[2023-10-23 12:00:37,215::train::INFO] [train] Iter 570519 | loss 0.2015 | loss(rot) 0.1161 | loss(pos) 0.0165 | loss(seq) 0.0690 | grad 1.9081 | lr 0.0000 | time_forward 3.0270 | time_backward 4.0240 |
[2023-10-23 12:00:39,968::train::INFO] [train] Iter 570520 | loss 1.3300 | loss(rot) 1.2959 | loss(pos) 0.0194 | loss(seq) 0.0147 | grad 9.7866 | lr 0.0000 | time_forward 1.2870 | time_backward 1.4640 |
[2023-10-23 12:00:47,300::train::INFO] [train] Iter 570521 | loss 0.1290 | loss(rot) 0.0961 | loss(pos) 0.0103 | loss(seq) 0.0226 | grad 1.6008 | lr 0.0000 | time_forward 3.1570 | time_backward 4.1720 |
[2023-10-23 12:00:54,422::train::INFO] [train] Iter 570522 | loss 1.3099 | loss(rot) 0.7333 | loss(pos) 0.2863 | loss(seq) 0.2903 | grad 3.1686 | lr 0.0000 | time_forward 3.0800 | time_backward 4.0390 |
[2023-10-23 12:01:01,818::train::INFO] [train] Iter 570523 | loss 0.9584 | loss(rot) 0.5094 | loss(pos) 0.0855 | loss(seq) 0.3634 | grad 3.6193 | lr 0.0000 | time_forward 3.1900 | time_backward 4.2030 |
[2023-10-23 12:01:09,539::train::INFO] [train] Iter 570524 | loss 1.1611 | loss(rot) 0.0695 | loss(pos) 1.0900 | loss(seq) 0.0016 | grad 7.5790 | lr 0.0000 | time_forward 3.3420 | time_backward 4.3750 |
[2023-10-23 12:01:17,931::train::INFO] [train] Iter 570525 | loss 0.3193 | loss(rot) 0.0996 | loss(pos) 0.1174 | loss(seq) 0.1023 | grad 2.9229 | lr 0.0000 | time_forward 3.4890 | time_backward 4.9010 |
[2023-10-23 12:01:25,521::train::INFO] [train] Iter 570526 | loss 0.6986 | loss(rot) 0.4384 | loss(pos) 0.0230 | loss(seq) 0.2371 | grad 3.1686 | lr 0.0000 | time_forward 3.2950 | time_backward 4.2910 |
[2023-10-23 12:01:32,602::train::INFO] [train] Iter 570527 | loss 1.3181 | loss(rot) 1.2907 | loss(pos) 0.0178 | loss(seq) 0.0095 | grad 4.6919 | lr 0.0000 | time_forward 3.0570 | time_backward 4.0220 |
[2023-10-23 12:01:35,323::train::INFO] [train] Iter 570528 | loss 0.8978 | loss(rot) 0.8031 | loss(pos) 0.0310 | loss(seq) 0.0637 | grad 3.9507 | lr 0.0000 | time_forward 1.2690 | time_backward 1.4480 |
[2023-10-23 12:01:43,665::train::INFO] [train] Iter 570529 | loss 0.2961 | loss(rot) 0.2087 | loss(pos) 0.0200 | loss(seq) 0.0674 | grad 4.8219 | lr 0.0000 | time_forward 3.4230 | time_backward 4.9150 |
[2023-10-23 12:01:46,557::train::INFO] [train] Iter 570530 | loss 0.1663 | loss(rot) 0.1389 | loss(pos) 0.0271 | loss(seq) 0.0002 | grad 2.3626 | lr 0.0000 | time_forward 1.3530 | time_backward 1.5370 |
[2023-10-23 12:01:55,052::train::INFO] [train] Iter 570531 | loss 0.2675 | loss(rot) 0.0718 | loss(pos) 0.0691 | loss(seq) 0.1266 | grad 2.7222 | lr 0.0000 | time_forward 3.5710 | time_backward 4.9200 |
[2023-10-23 12:01:58,330::train::INFO] [train] Iter 570532 | loss 0.9445 | loss(rot) 0.4816 | loss(pos) 0.1259 | loss(seq) 0.3370 | grad 5.6319 | lr 0.0000 | time_forward 1.4680 | time_backward 1.8080 |
[2023-10-23 12:02:06,714::train::INFO] [train] Iter 570533 | loss 0.5734 | loss(rot) 0.2633 | loss(pos) 0.0651 | loss(seq) 0.2450 | grad 6.0330 | lr 0.0000 | time_forward 3.4640 | time_backward 4.9070 |
