text stringlengths 56 1.16k |
|---|
[2023-10-23 12:08:27,406::train::INFO] [train] Iter 570592 | loss 0.1959 | loss(rot) 0.1426 | loss(pos) 0.0474 | loss(seq) 0.0059 | grad 1.9326 | lr 0.0000 | time_forward 1.3230 | time_backward 1.4430 |
[2023-10-23 12:08:34,950::train::INFO] [train] Iter 570593 | loss 0.1892 | loss(rot) 0.1360 | loss(pos) 0.0532 | loss(seq) 0.0000 | grad 3.6128 | lr 0.0000 | time_forward 3.2430 | time_backward 4.2980 |
[2023-10-23 12:08:43,839::train::INFO] [train] Iter 570594 | loss 0.4144 | loss(rot) 0.0446 | loss(pos) 0.3593 | loss(seq) 0.0105 | grad 6.9426 | lr 0.0000 | time_forward 3.3570 | time_backward 5.5280 |
[2023-10-23 12:08:51,296::train::INFO] [train] Iter 570595 | loss 0.3518 | loss(rot) 0.1509 | loss(pos) 0.0294 | loss(seq) 0.1715 | grad 2.9644 | lr 0.0000 | time_forward 3.2710 | time_backward 4.1820 |
[2023-10-23 12:09:00,496::train::INFO] [train] Iter 570596 | loss 0.8523 | loss(rot) 0.8241 | loss(pos) 0.0214 | loss(seq) 0.0067 | grad 39.9313 | lr 0.0000 | time_forward 3.8570 | time_backward 5.3390 |
[2023-10-23 12:09:08,479::train::INFO] [train] Iter 570597 | loss 0.2286 | loss(rot) 0.0504 | loss(pos) 0.1758 | loss(seq) 0.0024 | grad 3.5902 | lr 0.0000 | time_forward 3.4120 | time_backward 4.5680 |
[2023-10-23 12:09:17,337::train::INFO] [train] Iter 570598 | loss 2.3426 | loss(rot) 2.1516 | loss(pos) 0.0706 | loss(seq) 0.1204 | grad 6.7103 | lr 0.0000 | time_forward 3.4250 | time_backward 5.4290 |
[2023-10-23 12:09:20,903::train::INFO] [train] Iter 570599 | loss 0.5922 | loss(rot) 0.0961 | loss(pos) 0.0895 | loss(seq) 0.4066 | grad 4.7176 | lr 0.0000 | time_forward 1.7260 | time_backward 1.8360 |
[2023-10-23 12:09:32,089::train::INFO] [train] Iter 570600 | loss 0.8805 | loss(rot) 0.3892 | loss(pos) 0.0391 | loss(seq) 0.4523 | grad 70.0474 | lr 0.0000 | time_forward 4.4310 | time_backward 6.7290 |
[2023-10-23 12:09:38,055::train::INFO] [train] Iter 570601 | loss 1.3581 | loss(rot) 0.5868 | loss(pos) 0.3765 | loss(seq) 0.3948 | grad 3.7493 | lr 0.0000 | time_forward 2.7230 | time_backward 3.2400 |
[2023-10-23 12:09:45,843::train::INFO] [train] Iter 570602 | loss 1.6117 | loss(rot) 1.0703 | loss(pos) 0.0731 | loss(seq) 0.4683 | grad 5.6389 | lr 0.0000 | time_forward 3.4480 | time_backward 4.3360 |
[2023-10-23 12:09:52,680::train::INFO] [train] Iter 570603 | loss 0.6737 | loss(rot) 0.0282 | loss(pos) 0.6409 | loss(seq) 0.0046 | grad 7.6542 | lr 0.0000 | time_forward 3.0190 | time_backward 3.8130 |
[2023-10-23 12:09:55,180::train::INFO] [train] Iter 570604 | loss 0.8791 | loss(rot) 0.0477 | loss(pos) 0.8141 | loss(seq) 0.0173 | grad 5.8496 | lr 0.0000 | time_forward 1.1470 | time_backward 1.3490 |
[2023-10-23 12:10:01,816::train::INFO] [train] Iter 570605 | loss 1.5528 | loss(rot) 1.3720 | loss(pos) 0.0623 | loss(seq) 0.1185 | grad 6.2240 | lr 0.0000 | time_forward 2.8540 | time_backward 3.7780 |
