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HuangJunJie2017 / BEVDet

Official code base of the BEVDet series .
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Errors occured in distributed training #77

Closed bruce1408 closed 1 year ago

bruce1408 commented 2 years ago

when i want to train this model with distributed training, and execute tools/dist_train.sh configs/bevdet/bevdet-sttiny-accelerated.py Errors occured as follows:

Note that --use_env is set by default in torchrun.If your script expects `--local_rank` argument to be set, pleasechange it to read from `os.environ['LOCAL_RANK']` instead. See https://pytorch.org/docs/stable/distributed.html#launch-utility for further instructions  FutureWarning,
Traceback (most recent call last):
  File "/anaconda3/envs/torch1.9.0/lib/python3.7/site-packages/torch/distributed/run.py", line 564, in determine_local_world_size    return int(nproc_per_node)ValueError: invalid literal for int() with base 10: ''During handling of the above exception, another exception occurred:Traceback (most recent call last):
  File "/anaconda3/envs/torch1.9.0/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/anaconda3/envs/torch1.9.0/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/anaconda3/envs/torch1.9.0/lib/python3.7/site-packages/torch/distributed/launch.py", line 193, in <module>    main()
  File "/anaconda3/envs/torch1.9.0/lib/python3.7/site-packages/torch/distributed/launch.py", line 189, in main    launch(args)
  File "/anaconda3/envs/torch1.9.0/lib/python3.7/site-packages/torch/distributed/launch.py", line 174, in launch
    run(args)
  File "/anaconda3/envs/torch1.9.0/lib/python3.7/site-packages/torch/distributed/run.py", line 709, in run    config, cmd, cmd_args = config_from_args(args)
  File "/anaconda3/envs/torch1.9.0/lib/python3.7/site-packages/torch/distributed/run.py", line 617, in config_from_args
    nproc_per_node = determine_local_world_size(args.nproc_per_node)  File "/anaconda3/envs/torch1.9.0/lib/python3.7/site-packages/torch/distributed/run.py", line 582, in determine_local_world_size
    raise ValueError(f"Unsupported nproc_per_node value: {nproc_per_node}")
ValueError: Unsupported nproc_per_node value:

When I execute dist_train, I got this error. Anyone can help me to fix this error? Thx~

HuangJunJie2017 commented 2 years ago

Train with configs/bevdet/bevdet-sttiny.py instead

bruce1408 commented 2 years ago

I tried but got the same errors

HuangJunJie2017 commented 2 years ago

can you train this config: ? configs/centerpoint/centerpoint_01voxel_second_secfpn_4x8_cyclic_20e_nus.py

bruce1408 commented 2 years ago

can you train this config: ? configs/centerpoint/centerpoint_01voxel_second_secfpn_4x8_cyclic_20e_nus.py

image

bruce1408 commented 2 years ago

when i use single GPU to train this model , python tools/train.py --gpu-ids=1 configs/bevdet/bevdet-r50.py, it works well, but distributed training errors occured.

HuangJunJie2017 commented 2 years ago

It means that your mechine or environment does not support distributed training, you can refer to the official mmdet3d in openmmlab for extra cues.

bruce1408 commented 2 years ago

I will refer to mmdet3d repo about how to distributed training.Thanks a lot!

BTW： When i traind the BEVDet paradigm that used bevdet、bevdet-r50-fp16、bevdet-sttiny separately with nuscenes dataset. The result of my training results are not as accurate as you described.

The results are as follows:

bevdet.py

  self.post_center_range, device=heat.device)                                                                               
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 81/81, 12.1 task/s, elapsed: 7s, ETA:     0s                           
Formating bboxes of pts_bbox                                                                                                
Start to convert detection format...                                                                                        
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 81/81, 32.7 task/s, elapsed: 2s, ETA:     0s                           
Results writes to /tmp/tmpug3i1czi/results/pts_bbox/results_nusc.json                                                       
Evaluating bboxes of pts_bbox                                                                                               
mAP: 0.0017                                                                                                                 

mATE: 1.0643                                                                                                                
mASE: 0.8700                                                                                                                
mAOE: 1.0706                                                                                                                
mAVE: 0.8872                                                                                                                
mAAE: 0.8811                                                                                                                
NDS: 0.0370                                                                                                                 
Eval time: 5.0s                                                                                                             

Per-class results:                                                                                                          
Object Class    AP      ATE     ASE     AOE     AVE     AAE                                                                 
car     0.017   1.314   0.226   1.636   0.098   0.049                                                                       
truck   0.000   1.000   1.000   1.000   1.000   1.000
bus     0.000   1.000   1.000   1.000   1.000   1.000
trailer 0.000   1.000   1.000   1.000   1.000   1.000
construction_vehicle    0.000   1.000   1.000   1.000   1.000   1.000
pedestrian      0.000   1.000   1.000   1.000   1.000   1.000
motorcycle      0.000   1.000   1.000   1.000   1.000   1.000
bicycle 0.000   1.000   1.000   1.000   1.000   1.000
traffic_cone    0.000   1.329   0.475   nan     nan     nan
barrier 0.000   1.000   1.000   1.000   nan     nan

bevdet-r50-fp16.py

Formating bboxes of pts_bbox                                                                                                
Start to convert detection format...                                                                                        
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 81/81, 23.5 task/s, elapsed: 3s, ETA:     0s                           
Results writes to /tmp/tmphg9qo7rg/results/pts_bbox/results_nusc.json                                                       
Evaluating bboxes of pts_bbox                                                                                               
mAP: 0.0056                                                                                                                 

mATE: 1.1395                                                                                                                
mASE: 0.7507                                                                                                                
mAOE: 1.1528                                                                                                                
mAVE: 0.7149                                                                                                                
mAAE: 0.7159                                                                                                                
NDS: 0.0847                                                                                                                 
Eval time: 6.0s                                                                                                             

Per-class results:                                                                                                          
Object Class    AP      ATE     ASE     AOE     AVE     AAE                                                                 
car     0.034   1.306   0.226   1.438   0.143   0.084                                                                       
truck   0.008   1.238   0.479   1.496   0.074   0.000                                                                       
bus     0.000   1.000   1.000   1.000   1.000   1.000                                                                       
trailer 0.000   1.000   1.000   1.000   1.000   1.000                                                                       
construction_vehicle    0.000   1.000   1.000   1.000   1.000   1.000                                                       
pedestrian      0.015   1.243   0.334   1.441   0.502   0.644                                                               
motorcycle      0.000   1.000   1.000   1.000   1.000   1.000
bicycle 0.000   1.000   1.000   1.000   1.000   1.000
traffic_cone    0.000   1.607   0.469   nan     nan     nan
barrier 0.000   1.000   1.000   1.000   nan     nan
2022-09-14 22:07:12,750 - mmdet - INFO - Exp name: bevdet-r50-fp16.py

bevdet-sttiny.py

2022-09-14 20:56:17,425 - mmdet - INFO - Epoch [20][200/204]    lr: 3.029e-08, eta: 0:00:07, time: 1.799, data_time: 0.027, 
memory: 7109, task0.loss_xy: 0.1222, task0.loss_z: 0.0667, task0.loss_whl: 0.0445, task0.loss_yaw: 0.2361, task0.loss_vel: 0
.1668, task0.loss_heatmap: 1.7803, task1.loss_xy: 0.1121, task1.loss_z: 0.0449, task1.loss_whl: 0.0444, task1.loss_yaw: 0.16
37, task1.loss_vel: 0.0716, task1.loss_heatmap: 1.2584, task2.loss_xy: 0.1120, task2.loss_z: 0.0435, task2.loss_whl: 0.0406,
 task2.loss_yaw: 0.1813, task2.loss_vel: 0.1286, task2.loss_heatmap: 1.0837, task3.loss_xy: 0.1098, task3.loss_z: 0.0327, ta
sk3.loss_whl: 0.0333, task3.loss_yaw: 0.0742, task3.loss_vel: 0.0005, task3.loss_heatmap: 0.7614, task4.loss_xy: 0.1247, tas
k4.loss_z: 0.0433, task4.loss_whl: 0.0447, task4.loss_yaw: 0.1622, task4.loss_vel: 0.0898, task4.loss_heatmap: 1.2974, task5
.loss_xy: 0.1237, task5.loss_z: 0.0610, task5.loss_whl: 0.0713, task5.loss_yaw: 0.2509, task5.loss_vel: 0.0393, task5.loss_h
eatmap: 1.4174, loss: 10.4391, grad_norm: 10.8853

i wonder why the MAP is so slow, and i did not change any parameters of your config files

HuangJunJie2017 commented 2 years ago

have you tested the released models before training?

bruce1408 commented 2 years ago

I didn't test, just train the model from scratch

HuangJunJie2017 commented 2 years ago

.... you should check if you can properly test with the released models~

DevLinyan commented 2 years ago

I have encounter the same problem, but I have tested the released model, the inference results is the same as README. When I just train one epoch from scratch, but the value is very low, e.g. mAP = 0.01

HuangJunJie2017 commented 2 years ago

@linyanAI It is ok for the first epoch to have low precision.

DevLinyan commented 2 years ago

but in the 2 and 3 epoch, mAP is also 0.03. Do you have the metrics in every epoch?

HuangJunJie2017 commented 2 years ago

@linyanAI Is the loss normal? is that close to that in the log I provided?

