text
stringlengths 56
1.16k
|
|---|
[2023-10-25 15:12:50,885::train::INFO] [train] Iter 597165 | loss 1.9795 | loss(rot) 1.6211 | loss(pos) 0.0911 | loss(seq) 0.2672 | grad 15.3762 | lr 0.0000 | time_forward 1.0320 | time_backward 1.2170 |
[2023-10-25 15:13:00,208::train::INFO] [train] Iter 597166 | loss 1.5586 | loss(rot) 1.3326 | loss(pos) 0.0476 | loss(seq) 0.1784 | grad 5.7292 | lr 0.0000 | time_forward 3.8400 | time_backward 5.4790 |
[2023-10-25 15:13:09,590::train::INFO] [train] Iter 597167 | loss 2.0066 | loss(rot) 1.8050 | loss(pos) 0.0622 | loss(seq) 0.1394 | grad 10.9186 | lr 0.0000 | time_forward 3.7830 | time_backward 5.5960 |
[2023-10-25 15:13:18,104::train::INFO] [train] Iter 597168 | loss 0.9120 | loss(rot) 0.3853 | loss(pos) 0.1245 | loss(seq) 0.4022 | grad 5.5309 | lr 0.0000 | time_forward 3.5780 | time_backward 4.9320 |
[2023-10-25 15:13:20,701::train::INFO] [train] Iter 597169 | loss 0.1355 | loss(rot) 0.1164 | loss(pos) 0.0162 | loss(seq) 0.0028 | grad 1.9051 | lr 0.0000 | time_forward 1.1990 | time_backward 1.3950 |
[2023-10-25 15:13:23,472::train::INFO] [train] Iter 597170 | loss 0.7416 | loss(rot) 0.2478 | loss(pos) 0.0232 | loss(seq) 0.4706 | grad 4.7938 | lr 0.0000 | time_forward 1.3350 | time_backward 1.4340 |
[2023-10-25 15:13:32,347::train::INFO] [train] Iter 597171 | loss 0.6549 | loss(rot) 0.5102 | loss(pos) 0.0064 | loss(seq) 0.1382 | grad 49.7486 | lr 0.0000 | time_forward 3.7820 | time_backward 5.0550 |
[2023-10-25 15:13:34,609::train::INFO] [train] Iter 597172 | loss 0.7379 | loss(rot) 0.3993 | loss(pos) 0.1742 | loss(seq) 0.1644 | grad 3.3534 | lr 0.0000 | time_forward 1.0460 | time_backward 1.2120 |
[2023-10-25 15:13:37,385::train::INFO] [train] Iter 597173 | loss 1.1985 | loss(rot) 0.1009 | loss(pos) 1.0972 | loss(seq) 0.0004 | grad 13.7929 | lr 0.0000 | time_forward 1.2500 | time_backward 1.5220 |
[2023-10-25 15:13:40,155::train::INFO] [train] Iter 597174 | loss 0.1979 | loss(rot) 0.1630 | loss(pos) 0.0263 | loss(seq) 0.0086 | grad 2.6254 | lr 0.0000 | time_forward 1.3150 | time_backward 1.4500 |
[2023-10-25 15:13:48,087::train::INFO] [train] Iter 597175 | loss 0.2381 | loss(rot) 0.1758 | loss(pos) 0.0365 | loss(seq) 0.0258 | grad 2.1085 | lr 0.0000 | time_forward 3.3500 | time_backward 4.5800 |
[2023-10-25 15:13:50,600::train::INFO] [train] Iter 597176 | loss 1.3197 | loss(rot) 1.3030 | loss(pos) 0.0167 | loss(seq) 0.0000 | grad 4.2823 | lr 0.0000 | time_forward 1.2340 | time_backward 1.2750 |
[2023-10-25 15:13:58,986::train::INFO] [train] Iter 597177 | loss 1.8993 | loss(rot) 1.3288 | loss(pos) 0.1425 | loss(seq) 0.4280 | grad 6.3060 | lr 0.0000 | time_forward 3.4680 | time_backward 4.8960 |
[2023-10-25 15:14:08,588::train::INFO] [train] Iter 597178 | loss 0.5126 | loss(rot) 0.3758 | loss(pos) 0.0477 | loss(seq) 0.0891 | grad 2.1704 | lr 0.0000 | time_forward 3.9320 | time_backward 5.6660 |