[2023-10-23 12:10:04,370::train::INFO] [train] Iter 570606 | loss 1.2218 | loss(rot) 0.9912 | loss(pos) 0.0483 | loss(seq) 0.1822 | grad 17.5035 | lr 0.0000 | time_forward 1.1980 | time_backward 1.3530 |
[2023-10-23 12:10:11,470::train::INFO] [train] Iter 570607 | loss 0.4651 | loss(rot) 0.2396 | loss(pos) 0.0330 | loss(seq) 0.1925 | grad 4.4483 | lr 0.0000 | time_forward 3.0850 | time_backward 4.0130 |
[2023-10-23 12:10:19,724::train::INFO] [train] Iter 570608 | loss 0.2256 | loss(rot) 0.0508 | loss(pos) 0.1442 | loss(seq) 0.0307 | grad 2.8573 | lr 0.0000 | time_forward 3.3520 | time_backward 4.8990 |
[2023-10-23 12:10:21,995::train::INFO] [train] Iter 570609 | loss 1.2040 | loss(rot) 0.6736 | loss(pos) 0.1394 | loss(seq) 0.3910 | grad 3.6538 | lr 0.0000 | time_forward 1.0440 | time_backward 1.2230 |
[2023-10-23 12:10:31,027::train::INFO] [train] Iter 570610 | loss 0.8482 | loss(rot) 0.7499 | loss(pos) 0.0450 | loss(seq) 0.0533 | grad 13.3431 | lr 0.0000 | time_forward 3.6720 | time_backward 5.3570 |
[2023-10-23 12:10:40,026::train::INFO] [train] Iter 570611 | loss 0.4936 | loss(rot) 0.2376 | loss(pos) 0.1002 | loss(seq) 0.1557 | grad 2.9348 | lr 0.0000 | time_forward 3.6680 | time_backward 5.3270 |
[2023-10-23 12:10:42,767::train::INFO] [train] Iter 570612 | loss 0.2893 | loss(rot) 0.0644 | loss(pos) 0.1474 | loss(seq) 0.0775 | grad 5.3950 | lr 0.0000 | time_forward 1.3170 | time_backward 1.4200 |
[2023-10-23 12:10:51,657::train::INFO] [train] Iter 570613 | loss 0.4290 | loss(rot) 0.1217 | loss(pos) 0.1782 | loss(seq) 0.1290 | grad 4.1629 | lr 0.0000 | time_forward 3.6980 | time_backward 5.1880 |
[2023-10-23 12:10:59,298::train::INFO] [train] Iter 570614 | loss 0.1697 | loss(rot) 0.1391 | loss(pos) 0.0303 | loss(seq) 0.0002 | grad 1.8314 | lr 0.0000 | time_forward 3.2220 | time_backward 4.4160 |
[2023-10-23 12:11:06,945::train::INFO] [train] Iter 570615 | loss 0.8130 | loss(rot) 0.7193 | loss(pos) 0.0623 | loss(seq) 0.0313 | grad 3.4943 | lr 0.0000 | time_forward 3.2470 | time_backward 4.3970 |
[2023-10-23 12:11:15,164::train::INFO] [train] Iter 570616 | loss 0.4775 | loss(rot) 0.4405 | loss(pos) 0.0368 | loss(seq) 0.0002 | grad 13.8708 | lr 0.0000 | time_forward 3.4960 | time_backward 4.7200 |
[2023-10-23 12:11:24,473::train::INFO] [train] Iter 570617 | loss 0.5843 | loss(rot) 0.5647 | loss(pos) 0.0190 | loss(seq) 0.0005 | grad 2.8671 | lr 0.0000 | time_forward 4.0090 | time_backward 5.2970 |
[2023-10-23 12:11:27,577::train::INFO] [train] Iter 570618 | loss 0.2509 | loss(rot) 0.0413 | loss(pos) 0.2056 | loss(seq) 0.0039 | grad 5.5345 | lr 0.0000 | time_forward 1.4580 | time_backward 1.6410 |
[2023-10-23 12:11:36,517::train::INFO] [train] Iter 570619 | loss 0.6975 | loss(rot) 0.6072 | loss(pos) 0.0429 | loss(seq) 0.0473 | grad 2.7730 | lr 0.0000 | time_forward 3.8590 | time_backward 5.0780 |
[2023-10-23 12:11:46,720::train::INFO] [train] Iter 570620 | loss 1.5149 | loss(rot) 1.4839 | loss(pos) 0.0291 | loss(seq) 0.0019 | grad 32.2458 | lr 0.0000 | time_forward 4.1790 | time_backward 6.0200 |
