text
stringlengths 56
1.16k
|
|---|
[2023-09-01 21:00:36,045::train::INFO] [train] Iter 02293 | loss 2.2278 | loss(rot) 1.0406 | loss(pos) 0.7096 | loss(seq) 0.4776 | grad 3.0409 | lr 0.0010 | time_forward 3.6720 | time_backward 5.1830 |
[2023-09-01 21:00:38,961::train::INFO] [train] Iter 02294 | loss 0.8117 | loss(rot) 0.2296 | loss(pos) 0.3552 | loss(seq) 0.2268 | grad 2.9745 | lr 0.0010 | time_forward 1.3970 | time_backward 1.5140 |
[2023-09-01 21:00:50,674::train::INFO] [train] Iter 02295 | loss 3.4797 | loss(rot) 3.2791 | loss(pos) 0.2004 | loss(seq) 0.0001 | grad 3.5782 | lr 0.0010 | time_forward 4.5080 | time_backward 7.2010 |
[2023-09-01 21:01:01,575::train::INFO] [train] Iter 02296 | loss 1.8667 | loss(rot) 0.8830 | loss(pos) 0.4996 | loss(seq) 0.4841 | grad 3.1525 | lr 0.0010 | time_forward 4.6520 | time_backward 6.2420 |
[2023-09-01 21:01:12,310::train::INFO] [train] Iter 02297 | loss 2.3812 | loss(rot) 1.1729 | loss(pos) 0.5424 | loss(seq) 0.6659 | grad 3.9517 | lr 0.0010 | time_forward 4.4960 | time_backward 6.2360 |
[2023-09-01 21:01:22,955::train::INFO] [train] Iter 02298 | loss 2.5246 | loss(rot) 1.5481 | loss(pos) 0.3168 | loss(seq) 0.6597 | grad 3.6510 | lr 0.0010 | time_forward 4.3270 | time_backward 6.3130 |
[2023-09-01 21:01:33,067::train::INFO] [train] Iter 02299 | loss 2.7991 | loss(rot) 2.1455 | loss(pos) 0.2764 | loss(seq) 0.3772 | grad 3.6160 | lr 0.0010 | time_forward 4.0880 | time_backward 6.0200 |
[2023-09-01 21:01:35,319::train::INFO] [train] Iter 02300 | loss 3.5690 | loss(rot) 3.1745 | loss(pos) 0.3933 | loss(seq) 0.0012 | grad 3.3273 | lr 0.0010 | time_forward 1.0250 | time_backward 1.2230 |
[2023-09-01 21:01:42,885::train::INFO] [train] Iter 02301 | loss 2.7906 | loss(rot) 1.9621 | loss(pos) 0.3633 | loss(seq) 0.4652 | grad 4.4854 | lr 0.0010 | time_forward 3.2010 | time_backward 4.3620 |
[2023-09-01 21:01:51,676::train::INFO] [train] Iter 02302 | loss 3.0951 | loss(rot) 2.1586 | loss(pos) 0.4650 | loss(seq) 0.4715 | grad 3.5660 | lr 0.0010 | time_forward 3.7160 | time_backward 5.0720 |
[2023-09-01 21:01:54,381::train::INFO] [train] Iter 02303 | loss 2.1418 | loss(rot) 1.3431 | loss(pos) 0.3722 | loss(seq) 0.4266 | grad 5.8725 | lr 0.0010 | time_forward 1.2560 | time_backward 1.4460 |
[2023-09-01 21:01:57,116::train::INFO] [train] Iter 02304 | loss 1.5044 | loss(rot) 0.2003 | loss(pos) 1.2914 | loss(seq) 0.0127 | grad 5.5095 | lr 0.0010 | time_forward 1.2820 | time_backward 1.4490 |
[2023-09-01 21:02:07,205::train::INFO] [train] Iter 02305 | loss 2.8230 | loss(rot) 2.5441 | loss(pos) 0.2288 | loss(seq) 0.0501 | grad 2.9971 | lr 0.0010 | time_forward 4.0970 | time_backward 5.9880 |
[2023-09-01 21:02:16,589::train::INFO] [train] Iter 02306 | loss 3.2991 | loss(rot) 2.1620 | loss(pos) 0.5490 | loss(seq) 0.5881 | grad 3.3197 | lr 0.0010 | time_forward 4.0130 | time_backward 5.3670 |