DevLinyan commented 2 years ago

but your log is resume from 3 epoch, so we can't see the log before. our loss in the 1 epoch is as follow:

2022-09-22 11:29:18,792 - mmdet - INFO - Epoch [1][50/1931] lr: 1.978e-05, eta: 1 day, 5:25:50, time: 2.289, data_time: 0.300, memory: 15117, loss_depth: 63.1026, task0.loss_xy: 0.2635, task0.loss_z: 0.2677, task0.loss_whl: 0.7887, task0.loss_yaw: 0.3457, task0.loss_vel: 0.8095, task0.loss_heatmap: 171.3891, task1.loss_xy: 0.2656, task1.loss_z: 0.3019, task1.loss_whl: 0.8801, task1.loss_yaw: 0.3404, task1.loss_vel: 0.6529, task1.loss_heatmap: 3124.6201, task2.loss_xy: 0.3334, task2.loss_z: 0.2767, task2.loss_whl: 1.2884, task2.loss_yaw: 0.3540, task2.loss_vel: 0.8363, task2.loss_heatmap: 5466.7150, task3.loss_xy: 0.3451, task3.loss_z: 0.2965, task3.loss_whl: 0.5001, task3.loss_yaw: 0.3578, task3.loss_vel: 0.2662, task3.loss_heatmap: 937.5460, task4.loss_xy: 0.3186, task4.loss_z: 0.3518, task4.loss_whl: 0.5049, task4.loss_yaw: 0.3680, task4.loss_vel: 0.7420, task4.loss_heatmap: 9464.6608, task5.loss_xy: 0.3423, task5.loss_z: 0.2176, task5.loss_whl: 0.4381, task5.loss_yaw: 0.4172, task5.loss_vel: 0.4182, task5.loss_heatmap: 796.0191, loss: 20037.9418 2022-09-22 11:31:04,463 - mmdet - INFO - Epoch [1][100/1931] lr: 3.976e-05, eta: 1 day, 4:16:23, time: 2.113, data_time: 0.061, memory: 15117, loss_depth: 63.4890, task0.loss_xy: 0.1802, task0.loss_z: 0.2485, task0.loss_whl: 0.5913, task0.loss_yaw: 0.3107, task0.loss_vel: 0.7486, task0.loss_heatmap: 90.3982, task1.loss_xy: 0.1966, task1.loss_z: 0.2736, task1.loss_whl: 0.7114, task1.loss_yaw: 0.3194, task1.loss_vel: 0.5437, task1.loss_heatmap: 1722.9769, task2.loss_xy: 0.2400, task2.loss_z: 0.2631, task2.loss_whl: 1.0696, task2.loss_yaw: 0.3246, task2.loss_vel: 0.7909, task2.loss_heatmap: 2356.4639, task3.loss_xy: 0.1960, task3.loss_z: 0.2353, task3.loss_whl: 0.3672, task3.loss_yaw: 0.3249, task3.loss_vel: 0.1440, task3.loss_heatmap: 379.1518, task4.loss_xy: 0.2067, task4.loss_z: 0.2603, task4.loss_whl: 0.3382, task4.loss_yaw: 0.3175, task4.loss_vel: 0.6080, task4.loss_heatmap: 5056.4560, task5.loss_xy: 0.2300, task5.loss_z: 0.2083, task5.loss_whl: 0.3126, task5.loss_yaw: 0.3332, task5.loss_vel: 0.3699, task5.loss_heatmap: 421.3873, loss: 10101.5874 2022-09-22 11:32:59,496 - mmdet - INFO - Epoch [1][150/1931] lr: 5.974e-05, eta: 1 day, 4:40:08, time: 2.301, data_time: 0.065, memory: 15117, loss_depth: 63.9676, task0.loss_xy: 0.1356, task0.loss_z: 0.2274, task0.loss_whl: 0.2444, task0.loss_yaw: 0.3029, task0.loss_vel: 0.7471, task0.loss_heatmap: 30.1273, task1.loss_xy: 0.1420, task1.loss_z: 0.2585, task1.loss_whl: 0.4334, task1.loss_yaw: 0.3046, task1.loss_vel: 0.5359, task1.loss_heatmap: 763.6254, task2.loss_xy: 0.1558, task2.loss_z: 0.2498, task2.loss_whl: 0.6285, task2.loss_yaw: 0.3065, task2.loss_vel: 0.7645, task2.loss_heatmap: 1037.2942, task3.loss_xy: 0.1552, task3.loss_z: 0.1815, task3.loss_whl: 0.2121, task3.loss_yaw: 0.3095, task3.loss_vel: 0.0709, task3.loss_heatmap: 128.9273, task4.loss_xy: 0.1452, task4.loss_z: 0.1865, task4.loss_whl: 0.1992, task4.loss_yaw: 0.3124, task4.loss_vel: 0.6562, task4.loss_heatmap: 2286.8732, task5.loss_xy: 0.1526, task5.loss_z: 0.1935, task5.loss_whl: 0.2270, task5.loss_yaw: 0.3170, task5.loss_vel: 0.2892, task5.loss_heatmap: 162.2924, loss: 4482.1525 2022-09-22 11:34:48,465 - mmdet - INFO - Epoch [1][200/1931] lr: 7.972e-05, eta: 1 day, 4:27:43, time: 2.179, data_time: 0.063, memory: 15117, loss_depth: 63.7251, task0.loss_xy: 0.1296, task0.loss_z: 0.2270, task0.loss_whl: 0.0985, task0.loss_yaw: 0.2992, task0.loss_vel: 0.7064, task0.loss_heatmap: 12.0996, task1.loss_xy: 0.1302, task1.loss_z: 0.2584, task1.loss_whl: 0.2525, task1.loss_yaw: 0.3017, task1.loss_vel: 0.5136, task1.loss_heatmap: 356.3111, task2.loss_xy: 0.1346, task2.loss_z: 0.2485, task2.loss_whl: 0.3362, task2.loss_yaw: 0.3062, task2.loss_vel: 0.7972, task2.loss_heatmap: 471.4046, task3.loss_xy: 0.1339, task3.loss_z: 0.1825, task3.loss_whl: 0.1668, task3.loss_yaw: 0.3039, task3.loss_vel: 0.0467, task3.loss_heatmap: 81.4225, task4.loss_xy: 0.1320, task4.loss_z: 0.1841, task4.loss_whl: 0.1588, task4.loss_yaw: 0.3068, task4.loss_vel: 0.6629, task4.loss_heatmap: 1195.0932, task5.loss_xy: 0.1356, task5.loss_z: 0.1972, task5.loss_whl: 0.2102, task5.loss_yaw: 0.3100, task5.loss_vel: 0.2886, task5.loss_heatmap: 85.9101, loss: 2274.1262 2022-09-22 11:36:40,694 - mmdet - INFO - Epoch [1][250/1931] lr: 9.970e-05, eta: 1 day, 4:29:33, time: 2.244, data_time: 0.065, memory: 15117, loss_depth: 63.2468, task0.loss_xy: 0.1281, task0.loss_z: 0.2230, task0.loss_whl: 0.0782, task0.loss_yaw: 0.2988, task0.loss_vel: 0.7257, task0.loss_heatmap: 7.5176, task1.loss_xy: 0.1274, task1.loss_z: 0.2585, task1.loss_whl: 0.2004, task1.loss_yaw: 0.3017, task1.loss_vel: 0.5375, task1.loss_heatmap: 184.7893, task2.loss_xy: 0.1309, task2.loss_z: 0.2496, task2.loss_whl: 0.2183, task2.loss_yaw: 0.3030, task2.loss_vel: 0.7571, task2.loss_heatmap: 285.6501, task3.loss_xy: 0.1295, task3.loss_z: 0.1795, task3.loss_whl: 0.1586, task3.loss_yaw: 0.2990, task3.loss_vel: 0.0431, task3.loss_heatmap: 50.8334, task4.loss_xy: 0.1304, task4.loss_z: 0.1768, task4.loss_whl: 0.1473, task4.loss_yaw: 0.3054, task4.loss_vel: 0.5624, task4.loss_heatmap: 650.2212, task5.loss_xy: 0.1306, task5.loss_z: 0.1962, task5.loss_whl: 0.2065, task5.loss_yaw: 0.3088, task5.loss_vel: 0.2881, task5.loss_heatmap: 49.6195, loss: 1299.6783 2022-09-22 11:38:30,566 - mmdet - INFO - Epoch [1][300/1931] lr: 1.197e-04, eta: 1 day, 4:24:09, time: 2.198, data_time: 0.065, memory: 15117, loss_depth: 62.4146, task0.loss_xy: 0.1270, task0.loss_z: 0.2237, task0.loss_whl: 0.0733, task0.loss_yaw: 0.2976, task0.loss_vel: 0.6947, task0.loss_heatmap: 5.2968, task1.loss_xy: 0.1275, task1.loss_z: 0.2534, task1.loss_whl: 0.1807, task1.loss_yaw: 0.3001, task1.loss_vel: 0.5299, task1.loss_heatmap: 106.9233, task2.loss_xy: 0.1253, task2.loss_z: 0.2499, task2.loss_whl: 0.1797, task2.loss_yaw: 0.2997, task2.loss_vel: 0.7987, task2.loss_heatmap: 171.9879, task3.loss_xy: 0.1287, task3.loss_z: 0.1724, task3.loss_whl: 0.1583, task3.loss_yaw: 0.2993, task3.loss_vel: 0.0404, task3.loss_heatmap: 29.6173, task4.loss_xy: 0.1301, task4.loss_z: 0.1728, task4.loss_whl: 0.1439, task4.loss_yaw: 0.3061, task4.loss_vel: 0.5974, task4.loss_heatmap: 394.9211, task5.loss_xy: 0.1291, task5.loss_z: 0.1960, task5.loss_whl: 0.2038, task5.loss_yaw: 0.3061, task5.loss_vel: 