text stringlengths 56 1.16k |
|---|
[2023-10-23 10:16:06,757::train::INFO] [train] Iter 569593 | loss 0.6046 | loss(rot) 0.3287 | loss(pos) 0.0458 | loss(seq) 0.2301 | grad 3.0245 | lr 0.0000 | time_forward 5.2150 | time_backward 4.6910 |
[2023-10-23 10:16:18,001::train::INFO] [train] Iter 569594 | loss 0.3118 | loss(rot) 0.0457 | loss(pos) 0.0292 | loss(seq) 0.2369 | grad 1.7541 | lr 0.0000 | time_forward 5.8840 | time_backward 5.3570 |
[2023-10-23 10:16:26,224::train::INFO] [train] Iter 569595 | loss 1.6445 | loss(rot) 1.0126 | loss(pos) 0.2282 | loss(seq) 0.4038 | grad 3.3125 | lr 0.0000 | time_forward 3.4590 | time_backward 4.7610 |
[2023-10-23 10:16:33,219::train::INFO] [train] Iter 569596 | loss 0.6946 | loss(rot) 0.0575 | loss(pos) 0.4401 | loss(seq) 0.1970 | grad 5.0918 | lr 0.0000 | time_forward 3.1090 | time_backward 3.8820 |
[2023-10-23 10:16:41,225::train::INFO] [train] Iter 569597 | loss 1.2053 | loss(rot) 0.7421 | loss(pos) 0.0790 | loss(seq) 0.3841 | grad 4.6510 | lr 0.0000 | time_forward 3.2650 | time_backward 4.7380 |
[2023-10-23 10:16:49,349::train::INFO] [train] Iter 569598 | loss 1.2731 | loss(rot) 0.6715 | loss(pos) 0.2507 | loss(seq) 0.3508 | grad 8.2682 | lr 0.0000 | time_forward 3.3830 | time_backward 4.7370 |
[2023-10-23 10:16:57,836::train::INFO] [train] Iter 569599 | loss 0.5757 | loss(rot) 0.1344 | loss(pos) 0.3130 | loss(seq) 0.1284 | grad 4.9148 | lr 0.0000 | time_forward 3.6440 | time_backward 4.8400 |
[2023-10-23 10:17:00,629::train::INFO] [train] Iter 569600 | loss 0.7702 | loss(rot) 0.5139 | loss(pos) 0.0239 | loss(seq) 0.2325 | grad 3.7047 | lr 0.0000 | time_forward 1.5300 | time_backward 1.2590 |
[2023-10-23 10:17:03,496::train::INFO] [train] Iter 569601 | loss 0.6465 | loss(rot) 0.4047 | loss(pos) 0.0530 | loss(seq) 0.1889 | grad 2.8062 | lr 0.0000 | time_forward 1.3380 | time_backward 1.5270 |
[2023-10-23 10:17:06,215::train::INFO] [train] Iter 569602 | loss 0.8321 | loss(rot) 0.3523 | loss(pos) 0.0965 | loss(seq) 0.3834 | grad 3.3777 | lr 0.0000 | time_forward 1.3370 | time_backward 1.3770 |
[2023-10-23 10:17:13,667::train::INFO] [train] Iter 569603 | loss 0.2031 | loss(rot) 0.0736 | loss(pos) 0.0330 | loss(seq) 0.0964 | grad 2.1786 | lr 0.0000 | time_forward 3.2990 | time_backward 4.1490 |
[2023-10-23 10:17:20,619::train::INFO] [train] Iter 569604 | loss 0.5372 | loss(rot) 0.2278 | loss(pos) 0.0772 | loss(seq) 0.2322 | grad 3.4693 | lr 0.0000 | time_forward 3.0100 | time_backward 3.9390 |
[2023-10-23 10:17:27,719::train::INFO] [train] Iter 569605 | loss 1.6626 | loss(rot) 1.6270 | loss(pos) 0.0342 | loss(seq) 0.0014 | grad 24.9385 | lr 0.0000 | time_forward 3.1180 | time_backward 3.9800 |
[2023-10-23 10:17:34,564::train::INFO] [train] Iter 569606 | loss 0.1811 | loss(rot) 0.1222 | loss(pos) 0.0224 | loss(seq) 0.0364 | grad 1.8434 | lr 0.0000 | time_forward 2.9130 | time_backward 3.9280 |
