text
stringlengths 56
1.16k
|
|---|
[2023-09-02 17:41:03,052::train::INFO] [train] Iter 12583 | loss 2.2083 | loss(rot) 1.3485 | loss(pos) 0.3123 | loss(seq) 0.5475 | grad 4.8208 | lr 0.0010 | time_forward 3.0460 | time_backward 4.2960 |
[2023-09-02 17:41:11,833::train::INFO] [train] Iter 12584 | loss 2.0403 | loss(rot) 0.0287 | loss(pos) 2.0102 | loss(seq) 0.0015 | grad 6.3097 | lr 0.0010 | time_forward 3.5820 | time_backward 5.1950 |
[2023-09-02 17:41:20,485::train::INFO] [train] Iter 12585 | loss 0.7906 | loss(rot) 0.5378 | loss(pos) 0.2528 | loss(seq) 0.0000 | grad 4.7011 | lr 0.0010 | time_forward 3.5220 | time_backward 5.1260 |
[2023-09-02 17:41:29,377::train::INFO] [train] Iter 12586 | loss 0.4690 | loss(rot) 0.1455 | loss(pos) 0.2866 | loss(seq) 0.0369 | grad 2.5979 | lr 0.0010 | time_forward 3.6180 | time_backward 5.2700 |
[2023-09-02 17:41:37,208::train::INFO] [train] Iter 12587 | loss 2.2527 | loss(rot) 0.0185 | loss(pos) 2.2343 | loss(seq) 0.0000 | grad 5.7067 | lr 0.0010 | time_forward 3.2330 | time_backward 4.5950 |
[2023-09-02 17:41:45,432::train::INFO] [train] Iter 12588 | loss 0.6992 | loss(rot) 0.2758 | loss(pos) 0.2989 | loss(seq) 0.1244 | grad 2.7691 | lr 0.0010 | time_forward 3.4410 | time_backward 4.7790 |
[2023-09-02 17:41:54,760::train::INFO] [train] Iter 12589 | loss 1.5827 | loss(rot) 0.5376 | loss(pos) 0.3111 | loss(seq) 0.7339 | grad 4.1255 | lr 0.0010 | time_forward 4.1580 | time_backward 5.1660 |
[2023-09-02 17:41:57,648::train::INFO] [train] Iter 12590 | loss 1.6162 | loss(rot) 0.8031 | loss(pos) 0.4511 | loss(seq) 0.3620 | grad 4.7705 | lr 0.0010 | time_forward 1.3580 | time_backward 1.5270 |
[2023-09-02 17:42:00,345::train::INFO] [train] Iter 12591 | loss 1.0911 | loss(rot) 0.3005 | loss(pos) 0.2724 | loss(seq) 0.5182 | grad 3.0157 | lr 0.0010 | time_forward 1.2150 | time_backward 1.4780 |
[2023-09-02 17:42:03,774::train::INFO] [train] Iter 12592 | loss 1.7873 | loss(rot) 0.9149 | loss(pos) 0.3474 | loss(seq) 0.5250 | grad 4.5610 | lr 0.0010 | time_forward 2.0350 | time_backward 1.3900 |
[2023-09-02 17:42:06,081::train::INFO] [train] Iter 12593 | loss 2.3571 | loss(rot) 2.1354 | loss(pos) 0.0848 | loss(seq) 0.1369 | grad 5.1797 | lr 0.0010 | time_forward 1.0330 | time_backward 1.2700 |
[2023-09-02 17:42:16,072::train::INFO] [train] Iter 12594 | loss 1.0124 | loss(rot) 0.6344 | loss(pos) 0.1652 | loss(seq) 0.2127 | grad 4.2594 | lr 0.0010 | time_forward 3.3020 | time_backward 6.6860 |
[2023-09-02 17:42:24,923::train::INFO] [train] Iter 12595 | loss 1.5094 | loss(rot) 0.4875 | loss(pos) 0.3082 | loss(seq) 0.7138 | grad 3.5387 | lr 0.0010 | time_forward 2.7890 | time_backward 6.0580 |
[2023-09-02 17:43:34,633::train::INFO] [train] Iter 12596 | loss 1.3846 | loss(rot) 0.6962 | loss(pos) 0.1299 | loss(seq) 0.5586 | grad 4.2234 | lr 0.0010 | time_forward 63.7980 | time_backward 5.9080 |
