text
stringlengths 56
1.16k
|
|---|
[2023-10-25 18:39:25,919::train::INFO] [train] Iter 599063 | loss 0.8132 | loss(rot) 0.1408 | loss(pos) 0.6703 | loss(seq) 0.0021 | grad 8.7134 | lr 0.0000 | time_forward 2.9720 | time_backward 4.0020 |
[2023-10-25 18:39:28,563::train::INFO] [train] Iter 599064 | loss 0.5522 | loss(rot) 0.1676 | loss(pos) 0.0448 | loss(seq) 0.3399 | grad 12.3328 | lr 0.0000 | time_forward 1.2700 | time_backward 1.3710 |
[2023-10-25 18:39:30,775::train::INFO] [train] Iter 599065 | loss 0.6945 | loss(rot) 0.4460 | loss(pos) 0.0441 | loss(seq) 0.2043 | grad 2.7177 | lr 0.0000 | time_forward 1.0330 | time_backward 1.1760 |
[2023-10-25 18:39:33,408::train::INFO] [train] Iter 599066 | loss 0.6785 | loss(rot) 0.2601 | loss(pos) 0.2992 | loss(seq) 0.1192 | grad 4.9788 | lr 0.0000 | time_forward 1.2510 | time_backward 1.3790 |
[2023-10-25 18:39:40,125::train::INFO] [train] Iter 599067 | loss 1.1385 | loss(rot) 0.6724 | loss(pos) 0.1153 | loss(seq) 0.3507 | grad 2.7835 | lr 0.0000 | time_forward 2.9110 | time_backward 3.7790 |
[2023-10-25 18:39:48,589::train::INFO] [train] Iter 599068 | loss 0.8640 | loss(rot) 0.0541 | loss(pos) 0.8074 | loss(seq) 0.0025 | grad 7.7478 | lr 0.0000 | time_forward 3.4960 | time_backward 4.9650 |
[2023-10-25 18:39:57,056::train::INFO] [train] Iter 599069 | loss 0.4712 | loss(rot) 0.0484 | loss(pos) 0.4138 | loss(seq) 0.0090 | grad 3.9880 | lr 0.0000 | time_forward 3.4940 | time_backward 4.9700 |
[2023-10-25 18:40:05,503::train::INFO] [train] Iter 599070 | loss 1.0083 | loss(rot) 0.1775 | loss(pos) 0.4130 | loss(seq) 0.4179 | grad 4.9640 | lr 0.0000 | time_forward 3.4930 | time_backward 4.9500 |
[2023-10-25 18:40:13,973::train::INFO] [train] Iter 599071 | loss 0.5480 | loss(rot) 0.1957 | loss(pos) 0.0627 | loss(seq) 0.2896 | grad 2.5496 | lr 0.0000 | time_forward 3.4870 | time_backward 4.9800 |
[2023-10-25 18:40:16,715::train::INFO] [train] Iter 599072 | loss 0.4680 | loss(rot) 0.1348 | loss(pos) 0.3182 | loss(seq) 0.0150 | grad 8.8030 | lr 0.0000 | time_forward 1.2980 | time_backward 1.4410 |
[2023-10-25 18:40:25,325::train::INFO] [train] Iter 599073 | loss 0.4009 | loss(rot) 0.3332 | loss(pos) 0.0232 | loss(seq) 0.0446 | grad 2.8074 | lr 0.0000 | time_forward 3.5500 | time_backward 5.0580 |
[2023-10-25 18:40:32,470::train::INFO] [train] Iter 599074 | loss 0.5988 | loss(rot) 0.4221 | loss(pos) 0.0326 | loss(seq) 0.1441 | grad 4.3593 | lr 0.0000 | time_forward 3.0290 | time_backward 4.1130 |
[2023-10-25 18:40:40,230::train::INFO] [train] Iter 599075 | loss 0.5452 | loss(rot) 0.4932 | loss(pos) 0.0349 | loss(seq) 0.0170 | grad 9.1561 | lr 0.0000 | time_forward 3.3390 | time_backward 4.4180 |
[2023-10-25 18:40:48,994::train::INFO] [train] Iter 599076 | loss 1.4603 | loss(rot) 0.9962 | loss(pos) 0.0910 | loss(seq) 0.3731 | grad 12.0294 | lr 0.0000 | time_forward 3.5490 | time_backward 5.2110 |
