text stringlengths 56 1.16k |
|---|
[2023-10-25 00:57:22,755::train::INFO] [train] Iter 589673 | loss 0.2820 | loss(rot) 0.0513 | loss(pos) 0.2195 | loss(seq) 0.0112 | grad 6.8587 | lr 0.0000 | time_forward 1.2600 | time_backward 1.4160 |
[2023-10-25 00:57:31,658::train::INFO] [train] Iter 589674 | loss 0.3376 | loss(rot) 0.0945 | loss(pos) 0.0156 | loss(seq) 0.2275 | grad 2.2469 | lr 0.0000 | time_forward 3.6640 | time_backward 5.2360 |
[2023-10-25 00:57:39,732::train::INFO] [train] Iter 589675 | loss 0.5061 | loss(rot) 0.4487 | loss(pos) 0.0301 | loss(seq) 0.0273 | grad 1.8139 | lr 0.0000 | time_forward 3.4530 | time_backward 4.6120 |
[2023-10-25 00:57:42,529::train::INFO] [train] Iter 589676 | loss 0.6997 | loss(rot) 0.3643 | loss(pos) 0.1298 | loss(seq) 0.2056 | grad 3.4606 | lr 0.0000 | time_forward 1.2780 | time_backward 1.5160 |
[2023-10-25 00:57:50,615::train::INFO] [train] Iter 589677 | loss 0.2120 | loss(rot) 0.1891 | loss(pos) 0.0204 | loss(seq) 0.0025 | grad 1.9659 | lr 0.0000 | time_forward 3.4510 | time_backward 4.6320 |
[2023-10-25 00:57:53,322::train::INFO] [train] Iter 589678 | loss 0.3150 | loss(rot) 0.2792 | loss(pos) 0.0329 | loss(seq) 0.0029 | grad 3.1915 | lr 0.0000 | time_forward 1.2970 | time_backward 1.4070 |
[2023-10-25 00:58:01,485::train::INFO] [train] Iter 589679 | loss 0.5112 | loss(rot) 0.2806 | loss(pos) 0.0916 | loss(seq) 0.1391 | grad 3.4614 | lr 0.0000 | time_forward 3.5300 | time_backward 4.6290 |
[2023-10-25 00:58:09,566::train::INFO] [train] Iter 589680 | loss 2.2807 | loss(rot) 2.2186 | loss(pos) 0.0620 | loss(seq) 0.0001 | grad 10.1408 | lr 0.0000 | time_forward 3.4790 | time_backward 4.5980 |
[2023-10-25 00:58:12,280::train::INFO] [train] Iter 589681 | loss 0.9423 | loss(rot) 0.6870 | loss(pos) 0.0434 | loss(seq) 0.2119 | grad 4.6956 | lr 0.0000 | time_forward 1.2850 | time_backward 1.4270 |
[2023-10-25 00:58:20,379::train::INFO] [train] Iter 589682 | loss 2.4624 | loss(rot) 2.3512 | loss(pos) 0.0958 | loss(seq) 0.0154 | grad 25.3644 | lr 0.0000 | time_forward 3.4590 | time_backward 4.6020 |
[2023-10-25 00:58:28,162::train::INFO] [train] Iter 589683 | loss 0.2747 | loss(rot) 0.2376 | loss(pos) 0.0289 | loss(seq) 0.0082 | grad 2.4583 | lr 0.0000 | time_forward 3.2900 | time_backward 4.4910 |
[2023-10-25 00:58:34,965::train::INFO] [train] Iter 589684 | loss 0.8578 | loss(rot) 0.4822 | loss(pos) 0.0627 | loss(seq) 0.3129 | grad 3.6608 | lr 0.0000 | time_forward 2.8840 | time_backward 3.9150 |
[2023-10-25 00:58:43,809::train::INFO] [train] Iter 589685 | loss 0.6797 | loss(rot) 0.4454 | loss(pos) 0.0313 | loss(seq) 0.2030 | grad 3.5876 | lr 0.0000 | time_forward 3.6270 | time_backward 5.2140 |
[2023-10-25 00:58:51,851::train::INFO] [train] Iter 589686 | loss 1.5758 | loss(rot) 1.5361 | loss(pos) 0.0193 | loss(seq) 0.0204 | grad 6.1791 | lr 0.0000 | time_forward 3.4610 | time_backward 4.5780 |