[2023-10-23 10:17:36,869::train::INFO] [train] Iter 569607 | loss 1.0941 | loss(rot) 0.5357 | loss(pos) 0.1034 | loss(seq) 0.4550 | grad 3.8284 | lr 0.0000 | time_forward 1.0630 | time_backward 1.2380 |
[2023-10-23 10:17:52,871::train::INFO] [train] Iter 569608 | loss 0.6161 | loss(rot) 0.5070 | loss(pos) 0.0873 | loss(seq) 0.0217 | grad 3.4857 | lr 0.0000 | time_forward 11.2560 | time_backward 4.7420 |
[2023-10-23 10:17:55,541::train::INFO] [train] Iter 569609 | loss 0.8035 | loss(rot) 0.0314 | loss(pos) 0.6844 | loss(seq) 0.0876 | grad 7.8265 | lr 0.0000 | time_forward 1.2720 | time_backward 1.3940 |
[2023-10-23 10:18:03,176::train::INFO] [train] Iter 569610 | loss 0.4039 | loss(rot) 0.1419 | loss(pos) 0.0148 | loss(seq) 0.2471 | grad 2.2579 | lr 0.0000 | time_forward 3.2110 | time_backward 4.4200 |
[2023-10-23 10:18:10,947::train::INFO] [train] Iter 569611 | loss 1.0030 | loss(rot) 0.6866 | loss(pos) 0.0434 | loss(seq) 0.2730 | grad 55.5361 | lr 0.0000 | time_forward 3.4600 | time_backward 4.3080 |
[2023-10-23 10:18:13,993::train::INFO] [train] Iter 569612 | loss 0.3270 | loss(rot) 0.0717 | loss(pos) 0.2276 | loss(seq) 0.0278 | grad 2.7885 | lr 0.0000 | time_forward 1.4000 | time_backward 1.6430 |
[2023-10-23 10:18:21,832::train::INFO] [train] Iter 569613 | loss 1.9203 | loss(rot) 1.8497 | loss(pos) 0.0350 | loss(seq) 0.0356 | grad 3.6637 | lr 0.0000 | time_forward 3.3420 | time_backward 4.4870 |
[2023-10-23 10:18:28,927::train::INFO] [train] Iter 569614 | loss 1.3858 | loss(rot) 1.2682 | loss(pos) 0.0189 | loss(seq) 0.0987 | grad 6.2366 | lr 0.0000 | time_forward 3.1660 | time_backward 3.9260 |
[2023-10-23 10:18:35,877::train::INFO] [train] Iter 569615 | loss 1.1018 | loss(rot) 0.9573 | loss(pos) 0.1379 | loss(seq) 0.0067 | grad 4.5373 | lr 0.0000 | time_forward 3.0070 | time_backward 3.9400 |
[2023-10-23 10:18:42,612::train::INFO] [train] Iter 569616 | loss 0.6653 | loss(rot) 0.1804 | loss(pos) 0.4518 | loss(seq) 0.0331 | grad 6.9721 | lr 0.0000 | time_forward 2.9510 | time_backward 3.7820 |
[2023-10-23 10:18:45,345::train::INFO] [train] Iter 569617 | loss 0.3425 | loss(rot) 0.1977 | loss(pos) 0.0259 | loss(seq) 0.1189 | grad 2.7942 | lr 0.0000 | time_forward 1.2470 | time_backward 1.4820 |
[2023-10-23 10:18:47,494::train::INFO] [train] Iter 569618 | loss 0.7614 | loss(rot) 0.5780 | loss(pos) 0.0430 | loss(seq) 0.1404 | grad 3.0841 | lr 0.0000 | time_forward 0.9900 | time_backward 1.1550 |
[2023-10-23 10:18:55,210::train::INFO] [train] Iter 569619 | loss 0.4671 | loss(rot) 0.1331 | loss(pos) 0.1062 | loss(seq) 0.2278 | grad 3.3477 | lr 0.0000 | time_forward 3.2280 | time_backward 4.4850 |
[2023-10-23 10:18:57,351::train::INFO] [train] Iter 569620 | loss 0.3714 | loss(rot) 0.1990 | loss(pos) 0.0582 | loss(seq) 0.1142 | grad 2.4394 | lr 0.0000 | time_forward 0.9820 | time_backward 1.1550 |
[2023-10-23 10:19:03,624::train::INFO] [train] Iter 569621 | loss 1.4398 | loss(rot) 1.3578 | loss(pos) 0.0740 | loss(seq) 0.0080 | grad 2.7616 | lr 0.0000 | time_forward 2.7030 | time_backward 3.5670 |
