text
stringlengths 56
1.16k
|
|---|
[2023-09-02 06:20:24,142::train::INFO] [train] Iter 06889 | loss 1.3781 | loss(rot) 0.7797 | loss(pos) 0.4119 | loss(seq) 0.1865 | grad 4.5843 | lr 0.0010 | time_forward 3.9120 | time_backward 5.3700 |
[2023-09-02 06:20:31,496::train::INFO] [train] Iter 06890 | loss 2.5953 | loss(rot) 2.3853 | loss(pos) 0.1890 | loss(seq) 0.0210 | grad 4.7155 | lr 0.0010 | time_forward 3.1440 | time_backward 4.2060 |
[2023-09-02 06:20:41,352::train::INFO] [train] Iter 06891 | loss 2.1131 | loss(rot) 0.2914 | loss(pos) 1.8145 | loss(seq) 0.0073 | grad 5.8656 | lr 0.0010 | time_forward 4.0090 | time_backward 5.8450 |
[2023-09-02 06:20:51,356::train::INFO] [train] Iter 06892 | loss 2.2348 | loss(rot) 2.0981 | loss(pos) 0.1322 | loss(seq) 0.0045 | grad 4.3630 | lr 0.0010 | time_forward 4.0200 | time_backward 5.9800 |
[2023-09-02 06:20:54,066::train::INFO] [train] Iter 06893 | loss 1.4153 | loss(rot) 1.2662 | loss(pos) 0.1314 | loss(seq) 0.0177 | grad 3.1003 | lr 0.0010 | time_forward 1.2860 | time_backward 1.4200 |
[2023-09-02 06:21:04,125::train::INFO] [train] Iter 06894 | loss 1.8639 | loss(rot) 0.7130 | loss(pos) 0.7833 | loss(seq) 0.3676 | grad 4.7750 | lr 0.0010 | time_forward 4.0610 | time_backward 5.9950 |
[2023-09-02 06:21:06,919::train::INFO] [train] Iter 06895 | loss 2.5428 | loss(rot) 2.3501 | loss(pos) 0.1927 | loss(seq) 0.0000 | grad 5.9621 | lr 0.0010 | time_forward 1.2630 | time_backward 1.5270 |
[2023-09-02 06:21:17,726::train::INFO] [train] Iter 06896 | loss 1.4331 | loss(rot) 0.8192 | loss(pos) 0.1760 | loss(seq) 0.4379 | grad 5.5932 | lr 0.0010 | time_forward 4.7780 | time_backward 6.0250 |
[2023-09-02 06:21:26,531::train::INFO] [train] Iter 06897 | loss 2.2529 | loss(rot) 2.1112 | loss(pos) 0.1406 | loss(seq) 0.0012 | grad 3.8680 | lr 0.0010 | time_forward 3.6070 | time_backward 5.1840 |
[2023-09-02 06:21:36,692::train::INFO] [train] Iter 06898 | loss 2.5748 | loss(rot) 2.3083 | loss(pos) 0.2077 | loss(seq) 0.0588 | grad 3.7350 | lr 0.0010 | time_forward 4.1350 | time_backward 6.0210 |
[2023-09-02 06:21:45,452::train::INFO] [train] Iter 06899 | loss 1.3220 | loss(rot) 0.1508 | loss(pos) 1.1262 | loss(seq) 0.0449 | grad 6.5985 | lr 0.0010 | time_forward 3.7000 | time_backward 5.0580 |
[2023-09-02 06:21:52,958::train::INFO] [train] Iter 06900 | loss 0.9999 | loss(rot) 0.3465 | loss(pos) 0.6220 | loss(seq) 0.0315 | grad 4.7694 | lr 0.0010 | time_forward 3.1800 | time_backward 4.3230 |
[2023-09-02 06:21:55,643::train::INFO] [train] Iter 06901 | loss 1.9531 | loss(rot) 1.3308 | loss(pos) 0.1140 | loss(seq) 0.5083 | grad 4.7388 | lr 0.0010 | time_forward 1.2760 | time_backward 1.4050 |
[2023-09-02 06:22:04,034::train::INFO] [train] Iter 06902 | loss 1.7141 | loss(rot) 0.6962 | loss(pos) 0.6642 | loss(seq) 0.3538 | grad 4.4020 | lr 0.0010 | time_forward 3.5290 | time_backward 4.8580 |
