text stringlengths 56 1.16k |
|---|
[2023-10-24 21:42:14,103::train::INFO] [train] Iter 588074 | loss 2.2409 | loss(rot) 2.0411 | loss(pos) 0.1171 | loss(seq) 0.0827 | grad 4.5820 | lr 0.0000 | time_forward 4.5880 | time_backward 6.1870 |
[2023-10-24 21:42:16,817::train::INFO] [train] Iter 588075 | loss 0.4281 | loss(rot) 0.1169 | loss(pos) 0.1420 | loss(seq) 0.1693 | grad 2.5258 | lr 0.0000 | time_forward 1.2970 | time_backward 1.4140 |
[2023-10-24 21:42:24,965::train::INFO] [train] Iter 588076 | loss 2.3663 | loss(rot) 2.3392 | loss(pos) 0.0271 | loss(seq) 0.0000 | grad 5.0632 | lr 0.0000 | time_forward 3.4450 | time_backward 4.7010 |
[2023-10-24 21:42:30,097::train::INFO] [train] Iter 588077 | loss 0.8030 | loss(rot) 0.4408 | loss(pos) 0.0496 | loss(seq) 0.3126 | grad 3.5162 | lr 0.0000 | time_forward 2.3080 | time_backward 2.8210 |
[2023-10-24 21:42:36,682::train::INFO] [train] Iter 588078 | loss 1.0745 | loss(rot) 0.5952 | loss(pos) 0.1494 | loss(seq) 0.3299 | grad 4.8285 | lr 0.0000 | time_forward 2.7970 | time_backward 3.7840 |
[2023-10-24 21:42:45,244::train::INFO] [train] Iter 588079 | loss 0.7046 | loss(rot) 0.0053 | loss(pos) 0.6991 | loss(seq) 0.0002 | grad 14.0850 | lr 0.0000 | time_forward 3.5890 | time_backward 4.9570 |
[2023-10-24 21:42:55,416::train::INFO] [train] Iter 588080 | loss 1.4290 | loss(rot) 0.9315 | loss(pos) 0.2029 | loss(seq) 0.2945 | grad 3.5007 | lr 0.0000 | time_forward 4.2680 | time_backward 5.8540 |
[2023-10-24 21:43:05,396::train::INFO] [train] Iter 588081 | loss 0.5782 | loss(rot) 0.0384 | loss(pos) 0.5374 | loss(seq) 0.0025 | grad 6.6942 | lr 0.0000 | time_forward 4.0870 | time_backward 5.8900 |
[2023-10-24 21:43:14,099::train::INFO] [train] Iter 588082 | loss 0.4945 | loss(rot) 0.0083 | loss(pos) 0.4860 | loss(seq) 0.0002 | grad 10.1212 | lr 0.0000 | time_forward 3.6730 | time_backward 5.0270 |
[2023-10-24 21:43:24,570::train::INFO] [train] Iter 588083 | loss 1.0076 | loss(rot) 0.6196 | loss(pos) 0.0777 | loss(seq) 0.3103 | grad 4.3430 | lr 0.0000 | time_forward 4.1330 | time_backward 6.3340 |
[2023-10-24 21:43:27,362::train::INFO] [train] Iter 588084 | loss 0.4530 | loss(rot) 0.0808 | loss(pos) 0.3619 | loss(seq) 0.0103 | grad 4.7502 | lr 0.0000 | time_forward 1.3150 | time_backward 1.4740 |
[2023-10-24 21:43:36,381::train::INFO] [train] Iter 588085 | loss 2.0523 | loss(rot) 2.0343 | loss(pos) 0.0181 | loss(seq) 0.0000 | grad 5.7870 | lr 0.0000 | time_forward 3.8090 | time_backward 5.2080 |
[2023-10-24 21:43:47,389::train::INFO] [train] Iter 588086 | loss 0.3428 | loss(rot) 0.2696 | loss(pos) 0.0336 | loss(seq) 0.0397 | grad 3.1951 | lr 0.0000 | time_forward 4.7340 | time_backward 6.2700 |
[2023-10-24 21:43:56,969::train::INFO] [train] Iter 588087 | loss 0.7141 | loss(rot) 0.0234 | loss(pos) 0.6834 | loss(seq) 0.0072 | grad 7.0274 | lr 0.0000 | time_forward 3.9690 | time_backward 5.6080 |