[2023-10-25 00:59:00,747::train::INFO] [train] Iter 589687 | loss 0.4220 | loss(rot) 0.2053 | loss(pos) 0.1871 | loss(seq) 0.0296 | grad 2.1742 | lr 0.0000 | time_forward 3.5720 | time_backward 5.3210 |
[2023-10-25 00:59:09,715::train::INFO] [train] Iter 589688 | loss 0.3572 | loss(rot) 0.2941 | loss(pos) 0.0330 | loss(seq) 0.0302 | grad 19.1388 | lr 0.0000 | time_forward 3.8220 | time_backward 5.1440 |
[2023-10-25 00:59:16,671::train::INFO] [train] Iter 589689 | loss 1.0503 | loss(rot) 0.0111 | loss(pos) 1.0368 | loss(seq) 0.0024 | grad 12.1376 | lr 0.0000 | time_forward 2.9780 | time_backward 3.9750 |
[2023-10-25 00:59:19,243::train::INFO] [train] Iter 589690 | loss 0.4299 | loss(rot) 0.1722 | loss(pos) 0.2410 | loss(seq) 0.0166 | grad 4.6262 | lr 0.0000 | time_forward 1.2460 | time_backward 1.3220 |
[2023-10-25 00:59:22,097::train::INFO] [train] Iter 589691 | loss 0.4437 | loss(rot) 0.3871 | loss(pos) 0.0298 | loss(seq) 0.0268 | grad 3.9135 | lr 0.0000 | time_forward 1.3680 | time_backward 1.4820 |
[2023-10-25 00:59:31,232::train::INFO] [train] Iter 589692 | loss 0.3855 | loss(rot) 0.0993 | loss(pos) 0.0674 | loss(seq) 0.2188 | grad 2.6403 | lr 0.0000 | time_forward 3.7590 | time_backward 5.3520 |
[2023-10-25 00:59:40,223::train::INFO] [train] Iter 589693 | loss 1.3798 | loss(rot) 1.1302 | loss(pos) 0.0696 | loss(seq) 0.1801 | grad 30.3614 | lr 0.0000 | time_forward 3.7020 | time_backward 5.2860 |
[2023-10-25 00:59:49,266::train::INFO] [train] Iter 589694 | loss 0.9849 | loss(rot) 0.5940 | loss(pos) 0.0372 | loss(seq) 0.3538 | grad 5.2831 | lr 0.0000 | time_forward 3.7440 | time_backward 5.2970 |
[2023-10-25 00:59:58,224::train::INFO] [train] Iter 589695 | loss 1.6106 | loss(rot) 0.0100 | loss(pos) 1.5993 | loss(seq) 0.0012 | grad 5.1144 | lr 0.0000 | time_forward 3.8550 | time_backward 5.0990 |
[2023-10-25 01:00:06,114::train::INFO] [train] Iter 589696 | loss 2.4355 | loss(rot) 0.0150 | loss(pos) 2.4206 | loss(seq) 0.0000 | grad 20.3760 | lr 0.0000 | time_forward 3.3880 | time_backward 4.4980 |
[2023-10-25 01:00:08,847::train::INFO] [train] Iter 589697 | loss 1.7098 | loss(rot) 1.6720 | loss(pos) 0.0373 | loss(seq) 0.0005 | grad 5.5073 | lr 0.0000 | time_forward 1.3010 | time_backward 1.4290 |
[2023-10-25 01:00:17,924::train::INFO] [train] Iter 589698 | loss 0.1884 | loss(rot) 0.0901 | loss(pos) 0.0299 | loss(seq) 0.0683 | grad 1.6283 | lr 0.0000 | time_forward 3.7460 | time_backward 5.3270 |
[2023-10-25 01:00:26,931::train::INFO] [train] Iter 589699 | loss 4.6823 | loss(rot) 0.0090 | loss(pos) 4.6733 | loss(seq) 0.0000 | grad 21.9119 | lr 0.0000 | time_forward 3.6450 | time_backward 5.3590 |
[2023-10-25 01:00:34,355::train::INFO] [train] Iter 589700 | loss 0.5666 | loss(rot) 0.4408 | loss(pos) 0.0480 | loss(seq) 0.0779 | grad 5.9700 | lr 0.0000 | time_forward 3.1280 | time_backward 4.2940 |
[2023-10-25 01:00:43,561::train::INFO] [train] Iter 589701 | loss 1.0490 | loss(rot) 0.9038 | loss(pos) 0.0316 | loss(seq) 0.1136 | grad 4.5428 | lr 0.0000 | time_forward 3.7180 | time_backward 5.4840 |