[2023-09-02 06:22:12,863::train::INFO] [train] Iter 06903 | loss 1.8920 | loss(rot) 1.4889 | loss(pos) 0.4012 | loss(seq) 0.0019 | grad 5.3441 | lr 0.0010 | time_forward 3.6520 | time_backward 5.1750 |
[2023-09-02 06:22:20,401::train::INFO] [train] Iter 06904 | loss 1.0152 | loss(rot) 0.4791 | loss(pos) 0.4408 | loss(seq) 0.0953 | grad 3.3752 | lr 0.0010 | time_forward 3.2010 | time_backward 4.3330 |
[2023-09-02 06:22:30,627::train::INFO] [train] Iter 06905 | loss 2.2419 | loss(rot) 1.2031 | loss(pos) 0.4244 | loss(seq) 0.6144 | grad 4.4035 | lr 0.0010 | time_forward 4.1710 | time_backward 6.0530 |
[2023-09-02 06:22:39,156::train::INFO] [train] Iter 06906 | loss 0.8708 | loss(rot) 0.2304 | loss(pos) 0.3993 | loss(seq) 0.2411 | grad 3.5674 | lr 0.0010 | time_forward 3.6380 | time_backward 4.8870 |
[2023-09-02 06:22:47,165::train::INFO] [train] Iter 06907 | loss 3.4555 | loss(rot) 2.5539 | loss(pos) 0.4201 | loss(seq) 0.4815 | grad 4.3856 | lr 0.0010 | time_forward 3.3770 | time_backward 4.6280 |
[2023-09-02 06:22:56,459::train::INFO] [train] Iter 06908 | loss 1.0230 | loss(rot) 0.4252 | loss(pos) 0.5780 | loss(seq) 0.0199 | grad 4.5574 | lr 0.0010 | time_forward 3.9560 | time_backward 5.3350 |
[2023-09-02 06:23:04,865::train::INFO] [train] Iter 06909 | loss 3.3313 | loss(rot) 3.1928 | loss(pos) 0.1225 | loss(seq) 0.0160 | grad 5.8516 | lr 0.0010 | time_forward 3.5160 | time_backward 4.8860 |
[2023-09-02 06:23:13,232::train::INFO] [train] Iter 06910 | loss 2.8344 | loss(rot) 2.6052 | loss(pos) 0.1057 | loss(seq) 0.1235 | grad 5.4595 | lr 0.0010 | time_forward 3.4770 | time_backward 4.8870 |
[2023-09-02 06:23:22,405::train::INFO] [train] Iter 06911 | loss 1.1198 | loss(rot) 0.5261 | loss(pos) 0.0724 | loss(seq) 0.5213 | grad 3.3373 | lr 0.0010 | time_forward 3.8700 | time_backward 5.3000 |
[2023-09-02 06:23:30,743::train::INFO] [train] Iter 06912 | loss 1.7695 | loss(rot) 1.6409 | loss(pos) 0.0804 | loss(seq) 0.0482 | grad 4.5638 | lr 0.0010 | time_forward 3.4770 | time_backward 4.8580 |
[2023-09-02 06:23:39,048::train::INFO] [train] Iter 06913 | loss 2.1428 | loss(rot) 1.8741 | loss(pos) 0.1264 | loss(seq) 0.1423 | grad 5.8448 | lr 0.0010 | time_forward 3.4580 | time_backward 4.8430 |
[2023-09-02 06:23:48,873::train::INFO] [train] Iter 06914 | loss 2.0887 | loss(rot) 1.3893 | loss(pos) 0.2319 | loss(seq) 0.4675 | grad 3.4900 | lr 0.0010 | time_forward 3.9280 | time_backward 5.8940 |
[2023-09-02 06:23:56,970::train::INFO] [train] Iter 06915 | loss 1.7433 | loss(rot) 1.1867 | loss(pos) 0.1463 | loss(seq) 0.4103 | grad 5.3502 | lr 0.0010 | time_forward 3.3800 | time_backward 4.7130 |
[2023-09-02 06:24:06,255::train::INFO] [train] Iter 06916 | loss 1.7606 | loss(rot) 1.2071 | loss(pos) 0.2188 | loss(seq) 0.3348 | grad 4.4979 | lr 0.0010 | time_forward 4.0210 | time_backward 5.2590 |
[2023-09-02 06:24:16,243::train::INFO] [train] Iter 06917 | loss 0.4124 | loss(rot) 0.2448 | loss(pos) 0.0624 | loss(seq) 0.1052 | grad 2.8523 | lr 0.0010 | time_forward 3.9880 | time_backward 5.9960 |