[2023-10-25 18:40:51,212::train::INFO] [train] Iter 599077 | loss 1.1934 | loss(rot) 0.7182 | loss(pos) 0.1120 | loss(seq) 0.3633 | grad 3.1348 | lr 0.0000 | time_forward 1.0260 | time_backward 1.1880 |
[2023-10-25 18:40:59,704::train::INFO] [train] Iter 599078 | loss 0.8344 | loss(rot) 0.0100 | loss(pos) 0.8232 | loss(seq) 0.0013 | grad 8.5767 | lr 0.0000 | time_forward 3.5140 | time_backward 4.9760 |
[2023-10-25 18:41:02,500::train::INFO] [train] Iter 599079 | loss 0.6271 | loss(rot) 0.1599 | loss(pos) 0.0646 | loss(seq) 0.4027 | grad 3.2466 | lr 0.0000 | time_forward 1.2930 | time_backward 1.5000 |
[2023-10-25 18:41:05,805::train::INFO] [train] Iter 599080 | loss 0.6056 | loss(rot) 0.5511 | loss(pos) 0.0545 | loss(seq) 0.0000 | grad 2.4911 | lr 0.0000 | time_forward 1.4730 | time_backward 1.8290 |
[2023-10-25 18:41:14,237::train::INFO] [train] Iter 599081 | loss 0.7038 | loss(rot) 0.3520 | loss(pos) 0.1646 | loss(seq) 0.1872 | grad 3.0380 | lr 0.0000 | time_forward 3.4810 | time_backward 4.9470 |
[2023-10-25 18:41:22,784::train::INFO] [train] Iter 599082 | loss 0.7745 | loss(rot) 0.1956 | loss(pos) 0.0890 | loss(seq) 0.4899 | grad 3.3002 | lr 0.0000 | time_forward 3.5140 | time_backward 5.0300 |
[2023-10-25 18:41:30,861::train::INFO] [train] Iter 599083 | loss 1.7023 | loss(rot) 1.1650 | loss(pos) 0.0448 | loss(seq) 0.4925 | grad 6.1561 | lr 0.0000 | time_forward 3.4670 | time_backward 4.6070 |
[2023-10-25 18:41:39,409::train::INFO] [train] Iter 599084 | loss 1.5534 | loss(rot) 1.4149 | loss(pos) 0.0410 | loss(seq) 0.0976 | grad 3.6942 | lr 0.0000 | time_forward 3.5550 | time_backward 4.9890 |
[2023-10-25 18:41:46,730::train::INFO] [train] Iter 599085 | loss 0.6662 | loss(rot) 0.0373 | loss(pos) 0.1170 | loss(seq) 0.5119 | grad 5.4778 | lr 0.0000 | time_forward 3.1160 | time_backward 4.2000 |
[2023-10-25 18:41:54,263::train::INFO] [train] Iter 599086 | loss 0.5313 | loss(rot) 0.1596 | loss(pos) 0.0558 | loss(seq) 0.3160 | grad 3.2359 | lr 0.0000 | time_forward 3.2390 | time_backward 4.2910 |
[2023-10-25 18:42:02,732::train::INFO] [train] Iter 599087 | loss 0.4829 | loss(rot) 0.2508 | loss(pos) 0.1868 | loss(seq) 0.0453 | grad 2.7850 | lr 0.0000 | time_forward 3.4730 | time_backward 4.9930 |
[2023-10-25 18:42:05,862::train::INFO] [train] Iter 599088 | loss 1.1148 | loss(rot) 0.5812 | loss(pos) 0.0592 | loss(seq) 0.4744 | grad 7.6931 | lr 0.0000 | time_forward 1.4350 | time_backward 1.6920 |
[2023-10-25 18:42:08,590::train::INFO] [train] Iter 599089 | loss 1.1126 | loss(rot) 0.4061 | loss(pos) 0.2728 | loss(seq) 0.4337 | grad 4.0529 | lr 0.0000 | time_forward 1.2740 | time_backward 1.4380 |
[2023-10-25 18:42:11,301::train::INFO] [train] Iter 599090 | loss 1.8057 | loss(rot) 1.5185 | loss(pos) 0.1256 | loss(seq) 0.1616 | grad 3.7542 | lr 0.0000 | time_forward 1.2770 | time_backward 1.4310 |
[2023-10-25 18:42:19,769::train::INFO] [train] Iter 599091 | loss 0.5046 | loss(rot) 0.4585 | loss(pos) 0.0461 | loss(seq) 0.0000 | grad 1.9102 | lr 0.0000 | time_forward 3.5010 | time_backward 4.9400 |