[2023-09-01 21:02:26,526::train::INFO] [train] Iter 02307 | loss 2.7112 | loss(rot) 2.2948 | loss(pos) 0.1970 | loss(seq) 0.2195 | grad 2.9903 | lr 0.0010 | time_forward 4.0070 | time_backward 5.9280 |
[2023-09-01 21:02:29,188::train::INFO] [train] Iter 02308 | loss 2.4464 | loss(rot) 1.6032 | loss(pos) 0.4574 | loss(seq) 0.3859 | grad 5.0747 | lr 0.0010 | time_forward 1.2220 | time_backward 1.4370 |
[2023-09-01 21:02:39,145::train::INFO] [train] Iter 02309 | loss 2.7093 | loss(rot) 2.3180 | loss(pos) 0.1627 | loss(seq) 0.2286 | grad 3.1098 | lr 0.0010 | time_forward 4.0560 | time_backward 5.8810 |
[2023-09-01 21:02:41,776::train::INFO] [train] Iter 02310 | loss 1.2877 | loss(rot) 0.4728 | loss(pos) 0.5749 | loss(seq) 0.2400 | grad 4.6526 | lr 0.0010 | time_forward 1.2300 | time_backward 1.3970 |
[2023-09-01 21:02:44,522::train::INFO] [train] Iter 02311 | loss 2.3834 | loss(rot) 1.2139 | loss(pos) 0.2953 | loss(seq) 0.8741 | grad 3.6851 | lr 0.0010 | time_forward 1.2730 | time_backward 1.4680 |
[2023-09-01 21:02:54,399::train::INFO] [train] Iter 02312 | loss 1.7308 | loss(rot) 1.0999 | loss(pos) 0.1526 | loss(seq) 0.4783 | grad 3.3736 | lr 0.0010 | time_forward 4.0560 | time_backward 5.8180 |
[2023-09-01 21:03:03,412::train::INFO] [train] Iter 02313 | loss 1.7935 | loss(rot) 0.8744 | loss(pos) 0.8290 | loss(seq) 0.0901 | grad 6.4236 | lr 0.0010 | time_forward 3.8490 | time_backward 5.1480 |
[2023-09-01 21:03:06,130::train::INFO] [train] Iter 02314 | loss 2.3431 | loss(rot) 1.7273 | loss(pos) 0.1825 | loss(seq) 0.4333 | grad 3.4520 | lr 0.0010 | time_forward 1.2380 | time_backward 1.4760 |
[2023-09-01 21:03:09,377::train::INFO] [train] Iter 02315 | loss 2.1231 | loss(rot) 1.1500 | loss(pos) 0.4714 | loss(seq) 0.5016 | grad 3.7812 | lr 0.0010 | time_forward 1.4530 | time_backward 1.7910 |
[2023-09-01 21:03:12,084::train::INFO] [train] Iter 02316 | loss 3.1222 | loss(rot) 2.7516 | loss(pos) 0.3694 | loss(seq) 0.0012 | grad 4.5676 | lr 0.0010 | time_forward 1.2710 | time_backward 1.4330 |
[2023-09-01 21:03:19,318::train::INFO] [train] Iter 02317 | loss 3.2243 | loss(rot) 3.0711 | loss(pos) 0.1532 | loss(seq) 0.0000 | grad 2.0041 | lr 0.0010 | time_forward 3.0740 | time_backward 4.1580 |
[2023-09-01 21:03:29,324::train::INFO] [train] Iter 02318 | loss 3.1168 | loss(rot) 2.8491 | loss(pos) 0.2677 | loss(seq) 0.0000 | grad 3.1604 | lr 0.0010 | time_forward 4.2210 | time_backward 5.7820 |
[2023-09-01 21:03:39,421::train::INFO] [train] Iter 02319 | loss 3.0022 | loss(rot) 1.6756 | loss(pos) 0.7772 | loss(seq) 0.5494 | grad 5.0390 | lr 0.0010 | time_forward 4.1050 | time_backward 5.9880 |
[2023-09-01 21:03:42,074::train::INFO] [train] Iter 02320 | loss 3.5046 | loss(rot) 3.1404 | loss(pos) 0.3547 | loss(seq) 0.0094 | grad 6.2366 | lr 0.0010 | time_forward 1.2580 | time_backward 1.3800 |
[2023-09-01 21:03:51,337::train::INFO] [train] Iter 02321 | loss 2.6803 | loss(rot) 1.6539 | loss(pos) 0.3653 | loss(seq) 0.6610 | grad 4.4105 | lr 0.0010 | time_forward 3.9040 | time_backward 5.3550 |