[2023-10-24 21:44:06,551::train::INFO] [train] Iter 588088 | loss 0.5294 | loss(rot) 0.2478 | loss(pos) 0.2619 | loss(seq) 0.0197 | grad 5.2641 | lr 0.0000 | time_forward 3.9070 | time_backward 5.6720 |
[2023-10-24 21:44:09,265::train::INFO] [train] Iter 588089 | loss 0.3093 | loss(rot) 0.0839 | loss(pos) 0.1598 | loss(seq) 0.0655 | grad 2.8209 | lr 0.0000 | time_forward 1.2950 | time_backward 1.4170 |
[2023-10-24 21:44:12,121::train::INFO] [train] Iter 588090 | loss 0.3470 | loss(rot) 0.3080 | loss(pos) 0.0175 | loss(seq) 0.0215 | grad 5.6506 | lr 0.0000 | time_forward 1.4150 | time_backward 1.4370 |
[2023-10-24 21:44:21,369::train::INFO] [train] Iter 588091 | loss 0.3758 | loss(rot) 0.0987 | loss(pos) 0.0756 | loss(seq) 0.2015 | grad 3.0890 | lr 0.0000 | time_forward 3.9670 | time_backward 5.2790 |
[2023-10-24 21:44:31,817::train::INFO] [train] Iter 588092 | loss 1.3695 | loss(rot) 0.8269 | loss(pos) 0.0649 | loss(seq) 0.4778 | grad 4.5246 | lr 0.0000 | time_forward 4.4180 | time_backward 6.0260 |
[2023-10-24 21:44:42,655::train::INFO] [train] Iter 588093 | loss 0.3359 | loss(rot) 0.0716 | loss(pos) 0.0339 | loss(seq) 0.2305 | grad 2.4511 | lr 0.0000 | time_forward 4.6940 | time_backward 6.1420 |
[2023-10-24 21:44:52,709::train::INFO] [train] Iter 588094 | loss 1.0531 | loss(rot) 0.0269 | loss(pos) 1.0203 | loss(seq) 0.0059 | grad 9.6920 | lr 0.0000 | time_forward 3.9890 | time_backward 6.0620 |
[2023-10-24 21:45:00,149::train::INFO] [train] Iter 588095 | loss 0.3100 | loss(rot) 0.0281 | loss(pos) 0.2524 | loss(seq) 0.0295 | grad 4.3816 | lr 0.0000 | time_forward 3.1740 | time_backward 4.2620 |
[2023-10-24 21:45:09,229::train::INFO] [train] Iter 588096 | loss 0.2256 | loss(rot) 0.0802 | loss(pos) 0.0184 | loss(seq) 0.1269 | grad 1.7056 | lr 0.0000 | time_forward 3.8330 | time_backward 5.2440 |
[2023-10-24 21:45:17,691::train::INFO] [train] Iter 588097 | loss 0.7769 | loss(rot) 0.0767 | loss(pos) 0.6915 | loss(seq) 0.0087 | grad 13.7713 | lr 0.0000 | time_forward 3.5010 | time_backward 4.9580 |
[2023-10-24 21:45:26,403::train::INFO] [train] Iter 588098 | loss 1.9405 | loss(rot) 1.4133 | loss(pos) 0.1268 | loss(seq) 0.4005 | grad 5.5007 | lr 0.0000 | time_forward 3.6510 | time_backward 5.0570 |
[2023-10-24 21:45:34,654::train::INFO] [train] Iter 588099 | loss 0.7516 | loss(rot) 0.5922 | loss(pos) 0.0693 | loss(seq) 0.0900 | grad 2.5446 | lr 0.0000 | time_forward 3.4670 | time_backward 4.7790 |
[2023-10-24 21:45:44,569::train::INFO] [train] Iter 588100 | loss 2.4390 | loss(rot) 1.9811 | loss(pos) 0.0920 | loss(seq) 0.3659 | grad 3.4619 | lr 0.0000 | time_forward 4.0000 | time_backward 5.9120 |
[2023-10-24 21:45:47,322::train::INFO] [train] Iter 588101 | loss 0.3093 | loss(rot) 0.2622 | loss(pos) 0.0445 | loss(seq) 0.0026 | grad 9.5389 | lr 0.0000 | time_forward 1.2940 | time_backward 1.4570 |
[2023-10-24 21:45:57,639::train::INFO] [train] Iter 588102 | loss 0.8286 | loss(rot) 0.7973 | loss(pos) 0.0271 | loss(seq) 0.0042 | grad 7.0764 | lr 0.0000 | time_forward 4.2180 | time_backward 6.0570 |