[2023-10-25 18:42:27,001::train::INFO] [train] Iter 599092 | loss 0.9001 | loss(rot) 0.8303 | loss(pos) 0.0221 | loss(seq) 0.0477 | grad 6.4447 | lr 0.0000 | time_forward 3.1150 | time_backward 4.1130 |
[2023-10-25 18:42:34,187::train::INFO] [train] Iter 599093 | loss 0.8302 | loss(rot) 0.0403 | loss(pos) 0.2256 | loss(seq) 0.5644 | grad 7.2330 | lr 0.0000 | time_forward 3.1130 | time_backward 4.0710 |
[2023-10-25 18:42:42,379::train::INFO] [train] Iter 599094 | loss 1.7093 | loss(rot) 0.7909 | loss(pos) 0.3697 | loss(seq) 0.5487 | grad 3.9841 | lr 0.0000 | time_forward 3.4850 | time_backward 4.7040 |
[2023-10-25 18:42:50,801::train::INFO] [train] Iter 599095 | loss 1.3226 | loss(rot) 1.2594 | loss(pos) 0.0443 | loss(seq) 0.0189 | grad 3.1697 | lr 0.0000 | time_forward 3.4680 | time_backward 4.9520 |
[2023-10-25 18:42:59,257::train::INFO] [train] Iter 599096 | loss 0.4469 | loss(rot) 0.1636 | loss(pos) 0.0403 | loss(seq) 0.2431 | grad 2.2842 | lr 0.0000 | time_forward 3.5490 | time_backward 4.9040 |
[2023-10-25 18:43:01,969::train::INFO] [train] Iter 599097 | loss 0.2739 | loss(rot) 0.0767 | loss(pos) 0.0117 | loss(seq) 0.1855 | grad 2.0886 | lr 0.0000 | time_forward 1.2950 | time_backward 1.4140 |
[2023-10-25 18:43:09,871::train::INFO] [train] Iter 599098 | loss 0.9167 | loss(rot) 0.8904 | loss(pos) 0.0235 | loss(seq) 0.0029 | grad 4.0381 | lr 0.0000 | time_forward 3.4150 | time_backward 4.4630 |
[2023-10-25 18:43:18,444::train::INFO] [train] Iter 599099 | loss 0.1155 | loss(rot) 0.0799 | loss(pos) 0.0210 | loss(seq) 0.0146 | grad 1.9757 | lr 0.0000 | time_forward 3.5880 | time_backward 4.9820 |
[2023-10-25 18:43:26,868::train::INFO] [train] Iter 599100 | loss 0.7603 | loss(rot) 0.6271 | loss(pos) 0.0582 | loss(seq) 0.0749 | grad 5.5126 | lr 0.0000 | time_forward 3.6540 | time_backward 4.7660 |
[2023-10-25 18:43:34,459::train::INFO] [train] Iter 599101 | loss 0.6372 | loss(rot) 0.4099 | loss(pos) 0.0899 | loss(seq) 0.1374 | grad 3.1573 | lr 0.0000 | time_forward 3.2580 | time_backward 4.3310 |
[2023-10-25 18:43:42,771::train::INFO] [train] Iter 599102 | loss 1.5129 | loss(rot) 1.4766 | loss(pos) 0.0363 | loss(seq) 0.0000 | grad 7.7884 | lr 0.0000 | time_forward 3.4440 | time_backward 4.8650 |
[2023-10-25 18:43:51,110::train::INFO] [train] Iter 599103 | loss 0.4117 | loss(rot) 0.1969 | loss(pos) 0.0744 | loss(seq) 0.1404 | grad 2.6785 | lr 0.0000 | time_forward 3.6170 | time_backward 4.7190 |
[2023-10-25 18:43:53,797::train::INFO] [train] Iter 599104 | loss 1.0665 | loss(rot) 0.1392 | loss(pos) 0.1968 | loss(seq) 0.7305 | grad 5.1773 | lr 0.0000 | time_forward 1.2380 | time_backward 1.4470 |
[2023-10-25 18:44:01,163::train::INFO] [train] Iter 599105 | loss 0.5376 | loss(rot) 0.1122 | loss(pos) 0.2429 | loss(seq) 0.1825 | grad 3.4581 | lr 0.0000 | time_forward 3.1430 | time_backward 4.2200 |
