text stringlengths 56 1.16k |
|---|
[2023-10-24 21:17:16,947::train::INFO] [train] Iter 587875 | loss 1.1759 | loss(rot) 0.9917 | loss(pos) 0.0510 | loss(seq) 0.1333 | grad 6.3423 | lr 0.0000 | time_forward 3.6360 | time_backward 5.2460 |
[2023-10-24 21:17:25,185::train::INFO] [train] Iter 587876 | loss 0.7403 | loss(rot) 0.2619 | loss(pos) 0.1961 | loss(seq) 0.2822 | grad 3.0014 | lr 0.0000 | time_forward 3.4650 | time_backward 4.7700 |
[2023-10-24 21:17:34,133::train::INFO] [train] Iter 587877 | loss 0.6747 | loss(rot) 0.2306 | loss(pos) 0.2326 | loss(seq) 0.2115 | grad 4.3370 | lr 0.0000 | time_forward 3.6330 | time_backward 5.3120 |
[2023-10-24 21:17:42,209::train::INFO] [train] Iter 587878 | loss 0.3967 | loss(rot) 0.1234 | loss(pos) 0.0239 | loss(seq) 0.2494 | grad 3.1416 | lr 0.0000 | time_forward 3.4160 | time_backward 4.6560 |
[2023-10-24 21:17:50,965::train::INFO] [train] Iter 587879 | loss 0.9456 | loss(rot) 0.5279 | loss(pos) 0.0844 | loss(seq) 0.3334 | grad 3.1427 | lr 0.0000 | time_forward 3.6890 | time_backward 5.0640 |
[2023-10-24 21:17:59,852::train::INFO] [train] Iter 587880 | loss 1.0320 | loss(rot) 0.5813 | loss(pos) 0.0773 | loss(seq) 0.3735 | grad 4.8187 | lr 0.0000 | time_forward 3.5480 | time_backward 5.3360 |
[2023-10-24 21:18:02,106::train::INFO] [train] Iter 587881 | loss 1.4263 | loss(rot) 1.0949 | loss(pos) 0.0542 | loss(seq) 0.2771 | grad 3.9214 | lr 0.0000 | time_forward 1.0330 | time_backward 1.2190 |
[2023-10-24 21:18:11,067::train::INFO] [train] Iter 587882 | loss 0.8699 | loss(rot) 0.0139 | loss(pos) 0.8556 | loss(seq) 0.0004 | grad 15.2198 | lr 0.0000 | time_forward 3.6680 | time_backward 5.2760 |
[2023-10-24 21:18:19,280::train::INFO] [train] Iter 587883 | loss 0.1504 | loss(rot) 0.1004 | loss(pos) 0.0292 | loss(seq) 0.0209 | grad 2.2372 | lr 0.0000 | time_forward 3.5210 | time_backward 4.6890 |
[2023-10-24 21:18:21,084::train::INFO] [train] Iter 587884 | loss 2.5056 | loss(rot) 2.3894 | loss(pos) 0.0970 | loss(seq) 0.0191 | grad 6.8525 | lr 0.0000 | time_forward 0.8680 | time_backward 0.9330 |
[2023-10-24 21:18:23,303::train::INFO] [train] Iter 587885 | loss 0.1751 | loss(rot) 0.1236 | loss(pos) 0.0181 | loss(seq) 0.0333 | grad 1.7000 | lr 0.0000 | time_forward 1.0090 | time_backward 1.2080 |
[2023-10-24 21:18:31,458::train::INFO] [train] Iter 587886 | loss 0.2962 | loss(rot) 0.1345 | loss(pos) 0.0307 | loss(seq) 0.1310 | grad 2.4666 | lr 0.0000 | time_forward 3.4860 | time_backward 4.6650 |
[2023-10-24 21:18:34,152::train::INFO] [train] Iter 587887 | loss 0.0803 | loss(rot) 0.0369 | loss(pos) 0.0286 | loss(seq) 0.0148 | grad 1.8036 | lr 0.0000 | time_forward 1.3040 | time_backward 1.3860 |
[2023-10-24 21:18:44,349::train::INFO] [train] Iter 587888 | loss 0.6771 | loss(rot) 0.3508 | loss(pos) 0.0893 | loss(seq) 0.2370 | grad 9.6114 | lr 0.0000 | time_forward 4.8170 | time_backward 5.3770 |