[2023-09-02 17:43:42,547::train::INFO] [train] Iter 12597 | loss 1.0678 | loss(rot) 0.7176 | loss(pos) 0.0738 | loss(seq) 0.2763 | grad 5.8871 | lr 0.0010 | time_forward 3.3390 | time_backward 4.5710 |
[2023-09-02 17:43:49,281::train::INFO] [train] Iter 12598 | loss 1.2604 | loss(rot) 1.1750 | loss(pos) 0.0842 | loss(seq) 0.0012 | grad 4.1982 | lr 0.0010 | time_forward 2.9490 | time_backward 3.7810 |
[2023-09-02 17:43:56,028::train::INFO] [train] Iter 12599 | loss 2.4546 | loss(rot) 1.4710 | loss(pos) 0.4228 | loss(seq) 0.5609 | grad 3.7216 | lr 0.0010 | time_forward 2.9610 | time_backward 3.7830 |
[2023-09-02 17:43:58,157::train::INFO] [train] Iter 12600 | loss 2.0945 | loss(rot) 1.9202 | loss(pos) 0.1076 | loss(seq) 0.0667 | grad 4.6348 | lr 0.0010 | time_forward 0.9950 | time_backward 1.1300 |
[2023-09-02 17:44:06,448::train::INFO] [train] Iter 12601 | loss 1.3036 | loss(rot) 0.4441 | loss(pos) 0.3489 | loss(seq) 0.5106 | grad 2.6525 | lr 0.0010 | time_forward 3.4880 | time_backward 4.7980 |
[2023-09-02 17:44:09,051::train::INFO] [train] Iter 12602 | loss 1.0622 | loss(rot) 0.3536 | loss(pos) 0.2752 | loss(seq) 0.4334 | grad 4.7374 | lr 0.0010 | time_forward 1.1930 | time_backward 1.4060 |
[2023-09-02 17:44:16,723::train::INFO] [train] Iter 12603 | loss 2.0144 | loss(rot) 1.4458 | loss(pos) 0.1360 | loss(seq) 0.4326 | grad 6.1327 | lr 0.0010 | time_forward 3.1530 | time_backward 4.5160 |
[2023-09-02 17:44:19,284::train::INFO] [train] Iter 12604 | loss 0.6383 | loss(rot) 0.1526 | loss(pos) 0.4154 | loss(seq) 0.0702 | grad 2.7935 | lr 0.0010 | time_forward 1.1460 | time_backward 1.4110 |
[2023-09-02 17:44:26,852::train::INFO] [train] Iter 12605 | loss 1.4628 | loss(rot) 0.5066 | loss(pos) 0.3409 | loss(seq) 0.6153 | grad 5.8313 | lr 0.0010 | time_forward 3.0950 | time_backward 4.4670 |
[2023-09-02 17:44:29,828::train::INFO] [train] Iter 12606 | loss 1.5551 | loss(rot) 0.7750 | loss(pos) 0.2753 | loss(seq) 0.5048 | grad 3.4507 | lr 0.0010 | time_forward 1.3020 | time_backward 1.6710 |
[2023-09-02 17:44:31,997::train::INFO] [train] Iter 12607 | loss 0.8332 | loss(rot) 0.0877 | loss(pos) 0.3925 | loss(seq) 0.3529 | grad 4.1642 | lr 0.0010 | time_forward 0.9740 | time_backward 1.1910 |
[2023-09-02 17:44:38,818::train::INFO] [train] Iter 12608 | loss 3.7200 | loss(rot) 3.1777 | loss(pos) 0.1614 | loss(seq) 0.3809 | grad 12.1565 | lr 0.0010 | time_forward 2.8270 | time_backward 3.9900 |
[2023-09-02 17:44:46,921::train::INFO] [train] Iter 12609 | loss 1.2194 | loss(rot) 1.1102 | loss(pos) 0.0358 | loss(seq) 0.0734 | grad 4.7819 | lr 0.0010 | time_forward 3.3890 | time_backward 4.7110 |
[2023-09-02 17:44:53,932::train::INFO] [train] Iter 12610 | loss 1.6170 | loss(rot) 0.6853 | loss(pos) 0.6825 | loss(seq) 0.2492 | grad 6.5175 | lr 0.0010 | time_forward 3.0610 | time_backward 3.9460 |
[2023-09-02 17:45:00,928::train::INFO] [train] Iter 12611 | loss 2.5654 | loss(rot) 2.4280 | loss(pos) 0.0774 | loss(seq) 0.0600 | grad 4.9016 | lr 0.0010 | time_forward 2.9380 | time_backward 4.0540 |