[2023-09-02 06:24:26,298::train::INFO] [train] Iter 06918 | loss 0.5792 | loss(rot) 0.0909 | loss(pos) 0.4558 | loss(seq) 0.0325 | grad 4.2204 | lr 0.0010 | time_forward 4.0330 | time_backward 6.0180 |
[2023-09-02 06:24:34,987::train::INFO] [train] Iter 06919 | loss 1.6346 | loss(rot) 0.9988 | loss(pos) 0.1536 | loss(seq) 0.4822 | grad 3.8670 | lr 0.0010 | time_forward 3.7250 | time_backward 4.9600 |
[2023-09-02 06:24:37,640::train::INFO] [train] Iter 06920 | loss 1.4306 | loss(rot) 1.2452 | loss(pos) 0.1008 | loss(seq) 0.0846 | grad 3.2003 | lr 0.0010 | time_forward 1.2690 | time_backward 1.3800 |
[2023-09-02 06:24:40,354::train::INFO] [train] Iter 06921 | loss 2.2960 | loss(rot) 1.5936 | loss(pos) 0.3509 | loss(seq) 0.3515 | grad 5.4288 | lr 0.0010 | time_forward 1.2880 | time_backward 1.3860 |
[2023-09-02 06:24:43,029::train::INFO] [train] Iter 06922 | loss 3.1101 | loss(rot) 2.4775 | loss(pos) 0.3205 | loss(seq) 0.3120 | grad 5.0991 | lr 0.0010 | time_forward 1.2920 | time_backward 1.3800 |
[2023-09-02 06:24:45,711::train::INFO] [train] Iter 06923 | loss 2.4606 | loss(rot) 1.5188 | loss(pos) 0.4066 | loss(seq) 0.5352 | grad 5.0074 | lr 0.0010 | time_forward 1.2850 | time_backward 1.3940 |
[2023-09-02 06:24:55,129::train::INFO] [train] Iter 06924 | loss 1.0319 | loss(rot) 0.3750 | loss(pos) 0.3933 | loss(seq) 0.2636 | grad 4.6493 | lr 0.0010 | time_forward 4.0860 | time_backward 5.3270 |
[2023-09-02 06:24:57,793::train::INFO] [train] Iter 06925 | loss 1.4800 | loss(rot) 0.2559 | loss(pos) 1.1383 | loss(seq) 0.0858 | grad 6.2745 | lr 0.0010 | time_forward 1.2590 | time_backward 1.4010 |
[2023-09-02 06:25:07,264::train::INFO] [train] Iter 06926 | loss 2.3180 | loss(rot) 0.0720 | loss(pos) 2.2436 | loss(seq) 0.0025 | grad 6.0253 | lr 0.0010 | time_forward 3.9750 | time_backward 5.4930 |
[2023-09-02 06:25:15,470::train::INFO] [train] Iter 06927 | loss 1.4103 | loss(rot) 0.0502 | loss(pos) 1.3587 | loss(seq) 0.0013 | grad 5.3326 | lr 0.0010 | time_forward 3.4300 | time_backward 4.7710 |
[2023-09-02 06:25:21,573::train::INFO] [train] Iter 06928 | loss 1.1961 | loss(rot) 0.3492 | loss(pos) 0.5731 | loss(seq) 0.2738 | grad 6.3417 | lr 0.0010 | time_forward 2.5720 | time_backward 3.5280 |
[2023-09-02 06:25:28,081::train::INFO] [train] Iter 06929 | loss 0.8534 | loss(rot) 0.1369 | loss(pos) 0.6821 | loss(seq) 0.0344 | grad 5.6705 | lr 0.0010 | time_forward 2.7570 | time_backward 3.7470 |
[2023-09-02 06:25:37,308::train::INFO] [train] Iter 06930 | loss 1.5742 | loss(rot) 0.0482 | loss(pos) 1.5229 | loss(seq) 0.0031 | grad 6.5257 | lr 0.0010 | time_forward 3.9190 | time_backward 5.3040 |
[2023-09-02 06:25:40,554::train::INFO] [train] Iter 06931 | loss 1.8807 | loss(rot) 0.7224 | loss(pos) 0.8210 | loss(seq) 0.3372 | grad 5.9804 | lr 0.0010 | time_forward 1.4430 | time_backward 1.8000 |