[2023-10-25 15:14:11,350::train::INFO] [train] Iter 597179 | loss 2.3111 | loss(rot) 0.0054 | loss(pos) 2.3049 | loss(seq) 0.0008 | grad 16.4250 | lr 0.0000 | time_forward 1.3280 | time_backward 1.4310 |
[2023-10-25 15:14:20,822::train::INFO] [train] Iter 597180 | loss 0.9204 | loss(rot) 0.2349 | loss(pos) 0.3671 | loss(seq) 0.3184 | grad 4.0809 | lr 0.0000 | time_forward 3.8840 | time_backward 5.5850 |
[2023-10-25 15:14:23,633::train::INFO] [train] Iter 597181 | loss 0.7316 | loss(rot) 0.7061 | loss(pos) 0.0255 | loss(seq) 0.0000 | grad 3.5662 | lr 0.0000 | time_forward 1.3800 | time_backward 1.4270 |
[2023-10-25 15:14:32,038::train::INFO] [train] Iter 597182 | loss 0.3021 | loss(rot) 0.1466 | loss(pos) 0.0260 | loss(seq) 0.1296 | grad 2.0281 | lr 0.0000 | time_forward 3.4810 | time_backward 4.8810 |
[2023-10-25 15:14:42,585::train::INFO] [train] Iter 597183 | loss 0.3567 | loss(rot) 0.2627 | loss(pos) 0.0188 | loss(seq) 0.0752 | grad 2.8528 | lr 0.0000 | time_forward 4.3870 | time_backward 6.1560 |
[2023-10-25 15:14:45,117::train::INFO] [train] Iter 597184 | loss 0.8229 | loss(rot) 0.6447 | loss(pos) 0.0453 | loss(seq) 0.1329 | grad 54.7931 | lr 0.0000 | time_forward 1.2650 | time_backward 1.2630 |
[2023-10-25 15:14:54,108::train::INFO] [train] Iter 597185 | loss 1.9866 | loss(rot) 1.4165 | loss(pos) 0.1457 | loss(seq) 0.4244 | grad 5.9791 | lr 0.0000 | time_forward 3.8110 | time_backward 5.1450 |
[2023-10-25 15:15:02,923::train::INFO] [train] Iter 597186 | loss 0.4636 | loss(rot) 0.3906 | loss(pos) 0.0669 | loss(seq) 0.0060 | grad 3.7318 | lr 0.0000 | time_forward 3.7120 | time_backward 5.1010 |
[2023-10-25 15:15:12,689::train::INFO] [train] Iter 597187 | loss 0.4581 | loss(rot) 0.0660 | loss(pos) 0.3640 | loss(seq) 0.0280 | grad 5.5321 | lr 0.0000 | time_forward 3.9050 | time_backward 5.8570 |
[2023-10-25 15:15:21,109::train::INFO] [train] Iter 597188 | loss 0.3670 | loss(rot) 0.0421 | loss(pos) 0.2433 | loss(seq) 0.0816 | grad 2.6751 | lr 0.0000 | time_forward 3.6580 | time_backward 4.7600 |
[2023-10-25 15:15:23,899::train::INFO] [train] Iter 597189 | loss 0.2648 | loss(rot) 0.0714 | loss(pos) 0.1779 | loss(seq) 0.0155 | grad 3.3251 | lr 0.0000 | time_forward 1.3160 | time_backward 1.4700 |
[2023-10-25 15:15:26,230::train::INFO] [train] Iter 597190 | loss 0.3093 | loss(rot) 0.0718 | loss(pos) 0.2248 | loss(seq) 0.0127 | grad 3.7055 | lr 0.0000 | time_forward 1.1000 | time_backward 1.2290 |
[2023-10-25 15:15:29,065::train::INFO] [train] Iter 597191 | loss 0.2913 | loss(rot) 0.0204 | loss(pos) 0.2606 | loss(seq) 0.0103 | grad 7.1068 | lr 0.0000 | time_forward 1.3450 | time_backward 1.4870 |
[2023-10-25 15:15:31,958::train::INFO] [train] Iter 597192 | loss 0.2483 | loss(rot) 0.0586 | loss(pos) 0.1532 | loss(seq) 0.0365 | grad 3.3180 | lr 0.0000 | time_forward 1.3970 | time_backward 1.4910 |
[2023-10-25 15:15:41,720::train::INFO] [train] Iter 597193 | loss 1.1551 | loss(rot) 1.1008 | loss(pos) 0.0381 | loss(seq) 0.0162 | grad 3.3913 | lr 0.0000 | time_forward 4.0080 | time_backward 5.7510 |