[2023-09-02 17:45:07,798::train::INFO] [train] Iter 12612 | loss 2.1655 | loss(rot) 1.4806 | loss(pos) 0.4241 | loss(seq) 0.2608 | grad 4.6936 | lr 0.0010 | time_forward 2.6840 | time_backward 4.1820 |
[2023-09-02 17:45:14,262::train::INFO] [train] Iter 12613 | loss 2.2082 | loss(rot) 0.7301 | loss(pos) 0.8599 | loss(seq) 0.6182 | grad 6.3789 | lr 0.0010 | time_forward 2.7480 | time_backward 3.7130 |
[2023-09-02 17:45:26,042::train::INFO] [train] Iter 12614 | loss 2.5517 | loss(rot) 2.0059 | loss(pos) 0.2372 | loss(seq) 0.3086 | grad 4.7168 | lr 0.0010 | time_forward 5.4520 | time_backward 6.3240 |
[2023-09-02 17:46:06,156::train::INFO] [train] Iter 12615 | loss 1.7389 | loss(rot) 0.5583 | loss(pos) 1.1483 | loss(seq) 0.0323 | grad 6.2889 | lr 0.0010 | time_forward 30.9450 | time_backward 9.1660 |
[2023-09-02 17:46:20,805::train::INFO] [train] Iter 12616 | loss 1.1309 | loss(rot) 0.7242 | loss(pos) 0.2314 | loss(seq) 0.1753 | grad 4.4624 | lr 0.0010 | time_forward 7.2790 | time_backward 7.3660 |
[2023-09-02 17:46:23,547::train::INFO] [train] Iter 12617 | loss 1.1451 | loss(rot) 0.3567 | loss(pos) 0.1898 | loss(seq) 0.5986 | grad 4.5083 | lr 0.0010 | time_forward 1.2780 | time_backward 1.4600 |
[2023-09-02 17:46:33,939::train::INFO] [train] Iter 12618 | loss 2.6894 | loss(rot) 2.3862 | loss(pos) 0.1302 | loss(seq) 0.1730 | grad 3.7451 | lr 0.0010 | time_forward 5.2530 | time_backward 5.1360 |
[2023-09-02 17:46:36,274::train::INFO] [train] Iter 12619 | loss 0.9571 | loss(rot) 0.4298 | loss(pos) 0.4126 | loss(seq) 0.1147 | grad 4.3878 | lr 0.0010 | time_forward 1.1140 | time_backward 1.2180 |
[2023-09-02 17:46:47,519::train::INFO] [train] Iter 12620 | loss 1.8102 | loss(rot) 0.9727 | loss(pos) 0.4352 | loss(seq) 0.4024 | grad 5.3650 | lr 0.0010 | time_forward 5.3310 | time_backward 5.9100 |
[2023-09-02 17:46:50,196::train::INFO] [train] Iter 12621 | loss 1.1741 | loss(rot) 0.3695 | loss(pos) 0.5472 | loss(seq) 0.2574 | grad 4.4413 | lr 0.0010 | time_forward 1.2560 | time_backward 1.4170 |
[2023-09-02 17:46:59,705::train::INFO] [train] Iter 12622 | loss 1.7020 | loss(rot) 1.1224 | loss(pos) 0.1291 | loss(seq) 0.4505 | grad 3.9964 | lr 0.0010 | time_forward 3.9420 | time_backward 5.5430 |
[2023-09-02 17:47:08,758::train::INFO] [train] Iter 12623 | loss 1.4562 | loss(rot) 1.2576 | loss(pos) 0.1974 | loss(seq) 0.0012 | grad 4.7269 | lr 0.0010 | time_forward 3.7840 | time_backward 5.2500 |
[2023-09-02 17:47:17,479::train::INFO] [train] Iter 12624 | loss 0.8288 | loss(rot) 0.0321 | loss(pos) 0.7903 | loss(seq) 0.0063 | grad 5.4588 | lr 0.0010 | time_forward 3.3730 | time_backward 5.3450 |
[2023-09-02 17:47:20,184::train::INFO] [train] Iter 12625 | loss 1.6218 | loss(rot) 1.4720 | loss(pos) 0.1496 | loss(seq) 0.0001 | grad 5.1465 | lr 0.0010 | time_forward 1.2500 | time_backward 1.4440 |