[2023-09-02 06:25:48,689::train::INFO] [train] Iter 06932 | loss 0.9633 | loss(rot) 0.4680 | loss(pos) 0.2992 | loss(seq) 0.1961 | grad 4.4205 | lr 0.0010 | time_forward 3.4400 | time_backward 4.6800 |
[2023-09-02 06:25:55,800::train::INFO] [train] Iter 06933 | loss 1.8415 | loss(rot) 0.6715 | loss(pos) 0.5899 | loss(seq) 0.5801 | grad 8.2917 | lr 0.0010 | time_forward 3.0640 | time_backward 4.0430 |
[2023-09-02 06:26:04,309::train::INFO] [train] Iter 06934 | loss 3.0271 | loss(rot) 2.5711 | loss(pos) 0.4492 | loss(seq) 0.0067 | grad 6.1349 | lr 0.0010 | time_forward 3.6470 | time_backward 4.8580 |
[2023-09-02 06:26:07,082::train::INFO] [train] Iter 06935 | loss 1.0107 | loss(rot) 0.1586 | loss(pos) 0.8316 | loss(seq) 0.0206 | grad 6.3205 | lr 0.0010 | time_forward 1.3580 | time_backward 1.4110 |
[2023-09-02 06:26:13,631::train::INFO] [train] Iter 06936 | loss 1.1314 | loss(rot) 0.1868 | loss(pos) 0.8864 | loss(seq) 0.0582 | grad 5.1916 | lr 0.0010 | time_forward 2.7330 | time_backward 3.8130 |
[2023-09-02 06:26:23,906::train::INFO] [train] Iter 06937 | loss 2.8283 | loss(rot) 1.9854 | loss(pos) 0.3178 | loss(seq) 0.5251 | grad 4.0630 | lr 0.0010 | time_forward 4.1450 | time_backward 6.1260 |
[2023-09-02 06:26:32,801::train::INFO] [train] Iter 06938 | loss 1.7942 | loss(rot) 1.1960 | loss(pos) 0.2081 | loss(seq) 0.3901 | grad 4.1838 | lr 0.0010 | time_forward 3.7370 | time_backward 5.1540 |
[2023-09-02 06:26:35,520::train::INFO] [train] Iter 06939 | loss 2.6069 | loss(rot) 1.7485 | loss(pos) 0.3825 | loss(seq) 0.4760 | grad 3.5535 | lr 0.0010 | time_forward 1.2880 | time_backward 1.4270 |
[2023-09-02 06:26:45,471::train::INFO] [train] Iter 06940 | loss 1.9808 | loss(rot) 1.1217 | loss(pos) 0.3220 | loss(seq) 0.5370 | grad 3.2895 | lr 0.0010 | time_forward 3.9620 | time_backward 5.9500 |
[2023-09-02 06:26:54,990::train::INFO] [train] Iter 06941 | loss 1.7839 | loss(rot) 0.1311 | loss(pos) 1.4519 | loss(seq) 0.2010 | grad 6.3025 | lr 0.0010 | time_forward 3.9710 | time_backward 5.5450 |
[2023-09-02 06:26:57,734::train::INFO] [train] Iter 06942 | loss 2.2998 | loss(rot) 2.0923 | loss(pos) 0.1429 | loss(seq) 0.0645 | grad 4.1476 | lr 0.0010 | time_forward 1.2730 | time_backward 1.4560 |
[2023-09-02 06:27:06,292::train::INFO] [train] Iter 06943 | loss 1.3247 | loss(rot) 0.0359 | loss(pos) 1.2858 | loss(seq) 0.0030 | grad 6.0790 | lr 0.0010 | time_forward 3.7110 | time_backward 4.8440 |
[2023-09-02 06:27:16,420::train::INFO] [train] Iter 06944 | loss 2.0107 | loss(rot) 1.1021 | loss(pos) 0.2970 | loss(seq) 0.6117 | grad 3.7027 | lr 0.0010 | time_forward 4.1000 | time_backward 6.0240 |
[2023-09-02 06:27:19,170::train::INFO] [train] Iter 06945 | loss 1.6572 | loss(rot) 1.0579 | loss(pos) 0.1665 | loss(seq) 0.4329 | grad 3.3252 | lr 0.0010 | time_forward 1.2520 | time_backward 1.4930 |
