text stringlengths 56 1.16k |
|---|
[2023-10-22 20:02:13,517::train::INFO] [train] Iter 561401 | loss 0.7334 | loss(rot) 0.7136 | loss(pos) 0.0134 | loss(seq) 0.0064 | grad 2.6569 | lr 0.0000 | time_forward 3.4860 | time_backward 4.8440 |
[2023-10-22 20:02:21,893::train::INFO] [train] Iter 561402 | loss 0.3302 | loss(rot) 0.1261 | loss(pos) 0.0499 | loss(seq) 0.1543 | grad 3.7113 | lr 0.0000 | time_forward 3.6150 | time_backward 4.7580 |
[2023-10-22 20:02:24,665::train::INFO] [train] Iter 561403 | loss 0.4869 | loss(rot) 0.4664 | loss(pos) 0.0162 | loss(seq) 0.0044 | grad 2.1248 | lr 0.0000 | time_forward 1.3090 | time_backward 1.4600 |
[2023-10-22 20:02:32,177::train::INFO] [train] Iter 561404 | loss 0.2824 | loss(rot) 0.0770 | loss(pos) 0.0528 | loss(seq) 0.1526 | grad 2.6774 | lr 0.0000 | time_forward 3.1920 | time_backward 4.3160 |
[2023-10-22 20:02:41,065::train::INFO] [train] Iter 561405 | loss 0.1321 | loss(rot) 0.1060 | loss(pos) 0.0258 | loss(seq) 0.0004 | grad 1.6565 | lr 0.0000 | time_forward 3.6610 | time_backward 5.2220 |
[2023-10-22 20:02:50,012::train::INFO] [train] Iter 561406 | loss 0.6740 | loss(rot) 0.2743 | loss(pos) 0.3835 | loss(seq) 0.0162 | grad 7.0782 | lr 0.0000 | time_forward 3.6400 | time_backward 5.3040 |
[2023-10-22 20:02:58,319::train::INFO] [train] Iter 561407 | loss 0.5003 | loss(rot) 0.3405 | loss(pos) 0.0875 | loss(seq) 0.0723 | grad 4.2866 | lr 0.0000 | time_forward 3.6190 | time_backward 4.6850 |
[2023-10-22 20:03:00,621::train::INFO] [train] Iter 561408 | loss 1.3561 | loss(rot) 1.2491 | loss(pos) 0.0275 | loss(seq) 0.0795 | grad 7.3544 | lr 0.0000 | time_forward 1.0280 | time_backward 1.2710 |
[2023-10-22 20:03:08,470::train::INFO] [train] Iter 561409 | loss 0.3533 | loss(rot) 0.1802 | loss(pos) 0.0205 | loss(seq) 0.1526 | grad 2.6678 | lr 0.0000 | time_forward 3.3540 | time_backward 4.4920 |
[2023-10-22 20:03:17,382::train::INFO] [train] Iter 561410 | loss 0.6242 | loss(rot) 0.0748 | loss(pos) 0.5472 | loss(seq) 0.0023 | grad 11.1924 | lr 0.0000 | time_forward 3.6650 | time_backward 5.2450 |
[2023-10-22 20:03:26,425::train::INFO] [train] Iter 561411 | loss 0.4441 | loss(rot) 0.1987 | loss(pos) 0.0199 | loss(seq) 0.2254 | grad 2.8230 | lr 0.0000 | time_forward 3.7250 | time_backward 5.3140 |
[2023-10-22 20:03:34,190::train::INFO] [train] Iter 561412 | loss 0.2370 | loss(rot) 0.1637 | loss(pos) 0.0225 | loss(seq) 0.0508 | grad 2.0513 | lr 0.0000 | time_forward 3.3130 | time_backward 4.4490 |
[2023-10-22 20:03:40,987::train::INFO] [train] Iter 561413 | loss 0.1347 | loss(rot) 0.0383 | loss(pos) 0.0892 | loss(seq) 0.0072 | grad 3.0073 | lr 0.0000 | time_forward 2.8370 | time_backward 3.9570 |
[2023-10-22 20:03:49,331::train::INFO] [train] Iter 561414 | loss 0.2504 | loss(rot) 0.1461 | loss(pos) 0.0196 | loss(seq) 0.0848 | grad 3.2157 | lr 0.0000 | time_forward 3.6210 | time_backward 4.7190 |