[2023-10-25 01:00:52,653::train::INFO] [train] Iter 589702 | loss 0.1440 | loss(rot) 0.1029 | loss(pos) 0.0261 | loss(seq) 0.0151 | grad 1.4774 | lr 0.0000 | time_forward 3.8760 | time_backward 5.2140 |
[2023-10-25 01:00:56,011::train::INFO] [train] Iter 589703 | loss 0.7231 | loss(rot) 0.0258 | loss(pos) 0.6945 | loss(seq) 0.0028 | grad 5.7802 | lr 0.0000 | time_forward 1.4780 | time_backward 1.8760 |
[2023-10-25 01:01:00,509::train::INFO] [train] Iter 589704 | loss 0.7054 | loss(rot) 0.1041 | loss(pos) 0.0268 | loss(seq) 0.5745 | grad 2.6311 | lr 0.0000 | time_forward 2.0350 | time_backward 2.4610 |
[2023-10-25 01:01:03,030::train::INFO] [train] Iter 589705 | loss 0.9189 | loss(rot) 0.4282 | loss(pos) 0.1477 | loss(seq) 0.3431 | grad 4.2276 | lr 0.0000 | time_forward 1.1360 | time_backward 1.3810 |
[2023-10-25 01:01:10,354::train::INFO] [train] Iter 589706 | loss 0.5684 | loss(rot) 0.5071 | loss(pos) 0.0613 | loss(seq) 0.0000 | grad 2.4728 | lr 0.0000 | time_forward 3.1050 | time_backward 4.1600 |
[2023-10-25 01:01:17,623::train::INFO] [train] Iter 589707 | loss 0.6012 | loss(rot) 0.1407 | loss(pos) 0.1732 | loss(seq) 0.2873 | grad 4.3333 | lr 0.0000 | time_forward 3.0910 | time_backward 4.1740 |
[2023-10-25 01:01:20,336::train::INFO] [train] Iter 589708 | loss 0.8190 | loss(rot) 0.3912 | loss(pos) 0.4234 | loss(seq) 0.0044 | grad 6.0023 | lr 0.0000 | time_forward 1.2960 | time_backward 1.4150 |
[2023-10-25 01:01:23,100::train::INFO] [train] Iter 589709 | loss 0.9004 | loss(rot) 0.8809 | loss(pos) 0.0126 | loss(seq) 0.0069 | grad 7.5903 | lr 0.0000 | time_forward 1.3310 | time_backward 1.4290 |
[2023-10-25 01:01:32,017::train::INFO] [train] Iter 589710 | loss 0.7543 | loss(rot) 0.4234 | loss(pos) 0.0436 | loss(seq) 0.2873 | grad 3.6085 | lr 0.0000 | time_forward 3.7940 | time_backward 5.1200 |
[2023-10-25 01:01:39,547::train::INFO] [train] Iter 589711 | loss 0.4343 | loss(rot) 0.1247 | loss(pos) 0.0536 | loss(seq) 0.2560 | grad 2.5906 | lr 0.0000 | time_forward 3.2250 | time_backward 4.3020 |
[2023-10-25 01:01:47,423::train::INFO] [train] Iter 589712 | loss 1.2567 | loss(rot) 0.7265 | loss(pos) 0.1413 | loss(seq) 0.3888 | grad 3.8726 | lr 0.0000 | time_forward 3.3800 | time_backward 4.4930 |
[2023-10-25 01:01:56,388::train::INFO] [train] Iter 589713 | loss 1.5377 | loss(rot) 1.3789 | loss(pos) 0.0687 | loss(seq) 0.0901 | grad 5.8309 | lr 0.0000 | time_forward 3.6630 | time_backward 5.2990 |
[2023-10-25 01:02:05,393::train::INFO] [train] Iter 589714 | loss 1.6630 | loss(rot) 1.0314 | loss(pos) 0.1743 | loss(seq) 0.4573 | grad 3.7093 | lr 0.0000 | time_forward 3.6220 | time_backward 5.3790 |
[2023-10-25 01:02:14,316::train::INFO] [train] Iter 589715 | loss 0.4427 | loss(rot) 0.1969 | loss(pos) 0.2007 | loss(seq) 0.0450 | grad 2.2269 | lr 0.0000 | time_forward 3.6470 | time_backward 5.2730 |