[2023-10-24 21:18:55,184::train::INFO] [train] Iter 587889 | loss 0.3698 | loss(rot) 0.2002 | loss(pos) 0.0391 | loss(seq) 0.1305 | grad 2.8168 | lr 0.0000 | time_forward 4.7320 | time_backward 6.1010 |
[2023-10-24 21:19:03,485::train::INFO] [train] Iter 587890 | loss 0.8644 | loss(rot) 0.2443 | loss(pos) 0.2061 | loss(seq) 0.4140 | grad 10.3216 | lr 0.0000 | time_forward 3.4740 | time_backward 4.8240 |
[2023-10-24 21:19:11,435::train::INFO] [train] Iter 587891 | loss 0.3568 | loss(rot) 0.0840 | loss(pos) 0.0216 | loss(seq) 0.2513 | grad 2.3911 | lr 0.0000 | time_forward 3.3030 | time_backward 4.6430 |
[2023-10-24 21:19:14,282::train::INFO] [train] Iter 587892 | loss 0.8349 | loss(rot) 0.5535 | loss(pos) 0.0381 | loss(seq) 0.2433 | grad 3.1456 | lr 0.0000 | time_forward 1.3090 | time_backward 1.5350 |
[2023-10-24 21:19:17,118::train::INFO] [train] Iter 587893 | loss 0.1236 | loss(rot) 0.0894 | loss(pos) 0.0337 | loss(seq) 0.0005 | grad 2.0073 | lr 0.0000 | time_forward 1.3280 | time_backward 1.5050 |
[2023-10-24 21:19:19,971::train::INFO] [train] Iter 587894 | loss 0.4998 | loss(rot) 0.4564 | loss(pos) 0.0277 | loss(seq) 0.0157 | grad 4.2622 | lr 0.0000 | time_forward 1.3520 | time_backward 1.4980 |
[2023-10-24 21:19:29,809::train::INFO] [train] Iter 587895 | loss 0.4757 | loss(rot) 0.2226 | loss(pos) 0.1735 | loss(seq) 0.0795 | grad 3.2196 | lr 0.0000 | time_forward 4.0380 | time_backward 5.7980 |
[2023-10-24 21:19:39,539::train::INFO] [train] Iter 587896 | loss 1.2812 | loss(rot) 0.0718 | loss(pos) 1.2075 | loss(seq) 0.0019 | grad 8.6651 | lr 0.0000 | time_forward 3.9240 | time_backward 5.8020 |
[2023-10-24 21:19:49,458::train::INFO] [train] Iter 587897 | loss 0.7563 | loss(rot) 0.4064 | loss(pos) 0.0421 | loss(seq) 0.3077 | grad 4.7320 | lr 0.0000 | time_forward 3.9590 | time_backward 5.9560 |
[2023-10-24 21:19:52,836::train::INFO] [train] Iter 587898 | loss 0.7164 | loss(rot) 0.2585 | loss(pos) 0.1166 | loss(seq) 0.3413 | grad 3.6782 | lr 0.0000 | time_forward 1.5110 | time_backward 1.8630 |
[2023-10-24 21:20:01,099::train::INFO] [train] Iter 587899 | loss 0.6577 | loss(rot) 0.0554 | loss(pos) 0.4771 | loss(seq) 0.1252 | grad 12.2896 | lr 0.0000 | time_forward 3.4770 | time_backward 4.7830 |
[2023-10-24 21:20:08,613::train::INFO] [train] Iter 587900 | loss 0.6834 | loss(rot) 0.5307 | loss(pos) 0.0308 | loss(seq) 0.1218 | grad 2.4524 | lr 0.0000 | time_forward 3.2000 | time_backward 4.2990 |
[2023-10-24 21:20:11,360::train::INFO] [train] Iter 587901 | loss 0.4280 | loss(rot) 0.3988 | loss(pos) 0.0285 | loss(seq) 0.0007 | grad 2.5531 | lr 0.0000 | time_forward 1.3090 | time_backward 1.4360 |
[2023-10-24 21:20:14,167::train::INFO] [train] Iter 587902 | loss 1.0568 | loss(rot) 0.7494 | loss(pos) 0.0448 | loss(seq) 0.2626 | grad 7.6057 | lr 0.0000 | time_forward 1.3590 | time_backward 1.4450 |
[2023-10-24 21:20:23,467::train::INFO] [train] Iter 587903 | loss 0.7286 | loss(rot) 0.3220 | loss(pos) 0.1551 | loss(seq) 0.2516 | grad 4.9306 | lr 0.0000 | time_forward 3.9210 | time_backward 5.3750 |