[2023-09-02 06:27:29,183::train::INFO] [train] Iter 06946 | loss 2.5230 | loss(rot) 2.0685 | loss(pos) 0.1095 | loss(seq) 0.3450 | grad 3.9180 | lr 0.0010 | time_forward 4.1230 | time_backward 5.8860 |
[2023-09-02 06:27:38,194::train::INFO] [train] Iter 06947 | loss 1.4766 | loss(rot) 0.4963 | loss(pos) 0.5700 | loss(seq) 0.4103 | grad 5.9866 | lr 0.0010 | time_forward 3.8600 | time_backward 5.1470 |
[2023-09-02 06:27:40,864::train::INFO] [train] Iter 06948 | loss 3.0902 | loss(rot) 2.8372 | loss(pos) 0.2482 | loss(seq) 0.0048 | grad 4.5057 | lr 0.0010 | time_forward 1.2500 | time_backward 1.4160 |
[2023-09-02 06:27:49,406::train::INFO] [train] Iter 06949 | loss 0.8698 | loss(rot) 0.4263 | loss(pos) 0.2044 | loss(seq) 0.2391 | grad 3.5048 | lr 0.0010 | time_forward 3.5610 | time_backward 4.9760 |
[2023-09-02 06:27:59,850::train::INFO] [train] Iter 06950 | loss 2.7478 | loss(rot) 2.5887 | loss(pos) 0.1344 | loss(seq) 0.0248 | grad 4.0315 | lr 0.0010 | time_forward 4.2540 | time_backward 6.1860 |
[2023-09-02 06:28:10,228::train::INFO] [train] Iter 06951 | loss 1.8473 | loss(rot) 1.1517 | loss(pos) 0.2302 | loss(seq) 0.4654 | grad 4.1110 | lr 0.0010 | time_forward 4.0930 | time_backward 6.2810 |
[2023-09-02 06:28:19,869::train::INFO] [train] Iter 06952 | loss 2.0921 | loss(rot) 1.8506 | loss(pos) 0.1196 | loss(seq) 0.1219 | grad 5.4519 | lr 0.0010 | time_forward 4.0910 | time_backward 5.5470 |
[2023-09-02 06:28:23,256::train::INFO] [train] Iter 06953 | loss 2.4759 | loss(rot) 2.3127 | loss(pos) 0.0542 | loss(seq) 0.1089 | grad 3.2774 | lr 0.0010 | time_forward 1.4590 | time_backward 1.9250 |
[2023-09-02 06:28:34,022::train::INFO] [train] Iter 06954 | loss 2.9010 | loss(rot) 2.1489 | loss(pos) 0.2703 | loss(seq) 0.4818 | grad 2.9024 | lr 0.0010 | time_forward 4.3700 | time_backward 6.3910 |
[2023-09-02 06:28:43,396::train::INFO] [train] Iter 06955 | loss 1.3956 | loss(rot) 0.1562 | loss(pos) 0.8557 | loss(seq) 0.3837 | grad 6.9412 | lr 0.0010 | time_forward 3.8870 | time_backward 5.4840 |
[2023-09-02 06:28:45,622::train::INFO] [train] Iter 06956 | loss 2.6619 | loss(rot) 2.0324 | loss(pos) 0.2291 | loss(seq) 0.4004 | grad 2.5515 | lr 0.0010 | time_forward 1.0370 | time_backward 1.1850 |
[2023-09-02 06:28:48,589::train::INFO] [train] Iter 06957 | loss 1.7341 | loss(rot) 1.3095 | loss(pos) 0.1235 | loss(seq) 0.3010 | grad 3.5429 | lr 0.0010 | time_forward 1.5540 | time_backward 1.4090 |
[2023-09-02 06:28:59,520::train::INFO] [train] Iter 06958 | loss 2.7523 | loss(rot) 2.3836 | loss(pos) 0.1973 | loss(seq) 0.1714 | grad 3.6778 | lr 0.0010 | time_forward 4.5880 | time_backward 6.3400 |
[2023-09-02 06:29:02,420::train::INFO] [train] Iter 06959 | loss 1.6715 | loss(rot) 1.2274 | loss(pos) 0.1174 | loss(seq) 0.3267 | grad 3.7253 | lr 0.0010 | time_forward 1.3200 | time_backward 1.5770 |
[2023-09-02 06:29:10,461::train::INFO] [train] Iter 06960 | loss 1.7171 | loss(rot) 1.1217 | loss(pos) 0.1419 | loss(seq) 0.4535 | grad 4.4891 | lr 0.0010 | time_forward 3.3520 | time_backward 4.6850 |