[2023-09-02 17:47:27,451::train::INFO] [train] Iter 12626 | loss 1.8261 | loss(rot) 1.6707 | loss(pos) 0.1433 | loss(seq) 0.0120 | grad 6.8901 | lr 0.0010 | time_forward 3.1740 | time_backward 4.0890 |
[2023-09-02 17:47:33,834::train::INFO] [train] Iter 12627 | loss 2.6833 | loss(rot) 1.8958 | loss(pos) 0.3006 | loss(seq) 0.4868 | grad 3.4966 | lr 0.0010 | time_forward 1.3200 | time_backward 5.0590 |
[2023-09-02 17:47:46,466::train::INFO] [train] Iter 12628 | loss 0.9616 | loss(rot) 0.2782 | loss(pos) 0.3347 | loss(seq) 0.3487 | grad 4.0248 | lr 0.0010 | time_forward 7.4270 | time_backward 5.2010 |
[2023-09-02 17:47:48,840::train::INFO] [train] Iter 12629 | loss 1.3878 | loss(rot) 1.1622 | loss(pos) 0.1595 | loss(seq) 0.0661 | grad 4.8403 | lr 0.0010 | time_forward 1.0750 | time_backward 1.2960 |
[2023-09-02 17:47:57,632::train::INFO] [train] Iter 12630 | loss 2.4051 | loss(rot) 1.3188 | loss(pos) 0.2530 | loss(seq) 0.8332 | grad 4.3199 | lr 0.0010 | time_forward 4.1070 | time_backward 4.6820 |
[2023-09-02 17:48:01,003::train::INFO] [train] Iter 12631 | loss 1.5361 | loss(rot) 0.6732 | loss(pos) 0.3488 | loss(seq) 0.5140 | grad 3.6927 | lr 0.0010 | time_forward 1.4070 | time_backward 1.9600 |
[2023-09-02 17:48:13,797::train::INFO] [train] Iter 12632 | loss 1.0168 | loss(rot) 0.3004 | loss(pos) 0.3309 | loss(seq) 0.3855 | grad 3.9617 | lr 0.0010 | time_forward 6.7370 | time_backward 6.0530 |
[2023-09-02 17:48:23,343::train::INFO] [train] Iter 12633 | loss 2.5995 | loss(rot) 2.1552 | loss(pos) 0.1429 | loss(seq) 0.3015 | grad 4.2275 | lr 0.0010 | time_forward 3.8250 | time_backward 5.7120 |
[2023-09-02 17:48:26,018::train::INFO] [train] Iter 12634 | loss 1.7514 | loss(rot) 1.4054 | loss(pos) 0.1064 | loss(seq) 0.2396 | grad 3.0332 | lr 0.0010 | time_forward 1.2250 | time_backward 1.4460 |
[2023-09-02 17:48:28,759::train::INFO] [train] Iter 12635 | loss 2.3008 | loss(rot) 1.8345 | loss(pos) 0.1095 | loss(seq) 0.3569 | grad 7.0781 | lr 0.0010 | time_forward 1.2600 | time_backward 1.4770 |
[2023-09-02 17:48:38,613::train::INFO] [train] Iter 12636 | loss 2.3524 | loss(rot) 2.1072 | loss(pos) 0.1579 | loss(seq) 0.0873 | grad 3.7939 | lr 0.0010 | time_forward 3.9810 | time_backward 5.8510 |
[2023-09-02 17:48:41,283::train::INFO] [train] Iter 12637 | loss 0.6054 | loss(rot) 0.2248 | loss(pos) 0.3358 | loss(seq) 0.0448 | grad 3.8256 | lr 0.0010 | time_forward 1.2410 | time_backward 1.4260 |
[2023-09-02 17:48:44,612::train::INFO] [train] Iter 12638 | loss 1.3808 | loss(rot) 0.5154 | loss(pos) 0.2581 | loss(seq) 0.6073 | grad 6.5739 | lr 0.0010 | time_forward 1.4740 | time_backward 1.8500 |
[2023-09-02 17:48:51,930::train::INFO] [train] Iter 12639 | loss 1.1200 | loss(rot) 0.7386 | loss(pos) 0.0613 | loss(seq) 0.3200 | grad 3.4271 | lr 0.0010 | time_forward 3.0040 | time_backward 4.3100 |