[2023-10-24 21:46:07,858::train::INFO] [train] Iter 588103 | loss 0.4247 | loss(rot) 0.1731 | loss(pos) 0.1897 | loss(seq) 0.0620 | grad 2.1969 | lr 0.0000 | time_forward 4.3670 | time_backward 5.8480 |
[2023-10-24 21:46:15,924::train::INFO] [train] Iter 588104 | loss 0.1669 | loss(rot) 0.1342 | loss(pos) 0.0327 | loss(seq) 0.0000 | grad 2.1544 | lr 0.0000 | time_forward 3.4090 | time_backward 4.6540 |
[2023-10-24 21:46:24,018::train::INFO] [train] Iter 588105 | loss 0.8543 | loss(rot) 0.6231 | loss(pos) 0.0206 | loss(seq) 0.2106 | grad 4.8811 | lr 0.0000 | time_forward 3.4630 | time_backward 4.6280 |
[2023-10-24 21:46:33,966::train::INFO] [train] Iter 588106 | loss 0.5122 | loss(rot) 0.3999 | loss(pos) 0.0344 | loss(seq) 0.0779 | grad 3.1004 | lr 0.0000 | time_forward 4.0330 | time_backward 5.9110 |
[2023-10-24 21:46:36,761::train::INFO] [train] Iter 588107 | loss 0.3715 | loss(rot) 0.0859 | loss(pos) 0.0435 | loss(seq) 0.2421 | grad 2.6599 | lr 0.0000 | time_forward 1.3180 | time_backward 1.4730 |
[2023-10-24 21:46:46,839::train::INFO] [train] Iter 588108 | loss 1.2621 | loss(rot) 0.8630 | loss(pos) 0.1482 | loss(seq) 0.2508 | grad 4.0545 | lr 0.0000 | time_forward 4.0570 | time_backward 5.9850 |
[2023-10-24 21:46:49,634::train::INFO] [train] Iter 588109 | loss 1.0638 | loss(rot) 0.2978 | loss(pos) 0.2094 | loss(seq) 0.5565 | grad 4.3087 | lr 0.0000 | time_forward 1.3490 | time_backward 1.4430 |
[2023-10-24 21:46:58,101::train::INFO] [train] Iter 588110 | loss 0.5958 | loss(rot) 0.5405 | loss(pos) 0.0186 | loss(seq) 0.0367 | grad 3.9209 | lr 0.0000 | time_forward 3.5240 | time_backward 4.9050 |
[2023-10-24 21:47:00,625::train::INFO] [train] Iter 588111 | loss 4.9527 | loss(rot) 0.0127 | loss(pos) 4.9400 | loss(seq) 0.0000 | grad 18.0867 | lr 0.0000 | time_forward 1.2200 | time_backward 1.3020 |
[2023-10-24 21:47:09,067::train::INFO] [train] Iter 588112 | loss 0.1637 | loss(rot) 0.1085 | loss(pos) 0.0187 | loss(seq) 0.0365 | grad 1.6988 | lr 0.0000 | time_forward 3.6150 | time_backward 4.8240 |
[2023-10-24 21:47:19,053::train::INFO] [train] Iter 588113 | loss 2.6264 | loss(rot) 1.9424 | loss(pos) 0.2556 | loss(seq) 0.4284 | grad 6.7328 | lr 0.0000 | time_forward 4.2200 | time_backward 5.7630 |
[2023-10-24 21:47:29,345::train::INFO] [train] Iter 588114 | loss 1.9189 | loss(rot) 1.7514 | loss(pos) 0.0838 | loss(seq) 0.0837 | grad 5.3410 | lr 0.0000 | time_forward 4.3720 | time_backward 5.9160 |
[2023-10-24 21:47:37,618::train::INFO] [train] Iter 588115 | loss 0.4444 | loss(rot) 0.2322 | loss(pos) 0.0426 | loss(seq) 0.1697 | grad 2.8579 | lr 0.0000 | time_forward 3.5020 | time_backward 4.7690 |
[2023-10-24 21:47:40,511::train::INFO] [train] Iter 588116 | loss 0.1075 | loss(rot) 0.0935 | loss(pos) 0.0136 | loss(seq) 0.0003 | grad 2.3197 | lr 0.0000 | time_forward 1.4260 | time_backward 1.4640 |