[2023-10-24 21:20:30,122::train::INFO] [train] Iter 587904 | loss 1.5271 | loss(rot) 1.4611 | loss(pos) 0.0660 | loss(seq) 0.0000 | grad 5.2917 | lr 0.0000 | time_forward 2.8690 | time_backward 3.7820 |
[2023-10-24 21:20:35,960::train::INFO] [train] Iter 587905 | loss 0.2341 | loss(rot) 0.0660 | loss(pos) 0.0140 | loss(seq) 0.1541 | grad 2.3647 | lr 0.0000 | time_forward 2.5000 | time_backward 3.3350 |
[2023-10-24 21:20:44,660::train::INFO] [train] Iter 587906 | loss 0.9163 | loss(rot) 0.6687 | loss(pos) 0.0588 | loss(seq) 0.1888 | grad 21.5988 | lr 0.0000 | time_forward 3.6570 | time_backward 5.0410 |
[2023-10-24 21:20:53,739::train::INFO] [train] Iter 587907 | loss 0.7016 | loss(rot) 0.4260 | loss(pos) 0.1217 | loss(seq) 0.1539 | grad 4.1995 | lr 0.0000 | time_forward 3.8130 | time_backward 5.2620 |
[2023-10-24 21:21:00,411::train::INFO] [train] Iter 587908 | loss 0.6790 | loss(rot) 0.1878 | loss(pos) 0.1461 | loss(seq) 0.3452 | grad 5.0162 | lr 0.0000 | time_forward 2.8410 | time_backward 3.8270 |
[2023-10-24 21:21:10,351::train::INFO] [train] Iter 587909 | loss 0.8933 | loss(rot) 0.8658 | loss(pos) 0.0205 | loss(seq) 0.0071 | grad 9.2632 | lr 0.0000 | time_forward 4.1000 | time_backward 5.8370 |
[2023-10-24 21:21:18,223::train::INFO] [train] Iter 587910 | loss 0.6632 | loss(rot) 0.6238 | loss(pos) 0.0259 | loss(seq) 0.0135 | grad 3.9779 | lr 0.0000 | time_forward 3.3090 | time_backward 4.5590 |
[2023-10-24 21:21:27,246::train::INFO] [train] Iter 587911 | loss 0.6718 | loss(rot) 0.3145 | loss(pos) 0.1471 | loss(seq) 0.2102 | grad 3.2627 | lr 0.0000 | time_forward 3.7730 | time_backward 5.2480 |
[2023-10-24 21:21:36,496::train::INFO] [train] Iter 587912 | loss 1.2073 | loss(rot) 0.0186 | loss(pos) 1.1863 | loss(seq) 0.0023 | grad 7.2945 | lr 0.0000 | time_forward 3.8000 | time_backward 5.4460 |
[2023-10-24 21:21:39,245::train::INFO] [train] Iter 587913 | loss 0.6092 | loss(rot) 0.3893 | loss(pos) 0.1115 | loss(seq) 0.1084 | grad 3.7849 | lr 0.0000 | time_forward 1.2990 | time_backward 1.4480 |
[2023-10-24 21:21:48,406::train::INFO] [train] Iter 587914 | loss 0.2510 | loss(rot) 0.2170 | loss(pos) 0.0126 | loss(seq) 0.0214 | grad 2.7223 | lr 0.0000 | time_forward 3.9090 | time_backward 5.2490 |
[2023-10-24 21:21:56,413::train::INFO] [train] Iter 587915 | loss 0.4909 | loss(rot) 0.0349 | loss(pos) 0.4469 | loss(seq) 0.0091 | grad 6.5852 | lr 0.0000 | time_forward 3.3680 | time_backward 4.6350 |
[2023-10-24 21:22:04,759::train::INFO] [train] Iter 587916 | loss 1.9128 | loss(rot) 1.3499 | loss(pos) 0.0882 | loss(seq) 0.4747 | grad 5.0113 | lr 0.0000 | time_forward 3.5160 | time_backward 4.8280 |
[2023-10-24 21:22:13,857::train::INFO] [train] Iter 587917 | loss 0.0988 | loss(rot) 0.0579 | loss(pos) 0.0407 | loss(seq) 0.0001 | grad 2.2012 | lr 0.0000 | time_forward 3.8450 | time_backward 5.2490 |