[2023-10-23 10:19:10,449::train::INFO] [train] Iter 569622 | loss 1.3369 | loss(rot) 1.0669 | loss(pos) 0.0583 | loss(seq) 0.2117 | grad 4.1564 | lr 0.0000 | time_forward 3.0030 | time_backward 3.8180 |
[2023-10-23 10:19:13,018::train::INFO] [train] Iter 569623 | loss 0.4115 | loss(rot) 0.0915 | loss(pos) 0.1436 | loss(seq) 0.1764 | grad 3.7198 | lr 0.0000 | time_forward 1.1960 | time_backward 1.3710 |
[2023-10-23 10:19:19,843::train::INFO] [train] Iter 569624 | loss 0.6154 | loss(rot) 0.3700 | loss(pos) 0.1488 | loss(seq) 0.0966 | grad 4.5634 | lr 0.0000 | time_forward 3.0000 | time_backward 3.8210 |
[2023-10-23 10:19:22,465::train::INFO] [train] Iter 569625 | loss 0.5455 | loss(rot) 0.2697 | loss(pos) 0.2205 | loss(seq) 0.0553 | grad 3.8188 | lr 0.0000 | time_forward 1.2390 | time_backward 1.3800 |
[2023-10-23 10:19:24,912::train::INFO] [train] Iter 569626 | loss 0.9578 | loss(rot) 0.9254 | loss(pos) 0.0256 | loss(seq) 0.0068 | grad 4.2290 | lr 0.0000 | time_forward 1.2050 | time_backward 1.2400 |
[2023-10-23 10:19:32,790::train::INFO] [train] Iter 569627 | loss 0.2162 | loss(rot) 0.1822 | loss(pos) 0.0313 | loss(seq) 0.0027 | grad 2.5107 | lr 0.0000 | time_forward 3.3020 | time_backward 4.5710 |
[2023-10-23 10:19:40,643::train::INFO] [train] Iter 569628 | loss 1.8633 | loss(rot) 1.8203 | loss(pos) 0.0368 | loss(seq) 0.0061 | grad 4.6827 | lr 0.0000 | time_forward 3.3590 | time_backward 4.4910 |
[2023-10-23 10:19:43,231::train::INFO] [train] Iter 569629 | loss 0.6237 | loss(rot) 0.2148 | loss(pos) 0.2814 | loss(seq) 0.1274 | grad 5.3309 | lr 0.0000 | time_forward 1.1960 | time_backward 1.3890 |
[2023-10-23 10:19:50,982::train::INFO] [train] Iter 569630 | loss 1.1975 | loss(rot) 0.9421 | loss(pos) 0.0562 | loss(seq) 0.1992 | grad 3.5035 | lr 0.0000 | time_forward 3.2450 | time_backward 4.5040 |
[2023-10-23 10:19:57,574::train::INFO] [train] Iter 569631 | loss 0.3698 | loss(rot) 0.1542 | loss(pos) 0.0283 | loss(seq) 0.1873 | grad 2.9458 | lr 0.0000 | time_forward 2.8590 | time_backward 3.7290 |
[2023-10-23 10:20:00,244::train::INFO] [train] Iter 569632 | loss 1.8532 | loss(rot) 0.4533 | loss(pos) 0.7658 | loss(seq) 0.6341 | grad 9.4184 | lr 0.0000 | time_forward 1.3050 | time_backward 1.3620 |
[2023-10-23 10:20:08,067::train::INFO] [train] Iter 569633 | loss 0.4503 | loss(rot) 0.1478 | loss(pos) 0.2964 | loss(seq) 0.0061 | grad 3.5242 | lr 0.0000 | time_forward 3.2700 | time_backward 4.5500 |
[2023-10-23 10:20:15,106::train::INFO] [train] Iter 569634 | loss 0.8309 | loss(rot) 0.4287 | loss(pos) 0.0896 | loss(seq) 0.3126 | grad 2.9920 | lr 0.0000 | time_forward 3.0880 | time_backward 3.9470 |
[2023-10-23 10:20:17,274::train::INFO] [train] Iter 569635 | loss 1.1986 | loss(rot) 0.5982 | loss(pos) 0.1141 | loss(seq) 0.4862 | grad 3.9764 | lr 0.0000 | time_forward 1.0170 | time_backward 1.1480 |