[2023-10-25 15:15:44,483::train::INFO] [train] Iter 597194 | loss 0.8035 | loss(rot) 0.1023 | loss(pos) 0.4678 | loss(seq) 0.2334 | grad 3.9510 | lr 0.0000 | time_forward 1.2810 | time_backward 1.4790 |
[2023-10-25 15:15:54,352::train::INFO] [train] Iter 597195 | loss 0.3378 | loss(rot) 0.2426 | loss(pos) 0.0278 | loss(seq) 0.0674 | grad 3.1571 | lr 0.0000 | time_forward 3.9870 | time_backward 5.8790 |
[2023-10-25 15:16:02,993::train::INFO] [train] Iter 597196 | loss 0.4450 | loss(rot) 0.0808 | loss(pos) 0.3520 | loss(seq) 0.0122 | grad 5.7459 | lr 0.0000 | time_forward 3.6340 | time_backward 5.0040 |
[2023-10-25 15:16:05,235::train::INFO] [train] Iter 597197 | loss 0.7296 | loss(rot) 0.4863 | loss(pos) 0.0494 | loss(seq) 0.1939 | grad 2.3245 | lr 0.0000 | time_forward 1.0350 | time_backward 1.2030 |
[2023-10-25 15:16:15,088::train::INFO] [train] Iter 597198 | loss 0.6206 | loss(rot) 0.4492 | loss(pos) 0.0235 | loss(seq) 0.1479 | grad 2.4440 | lr 0.0000 | time_forward 3.9940 | time_backward 5.8560 |
[2023-10-25 15:16:23,970::train::INFO] [train] Iter 597199 | loss 1.7566 | loss(rot) 1.0116 | loss(pos) 0.3286 | loss(seq) 0.4163 | grad 3.4252 | lr 0.0000 | time_forward 3.7310 | time_backward 5.1480 |
[2023-10-25 15:16:26,742::train::INFO] [train] Iter 597200 | loss 0.3555 | loss(rot) 0.2310 | loss(pos) 0.0200 | loss(seq) 0.1045 | grad 3.9991 | lr 0.0000 | time_forward 1.3020 | time_backward 1.4670 |
[2023-10-25 15:16:29,625::train::INFO] [train] Iter 597201 | loss 0.4980 | loss(rot) 0.1619 | loss(pos) 0.0467 | loss(seq) 0.2894 | grad 3.2786 | lr 0.0000 | time_forward 1.3050 | time_backward 1.5330 |
[2023-10-25 15:16:37,882::train::INFO] [train] Iter 597202 | loss 0.4384 | loss(rot) 0.0628 | loss(pos) 0.1825 | loss(seq) 0.1931 | grad 4.9808 | lr 0.0000 | time_forward 3.4450 | time_backward 4.8100 |
[2023-10-25 15:16:46,977::train::INFO] [train] Iter 597203 | loss 1.8509 | loss(rot) 1.0107 | loss(pos) 0.2536 | loss(seq) 0.5866 | grad 7.4705 | lr 0.0000 | time_forward 3.8750 | time_backward 5.2150 |
[2023-10-25 15:16:57,047::train::INFO] [train] Iter 597204 | loss 1.0952 | loss(rot) 0.2657 | loss(pos) 0.8121 | loss(seq) 0.0173 | grad 5.9613 | lr 0.0000 | time_forward 4.1240 | time_backward 5.9430 |
[2023-10-25 15:17:06,230::train::INFO] [train] Iter 597205 | loss 0.3307 | loss(rot) 0.1097 | loss(pos) 0.1070 | loss(seq) 0.1140 | grad 4.0881 | lr 0.0000 | time_forward 3.8740 | time_backward 5.3060 |
[2023-10-25 15:17:16,200::train::INFO] [train] Iter 597206 | loss 0.4635 | loss(rot) 0.4057 | loss(pos) 0.0268 | loss(seq) 0.0309 | grad 2.8285 | lr 0.0000 | time_forward 3.9740 | time_backward 5.9910 |
[2023-10-25 15:17:24,398::train::INFO] [train] Iter 597207 | loss 0.8527 | loss(rot) 0.7601 | loss(pos) 0.0243 | loss(seq) 0.0682 | grad 6.1593 | lr 0.0000 | time_forward 3.4750 | time_backward 4.7190 |