[2023-10-25 18:44:03,923::train::INFO] [train] Iter 599106 | loss 0.5645 | loss(rot) 0.1824 | loss(pos) 0.1216 | loss(seq) 0.2605 | grad 3.6140 | lr 0.0000 | time_forward 1.2460 | time_backward 1.5100 |
[2023-10-25 18:44:12,421::train::INFO] [train] Iter 599107 | loss 0.4603 | loss(rot) 0.2190 | loss(pos) 0.1375 | loss(seq) 0.1039 | grad 2.6852 | lr 0.0000 | time_forward 3.5690 | time_backward 4.9250 |
[2023-10-25 18:44:19,334::train::INFO] [train] Iter 599108 | loss 1.7275 | loss(rot) 0.0031 | loss(pos) 1.7237 | loss(seq) 0.0007 | grad 16.4960 | lr 0.0000 | time_forward 2.9360 | time_backward 3.9740 |
[2023-10-25 18:44:22,006::train::INFO] [train] Iter 599109 | loss 1.2218 | loss(rot) 0.5363 | loss(pos) 0.2641 | loss(seq) 0.4214 | grad 3.7134 | lr 0.0000 | time_forward 1.2630 | time_backward 1.4060 |
[2023-10-25 18:44:30,424::train::INFO] [train] Iter 599110 | loss 0.3316 | loss(rot) 0.1430 | loss(pos) 0.0307 | loss(seq) 0.1579 | grad 2.2511 | lr 0.0000 | time_forward 3.5230 | time_backward 4.8920 |
[2023-10-25 18:44:37,884::train::INFO] [train] Iter 599111 | loss 0.1620 | loss(rot) 0.0903 | loss(pos) 0.0489 | loss(seq) 0.0228 | grad 3.2622 | lr 0.0000 | time_forward 3.1940 | time_backward 4.2630 |
[2023-10-25 18:44:40,501::train::INFO] [train] Iter 599112 | loss 0.3658 | loss(rot) 0.3473 | loss(pos) 0.0184 | loss(seq) 0.0000 | grad 3.6952 | lr 0.0000 | time_forward 1.2000 | time_backward 1.4130 |
[2023-10-25 18:44:47,689::train::INFO] [train] Iter 599113 | loss 0.4756 | loss(rot) 0.1729 | loss(pos) 0.2813 | loss(seq) 0.0214 | grad 5.2762 | lr 0.0000 | time_forward 2.9540 | time_backward 4.2310 |
[2023-10-25 18:44:53,402::train::INFO] [train] Iter 599114 | loss 0.4094 | loss(rot) 0.1532 | loss(pos) 0.0574 | loss(seq) 0.1989 | grad 2.7949 | lr 0.0000 | time_forward 2.4520 | time_backward 3.2590 |
[2023-10-25 18:45:01,674::train::INFO] [train] Iter 599115 | loss 1.6111 | loss(rot) 0.7686 | loss(pos) 0.2527 | loss(seq) 0.5897 | grad 3.5260 | lr 0.0000 | time_forward 3.5770 | time_backward 4.6820 |
[2023-10-25 18:45:04,917::train::INFO] [train] Iter 599116 | loss 0.8899 | loss(rot) 0.5894 | loss(pos) 0.0398 | loss(seq) 0.2607 | grad 2.7994 | lr 0.0000 | time_forward 1.4420 | time_backward 1.7980 |
[2023-10-25 18:45:12,261::train::INFO] [train] Iter 599117 | loss 0.2477 | loss(rot) 0.0716 | loss(pos) 0.0288 | loss(seq) 0.1473 | grad 2.0500 | lr 0.0000 | time_forward 3.2050 | time_backward 4.1250 |
[2023-10-25 18:45:15,082::train::INFO] [train] Iter 599118 | loss 0.4020 | loss(rot) 0.3242 | loss(pos) 0.0259 | loss(seq) 0.0519 | grad 6.5907 | lr 0.0000 | time_forward 1.2980 | time_backward 1.5200 |
[2023-10-25 18:45:17,797::train::INFO] [train] Iter 599119 | loss 0.7145 | loss(rot) 0.2993 | loss(pos) 0.0318 | loss(seq) 0.3834 | grad 3.0107 | lr 0.0000 | time_forward 1.3120 | time_backward 1.4000 |