[2023-10-25 01:02:22,618::train::INFO] [train] Iter 589716 | loss 0.1574 | loss(rot) 0.0313 | loss(pos) 0.0317 | loss(seq) 0.0944 | grad 2.1052 | lr 0.0000 | time_forward 3.5800 | time_backward 4.7190 |
[2023-10-25 01:02:29,201::train::INFO] [train] Iter 589717 | loss 0.3147 | loss(rot) 0.1242 | loss(pos) 0.0365 | loss(seq) 0.1541 | grad 2.3312 | lr 0.0000 | time_forward 2.8050 | time_backward 3.7750 |
[2023-10-25 01:02:38,143::train::INFO] [train] Iter 589718 | loss 0.3277 | loss(rot) 0.2902 | loss(pos) 0.0338 | loss(seq) 0.0037 | grad 3.0850 | lr 0.0000 | time_forward 3.6780 | time_backward 5.2600 |
[2023-10-25 01:02:47,202::train::INFO] [train] Iter 589719 | loss 0.7241 | loss(rot) 0.1286 | loss(pos) 0.5841 | loss(seq) 0.0114 | grad 6.4552 | lr 0.0000 | time_forward 3.7260 | time_backward 5.3300 |
[2023-10-25 01:02:54,888::train::INFO] [train] Iter 589720 | loss 0.1561 | loss(rot) 0.1076 | loss(pos) 0.0485 | loss(seq) 0.0000 | grad 2.8353 | lr 0.0000 | time_forward 3.2430 | time_backward 4.4390 |
[2023-10-25 01:02:58,196::train::INFO] [train] Iter 589721 | loss 0.2580 | loss(rot) 0.2264 | loss(pos) 0.0186 | loss(seq) 0.0129 | grad 1.2842 | lr 0.0000 | time_forward 1.5140 | time_backward 1.7920 |
[2023-10-25 01:03:00,992::train::INFO] [train] Iter 589722 | loss 0.5453 | loss(rot) 0.3251 | loss(pos) 0.0189 | loss(seq) 0.2014 | grad 12.5643 | lr 0.0000 | time_forward 1.3420 | time_backward 1.4360 |
[2023-10-25 01:03:08,981::train::INFO] [train] Iter 589723 | loss 0.7376 | loss(rot) 0.4794 | loss(pos) 0.0277 | loss(seq) 0.2304 | grad 5.4148 | lr 0.0000 | time_forward 3.3890 | time_backward 4.5960 |
[2023-10-25 01:03:17,257::train::INFO] [train] Iter 589724 | loss 0.1769 | loss(rot) 0.1328 | loss(pos) 0.0438 | loss(seq) 0.0003 | grad 1.7790 | lr 0.0000 | time_forward 3.5460 | time_backward 4.7270 |
[2023-10-25 01:03:19,996::train::INFO] [train] Iter 589725 | loss 0.4000 | loss(rot) 0.1857 | loss(pos) 0.0724 | loss(seq) 0.1419 | grad 2.9152 | lr 0.0000 | time_forward 1.3050 | time_backward 1.4320 |
[2023-10-25 01:03:28,271::train::INFO] [train] Iter 589726 | loss 0.4204 | loss(rot) 0.3786 | loss(pos) 0.0418 | loss(seq) 0.0000 | grad 6.3728 | lr 0.0000 | time_forward 3.5500 | time_backward 4.7220 |
[2023-10-25 01:03:30,548::train::INFO] [train] Iter 589727 | loss 0.7255 | loss(rot) 0.0373 | loss(pos) 0.6815 | loss(seq) 0.0068 | grad 8.9713 | lr 0.0000 | time_forward 1.0470 | time_backward 1.2270 |
[2023-10-25 01:03:38,842::train::INFO] [train] Iter 589728 | loss 0.7237 | loss(rot) 0.0919 | loss(pos) 0.1951 | loss(seq) 0.4367 | grad 5.3461 | lr 0.0000 | time_forward 3.5560 | time_backward 4.7340 |
[2023-10-25 01:03:41,595::train::INFO] [train] Iter 589729 | loss 0.3246 | loss(rot) 0.2147 | loss(pos) 0.0426 | loss(seq) 0.0673 | grad 2.6866 | lr 0.0000 | time_forward 1.3200 | time_backward 1.4280 |