[2023-10-23 12:02:13,903::train::INFO] [train] Iter 570534 | loss 0.3415 | loss(rot) 0.3220 | loss(pos) 0.0148 | loss(seq) 0.0047 | grad 4.3519 | lr 0.0000 | time_forward 3.1210 | time_backward 4.0640 |
[2023-10-23 12:02:19,513::train::INFO] [train] Iter 570535 | loss 1.3703 | loss(rot) 0.0267 | loss(pos) 1.3428 | loss(seq) 0.0008 | grad 10.4744 | lr 0.0000 | time_forward 2.3810 | time_backward 3.2260 |
[2023-10-23 12:02:27,747::train::INFO] [train] Iter 570536 | loss 1.3385 | loss(rot) 1.0989 | loss(pos) 0.0451 | loss(seq) 0.1945 | grad 4.6708 | lr 0.0000 | time_forward 3.4050 | time_backward 4.8170 |
[2023-10-23 12:02:35,503::train::INFO] [train] Iter 570537 | loss 0.1798 | loss(rot) 0.1594 | loss(pos) 0.0196 | loss(seq) 0.0008 | grad 2.4908 | lr 0.0000 | time_forward 3.2840 | time_backward 4.4700 |
[2023-10-23 12:02:42,148::train::INFO] [train] Iter 570538 | loss 0.4049 | loss(rot) 0.1779 | loss(pos) 0.1126 | loss(seq) 0.1143 | grad 3.7242 | lr 0.0000 | time_forward 2.8480 | time_backward 3.7930 |
[2023-10-23 12:02:49,410::train::INFO] [train] Iter 570539 | loss 1.3628 | loss(rot) 0.9255 | loss(pos) 0.1268 | loss(seq) 0.3105 | grad 5.9591 | lr 0.0000 | time_forward 3.2470 | time_backward 4.0120 |
[2023-10-23 12:02:52,112::train::INFO] [train] Iter 570540 | loss 0.4539 | loss(rot) 0.1099 | loss(pos) 0.0977 | loss(seq) 0.2463 | grad 3.4403 | lr 0.0000 | time_forward 1.2910 | time_backward 1.4080 |
[2023-10-23 12:02:59,580::train::INFO] [train] Iter 570541 | loss 1.0642 | loss(rot) 0.7094 | loss(pos) 0.0629 | loss(seq) 0.2919 | grad 3.0312 | lr 0.0000 | time_forward 3.2680 | time_backward 4.1960 |
[2023-10-23 12:03:01,840::train::INFO] [train] Iter 570542 | loss 1.0319 | loss(rot) 0.7077 | loss(pos) 0.0674 | loss(seq) 0.2567 | grad 3.7083 | lr 0.0000 | time_forward 1.0210 | time_backward 1.2360 |
[2023-10-23 12:03:10,207::train::INFO] [train] Iter 570543 | loss 0.5165 | loss(rot) 0.2535 | loss(pos) 0.0472 | loss(seq) 0.2158 | grad 3.5249 | lr 0.0000 | time_forward 3.4620 | time_backward 4.9020 |
[2023-10-23 12:03:18,525::train::INFO] [train] Iter 570544 | loss 1.0341 | loss(rot) 0.4743 | loss(pos) 0.2839 | loss(seq) 0.2759 | grad 3.4083 | lr 0.0000 | time_forward 3.5000 | time_backward 4.8160 |
[2023-10-23 12:03:26,707::train::INFO] [train] Iter 570545 | loss 0.7481 | loss(rot) 0.1260 | loss(pos) 0.6131 | loss(seq) 0.0089 | grad 5.0552 | lr 0.0000 | time_forward 3.5490 | time_backward 4.6290 |
[2023-10-23 12:03:34,479::train::INFO] [train] Iter 570546 | loss 0.3876 | loss(rot) 0.1978 | loss(pos) 0.0226 | loss(seq) 0.1672 | grad 3.2870 | lr 0.0000 | time_forward 3.3760 | time_backward 4.3930 |
[2023-10-23 12:03:42,922::train::INFO] [train] Iter 570547 | loss 0.4420 | loss(rot) 0.0986 | loss(pos) 0.2677 | loss(seq) 0.0756 | grad 3.0076 | lr 0.0000 | time_forward 3.4630 | time_backward 4.9770 |