[2023-10-24 21:47:50,708::train::INFO] [train] Iter 588117 | loss 0.8385 | loss(rot) 0.5726 | loss(pos) 0.0443 | loss(seq) 0.2216 | grad 4.1877 | lr 0.0000 | time_forward 4.1580 | time_backward 5.9940 |
[2023-10-24 21:47:53,487::train::INFO] [train] Iter 588118 | loss 0.2488 | loss(rot) 0.0441 | loss(pos) 0.1701 | loss(seq) 0.0346 | grad 4.5455 | lr 0.0000 | time_forward 1.3420 | time_backward 1.4340 |
[2023-10-24 21:47:56,523::train::INFO] [train] Iter 588119 | loss 0.5708 | loss(rot) 0.2848 | loss(pos) 0.0258 | loss(seq) 0.2602 | grad 2.9321 | lr 0.0000 | time_forward 1.4730 | time_backward 1.5180 |
[2023-10-24 21:48:05,134::train::INFO] [train] Iter 588120 | loss 0.2385 | loss(rot) 0.1024 | loss(pos) 0.0405 | loss(seq) 0.0956 | grad 2.3082 | lr 0.0000 | time_forward 3.7000 | time_backward 4.9080 |
[2023-10-24 21:48:08,118::train::INFO] [train] Iter 588121 | loss 0.9008 | loss(rot) 0.5272 | loss(pos) 0.0836 | loss(seq) 0.2900 | grad 3.2183 | lr 0.0000 | time_forward 1.4490 | time_backward 1.5310 |
[2023-10-24 21:48:16,772::train::INFO] [train] Iter 588122 | loss 0.4752 | loss(rot) 0.0210 | loss(pos) 0.3737 | loss(seq) 0.0804 | grad 4.9182 | lr 0.0000 | time_forward 3.6390 | time_backward 5.0130 |
[2023-10-24 21:48:19,627::train::INFO] [train] Iter 588123 | loss 2.9084 | loss(rot) 2.7635 | loss(pos) 0.1449 | loss(seq) 0.0000 | grad 13.6339 | lr 0.0000 | time_forward 1.4150 | time_backward 1.4370 |
[2023-10-24 21:48:29,917::train::INFO] [train] Iter 588124 | loss 0.6619 | loss(rot) 0.1190 | loss(pos) 0.3678 | loss(seq) 0.1751 | grad 5.2687 | lr 0.0000 | time_forward 4.2670 | time_backward 6.0200 |
[2023-10-24 21:48:40,114::train::INFO] [train] Iter 588125 | loss 0.5946 | loss(rot) 0.4727 | loss(pos) 0.0753 | loss(seq) 0.0466 | grad 2.4989 | lr 0.0000 | time_forward 4.1940 | time_backward 5.9990 |
[2023-10-24 21:48:42,926::train::INFO] [train] Iter 588126 | loss 0.2389 | loss(rot) 0.1335 | loss(pos) 0.0356 | loss(seq) 0.0699 | grad 2.3293 | lr 0.0000 | time_forward 1.3900 | time_backward 1.4190 |
[2023-10-24 21:48:52,004::train::INFO] [train] Iter 588127 | loss 1.0277 | loss(rot) 0.6374 | loss(pos) 0.0946 | loss(seq) 0.2957 | grad 4.5515 | lr 0.0000 | time_forward 3.9250 | time_backward 5.1290 |
[2023-10-24 21:49:01,463::train::INFO] [train] Iter 588128 | loss 0.2069 | loss(rot) 0.1954 | loss(pos) 0.0101 | loss(seq) 0.0014 | grad 2.8193 | lr 0.0000 | time_forward 4.0110 | time_backward 5.4450 |
[2023-10-24 21:49:11,532::train::INFO] [train] Iter 588129 | loss 0.2258 | loss(rot) 0.0815 | loss(pos) 0.1250 | loss(seq) 0.0193 | grad 3.8210 | lr 0.0000 | time_forward 4.0320 | time_backward 6.0330 |
[2023-10-24 21:49:14,555::train::INFO] [train] Iter 588130 | loss 0.3185 | loss(rot) 0.2270 | loss(pos) 0.0479 | loss(seq) 0.0436 | grad 3.2758 | lr 0.0000 | time_forward 1.5460 | time_backward 1.4740 |