[2023-10-23 12:11:49,706::train::INFO] [train] Iter 570621 | loss 1.1552 | loss(rot) 0.7829 | loss(pos) 0.0737 | loss(seq) 0.2986 | grad 4.4204 | lr 0.0000 | time_forward 1.3870 | time_backward 1.5960 |
[2023-10-23 12:11:59,186::train::INFO] [train] Iter 570622 | loss 1.2873 | loss(rot) 0.7196 | loss(pos) 0.3031 | loss(seq) 0.2646 | grad 3.7226 | lr 0.0000 | time_forward 3.9590 | time_backward 5.5180 |
[2023-10-23 12:12:08,709::train::INFO] [train] Iter 570623 | loss 0.4269 | loss(rot) 0.2140 | loss(pos) 0.0187 | loss(seq) 0.1943 | grad 4.2306 | lr 0.0000 | time_forward 4.1050 | time_backward 5.4140 |
[2023-10-23 12:12:11,521::train::INFO] [train] Iter 570624 | loss 0.5933 | loss(rot) 0.0069 | loss(pos) 0.5851 | loss(seq) 0.0013 | grad 13.7188 | lr 0.0000 | time_forward 1.3510 | time_backward 1.4570 |
[2023-10-23 12:12:20,795::train::INFO] [train] Iter 570625 | loss 1.1419 | loss(rot) 0.5538 | loss(pos) 0.2267 | loss(seq) 0.3614 | grad 5.6496 | lr 0.0000 | time_forward 3.8370 | time_backward 5.4340 |
[2023-10-23 12:12:28,951::train::INFO] [train] Iter 570626 | loss 1.9091 | loss(rot) 1.8410 | loss(pos) 0.0680 | loss(seq) 0.0001 | grad 6.3987 | lr 0.0000 | time_forward 3.4510 | time_backward 4.7000 |
[2023-10-23 12:12:37,459::train::INFO] [train] Iter 570627 | loss 1.5438 | loss(rot) 1.2675 | loss(pos) 0.0504 | loss(seq) 0.2259 | grad 5.5668 | lr 0.0000 | time_forward 3.6500 | time_backward 4.8540 |
[2023-10-23 12:12:40,188::train::INFO] [train] Iter 570628 | loss 0.5187 | loss(rot) 0.1842 | loss(pos) 0.0707 | loss(seq) 0.2638 | grad 2.5849 | lr 0.0000 | time_forward 1.3070 | time_backward 1.4180 |
[2023-10-23 12:12:48,687::train::INFO] [train] Iter 570629 | loss 0.4818 | loss(rot) 0.4147 | loss(pos) 0.0321 | loss(seq) 0.0351 | grad 3.9789 | lr 0.0000 | time_forward 3.7080 | time_backward 4.7620 |
[2023-10-23 12:12:56,431::train::INFO] [train] Iter 570630 | loss 1.9466 | loss(rot) 1.0240 | loss(pos) 0.3173 | loss(seq) 0.6053 | grad 4.2698 | lr 0.0000 | time_forward 3.1900 | time_backward 4.5500 |
[2023-10-23 12:13:05,014::train::INFO] [train] Iter 570631 | loss 0.9484 | loss(rot) 0.9272 | loss(pos) 0.0212 | loss(seq) 0.0000 | grad 3.5365 | lr 0.0000 | time_forward 3.6640 | time_backward 4.9160 |
[2023-10-23 12:13:13,378::train::INFO] [train] Iter 570632 | loss 1.5854 | loss(rot) 1.0004 | loss(pos) 0.1115 | loss(seq) 0.4735 | grad 20.2140 | lr 0.0000 | time_forward 3.3750 | time_backward 4.9840 |
[2023-10-23 12:13:22,082::train::INFO] [train] Iter 570633 | loss 0.1279 | loss(rot) 0.0856 | loss(pos) 0.0291 | loss(seq) 0.0132 | grad 1.9859 | lr 0.0000 | time_forward 3.8560 | time_backward 4.8450 |
[2023-10-23 12:13:29,000::train::INFO] [train] Iter 570634 | loss 0.7606 | loss(rot) 0.0698 | loss(pos) 0.6403 | loss(seq) 0.0505 | grad 7.8301 | lr 0.0000 | time_forward 2.9610 | time_backward 3.9540 |