[2023-10-23 12:03:50,465::train::INFO] [train] Iter 570548 | loss 0.7064 | loss(rot) 0.1293 | loss(pos) 0.5196 | loss(seq) 0.0575 | grad 6.0534 | lr 0.0000 | time_forward 3.2770 | time_backward 4.2620 |
[2023-10-23 12:03:53,296::train::INFO] [train] Iter 570549 | loss 1.3294 | loss(rot) 1.2995 | loss(pos) 0.0229 | loss(seq) 0.0070 | grad 5.2427 | lr 0.0000 | time_forward 1.2950 | time_backward 1.5320 |
[2023-10-23 12:04:01,984::train::INFO] [train] Iter 570550 | loss 0.3834 | loss(rot) 0.0389 | loss(pos) 0.3396 | loss(seq) 0.0048 | grad 4.4897 | lr 0.0000 | time_forward 3.6740 | time_backward 5.0120 |
[2023-10-23 12:04:09,418::train::INFO] [train] Iter 570551 | loss 0.4031 | loss(rot) 0.2365 | loss(pos) 0.1598 | loss(seq) 0.0069 | grad 4.6640 | lr 0.0000 | time_forward 3.3100 | time_backward 4.1190 |
[2023-10-23 12:04:17,774::train::INFO] [train] Iter 570552 | loss 0.2961 | loss(rot) 0.0889 | loss(pos) 0.0803 | loss(seq) 0.1268 | grad 2.3442 | lr 0.0000 | time_forward 3.4350 | time_backward 4.9190 |
[2023-10-23 12:04:20,572::train::INFO] [train] Iter 570553 | loss 1.1373 | loss(rot) 0.1054 | loss(pos) 0.8347 | loss(seq) 0.1972 | grad 5.7291 | lr 0.0000 | time_forward 1.3420 | time_backward 1.4520 |
[2023-10-23 12:04:23,454::train::INFO] [train] Iter 570554 | loss 0.9959 | loss(rot) 0.8937 | loss(pos) 0.0403 | loss(seq) 0.0619 | grad 3.9005 | lr 0.0000 | time_forward 1.3100 | time_backward 1.5490 |
[2023-10-23 12:04:26,069::train::INFO] [train] Iter 570555 | loss 0.3818 | loss(rot) 0.0552 | loss(pos) 0.3067 | loss(seq) 0.0199 | grad 8.6689 | lr 0.0000 | time_forward 1.3110 | time_backward 1.3010 |
[2023-10-23 12:04:28,366::train::INFO] [train] Iter 570556 | loss 1.0240 | loss(rot) 0.6690 | loss(pos) 0.0919 | loss(seq) 0.2631 | grad 3.0629 | lr 0.0000 | time_forward 1.0780 | time_backward 1.2150 |
[2023-10-23 12:04:36,735::train::INFO] [train] Iter 570557 | loss 0.8830 | loss(rot) 0.5961 | loss(pos) 0.0723 | loss(seq) 0.2146 | grad 16.3688 | lr 0.0000 | time_forward 3.6390 | time_backward 4.7270 |
[2023-10-23 12:04:45,200::train::INFO] [train] Iter 570558 | loss 1.5337 | loss(rot) 1.1068 | loss(pos) 0.0613 | loss(seq) 0.3656 | grad 3.1741 | lr 0.0000 | time_forward 3.4760 | time_backward 4.9860 |
[2023-10-23 12:04:51,519::train::INFO] [train] Iter 570559 | loss 0.2531 | loss(rot) 0.1162 | loss(pos) 0.0761 | loss(seq) 0.0608 | grad 3.4173 | lr 0.0000 | time_forward 2.7440 | time_backward 3.5720 |
[2023-10-23 12:04:58,833::train::INFO] [train] Iter 570560 | loss 0.6924 | loss(rot) 0.1679 | loss(pos) 0.1850 | loss(seq) 0.3395 | grad 6.7050 | lr 0.0000 | time_forward 3.1390 | time_backward 4.1730 |
[2023-10-23 12:05:07,336::train::INFO] [train] Iter 570561 | loss 0.4654 | loss(rot) 0.2101 | loss(pos) 0.0481 | loss(seq) 0.2072 | grad 2.2729 | lr 0.0000 | time_forward 3.4930 | time_backward 5.0050 |
[2023-10-23 12:05:15,758::train::INFO] [train] Iter 570562 | loss 0.3908 | loss(rot) 0.1328 | loss(pos) 0.0246 | loss(seq) 0.2334 | grad 2.8189 | lr 0.0000 | time_forward 3.5040 | time_backward 4.9150 |