[2023-09-01 21:04:01,504::train::INFO] [train] Iter 02322 | loss 4.0179 | loss(rot) 3.5269 | loss(pos) 0.1586 | loss(seq) 0.3324 | grad 3.2782 | lr 0.0010 | time_forward 4.1730 | time_backward 5.9910 |
[2023-09-01 21:04:11,525::train::INFO] [train] Iter 02323 | loss 2.0042 | loss(rot) 1.1381 | loss(pos) 0.4459 | loss(seq) 0.4202 | grad 2.6456 | lr 0.0010 | time_forward 4.2040 | time_backward 5.8140 |
[2023-09-01 21:04:19,952::train::INFO] [train] Iter 02324 | loss 2.1761 | loss(rot) 1.2013 | loss(pos) 0.4104 | loss(seq) 0.5645 | grad 3.4688 | lr 0.0010 | time_forward 3.4570 | time_backward 4.9490 |
[2023-09-01 21:04:27,575::train::INFO] [train] Iter 02325 | loss 2.9015 | loss(rot) 1.7815 | loss(pos) 0.5413 | loss(seq) 0.5788 | grad 7.7336 | lr 0.0010 | time_forward 3.2350 | time_backward 4.3850 |
[2023-09-01 21:04:37,605::train::INFO] [train] Iter 02326 | loss 2.9304 | loss(rot) 2.2380 | loss(pos) 0.1924 | loss(seq) 0.5000 | grad 2.7616 | lr 0.0010 | time_forward 4.0150 | time_backward 6.0120 |
[2023-09-01 21:04:40,331::train::INFO] [train] Iter 02327 | loss 2.8886 | loss(rot) 2.0874 | loss(pos) 0.2465 | loss(seq) 0.5546 | grad 3.1304 | lr 0.0010 | time_forward 1.2520 | time_backward 1.4690 |
[2023-09-01 21:04:49,000::train::INFO] [train] Iter 02328 | loss 1.3480 | loss(rot) 0.3677 | loss(pos) 0.5961 | loss(seq) 0.3842 | grad 4.6827 | lr 0.0010 | time_forward 3.7300 | time_backward 4.9350 |
[2023-09-01 21:04:52,259::train::INFO] [train] Iter 02329 | loss 3.0266 | loss(rot) 2.1117 | loss(pos) 0.4292 | loss(seq) 0.4857 | grad 5.0821 | lr 0.0010 | time_forward 1.7880 | time_backward 1.4690 |
[2023-09-01 21:05:00,863::train::INFO] [train] Iter 02330 | loss 1.7478 | loss(rot) 0.8224 | loss(pos) 0.5195 | loss(seq) 0.4059 | grad 4.9215 | lr 0.0010 | time_forward 3.6480 | time_backward 4.9530 |
[2023-09-01 21:05:10,789::train::INFO] [train] Iter 02331 | loss 3.7256 | loss(rot) 3.3151 | loss(pos) 0.3914 | loss(seq) 0.0192 | grad 6.3766 | lr 0.0010 | time_forward 4.0000 | time_backward 5.9220 |
[2023-09-01 21:05:20,613::train::INFO] [train] Iter 02332 | loss 1.5872 | loss(rot) 0.3973 | loss(pos) 1.1117 | loss(seq) 0.0782 | grad 5.4153 | lr 0.0010 | time_forward 4.0750 | time_backward 5.7450 |
[2023-09-01 21:05:23,254::train::INFO] [train] Iter 02333 | loss 3.1082 | loss(rot) 2.9211 | loss(pos) 0.1803 | loss(seq) 0.0068 | grad 4.0723 | lr 0.0010 | time_forward 1.2390 | time_backward 1.3990 |
[2023-09-01 21:05:31,948::train::INFO] [train] Iter 02334 | loss 1.8267 | loss(rot) 0.0738 | loss(pos) 1.7487 | loss(seq) 0.0042 | grad 5.3690 | lr 0.0010 | time_forward 3.6760 | time_backward 5.0130 |
[2023-09-01 21:05:41,161::train::INFO] [train] Iter 02335 | loss 2.6480 | loss(rot) 2.5311 | loss(pos) 0.1157 | loss(seq) 0.0011 | grad 2.7463 | lr 0.0010 | time_forward 3.8870 | time_backward 5.3220 |