[2023-10-23 12:13:36,038::train::INFO] [train] Iter 570635 | loss 1.6384 | loss(rot) 1.2686 | loss(pos) 0.0314 | loss(seq) 0.3385 | grad 11.7348 | lr 0.0000 | time_forward 3.0410 | time_backward 3.9940 |
[2023-10-23 12:13:38,295::train::INFO] [train] Iter 570636 | loss 0.9325 | loss(rot) 0.5655 | loss(pos) 0.1504 | loss(seq) 0.2166 | grad 4.9680 | lr 0.0000 | time_forward 1.0420 | time_backward 1.2120 |
[2023-10-23 12:13:41,429::train::INFO] [train] Iter 570637 | loss 0.4657 | loss(rot) 0.1162 | loss(pos) 0.2270 | loss(seq) 0.1225 | grad 2.4864 | lr 0.0000 | time_forward 1.4170 | time_backward 1.7050 |
[2023-10-23 12:13:48,760::train::INFO] [train] Iter 570638 | loss 0.3538 | loss(rot) 0.0642 | loss(pos) 0.0365 | loss(seq) 0.2532 | grad 2.5214 | lr 0.0000 | time_forward 3.1900 | time_backward 4.1260 |
[2023-10-23 12:13:56,279::train::INFO] [train] Iter 570639 | loss 0.3317 | loss(rot) 0.0553 | loss(pos) 0.0320 | loss(seq) 0.2444 | grad 2.1205 | lr 0.0000 | time_forward 3.2250 | time_backward 4.2910 |
[2023-10-23 12:14:03,717::train::INFO] [train] Iter 570640 | loss 0.2829 | loss(rot) 0.2618 | loss(pos) 0.0211 | loss(seq) 0.0000 | grad 15.0350 | lr 0.0000 | time_forward 3.2170 | time_backward 4.2180 |
[2023-10-23 12:14:06,501::train::INFO] [train] Iter 570641 | loss 2.2520 | loss(rot) 0.0506 | loss(pos) 2.1986 | loss(seq) 0.0028 | grad 17.2657 | lr 0.0000 | time_forward 1.2930 | time_backward 1.4880 |
[2023-10-23 12:14:14,939::train::INFO] [train] Iter 570642 | loss 1.2668 | loss(rot) 1.1499 | loss(pos) 0.0320 | loss(seq) 0.0849 | grad 3.9944 | lr 0.0000 | time_forward 3.5850 | time_backward 4.8510 |
[2023-10-23 12:14:23,365::train::INFO] [train] Iter 570643 | loss 0.4662 | loss(rot) 0.1431 | loss(pos) 0.0382 | loss(seq) 0.2849 | grad 2.5557 | lr 0.0000 | time_forward 3.5140 | time_backward 4.9080 |
[2023-10-23 12:14:30,921::train::INFO] [train] Iter 570644 | loss 1.8676 | loss(rot) 1.8187 | loss(pos) 0.0483 | loss(seq) 0.0006 | grad 3.1523 | lr 0.0000 | time_forward 3.2010 | time_backward 4.3530 |
[2023-10-23 12:14:33,552::train::INFO] [train] Iter 570645 | loss 0.7043 | loss(rot) 0.3725 | loss(pos) 0.0295 | loss(seq) 0.3023 | grad 4.0390 | lr 0.0000 | time_forward 1.2370 | time_backward 1.3910 |
[2023-10-23 12:14:40,186::train::INFO] [train] Iter 570646 | loss 0.1628 | loss(rot) 0.1433 | loss(pos) 0.0173 | loss(seq) 0.0022 | grad 2.6797 | lr 0.0000 | time_forward 2.8420 | time_backward 3.7880 |
[2023-10-23 12:14:48,051::train::INFO] [train] Iter 570647 | loss 0.8140 | loss(rot) 0.5585 | loss(pos) 0.0574 | loss(seq) 0.1981 | grad 2.6747 | lr 0.0000 | time_forward 3.3910 | time_backward 4.4720 |
[2023-10-23 12:14:54,391::train::INFO] [train] Iter 570648 | loss 0.2981 | loss(rot) 0.0446 | loss(pos) 0.0718 | loss(seq) 0.1817 | grad 3.3105 | lr 0.0000 | time_forward 2.7770 | time_backward 3.5590 |