[2023-10-22 20:03:57,649::train::INFO] [train] Iter 561415 | loss 0.8617 | loss(rot) 0.4146 | loss(pos) 0.1817 | loss(seq) 0.2655 | grad 4.1894 | lr 0.0000 | time_forward 3.5260 | time_backward 4.7880 |
[2023-10-22 20:04:00,373::train::INFO] [train] Iter 561416 | loss 1.2172 | loss(rot) 0.9739 | loss(pos) 0.0554 | loss(seq) 0.1879 | grad 2.5028 | lr 0.0000 | time_forward 1.2970 | time_backward 1.4230 |
[2023-10-22 20:04:08,375::train::INFO] [train] Iter 561417 | loss 1.0987 | loss(rot) 0.0851 | loss(pos) 0.7250 | loss(seq) 0.2885 | grad 7.8418 | lr 0.0000 | time_forward 3.4780 | time_backward 4.5190 |
[2023-10-22 20:04:17,228::train::INFO] [train] Iter 561418 | loss 0.6135 | loss(rot) 0.1258 | loss(pos) 0.2432 | loss(seq) 0.2446 | grad 4.9091 | lr 0.0000 | time_forward 3.7420 | time_backward 5.1080 |
[2023-10-22 20:04:24,872::train::INFO] [train] Iter 561419 | loss 0.2687 | loss(rot) 0.0698 | loss(pos) 0.0152 | loss(seq) 0.1837 | grad 1.8361 | lr 0.0000 | time_forward 3.2620 | time_backward 4.3790 |
[2023-10-22 20:04:27,656::train::INFO] [train] Iter 561420 | loss 0.7297 | loss(rot) 0.6903 | loss(pos) 0.0393 | loss(seq) 0.0000 | grad 14.9310 | lr 0.0000 | time_forward 1.3260 | time_backward 1.4550 |
[2023-10-22 20:04:30,514::train::INFO] [train] Iter 561421 | loss 0.7802 | loss(rot) 0.3467 | loss(pos) 0.0750 | loss(seq) 0.3585 | grad 3.8173 | lr 0.0000 | time_forward 1.3810 | time_backward 1.4740 |
[2023-10-22 20:04:38,566::train::INFO] [train] Iter 561422 | loss 0.4840 | loss(rot) 0.0552 | loss(pos) 0.4142 | loss(seq) 0.0147 | grad 5.3495 | lr 0.0000 | time_forward 3.4180 | time_backward 4.6300 |
[2023-10-22 20:04:41,341::train::INFO] [train] Iter 561423 | loss 0.3363 | loss(rot) 0.1322 | loss(pos) 0.1383 | loss(seq) 0.0658 | grad 3.0755 | lr 0.0000 | time_forward 1.3230 | time_backward 1.4490 |
[2023-10-22 20:04:49,198::train::INFO] [train] Iter 561424 | loss 0.4528 | loss(rot) 0.1219 | loss(pos) 0.0205 | loss(seq) 0.3104 | grad 2.4974 | lr 0.0000 | time_forward 3.3340 | time_backward 4.5200 |
[2023-10-22 20:04:51,951::train::INFO] [train] Iter 561425 | loss 0.1833 | loss(rot) 0.1388 | loss(pos) 0.0244 | loss(seq) 0.0202 | grad 2.5082 | lr 0.0000 | time_forward 1.3050 | time_backward 1.4450 |
[2023-10-22 20:05:00,220::train::INFO] [train] Iter 561426 | loss 1.3887 | loss(rot) 0.8056 | loss(pos) 0.0991 | loss(seq) 0.4840 | grad 4.0374 | lr 0.0000 | time_forward 3.5390 | time_backward 4.7270 |
[2023-10-22 20:05:08,507::train::INFO] [train] Iter 561427 | loss 0.2704 | loss(rot) 0.1799 | loss(pos) 0.0258 | loss(seq) 0.0647 | grad 2.8897 | lr 0.0000 | time_forward 3.5200 | time_backward 4.7620 |
[2023-10-22 20:05:11,240::train::INFO] [train] Iter 561428 | loss 0.3911 | loss(rot) 0.3651 | loss(pos) 0.0259 | loss(seq) 0.0001 | grad 1.6768 | lr 0.0000 | time_forward 1.2990 | time_backward 1.4310 |
[2023-10-22 20:05:20,196::train::INFO] [train] Iter 561429 | loss 0.3776 | loss(rot) 0.1944 | loss(pos) 0.0757 | loss(seq) 0.1075 | grad 2.9923 | lr 0.0000 | time_forward 3.6550 | time_backward 5.2570 |