[2023-09-01 21:05:43,375::train::INFO] [train] Iter 02336 | loss 2.5804 | loss(rot) 1.8776 | loss(pos) 0.3289 | loss(seq) 0.3739 | grad 4.2758 | lr 0.0010 | time_forward 1.0200 | time_backward 1.1920 |
[2023-09-01 21:05:46,068::train::INFO] [train] Iter 02337 | loss 1.4871 | loss(rot) 0.5794 | loss(pos) 0.6797 | loss(seq) 0.2280 | grad 4.8773 | lr 0.0010 | time_forward 1.2350 | time_backward 1.4530 |
[2023-09-01 21:05:55,121::train::INFO] [train] Iter 02338 | loss 1.2678 | loss(rot) 0.7920 | loss(pos) 0.2554 | loss(seq) 0.2205 | grad 3.4069 | lr 0.0010 | time_forward 3.8080 | time_backward 5.2410 |
[2023-09-01 21:06:03,705::train::INFO] [train] Iter 02339 | loss 3.5496 | loss(rot) 2.8661 | loss(pos) 0.2521 | loss(seq) 0.4314 | grad 4.8348 | lr 0.0010 | time_forward 3.5820 | time_backward 4.9990 |
[2023-09-01 21:06:11,750::train::INFO] [train] Iter 02340 | loss 3.0777 | loss(rot) 2.4772 | loss(pos) 0.2407 | loss(seq) 0.3599 | grad 4.1265 | lr 0.0010 | time_forward 3.3400 | time_backward 4.7010 |
[2023-09-01 21:06:22,437::train::INFO] [train] Iter 02341 | loss 2.4803 | loss(rot) 2.3323 | loss(pos) 0.1243 | loss(seq) 0.0237 | grad 2.6948 | lr 0.0010 | time_forward 4.6560 | time_backward 6.0270 |
[2023-09-01 21:06:32,395::train::INFO] [train] Iter 02342 | loss 2.8942 | loss(rot) 1.8756 | loss(pos) 0.4294 | loss(seq) 0.5892 | grad 4.3163 | lr 0.0010 | time_forward 3.9330 | time_backward 6.0220 |
[2023-09-01 21:06:35,099::train::INFO] [train] Iter 02343 | loss 2.8325 | loss(rot) 2.6448 | loss(pos) 0.0912 | loss(seq) 0.0965 | grad 4.4110 | lr 0.0010 | time_forward 1.2430 | time_backward 1.4570 |
[2023-09-01 21:06:45,057::train::INFO] [train] Iter 02344 | loss 3.4058 | loss(rot) 3.2669 | loss(pos) 0.1388 | loss(seq) 0.0001 | grad 2.4917 | lr 0.0010 | time_forward 4.0780 | time_backward 5.8770 |
[2023-09-01 21:06:47,543::train::INFO] [train] Iter 02345 | loss 1.6296 | loss(rot) 0.1845 | loss(pos) 1.4419 | loss(seq) 0.0032 | grad 5.7477 | lr 0.0010 | time_forward 1.1610 | time_backward 1.3220 |
[2023-09-01 21:06:55,089::train::INFO] [train] Iter 02346 | loss 1.3519 | loss(rot) 1.0190 | loss(pos) 0.2104 | loss(seq) 0.1225 | grad 3.6106 | lr 0.0010 | time_forward 3.1530 | time_backward 4.3890 |
[2023-09-01 21:06:57,632::train::INFO] [train] Iter 02347 | loss 3.3481 | loss(rot) 2.6091 | loss(pos) 0.2725 | loss(seq) 0.4664 | grad 4.3014 | lr 0.0010 | time_forward 1.1740 | time_backward 1.3650 |
[2023-09-01 21:07:06,221::train::INFO] [train] Iter 02348 | loss 3.4846 | loss(rot) 2.7039 | loss(pos) 0.3622 | loss(seq) 0.4185 | grad 4.7482 | lr 0.0010 | time_forward 3.5480 | time_backward 4.9970 |
[2023-09-01 21:07:09,041::train::INFO] [train] Iter 02349 | loss 3.4975 | loss(rot) 2.9260 | loss(pos) 0.5714 | loss(seq) 0.0000 | grad 7.8328 | lr 0.0010 | time_forward 1.2690 | time_backward 1.5470 |