[2023-10-23 12:15:02,697::train::INFO] [train] Iter 570649 | loss 0.3809 | loss(rot) 0.0558 | loss(pos) 0.3190 | loss(seq) 0.0062 | grad 5.4970 | lr 0.0000 | time_forward 3.6010 | time_backward 4.7020 |
[2023-10-23 12:15:11,166::train::INFO] [train] Iter 570650 | loss 0.2887 | loss(rot) 0.2024 | loss(pos) 0.0148 | loss(seq) 0.0715 | grad 2.2783 | lr 0.0000 | time_forward 3.4870 | time_backward 4.9800 |
[2023-10-23 12:15:19,751::train::INFO] [train] Iter 570651 | loss 0.6448 | loss(rot) 0.5782 | loss(pos) 0.0487 | loss(seq) 0.0179 | grad 2.2325 | lr 0.0000 | time_forward 3.6620 | time_backward 4.9190 |
[2023-10-23 12:15:28,441::train::INFO] [train] Iter 570652 | loss 1.1810 | loss(rot) 1.0467 | loss(pos) 0.0309 | loss(seq) 0.1034 | grad 6.1078 | lr 0.0000 | time_forward 3.6000 | time_backward 5.0870 |
[2023-10-23 12:15:31,205::train::INFO] [train] Iter 570653 | loss 1.6799 | loss(rot) 1.1664 | loss(pos) 0.1515 | loss(seq) 0.3620 | grad 8.0746 | lr 0.0000 | time_forward 1.3370 | time_backward 1.4240 |
[2023-10-23 12:15:33,907::train::INFO] [train] Iter 570654 | loss 0.2864 | loss(rot) 0.2465 | loss(pos) 0.0182 | loss(seq) 0.0217 | grad 3.6161 | lr 0.0000 | time_forward 1.2810 | time_backward 1.4180 |
[2023-10-23 12:15:42,477::train::INFO] [train] Iter 570655 | loss 0.6579 | loss(rot) 0.4678 | loss(pos) 0.0287 | loss(seq) 0.1614 | grad 2.9420 | lr 0.0000 | time_forward 3.7290 | time_backward 4.8370 |
[2023-10-23 12:15:52,440::train::INFO] [train] Iter 570656 | loss 0.7148 | loss(rot) 0.2419 | loss(pos) 0.3162 | loss(seq) 0.1567 | grad 3.3546 | lr 0.0000 | time_forward 3.7140 | time_backward 6.2460 |
[2023-10-23 12:15:55,529::train::INFO] [train] Iter 570657 | loss 0.3419 | loss(rot) 0.1452 | loss(pos) 0.0953 | loss(seq) 0.1013 | grad 2.4431 | lr 0.0000 | time_forward 1.4550 | time_backward 1.6320 |
[2023-10-23 12:15:58,270::train::INFO] [train] Iter 570658 | loss 0.7573 | loss(rot) 0.7207 | loss(pos) 0.0339 | loss(seq) 0.0027 | grad 5.0399 | lr 0.0000 | time_forward 1.3520 | time_backward 1.3850 |
[2023-10-23 12:16:08,994::train::INFO] [train] Iter 570659 | loss 1.7875 | loss(rot) 1.1055 | loss(pos) 0.1170 | loss(seq) 0.5650 | grad 2.5190 | lr 0.0000 | time_forward 4.5960 | time_backward 6.1000 |
[2023-10-23 12:16:18,517::train::INFO] [train] Iter 570660 | loss 0.9097 | loss(rot) 0.3321 | loss(pos) 0.3231 | loss(seq) 0.2545 | grad 3.5088 | lr 0.0000 | time_forward 3.9640 | time_backward 5.5560 |
[2023-10-23 12:16:28,356::train::INFO] [train] Iter 570661 | loss 0.6329 | loss(rot) 0.5970 | loss(pos) 0.0156 | loss(seq) 0.0203 | grad 3.1634 | lr 0.0000 | time_forward 4.0300 | time_backward 5.8050 |
[2023-10-23 12:16:38,007::train::INFO] [train] Iter 570662 | loss 0.4112 | loss(rot) 0.1879 | loss(pos) 0.0431 | loss(seq) 0.1803 | grad 2.5227 | lr 0.0000 | time_forward 4.1050 | time_backward 5.5440 |
[2023-10-23 12:16:47,361::train::INFO] [train] Iter 570663 | loss 1.6811 | loss(rot) 1.2775 | loss(pos) 0.1191 | loss(seq) 0.2845 | grad 7.5920 | lr 0.0000 | time_forward 3.8920 | time_backward 5.4590 |