[2023-10-25 15:17:32,320::train::INFO] [train] Iter 597208 | loss 0.1490 | loss(rot) 0.0397 | loss(pos) 0.0788 | loss(seq) 0.0305 | grad 2.1734 | lr 0.0000 | time_forward 3.4240 | time_backward 4.4960 |
[2023-10-25 15:17:41,199::train::INFO] [train] Iter 597209 | loss 0.3924 | loss(rot) 0.1781 | loss(pos) 0.0268 | loss(seq) 0.1875 | grad 3.2818 | lr 0.0000 | time_forward 3.8180 | time_backward 5.0570 |
[2023-10-25 15:17:49,660::train::INFO] [train] Iter 597210 | loss 1.6287 | loss(rot) 0.2468 | loss(pos) 1.3798 | loss(seq) 0.0021 | grad 11.0234 | lr 0.0000 | time_forward 3.5020 | time_backward 4.9540 |
[2023-10-25 15:17:51,918::train::INFO] [train] Iter 597211 | loss 0.7234 | loss(rot) 0.3592 | loss(pos) 0.1578 | loss(seq) 0.2064 | grad 2.5211 | lr 0.0000 | time_forward 1.0280 | time_backward 1.2270 |
[2023-10-25 15:17:54,664::train::INFO] [train] Iter 597212 | loss 0.8542 | loss(rot) 0.5513 | loss(pos) 0.0384 | loss(seq) 0.2644 | grad 3.6950 | lr 0.0000 | time_forward 1.3210 | time_backward 1.4220 |
[2023-10-25 15:18:01,601::train::INFO] [train] Iter 597213 | loss 1.1454 | loss(rot) 0.5852 | loss(pos) 0.1132 | loss(seq) 0.4469 | grad 4.0299 | lr 0.0000 | time_forward 3.0450 | time_backward 3.8870 |
[2023-10-25 15:18:11,101::train::INFO] [train] Iter 597214 | loss 0.3854 | loss(rot) 0.0904 | loss(pos) 0.0325 | loss(seq) 0.2625 | grad 2.2851 | lr 0.0000 | time_forward 3.9100 | time_backward 5.5730 |
[2023-10-25 15:18:13,897::train::INFO] [train] Iter 597215 | loss 1.2278 | loss(rot) 0.9461 | loss(pos) 0.0390 | loss(seq) 0.2428 | grad 10.9890 | lr 0.0000 | time_forward 1.3040 | time_backward 1.4880 |
[2023-10-25 15:18:16,723::train::INFO] [train] Iter 597216 | loss 0.2444 | loss(rot) 0.0209 | loss(pos) 0.2227 | loss(seq) 0.0007 | grad 5.5013 | lr 0.0000 | time_forward 1.3030 | time_backward 1.4800 |
[2023-10-25 15:18:26,127::train::INFO] [train] Iter 597217 | loss 0.5837 | loss(rot) 0.4847 | loss(pos) 0.0198 | loss(seq) 0.0791 | grad 14.0318 | lr 0.0000 | time_forward 3.8660 | time_backward 5.5340 |
[2023-10-25 15:18:35,583::train::INFO] [train] Iter 597218 | loss 0.9771 | loss(rot) 0.2703 | loss(pos) 0.1065 | loss(seq) 0.6003 | grad 4.9706 | lr 0.0000 | time_forward 3.8820 | time_backward 5.5710 |
[2023-10-25 15:18:38,479::train::INFO] [train] Iter 597219 | loss 0.9973 | loss(rot) 0.4895 | loss(pos) 0.5013 | loss(seq) 0.0065 | grad 8.1972 | lr 0.0000 | time_forward 1.3170 | time_backward 1.5770 |
[2023-10-25 15:18:45,688::train::INFO] [train] Iter 597220 | loss 1.6811 | loss(rot) 1.5751 | loss(pos) 0.0395 | loss(seq) 0.0665 | grad 4.5704 | lr 0.0000 | time_forward 3.0630 | time_backward 4.1420 |
[2023-10-25 15:18:53,884::train::INFO] [train] Iter 597221 | loss 0.5774 | loss(rot) 0.0994 | loss(pos) 0.4269 | loss(seq) 0.0512 | grad 4.4200 | lr 0.0000 | time_forward 3.4880 | time_backward 4.7050 |