[2023-10-25 18:45:25,126::train::INFO] [train] Iter 599120 | loss 0.3449 | loss(rot) 0.1197 | loss(pos) 0.0320 | loss(seq) 0.1932 | grad 2.4005 | lr 0.0000 | time_forward 3.1710 | time_backward 4.1550 |
[2023-10-25 18:45:28,309::train::INFO] [train] Iter 599121 | loss 1.0372 | loss(rot) 0.8761 | loss(pos) 0.0660 | loss(seq) 0.0950 | grad 3.6636 | lr 0.0000 | time_forward 1.4580 | time_backward 1.7230 |
[2023-10-25 18:45:36,801::train::INFO] [train] Iter 599122 | loss 0.3287 | loss(rot) 0.1179 | loss(pos) 0.0147 | loss(seq) 0.1962 | grad 2.6244 | lr 0.0000 | time_forward 3.5190 | time_backward 4.9580 |
[2023-10-25 18:45:39,504::train::INFO] [train] Iter 599123 | loss 0.7316 | loss(rot) 0.2343 | loss(pos) 0.2322 | loss(seq) 0.2650 | grad 3.1427 | lr 0.0000 | time_forward 1.2920 | time_backward 1.4070 |
[2023-10-25 18:45:48,089::train::INFO] [train] Iter 599124 | loss 0.6417 | loss(rot) 0.1419 | loss(pos) 0.1784 | loss(seq) 0.3214 | grad 3.2804 | lr 0.0000 | time_forward 3.5640 | time_backward 5.0190 |
[2023-10-25 18:45:50,766::train::INFO] [train] Iter 599125 | loss 1.0237 | loss(rot) 0.9380 | loss(pos) 0.0253 | loss(seq) 0.0604 | grad 134.1055 | lr 0.0000 | time_forward 1.2830 | time_backward 1.3910 |
[2023-10-25 18:45:53,578::train::INFO] [train] Iter 599126 | loss 0.2499 | loss(rot) 0.2106 | loss(pos) 0.0236 | loss(seq) 0.0158 | grad 17.0977 | lr 0.0000 | time_forward 1.3320 | time_backward 1.4770 |
[2023-10-25 18:45:56,256::train::INFO] [train] Iter 599127 | loss 0.3013 | loss(rot) 0.2691 | loss(pos) 0.0282 | loss(seq) 0.0039 | grad 2.8131 | lr 0.0000 | time_forward 1.2990 | time_backward 1.3760 |
[2023-10-25 18:45:58,950::train::INFO] [train] Iter 599128 | loss 0.2115 | loss(rot) 0.0487 | loss(pos) 0.0302 | loss(seq) 0.1325 | grad 1.7117 | lr 0.0000 | time_forward 1.2740 | time_backward 1.4010 |
[2023-10-25 18:46:06,039::train::INFO] [train] Iter 599129 | loss 0.8280 | loss(rot) 0.3148 | loss(pos) 0.1651 | loss(seq) 0.3481 | grad 5.0313 | lr 0.0000 | time_forward 3.0260 | time_backward 4.0370 |
[2023-10-25 18:46:13,050::train::INFO] [train] Iter 599130 | loss 0.7102 | loss(rot) 0.3962 | loss(pos) 0.1081 | loss(seq) 0.2059 | grad 5.4440 | lr 0.0000 | time_forward 2.9650 | time_backward 4.0420 |
[2023-10-25 18:46:20,594::train::INFO] [train] Iter 599131 | loss 0.4078 | loss(rot) 0.2847 | loss(pos) 0.0180 | loss(seq) 0.1051 | grad 3.9561 | lr 0.0000 | time_forward 3.2250 | time_backward 4.3160 |
[2023-10-25 18:46:29,020::train::INFO] [train] Iter 599132 | loss 0.8775 | loss(rot) 0.6193 | loss(pos) 0.0383 | loss(seq) 0.2199 | grad 2.4975 | lr 0.0000 | time_forward 3.4570 | time_backward 4.9650 |
[2023-10-25 18:46:36,331::train::INFO] [train] Iter 599133 | loss 0.6588 | loss(rot) 0.2849 | loss(pos) 0.1114 | loss(seq) 0.2626 | grad 2.6898 | lr 0.0000 | time_forward 3.1420 | time_backward 4.1660 |
[2023-10-25 18:46:39,509::train::INFO] [train] Iter 599134 | loss 1.1905 | loss(rot) 0.7740 | loss(pos) 0.1842 | loss(seq) 0.2323 | grad 4.5083 | lr 0.0000 | time_forward 1.4430 | time_backward 1.7320 |