[2023-10-25 01:03:49,963::train::INFO] [train] Iter 589730 | loss 0.5185 | loss(rot) 0.0389 | loss(pos) 0.4736 | loss(seq) 0.0060 | grad 5.7445 | lr 0.0000 | time_forward 3.5420 | time_backward 4.8230 |
[2023-10-25 01:03:52,734::train::INFO] [train] Iter 589731 | loss 0.4884 | loss(rot) 0.0442 | loss(pos) 0.4353 | loss(seq) 0.0089 | grad 8.4421 | lr 0.0000 | time_forward 1.3510 | time_backward 1.4170 |
[2023-10-25 01:04:00,789::train::INFO] [train] Iter 589732 | loss 0.4406 | loss(rot) 0.1455 | loss(pos) 0.1124 | loss(seq) 0.1827 | grad 4.5623 | lr 0.0000 | time_forward 3.4390 | time_backward 4.5920 |
[2023-10-25 01:04:03,499::train::INFO] [train] Iter 589733 | loss 0.4057 | loss(rot) 0.1123 | loss(pos) 0.2899 | loss(seq) 0.0036 | grad 5.9792 | lr 0.0000 | time_forward 1.2440 | time_backward 1.4620 |
[2023-10-25 01:04:10,877::train::INFO] [train] Iter 589734 | loss 0.8777 | loss(rot) 0.2900 | loss(pos) 0.3260 | loss(seq) 0.2617 | grad 3.3842 | lr 0.0000 | time_forward 3.1840 | time_backward 4.1910 |
[2023-10-25 01:04:13,595::train::INFO] [train] Iter 589735 | loss 1.1256 | loss(rot) 1.0537 | loss(pos) 0.0245 | loss(seq) 0.0474 | grad 6.5133 | lr 0.0000 | time_forward 1.2790 | time_backward 1.4360 |
[2023-10-25 01:04:21,934::train::INFO] [train] Iter 589736 | loss 0.7285 | loss(rot) 0.3276 | loss(pos) 0.0356 | loss(seq) 0.3652 | grad 10.9666 | lr 0.0000 | time_forward 3.5590 | time_backward 4.7540 |
[2023-10-25 01:04:28,512::train::INFO] [train] Iter 589737 | loss 0.6685 | loss(rot) 0.6383 | loss(pos) 0.0099 | loss(seq) 0.0203 | grad 1.8868 | lr 0.0000 | time_forward 2.8440 | time_backward 3.7310 |
[2023-10-25 01:04:35,803::train::INFO] [train] Iter 589738 | loss 0.5986 | loss(rot) 0.5104 | loss(pos) 0.0202 | loss(seq) 0.0680 | grad 4.0049 | lr 0.0000 | time_forward 3.0870 | time_backward 4.2010 |
[2023-10-25 01:04:38,558::train::INFO] [train] Iter 589739 | loss 0.3345 | loss(rot) 0.1547 | loss(pos) 0.0364 | loss(seq) 0.1434 | grad 1.6256 | lr 0.0000 | time_forward 1.3050 | time_backward 1.4480 |
[2023-10-25 01:04:45,832::train::INFO] [train] Iter 589740 | loss 0.1994 | loss(rot) 0.1296 | loss(pos) 0.0321 | loss(seq) 0.0376 | grad 1.9915 | lr 0.0000 | time_forward 3.0990 | time_backward 4.1710 |
[2023-10-25 01:04:53,681::train::INFO] [train] Iter 589741 | loss 0.2290 | loss(rot) 0.2018 | loss(pos) 0.0267 | loss(seq) 0.0005 | grad 2.5671 | lr 0.0000 | time_forward 3.3340 | time_backward 4.5100 |
[2023-10-25 01:05:02,707::train::INFO] [train] Iter 589742 | loss 0.5510 | loss(rot) 0.0478 | loss(pos) 0.4786 | loss(seq) 0.0246 | grad 7.8599 | lr 0.0000 | time_forward 3.6940 | time_backward 5.3290 |
[2023-10-25 01:05:11,676::train::INFO] [train] Iter 589743 | loss 1.0884 | loss(rot) 0.1852 | loss(pos) 0.8912 | loss(seq) 0.0120 | grad 7.2335 | lr 0.0000 | time_forward 3.8000 | time_backward 5.1660 |
[2023-10-25 01:05:13,995::train::INFO] [train] Iter 589744 | loss 1.4593 | loss(rot) 1.0026 | loss(pos) 0.1251 | loss(seq) 0.3316 | grad 17.1767 | lr 0.0000 | time_forward 1.0480 | time_backward 1.2670 |