0.2879, task5.loss_heatmap: 30.2799, loss: 809.1746 2022-09-22 11:40:20,715 - mmdet - INFO - Epoch [1][350/1931] lr: 1.397e-04, eta: 1 day, 4:20:20, time: 2.203, data_time: 0.065, memory: 15117, loss_depth: 61.1280, task0.loss_xy: 0.1263, task0.loss_z: 0.2248, task0.loss_whl: 0.0702, task0.loss_yaw: 0.2986, task0.loss_vel: 0.7094, task0.loss_heatmap: 4.2735, task1.loss_xy: 0.1273, task1.loss_z: 0.2560, task1.loss_whl: 0.1772, task1.loss_yaw: 0.3015, task1.loss_vel: 0.5243, task1.loss_heatmap: 69.8363, task2.loss_xy: 0.1286, task2.loss_z: 0.2554, task2.loss_whl: 0.1709, task2.loss_yaw: 0.3028, task2.loss_vel: 0.7690, task2.loss_heatmap: 105.9888, task3.loss_xy: 0.1290, task3.loss_z: 0.1845, task3.loss_whl: 0.1591, task3.loss_yaw: 0.3014, task3.loss_vel: 0.0408, task3.loss_heatmap: 19.3892, task4.loss_xy: 0.1257, task4.loss_z: 0.1775, task4.loss_whl: 0.1395, task4.loss_yaw: 0.3049, task4.loss_vel: 0.6040, task4.loss_heatmap: 235.6434, task5.loss_xy: 0.1274, task5.loss_z: 0.1917, task5.loss_whl: 0.2024, task5.loss_yaw: 0.3070, task5.loss_vel: 0.2866, task5.loss_heatmap: 19.1237, loss: 523.1066 2022-09-22 11:42:10,423 - mmdet - INFO - Epoch [1][400/1931] lr: 1.596e-04, eta: 1 day, 4:16:12, time: 2.194, data_time: 0.067, memory: 15117, loss_depth: 59.7841, task0.loss_xy: 0.1256, task0.loss_z: 0.2271, task0.loss_whl: 0.0681, task0.loss_yaw: 0.2966, task0.loss_vel: 0.7423, task0.loss_heatmap: 3.6294, task1.loss_xy: 0.1264, task1.loss_z: 0.2591, task1.loss_whl: 0.1743, task1.loss_yaw: 0.2996, task1.loss_vel: 0.5107, task1.loss_heatmap: 47.3633, task2.loss_xy: 0.1275, task2.loss_z: 0.2594, task2.loss_whl: 0.1652, task2.loss_yaw: 0.3035, task2.loss_vel: 0.7356, task2.loss_heatmap: 74.8740, task3.loss_xy: 0.1284, task3.loss_z: 0.1857, task3.loss_whl: 0.1600, task3.loss_yaw: 0.3033, task3.loss_vel: 0.0370, task3.loss_heatmap: 16.2879, task4.loss_xy: 0.1297, task4.loss_z: 0.1755, task4.loss_whl: 0.1425, task4.loss_yaw: 0.3060, task4.loss_vel: 0.5943, task4.loss_heatmap: 152.3614, task5.loss_xy: 0.1272, task5.loss_z: 0.1943, task5.loss_whl: 0.2027, task5.loss_yaw: 0.3076, task5.loss_vel: 0.2812, task5.loss_heatmap: 12.9809, loss: 374.9775 2022-09-22 11:44:04,658 - mmdet - INFO - Epoch [1][450/1931] lr: 1.796e-04, eta: 1 day, 4:20:15, time: 2.285, data_time: 0.064, memory: 15117, loss_depth: 58.2687, task0.loss_xy: 0.1266, task0.loss_z: 0.2230, task0.loss_whl: 0.0670, task0.loss_yaw: 0.2976, task0.loss_vel: 0.7137, task0.loss_heatmap: 3.3067, task1.loss_xy: 0.1265, task1.loss_z: 0.2553, task1.loss_whl: 0.1747, task1.loss_yaw: 0.3019, task1.loss_vel: 0.5313, task1.loss_heatmap: 33.9060, task2.loss_xy: 0.1292, task2.loss_z: 0.2544, task2.loss_whl: 0.1683, task2.loss_yaw: 0.3026, task2.loss_vel: 0.7990, task2.loss_heatmap: 50.9476, task3.loss_xy: 0.1263, task3.loss_z: 0.1863, task3.loss_whl: 0.1570, task3.loss_yaw: 0.3018, task3.loss_vel: 0.0370, task3.loss_heatmap: 11.1971, task4.loss_xy: 0.1268, task4.loss_z: 0.1744, task4.loss_whl: 0.1397, task4.loss_yaw: 0.3061, task4.loss_vel: 0.5806, task4.loss_heatmap: 104.2418, task5.loss_xy: 0.1271, task5.loss_z: 0.1944, task5.loss_whl: 0.2049, task5.loss_yaw: 0.3055, task5.loss_vel: 0.2838, task5.loss_heatmap: 10.1436, loss: 279.7343 2022-09-22 11:45:51,278 - mmdet - INFO - Epoch [1][500/1931] lr: 1.996e-04, eta: 1 day, 4:11:30, time: 2.133, data_time: 0.098, memory: 15117, loss_depth: 56.5829, task0.loss_xy: 0.1260, task0.loss_z: 0.2257, task0.loss_whl: 0.0661, task0.loss_yaw: 0.2974, task0.loss_vel: 0.7293, task0.loss_heatmap: 3.0938, task1.loss_xy: 0.1255, task1.loss_z: 0.2558, task1.loss_whl: 0.1728, task1.loss_yaw: 0.2995, task1.loss_vel: 0.5244, task1.loss_heatmap: 24.6030, task2.loss_xy: 0.1282, task2.loss_z: 0.2579, task2.loss_whl: 0.1599, task2.loss_yaw: 0.3035, task2.loss_vel: 0.7715, task2.loss_heatmap: 38.3596, task3.loss_xy: 0.1263, task3.loss_z: 0.1798, task3.loss_whl: 0.1536, task3.loss_yaw: 0.3009, task3.loss_vel: 0.0429, task3.loss_heatmap: 9.3086, task4.loss_xy: 0.1287, task4.loss_z: 0.1689, task4.loss_whl: 0.1396, task4.loss_yaw: 0.3059, task4.loss_vel: 0.5845, task4.loss_heatmap: 79.4287, task5.loss_xy: 0.1267, task5.loss_z: 0.1930, task5.loss_whl: 0.2027, task5.loss_yaw: 0.3059, task5.loss_vel: 0.2831, task5.loss_heatmap: 7.9173, loss: 226.9796 2022-09-22 11:47:36,734 - mmdet - INFO - Epoch [1][550/1931] lr: 2.000e-04, eta: 1 day, 4:02:23, time: 2.109, data_time: 0.069, memory: 15117, loss_depth: 55.0667, task0.loss_xy: 0.1259, task0.loss_z: 0.2267, task0.loss_whl: 0.0662, task0.loss_yaw: 0.2964, task0.loss_vel: 0.7060, task0.loss_heatmap: 2.9700, task1.loss_xy: 0.1264, task1.loss_z: 0.2584, task1.loss_whl: 0.1707, task1.loss_yaw: 0.3002, task1.loss_vel: 0.5013, task1.loss_heatmap: 18.7812, task2.loss_xy: 0.1275, task2.loss_z: 0.2567, task2.loss_whl: 0.1585, task2.loss_yaw: 0.3000, task2.loss_vel: 0.7837, task2.loss_heatmap: 31.5963, task3.loss_xy: 0.1259, task3.loss_z: 0.1774, task3.loss_whl: 0.1512, task3.loss_yaw: 0.2994, task3.loss_vel: 0.0354, task3.loss_heatmap: 7.4733, task4.loss_xy: 0.1256, task4.loss_z: 0.1781, task4.loss_whl: 0.1343, task4.loss_yaw: 0.3034, task4.loss_vel: 0.5217, task4.loss_heatmap: 60.4710, task5.loss_xy: 0.1269, task5.loss_z: 0.1943, task5.loss_whl: 0.2018, task5.loss_yaw: 0.3062, task5.loss_vel: 0.2833, task5.loss_heatmap: 6.4567, loss: 190.3842 2022-09-22 11:49:29,842 - mmdet - INFO - Epoch [1][600/1931] lr: 2.000e-04, eta: 1 day, 4:04:13, time: 2.262, data_time: 0.064, memory: 15117, loss_depth: 53.2467, task0.loss_xy: 0.1262, task0.loss_z: 0.2236, task0.loss_whl: 0.0646, task0.loss_yaw: 0.2983, task0.loss_vel: 0.7391, task0.loss_heatmap: 2.8945, task1.loss_xy: 0.1264, task1.loss_z: 0.2585, task1.loss_whl: 0.1748, task1.loss_yaw: 0.2984, task1.loss_vel: 0.5223, task1.loss_heatmap: 15.9483, task2.loss_xy: 0.1264, task2.loss_z: 0.2449, task2.loss_whl: 0.1593, task2.loss_yaw: 0.3002, task2.loss_vel: 0.6989, task2.loss_heatmap: 22.7597, task3.loss_xy: 0.1249, task3.loss_z: 0.1806, task3.loss_whl: 0.1553, task3.loss_yaw: 0.2956, task3.loss_vel: 0.0328, task3.loss_heatmap: 7.0580, task4.loss_xy: 0.1273, task4.loss_z: 0.1718, task4.loss_whl: 0.1374, task4.loss_yaw: 0.3048, task4.loss_vel: 0.5881, task4.loss_heatmap: 48.9459, task5.loss_xy: 0.1267, task5.loss_z: 0.1955, task5.loss_whl: 0.1987, task5.loss_yaw: 0.3060, task5.loss_vel: 0.2776, task5.loss_heatmap: 5.8097, loss: 164.2475 2022-09-22 11:51:23,723 - mmdet - INFO - Epoch [1][650/1931] lr: 2.000e-04, eta: 