[2023-10-25 15:19:03,329::train::INFO] [train] Iter 597222 | loss 0.3726 | loss(rot) 0.3572 | loss(pos) 0.0116 | loss(seq) 0.0038 | grad 3.9659 | lr 0.0000 | time_forward 3.9750 | time_backward 5.4660 |
[2023-10-25 15:19:06,175::train::INFO] [train] Iter 597223 | loss 0.6448 | loss(rot) 0.0117 | loss(pos) 0.6315 | loss(seq) 0.0017 | grad 7.5315 | lr 0.0000 | time_forward 1.3040 | time_backward 1.5390 |
[2023-10-25 15:19:15,516::train::INFO] [train] Iter 597224 | loss 0.5700 | loss(rot) 0.2903 | loss(pos) 0.2446 | loss(seq) 0.0352 | grad 4.7632 | lr 0.0000 | time_forward 3.9240 | time_backward 5.4120 |
[2023-10-25 15:19:24,557::train::INFO] [train] Iter 597225 | loss 0.6474 | loss(rot) 0.5527 | loss(pos) 0.0155 | loss(seq) 0.0793 | grad 4.4625 | lr 0.0000 | time_forward 3.6520 | time_backward 5.3870 |
[2023-10-25 15:19:27,466::train::INFO] [train] Iter 597226 | loss 0.7910 | loss(rot) 0.1720 | loss(pos) 0.1673 | loss(seq) 0.4517 | grad 4.8745 | lr 0.0000 | time_forward 1.3760 | time_backward 1.5300 |
[2023-10-25 15:19:30,301::train::INFO] [train] Iter 597227 | loss 0.2115 | loss(rot) 0.1203 | loss(pos) 0.0244 | loss(seq) 0.0667 | grad 1.6356 | lr 0.0000 | time_forward 1.3460 | time_backward 1.4860 |
[2023-10-25 15:19:33,032::train::INFO] [train] Iter 597228 | loss 0.4244 | loss(rot) 0.3385 | loss(pos) 0.0505 | loss(seq) 0.0354 | grad 2.4793 | lr 0.0000 | time_forward 1.2600 | time_backward 1.4460 |
[2023-10-25 15:19:35,745::train::INFO] [train] Iter 597229 | loss 0.9464 | loss(rot) 0.3501 | loss(pos) 0.5955 | loss(seq) 0.0007 | grad 9.0081 | lr 0.0000 | time_forward 1.2630 | time_backward 1.4190 |
[2023-10-25 15:19:38,656::train::INFO] [train] Iter 597230 | loss 0.1853 | loss(rot) 0.0755 | loss(pos) 0.0251 | loss(seq) 0.0847 | grad 1.7698 | lr 0.0000 | time_forward 1.3450 | time_backward 1.5460 |
[2023-10-25 15:19:48,070::train::INFO] [train] Iter 597231 | loss 1.1406 | loss(rot) 0.5817 | loss(pos) 0.2071 | loss(seq) 0.3519 | grad 4.3569 | lr 0.0000 | time_forward 4.0340 | time_backward 5.3580 |
[2023-10-25 15:19:57,139::train::INFO] [train] Iter 597232 | loss 0.5241 | loss(rot) 0.4686 | loss(pos) 0.0309 | loss(seq) 0.0246 | grad 23.4648 | lr 0.0000 | time_forward 3.8900 | time_backward 5.1750 |
[2023-10-25 15:19:59,882::train::INFO] [train] Iter 597233 | loss 0.7423 | loss(rot) 0.2816 | loss(pos) 0.2903 | loss(seq) 0.1704 | grad 3.7296 | lr 0.0000 | time_forward 1.3360 | time_backward 1.4050 |
[2023-10-25 15:20:02,675::train::INFO] [train] Iter 597234 | loss 0.4053 | loss(rot) 0.0292 | loss(pos) 0.3712 | loss(seq) 0.0049 | grad 5.5343 | lr 0.0000 | time_forward 1.3800 | time_backward 1.4090 |
[2023-10-25 15:20:10,220::train::INFO] [train] Iter 597235 | loss 1.9235 | loss(rot) 1.3388 | loss(pos) 0.0979 | loss(seq) 0.4868 | grad 5.4561 | lr 0.0000 | time_forward 3.2220 | time_backward 4.2910 |
[2023-10-25 15:20:17,824::train::INFO] [train] Iter 597236 | loss 1.0101 | loss(rot) 0.7695 | loss(pos) 0.0894 | loss(seq) 0.1512 | grad 3.0377 | lr 0.0000 | time_forward 3.2690 | time_backward 4.3310 |