[2023-10-24 21:49:25,245::train::INFO] [train] Iter 588131 | loss 0.3958 | loss(rot) 0.0820 | loss(pos) 0.1592 | loss(seq) 0.1546 | grad 4.2525 | lr 0.0000 | time_forward 4.5770 | time_backward 6.0830 |
[2023-10-24 21:49:35,161::train::INFO] [train] Iter 588132 | loss 0.6166 | loss(rot) 0.5905 | loss(pos) 0.0256 | loss(seq) 0.0004 | grad 4.0048 | lr 0.0000 | time_forward 4.7860 | time_backward 5.1270 |
[2023-10-24 21:49:42,807::train::INFO] [train] Iter 588133 | loss 0.3231 | loss(rot) 0.0894 | loss(pos) 0.0620 | loss(seq) 0.1717 | grad 2.5864 | lr 0.0000 | time_forward 3.4940 | time_backward 4.1490 |
[2023-10-24 21:49:54,141::train::INFO] [train] Iter 588134 | loss 2.2115 | loss(rot) 1.5877 | loss(pos) 0.4703 | loss(seq) 0.1535 | grad 4.7950 | lr 0.0000 | time_forward 4.9420 | time_backward 6.3890 |
[2023-10-24 21:49:57,593::train::INFO] [train] Iter 588135 | loss 0.4921 | loss(rot) 0.2130 | loss(pos) 0.0462 | loss(seq) 0.2329 | grad 2.9886 | lr 0.0000 | time_forward 1.7640 | time_backward 1.6840 |
[2023-10-24 21:50:08,202::train::INFO] [train] Iter 588136 | loss 0.8268 | loss(rot) 0.4867 | loss(pos) 0.0372 | loss(seq) 0.3030 | grad 2.8899 | lr 0.0000 | time_forward 4.3910 | time_backward 6.2020 |
[2023-10-24 21:50:17,437::train::INFO] [train] Iter 588137 | loss 0.2952 | loss(rot) 0.0530 | loss(pos) 0.1447 | loss(seq) 0.0974 | grad 3.8423 | lr 0.0000 | time_forward 3.7400 | time_backward 5.4930 |
[2023-10-24 21:50:26,990::train::INFO] [train] Iter 588138 | loss 1.4428 | loss(rot) 0.7176 | loss(pos) 0.1135 | loss(seq) 0.6117 | grad 10.9862 | lr 0.0000 | time_forward 4.1040 | time_backward 5.4450 |
[2023-10-24 21:50:37,487::train::INFO] [train] Iter 588139 | loss 0.6620 | loss(rot) 0.3845 | loss(pos) 0.1938 | loss(seq) 0.0837 | grad 3.3975 | lr 0.0000 | time_forward 4.4660 | time_backward 6.0280 |
[2023-10-24 21:50:40,357::train::INFO] [train] Iter 588140 | loss 0.3197 | loss(rot) 0.0556 | loss(pos) 0.2427 | loss(seq) 0.0214 | grad 3.6522 | lr 0.0000 | time_forward 1.3790 | time_backward 1.4880 |
[2023-10-24 21:50:43,069::train::INFO] [train] Iter 588141 | loss 0.8869 | loss(rot) 0.7946 | loss(pos) 0.0869 | loss(seq) 0.0054 | grad 3.3698 | lr 0.0000 | time_forward 1.3770 | time_backward 1.3310 |
[2023-10-24 21:50:53,659::train::INFO] [train] Iter 588142 | loss 0.3653 | loss(rot) 0.0587 | loss(pos) 0.1467 | loss(seq) 0.1599 | grad 3.3762 | lr 0.0000 | time_forward 4.2820 | time_backward 6.2920 |
[2023-10-24 21:50:56,522::train::INFO] [train] Iter 588143 | loss 0.0516 | loss(rot) 0.0248 | loss(pos) 0.0258 | loss(seq) 0.0011 | grad 1.4762 | lr 0.0000 | time_forward 1.3440 | time_backward 1.5150 |
[2023-10-24 21:51:06,998::train::INFO] [train] Iter 588144 | loss 0.4906 | loss(rot) 0.1261 | loss(pos) 0.2360 | loss(seq) 0.1285 | grad 5.7318 | lr 0.0000 | time_forward 4.1840 | time_backward 6.2550 |
[2023-10-24 21:51:16,641::train::INFO] [train] Iter 588145 | loss 0.8677 | loss(rot) 0.7335 | loss(pos) 0.0458 | loss(seq) 0.0883 | grad 3.8534 | lr 0.0000 | time_forward 4.0940 | time_backward 5.5460 |