1 day, 4:06:23, time: 2.278, data_time: 0.067, memory: 15117, loss_depth: 51.7517, task0.loss_xy: 0.1257, task0.loss_z: 0.2249, task0.loss_whl: 0.0643, task0.loss_yaw: 0.2971, task0.loss_vel: 0.6927, task0.loss_heatmap: 2.8228, task1.loss_xy: 0.1265, task1.loss_z: 0.2646, task1.loss_whl: 0.1724, task1.loss_yaw: 0.3009, task1.loss_vel: 0.5309, task1.loss_heatmap: 12.7825, task2.loss_xy: 0.1294, task2.loss_z: 0.2505, task2.loss_whl: 0.1626, task2.loss_yaw: 0.3035, task2.loss_vel: 0.7691, task2.loss_heatmap: 19.6847, task3.loss_xy: 0.1248, task3.loss_z: 0.1768, task3.loss_whl: 0.1531, task3.loss_yaw: 0.3001, task3.loss_vel: 0.0326, task3.loss_heatmap: 6.1395, task4.loss_xy: 0.1281, task4.loss_z: 0.1819, task4.loss_whl: 0.1384, task4.loss_yaw: 0.3058, task4.loss_vel: 0.5297, task4.loss_heatmap: 38.3391, task5.loss_xy: 0.1263, task5.loss_z: 0.1972, task5.loss_whl: 0.1978, task5.loss_yaw: 0.3070, task5.loss_vel: 0.2873, task5.loss_heatmap: 4.8742, loss: 143.9965 2022-09-22 11:53:12,881 - mmdet - INFO - Epoch [1][700/1931] lr: 2.000e-04, eta: 1 day, 4:02:51, time: 2.183, data_time: 0.064, memory: 15117, loss_depth: 50.0947, task0.loss_xy: 0.1256, task0.loss_z: 0.2284, task0.loss_whl: 0.0641, task0.loss_yaw: 0.2963, task0.loss_vel: 0.7151, task0.loss_heatmap: 2.8130, task1.loss_xy: 0.1261, task1.loss_z: 0.2630, task1.loss_whl: 0.1758, task1.loss_yaw: 0.3007, task1.loss_vel: 0.5350, task1.loss_heatmap: 11.5361, task2.loss_xy: 0.1261, task2.loss_z: 0.2512, task2.loss_whl: 0.1578, task2.loss_yaw: 0.3031, task2.loss_vel: 0.7434, task2.loss_heatmap: 17.8078, task3.loss_xy: 0.1244, task3.loss_z: 0.1779, task3.loss_whl: 0.1552, task3.loss_yaw: 0.2954, task3.loss_vel: 0.0333, task3.loss_heatmap: 5.7780, task4.loss_xy: 0.1240, task4.loss_z: 0.1756, task4.loss_whl: 0.1373, task4.loss_yaw: 0.3056, task4.loss_vel: 0.5953, task4.loss_heatmap: 32.4179, task5.loss_xy: 0.1258, task5.loss_z: 0.1929, task5.loss_whl: 0.2000, task5.loss_yaw: 0.3059, task5.loss_vel: 0.2825, task5.loss_heatmap: 4.6283, loss: 132.7187 2022-09-22 11:55:03,518 - mmdet - INFO - Epoch [1][750/1931] lr: 2.000e-04, eta: 1 day, 4:01:02, time: 2.213, data_time: 0.069, memory: 15117, loss_depth: 48.4607, task0.loss_xy: 0.1259, task0.loss_z: 0.2260, task0.loss_whl: 0.0638, task0.loss_yaw: 0.2962, task0.loss_vel: 0.7475, task0.loss_heatmap: 2.8051, task1.loss_xy: 0.1264, task1.loss_z: 0.2549, task1.loss_whl: 0.1740, task1.loss_yaw: 0.2986, task1.loss_vel: 0.5428, task1.loss_heatmap: 10.0218, task2.loss_xy: 0.1280, task2.loss_z: 0.2508, task2.loss_whl: 0.1580, task2.loss_yaw: 0.3028, task2.loss_vel: 0.7459, task2.loss_heatmap: 15.2719, task3.loss_xy: 0.1251, task3.loss_z: 0.1817, task3.loss_whl: 0.1508, task3.loss_yaw: 0.2993, task3.loss_vel: 0.0363, task3.loss_heatmap: 4.4544, task4.loss_xy: 0.1271, task4.loss_z: 0.1767, task4.loss_whl: 0.1376, task4.loss_yaw: 0.3045, task4.loss_vel: 0.6143, task4.loss_heatmap: 28.4187, task5.loss_xy: 0.1258, task5.loss_z: 0.1943, task5.loss_whl: 0.2015, task5.loss_yaw: 0.3060, task5.loss_vel: 0.2811, task5.loss_heatmap: 4.4087, loss: 121.5451 2022-09-22 11:56:46,256 - mmdet - INFO - Epoch [1][800/1931] lr: 2.000e-04, eta: 1 day, 3:51:44, time: 2.055, data_time: 0.070, memory: 15117, loss_depth: 46.8271, task0.loss_xy: 0.1254, task0.loss_z: 0.2254, task0.loss_whl: 0.0634, task0.loss_yaw: 0.2961, task0.loss_vel: 0.7200, task0.loss_heatmap: 2.7646, task1.loss_xy: 0.1254, task1.loss_z: 0.2588, task1.loss_whl: 0.1723, task1.loss_yaw: 0.2996, task1.loss_vel: 0.5508, task1.loss_heatmap: 9.0330, task2.loss_xy: 0.1267, task2.loss_z: 0.2462, task2.loss_whl: 0.1537, task2.loss_yaw: 0.3000, task2.loss_vel: 0.7430, task2.loss_heatmap: 13.0332, task3.loss_xy: 0.1251, task3.loss_z: 0.1827, task3.loss_whl: 0.1586, task3.loss_yaw: 0.2991, task3.loss_vel: 0.0301, task3.loss_heatmap: 4.8689, task4.loss_xy: 0.1256, task4.loss_z: 0.1742, task4.loss_whl: 0.1357, task4.loss_yaw: 0.3061, task4.loss_vel: 0.5735, task4.loss_heatmap: 24.4946, task5.loss_xy: 0.1258, task5.loss_z: 0.1919, task5.loss_whl: 0.1992, task5.loss_yaw: 0.3054, task5.loss_vel: 0.2799, task5.loss_heatmap: 4.0047, loss: 112.6462 2022-09-22 11:58:39,383 - mmdet - INFO - Epoch [1][850/1931] lr: 2.000e-04, eta: 1 day, 3:52:34, time: 2.262, data_time: 0.067, memory: 15117, loss_depth: 45.2992, task0.loss_xy: 0.1260, task0.loss_z: 0.2256, task0.loss_whl: 0.0634, task0.loss_yaw: 0.2963, task0.loss_vel: 0.6939, task0.loss_heatmap: 2.7520, task1.loss_xy: 0.1253, task1.loss_z: 0.2630, task1.loss_whl: 0.1677, task1.loss_yaw: 0.2988, task1.loss_vel: 0.5425, task1.loss_heatmap: 7.9919, task2.loss_xy: 0.1279, task2.loss_z: 0.2513, task2.loss_whl: 0.1589, task2.loss_yaw: 0.3010, task2.loss_vel: 0.7300, task2.loss_heatmap: 12.1269, task3.loss_xy: 0.1260, task3.loss_z: 0.1795, task3.loss_whl: 0.1506, task3.loss_yaw: 0.2958, task3.loss_vel: 0.0346, task3.loss_heatmap: 4.6487, task4.loss_xy: 0.1257, task4.loss_z: 0.1837, task4.loss_whl: 0.1386, task4.loss_yaw: 0.3068, task4.loss_vel: 0.5659, task4.loss_heatmap: 21.9733, task5.loss_xy: 0.1271, task5.loss_z: 0.1958, task5.loss_whl: 0.2046, task5.loss_yaw: 0.3113, task5.loss_vel: 0.2755, task5.loss_heatmap: 3.9989, loss: 106.3843 2022-09-22 12:00:29,302 - mmdet - INFO - Epoch [1][900/1931] lr: 2.000e-04, eta: 1 day, 3:50:25, time: 2.198, data_time: 0.069, memory: 15117, loss_depth: 43.9389, task0.loss_xy: 0.1258, task0.loss_z: 0.2264, task0.loss_whl: 0.0637, task0.loss_yaw: 0.2956, task0.loss_vel: 0.7250, task0.loss_heatmap: 2.7637, task1.loss_xy: 0.1248, task1.loss_z: 0.2562, task1.loss_whl: 0.1697, task1.loss_yaw: 0.2988, task1.loss_vel: 0.5305, task1.loss_heatmap: 7.4886, task2.loss_xy: 0.1287, task2.loss_z: 0.2568, task2.loss_whl: 0.1693, task2.loss_yaw: 0.3037, task2.loss_vel: 0.7163, task2.loss_heatmap: 11.1626, task3.loss_xy: 0.1250, task3.loss_z: 0.1764, task3.loss_whl: 0.1504, task3.loss_yaw: 0.2950, task3.loss_vel: 0.0333, task3.loss_heatmap: 4.1117, task4.loss_xy: 0.1259, task4.loss_z: 0.1725, task4.loss_whl: 0.1368, task4.loss_yaw: 0.3045, task4.loss_vel: 0.5370, task4.loss_heatmap: 19.3907, task5.loss_xy: 0.1261, task5.loss_z: 0.1919, task5.loss_whl: 0.2036, task5.loss_yaw: 0.3057, task5.loss_vel: 0.2809, task5.loss_heatmap: 3.7444, loss: 100.1569 2022-09-22 12:02:22,102 - mmdet - INFO - Epoch [1][950/1931] lr: 2.000e-04, eta: 1 day, 3:50:36, time: 2.256, data_time: 0.067, memory: 15117, loss_depth: 42.3467, task0.loss_xy: 0.1252, task0.loss_z: 0.2297, task0.loss_whl: 