[2023-10-25 15:20:25,724::train::INFO] [train] Iter 597237 | loss 0.2529 | loss(rot) 0.0730 | loss(pos) 0.0146 | loss(seq) 0.1652 | grad 1.8778 | lr 0.0000 | time_forward 3.3990 | time_backward 4.4970 |
[2023-10-25 15:20:34,995::train::INFO] [train] Iter 597238 | loss 0.2722 | loss(rot) 0.1844 | loss(pos) 0.0876 | loss(seq) 0.0002 | grad 2.7497 | lr 0.0000 | time_forward 3.8510 | time_backward 5.4170 |
[2023-10-25 15:20:44,017::train::INFO] [train] Iter 597239 | loss 0.2792 | loss(rot) 0.2403 | loss(pos) 0.0192 | loss(seq) 0.0198 | grad 2.8609 | lr 0.0000 | time_forward 3.7070 | time_backward 5.3120 |
[2023-10-25 15:20:51,532::train::INFO] [train] Iter 597240 | loss 1.0265 | loss(rot) 0.5970 | loss(pos) 0.0309 | loss(seq) 0.3986 | grad 11.2905 | lr 0.0000 | time_forward 3.2430 | time_backward 4.2700 |
[2023-10-25 15:20:59,306::train::INFO] [train] Iter 597241 | loss 0.1657 | loss(rot) 0.0679 | loss(pos) 0.0201 | loss(seq) 0.0777 | grad 2.1073 | lr 0.0000 | time_forward 3.3830 | time_backward 4.3880 |
[2023-10-25 15:21:07,542::train::INFO] [train] Iter 597242 | loss 0.4689 | loss(rot) 0.3130 | loss(pos) 0.0278 | loss(seq) 0.1282 | grad 11.6894 | lr 0.0000 | time_forward 3.5330 | time_backward 4.6990 |
[2023-10-25 15:21:16,027::train::INFO] [train] Iter 597243 | loss 0.5347 | loss(rot) 0.1208 | loss(pos) 0.2358 | loss(seq) 0.1781 | grad 4.5932 | lr 0.0000 | time_forward 3.3630 | time_backward 5.1200 |
[2023-10-25 15:21:24,390::train::INFO] [train] Iter 597244 | loss 0.5619 | loss(rot) 0.3437 | loss(pos) 0.0249 | loss(seq) 0.1933 | grad 5.5246 | lr 0.0000 | time_forward 3.6290 | time_backward 4.7310 |
[2023-10-25 15:21:34,035::train::INFO] [train] Iter 597245 | loss 0.7687 | loss(rot) 0.3580 | loss(pos) 0.0338 | loss(seq) 0.3769 | grad 4.9539 | lr 0.0000 | time_forward 4.0190 | time_backward 5.6220 |
[2023-10-25 15:21:43,353::train::INFO] [train] Iter 597246 | loss 2.9428 | loss(rot) 0.2414 | loss(pos) 2.7014 | loss(seq) 0.0000 | grad 27.0905 | lr 0.0000 | time_forward 3.6980 | time_backward 5.6160 |
[2023-10-25 15:21:51,530::train::INFO] [train] Iter 597247 | loss 0.6007 | loss(rot) 0.0941 | loss(pos) 0.2489 | loss(seq) 0.2578 | grad 5.7192 | lr 0.0000 | time_forward 3.4970 | time_backward 4.6780 |
[2023-10-25 15:22:00,730::train::INFO] [train] Iter 597248 | loss 1.2541 | loss(rot) 0.4563 | loss(pos) 0.3545 | loss(seq) 0.4432 | grad 2.4898 | lr 0.0000 | time_forward 4.0720 | time_backward 5.1260 |
[2023-10-25 15:22:08,912::train::INFO] [train] Iter 597249 | loss 0.3315 | loss(rot) 0.0465 | loss(pos) 0.2757 | loss(seq) 0.0092 | grad 7.8090 | lr 0.0000 | time_forward 3.4460 | time_backward 4.7330 |
[2023-10-25 15:22:11,726::train::INFO] [train] Iter 597250 | loss 1.0891 | loss(rot) 0.4920 | loss(pos) 0.0558 | loss(seq) 0.5413 | grad 4.4266 | lr 0.0000 | time_forward 1.3270 | time_backward 1.4840 |