[2023-10-22 20:05:27,868::train::INFO] [train] Iter 561430 | loss 0.1687 | loss(rot) 0.1312 | loss(pos) 0.0364 | loss(seq) 0.0012 | grad 1.8931 | lr 0.0000 | time_forward 3.2730 | time_backward 4.3960 |
[2023-10-22 20:05:36,997::train::INFO] [train] Iter 561431 | loss 0.7700 | loss(rot) 0.3195 | loss(pos) 0.3651 | loss(seq) 0.0854 | grad 4.4277 | lr 0.0000 | time_forward 3.7360 | time_backward 5.3910 |
[2023-10-22 20:05:45,414::train::INFO] [train] Iter 561432 | loss 0.2273 | loss(rot) 0.1734 | loss(pos) 0.0237 | loss(seq) 0.0301 | grad 9.6691 | lr 0.0000 | time_forward 3.6820 | time_backward 4.7310 |
[2023-10-22 20:05:52,962::train::INFO] [train] Iter 561433 | loss 0.8345 | loss(rot) 0.7569 | loss(pos) 0.0271 | loss(seq) 0.0505 | grad 59.6015 | lr 0.0000 | time_forward 3.2070 | time_backward 4.3380 |
[2023-10-22 20:05:55,717::train::INFO] [train] Iter 561434 | loss 0.3878 | loss(rot) 0.1363 | loss(pos) 0.2285 | loss(seq) 0.0230 | grad 3.0728 | lr 0.0000 | time_forward 1.3030 | time_backward 1.4490 |
[2023-10-22 20:05:58,527::train::INFO] [train] Iter 561435 | loss 0.2224 | loss(rot) 0.1941 | loss(pos) 0.0212 | loss(seq) 0.0070 | grad 2.6962 | lr 0.0000 | time_forward 1.3290 | time_backward 1.4780 |
[2023-10-22 20:06:06,795::train::INFO] [train] Iter 561436 | loss 0.3203 | loss(rot) 0.1207 | loss(pos) 0.0808 | loss(seq) 0.1188 | grad 3.7110 | lr 0.0000 | time_forward 3.5070 | time_backward 4.7580 |
[2023-10-22 20:06:14,601::train::INFO] [train] Iter 561437 | loss 0.3936 | loss(rot) 0.1244 | loss(pos) 0.0675 | loss(seq) 0.2017 | grad 3.2965 | lr 0.0000 | time_forward 3.2730 | time_backward 4.5290 |
[2023-10-22 20:06:23,497::train::INFO] [train] Iter 561438 | loss 0.4271 | loss(rot) 0.0385 | loss(pos) 0.3831 | loss(seq) 0.0055 | grad 4.9554 | lr 0.0000 | time_forward 3.6500 | time_backward 5.2430 |
[2023-10-22 20:06:32,387::train::INFO] [train] Iter 561439 | loss 0.4877 | loss(rot) 0.2162 | loss(pos) 0.1246 | loss(seq) 0.1470 | grad 2.8437 | lr 0.0000 | time_forward 3.7280 | time_backward 5.1590 |
[2023-10-22 20:06:40,108::train::INFO] [train] Iter 561440 | loss 0.6456 | loss(rot) 0.3976 | loss(pos) 0.0954 | loss(seq) 0.1526 | grad 3.8144 | lr 0.0000 | time_forward 3.2600 | time_backward 4.4570 |
[2023-10-22 20:06:42,627::train::INFO] [train] Iter 561441 | loss 0.3213 | loss(rot) 0.1706 | loss(pos) 0.0313 | loss(seq) 0.1194 | grad 2.8695 | lr 0.0000 | time_forward 1.2070 | time_backward 1.3080 |
[2023-10-22 20:06:50,298::train::INFO] [train] Iter 561442 | loss 0.2411 | loss(rot) 0.2223 | loss(pos) 0.0150 | loss(seq) 0.0038 | grad 2.9526 | lr 0.0000 | time_forward 3.2730 | time_backward 4.3950 |
[2023-10-22 20:06:58,042::train::INFO] [train] Iter 561443 | loss 0.3170 | loss(rot) 0.1489 | loss(pos) 0.0475 | loss(seq) 0.1205 | grad 3.4842 | lr 0.0000 | time_forward 3.2690 | time_backward 4.4720 |