[2023-10-23 12:16:55,460::train::INFO] [train] Iter 570664 | loss 0.7953 | loss(rot) 0.4234 | loss(pos) 0.0327 | loss(seq) 0.3393 | grad 3.5839 | lr 0.0000 | time_forward 3.4300 | time_backward 4.6660 |
[2023-10-23 12:16:58,232::train::INFO] [train] Iter 570665 | loss 0.4187 | loss(rot) 0.0865 | loss(pos) 0.2961 | loss(seq) 0.0361 | grad 4.0409 | lr 0.0000 | time_forward 1.2670 | time_backward 1.5020 |
[2023-10-23 12:17:07,477::train::INFO] [train] Iter 570666 | loss 0.1872 | loss(rot) 0.0393 | loss(pos) 0.1373 | loss(seq) 0.0106 | grad 2.1556 | lr 0.0000 | time_forward 3.8120 | time_backward 5.3880 |
[2023-10-23 12:17:10,204::train::INFO] [train] Iter 570667 | loss 0.8027 | loss(rot) 0.1423 | loss(pos) 0.2593 | loss(seq) 0.4012 | grad 4.4118 | lr 0.0000 | time_forward 1.2860 | time_backward 1.4380 |
[2023-10-23 12:17:18,015::train::INFO] [train] Iter 570668 | loss 1.7372 | loss(rot) 1.6792 | loss(pos) 0.0560 | loss(seq) 0.0020 | grad 5.2878 | lr 0.0000 | time_forward 3.3550 | time_backward 4.4530 |
[2023-10-23 12:17:26,310::train::INFO] [train] Iter 570669 | loss 0.3512 | loss(rot) 0.1862 | loss(pos) 0.0403 | loss(seq) 0.1248 | grad 3.0864 | lr 0.0000 | time_forward 3.5300 | time_backward 4.7610 |
[2023-10-23 12:17:34,950::train::INFO] [train] Iter 570670 | loss 0.2966 | loss(rot) 0.0928 | loss(pos) 0.0482 | loss(seq) 0.1556 | grad 2.3841 | lr 0.0000 | time_forward 3.5070 | time_backward 5.1290 |
[2023-10-23 12:17:43,508::train::INFO] [train] Iter 570671 | loss 0.9923 | loss(rot) 0.2885 | loss(pos) 0.1487 | loss(seq) 0.5551 | grad 6.4131 | lr 0.0000 | time_forward 3.6990 | time_backward 4.8560 |
[2023-10-23 12:17:51,691::train::INFO] [train] Iter 570672 | loss 0.7496 | loss(rot) 0.6593 | loss(pos) 0.0161 | loss(seq) 0.0743 | grad 6.1488 | lr 0.0000 | time_forward 3.4100 | time_backward 4.7700 |
[2023-10-23 12:18:01,636::train::INFO] [train] Iter 570673 | loss 0.1985 | loss(rot) 0.0340 | loss(pos) 0.1475 | loss(seq) 0.0170 | grad 3.2313 | lr 0.0000 | time_forward 4.3070 | time_backward 5.6350 |
[2023-10-23 12:18:09,559::train::INFO] [train] Iter 570674 | loss 1.5953 | loss(rot) 1.2974 | loss(pos) 0.0846 | loss(seq) 0.2133 | grad 4.4651 | lr 0.0000 | time_forward 3.4370 | time_backward 4.4830 |
[2023-10-23 12:18:17,864::train::INFO] [train] Iter 570675 | loss 0.9189 | loss(rot) 0.0881 | loss(pos) 0.7744 | loss(seq) 0.0565 | grad 5.3286 | lr 0.0000 | time_forward 3.5610 | time_backward 4.7410 |
[2023-10-23 12:18:24,872::train::INFO] [train] Iter 570676 | loss 0.2354 | loss(rot) 0.0612 | loss(pos) 0.0431 | loss(seq) 0.1310 | grad 2.1920 | lr 0.0000 | time_forward 3.0300 | time_backward 3.9750 |
[2023-10-23 12:18:27,505::train::INFO] [train] Iter 570677 | loss 0.1424 | loss(rot) 0.1184 | loss(pos) 0.0211 | loss(seq) 0.0029 | grad 1.9599 | lr 0.0000 | time_forward 1.2670 | time_backward 1.3620 |