[2023-10-23 12:05:23,310::train::INFO] [train] Iter 570563 | loss 1.1513 | loss(rot) 1.0904 | loss(pos) 0.0425 | loss(seq) 0.0184 | grad 3.4407 | lr 0.0000 | time_forward 3.2430 | time_backward 4.3060 |
[2023-10-23 12:05:30,076::train::INFO] [train] Iter 570564 | loss 1.5393 | loss(rot) 1.1814 | loss(pos) 0.1091 | loss(seq) 0.2488 | grad 3.8327 | lr 0.0000 | time_forward 2.8940 | time_backward 3.8680 |
[2023-10-23 12:05:38,490::train::INFO] [train] Iter 570565 | loss 0.6311 | loss(rot) 0.3531 | loss(pos) 0.0413 | loss(seq) 0.2368 | grad 3.8452 | lr 0.0000 | time_forward 3.5230 | time_backward 4.8880 |
[2023-10-23 12:05:46,946::train::INFO] [train] Iter 570566 | loss 0.4681 | loss(rot) 0.0766 | loss(pos) 0.3315 | loss(seq) 0.0600 | grad 7.8674 | lr 0.0000 | time_forward 3.5000 | time_backward 4.9540 |
[2023-10-23 12:05:54,183::train::INFO] [train] Iter 570567 | loss 0.3004 | loss(rot) 0.0649 | loss(pos) 0.2305 | loss(seq) 0.0050 | grad 2.4635 | lr 0.0000 | time_forward 3.1420 | time_backward 4.0920 |
[2023-10-23 12:06:02,576::train::INFO] [train] Iter 570568 | loss 0.7410 | loss(rot) 0.4449 | loss(pos) 0.0357 | loss(seq) 0.2604 | grad 4.1431 | lr 0.0000 | time_forward 3.4870 | time_backward 4.9030 |
[2023-10-23 12:06:10,889::train::INFO] [train] Iter 570569 | loss 0.3451 | loss(rot) 0.2692 | loss(pos) 0.0145 | loss(seq) 0.0615 | grad 2.9505 | lr 0.0000 | time_forward 3.4260 | time_backward 4.8830 |
[2023-10-23 12:06:18,639::train::INFO] [train] Iter 570570 | loss 0.2200 | loss(rot) 0.1781 | loss(pos) 0.0291 | loss(seq) 0.0128 | grad 2.9360 | lr 0.0000 | time_forward 3.3670 | time_backward 4.3800 |
[2023-10-23 12:06:26,885::train::INFO] [train] Iter 570571 | loss 1.0897 | loss(rot) 0.6031 | loss(pos) 0.0929 | loss(seq) 0.3937 | grad 3.8149 | lr 0.0000 | time_forward 3.6010 | time_backward 4.6410 |
[2023-10-23 12:06:34,214::train::INFO] [train] Iter 570572 | loss 0.3531 | loss(rot) 0.1998 | loss(pos) 0.0588 | loss(seq) 0.0945 | grad 2.4531 | lr 0.0000 | time_forward 3.1750 | time_backward 4.1510 |
[2023-10-23 12:06:41,840::train::INFO] [train] Iter 570573 | loss 1.8066 | loss(rot) 1.3495 | loss(pos) 0.0638 | loss(seq) 0.3934 | grad 7.1979 | lr 0.0000 | time_forward 3.2980 | time_backward 4.3250 |
[2023-10-23 12:06:49,336::train::INFO] [train] Iter 570574 | loss 0.6894 | loss(rot) 0.1376 | loss(pos) 0.2712 | loss(seq) 0.2805 | grad 3.3864 | lr 0.0000 | time_forward 3.2320 | time_backward 4.2600 |
[2023-10-23 12:06:57,641::train::INFO] [train] Iter 570575 | loss 0.3474 | loss(rot) 0.1208 | loss(pos) 0.0715 | loss(seq) 0.1552 | grad 2.3113 | lr 0.0000 | time_forward 3.5780 | time_backward 4.7230 |
[2023-10-23 12:07:05,244::train::INFO] [train] Iter 570576 | loss 1.1195 | loss(rot) 0.8678 | loss(pos) 0.1569 | loss(seq) 0.0947 | grad 6.1811 | lr 0.0000 | time_forward 3.2800 | time_backward 4.3200 |
[2023-10-23 12:07:13,500::train::INFO] [train] Iter 570577 | loss 0.2200 | loss(rot) 0.0678 | loss(pos) 0.1175 | loss(seq) 0.0347 | grad 2.3943 | lr 0.0000 | time_forward 3.4070 | time_backward 4.8450 |