[2023-10-22 20:07:04,795::train::INFO] [train] Iter 561444 | loss 0.3700 | loss(rot) 0.2260 | loss(pos) 0.0163 | loss(seq) 0.1276 | grad 15.1845 | lr 0.0000 | time_forward 2.8510 | time_backward 3.8980 |
[2023-10-22 20:07:12,837::train::INFO] [train] Iter 561445 | loss 0.6512 | loss(rot) 0.1912 | loss(pos) 0.0471 | loss(seq) 0.4129 | grad 3.2480 | lr 0.0000 | time_forward 3.4650 | time_backward 4.5750 |
[2023-10-22 20:07:21,984::train::INFO] [train] Iter 561446 | loss 0.9323 | loss(rot) 0.5956 | loss(pos) 0.1235 | loss(seq) 0.2133 | grad 6.7232 | lr 0.0000 | time_forward 3.6360 | time_backward 5.5070 |
[2023-10-22 20:07:31,064::train::INFO] [train] Iter 561447 | loss 0.3990 | loss(rot) 0.0463 | loss(pos) 0.3500 | loss(seq) 0.0028 | grad 4.8682 | lr 0.0000 | time_forward 3.7520 | time_backward 5.3260 |
[2023-10-22 20:07:38,568::train::INFO] [train] Iter 561448 | loss 1.6896 | loss(rot) 1.3506 | loss(pos) 0.0917 | loss(seq) 0.2473 | grad 4.6633 | lr 0.0000 | time_forward 3.1740 | time_backward 4.3260 |
[2023-10-22 20:07:41,070::train::INFO] [train] Iter 561449 | loss 1.9478 | loss(rot) 1.8951 | loss(pos) 0.0437 | loss(seq) 0.0090 | grad 4.4877 | lr 0.0000 | time_forward 1.2220 | time_backward 1.2770 |
[2023-10-22 20:07:50,111::train::INFO] [train] Iter 561450 | loss 0.9457 | loss(rot) 0.2754 | loss(pos) 0.2904 | loss(seq) 0.3799 | grad 2.2349 | lr 0.0000 | time_forward 3.8730 | time_backward 5.1650 |
[2023-10-22 20:07:58,436::train::INFO] [train] Iter 561451 | loss 0.9202 | loss(rot) 0.3874 | loss(pos) 0.2561 | loss(seq) 0.2767 | grad 2.7431 | lr 0.0000 | time_forward 3.5820 | time_backward 4.7400 |
[2023-10-22 20:08:06,161::train::INFO] [train] Iter 561452 | loss 0.9039 | loss(rot) 0.8481 | loss(pos) 0.0361 | loss(seq) 0.0198 | grad 11.7425 | lr 0.0000 | time_forward 3.2830 | time_backward 4.4380 |
[2023-10-22 20:08:15,176::train::INFO] [train] Iter 561453 | loss 0.5089 | loss(rot) 0.3775 | loss(pos) 0.0292 | loss(seq) 0.1022 | grad 3.3562 | lr 0.0000 | time_forward 3.6880 | time_backward 5.3230 |
[2023-10-22 20:08:23,553::train::INFO] [train] Iter 561454 | loss 0.7412 | loss(rot) 0.5871 | loss(pos) 0.0306 | loss(seq) 0.1235 | grad 3.8409 | lr 0.0000 | time_forward 3.5940 | time_backward 4.7800 |
[2023-10-22 20:08:31,019::train::INFO] [train] Iter 561455 | loss 0.3041 | loss(rot) 0.1334 | loss(pos) 0.0274 | loss(seq) 0.1433 | grad 2.8201 | lr 0.0000 | time_forward 3.1370 | time_backward 4.3260 |
[2023-10-22 20:08:39,295::train::INFO] [train] Iter 561456 | loss 0.5935 | loss(rot) 0.1173 | loss(pos) 0.0533 | loss(seq) 0.4229 | grad 2.9949 | lr 0.0000 | time_forward 3.5340 | time_backward 4.7390 |
[2023-10-22 20:08:43,598::train::INFO] [train] Iter 561457 | loss 1.3443 | loss(rot) 0.6435 | loss(pos) 0.3486 | loss(seq) 0.3522 | grad 6.3610 | lr 0.0000 | time_forward 1.9280 | time_backward 2.3710 |
