mcg-nju / link Goto Github PK

Hi,

Thank you for the great work!

I tried to use pcdet to run Link, I modified SpMiddleResNetFHDELKv3 model. It works, but I had some questions during the debug process.

1. Funtion small_to_large_v2: (line 84 in ts_elk.py). F.spdevoxelize requires only 3 input elements. So can I change the new_feat = F.spdevoxelize(f, idx_query, weights, kernel_size) to new_feat = F.spdevoxelize(f, idx_query, weights)?
2. I had nan in training process. When I debug it, I found that here divides by 0 （Funtion small_to_large_v2: new_feat = new_feat[:,:-1] / (new_feat[:,-1:]) ). Can I add a small value here (e.g. 1e-8) ?

When I finished training, I found that Link was a little better (1 mAP) than CenterPoint(3d spconv). Is this normal? Is it possible that the changes I made above are causing poor performance?

Thanks for this excellent piece of work! I encountered "Warning: could not find environment variable "-x", mpirun was unable to find the specified executable file, and therefore did not launch the job. Warning: could not find environment variable "-x", mpirun was unable to find the specified executable file, and therefore did not launch the job. This error was first reported for process rank 0; it may have occurred for other processes as well", but I have already run the chmod +x evaluate.sh command, and I would like to know how to create a hyperlink to the semantickitti dataset if it is placed in a different location, and is hyperlink necessary?

when I run the follow command:

python -m torch.distributed.launch --nproc_per_node=4 --master_port 23456 ./tools/train.py \
  configs/nusc/voxelnet/nusc_centerpoint_voxelnet_0075voxel_fix_bn_z_elkv3.py \
  --work_dir work_dirs/0075cbgs_cos3x7_group2_15+5 \
  --resume_from work_dirs/0075cbgs_cos3x7_group2/epoch_15.pth

I get a prediction.pkl in work_dir work_dirs/0075cbgs_cos3x7_group2_15+5 directory, I want visualize the result, but I find the ./detection/tools/visual.py can not work well, so where can I find the scrip to visualize predict result.

As mentioned in the paper, training with 3090 is feasible. Installing CUDA10.2 in 3090 is difficult. So I'm using CUDA11.3, Python3.7.10, pytorch1.10.1. The previous steps performed according to installation, and detectioin was added to PYTHONPATH, but when executing the last step, "pip install./torchsparse-u/", the error is as follows

Processing ./torchsparse-u
Preparing metadata (setup.py) ... done
Building wheels for collected packages: torchsparse
Building wheel for torchsparse (setup.py) ... error
error: subprocess-exited-with-error

× python setup.py bdist_wheel did not run successfully.
│ exit code: 1
╰─> [112 lines of output]
...
subprocess.CalledProcessError: Command '['which', 'c++']' returned non-zero exit status 1.
[end of output]

note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for torchsparse
Running setup.py clean for torchsparse
Failed to build torchsparse
ERROR: Could not build wheels for torchsparse, which is required to install pyproject.toml-based projects

What might be the cause of this problem? Could you please give some reference configuration advice on using CUDA11.3 installation project environment on 3090?Besides,is the requirement.txt exactly the same as the requirement.txt in CenterPoint?

Hi,could you please tell the differences between torchsparse1.14.0 and the modified Torchsparse ? cause there is a new version Torchsparse that is easier to install , when i want to use your torchsparse I need to change my gcc version to lower version.

只刷点云，刷了nuscenes数据集，效果还可以，这样投稿能中吗？我看你还专门刷了kitti，有谁告诉你的，请指点一下，我论文刚写完，准备投稿