[2023-10-24 21:22:23,724::train::INFO] [train] Iter 587918 | loss 1.6766 | loss(rot) 1.2289 | loss(pos) 0.0667 | loss(seq) 0.3810 | grad 4.2474 | lr 0.0000 | time_forward 3.9970 | time_backward 5.8670 |
[2023-10-24 21:22:32,952::train::INFO] [train] Iter 587919 | loss 0.5401 | loss(rot) 0.2875 | loss(pos) 0.1870 | loss(seq) 0.0657 | grad 3.9868 | lr 0.0000 | time_forward 3.8680 | time_backward 5.3580 |
[2023-10-24 21:22:42,992::train::INFO] [train] Iter 587920 | loss 1.0977 | loss(rot) 0.7428 | loss(pos) 0.1475 | loss(seq) 0.2074 | grad 6.5043 | lr 0.0000 | time_forward 4.1280 | time_backward 5.9080 |
[2023-10-24 21:22:45,716::train::INFO] [train] Iter 587921 | loss 0.4561 | loss(rot) 0.1109 | loss(pos) 0.0324 | loss(seq) 0.3128 | grad 2.2398 | lr 0.0000 | time_forward 1.2910 | time_backward 1.4300 |
[2023-10-24 21:22:48,487::train::INFO] [train] Iter 587922 | loss 0.1227 | loss(rot) 0.1051 | loss(pos) 0.0105 | loss(seq) 0.0070 | grad 1.4439 | lr 0.0000 | time_forward 1.3130 | time_backward 1.4310 |
[2023-10-24 21:22:58,627::train::INFO] [train] Iter 587923 | loss 0.8235 | loss(rot) 0.5300 | loss(pos) 0.2643 | loss(seq) 0.0292 | grad 3.8614 | lr 0.0000 | time_forward 4.1310 | time_backward 6.0050 |
[2023-10-24 21:23:04,526::train::INFO] [train] Iter 587924 | loss 0.2247 | loss(rot) 0.1446 | loss(pos) 0.0801 | loss(seq) 0.0000 | grad 2.0782 | lr 0.0000 | time_forward 2.5340 | time_backward 3.3620 |
[2023-10-24 21:23:14,502::train::INFO] [train] Iter 587925 | loss 0.1612 | loss(rot) 0.1347 | loss(pos) 0.0265 | loss(seq) 0.0000 | grad 1.7585 | lr 0.0000 | time_forward 4.0380 | time_backward 5.9340 |
[2023-10-24 21:23:17,204::train::INFO] [train] Iter 587926 | loss 0.3588 | loss(rot) 0.2686 | loss(pos) 0.0272 | loss(seq) 0.0629 | grad 2.1020 | lr 0.0000 | time_forward 1.2920 | time_backward 1.4070 |
[2023-10-24 21:23:20,025::train::INFO] [train] Iter 587927 | loss 0.2308 | loss(rot) 0.2019 | loss(pos) 0.0158 | loss(seq) 0.0131 | grad 5.8933 | lr 0.0000 | time_forward 1.3850 | time_backward 1.4330 |
[2023-10-24 21:23:22,804::train::INFO] [train] Iter 587928 | loss 0.2227 | loss(rot) 0.0368 | loss(pos) 0.1304 | loss(seq) 0.0555 | grad 3.2598 | lr 0.0000 | time_forward 1.3590 | time_backward 1.4160 |
[2023-10-24 21:23:31,610::train::INFO] [train] Iter 587929 | loss 1.1002 | loss(rot) 0.2408 | loss(pos) 0.5198 | loss(seq) 0.3395 | grad 5.3123 | lr 0.0000 | time_forward 3.7330 | time_backward 5.0690 |
[2023-10-24 21:23:40,405::train::INFO] [train] Iter 587930 | loss 0.7840 | loss(rot) 0.7482 | loss(pos) 0.0104 | loss(seq) 0.0254 | grad 3.9158 | lr 0.0000 | time_forward 3.7280 | time_backward 5.0540 |
[2023-10-24 21:23:47,868::train::INFO] [train] Iter 587931 | loss 1.7192 | loss(rot) 0.8039 | loss(pos) 0.1200 | loss(seq) 0.7953 | grad 5.2150 | lr 0.0000 | time_forward 3.1710 | time_backward 4.2890 |