[2023-09-01 21:07:19,021::train::INFO] [train] Iter 02350 | loss 2.8735 | loss(rot) 2.4808 | loss(pos) 0.3059 | loss(seq) 0.0868 | grad 3.9209 | lr 0.0010 | time_forward 4.0260 | time_backward 5.9290 |
[2023-09-01 21:07:25,513::train::INFO] [train] Iter 02351 | loss 3.3175 | loss(rot) 2.8753 | loss(pos) 0.3725 | loss(seq) 0.0697 | grad 6.3359 | lr 0.0010 | time_forward 2.6950 | time_backward 3.7880 |
[2023-09-01 21:07:28,710::train::INFO] [train] Iter 02352 | loss 3.2899 | loss(rot) 2.9044 | loss(pos) 0.3342 | loss(seq) 0.0513 | grad 3.7568 | lr 0.0010 | time_forward 1.3850 | time_backward 1.8080 |
[2023-09-01 21:07:31,444::train::INFO] [train] Iter 02353 | loss 3.5549 | loss(rot) 2.6767 | loss(pos) 0.3353 | loss(seq) 0.5430 | grad 2.9439 | lr 0.0010 | time_forward 1.2310 | time_backward 1.5010 |
[2023-09-01 21:07:40,291::train::INFO] [train] Iter 02354 | loss 1.8227 | loss(rot) 0.1549 | loss(pos) 1.6521 | loss(seq) 0.0156 | grad 13.0079 | lr 0.0010 | time_forward 3.7800 | time_backward 5.0630 |
[2023-09-01 21:07:48,257::train::INFO] [train] Iter 02355 | loss 2.3513 | loss(rot) 1.9460 | loss(pos) 0.3176 | loss(seq) 0.0877 | grad 6.0611 | lr 0.0010 | time_forward 3.3360 | time_backward 4.6270 |
[2023-09-01 21:07:51,049::train::INFO] [train] Iter 02356 | loss 1.5761 | loss(rot) 0.4168 | loss(pos) 1.1510 | loss(seq) 0.0084 | grad 4.9668 | lr 0.0010 | time_forward 1.2750 | time_backward 1.5130 |
[2023-09-01 21:07:58,623::train::INFO] [train] Iter 02357 | loss 3.1423 | loss(rot) 2.2445 | loss(pos) 0.5007 | loss(seq) 0.3971 | grad 4.7474 | lr 0.0010 | time_forward 3.2100 | time_backward 4.3600 |
[2023-09-01 21:08:08,650::train::INFO] [train] Iter 02358 | loss 1.1658 | loss(rot) 0.1857 | loss(pos) 0.9473 | loss(seq) 0.0328 | grad 4.9588 | lr 0.0010 | time_forward 4.1040 | time_backward 5.9190 |
[2023-09-01 21:08:18,687::train::INFO] [train] Iter 02359 | loss 2.9678 | loss(rot) 2.0192 | loss(pos) 0.4482 | loss(seq) 0.5004 | grad 5.6068 | lr 0.0010 | time_forward 4.0330 | time_backward 6.0000 |
[2023-09-01 21:08:28,467::train::INFO] [train] Iter 02360 | loss 3.1450 | loss(rot) 1.9989 | loss(pos) 0.6352 | loss(seq) 0.5108 | grad 7.5192 | lr 0.0010 | time_forward 4.0150 | time_backward 5.7610 |
[2023-09-01 21:08:31,141::train::INFO] [train] Iter 02361 | loss 1.3173 | loss(rot) 0.2134 | loss(pos) 0.8729 | loss(seq) 0.2310 | grad 4.9538 | lr 0.0010 | time_forward 1.2300 | time_backward 1.4400 |
[2023-09-01 21:08:40,908::train::INFO] [train] Iter 02362 | loss 1.1541 | loss(rot) 0.0828 | loss(pos) 1.0611 | loss(seq) 0.0102 | grad 7.5775 | lr 0.0010 | time_forward 3.9660 | time_backward 5.7970 |
[2023-09-01 21:08:50,742::train::INFO] [train] Iter 02363 | loss 2.3944 | loss(rot) 1.5930 | loss(pos) 0.3273 | loss(seq) 0.4742 | grad 3.4532 | lr 0.0010 | time_forward 3.9580 | time_backward 5.8720 |
[2023-09-01 21:09:00,354::train::INFO] [train] Iter 02364 | loss 2.7656 | loss(rot) 1.9295 | loss(pos) 0.3334 | loss(seq) 0.5027 | grad 3.5172 | lr 0.0010 | time_forward 4.1860 | time_backward 5.4200 |