[2023-10-23 10:20:23,352::train::INFO] [train] Iter 569636 | loss 0.7957 | loss(rot) 0.7712 | loss(pos) 0.0238 | loss(seq) 0.0007 | grad 4.6961 | lr 0.0000 | time_forward 2.6810 | time_backward 3.3870 |
[2023-10-23 10:20:25,990::train::INFO] [train] Iter 569637 | loss 2.2368 | loss(rot) 1.5754 | loss(pos) 0.3890 | loss(seq) 0.2724 | grad 8.7714 | lr 0.0000 | time_forward 1.2550 | time_backward 1.3790 |
[2023-10-23 10:20:31,171::train::INFO] [train] Iter 569638 | loss 2.1526 | loss(rot) 0.0021 | loss(pos) 2.1504 | loss(seq) 0.0000 | grad 18.3861 | lr 0.0000 | time_forward 2.2280 | time_backward 2.9480 |
[2023-10-23 10:20:33,802::train::INFO] [train] Iter 569639 | loss 1.3590 | loss(rot) 1.2139 | loss(pos) 0.1403 | loss(seq) 0.0048 | grad 3.6143 | lr 0.0000 | time_forward 1.2670 | time_backward 1.3520 |
[2023-10-23 10:20:40,147::train::INFO] [train] Iter 569640 | loss 1.3724 | loss(rot) 0.0368 | loss(pos) 1.3354 | loss(seq) 0.0001 | grad 11.1550 | lr 0.0000 | time_forward 2.7420 | time_backward 3.5990 |
[2023-10-23 10:20:42,744::train::INFO] [train] Iter 569641 | loss 0.8875 | loss(rot) 0.5153 | loss(pos) 0.1085 | loss(seq) 0.2637 | grad 3.6605 | lr 0.0000 | time_forward 1.2310 | time_backward 1.3630 |
[2023-10-23 10:20:49,235::train::INFO] [train] Iter 569642 | loss 0.8114 | loss(rot) 0.2031 | loss(pos) 0.1668 | loss(seq) 0.4415 | grad 3.8946 | lr 0.0000 | time_forward 2.7740 | time_backward 3.7140 |
[2023-10-23 10:20:51,912::train::INFO] [train] Iter 569643 | loss 0.4118 | loss(rot) 0.0284 | loss(pos) 0.3813 | loss(seq) 0.0020 | grad 7.1243 | lr 0.0000 | time_forward 1.2610 | time_backward 1.4130 |
[2023-10-23 10:20:59,762::train::INFO] [train] Iter 569644 | loss 1.3840 | loss(rot) 1.0654 | loss(pos) 0.0719 | loss(seq) 0.2467 | grad 2.6494 | lr 0.0000 | time_forward 3.2960 | time_backward 4.5390 |
[2023-10-23 10:21:02,502::train::INFO] [train] Iter 569645 | loss 0.7047 | loss(rot) 0.0310 | loss(pos) 0.6728 | loss(seq) 0.0008 | grad 6.3437 | lr 0.0000 | time_forward 1.2970 | time_backward 1.4410 |
[2023-10-23 10:21:09,783::train::INFO] [train] Iter 569646 | loss 0.7169 | loss(rot) 0.0039 | loss(pos) 0.7128 | loss(seq) 0.0001 | grad 12.8932 | lr 0.0000 | time_forward 3.2080 | time_backward 4.0560 |
[2023-10-23 10:21:17,554::train::INFO] [train] Iter 569647 | loss 1.3452 | loss(rot) 1.3085 | loss(pos) 0.0192 | loss(seq) 0.0175 | grad 5.1455 | lr 0.0000 | time_forward 3.2370 | time_backward 4.5300 |
[2023-10-23 10:21:20,304::train::INFO] [train] Iter 569648 | loss 0.6778 | loss(rot) 0.0277 | loss(pos) 0.2690 | loss(seq) 0.3810 | grad 7.9577 | lr 0.0000 | time_forward 1.2790 | time_backward 1.4680 |
[2023-10-23 10:21:28,172::train::INFO] [train] Iter 569649 | loss 1.0752 | loss(rot) 0.3048 | loss(pos) 0.5317 | loss(seq) 0.2387 | grad 4.7039 | lr 0.0000 | time_forward 3.4430 | time_backward 4.4220 |