[2023-10-23 12:18:30,273::train::INFO] [train] Iter 570678 | loss 0.5963 | loss(rot) 0.1873 | loss(pos) 0.0184 | loss(seq) 0.3906 | grad 2.6469 | lr 0.0000 | time_forward 1.3210 | time_backward 1.3890 |
[2023-10-23 12:18:37,097::train::INFO] [train] Iter 570679 | loss 0.4181 | loss(rot) 0.3024 | loss(pos) 0.0383 | loss(seq) 0.0775 | grad 2.6897 | lr 0.0000 | time_forward 2.9560 | time_backward 3.8640 |
[2023-10-23 12:18:44,400::train::INFO] [train] Iter 570680 | loss 1.4649 | loss(rot) 1.1277 | loss(pos) 0.0213 | loss(seq) 0.3160 | grad 4.7243 | lr 0.0000 | time_forward 3.1060 | time_backward 4.1940 |
[2023-10-23 12:18:47,075::train::INFO] [train] Iter 570681 | loss 0.3908 | loss(rot) 0.2104 | loss(pos) 0.0852 | loss(seq) 0.0953 | grad 3.8998 | lr 0.0000 | time_forward 1.2790 | time_backward 1.3920 |
[2023-10-23 12:18:49,753::train::INFO] [train] Iter 570682 | loss 0.4454 | loss(rot) 0.4107 | loss(pos) 0.0346 | loss(seq) 0.0001 | grad 5.2816 | lr 0.0000 | time_forward 1.2810 | time_backward 1.3800 |
[2023-10-23 12:18:57,944::train::INFO] [train] Iter 570683 | loss 1.8813 | loss(rot) 1.7886 | loss(pos) 0.0544 | loss(seq) 0.0382 | grad 3.2675 | lr 0.0000 | time_forward 3.5400 | time_backward 4.6480 |
[2023-10-23 12:19:06,123::train::INFO] [train] Iter 570684 | loss 0.9261 | loss(rot) 0.4781 | loss(pos) 0.0540 | loss(seq) 0.3941 | grad 4.7909 | lr 0.0000 | time_forward 3.3910 | time_backward 4.7840 |
[2023-10-23 12:19:13,341::train::INFO] [train] Iter 570685 | loss 1.0461 | loss(rot) 0.3080 | loss(pos) 0.0941 | loss(seq) 0.6440 | grad 3.9399 | lr 0.0000 | time_forward 3.1060 | time_backward 4.1100 |
[2023-10-23 12:19:20,251::train::INFO] [train] Iter 570686 | loss 1.1839 | loss(rot) 0.3480 | loss(pos) 0.3799 | loss(seq) 0.4560 | grad 3.4009 | lr 0.0000 | time_forward 3.0200 | time_backward 3.8870 |
[2023-10-23 12:19:28,484::train::INFO] [train] Iter 570687 | loss 0.5506 | loss(rot) 0.4914 | loss(pos) 0.0522 | loss(seq) 0.0070 | grad 60.0934 | lr 0.0000 | time_forward 3.3220 | time_backward 4.9070 |
[2023-10-23 12:19:31,151::train::INFO] [train] Iter 570688 | loss 0.3509 | loss(rot) 0.0883 | loss(pos) 0.0166 | loss(seq) 0.2460 | grad 5.2827 | lr 0.0000 | time_forward 1.2280 | time_backward 1.4370 |
[2023-10-23 12:19:39,555::train::INFO] [train] Iter 570689 | loss 0.8578 | loss(rot) 0.5891 | loss(pos) 0.0538 | loss(seq) 0.2149 | grad 4.0028 | lr 0.0000 | time_forward 3.4390 | time_backward 4.9610 |
[2023-10-23 12:19:48,051::train::INFO] [train] Iter 570690 | loss 1.1009 | loss(rot) 0.4976 | loss(pos) 0.5030 | loss(seq) 0.1003 | grad 6.4825 | lr 0.0000 | time_forward 3.5420 | time_backward 4.9510 |
[2023-10-23 12:19:56,546::train::INFO] [train] Iter 570691 | loss 0.1423 | loss(rot) 0.0897 | loss(pos) 0.0320 | loss(seq) 0.0206 | grad 3.1574 | lr 0.0000 | time_forward 3.5280 | time_backward 4.9640 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.