[2023-10-24 21:23:56,211::train::INFO] [train] Iter 587932 | loss 1.6048 | loss(rot) 0.8157 | loss(pos) 0.1106 | loss(seq) 0.6785 | grad 7.4600 | lr 0.0000 | time_forward 3.5280 | time_backward 4.8130 |
[2023-10-24 21:24:06,264::train::INFO] [train] Iter 587933 | loss 4.1319 | loss(rot) 0.0053 | loss(pos) 4.1266 | loss(seq) 0.0000 | grad 27.3864 | lr 0.0000 | time_forward 4.0480 | time_backward 6.0030 |
[2023-10-24 21:24:14,286::train::INFO] [train] Iter 587934 | loss 0.9406 | loss(rot) 0.1048 | loss(pos) 0.5728 | loss(seq) 0.2631 | grad 6.4849 | lr 0.0000 | time_forward 3.3860 | time_backward 4.6330 |
[2023-10-24 21:24:24,375::train::INFO] [train] Iter 587935 | loss 0.6287 | loss(rot) 0.4116 | loss(pos) 0.0303 | loss(seq) 0.1868 | grad 3.2061 | lr 0.0000 | time_forward 4.1190 | time_backward 5.9660 |
[2023-10-24 21:24:34,379::train::INFO] [train] Iter 587936 | loss 0.1944 | loss(rot) 0.1743 | loss(pos) 0.0189 | loss(seq) 0.0012 | grad 3.1715 | lr 0.0000 | time_forward 4.1020 | time_backward 5.8990 |
[2023-10-24 21:24:37,710::train::INFO] [train] Iter 587937 | loss 0.9980 | loss(rot) 0.4006 | loss(pos) 0.3164 | loss(seq) 0.2810 | grad 6.0820 | lr 0.0000 | time_forward 1.4690 | time_backward 1.8580 |
[2023-10-24 21:24:47,889::train::INFO] [train] Iter 587938 | loss 0.9106 | loss(rot) 0.3855 | loss(pos) 0.1083 | loss(seq) 0.4168 | grad 3.0540 | lr 0.0000 | time_forward 4.3260 | time_backward 5.8510 |
[2023-10-24 21:24:56,568::train::INFO] [train] Iter 587939 | loss 0.2939 | loss(rot) 0.0756 | loss(pos) 0.1708 | loss(seq) 0.0474 | grad 3.8217 | lr 0.0000 | time_forward 3.6700 | time_backward 5.0050 |
[2023-10-24 21:24:59,345::train::INFO] [train] Iter 587940 | loss 2.9102 | loss(rot) 2.8158 | loss(pos) 0.0533 | loss(seq) 0.0410 | grad 4.3081 | lr 0.0000 | time_forward 1.3190 | time_backward 1.4540 |
[2023-10-24 21:25:02,028::train::INFO] [train] Iter 587941 | loss 0.6524 | loss(rot) 0.3114 | loss(pos) 0.1404 | loss(seq) 0.2006 | grad 4.4178 | lr 0.0000 | time_forward 1.2600 | time_backward 1.4210 |
[2023-10-24 21:25:11,321::train::INFO] [train] Iter 587942 | loss 0.9081 | loss(rot) 0.4746 | loss(pos) 0.0883 | loss(seq) 0.3452 | grad 5.1835 | lr 0.0000 | time_forward 3.9240 | time_backward 5.3280 |
[2023-10-24 21:25:21,390::train::INFO] [train] Iter 587943 | loss 1.0029 | loss(rot) 0.1528 | loss(pos) 0.8389 | loss(seq) 0.0112 | grad 5.6527 | lr 0.0000 | time_forward 4.0820 | time_backward 5.9840 |
[2023-10-24 21:25:31,481::train::INFO] [train] Iter 587944 | loss 0.9718 | loss(rot) 0.0520 | loss(pos) 0.9142 | loss(seq) 0.0056 | grad 8.9892 | lr 0.0000 | time_forward 4.0630 | time_backward 6.0250 |
[2023-10-24 21:25:40,769::train::INFO] [train] Iter 587945 | loss 0.6017 | loss(rot) 0.3821 | loss(pos) 0.2076 | loss(seq) 0.0120 | grad 6.3352 | lr 0.0000 | time_forward 3.8880 | time_backward 5.3960 |
[2023-10-24 21:25:46,825::train::INFO] [train] Iter 587946 | loss 0.6725 | loss(rot) 0.6178 | loss(pos) 0.0502 | loss(seq) 0.0045 | grad 3.3596 | lr 0.0000 | time_forward 2.5810 | time_backward 3.4710 |