[2023-10-24 21:51:19,541::train::INFO] [train] Iter 588146 | loss 0.2698 | loss(rot) 0.2255 | loss(pos) 0.0297 | loss(seq) 0.0146 | grad 3.6296 | lr 0.0000 | time_forward 1.3690 | time_backward 1.5280 |
[2023-10-24 21:51:28,786::train::INFO] [train] Iter 588147 | loss 0.4605 | loss(rot) 0.4280 | loss(pos) 0.0324 | loss(seq) 0.0001 | grad 6.4494 | lr 0.0000 | time_forward 3.9920 | time_backward 5.2500 |
[2023-10-24 21:51:36,539::train::INFO] [train] Iter 588148 | loss 1.6160 | loss(rot) 1.5797 | loss(pos) 0.0099 | loss(seq) 0.0264 | grad 8.0231 | lr 0.0000 | time_forward 3.3400 | time_backward 4.4100 |
[2023-10-24 21:51:39,394::train::INFO] [train] Iter 588149 | loss 1.3131 | loss(rot) 1.2719 | loss(pos) 0.0396 | loss(seq) 0.0016 | grad 4.2887 | lr 0.0000 | time_forward 1.3550 | time_backward 1.4970 |
[2023-10-24 21:51:49,821::train::INFO] [train] Iter 588150 | loss 0.7494 | loss(rot) 0.7174 | loss(pos) 0.0320 | loss(seq) 0.0000 | grad 8.8304 | lr 0.0000 | time_forward 4.3340 | time_backward 6.0600 |
[2023-10-24 21:51:52,719::train::INFO] [train] Iter 588151 | loss 0.2539 | loss(rot) 0.1814 | loss(pos) 0.0437 | loss(seq) 0.0288 | grad 2.1521 | lr 0.0000 | time_forward 1.3570 | time_backward 1.5380 |
[2023-10-24 21:52:03,014::train::INFO] [train] Iter 588152 | loss 0.4088 | loss(rot) 0.0204 | loss(pos) 0.3834 | loss(seq) 0.0050 | grad 6.5200 | lr 0.0000 | time_forward 4.3430 | time_backward 5.9240 |
[2023-10-24 21:52:05,680::train::INFO] [train] Iter 588153 | loss 0.9566 | loss(rot) 0.4103 | loss(pos) 0.2782 | loss(seq) 0.2682 | grad 3.3461 | lr 0.0000 | time_forward 1.2540 | time_backward 1.4100 |
[2023-10-24 21:52:14,006::train::INFO] [train] Iter 588154 | loss 1.1375 | loss(rot) 0.5411 | loss(pos) 0.1507 | loss(seq) 0.4457 | grad 3.1588 | lr 0.0000 | time_forward 3.4520 | time_backward 4.8690 |
[2023-10-24 21:52:23,732::train::INFO] [train] Iter 588155 | loss 1.2721 | loss(rot) 0.0053 | loss(pos) 1.2663 | loss(seq) 0.0006 | grad 15.0496 | lr 0.0000 | time_forward 4.1440 | time_backward 5.5790 |
[2023-10-24 21:52:26,556::train::INFO] [train] Iter 588156 | loss 0.4448 | loss(rot) 0.1372 | loss(pos) 0.0405 | loss(seq) 0.2672 | grad 2.6961 | lr 0.0000 | time_forward 1.3590 | time_backward 1.4610 |
[2023-10-24 21:52:29,339::train::INFO] [train] Iter 588157 | loss 1.2971 | loss(rot) 0.6702 | loss(pos) 0.1392 | loss(seq) 0.4878 | grad 78.2701 | lr 0.0000 | time_forward 1.2910 | time_backward 1.2780 |
[2023-10-24 21:52:39,730::train::INFO] [train] Iter 588158 | loss 0.6976 | loss(rot) 0.1807 | loss(pos) 0.1257 | loss(seq) 0.3912 | grad 3.4937 | lr 0.0000 | time_forward 4.2050 | time_backward 6.1490 |
[2023-10-24 21:52:50,202::train::INFO] [train] Iter 588159 | loss 0.5484 | loss(rot) 0.4696 | loss(pos) 0.0211 | loss(seq) 0.0577 | grad 4.8334 | lr 0.0000 | time_forward 4.1770 | time_backward 6.2920 |