[2023-09-01 21:09:09,016::train::INFO] [train] Iter 02365 | loss 3.0923 | loss(rot) 2.5549 | loss(pos) 0.5368 | loss(seq) 0.0007 | grad 4.1642 | lr 0.0010 | time_forward 3.6040 | time_backward 5.0540 |
[2023-09-01 21:09:15,421::train::INFO] [train] Iter 02366 | loss 1.7199 | loss(rot) 0.9539 | loss(pos) 0.3450 | loss(seq) 0.4210 | grad 4.8738 | lr 0.0010 | time_forward 2.6390 | time_backward 3.7630 |
[2023-09-01 21:09:21,119::train::INFO] [train] Iter 02367 | loss 3.4551 | loss(rot) 3.1743 | loss(pos) 0.2630 | loss(seq) 0.0177 | grad 3.8229 | lr 0.0010 | time_forward 2.3890 | time_backward 3.3050 |
[2023-09-01 21:09:29,764::train::INFO] [train] Iter 02368 | loss 3.0726 | loss(rot) 2.7109 | loss(pos) 0.2446 | loss(seq) 0.1170 | grad 4.0307 | lr 0.0010 | time_forward 3.6550 | time_backward 4.9860 |
[2023-09-01 21:09:39,365::train::INFO] [train] Iter 02369 | loss 1.4847 | loss(rot) 0.0916 | loss(pos) 1.3836 | loss(seq) 0.0094 | grad 3.8063 | lr 0.0010 | time_forward 4.1310 | time_backward 5.4670 |
[2023-09-01 21:09:48,783::train::INFO] [train] Iter 02370 | loss 3.2286 | loss(rot) 2.7601 | loss(pos) 0.3034 | loss(seq) 0.1650 | grad 4.2132 | lr 0.0010 | time_forward 3.9450 | time_backward 5.4690 |
[2023-09-01 21:09:56,351::train::INFO] [train] Iter 02371 | loss 3.0284 | loss(rot) 2.6727 | loss(pos) 0.2268 | loss(seq) 0.1288 | grad 2.8171 | lr 0.0010 | time_forward 3.1670 | time_backward 4.3970 |
[2023-09-01 21:10:03,969::train::INFO] [train] Iter 02372 | loss 3.3903 | loss(rot) 2.6058 | loss(pos) 0.2602 | loss(seq) 0.5244 | grad 4.5839 | lr 0.0010 | time_forward 3.2110 | time_backward 4.4030 |
[2023-09-01 21:10:12,647::train::INFO] [train] Iter 02373 | loss 3.6536 | loss(rot) 3.1057 | loss(pos) 0.2242 | loss(seq) 0.3237 | grad 2.9266 | lr 0.0010 | time_forward 3.7410 | time_backward 4.9330 |
[2023-09-01 21:10:15,693::train::INFO] [train] Iter 02374 | loss 3.5918 | loss(rot) 2.9168 | loss(pos) 0.3122 | loss(seq) 0.3628 | grad 3.0124 | lr 0.0010 | time_forward 1.3890 | time_backward 1.6540 |
[2023-09-01 21:10:25,858::train::INFO] [train] Iter 02375 | loss 1.3990 | loss(rot) 0.3267 | loss(pos) 0.8945 | loss(seq) 0.1778 | grad 4.6691 | lr 0.0010 | time_forward 4.2660 | time_backward 5.8950 |
[2023-09-01 21:10:28,512::train::INFO] [train] Iter 02376 | loss 2.9382 | loss(rot) 1.8164 | loss(pos) 0.5794 | loss(seq) 0.5424 | grad 3.8697 | lr 0.0010 | time_forward 1.2400 | time_backward 1.4100 |
[2023-09-01 21:10:36,689::train::INFO] [train] Iter 02377 | loss 2.3795 | loss(rot) 1.7108 | loss(pos) 0.2296 | loss(seq) 0.4392 | grad 3.8855 | lr 0.0010 | time_forward 3.4420 | time_backward 4.7320 |
[2023-09-01 21:10:45,321::train::INFO] [train] Iter 02378 | loss 2.0070 | loss(rot) 0.4562 | loss(pos) 1.5487 | loss(seq) 0.0022 | grad 4.2834 | lr 0.0010 | time_forward 3.6040 | time_backward 5.0240 |