[2023-10-24 21:25:49,749::train::INFO] [train] Iter 587947 | loss 0.8391 | loss(rot) 0.7663 | loss(pos) 0.0727 | loss(seq) 0.0000 | grad 29.0279 | lr 0.0000 | time_forward 1.3180 | time_backward 1.6030 |
[2023-10-24 21:25:57,757::train::INFO] [train] Iter 587948 | loss 1.5625 | loss(rot) 0.4991 | loss(pos) 0.9156 | loss(seq) 0.1478 | grad 4.6703 | lr 0.0000 | time_forward 3.3510 | time_backward 4.6550 |
[2023-10-24 21:26:00,494::train::INFO] [train] Iter 587949 | loss 0.2984 | loss(rot) 0.0546 | loss(pos) 0.2162 | loss(seq) 0.0276 | grad 3.2558 | lr 0.0000 | time_forward 1.2930 | time_backward 1.4400 |
[2023-10-24 21:26:03,279::train::INFO] [train] Iter 587950 | loss 0.0841 | loss(rot) 0.0712 | loss(pos) 0.0128 | loss(seq) 0.0001 | grad 1.7606 | lr 0.0000 | time_forward 1.2930 | time_backward 1.4870 |
[2023-10-24 21:26:11,662::train::INFO] [train] Iter 587951 | loss 0.4233 | loss(rot) 0.0459 | loss(pos) 0.0388 | loss(seq) 0.3386 | grad 2.8570 | lr 0.0000 | time_forward 3.5510 | time_backward 4.8290 |
[2023-10-24 21:26:20,690::train::INFO] [train] Iter 587952 | loss 0.6723 | loss(rot) 0.2832 | loss(pos) 0.3794 | loss(seq) 0.0097 | grad 6.8309 | lr 0.0000 | time_forward 3.7950 | time_backward 5.2290 |
[2023-10-24 21:26:28,995::train::INFO] [train] Iter 587953 | loss 1.9479 | loss(rot) 1.9104 | loss(pos) 0.0355 | loss(seq) 0.0020 | grad 4.1101 | lr 0.0000 | time_forward 3.4840 | time_backward 4.8170 |
[2023-10-24 21:26:37,688::train::INFO] [train] Iter 587954 | loss 0.6943 | loss(rot) 0.6683 | loss(pos) 0.0259 | loss(seq) 0.0000 | grad 4.4526 | lr 0.0000 | time_forward 3.6200 | time_backward 5.0700 |
[2023-10-24 21:26:46,777::train::INFO] [train] Iter 587955 | loss 0.1269 | loss(rot) 0.0696 | loss(pos) 0.0165 | loss(seq) 0.0408 | grad 1.9800 | lr 0.0000 | time_forward 3.8280 | time_backward 5.2580 |
[2023-10-24 21:26:56,872::train::INFO] [train] Iter 587956 | loss 1.4715 | loss(rot) 1.4098 | loss(pos) 0.0616 | loss(seq) 0.0001 | grad 21.7937 | lr 0.0000 | time_forward 4.2490 | time_backward 5.8420 |
[2023-10-24 21:27:06,880::train::INFO] [train] Iter 587957 | loss 0.7848 | loss(rot) 0.1073 | loss(pos) 0.6603 | loss(seq) 0.0172 | grad 4.9398 | lr 0.0000 | time_forward 4.0190 | time_backward 5.9850 |
[2023-10-24 21:27:10,234::train::INFO] [train] Iter 587958 | loss 0.7620 | loss(rot) 0.3704 | loss(pos) 0.3031 | loss(seq) 0.0885 | grad 3.2765 | lr 0.0000 | time_forward 1.4930 | time_backward 1.8580 |
[2023-10-24 21:27:20,187::train::INFO] [train] Iter 587959 | loss 0.3181 | loss(rot) 0.2125 | loss(pos) 0.0227 | loss(seq) 0.0829 | grad 2.1465 | lr 0.0000 | time_forward 4.0110 | time_backward 5.9380 |
[2023-10-24 21:27:22,912::train::INFO] [train] Iter 587960 | loss 0.9837 | loss(rot) 0.6741 | loss(pos) 0.0395 | loss(seq) 0.2701 | grad 2.6582 | lr 0.0000 | time_forward 1.2780 | time_backward 1.4450 |