[2023-10-25 18:46:46,943::train::INFO] [train] Iter 599135 | loss 1.1733 | loss(rot) 0.8638 | loss(pos) 0.0654 | loss(seq) 0.2441 | grad 9.4452 | lr 0.0000 | time_forward 3.2200 | time_backward 4.1990 |
[2023-10-25 18:46:55,427::train::INFO] [train] Iter 599136 | loss 0.2451 | loss(rot) 0.0317 | loss(pos) 0.0185 | loss(seq) 0.1950 | grad 1.3970 | lr 0.0000 | time_forward 3.5050 | time_backward 4.9760 |
[2023-10-25 18:47:03,961::train::INFO] [train] Iter 599137 | loss 1.0118 | loss(rot) 0.6553 | loss(pos) 0.0923 | loss(seq) 0.2642 | grad 2.9372 | lr 0.0000 | time_forward 3.4820 | time_backward 5.0500 |
[2023-10-25 18:47:06,202::train::INFO] [train] Iter 599138 | loss 0.5352 | loss(rot) 0.4703 | loss(pos) 0.0265 | loss(seq) 0.0384 | grad 1.6123 | lr 0.0000 | time_forward 1.0420 | time_backward 1.1960 |
[2023-10-25 18:47:14,641::train::INFO] [train] Iter 599139 | loss 1.7536 | loss(rot) 1.6405 | loss(pos) 0.0337 | loss(seq) 0.0794 | grad 3.7741 | lr 0.0000 | time_forward 3.5100 | time_backward 4.9260 |
[2023-10-25 18:47:21,844::train::INFO] [train] Iter 599140 | loss 1.0688 | loss(rot) 0.4692 | loss(pos) 0.0706 | loss(seq) 0.5289 | grad 4.7617 | lr 0.0000 | time_forward 2.9630 | time_backward 4.2360 |
[2023-10-25 18:47:30,406::train::INFO] [train] Iter 599141 | loss 0.1756 | loss(rot) 0.1184 | loss(pos) 0.0236 | loss(seq) 0.0336 | grad 1.8924 | lr 0.0000 | time_forward 3.5940 | time_backward 4.9650 |
[2023-10-25 18:47:33,098::train::INFO] [train] Iter 599142 | loss 1.3308 | loss(rot) 0.9236 | loss(pos) 0.0684 | loss(seq) 0.3388 | grad 5.7114 | lr 0.0000 | time_forward 1.2820 | time_backward 1.4080 |
[2023-10-25 18:47:40,494::train::INFO] [train] Iter 599143 | loss 0.3036 | loss(rot) 0.0752 | loss(pos) 0.0142 | loss(seq) 0.2142 | grad 2.2759 | lr 0.0000 | time_forward 3.1690 | time_backward 4.2240 |
[2023-10-25 18:47:47,000::train::INFO] [train] Iter 599144 | loss 0.2694 | loss(rot) 0.2123 | loss(pos) 0.0258 | loss(seq) 0.0314 | grad 3.4550 | lr 0.0000 | time_forward 2.7760 | time_backward 3.7260 |
[2023-10-25 18:47:55,663::train::INFO] [train] Iter 599145 | loss 0.5975 | loss(rot) 0.5495 | loss(pos) 0.0244 | loss(seq) 0.0236 | grad 61.4664 | lr 0.0000 | time_forward 3.6240 | time_backward 5.0350 |
[2023-10-25 18:48:03,562::train::INFO] [train] Iter 599146 | loss 0.2331 | loss(rot) 0.1942 | loss(pos) 0.0387 | loss(seq) 0.0002 | grad 6.4288 | lr 0.0000 | time_forward 3.3630 | time_backward 4.5330 |
[2023-10-25 18:48:12,112::train::INFO] [train] Iter 599147 | loss 1.3405 | loss(rot) 0.1643 | loss(pos) 1.1699 | loss(seq) 0.0063 | grad 7.7655 | lr 0.0000 | time_forward 3.4690 | time_backward 5.0770 |
[2023-10-25 18:48:20,514::train::INFO] [train] Iter 599148 | loss 1.0564 | loss(rot) 0.6202 | loss(pos) 0.0565 | loss(seq) 0.3797 | grad 3.7085 | lr 0.0000 | time_forward 3.4760 | time_backward 4.9240 |