[2023-10-23 12:07:20,553::train::INFO] [train] Iter 570578 | loss 0.4713 | loss(rot) 0.4350 | loss(pos) 0.0355 | loss(seq) 0.0008 | grad 4.0517 | lr 0.0000 | time_forward 2.9300 | time_backward 4.1210 |
[2023-10-23 12:07:29,109::train::INFO] [train] Iter 570579 | loss 0.2758 | loss(rot) 0.0378 | loss(pos) 0.0805 | loss(seq) 0.1575 | grad 2.9028 | lr 0.0000 | time_forward 3.5470 | time_backward 5.0050 |
[2023-10-23 12:07:31,778::train::INFO] [train] Iter 570580 | loss 3.4073 | loss(rot) 0.0015 | loss(pos) 3.4059 | loss(seq) 0.0000 | grad 18.4532 | lr 0.0000 | time_forward 1.2330 | time_backward 1.4340 |
[2023-10-23 12:07:33,602::train::INFO] [train] Iter 570581 | loss 2.3760 | loss(rot) 1.9375 | loss(pos) 0.1935 | loss(seq) 0.2451 | grad 8.8116 | lr 0.0000 | time_forward 0.8380 | time_backward 0.9650 |
[2023-10-23 12:07:39,567::train::INFO] [train] Iter 570582 | loss 0.2306 | loss(rot) 0.1782 | loss(pos) 0.0385 | loss(seq) 0.0140 | grad 2.1264 | lr 0.0000 | time_forward 2.5940 | time_backward 3.3680 |
[2023-10-23 12:07:47,373::train::INFO] [train] Iter 570583 | loss 0.8481 | loss(rot) 0.1382 | loss(pos) 0.6872 | loss(seq) 0.0227 | grad 4.9629 | lr 0.0000 | time_forward 3.3660 | time_backward 4.4360 |
[2023-10-23 12:07:49,177::train::INFO] [train] Iter 570584 | loss 0.7018 | loss(rot) 0.1220 | loss(pos) 0.5364 | loss(seq) 0.0434 | grad 2.9326 | lr 0.0000 | time_forward 0.8080 | time_backward 0.9930 |
[2023-10-23 12:07:56,313::train::INFO] [train] Iter 570585 | loss 0.6623 | loss(rot) 0.6334 | loss(pos) 0.0225 | loss(seq) 0.0064 | grad 131.6422 | lr 0.0000 | time_forward 3.0230 | time_backward 4.1090 |
[2023-10-23 12:07:59,101::train::INFO] [train] Iter 570586 | loss 0.4686 | loss(rot) 0.0276 | loss(pos) 0.4397 | loss(seq) 0.0013 | grad 10.7843 | lr 0.0000 | time_forward 1.3330 | time_backward 1.4520 |
[2023-10-23 12:08:02,031::train::INFO] [train] Iter 570587 | loss 0.4949 | loss(rot) 0.2742 | loss(pos) 0.1808 | loss(seq) 0.0399 | grad 3.7742 | lr 0.0000 | time_forward 1.3500 | time_backward 1.5770 |
[2023-10-23 12:08:08,394::train::INFO] [train] Iter 570588 | loss 0.3543 | loss(rot) 0.2447 | loss(pos) 0.0109 | loss(seq) 0.0987 | grad 3.1940 | lr 0.0000 | time_forward 2.7550 | time_backward 3.6050 |
[2023-10-23 12:08:10,856::train::INFO] [train] Iter 570589 | loss 0.8767 | loss(rot) 0.3280 | loss(pos) 0.4926 | loss(seq) 0.0561 | grad 5.8268 | lr 0.0000 | time_forward 1.1830 | time_backward 1.2760 |
[2023-10-23 12:08:17,945::train::INFO] [train] Iter 570590 | loss 0.7369 | loss(rot) 0.6049 | loss(pos) 0.0169 | loss(seq) 0.1151 | grad 23.9066 | lr 0.0000 | time_forward 3.0020 | time_backward 4.0660 |
[2023-10-23 12:08:24,636::train::INFO] [train] Iter 570591 | loss 0.4407 | loss(rot) 0.1561 | loss(pos) 0.0114 | loss(seq) 0.2732 | grad 2.4025 | lr 0.0000 | time_forward 2.8270 | time_backward 3.8620 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.