0.0632, task0.loss_yaw: 0.2950, task0.loss_vel: 0.7142, task0.loss_heatmap: 2.7656, task1.loss_xy: 0.1281, task1.loss_z: 0.2642, task1.loss_whl: 0.1709, task1.loss_yaw: 0.3010, task1.loss_vel: 0.5102, task1.loss_heatmap: 6.9991, task2.loss_xy: 0.1324, task2.loss_z: 0.2531, task2.loss_whl: 0.1637, task2.loss_yaw: 0.3074, task2.loss_vel: 0.7485, task2.loss_heatmap: 10.4697, task3.loss_xy: 0.1253, task3.loss_z: 0.1820, task3.loss_whl: 0.1521, task3.loss_yaw: 0.2998, task3.loss_vel: 0.0328, task3.loss_heatmap: 4.0475, task4.loss_xy: 0.1302, task4.loss_z: 0.1768, task4.loss_whl: 0.1412, task4.loss_yaw: 0.3084, task4.loss_vel: 0.6150, task4.loss_heatmap: 17.9199, task5.loss_xy: 0.1256, task5.loss_z: 0.1937, task5.loss_whl: 0.2017, task5.loss_yaw: 0.3058, task5.loss_vel: 0.2763, task5.loss_heatmap: 3.6733, loss: 95.8953 2022-09-22 12:04:05,760 - mmdet - INFO - Exp name: bevdepth4d-r50.py 2022-09-22 12:04:05,761 - mmdet - INFO - Epoch [1][1000/1931] lr: 2.000e-04, eta: 1 day, 3:43:39, time: 2.073, data_time: 0.070, memory: 15117, loss_depth: 41.1649, task0.loss_xy: 0.1256, task0.loss_z: 0.2252, task0.loss_whl: 0.0638, task0.loss_yaw: 0.2939, task0.loss_vel: 0.7360, task0.loss_heatmap: 2.7528, task1.loss_xy: 0.1267, task1.loss_z: 0.2620, task1.loss_whl: 0.1692, task1.loss_yaw: 0.2983, task1.loss_vel: 0.5360, task1.loss_heatmap: 6.6687, task2.loss_xy: 0.1257, task2.loss_z: 0.2488, task2.loss_whl: 0.1654, task2.loss_yaw: 0.3006, task2.loss_vel: 0.6750, task2.loss_heatmap: 9.5830, task3.loss_xy: 0.1252, task3.loss_z: 0.1797, task3.loss_whl: 0.1508, task3.loss_yaw: 0.2973, task3.loss_vel: 0.0328, task3.loss_heatmap: 4.1552, task4.loss_xy: 0.1245, task4.loss_z: 0.1758, task4.loss_whl: 0.1378, task4.loss_yaw: 0.3021, task4.loss_vel: 0.6255, task4.loss_heatmap: 16.9323, task5.loss_xy: 0.1256, task5.loss_z: 0.1903, task5.loss_whl: 0.2024, task5.loss_yaw: 0.3047, task5.loss_vel: 0.2826, task5.loss_heatmap: 3.6493, loss: 92.5155 2022-09-22 12:05:54,686 - mmdet - INFO - Epoch [1][1050/1931] lr: 2.000e-04, eta: 1 day, 3:41:00, time: 2.178, data_time: 0.068, memory: 15117, loss_depth: 39.7653, task0.loss_xy: 0.1255, task0.loss_z: 0.2271, task0.loss_whl: 0.0629, task0.loss_yaw: 0.2950, task0.loss_vel: 0.7349, task0.loss_heatmap: 2.7512, task1.loss_xy: 0.1272, task1.loss_z: 0.2616, task1.loss_whl: 0.1740, task1.loss_yaw: 0.2982, task1.loss_vel: 0.4986, task1.loss_heatmap: 6.3139, task2.loss_xy: 0.1282, task2.loss_z: 0.2605, task2.loss_whl: 0.1624, task2.loss_yaw: 0.3016, task2.loss_vel: 0.7288, task2.loss_heatmap: 8.6659, task3.loss_xy: 0.1255, task3.loss_z: 0.1803, task3.loss_whl: 0.1596, task3.loss_yaw: 0.2958, task3.loss_vel: 0.0335, task3.loss_heatmap: 3.6368, task4.loss_xy: 0.1267, task4.loss_z: 0.1794, task4.loss_whl: 0.1365, task4.loss_yaw: 0.3041, task4.loss_vel: 0.5825, task4.loss_heatmap: 15.1592, task5.loss_xy: 0.1253, task5.loss_z: 0.1946, task5.loss_whl: 0.1996, task5.loss_yaw: 0.3051, task5.loss_vel: 0.2734, task5.loss_heatmap: 3.5122, loss: 87.4128 2022-09-22 12:07:40,409 - mmdet - INFO - Epoch [1][1100/1931] lr: 2.000e-04, eta: 1 day, 3:36:13, time: 2.115, data_time: 0.069, memory: 15117, loss_depth: 38.4423, task0.loss_xy: 0.1250, task0.loss_z: 0.2221, task0.loss_whl: 0.0626, task0.loss_yaw: 0.2950, task0.loss_vel: 0.7208, task0.loss_heatmap: 2.7413, task1.loss_xy: 0.1260, task1.loss_z: 0.2571, task1.loss_whl: 0.1708, task1.loss_yaw: 0.2973, task1.loss_vel: 0.4959, task1.loss_heatmap: 5.7485, task2.loss_xy: 0.1257, task2.loss_z: 0.2501, task2.loss_whl: 0.1595, task2.loss_yaw: 0.2988, task2.loss_vel: 0.7557, task2.loss_heatmap: 8.4489, task3.loss_xy: 0.1243, task3.loss_z: 0.1743, task3.loss_whl: 0.1531, task3.loss_yaw: 0.2954, task3.loss_vel: 0.0333, task3.loss_heatmap: 3.6811, task4.loss_xy: 0.1260, task4.loss_z: 0.1739, task4.loss_whl: 0.1319, task4.loss_yaw: 0.3043, task4.loss_vel: 0.5461, task4.loss_heatmap: 14.2401, task5.loss_xy: 0.1256, task5.loss_z: 0.1893, task5.loss_whl: 0.1998, task5.loss_yaw: 0.3054, task5.loss_vel: 0.2762, task5.loss_heatmap: 3.4093, loss: 84.2328 2022-09-22 12:09:30,844 - mmdet - INFO - Epoch [1][1150/1931] lr: 2.000e-04, eta: 1 day, 3:34:48, time: 2.209, data_time: 0.071, memory: 15117, loss_depth: 37.2199, task0.loss_xy: 0.1261, task0.loss_z: 0.2226, task0.loss_whl: 0.0625, task0.loss_yaw: 0.2940, task0.loss_vel: 0.7340, task0.loss_heatmap: 2.7415, task1.loss_xy: 0.1268, task1.loss_z: 0.2613, task1.loss_whl: 0.1703, task1.loss_yaw: 0.2984, task1.loss_vel: 0.5433, task1.loss_heatmap: 5.7337, task2.loss_xy: 0.1256, task2.loss_z: 0.2508, task2.loss_whl: 0.1572, task2.loss_yaw: 0.3021, task2.loss_vel: 0.8226, task2.loss_heatmap: 8.1609, task3.loss_xy: 0.1260, task3.loss_z: 0.1832, task3.loss_whl: 0.1565, task3.loss_yaw: 0.3000, task3.loss_vel: 0.0350, task3.loss_heatmap: 3.8511, task4.loss_xy: 0.1256, task4.loss_z: 0.1765, task4.loss_whl: 0.1342, task4.loss_yaw: 0.3058, task4.loss_vel: 0.5945, task4.loss_heatmap: 12.8702, task5.loss_xy: 0.1254, task5.loss_z: 0.1908, task5.loss_whl: 0.2012, task5.loss_yaw: 0.3052, task5.loss_vel: 0.2855, task5.loss_heatmap: 3.4608, loss: 81.7813 2022-09-22 12:11:16,276 - mmdet - INFO - Epoch [1][1200/1931] lr: 2.000e-04, eta: 1 day, 3:30:12, time: 2.109, data_time: 0.066, memory: 15117, loss_depth: 36.0154, task0.loss_xy: 0.1256, task0.loss_z: 0.2190, task0.loss_whl: 0.0637, task0.loss_yaw: 0.2941, task0.loss_vel: 0.6900, task0.loss_heatmap: 2.7325, task1.loss_xy: 0.1265, task1.loss_z: 0.2548, task1.loss_whl: 0.1722, task1.loss_yaw: 0.2977, task1.loss_vel: 0.5281, task1.loss_heatmap: 5.4943, task2.loss_xy: 0.1281, task2.loss_z: 0.2482, task2.loss_whl: 0.1569, task2.loss_yaw: 0.3030, task2.loss_vel: 0.8390, task2.loss_heatmap: 7.6945, task3.loss_xy: 0.1264, task3.loss_z: 0.1759, task3.loss_whl: 0.1510, task3.loss_yaw: 0.2985, task3.loss_vel: 0.0374, task3.loss_heatmap: 4.0435, task4.loss_xy: 0.1263, task4.loss_z: 0.1720, task4.loss_whl: 0.1357, task4.loss_yaw: 0.3048, task4.loss_vel: 0.5248, task4.loss_heatmap: 12.1318, task5.loss_xy: 0.1261, task5.loss_z: 0.1902, task5.loss_whl: 0.1989, task5.loss_yaw: 0.3046, task5.loss_vel: 0.2756, task5.loss_heatmap: 3.3331, loss: 79.0399 2022-09-22 12:13:07,509 - mmdet - INFO - Epoch [1][1250/1931] lr: 2.000e-04, eta: 1 day, 3:29:18, time: 2.224, data_time: 0.070, memory: 15117, loss_depth: 34.4292, task0.loss_xy: 0.1254, task0.loss_z: 0.2179, task0.loss_whl: 0.0632, task0.loss_yaw: 0.2942, task0.loss_vel: 0.7129, task0.loss_heatmap: 