[2023-09-01 21:10:54,796::train::INFO] [train] Iter 02379 | loss 1.5899 | loss(rot) 0.7563 | loss(pos) 0.2476 | loss(seq) 0.5859 | grad 3.0673 | lr 0.0010 | time_forward 4.0460 | time_backward 5.4250 |
[2023-09-01 21:11:04,033::train::INFO] [train] Iter 02380 | loss 2.2401 | loss(rot) 1.5015 | loss(pos) 0.2725 | loss(seq) 0.4661 | grad 6.2637 | lr 0.0010 | time_forward 3.8590 | time_backward 5.3740 |
[2023-09-01 21:11:13,920::train::INFO] [train] Iter 02381 | loss 3.1910 | loss(rot) 2.7633 | loss(pos) 0.4064 | loss(seq) 0.0213 | grad 4.2675 | lr 0.0010 | time_forward 3.9710 | time_backward 5.9130 |
[2023-09-01 21:11:23,742::train::INFO] [train] Iter 02382 | loss 2.5039 | loss(rot) 0.1860 | loss(pos) 2.3152 | loss(seq) 0.0026 | grad 5.6892 | lr 0.0010 | time_forward 4.1340 | time_backward 5.6840 |
[2023-09-01 21:11:35,157::train::INFO] [train] Iter 02383 | loss 3.3033 | loss(rot) 3.0774 | loss(pos) 0.1546 | loss(seq) 0.0713 | grad 3.2065 | lr 0.0010 | time_forward 4.5190 | time_backward 6.8930 |
[2023-09-01 21:11:47,241::train::INFO] [train] Iter 02384 | loss 2.3879 | loss(rot) 1.1054 | loss(pos) 0.8628 | loss(seq) 0.4197 | grad 4.1081 | lr 0.0010 | time_forward 4.6800 | time_backward 7.4010 |
[2023-09-01 21:11:55,661::train::INFO] [train] Iter 02385 | loss 2.1340 | loss(rot) 1.0359 | loss(pos) 0.7528 | loss(seq) 0.3453 | grad 4.8135 | lr 0.0010 | time_forward 3.3900 | time_backward 5.0240 |
[2023-09-01 21:11:58,640::train::INFO] [train] Iter 02386 | loss 2.9826 | loss(rot) 0.3917 | loss(pos) 2.0152 | loss(seq) 0.5757 | grad 9.9537 | lr 0.0010 | time_forward 1.4310 | time_backward 1.5430 |
[2023-09-01 21:12:02,386::train::INFO] [train] Iter 02387 | loss 3.0655 | loss(rot) 2.1139 | loss(pos) 0.4340 | loss(seq) 0.5177 | grad 3.5832 | lr 0.0010 | time_forward 1.6870 | time_backward 2.0550 |
[2023-09-01 21:12:15,640::train::INFO] [train] Iter 02388 | loss 2.4330 | loss(rot) 1.3663 | loss(pos) 0.5042 | loss(seq) 0.5625 | grad 3.0211 | lr 0.0010 | time_forward 6.3690 | time_backward 6.8810 |
[2023-09-01 21:12:26,680::train::INFO] [train] Iter 02389 | loss 3.4038 | loss(rot) 2.4008 | loss(pos) 0.5190 | loss(seq) 0.4841 | grad 4.1020 | lr 0.0010 | time_forward 4.4890 | time_backward 6.5480 |
[2023-09-01 21:12:35,035::train::INFO] [train] Iter 02390 | loss 3.1164 | loss(rot) 2.7894 | loss(pos) 0.3239 | loss(seq) 0.0032 | grad 4.3145 | lr 0.0010 | time_forward 3.5010 | time_backward 4.8500 |
[2023-09-01 21:12:37,776::train::INFO] [train] Iter 02391 | loss 3.2745 | loss(rot) 2.8091 | loss(pos) 0.1845 | loss(seq) 0.2809 | grad 3.4754 | lr 0.0010 | time_forward 1.2650 | time_backward 1.4730 |
[2023-09-01 21:12:40,636::train::INFO] [train] Iter 02392 | loss 3.3554 | loss(rot) 2.6176 | loss(pos) 0.3691 | loss(seq) 0.3687 | grad 5.1841 | lr 0.0010 | time_forward 1.3640 | time_backward 1.4930 |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.