[2023-10-24 21:53:00,504::train::INFO] [train] Iter 588160 | loss 0.3142 | loss(rot) 0.2809 | loss(pos) 0.0208 | loss(seq) 0.0124 | grad 3.0225 | lr 0.0000 | time_forward 4.1270 | time_backward 6.1720 |
[2023-10-24 21:53:09,690::train::INFO] [train] Iter 588161 | loss 0.3691 | loss(rot) 0.1500 | loss(pos) 0.0212 | loss(seq) 0.1980 | grad 3.8424 | lr 0.0000 | time_forward 3.8560 | time_backward 5.3280 |
[2023-10-24 21:53:12,083::train::INFO] [train] Iter 588162 | loss 0.8591 | loss(rot) 0.5222 | loss(pos) 0.2937 | loss(seq) 0.0433 | grad 4.0363 | lr 0.0000 | time_forward 1.1010 | time_backward 1.2880 |
[2023-10-24 21:53:20,814::train::INFO] [train] Iter 588163 | loss 1.0553 | loss(rot) 0.7137 | loss(pos) 0.0464 | loss(seq) 0.2953 | grad 2.6016 | lr 0.0000 | time_forward 3.7840 | time_backward 4.9430 |
[2023-10-24 21:53:23,687::train::INFO] [train] Iter 588164 | loss 0.7574 | loss(rot) 0.1687 | loss(pos) 0.2442 | loss(seq) 0.3445 | grad 3.0592 | lr 0.0000 | time_forward 1.3450 | time_backward 1.5260 |
[2023-10-24 21:53:34,141::train::INFO] [train] Iter 588165 | loss 1.1289 | loss(rot) 0.6583 | loss(pos) 0.0733 | loss(seq) 0.3974 | grad 3.1652 | lr 0.0000 | time_forward 4.1480 | time_backward 6.2570 |
[2023-10-24 21:53:44,530::train::INFO] [train] Iter 588166 | loss 0.5722 | loss(rot) 0.2811 | loss(pos) 0.1044 | loss(seq) 0.1867 | grad 3.1820 | lr 0.0000 | time_forward 4.2010 | time_backward 6.1840 |
[2023-10-24 21:53:54,989::train::INFO] [train] Iter 588167 | loss 1.0555 | loss(rot) 0.7926 | loss(pos) 0.0409 | loss(seq) 0.2220 | grad 33.4899 | lr 0.0000 | time_forward 4.4510 | time_backward 6.0040 |
[2023-10-24 21:54:05,491::train::INFO] [train] Iter 588168 | loss 1.4701 | loss(rot) 1.3978 | loss(pos) 0.0266 | loss(seq) 0.0457 | grad 5.5143 | lr 0.0000 | time_forward 4.2160 | time_backward 6.2820 |
[2023-10-24 21:54:15,969::train::INFO] [train] Iter 588169 | loss 0.8992 | loss(rot) 0.5368 | loss(pos) 0.1233 | loss(seq) 0.2391 | grad 11.4442 | lr 0.0000 | time_forward 4.4000 | time_backward 6.0750 |
[2023-10-24 21:54:24,807::train::INFO] [train] Iter 588170 | loss 0.2794 | loss(rot) 0.0669 | loss(pos) 0.0630 | loss(seq) 0.1494 | grad 2.3026 | lr 0.0000 | time_forward 3.6700 | time_backward 5.1640 |
[2023-10-24 21:54:33,666::train::INFO] [train] Iter 588171 | loss 0.3982 | loss(rot) 0.0913 | loss(pos) 0.2856 | loss(seq) 0.0214 | grad 4.7358 | lr 0.0000 | time_forward 3.7380 | time_backward 5.1170 |
[2023-10-24 21:54:42,516::train::INFO] [train] Iter 588172 | loss 1.3652 | loss(rot) 0.2800 | loss(pos) 0.9554 | loss(seq) 0.1298 | grad 6.1528 | lr 0.0000 | time_forward 3.7380 | time_backward 5.1090 |
[2023-10-24 21:54:51,742::train::INFO] [train] Iter 588173 | loss 0.3056 | loss(rot) 0.0642 | loss(pos) 0.0250 | loss(seq) 0.2165 | grad 2.5804 | lr 0.0000 | time_forward 3.8920 | time_backward 5.3310 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.