2.7375, task1.loss_xy: 0.1270, task1.loss_z: 0.2549, task1.loss_whl: 0.1720, task1.loss_yaw: 0.2976, task1.loss_vel: 0.5137, task1.loss_heatmap: 5.2005, task2.loss_xy: 0.1261, task2.loss_z: 0.2512, task2.loss_whl: 0.1539, task2.loss_yaw: 0.2977, task2.loss_vel: 0.7510, task2.loss_heatmap: 7.1440, task3.loss_xy: 0.1246, task3.loss_z: 0.1758, task3.loss_whl: 0.1559, task3.loss_yaw: 0.2969, task3.loss_vel: 0.0349, task3.loss_heatmap: 3.5759, task4.loss_xy: 0.1241, task4.loss_z: 0.1787, task4.loss_whl: 0.1326, task4.loss_yaw: 0.2998, task4.loss_vel: 0.5123, task4.loss_heatmap: 12.1933, task5.loss_xy: 0.1257, task5.loss_z: 0.1866, task5.loss_whl: 0.1987, task5.loss_yaw: 0.3058, task5.loss_vel: 0.2845, task5.loss_heatmap: 3.2997, loss: 76.0757 2022-09-22 12:14:55,289 - mmdet - INFO - Epoch [1][1300/1931] lr: 2.000e-04, eta: 1 day, 3:26:21, time: 2.156, data_time: 0.066, memory: 15117, loss_depth: 33.0191, task0.loss_xy: 0.1258, task0.loss_z: 0.2108, task0.loss_whl: 0.0633, task0.loss_yaw: 0.2935, task0.loss_vel: 0.6716, task0.loss_heatmap: 2.6978, task1.loss_xy: 0.1259, task1.loss_z: 0.2547, task1.loss_whl: 0.1681, task1.loss_yaw: 0.2963, task1.loss_vel: 0.5054, task1.loss_heatmap: 5.0522, task2.loss_xy: 0.1267, task2.loss_z: 0.2460, task2.loss_whl: 0.1576, task2.loss_yaw: 0.2970, task2.loss_vel: 0.7385, task2.loss_heatmap: 6.8956, task3.loss_xy: 0.1258, task3.loss_z: 0.1732, task3.loss_whl: 0.1510, task3.loss_yaw: 0.2976, task3.loss_vel: 0.0348, task3.loss_heatmap: 3.7533, task4.loss_xy: 0.1252, task4.loss_z: 0.1675, task4.loss_whl: 0.1358, task4.loss_yaw: 0.3038, task4.loss_vel: 0.5456, task4.loss_heatmap: 10.9944, task5.loss_xy: 0.1254, task5.loss_z: 0.1869, task5.loss_whl: 0.2013, task5.loss_yaw: 0.3043, task5.loss_vel: 0.2755, task5.loss_heatmap: 3.2774, loss: 73.1246 2022-09-22 12:16:46,830 - mmdet - INFO - Epoch [1][1350/1931] lr: 2.000e-04, eta: 1 day, 3:25:35, time: 2.231, data_time: 0.067, memory: 15117, loss_depth: 32.1519, task0.loss_xy: 0.1256, task0.loss_z: 0.2088, task0.loss_whl: 0.0627, task0.loss_yaw: 0.2939, task0.loss_vel: 0.6711, task0.loss_heatmap: 2.7232, task1.loss_xy: 0.1265, task1.loss_z: 0.2520, task1.loss_whl: 0.1704, task1.loss_yaw: 0.2989, task1.loss_vel: 0.5111, task1.loss_heatmap: 4.9466, task2.loss_xy: 0.1278, task2.loss_z: 0.2399, task2.loss_whl: 0.1596, task2.loss_yaw: 0.2961, task2.loss_vel: 0.7066, task2.loss_heatmap: 6.8074, task3.loss_xy: 0.1267, task3.loss_z: 0.1670, task3.loss_whl: 0.1524, task3.loss_yaw: 0.2978, task3.loss_vel: 0.0342, task3.loss_heatmap: 3.5028, task4.loss_xy: 0.1245, task4.loss_z: 0.1755, task4.loss_whl: 0.1333, task4.loss_yaw: 0.3038, task4.loss_vel: 0.5317, task4.loss_heatmap: 10.2037, task5.loss_xy: 0.1256, task5.loss_z: 0.1817, task5.loss_whl: 0.1987, task5.loss_yaw: 0.3051, task5.loss_vel: 0.2784, task5.loss_heatmap: 3.2557, loss: 70.9787 2022-09-22 12:18:33,241 - mmdet - INFO - Epoch [1][1400/1931] lr: 2.000e-04, eta: 1 day, 3:21:59, time: 2.128, data_time: 0.068, memory: 15117, loss_depth: 30.2995, task0.loss_xy: 0.1254, task0.loss_z: 0.1994, task0.loss_whl: 0.0623, task0.loss_yaw: 0.2923, task0.loss_vel: 0.7293, task0.loss_heatmap: 2.7267, task1.loss_xy: 0.1259, task1.loss_z: 0.2466, task1.loss_whl: 0.1718, task1.loss_yaw: 0.2976, task1.loss_vel: 0.5313, task1.loss_heatmap: 4.8686, task2.loss_xy: 0.1249, task2.loss_z: 0.2451, task2.loss_whl: 0.1561, task2.loss_yaw: 0.2993, task2.loss_vel: 0.7896, task2.loss_heatmap: 6.3809, task3.loss_xy: 0.1239, task3.loss_z: 0.1560, task3.loss_whl: 0.1499, task3.loss_yaw: 0.2976, task3.loss_vel: 0.0331, task3.loss_heatmap: 3.5015, task4.loss_xy: 0.1243, task4.loss_z: 0.1700, task4.loss_whl: 0.1351, task4.loss_yaw: 0.3036, task4.loss_vel: 0.5792, task4.loss_heatmap: 10.0081, task5.loss_xy: 0.1262, task5.loss_z: 0.1762, task5.loss_whl: 0.2021, task5.loss_yaw: 0.3062, task5.loss_vel: 0.2949, task5.loss_heatmap: 3.2232, loss: 68.5839 2022-09-22 12:20:22,343 - mmdet - INFO - Epoch [1][1450/1931] lr: 2.000e-04, eta: 1 day, 3:19:54, time: 2.182, data_time: 0.068, memory: 15117, loss_depth: 29.1037, task0.loss_xy: 0.1258, task0.loss_z: 0.1943, task0.loss_whl: 0.0627, task0.loss_yaw: 0.2929, task0.loss_vel: 0.7196, task0.loss_heatmap: 2.7235, task1.loss_xy: 0.1272, task1.loss_z: 0.2406, task1.loss_whl: 0.1738, task1.loss_yaw: 0.2985, task1.loss_vel: 0.5084, task1.loss_heatmap: 4.7348, task2.loss_xy: 0.1262, task2.loss_z: 0.2498, task2.loss_whl: 0.1623, task2.loss_yaw: 0.3025, task2.loss_vel: 0.7460, task2.loss_heatmap: 5.9646, task3.loss_xy: 0.1258, task3.loss_z: 0.1539, task3.loss_whl: 0.1520, task3.loss_yaw: 0.2996, task3.loss_vel: 0.0339, task3.loss_heatmap: 3.5224, task4.loss_xy: 0.1284, task4.loss_z: 0.1676, task4.loss_whl: 0.1410, task4.loss_yaw: 0.3069, task4.loss_vel: 0.5737, task4.loss_heatmap: 9.5266, task5.loss_xy: 0.1259, task5.loss_z: 0.1721, task5.loss_whl: 0.1984, task5.loss_yaw: 0.3042, task5.loss_vel: 0.2822, task5.loss_heatmap: 3.2114, loss: 66.2837 2022-09-22 12:22:06,723 - mmdet - INFO - Epoch [1][1500/1931] lr: 2.000e-04, eta: 1 day, 3:15:29, time: 2.088, data_time: 0.065, memory: 15117, loss_depth: 28.1629, task0.loss_xy: 0.1256, task0.loss_z: 0.1900, task0.loss_whl: 0.0630, task0.loss_yaw: 0.2908, task0.loss_vel: 0.7252, task0.loss_heatmap: 2.7226, task1.loss_xy: 0.1261, task1.loss_z: 0.2319, task1.loss_whl: 0.1705, task1.loss_yaw: 0.2957, task1.loss_vel: 0.5383, task1.loss_heatmap: 4.7539, task2.loss_xy: 0.1280, task2.loss_z: 0.2260, task2.loss_whl: 0.1530, task2.loss_yaw: 0.2988, task2.loss_vel: 0.7809, task2.loss_heatmap: 6.0445, task3.loss_xy: 0.1250, task3.loss_z: 0.1412, task3.loss_whl: 0.1509, task3.loss_yaw: 0.2964, task3.loss_vel: 0.0327, task3.loss_heatmap: 3.4687, task4.loss_xy: 0.1273, task4.loss_z: 0.1665, task4.loss_whl: 0.1357, task4.loss_yaw: 0.3078, task4.loss_vel: 0.5930, task4.loss_heatmap: 8.6563, task5.loss_xy: 0.1258, task5.loss_z: 0.1641, task5.loss_whl: 0.1979, task5.loss_yaw: 0.3049, task5.loss_vel: 0.2798, task5.loss_heatmap: 3.1593, loss: 64.4613 2022-09-22 12:23:58,847 - mmdet - INFO - Epoch [1][1550/1931] lr: 2.000e-04, eta: 1 day, 3:14:58, time: 2.242, data_time: 0.065, memory: 15117, loss_depth: 26.7409, task0.loss_xy: 0.1256, task0.loss_z: 0.1908, task0.loss_whl: 0.0622, task0.loss_yaw: 0.2896, task0.loss_vel: 0.7267, task0.loss_heatmap: 2.7285, task1.loss_xy: 0.1253, task1.loss_z: 0.2312, task1.loss_whl: 0.1714, task1.loss_yaw: 0.2973, task1.loss_vel: 0.5296, task1.loss_heatmap: 4.4948, task2.loss_xy: 