[2023-09-02 17:49:01,341::train::INFO] [train] Iter 12640 | loss 2.0331 | loss(rot) 1.8119 | loss(pos) 0.2210 | loss(seq) 0.0001 | grad 3.5909 | lr 0.0010 | time_forward 3.8900 | time_backward 5.5170 |
[2023-09-02 17:49:09,200::train::INFO] [train] Iter 12641 | loss 1.2109 | loss(rot) 0.5107 | loss(pos) 0.1383 | loss(seq) 0.5619 | grad 3.0496 | lr 0.0010 | time_forward 3.3270 | time_backward 4.5300 |
[2023-09-02 17:49:11,916::train::INFO] [train] Iter 12642 | loss 2.0955 | loss(rot) 1.9439 | loss(pos) 0.1183 | loss(seq) 0.0334 | grad 6.1645 | lr 0.0010 | time_forward 1.2530 | time_backward 1.4490 |
[2023-09-02 17:49:14,597::train::INFO] [train] Iter 12643 | loss 2.6785 | loss(rot) 1.9332 | loss(pos) 0.2618 | loss(seq) 0.4835 | grad 8.0062 | lr 0.0010 | time_forward 1.2360 | time_backward 1.4420 |
[2023-09-02 17:49:17,227::train::INFO] [train] Iter 12644 | loss 1.4748 | loss(rot) 1.2458 | loss(pos) 0.2123 | loss(seq) 0.0168 | grad 4.1260 | lr 0.0010 | time_forward 1.1980 | time_backward 1.4280 |
[2023-09-02 17:49:19,940::train::INFO] [train] Iter 12645 | loss 1.7150 | loss(rot) 1.0891 | loss(pos) 0.2101 | loss(seq) 0.4158 | grad 3.0952 | lr 0.0010 | time_forward 1.2510 | time_backward 1.4600 |
[2023-09-02 17:49:29,080::train::INFO] [train] Iter 12646 | loss 1.8102 | loss(rot) 1.0115 | loss(pos) 0.3929 | loss(seq) 0.4059 | grad 5.3001 | lr 0.0010 | time_forward 3.9020 | time_backward 5.2340 |
[2023-09-02 17:49:43,613::train::INFO] [train] Iter 12647 | loss 1.1571 | loss(rot) 0.6442 | loss(pos) 0.2652 | loss(seq) 0.2476 | grad 4.3237 | lr 0.0010 | time_forward 8.6300 | time_backward 5.9000 |
[2023-09-02 17:49:52,311::train::INFO] [train] Iter 12648 | loss 1.3785 | loss(rot) 0.8310 | loss(pos) 0.2171 | loss(seq) 0.3304 | grad 5.1653 | lr 0.0010 | time_forward 3.6990 | time_backward 4.9940 |
[2023-09-02 17:49:55,179::train::INFO] [train] Iter 12649 | loss 2.5227 | loss(rot) 2.2528 | loss(pos) 0.2425 | loss(seq) 0.0274 | grad 3.9608 | lr 0.0010 | time_forward 1.2650 | time_backward 1.5990 |
[2023-09-02 17:50:01,844::train::INFO] [train] Iter 12650 | loss 3.1984 | loss(rot) 0.1075 | loss(pos) 2.8848 | loss(seq) 0.2062 | grad 10.7953 | lr 0.0010 | time_forward 2.7130 | time_backward 3.9350 |
[2023-09-02 17:50:10,312::train::INFO] [train] Iter 12651 | loss 2.1954 | loss(rot) 1.4747 | loss(pos) 0.2786 | loss(seq) 0.4421 | grad 9.5910 | lr 0.0010 | time_forward 3.5420 | time_backward 4.9220 |
[2023-09-02 17:50:19,423::train::INFO] [train] Iter 12652 | loss 1.2971 | loss(rot) 0.1717 | loss(pos) 0.2451 | loss(seq) 0.8804 | grad 3.0461 | lr 0.0010 | time_forward 4.0860 | time_backward 5.0210 |
[2023-09-02 17:50:27,617::train::INFO] [train] Iter 12653 | loss 1.5923 | loss(rot) 0.0180 | loss(pos) 1.5722 | loss(seq) 0.0022 | grad 8.4180 | lr 0.0010 | time_forward 3.3610 | time_backward 4.8290 |
[2023-09-02 17:50:34,993::train::INFO] [train] Iter 12654 | loss 2.0044 | loss(rot) 1.4423 | loss(pos) 0.3196 | loss(seq) 0.2425 | grad 7.7760 | lr 0.0010 | time_forward 3.1570 | time_backward 4.2150 |