[2023-10-23 10:21:34,560::train::INFO] [train] Iter 569650 | loss 2.1722 | loss(rot) 1.7786 | loss(pos) 0.1374 | loss(seq) 0.2562 | grad 6.7723 | lr 0.0000 | time_forward 2.7650 | time_backward 3.6200 |
[2023-10-23 10:21:37,231::train::INFO] [train] Iter 569651 | loss 0.7932 | loss(rot) 0.4393 | loss(pos) 0.0295 | loss(seq) 0.3244 | grad 2.4616 | lr 0.0000 | time_forward 1.2550 | time_backward 1.4120 |
[2023-10-23 10:21:45,046::train::INFO] [train] Iter 569652 | loss 1.7091 | loss(rot) 1.6813 | loss(pos) 0.0225 | loss(seq) 0.0052 | grad 4.2425 | lr 0.0000 | time_forward 3.2610 | time_backward 4.5520 |
[2023-10-23 10:21:49,164::train::INFO] [train] Iter 569653 | loss 0.2043 | loss(rot) 0.1645 | loss(pos) 0.0356 | loss(seq) 0.0042 | grad 2.5991 | lr 0.0000 | time_forward 1.8990 | time_backward 2.2150 |
[2023-10-23 10:21:55,365::train::INFO] [train] Iter 569654 | loss 0.7142 | loss(rot) 0.6639 | loss(pos) 0.0213 | loss(seq) 0.0290 | grad 2.2364 | lr 0.0000 | time_forward 2.6530 | time_backward 3.5450 |
[2023-10-23 10:21:57,503::train::INFO] [train] Iter 569655 | loss 0.2213 | loss(rot) 0.1577 | loss(pos) 0.0530 | loss(seq) 0.0105 | grad 2.3078 | lr 0.0000 | time_forward 0.9640 | time_backward 1.1710 |
[2023-10-23 10:22:00,279::train::INFO] [train] Iter 569656 | loss 0.1610 | loss(rot) 0.1272 | loss(pos) 0.0129 | loss(seq) 0.0209 | grad 2.0154 | lr 0.0000 | time_forward 1.3530 | time_backward 1.4210 |
[2023-10-23 10:22:07,501::train::INFO] [train] Iter 569657 | loss 0.8768 | loss(rot) 0.1603 | loss(pos) 0.2914 | loss(seq) 0.4251 | grad 3.5172 | lr 0.0000 | time_forward 3.2570 | time_backward 3.9600 |
[2023-10-23 10:22:10,587::train::INFO] [train] Iter 569658 | loss 0.7572 | loss(rot) 0.7035 | loss(pos) 0.0535 | loss(seq) 0.0001 | grad 6.1507 | lr 0.0000 | time_forward 1.4050 | time_backward 1.6770 |
[2023-10-23 10:22:13,665::train::INFO] [train] Iter 569659 | loss 0.8810 | loss(rot) 0.8140 | loss(pos) 0.0440 | loss(seq) 0.0230 | grad 8.3540 | lr 0.0000 | time_forward 1.3740 | time_backward 1.7000 |
[2023-10-23 10:22:21,799::train::INFO] [train] Iter 569660 | loss 1.1222 | loss(rot) 0.1920 | loss(pos) 0.9179 | loss(seq) 0.0122 | grad 6.3842 | lr 0.0000 | time_forward 3.4960 | time_backward 4.6250 |
[2023-10-23 10:22:24,509::train::INFO] [train] Iter 569661 | loss 0.8114 | loss(rot) 0.4982 | loss(pos) 0.0585 | loss(seq) 0.2547 | grad 2.2431 | lr 0.0000 | time_forward 1.3110 | time_backward 1.3940 |
[2023-10-23 10:22:27,095::train::INFO] [train] Iter 569662 | loss 0.3810 | loss(rot) 0.3294 | loss(pos) 0.0238 | loss(seq) 0.0279 | grad 2.7044 | lr 0.0000 | time_forward 1.1910 | time_backward 1.3760 |
[2023-10-23 10:22:29,726::train::INFO] [train] Iter 569663 | loss 0.7810 | loss(rot) 0.3830 | loss(pos) 0.0472 | loss(seq) 0.3507 | grad 5.5190 | lr 0.0000 | time_forward 1.2470 | time_backward 1.3810 |
[2023-10-23 10:22:35,902::train::INFO] [train] Iter 569664 | loss 2.3763 | loss(rot) 1.9885 | loss(pos) 0.0966 | loss(seq) 0.2911 | grad 19.1720 | lr 0.0000 | time_forward 2.6700 | time_backward 3.5030 |