0.1272, task2.loss_z: 0.2198, task2.loss_whl: 0.1526, task2.loss_yaw: 0.2956, task2.loss_vel: 0.8108, task2.loss_heatmap: 6.0758, task3.loss_xy: 0.1254, task3.loss_z: 0.1468, task3.loss_whl: 0.1528, task3.loss_yaw: 0.2997, task3.loss_vel: 0.0408, task3.loss_heatmap: 3.3492, task4.loss_xy: 0.1267, task4.loss_z: 0.1556, task4.loss_whl: 0.1375, task4.loss_yaw: 0.3075, task4.loss_vel: 0.5928, task4.loss_heatmap: 8.5124, task5.loss_xy: 0.1255, task5.loss_z: 0.1648, task5.loss_whl: 0.1946, task5.loss_yaw: 0.3055, task5.loss_vel: 0.2877, task5.loss_heatmap: 3.1690, loss: 62.5902 2022-09-22 12:25:44,794 - mmdet - INFO - Epoch [1][1600/1931] lr: 2.000e-04, eta: 1 day, 3:11:29, time: 2.119, data_time: 0.067, memory: 15117, loss_depth: 25.3202, task0.loss_xy: 0.1255, task0.loss_z: 0.1814, task0.loss_whl: 0.0626, task0.loss_yaw: 0.2900, task0.loss_vel: 0.6951, task0.loss_heatmap: 2.7071, task1.loss_xy: 0.1255, task1.loss_z: 0.2230, task1.loss_whl: 0.1708, task1.loss_yaw: 0.2964, task1.loss_vel: 0.5245, task1.loss_heatmap: 4.3924, task2.loss_xy: 0.1256, task2.loss_z: 0.2129, task2.loss_whl: 0.1588, task2.loss_yaw: 0.2959, task2.loss_vel: 0.7654, task2.loss_heatmap: 5.5895, task3.loss_xy: 0.1257, task3.loss_z: 0.1383, task3.loss_whl: 0.1519, task3.loss_yaw: 0.2979, task3.loss_vel: 0.0340, task3.loss_heatmap: 3.3557, task4.loss_xy: 0.1223, task4.loss_z: 0.1451, task4.loss_whl: 0.1367, task4.loss_yaw: 0.3001, task4.loss_vel: 0.5328, task4.loss_heatmap: 8.1549, task5.loss_xy: 0.1254, task5.loss_z: 0.1614, task5.loss_whl: 0.1964, task5.loss_yaw: 0.3047, task5.loss_vel: 0.2774, task5.loss_heatmap: 3.1365, loss: 59.9595 2022-09-22 12:27:33,685 - mmdet - INFO - Epoch [1][1650/1931] lr: 2.000e-04, eta: 1 day, 3:09:25, time: 2.177, data_time: 0.071, memory: 15117, loss_depth: 24.2832, task0.loss_xy: 0.1259, task0.loss_z: 0.1765, task0.loss_whl: 0.0629, task0.loss_yaw: 0.2881, task0.loss_vel: 0.7331, task0.loss_heatmap: 2.7228, task1.loss_xy: 0.1271, task1.loss_z: 0.2182, task1.loss_whl: 0.1743, task1.loss_yaw: 0.2957, task1.loss_vel: 0.5129, task1.loss_heatmap: 4.3540, task2.loss_xy: 0.1261, task2.loss_z: 0.2009, task2.loss_whl: 0.1609, task2.loss_yaw: 0.2995, task2.loss_vel: 0.7413, task2.loss_heatmap: 5.6247, task3.loss_xy: 0.1252, task3.loss_z: 0.1360, task3.loss_whl: 0.1490, task3.loss_yaw: 0.2967, task3.loss_vel: 0.0365, task3.loss_heatmap: 3.3252, task4.loss_xy: 0.1256, task4.loss_z: 0.1494, task4.loss_whl: 0.1370, task4.loss_yaw: 0.3032, task4.loss_vel: 0.5915, task4.loss_heatmap: 7.8014, task5.loss_xy: 0.1254, task5.loss_z: 0.1553, task5.loss_whl: 0.1950, task5.loss_yaw: 0.3046, task5.loss_vel: 0.2830, task5.loss_heatmap: 3.1119, loss: 58.5798 2022-09-22 12:29:23,637 - mmdet - INFO - Epoch [1][1700/1931] lr: 2.000e-04, eta: 1 day, 3:07:51, time: 2.199, data_time: 0.072, memory: 15117, loss_depth: 23.2570, task0.loss_xy: 0.1250, task0.loss_z: 0.1711, task0.loss_whl: 0.0623, task0.loss_yaw: 0.2884, task0.loss_vel: 0.7270, task0.loss_heatmap: 2.6995, task1.loss_xy: 0.1259, task1.loss_z: 0.2147, task1.loss_whl: 0.1688, task1.loss_yaw: 0.2955, task1.loss_vel: 0.4902, task1.loss_heatmap: 4.2529, task2.loss_xy: 0.1281, task2.loss_z: 0.2123, task2.loss_whl: 0.1593, task2.loss_yaw: 0.2973, task2.loss_vel: 0.7284, task2.loss_heatmap: 5.5704, task3.loss_xy: 0.1244, task3.loss_z: 0.1347, task3.loss_whl: 0.1516, task3.loss_yaw: 0.2972, task3.loss_vel: 0.0303, task3.loss_heatmap: 3.4994, task4.loss_xy: 0.1260, task4.loss_z: 0.1402, task4.loss_whl: 0.1364, task4.loss_yaw: 0.3085, task4.loss_vel: 0.5408, task4.loss_heatmap: 7.3849, task5.loss_xy: 0.1251, task5.loss_z: 0.1543, task5.loss_whl: 0.1961, task5.loss_yaw: 0.3042, task5.loss_vel: 0.2789, task5.loss_heatmap: 3.1099, loss: 57.0171 2022-09-22 12:31:17,178 - mmdet - INFO - Epoch [1][1750/1931] lr: 2.000e-04, eta: 1 day, 3:07:48, time: 2.271, data_time: 0.071, memory: 15117, loss_depth: 22.2095, task0.loss_xy: 0.1252, task0.loss_z: 0.1725, task0.loss_whl: 0.0626, task0.loss_yaw: 0.2875, task0.loss_vel: 0.7348, task0.loss_heatmap: 2.7081, task1.loss_xy: 0.1258, task1.loss_z: 0.2158, task1.loss_whl: 0.1705, task1.loss_yaw: 0.2957, task1.loss_vel: 0.4957, task1.loss_heatmap: 4.2211, task2.loss_xy: 0.1280, task2.loss_z: 0.2002, task2.loss_whl: 0.1609, task2.loss_yaw: 0.2942, task2.loss_vel: 0.7068, task2.loss_heatmap: 5.5323, task3.loss_xy: 0.1259, task3.loss_z: 0.1293, task3.loss_whl: 0.1540, task3.loss_yaw: 0.3001, task3.loss_vel: 0.0347, task3.loss_heatmap: 3.3889, task4.loss_xy: 0.1258, task4.loss_z: 0.1452, task4.loss_whl: 0.1367, task4.loss_yaw: 0.3041, task4.loss_vel: 0.6001, task4.loss_heatmap: 7.3396, task5.loss_xy: 0.1252, task5.loss_z: 0.1554, task5.loss_whl: 0.1975, task5.loss_yaw: 0.3038, task5.loss_vel: 0.2884, task5.loss_heatmap: 3.1097, loss: 55.8117 2022-09-22 12:33:05,557 - mmdet - INFO - Epoch [1][1800/1931] lr: 2.000e-04, eta: 1 day, 3:05:30, time: 2.168, data_time: 0.069, memory: 15117, loss_depth: 21.1855, task0.loss_xy: 0.1257, task0.loss_z: 0.1710, task0.loss_whl: 0.0626, task0.loss_yaw: 0.2891, task0.loss_vel: 0.7332, task0.loss_heatmap: 2.7168, task1.loss_xy: 0.1262, task1.loss_z: 0.2104, task1.loss_whl: 0.1725, task1.loss_yaw: 0.2960, task1.loss_vel: 0.5278, task1.loss_heatmap: 4.1663, task2.loss_xy: 0.1264, task2.loss_z: 0.2056, task2.loss_whl: 0.1617, task2.loss_yaw: 0.2979, task2.loss_vel: 0.7970, task2.loss_heatmap: 5.4125, task3.loss_xy: 0.1251, task3.loss_z: 0.1237, task3.loss_whl: 0.1484, task3.loss_yaw: 0.2952, task3.loss_vel: 0.0313, task3.loss_heatmap: 3.2912, task4.loss_xy: 0.1270, task4.loss_z: 0.1378, task4.loss_whl: 0.1371, task4.loss_yaw: 0.3067, task4.loss_vel: 0.6042, task4.loss_heatmap: 6.9747, task5.loss_xy: 0.1258, task5.loss_z: 0.1483, task5.loss_whl: 0.1968, task5.loss_yaw: 0.3041, task5.loss_vel: 0.2818, task5.loss_heatmap: 3.0643, loss: 54.2077 2022-09-22 12:34:59,898 - mmdet - INFO - Epoch [1][1850/1931] lr: 2.000e-04, eta: 1 day, 3:05:38, time: 2.287, data_time: 0.070, memory: 15117, loss_depth: 19.8763, task0.loss_xy: 0.1253, task0.loss_z: 0.1694, task0.loss_whl: 0.0629, task0.loss_yaw: 0.2871, task0.loss_vel: 0.7720, task0.loss_heatmap: 2.7095, task1.loss_xy: 0.1263, task1.loss_z: 0.2108, task1.loss_whl: 0.1681, task1.loss_yaw: 0.2934, task1.loss_vel: 0.5082, task1.loss_heatmap: 4.1583, task2.loss_xy: 0.1244, task2.loss_z: 0.1965, task2.loss_whl: 0.1584, task2.loss_yaw: 0.2958, task2.loss_vel: 0.7103, task2.loss_heatmap: 5.2746, task3.loss_xy: 0.1267, task3.loss_z: 