[2023-10-22 20:08:53,148::train::INFO] [train] Iter 561458 | loss 0.9657 | loss(rot) 0.3005 | loss(pos) 0.2129 | loss(seq) 0.4522 | grad 2.3376 | lr 0.0000 | time_forward 3.9860 | time_backward 5.5610 |
[2023-10-22 20:09:00,689::train::INFO] [train] Iter 561459 | loss 0.8585 | loss(rot) 0.0921 | loss(pos) 0.5991 | loss(seq) 0.1674 | grad 3.5990 | lr 0.0000 | time_forward 3.2020 | time_backward 4.3360 |
[2023-10-22 20:09:03,470::train::INFO] [train] Iter 561460 | loss 0.8133 | loss(rot) 0.7695 | loss(pos) 0.0435 | loss(seq) 0.0002 | grad 3.7263 | lr 0.0000 | time_forward 1.3220 | time_backward 1.3990 |
[2023-10-22 20:09:10,867::train::INFO] [train] Iter 561461 | loss 0.2745 | loss(rot) 0.1434 | loss(pos) 0.0268 | loss(seq) 0.1043 | grad 2.6338 | lr 0.0000 | time_forward 3.1530 | time_backward 4.2110 |
[2023-10-22 20:09:17,516::train::INFO] [train] Iter 561462 | loss 0.4409 | loss(rot) 0.0445 | loss(pos) 0.0752 | loss(seq) 0.3213 | grad 3.2961 | lr 0.0000 | time_forward 2.8140 | time_backward 3.8330 |
[2023-10-22 20:09:26,615::train::INFO] [train] Iter 561463 | loss 1.4624 | loss(rot) 1.2837 | loss(pos) 0.0388 | loss(seq) 0.1399 | grad 4.6828 | lr 0.0000 | time_forward 3.7290 | time_backward 5.3670 |
[2023-10-22 20:09:34,316::train::INFO] [train] Iter 561464 | loss 0.2124 | loss(rot) 0.0523 | loss(pos) 0.1485 | loss(seq) 0.0116 | grad 4.6023 | lr 0.0000 | time_forward 3.2800 | time_backward 4.4180 |
[2023-10-22 20:09:42,037::train::INFO] [train] Iter 561465 | loss 0.5266 | loss(rot) 0.4443 | loss(pos) 0.0421 | loss(seq) 0.0402 | grad 3.2583 | lr 0.0000 | time_forward 3.3240 | time_backward 4.3930 |
[2023-10-22 20:09:44,787::train::INFO] [train] Iter 561466 | loss 0.3608 | loss(rot) 0.1162 | loss(pos) 0.0166 | loss(seq) 0.2280 | grad 1.6359 | lr 0.0000 | time_forward 1.3160 | time_backward 1.4300 |
[2023-10-22 20:09:53,912::train::INFO] [train] Iter 561467 | loss 0.3773 | loss(rot) 0.1134 | loss(pos) 0.2572 | loss(seq) 0.0067 | grad 3.8438 | lr 0.0000 | time_forward 3.7440 | time_backward 5.3790 |
[2023-10-22 20:09:55,686::train::INFO] [train] Iter 561468 | loss 0.6390 | loss(rot) 0.2092 | loss(pos) 0.4258 | loss(seq) 0.0041 | grad 11.4245 | lr 0.0000 | time_forward 0.8560 | time_backward 0.9140 |
[2023-10-22 20:10:04,556::train::INFO] [train] Iter 561469 | loss 0.8691 | loss(rot) 0.4811 | loss(pos) 0.1139 | loss(seq) 0.2741 | grad 3.1384 | lr 0.0000 | time_forward 3.8590 | time_backward 5.0080 |
[2023-10-22 20:10:12,328::train::INFO] [train] Iter 561470 | loss 0.8183 | loss(rot) 0.4968 | loss(pos) 0.0886 | loss(seq) 0.2328 | grad 3.7197 | lr 0.0000 | time_forward 3.3200 | time_backward 4.4500 |
[2023-10-22 20:10:20,443::train::INFO] [train] Iter 561471 | loss 1.0139 | loss(rot) 0.2947 | loss(pos) 0.3850 | loss(seq) 0.3341 | grad 5.0555 | lr 0.0000 | time_forward 3.4650 | time_backward 4.6470 |
[2023-10-22 20:10:24,877::train::INFO] [train] Iter 561472 | loss 0.3164 | loss(rot) 0.0738 | loss(pos) 0.0201 | loss(seq) 0.2224 | grad 2.4534 | lr 0.0000 | time_forward 2.0610 | time_backward 2.3690 |