[2023-09-02 17:50:45,044::train::INFO] [train] Iter 12655 | loss 1.9339 | loss(rot) 0.8896 | loss(pos) 0.5793 | loss(seq) 0.4650 | grad 5.5057 | lr 0.0010 | time_forward 4.2160 | time_backward 5.8320 |
[2023-09-02 17:50:52,621::train::INFO] [train] Iter 12656 | loss 1.8343 | loss(rot) 1.7558 | loss(pos) 0.0785 | loss(seq) 0.0000 | grad 6.3893 | lr 0.0010 | time_forward 3.1820 | time_backward 4.3920 |
[2023-09-02 17:50:55,337::train::INFO] [train] Iter 12657 | loss 1.4660 | loss(rot) 0.2786 | loss(pos) 0.5691 | loss(seq) 0.6182 | grad 4.5440 | lr 0.0010 | time_forward 1.2560 | time_backward 1.4570 |
[2023-09-02 17:51:04,600::train::INFO] [train] Iter 12658 | loss 0.9761 | loss(rot) 0.2600 | loss(pos) 0.2631 | loss(seq) 0.4530 | grad 4.4938 | lr 0.0010 | time_forward 4.0030 | time_backward 5.2570 |
[2023-09-02 17:51:07,080::train::INFO] [train] Iter 12659 | loss 1.4613 | loss(rot) 0.6957 | loss(pos) 0.3412 | loss(seq) 0.4244 | grad 4.6363 | lr 0.0010 | time_forward 1.1750 | time_backward 1.3010 |
[2023-09-02 17:51:15,326::train::INFO] [train] Iter 12660 | loss 1.2710 | loss(rot) 0.4348 | loss(pos) 0.2822 | loss(seq) 0.5539 | grad 4.7623 | lr 0.0010 | time_forward 3.4010 | time_backward 4.8420 |
[2023-09-02 17:51:23,596::train::INFO] [train] Iter 12661 | loss 1.5681 | loss(rot) 1.3526 | loss(pos) 0.2155 | loss(seq) 0.0000 | grad 12.6514 | lr 0.0010 | time_forward 3.4720 | time_backward 4.7940 |
[2023-09-02 17:51:31,445::train::INFO] [train] Iter 12662 | loss 1.6030 | loss(rot) 1.1721 | loss(pos) 0.1058 | loss(seq) 0.3250 | grad 6.0292 | lr 0.0010 | time_forward 3.1020 | time_backward 4.7450 |
[2023-09-02 17:51:33,716::train::INFO] [train] Iter 12663 | loss 1.3126 | loss(rot) 0.3810 | loss(pos) 0.3691 | loss(seq) 0.5625 | grad 3.1761 | lr 0.0010 | time_forward 1.0450 | time_backward 1.2220 |
[2023-09-02 17:51:36,426::train::INFO] [train] Iter 12664 | loss 1.1620 | loss(rot) 0.5428 | loss(pos) 0.0383 | loss(seq) 0.5808 | grad 6.4403 | lr 0.0010 | time_forward 1.2140 | time_backward 1.4930 |
[2023-09-02 17:51:45,270::train::INFO] [train] Iter 12665 | loss 2.3166 | loss(rot) 1.3342 | loss(pos) 0.3589 | loss(seq) 0.6235 | grad 3.8206 | lr 0.0010 | time_forward 3.7510 | time_backward 5.0890 |
[2023-09-02 17:51:55,399::train::INFO] [train] Iter 12666 | loss 1.1529 | loss(rot) 0.0880 | loss(pos) 1.0548 | loss(seq) 0.0100 | grad 3.6448 | lr 0.0010 | time_forward 4.1060 | time_backward 6.0190 |
[2023-09-02 17:52:04,002::train::INFO] [train] Iter 12667 | loss 1.3911 | loss(rot) 0.8427 | loss(pos) 0.2062 | loss(seq) 0.3422 | grad 9.3425 | lr 0.0010 | time_forward 3.6670 | time_backward 4.9340 |
[2023-09-02 17:52:10,573::train::INFO] [train] Iter 12668 | loss 2.5662 | loss(rot) 2.1842 | loss(pos) 0.3820 | loss(seq) 0.0000 | grad 6.4171 | lr 0.0010 | time_forward 2.8300 | time_backward 3.7370 |