[2023-10-25 01:05:16,768::train::INFO] [train] Iter 589745 | loss 1.4557 | loss(rot) 0.4740 | loss(pos) 0.9727 | loss(seq) 0.0090 | grad 13.7899 | lr 0.0000 | time_forward 1.3730 | time_backward 1.3970 |
[2023-10-25 01:05:24,039::train::INFO] [train] Iter 589746 | loss 0.3389 | loss(rot) 0.2721 | loss(pos) 0.0189 | loss(seq) 0.0480 | grad 3.0078 | lr 0.0000 | time_forward 3.1210 | time_backward 4.1470 |
[2023-10-25 01:05:31,909::train::INFO] [train] Iter 589747 | loss 0.7720 | loss(rot) 0.7542 | loss(pos) 0.0163 | loss(seq) 0.0015 | grad 4.7542 | lr 0.0000 | time_forward 3.3750 | time_backward 4.4920 |
[2023-10-25 01:05:40,145::train::INFO] [train] Iter 589748 | loss 0.7596 | loss(rot) 0.5797 | loss(pos) 0.0445 | loss(seq) 0.1354 | grad 3.2657 | lr 0.0000 | time_forward 3.4990 | time_backward 4.7340 |
[2023-10-25 01:05:46,865::train::INFO] [train] Iter 589749 | loss 0.5476 | loss(rot) 0.3114 | loss(pos) 0.0139 | loss(seq) 0.2223 | grad 3.9030 | lr 0.0000 | time_forward 2.9000 | time_backward 3.8180 |
[2023-10-25 01:05:49,204::train::INFO] [train] Iter 589750 | loss 0.3666 | loss(rot) 0.1300 | loss(pos) 0.0231 | loss(seq) 0.2135 | grad 2.9771 | lr 0.0000 | time_forward 1.0730 | time_backward 1.2620 |
[2023-10-25 01:05:56,785::train::INFO] [train] Iter 589751 | loss 0.2759 | loss(rot) 0.0628 | loss(pos) 0.1343 | loss(seq) 0.0787 | grad 3.6469 | lr 0.0000 | time_forward 3.2430 | time_backward 4.3230 |
[2023-10-25 01:06:04,654::train::INFO] [train] Iter 589752 | loss 0.4752 | loss(rot) 0.4292 | loss(pos) 0.0201 | loss(seq) 0.0259 | grad 4.4490 | lr 0.0000 | time_forward 3.3480 | time_backward 4.5190 |
[2023-10-25 01:06:13,535::train::INFO] [train] Iter 589753 | loss 0.7816 | loss(rot) 0.5611 | loss(pos) 0.0935 | loss(seq) 0.1269 | grad 3.1693 | lr 0.0000 | time_forward 3.6260 | time_backward 5.2520 |
[2023-10-25 01:06:21,400::train::INFO] [train] Iter 589754 | loss 0.6698 | loss(rot) 0.3662 | loss(pos) 0.0416 | loss(seq) 0.2620 | grad 2.6556 | lr 0.0000 | time_forward 3.3730 | time_backward 4.4890 |
[2023-10-25 01:06:29,292::train::INFO] [train] Iter 589755 | loss 0.1415 | loss(rot) 0.1230 | loss(pos) 0.0186 | loss(seq) 0.0000 | grad 1.5719 | lr 0.0000 | time_forward 3.3770 | time_backward 4.5110 |
[2023-10-25 01:06:38,187::train::INFO] [train] Iter 589756 | loss 0.2155 | loss(rot) 0.1670 | loss(pos) 0.0095 | loss(seq) 0.0390 | grad 2.7481 | lr 0.0000 | time_forward 3.6650 | time_backward 5.2260 |
[2023-10-25 01:06:46,401::train::INFO] [train] Iter 589757 | loss 0.1952 | loss(rot) 0.1603 | loss(pos) 0.0241 | loss(seq) 0.0108 | grad 1.6048 | lr 0.0000 | time_forward 3.5280 | time_backward 4.6830 |
[2023-10-25 01:06:54,088::train::INFO] [train] Iter 589758 | loss 0.8084 | loss(rot) 0.2175 | loss(pos) 0.3099 | loss(seq) 0.2810 | grad 2.8047 | lr 0.0000 | time_forward 3.2550 | time_backward 4.4300 |