[2023-10-22 20:10:31,213::train::INFO] [train] Iter 561473 | loss 0.3687 | loss(rot) 0.1170 | loss(pos) 0.0253 | loss(seq) 0.2264 | grad 2.1229 | lr 0.0000 | time_forward 2.6570 | time_backward 3.6760 |
[2023-10-22 20:10:40,074::train::INFO] [train] Iter 561474 | loss 1.4281 | loss(rot) 0.8311 | loss(pos) 0.2761 | loss(seq) 0.3208 | grad 3.3855 | lr 0.0000 | time_forward 3.7530 | time_backward 5.1040 |
[2023-10-22 20:10:42,883::train::INFO] [train] Iter 561475 | loss 1.3872 | loss(rot) 1.0771 | loss(pos) 0.0928 | loss(seq) 0.2173 | grad 51.5780 | lr 0.0000 | time_forward 1.3040 | time_backward 1.4550 |
[2023-10-22 20:10:50,423::train::INFO] [train] Iter 561476 | loss 0.3849 | loss(rot) 0.0939 | loss(pos) 0.2474 | loss(seq) 0.0435 | grad 4.1403 | lr 0.0000 | time_forward 3.1730 | time_backward 4.3640 |
[2023-10-22 20:10:58,660::train::INFO] [train] Iter 561477 | loss 1.0690 | loss(rot) 0.5456 | loss(pos) 0.0703 | loss(seq) 0.4531 | grad 5.4344 | lr 0.0000 | time_forward 3.5740 | time_backward 4.6600 |
[2023-10-22 20:11:06,575::train::INFO] [train] Iter 561478 | loss 0.1928 | loss(rot) 0.1764 | loss(pos) 0.0149 | loss(seq) 0.0014 | grad 2.2134 | lr 0.0000 | time_forward 3.3420 | time_backward 4.5700 |
[2023-10-22 20:11:08,832::train::INFO] [train] Iter 561479 | loss 0.6428 | loss(rot) 0.5229 | loss(pos) 0.0335 | loss(seq) 0.0864 | grad 116.6263 | lr 0.0000 | time_forward 1.0290 | time_backward 1.2250 |
[2023-10-22 20:11:15,848::train::INFO] [train] Iter 561480 | loss 0.4514 | loss(rot) 0.0514 | loss(pos) 0.1703 | loss(seq) 0.2297 | grad 5.1831 | lr 0.0000 | time_forward 3.0020 | time_backward 4.0120 |
[2023-10-22 20:11:18,585::train::INFO] [train] Iter 561481 | loss 1.3098 | loss(rot) 0.8100 | loss(pos) 0.1139 | loss(seq) 0.3860 | grad 3.8329 | lr 0.0000 | time_forward 1.3130 | time_backward 1.4200 |
[2023-10-22 20:11:21,328::train::INFO] [train] Iter 561482 | loss 0.1494 | loss(rot) 0.0888 | loss(pos) 0.0390 | loss(seq) 0.0216 | grad 2.4249 | lr 0.0000 | time_forward 1.3180 | time_backward 1.4220 |
[2023-10-22 20:11:29,637::train::INFO] [train] Iter 561483 | loss 0.2631 | loss(rot) 0.2208 | loss(pos) 0.0335 | loss(seq) 0.0088 | grad 3.2700 | lr 0.0000 | time_forward 3.5700 | time_backward 4.7350 |
[2023-10-22 20:11:32,378::train::INFO] [train] Iter 561484 | loss 0.5821 | loss(rot) 0.5565 | loss(pos) 0.0251 | loss(seq) 0.0005 | grad 2.9347 | lr 0.0000 | time_forward 1.3030 | time_backward 1.4360 |
[2023-10-22 20:11:41,247::train::INFO] [train] Iter 561485 | loss 0.5530 | loss(rot) 0.0952 | loss(pos) 0.2095 | loss(seq) 0.2483 | grad 3.5672 | lr 0.0000 | time_forward 3.6700 | time_backward 5.1950 |
[2023-10-22 20:11:44,108::train::INFO] [train] Iter 561486 | loss 1.1517 | loss(rot) 0.5824 | loss(pos) 0.1650 | loss(seq) 0.4043 | grad 3.9378 | lr 0.0000 | time_forward 1.3270 | time_backward 1.5310 |