[2023-10-25 18:48:23,199::train::INFO] [train] Iter 599149 | loss 0.4526 | loss(rot) 0.1162 | loss(pos) 0.2988 | loss(seq) 0.0377 | grad 6.4599 | lr 0.0000 | time_forward 1.2660 | time_backward 1.4150 |
[2023-10-25 18:48:30,141::train::INFO] [train] Iter 599150 | loss 2.2171 | loss(rot) 1.8467 | loss(pos) 0.0916 | loss(seq) 0.2788 | grad 4.8693 | lr 0.0000 | time_forward 3.1200 | time_backward 3.8200 |
[2023-10-25 18:48:37,990::train::INFO] [train] Iter 599151 | loss 0.0863 | loss(rot) 0.0552 | loss(pos) 0.0309 | loss(seq) 0.0002 | grad 1.7909 | lr 0.0000 | time_forward 3.3650 | time_backward 4.4800 |
[2023-10-25 18:48:46,140::train::INFO] [train] Iter 599152 | loss 0.1723 | loss(rot) 0.1072 | loss(pos) 0.0483 | loss(seq) 0.0168 | grad 1.6065 | lr 0.0000 | time_forward 3.3950 | time_backward 4.7530 |
[2023-10-25 18:48:53,469::train::INFO] [train] Iter 599153 | loss 1.6680 | loss(rot) 1.5502 | loss(pos) 0.0675 | loss(seq) 0.0504 | grad 5.6181 | lr 0.0000 | time_forward 3.1360 | time_backward 4.1890 |
[2023-10-25 18:48:56,802::train::INFO] [train] Iter 599154 | loss 1.8380 | loss(rot) 1.8153 | loss(pos) 0.0180 | loss(seq) 0.0047 | grad 5.2857 | lr 0.0000 | time_forward 1.4980 | time_backward 1.8320 |
[2023-10-25 18:49:03,139::train::INFO] [train] Iter 599155 | loss 0.2790 | loss(rot) 0.0979 | loss(pos) 0.0226 | loss(seq) 0.1586 | grad 2.3781 | lr 0.0000 | time_forward 2.7580 | time_backward 3.5660 |
[2023-10-25 18:49:05,720::train::INFO] [train] Iter 599156 | loss 1.0746 | loss(rot) 0.0386 | loss(pos) 1.0307 | loss(seq) 0.0052 | grad 8.7423 | lr 0.0000 | time_forward 1.2170 | time_backward 1.3620 |
[2023-10-25 18:49:13,107::train::INFO] [train] Iter 599157 | loss 0.7633 | loss(rot) 0.1667 | loss(pos) 0.4627 | loss(seq) 0.1339 | grad 3.0374 | lr 0.0000 | time_forward 3.1590 | time_backward 4.2090 |
[2023-10-25 18:49:20,749::train::INFO] [train] Iter 599158 | loss 0.1145 | loss(rot) 0.0995 | loss(pos) 0.0125 | loss(seq) 0.0026 | grad 1.8241 | lr 0.0000 | time_forward 3.2830 | time_backward 4.3560 |
[2023-10-25 18:49:27,394::train::INFO] [train] Iter 599159 | loss 0.3509 | loss(rot) 0.0772 | loss(pos) 0.0277 | loss(seq) 0.2460 | grad 2.0843 | lr 0.0000 | time_forward 2.8720 | time_backward 3.7690 |
[2023-10-25 18:49:29,887::train::INFO] [train] Iter 599160 | loss 0.1065 | loss(rot) 0.0842 | loss(pos) 0.0144 | loss(seq) 0.0080 | grad 2.1436 | lr 0.0000 | time_forward 1.2190 | time_backward 1.2720 |
[2023-10-25 18:49:37,273::train::INFO] [train] Iter 599161 | loss 0.7880 | loss(rot) 0.5284 | loss(pos) 0.0312 | loss(seq) 0.2284 | grad 2.6203 | lr 0.0000 | time_forward 3.1470 | time_backward 4.2360 |
[2023-10-25 18:49:39,985::train::INFO] [train] Iter 599162 | loss 1.3790 | loss(rot) 0.9102 | loss(pos) 0.0643 | loss(seq) 0.4046 | grad 2.4594 | lr 0.0000 | time_forward 1.3060 | time_backward 1.4020 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.