[2023-10-25 01:07:03,121::train::INFO] [train] Iter 589759 | loss 0.8186 | loss(rot) 0.7923 | loss(pos) 0.0146 | loss(seq) 0.0117 | grad 3.6204 | lr 0.0000 | time_forward 3.6880 | time_backward 5.3410 |
[2023-10-25 01:07:05,856::train::INFO] [train] Iter 589760 | loss 1.0694 | loss(rot) 1.0377 | loss(pos) 0.0317 | loss(seq) 0.0000 | grad 3.7815 | lr 0.0000 | time_forward 1.3270 | time_backward 1.4060 |
[2023-10-25 01:07:14,864::train::INFO] [train] Iter 589761 | loss 0.9485 | loss(rot) 0.6205 | loss(pos) 0.0687 | loss(seq) 0.2593 | grad 3.8382 | lr 0.0000 | time_forward 3.6810 | time_backward 5.2980 |
[2023-10-25 01:07:18,040::train::INFO] [train] Iter 589762 | loss 0.9022 | loss(rot) 0.5620 | loss(pos) 0.0890 | loss(seq) 0.2512 | grad 3.3777 | lr 0.0000 | time_forward 1.4550 | time_backward 1.7170 |
[2023-10-25 01:07:26,072::train::INFO] [train] Iter 589763 | loss 0.3658 | loss(rot) 0.3402 | loss(pos) 0.0244 | loss(seq) 0.0011 | grad 3.4945 | lr 0.0000 | time_forward 3.4430 | time_backward 4.5730 |
[2023-10-25 01:07:35,044::train::INFO] [train] Iter 589764 | loss 1.4406 | loss(rot) 0.8929 | loss(pos) 0.0573 | loss(seq) 0.4905 | grad 4.8293 | lr 0.0000 | time_forward 3.7500 | time_backward 5.2200 |
[2023-10-25 01:07:37,808::train::INFO] [train] Iter 589765 | loss 0.8258 | loss(rot) 0.3695 | loss(pos) 0.1496 | loss(seq) 0.3067 | grad 4.7482 | lr 0.0000 | time_forward 1.3180 | time_backward 1.4420 |
[2023-10-25 01:07:45,398::train::INFO] [train] Iter 589766 | loss 1.1615 | loss(rot) 0.9198 | loss(pos) 0.0316 | loss(seq) 0.2101 | grad 8.2196 | lr 0.0000 | time_forward 3.2750 | time_backward 4.3120 |
[2023-10-25 01:07:52,960::train::INFO] [train] Iter 589767 | loss 1.8365 | loss(rot) 1.2950 | loss(pos) 0.1108 | loss(seq) 0.4306 | grad 4.3949 | lr 0.0000 | time_forward 3.1980 | time_backward 4.3610 |
[2023-10-25 01:08:01,895::train::INFO] [train] Iter 589768 | loss 0.6948 | loss(rot) 0.2528 | loss(pos) 0.0568 | loss(seq) 0.3852 | grad 3.8417 | lr 0.0000 | time_forward 3.6590 | time_backward 5.2730 |
[2023-10-25 01:08:10,882::train::INFO] [train] Iter 589769 | loss 0.7309 | loss(rot) 0.4237 | loss(pos) 0.1132 | loss(seq) 0.1940 | grad 3.6288 | lr 0.0000 | time_forward 3.6600 | time_backward 5.3250 |
[2023-10-25 01:08:19,792::train::INFO] [train] Iter 589770 | loss 0.2832 | loss(rot) 0.1732 | loss(pos) 0.0473 | loss(seq) 0.0627 | grad 2.4325 | lr 0.0000 | time_forward 3.6470 | time_backward 5.2590 |
[2023-10-25 01:08:28,847::train::INFO] [train] Iter 589771 | loss 0.4649 | loss(rot) 0.0433 | loss(pos) 0.1082 | loss(seq) 0.3133 | grad 3.1238 | lr 0.0000 | time_forward 3.6700 | time_backward 5.3820 |
[2023-10-25 01:08:36,159::train::INFO] [train] Iter 589772 | loss 1.1540 | loss(rot) 0.7908 | loss(pos) 0.1293 | loss(seq) 0.2340 | grad 14.2742 | lr 0.0000 | time_forward 3.0210 | time_backward 4.2880 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.