[2023-10-22 20:11:51,947::train::INFO] [train] Iter 561487 | loss 0.9851 | loss(rot) 0.7937 | loss(pos) 0.0362 | loss(seq) 0.1552 | grad 3.0039 | lr 0.0000 | time_forward 3.3160 | time_backward 4.5190 |
[2023-10-22 20:11:59,453::train::INFO] [train] Iter 561488 | loss 0.5290 | loss(rot) 0.4778 | loss(pos) 0.0246 | loss(seq) 0.0266 | grad 17.9134 | lr 0.0000 | time_forward 3.1660 | time_backward 4.3380 |
[2023-10-22 20:12:06,775::train::INFO] [train] Iter 561489 | loss 0.4312 | loss(rot) 0.1470 | loss(pos) 0.0676 | loss(seq) 0.2166 | grad 3.0220 | lr 0.0000 | time_forward 3.0370 | time_backward 4.2810 |
[2023-10-22 20:12:09,150::train::INFO] [train] Iter 561490 | loss 0.6603 | loss(rot) 0.2826 | loss(pos) 0.0338 | loss(seq) 0.3438 | grad 3.4729 | lr 0.0000 | time_forward 1.1120 | time_backward 1.2610 |
[2023-10-22 20:12:16,530::train::INFO] [train] Iter 561491 | loss 1.5427 | loss(rot) 0.2234 | loss(pos) 1.3108 | loss(seq) 0.0085 | grad 10.4618 | lr 0.0000 | time_forward 3.0520 | time_backward 4.3100 |
[2023-10-22 20:12:25,387::train::INFO] [train] Iter 561492 | loss 0.6761 | loss(rot) 0.3425 | loss(pos) 0.0971 | loss(seq) 0.2364 | grad 4.2889 | lr 0.0000 | time_forward 3.5830 | time_backward 5.2710 |
[2023-10-22 20:12:33,327::train::INFO] [train] Iter 561493 | loss 0.4812 | loss(rot) 0.4040 | loss(pos) 0.0772 | loss(seq) 0.0000 | grad 6.4291 | lr 0.0000 | time_forward 3.3920 | time_backward 4.5440 |
[2023-10-22 20:12:40,299::train::INFO] [train] Iter 561494 | loss 1.4349 | loss(rot) 0.4298 | loss(pos) 0.5195 | loss(seq) 0.4857 | grad 7.6381 | lr 0.0000 | time_forward 2.9770 | time_backward 3.9920 |
[2023-10-22 20:12:47,349::train::INFO] [train] Iter 561495 | loss 1.2164 | loss(rot) 0.5071 | loss(pos) 0.1357 | loss(seq) 0.5736 | grad 17.0449 | lr 0.0000 | time_forward 3.0070 | time_backward 4.0390 |
[2023-10-22 20:12:56,127::train::INFO] [train] Iter 561496 | loss 0.6015 | loss(rot) 0.4318 | loss(pos) 0.0506 | loss(seq) 0.1192 | grad 2.8538 | lr 0.0000 | time_forward 3.7210 | time_backward 5.0530 |
[2023-10-22 20:12:58,717::train::INFO] [train] Iter 561497 | loss 0.4373 | loss(rot) 0.1664 | loss(pos) 0.2280 | loss(seq) 0.0429 | grad 4.0140 | lr 0.0000 | time_forward 1.2500 | time_backward 1.3360 |
[2023-10-22 20:13:01,454::train::INFO] [train] Iter 561498 | loss 0.3277 | loss(rot) 0.1049 | loss(pos) 0.0483 | loss(seq) 0.1745 | grad 1.9172 | lr 0.0000 | time_forward 1.2940 | time_backward 1.4230 |
[2023-10-22 20:13:09,515::train::INFO] [train] Iter 561499 | loss 0.6511 | loss(rot) 0.1249 | loss(pos) 0.2704 | loss(seq) 0.2557 | grad 3.7449 | lr 0.0000 | time_forward 3.4340 | time_backward 4.6230 |
[2023-10-22 20:13:12,707::train::INFO] [train] Iter 561500 | loss 0.4648 | loss(rot) 0.3084 | loss(pos) 0.0233 | loss(seq) 0.1331 | grad 3.7957 | lr 0.0000 | time_forward 1.4600 | time_backward 1.7300 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.