0.1210, task3.loss_whl: 0.1524, task3.loss_yaw: 0.2966, task3.loss_vel: 0.0355, task3.loss_heatmap: 3.2889, task4.loss_xy: 0.1265, task4.loss_z: 0.1365, task4.loss_whl: 0.1360, task4.loss_yaw: 0.3007, task4.loss_vel: 0.6323, task4.loss_heatmap: 7.2621, task5.loss_xy: 0.1250, task5.loss_z: 0.1531, task5.loss_whl: 0.1968, task5.loss_yaw: 0.3042, task5.loss_vel: 0.2867, task5.loss_heatmap: 3.0660, loss: 52.9745 2022-09-22 12:36:45,100 - mmdet - INFO - Epoch [1][1900/1931] lr: 2.000e-04, eta: 1 day, 3:02:05, time: 2.104, data_time: 0.068, memory: 15117, loss_depth: 19.3292, task0.loss_xy: 0.1255, task0.loss_z: 0.1653, task0.loss_whl: 0.0625, task0.loss_yaw: 0.2864, task0.loss_vel: 0.7152, task0.loss_heatmap: 2.7039, task1.loss_xy: 0.1263, task1.loss_z: 0.2108, task1.loss_whl: 0.1703, task1.loss_yaw: 0.2926, task1.loss_vel: 0.5235, task1.loss_heatmap: 4.0956, task2.loss_xy: 0.1256, task2.loss_z: 0.1969, task2.loss_whl: 0.1612, task2.loss_yaw: 0.2964, task2.loss_vel: 0.7578, task2.loss_heatmap: 4.9921, task3.loss_xy: 0.1252, task3.loss_z: 0.1187, task3.loss_whl: 0.1479, task3.loss_yaw: 0.2946, task3.loss_vel: 0.0347, task3.loss_heatmap: 3.3646, task4.loss_xy: 0.1285, task4.loss_z: 0.1372, task4.loss_whl: 0.1325, task4.loss_yaw: 0.3047, task4.loss_vel: 0.5968, task4.loss_heatmap: 6.7560, task5.loss_xy: 0.1260, task5.loss_z: 0.1486, task5.loss_whl: 0.2002, task5.loss_yaw: 0.3054, task5.loss_vel: 0.2792, task5.loss_heatmap: 3.1032, loss: 51.6407 2022-09-22 12:37:49,791 - mmdet - INFO - Saving checkpoint at 1 epochs 2022-09-22 12:43:55,103 - mmdet - INFO - Exp name: bevdepth4d-r50.py 2022-09-22 12:43:55,104 - mmdet - INFO - Epoch(val) [1][753] pts_bbox_NuScenes/car_AP_dist_0.5: 0.0000, pts_bbox_NuScenes/car_AP_dist_1.0: 0.0000, pts_bbox_NuScenes/car_AP_dist_2.0: 0.0000, pts_bbox_NuScenes/car_AP_dist_4.0: 0.0000, pts_bbox_NuScenes/car_trans_err: 1.0000, pts_bbox_NuScenes/car_scale_err: 1.0000, pts_bbox_NuScenes/car_orient_err: 1.0000, pts_bbox_NuScenes/car_vel_err: 1.0000, pts_bbox_NuScenes/car_attr_err: 1.0000, pts_bbox_NuScenes/mATE: 1.0000, pts_bbox_NuScenes/mASE: 1.0000, pts_bbox_NuScenes/mAOE: 1.0000, pts_bbox_NuScenes/mAVE: 1.0000, pts_bbox_NuScenes/mAAE: 1.0000, pts_bbox_NuScenes/truck_AP_dist_0.5: 0.0000, pts_bbox_NuScenes/truck_AP_dist_1.0: 0.0000, pts_bbox_NuScenes/truck_AP_dist_2.0: 0.0000, pts_bbox_NuScenes/truck_AP_dist_4.0: 0.0000, pts_bbox_NuScenes/truck_trans_err: 1.0000, pts_bbox_NuScenes/truck_scale_err: 1.0000, pts_bbox_NuScenes/truck_orient_err: 1.0000, pts_bbox_NuScenes/truck_vel_err: 1.0000, pts_bbox_NuScenes/truck_attr_err: 1.0000, pts_bbox_NuScenes/construction_vehicle_AP_dist_0.5: 0.0000, pts_bbox_NuScenes/construction_vehicle_AP_dist_1.0: 0.0000, pts_bbox_NuScenes/construction_vehicle_AP_dist_2.0: 0.0000, pts_bbox_NuScenes/construction_vehicle_AP_dist_4.0: 0.0000, pts_bbox_NuScenes/construction_vehicle_trans_err: 1.0000, pts_bbox_NuScenes/construction_vehicle_scale_err: 1.0000, pts_bbox_NuScenes/construction_vehicle_orient_err: 1.0000, pts_bbox_NuScenes/construction_vehicle_vel_err: 1.0000, pts_bbox_NuScenes/construction_vehicle_attr_err: 1.0000, pts_bbox_NuScenes/bus_AP_dist_0.5: 0.0000, pts_bbox_NuScenes/bus_AP_dist_1.0: 0.0000, pts_bbox_NuScenes/bus_AP_dist_2.0: 0.0000, pts_bbox_NuScenes/bus_AP_dist_4.0: 0.0000, pts_bbox_NuScenes/bus_trans_err: 1.0000, pts_bbox_NuScenes/bus_scale_err: 1.0000, pts_bbox_NuScenes/bus_orient_err: 1.0000, pts_bbox_NuScenes/bus_vel_err: 1.0000, pts_bbox_NuScenes/bus_attr_err: 1.0000, pts_bbox_NuScenes/trailer_AP_dist_0.5: 0.0000, pts_bbox_NuScenes/trailer_AP_dist_1.0: 0.0000, pts_bbox_NuScenes/trailer_AP_dist_2.0: 0.0000, pts_bbox_NuScenes/trailer_AP_dist_4.0: 0.0000, pts_bbox_NuScenes/trailer_trans_err: 1.0000, pts_bbox_NuScenes/trailer_scale_err: 1.0000, pts_bbox_NuScenes/trailer_orient_err: 1.0000, pts_bbox_NuScenes/trailer_vel_err: 1.0000, pts_bbox_NuScenes/trailer_attr_err: 1.0000, pts_bbox_NuScenes/barrier_AP_dist_0.5: 0.0000, pts_bbox_NuScenes/barrier_AP_dist_1.0: 0.0000, pts_bbox_NuScenes/barrier_AP_dist_2.0: 0.0000, pts_bbox_NuScenes/barrier_AP_dist_4.0: 0.0000, pts_bbox_NuScenes/barrier_trans_err: 1.0000, pts_bbox_NuScenes/barrier_scale_err: 1.0000, pts_bbox_NuScenes/barrier_orient_err: 1.0000, pts_bbox_NuScenes/barrier_vel_err: nan, pts_bbox_NuScenes/barrier_attr_err: nan, pts_bbox_NuScenes/motorcycle_AP_dist_0.5: 0.0000, pts_bbox_NuScenes/motorcycle_AP_dist_1.0: 0.0000, pts_bbox_NuScenes/motorcycle_AP_dist_2.0: 0.0000, pts_bbox_NuScenes/motorcycle_AP_dist_4.0: 0.0000, pts_bbox_NuScenes/motorcycle_trans_err: 1.0000, pts_bbox_NuScenes/motorcycle_scale_err: 1.0000, pts_bbox_NuScenes/motorcycle_orient_err: 1.0000, pts_bbox_NuScenes/motorcycle_vel_err: 1.0000, pts_bbox_NuScenes/motorcycle_attr_err: 1.0000, pts_bbox_NuScenes/bicycle_AP_dist_0.5: 0.0000, pts_bbox_NuScenes/bicycle_AP_dist_1.0: 0.0000, pts_bbox_NuScenes/bicycle_AP_dist_2.0: 0.0000, pts_bbox_NuScenes/bicycle_AP_dist_4.0: 0.0000, pts_bbox_NuScenes/bicycle_trans_err: 1.0000, pts_bbox_NuScenes/bicycle_scale_err: 1.0000, pts_bbox_NuScenes/bicycle_orient_err: 1.0000, pts_bbox_NuScenes/bicycle_vel_err: 1.0000, pts_bbox_NuScenes/bicycle_attr_err: 1.0000, pts_bbox_NuScenes/pedestrian_AP_dist_0.5: 0.0000, pts_bbox_NuScenes/pedestrian_AP_dist_1.0: 0.0000, pts_bbox_NuScenes/pedestrian_AP_dist_2.0: 0.0000, pts_bbox_NuScenes/pedestrian_AP_dist_4.0: 0.0000, pts_bbox_NuScenes/pedestrian_trans_err: 1.0000, pts_bbox_NuScenes/pedestrian_scale_err: 1.0000, pts_bbox_NuScenes/pedestrian_orient_err: 1.0000, pts_bbox_NuScenes/pedestrian_vel_err: 1.0000, pts_bbox_NuScenes/pedestrian_attr_err: 1.0000, pts_bbox_NuScenes/traffic_cone_AP_dist_0.5: 0.0000, pts_bbox_NuScenes/traffic_cone_AP_dist_1.0: 0.0000, pts_bbox_NuScenes/traffic_cone_AP_dist_2.0: 0.0000, pts_bbox_NuScenes/traffic_cone_AP_dist_4.0: 0.0000, pts_bbox_NuScenes/traffic_cone_trans_err: 1.0000, pts_bbox_NuScenes/traffic_cone_scale_err: 1.0000, pts_bbox_NuScenes/traffic_cone_orient_err: nan, pts_bbox_NuScenes/traffic_cone_vel_err: nan, pts_bbox_NuScenes/traffic_cone_attr_err: nan, pts_bbox_NuScenes/NDS: 0.0000, pts_bbox_NuScenes/mAP: 0.0000

DevLinyan commented 2 years ago

Screen Shot 2022-09-22 at 13 20 44 The rotation is not right in the object in lidar.

To check the gt, I just use the lidar to show the matching, but found the gt cant match the points object, is there anything problem?

HuangJunJie2017 commented 2 years ago

@linyanAI be patient to see the result of final epoch