[2023-09-02 06:29:19,436::train::INFO] [train] Iter 06961 | loss 2.3759 | loss(rot) 1.9641 | loss(pos) 0.1857 | loss(seq) 0.2261 | grad 5.7244 | lr 0.0010 | time_forward 3.7230 | time_backward 5.2480 |
[2023-09-02 06:29:28,973::train::INFO] [train] Iter 06962 | loss 1.9394 | loss(rot) 1.4059 | loss(pos) 0.1223 | loss(seq) 0.4113 | grad 3.7681 | lr 0.0010 | time_forward 4.0610 | time_backward 5.4720 |
[2023-09-02 06:29:38,454::train::INFO] [train] Iter 06963 | loss 1.5923 | loss(rot) 1.0922 | loss(pos) 0.0776 | loss(seq) 0.4225 | grad 5.0832 | lr 0.0010 | time_forward 4.0450 | time_backward 5.4330 |
[2023-09-02 06:29:47,714::train::INFO] [train] Iter 06964 | loss 1.2944 | loss(rot) 0.6590 | loss(pos) 0.2792 | loss(seq) 0.3562 | grad 3.1073 | lr 0.0010 | time_forward 4.0140 | time_backward 5.2410 |
[2023-09-02 06:29:56,667::train::INFO] [train] Iter 06965 | loss 2.4985 | loss(rot) 1.7358 | loss(pos) 0.2603 | loss(seq) 0.5024 | grad 4.9522 | lr 0.0010 | time_forward 3.6630 | time_backward 5.2860 |
[2023-09-02 06:29:59,008::train::INFO] [train] Iter 06966 | loss 1.8761 | loss(rot) 1.2122 | loss(pos) 0.2846 | loss(seq) 0.3793 | grad 3.9450 | lr 0.0010 | time_forward 1.1850 | time_backward 1.1520 |
[2023-09-02 06:30:09,040::train::INFO] [train] Iter 06967 | loss 1.0895 | loss(rot) 0.2318 | loss(pos) 0.7921 | loss(seq) 0.0656 | grad 4.5358 | lr 0.0010 | time_forward 4.0800 | time_backward 5.9480 |
[2023-09-02 06:30:19,230::train::INFO] [train] Iter 06968 | loss 0.8407 | loss(rot) 0.2700 | loss(pos) 0.5357 | loss(seq) 0.0351 | grad 3.1445 | lr 0.0010 | time_forward 4.1110 | time_backward 6.0760 |
[2023-09-02 06:30:27,982::train::INFO] [train] Iter 06969 | loss 2.5719 | loss(rot) 2.2120 | loss(pos) 0.1829 | loss(seq) 0.1771 | grad 6.2884 | lr 0.0010 | time_forward 3.6240 | time_backward 5.1240 |
[2023-09-02 06:30:37,247::train::INFO] [train] Iter 06970 | loss 2.1903 | loss(rot) 0.7695 | loss(pos) 1.1620 | loss(seq) 0.2587 | grad 6.5280 | lr 0.0010 | time_forward 3.8160 | time_backward 5.4450 |
[2023-09-02 06:30:39,940::train::INFO] [train] Iter 06971 | loss 2.1174 | loss(rot) 1.2435 | loss(pos) 0.3109 | loss(seq) 0.5629 | grad 3.2495 | lr 0.0010 | time_forward 1.2510 | time_backward 1.4380 |
[2023-09-02 06:30:49,743::train::INFO] [train] Iter 06972 | loss 2.4801 | loss(rot) 2.2909 | loss(pos) 0.1772 | loss(seq) 0.0120 | grad 7.6482 | lr 0.0010 | time_forward 3.9950 | time_backward 5.8040 |
[2023-09-02 06:30:57,883::train::INFO] [train] Iter 06973 | loss 1.3256 | loss(rot) 0.8040 | loss(pos) 0.1542 | loss(seq) 0.3673 | grad 6.0678 | lr 0.0010 | time_forward 3.3960 | time_backward 4.7400 |
[2023-09-02 06:31:07,558::train::INFO] [train] Iter 06974 | loss 4.1455 | loss(rot) 0.3172 | loss(pos) 3.8283 | loss(seq) 0.0000 | grad 8.9073 | lr 0.0010 | time_forward 3.9110 | time_backward 5.7600 |