[2023-10-24 21:27:32,903::train::INFO] [train] Iter 587961 | loss 0.8731 | loss(rot) 0.4088 | loss(pos) 0.3006 | loss(seq) 0.1637 | grad 3.9094 | lr 0.0000 | time_forward 4.2010 | time_backward 5.7570 |
[2023-10-24 21:27:42,174::train::INFO] [train] Iter 587962 | loss 0.5155 | loss(rot) 0.2670 | loss(pos) 0.0208 | loss(seq) 0.2278 | grad 2.7136 | lr 0.0000 | time_forward 3.9090 | time_backward 5.3600 |
[2023-10-24 21:27:45,970::train::INFO] [train] Iter 587963 | loss 0.8194 | loss(rot) 0.7043 | loss(pos) 0.0706 | loss(seq) 0.0446 | grad 4.5414 | lr 0.0000 | time_forward 1.6660 | time_backward 2.1270 |
[2023-10-24 21:27:56,018::train::INFO] [train] Iter 587964 | loss 0.8733 | loss(rot) 0.8015 | loss(pos) 0.0595 | loss(seq) 0.0123 | grad 4.0846 | lr 0.0000 | time_forward 4.0140 | time_backward 6.0180 |
[2023-10-24 21:27:59,441::train::INFO] [train] Iter 587965 | loss 0.5809 | loss(rot) 0.2367 | loss(pos) 0.1508 | loss(seq) 0.1934 | grad 3.5542 | lr 0.0000 | time_forward 1.5010 | time_backward 1.8940 |
[2023-10-24 21:28:01,723::train::INFO] [train] Iter 587966 | loss 0.6400 | loss(rot) 0.2061 | loss(pos) 0.4270 | loss(seq) 0.0069 | grad 3.4489 | lr 0.0000 | time_forward 1.0480 | time_backward 1.2180 |
[2023-10-24 21:28:11,895::train::INFO] [train] Iter 587967 | loss 0.2972 | loss(rot) 0.1222 | loss(pos) 0.0293 | loss(seq) 0.1457 | grad 2.5363 | lr 0.0000 | time_forward 4.1510 | time_backward 6.0190 |
[2023-10-24 21:28:14,649::train::INFO] [train] Iter 587968 | loss 1.3629 | loss(rot) 0.9985 | loss(pos) 0.0387 | loss(seq) 0.3258 | grad 25.0264 | lr 0.0000 | time_forward 1.3070 | time_backward 1.4440 |
[2023-10-24 21:28:17,354::train::INFO] [train] Iter 587969 | loss 0.7026 | loss(rot) 0.1395 | loss(pos) 0.0578 | loss(seq) 0.5053 | grad 2.8714 | lr 0.0000 | time_forward 1.2760 | time_backward 1.4260 |
[2023-10-24 21:28:20,266::train::INFO] [train] Iter 587970 | loss 0.4818 | loss(rot) 0.1580 | loss(pos) 0.2553 | loss(seq) 0.0685 | grad 4.3002 | lr 0.0000 | time_forward 1.4070 | time_backward 1.4990 |
[2023-10-24 21:28:27,087::train::INFO] [train] Iter 587971 | loss 0.6468 | loss(rot) 0.6169 | loss(pos) 0.0149 | loss(seq) 0.0150 | grad 2.1780 | lr 0.0000 | time_forward 2.9370 | time_backward 3.8810 |
[2023-10-24 21:28:33,581::train::INFO] [train] Iter 587972 | loss 0.5952 | loss(rot) 0.1124 | loss(pos) 0.0411 | loss(seq) 0.4417 | grad 3.0637 | lr 0.0000 | time_forward 2.7240 | time_backward 3.7660 |
[2023-10-24 21:28:42,626::train::INFO] [train] Iter 587973 | loss 0.8688 | loss(rot) 0.8149 | loss(pos) 0.0538 | loss(seq) 0.0000 | grad 3.8295 | lr 0.0000 | time_forward 3.8440 | time_backward 5.1840 |
[2023-10-24 21:28:45,403::train::INFO] [train] Iter 587974 | loss 0.7169 | loss(rot) 0.2628 | loss(pos) 0.0646 | loss(seq) 0.3895 | grad 4.3281 | lr 0.0000 | time_forward 1.3010 | time_backward 1.4740 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.