[2023-10-23 10:22:43,907::train::INFO] [train] Iter 569665 | loss 1.0919 | loss(rot) 0.0033 | loss(pos) 1.0777 | loss(seq) 0.0109 | grad 14.1830 | lr 0.0000 | time_forward 3.9730 | time_backward 4.0280 |
[2023-10-23 10:22:46,909::train::INFO] [train] Iter 569666 | loss 1.6896 | loss(rot) 1.3931 | loss(pos) 0.0840 | loss(seq) 0.2125 | grad 3.7683 | lr 0.0000 | time_forward 1.3520 | time_backward 1.6470 |
[2023-10-23 10:22:58,262::train::INFO] [train] Iter 569667 | loss 1.3259 | loss(rot) 1.2862 | loss(pos) 0.0230 | loss(seq) 0.0167 | grad 5.4767 | lr 0.0000 | time_forward 6.7160 | time_backward 4.6340 |
[2023-10-23 10:23:00,858::train::INFO] [train] Iter 569668 | loss 0.7377 | loss(rot) 0.0432 | loss(pos) 0.6764 | loss(seq) 0.0181 | grad 9.6333 | lr 0.0000 | time_forward 1.2320 | time_backward 1.3610 |
[2023-10-23 10:23:06,110::train::INFO] [train] Iter 569669 | loss 0.9888 | loss(rot) 0.0115 | loss(pos) 0.9500 | loss(seq) 0.0273 | grad 9.5568 | lr 0.0000 | time_forward 2.3260 | time_backward 2.9140 |
[2023-10-23 10:23:17,365::train::INFO] [train] Iter 569670 | loss 0.6852 | loss(rot) 0.2775 | loss(pos) 0.3876 | loss(seq) 0.0202 | grad 4.9973 | lr 0.0000 | time_forward 7.1100 | time_backward 4.1360 |
[2023-10-23 10:23:25,790::train::INFO] [train] Iter 569671 | loss 0.3375 | loss(rot) 0.1202 | loss(pos) 0.0482 | loss(seq) 0.1691 | grad 2.3973 | lr 0.0000 | time_forward 4.1430 | time_backward 4.2800 |
[2023-10-23 10:23:36,312::train::INFO] [train] Iter 569672 | loss 0.4802 | loss(rot) 0.4392 | loss(pos) 0.0378 | loss(seq) 0.0031 | grad 2.0475 | lr 0.0000 | time_forward 5.3440 | time_backward 5.1740 |
[2023-10-23 10:23:38,995::train::INFO] [train] Iter 569673 | loss 0.3900 | loss(rot) 0.1257 | loss(pos) 0.0186 | loss(seq) 0.2458 | grad 3.4955 | lr 0.0000 | time_forward 1.2180 | time_backward 1.4610 |
[2023-10-23 10:23:49,005::train::INFO] [train] Iter 569674 | loss 0.5378 | loss(rot) 0.3182 | loss(pos) 0.0376 | loss(seq) 0.1821 | grad 3.3536 | lr 0.0000 | time_forward 4.6380 | time_backward 5.3560 |
[2023-10-23 10:23:56,603::train::INFO] [train] Iter 569675 | loss 0.4444 | loss(rot) 0.0261 | loss(pos) 0.4153 | loss(seq) 0.0030 | grad 12.0795 | lr 0.0000 | time_forward 3.1100 | time_backward 4.4850 |
[2023-10-23 10:23:59,677::train::INFO] [train] Iter 569676 | loss 0.7458 | loss(rot) 0.4712 | loss(pos) 0.0739 | loss(seq) 0.2008 | grad 3.8839 | lr 0.0000 | time_forward 1.4180 | time_backward 1.6520 |
[2023-10-23 10:24:10,591::train::INFO] [train] Iter 569677 | loss 0.3534 | loss(rot) 0.2466 | loss(pos) 0.0208 | loss(seq) 0.0860 | grad 4.2043 | lr 0.0000 | time_forward 5.5910 | time_backward 5.3120 |
[2023-10-23 10:24:17,343::train::INFO] [train] Iter 569678 | loss 0.4454 | loss(rot) 0.2194 | loss(pos) 0.0392 | loss(seq) 0.1868 | grad 3.0731 | lr 0.0000 | time_forward 2.7850 | time_backward 3.9640 |