[2023-09-02 06:31:17,146::train::INFO] [train] Iter 06975 | loss 0.6208 | loss(rot) 0.1377 | loss(pos) 0.4535 | loss(seq) 0.0295 | grad 2.7499 | lr 0.0010 | time_forward 3.9650 | time_backward 5.6200 |
[2023-09-02 06:31:27,140::train::INFO] [train] Iter 06976 | loss 1.2233 | loss(rot) 0.5537 | loss(pos) 0.2574 | loss(seq) 0.4122 | grad 2.7518 | lr 0.0010 | time_forward 4.0600 | time_backward 5.9300 |
[2023-09-02 06:31:37,095::train::INFO] [train] Iter 06977 | loss 1.0369 | loss(rot) 0.3572 | loss(pos) 0.4283 | loss(seq) 0.2514 | grad 3.6670 | lr 0.0010 | time_forward 4.1980 | time_backward 5.7540 |
[2023-09-02 06:31:47,010::train::INFO] [train] Iter 06978 | loss 2.3346 | loss(rot) 1.6243 | loss(pos) 0.2315 | loss(seq) 0.4788 | grad 3.5450 | lr 0.0010 | time_forward 3.9670 | time_backward 5.9440 |
[2023-09-02 06:31:55,605::train::INFO] [train] Iter 06979 | loss 1.5023 | loss(rot) 1.3419 | loss(pos) 0.1591 | loss(seq) 0.0013 | grad 4.5974 | lr 0.0010 | time_forward 3.6390 | time_backward 4.9520 |
[2023-09-02 06:31:58,454::train::INFO] [train] Iter 06980 | loss 1.5057 | loss(rot) 1.0264 | loss(pos) 0.1323 | loss(seq) 0.3470 | grad 3.4801 | lr 0.0010 | time_forward 1.4180 | time_backward 1.4270 |
[2023-09-02 06:32:07,007::train::INFO] [train] Iter 06981 | loss 2.0723 | loss(rot) 1.3965 | loss(pos) 0.2491 | loss(seq) 0.4267 | grad 4.3295 | lr 0.0010 | time_forward 3.5780 | time_backward 4.9710 |
[2023-09-02 06:32:15,334::train::INFO] [train] Iter 06982 | loss 1.7910 | loss(rot) 0.0713 | loss(pos) 1.7156 | loss(seq) 0.0041 | grad 6.6616 | lr 0.0010 | time_forward 3.4570 | time_backward 4.8670 |
[2023-09-02 06:32:23,867::train::INFO] [train] Iter 06983 | loss 1.9964 | loss(rot) 0.4130 | loss(pos) 0.9412 | loss(seq) 0.6422 | grad 5.5986 | lr 0.0010 | time_forward 3.5870 | time_backward 4.9420 |
[2023-09-02 06:32:26,386::train::INFO] [train] Iter 06984 | loss 2.2950 | loss(rot) 1.5109 | loss(pos) 0.2691 | loss(seq) 0.5150 | grad 4.3498 | lr 0.0010 | time_forward 1.1950 | time_backward 1.3210 |
[2023-09-02 06:32:36,497::train::INFO] [train] Iter 06985 | loss 1.3919 | loss(rot) 0.5074 | loss(pos) 0.4763 | loss(seq) 0.4082 | grad 3.7131 | lr 0.0010 | time_forward 4.2230 | time_backward 5.8850 |
[2023-09-02 06:32:44,509::train::INFO] [train] Iter 06986 | loss 2.5787 | loss(rot) 2.3578 | loss(pos) 0.2196 | loss(seq) 0.0013 | grad 3.1568 | lr 0.0010 | time_forward 3.3380 | time_backward 4.6700 |
[2023-09-02 06:32:54,560::train::INFO] [train] Iter 06987 | loss 2.1854 | loss(rot) 1.5556 | loss(pos) 0.1553 | loss(seq) 0.4745 | grad 4.6094 | lr 0.0010 | time_forward 4.1220 | time_backward 5.9250 |
[2023-09-02 06:32:56,821::train::INFO] [train] Iter 06988 | loss 1.7133 | loss(rot) 0.7547 | loss(pos) 0.3625 | loss(seq) 0.5961 | grad 3.7069 | lr 0.0010 | time_forward 1.0680 | time_backward 1.1900 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.