[2023-10-25 15:22:19,318::train::INFO] [train] Iter 597251 | loss 1.2882 | loss(rot) 1.0973 | loss(pos) 0.0592 | loss(seq) 0.1317 | grad 6.8070 | lr 0.0000 | time_forward 3.2480 | time_backward 4.3410 |
[2023-10-25 15:22:21,296::train::INFO] [train] Iter 597252 | loss 1.1391 | loss(rot) 0.3481 | loss(pos) 0.3617 | loss(seq) 0.4293 | grad 4.7084 | lr 0.0000 | time_forward 0.9020 | time_backward 1.0720 |
[2023-10-25 15:22:28,543::train::INFO] [train] Iter 597253 | loss 0.5888 | loss(rot) 0.1689 | loss(pos) 0.0640 | loss(seq) 0.3559 | grad 3.5374 | lr 0.0000 | time_forward 3.0470 | time_backward 4.1840 |
[2023-10-25 15:22:30,680::train::INFO] [train] Iter 597254 | loss 1.4847 | loss(rot) 0.8547 | loss(pos) 0.1249 | loss(seq) 0.5050 | grad 5.0353 | lr 0.0000 | time_forward 0.9910 | time_backward 1.1420 |
[2023-10-25 15:22:39,343::train::INFO] [train] Iter 597255 | loss 0.7562 | loss(rot) 0.7006 | loss(pos) 0.0273 | loss(seq) 0.0284 | grad 2.1952 | lr 0.0000 | time_forward 3.5280 | time_backward 5.1190 |
[2023-10-25 15:22:47,312::train::INFO] [train] Iter 597256 | loss 0.4094 | loss(rot) 0.3089 | loss(pos) 0.0317 | loss(seq) 0.0689 | grad 4.9897 | lr 0.0000 | time_forward 3.4340 | time_backward 4.5320 |
[2023-10-25 15:22:54,517::train::INFO] [train] Iter 597257 | loss 0.8130 | loss(rot) 0.2318 | loss(pos) 0.4606 | loss(seq) 0.1206 | grad 5.1063 | lr 0.0000 | time_forward 3.2080 | time_backward 3.9930 |
[2023-10-25 15:23:02,122::train::INFO] [train] Iter 597258 | loss 0.5196 | loss(rot) 0.2297 | loss(pos) 0.2220 | loss(seq) 0.0678 | grad 3.2912 | lr 0.0000 | time_forward 3.3490 | time_backward 4.2530 |
[2023-10-25 15:23:05,044::train::INFO] [train] Iter 597259 | loss 0.3740 | loss(rot) 0.3406 | loss(pos) 0.0308 | loss(seq) 0.0026 | grad 3.9442 | lr 0.0000 | time_forward 1.4630 | time_backward 1.4550 |
[2023-10-25 15:23:08,363::train::INFO] [train] Iter 597260 | loss 1.8419 | loss(rot) 1.3171 | loss(pos) 0.1394 | loss(seq) 0.3853 | grad 6.5984 | lr 0.0000 | time_forward 1.4890 | time_backward 1.8280 |
[2023-10-25 15:23:17,352::train::INFO] [train] Iter 597261 | loss 0.5220 | loss(rot) 0.1155 | loss(pos) 0.0765 | loss(seq) 0.3299 | grad 2.6143 | lr 0.0000 | time_forward 3.9540 | time_backward 5.0320 |
[2023-10-25 15:23:23,584::train::INFO] [train] Iter 597262 | loss 0.8258 | loss(rot) 0.0131 | loss(pos) 0.8075 | loss(seq) 0.0053 | grad 12.3990 | lr 0.0000 | time_forward 2.7630 | time_backward 3.4570 |
[2023-10-25 15:23:32,583::train::INFO] [train] Iter 597263 | loss 0.4595 | loss(rot) 0.0859 | loss(pos) 0.1917 | loss(seq) 0.1818 | grad 3.7067 | lr 0.0000 | time_forward 3.7220 | time_backward 5.2730 |
[2023-10-25 15:23:40,461::train::INFO] [train] Iter 597264 | loss 1.6762 | loss(rot) 1.4683 | loss(pos) 0.0759 | loss(seq) 0.1320 | grad 3.0878 | lr 0.0000 | time_forward 3.5580 | time_backward 4.3180 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.