[2023-09-02 17:52:20,317::train::INFO] [train] Iter 12669 | loss 1.7512 | loss(rot) 1.5434 | loss(pos) 0.1387 | loss(seq) 0.0691 | grad 5.7687 | lr 0.0010 | time_forward 4.0140 | time_backward 5.7270 |
[2023-09-02 17:52:22,770::train::INFO] [train] Iter 12670 | loss 3.2553 | loss(rot) 2.5743 | loss(pos) 0.2862 | loss(seq) 0.3949 | grad 12.3057 | lr 0.0010 | time_forward 1.1660 | time_backward 1.2830 |
[2023-09-02 17:52:31,986::train::INFO] [train] Iter 12671 | loss 1.1745 | loss(rot) 0.5806 | loss(pos) 0.1206 | loss(seq) 0.4734 | grad 4.7166 | lr 0.0010 | time_forward 3.8510 | time_backward 5.3360 |
[2023-09-02 17:52:40,128::train::INFO] [train] Iter 12672 | loss 1.2476 | loss(rot) 0.8781 | loss(pos) 0.1660 | loss(seq) 0.2035 | grad 4.6760 | lr 0.0010 | time_forward 3.4660 | time_backward 4.6720 |
[2023-09-02 17:52:43,611::train::INFO] [train] Iter 12673 | loss 2.4989 | loss(rot) 1.7843 | loss(pos) 0.2556 | loss(seq) 0.4590 | grad 4.4270 | lr 0.0010 | time_forward 1.4540 | time_backward 2.0250 |
[2023-09-02 17:53:03,145::train::INFO] [train] Iter 12674 | loss 1.6267 | loss(rot) 0.6677 | loss(pos) 0.3466 | loss(seq) 0.6124 | grad 3.6369 | lr 0.0010 | time_forward 11.3800 | time_backward 8.1510 |
[2023-09-02 17:53:06,490::train::INFO] [train] Iter 12675 | loss 1.3254 | loss(rot) 0.5759 | loss(pos) 0.2951 | loss(seq) 0.4544 | grad 2.8101 | lr 0.0010 | time_forward 1.4030 | time_backward 1.9380 |
[2023-09-02 17:53:09,246::train::INFO] [train] Iter 12676 | loss 0.3584 | loss(rot) 0.1236 | loss(pos) 0.1474 | loss(seq) 0.0874 | grad 3.0392 | lr 0.0010 | time_forward 1.3010 | time_backward 1.4530 |
[2023-09-02 17:53:18,054::train::INFO] [train] Iter 12677 | loss 0.8896 | loss(rot) 0.5131 | loss(pos) 0.3118 | loss(seq) 0.0646 | grad 4.6393 | lr 0.0010 | time_forward 3.7310 | time_backward 5.0720 |
[2023-09-02 17:53:20,882::train::INFO] [train] Iter 12678 | loss 1.6358 | loss(rot) 0.4823 | loss(pos) 0.7363 | loss(seq) 0.4171 | grad 5.2516 | lr 0.0010 | time_forward 1.3010 | time_backward 1.5230 |
[2023-09-02 17:53:30,906::train::INFO] [train] Iter 12679 | loss 2.8320 | loss(rot) 2.2755 | loss(pos) 0.1325 | loss(seq) 0.4240 | grad 5.9436 | lr 0.0010 | time_forward 4.1530 | time_backward 5.8470 |
[2023-09-02 17:53:39,940::train::INFO] [train] Iter 12680 | loss 1.3181 | loss(rot) 1.1056 | loss(pos) 0.1676 | loss(seq) 0.0449 | grad 5.2289 | lr 0.0010 | time_forward 3.8220 | time_backward 5.2070 |
[2023-09-02 17:53:42,745::train::INFO] [train] Iter 12681 | loss 1.4294 | loss(rot) 0.0218 | loss(pos) 1.4064 | loss(seq) 0.0012 | grad 6.9758 | lr 0.0010 | time_forward 1.3260 | time_backward 1.4750 |
[2023-09-02 17:53:50,013::train::INFO] [train] Iter 12682 | loss 1.6924 | loss(rot) 0.9778 | loss(pos) 0.2752 | loss(seq) 0.4394 | grad 4.2450 | lr 0.0010 | time_forward 3.0870 | time_backward 4.1780 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.