[2023-10-23 10:24:31,008::train::INFO] [train] Iter 569679 | loss 0.3711 | loss(rot) 0.0667 | loss(pos) 0.0469 | loss(seq) 0.2575 | grad 2.5202 | lr 0.0000 | time_forward 4.6850 | time_backward 8.9770 |
[2023-10-23 10:24:48,995::train::INFO] [train] Iter 569680 | loss 1.3352 | loss(rot) 1.3085 | loss(pos) 0.0249 | loss(seq) 0.0019 | grad 10.3636 | lr 0.0000 | time_forward 12.4270 | time_backward 5.5570 |
[2023-10-23 10:25:03,310::train::INFO] [train] Iter 569681 | loss 1.1844 | loss(rot) 0.6034 | loss(pos) 0.5736 | loss(seq) 0.0074 | grad 8.1606 | lr 0.0000 | time_forward 8.3090 | time_backward 6.0020 |
[2023-10-23 10:25:14,217::train::INFO] [train] Iter 569682 | loss 1.5619 | loss(rot) 0.5369 | loss(pos) 0.9903 | loss(seq) 0.0348 | grad 9.1664 | lr 0.0000 | time_forward 5.1100 | time_backward 5.7940 |
[2023-10-23 10:25:20,231::train::INFO] [train] Iter 569683 | loss 1.1200 | loss(rot) 0.8774 | loss(pos) 0.0848 | loss(seq) 0.1578 | grad 5.3755 | lr 0.0000 | time_forward 2.6030 | time_backward 3.4070 |
[2023-10-23 10:25:22,787::train::INFO] [train] Iter 569684 | loss 1.8193 | loss(rot) 1.7386 | loss(pos) 0.0602 | loss(seq) 0.0205 | grad 8.2689 | lr 0.0000 | time_forward 1.2130 | time_backward 1.3410 |
[2023-10-23 10:25:34,900::train::INFO] [train] Iter 569685 | loss 2.0829 | loss(rot) 1.5797 | loss(pos) 0.1643 | loss(seq) 0.3389 | grad 3.6498 | lr 0.0000 | time_forward 6.5190 | time_backward 5.5610 |
[2023-10-23 10:25:43,661::train::INFO] [train] Iter 569686 | loss 0.2902 | loss(rot) 0.0939 | loss(pos) 0.0411 | loss(seq) 0.1552 | grad 2.7490 | lr 0.0000 | time_forward 3.7070 | time_backward 5.0510 |
[2023-10-23 10:25:45,936::train::INFO] [train] Iter 569687 | loss 0.3715 | loss(rot) 0.1529 | loss(pos) 0.0600 | loss(seq) 0.1586 | grad 2.4327 | lr 0.0000 | time_forward 1.0440 | time_backward 1.2280 |
[2023-10-23 10:25:55,580::train::INFO] [train] Iter 569688 | loss 0.3324 | loss(rot) 0.0870 | loss(pos) 0.0982 | loss(seq) 0.1471 | grad 2.1835 | lr 0.0000 | time_forward 4.1400 | time_backward 5.4850 |
[2023-10-23 10:26:03,415::train::INFO] [train] Iter 569689 | loss 1.8525 | loss(rot) 1.3421 | loss(pos) 0.1341 | loss(seq) 0.3763 | grad 8.0536 | lr 0.0000 | time_forward 3.2830 | time_backward 4.5500 |
[2023-10-23 10:26:05,690::train::INFO] [train] Iter 569690 | loss 0.6180 | loss(rot) 0.4912 | loss(pos) 0.0818 | loss(seq) 0.0450 | grad 2.5725 | lr 0.0000 | time_forward 1.0630 | time_backward 1.2090 |
[2023-10-23 10:26:15,466::train::INFO] [train] Iter 569691 | loss 0.9043 | loss(rot) 0.2672 | loss(pos) 0.1454 | loss(seq) 0.4918 | grad 3.7129 | lr 0.0000 | time_forward 3.9440 | time_backward 5.8060 |
[2023-10-23 10:26:23,541::train::INFO] [train] Iter 569692 | loss 0.5348 | loss(rot) 0.0613 | loss(pos) 0.3395 | loss(seq) 0.1340 | grad 5.1208 | lr 0.0000 | time_forward 3.3480 | time_backward 4.7240 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.