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cmd.txt
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#####################################################
新建会话：tmux new -s SESSION-NAME
杀死会话：tmux kill-session -t 0
接入会话：tmux attach-session -t 0
退出并杀死当前会话：ctrl+d
# 远程连接tensorboard
ssh -p 40216 -NL 8008:localhost:9009 root@ssh.atom.ks.supremind.info
链接成功后会卡死，直接新开一个终端启动tb就好了
# 打开tb
tensorboard --logdir='./' --port=9009
http://127.0.0.1:8008/
# 上传数据
双卡工作台
scp -P 40612 E:\Dataset\Avgkitti\data_odometry_gray\dataset\events_final.zip root@ssh.atom.rk.supremind.info:/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/events_final
单卡工作台
scp -P 42169 E:\SSC_Dataset\SemanticKITTI\data_odometry_voxels_all.zip root@ssh.atom.rk.supremind.info:/workspace/mnt/storage/shihao/SSC-HR-PCD/SemanticKITTI_Voxels_All
# 下载数据
scp -P 40272 root@ssh.atom.ks.supremind.info:/workspace/mnt/storage/shihao/shihao-cephs/SSC_out/SCFormer_multi_b2_no-aug3D_com0-1.zip D:\
scp -P 40216 root@ssh.atom.ks.supremind.info:/workspace/mnt/storage/shihao/shihao-cephs/SSC_out/SCFormer_multi_b2_l6_com0-1.zip D:\
# SMMC
上传数据：smmc cp -r src.zip ks/personal/shihao/
查看数据：smmc ls ks/personal/shihao
下载数据：smmc cp -r ks/personal/shihao/src.zip ./
# 清除显卡进程
安装fuser： apt-get install psmisc
fuser -v /dev/nvidia* |awk '{for(i=1;i<=NF;i++)print "kill -9 " $i;}' | sh
# 查看所有进程
ps -aux
# 查看服务器端口占用
netstat -ap
# SemanticKITTI SSC数据集分布
训练集：01-10除了08，共3834帧点云; 不抽帧: 19130帧点云
验证集：08，共815帧点云; 不抽帧: 4071帧点云
测试集：11-21，3901帧点云; 不抽帧: 20351帧点云
# SemanticPOSS SSC数据集分布
训练集：01，03，04，05，共2488帧点云
验证集：02，共500帧点云
# 迭代删除
rm -rf 路径
# 对执行脚本赋予可读可写可执行权限
sudo chmod 777 ×××
# pip换源
pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/
# 查看磁盘空间
df -h
# vid2e环境配置
镜像：reg.supremind.info/algorithmteam/supreimage/tools/smarttrafficintegrateddemo:pytorch1.9-trt7.2.2-ubuntu20.04-cuda11.2
source /opt/conda/bin/activate
pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/
cd /workspace/mnt/storage/shihao/MyCode-02/rpg_vid2e
pip install -r requirements.txt
pip install pybind11 matplotlib
python -m pip install ./esim_torch/
CUDA_VISIBLE_DEVICES=0
#####################################################
# 上采样[序列 09 10]
CUDA_VISIBLE_DEVICES=0 python upsample.py \
--input_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/images_reorganize_cuda0 \
--output_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/imageFiles_Upsample_cuda0_2
# 上采样[cuda 1, 序列 08]
CUDA_VISIBLE_DEVICES=1 python upsample.py \
--input_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/images_reorganize_cuda1 \
--output_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/imageFiles_Upsample_cuda1_2
# 上采样[cuda 0, 序列 01]
CUDA_VISIBLE_DEVICES=0 python upsample.py \
--input_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/images_reorganize_cuda0 \
--output_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/imageFiles_Upsample_cuda0_3
# 上采样[cuda 1, 序列 03]
CUDA_VISIBLE_DEVICES=1 python upsample.py \
--input_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/images_reorganize_cuda1 \
--output_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/imageFiles_Upsample_cuda1_3
# 上采样[cuda 0, 序列 02]
CUDA_VISIBLE_DEVICES=0 python upsample.py \
--input_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/images_reorganize_cuda0 \
--output_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/imageFiles_Upsample_cuda0_4
# 生成事件[cuda 1, 序列 1,3,8,9,10]
CUDA_VISIBLE_DEVICES=1 python ./esim_torch/scripts/generate_events.py \
--input_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/imageFiles_Upsample_1-3-8-9-10 \
--output_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/events
# 上采样[cuda 1, 序列 00]
CUDA_VISIBLE_DEVICES=1 python upsample.py \
--input_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/images_reorganize_cuda1 \
--output_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/imageFiles_Upsample_cuda1
# 生成事件[cuda 0, 序列 02]
CUDA_VISIBLE_DEVICES=0 python ./esim_torch/scripts/generate_events.py \
--input_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/imageFiles_Upsample_cuda0_4 \
--output_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/events
# 生成时间戳[序列 02]
python generate_timestamp.py
# 合并事件[cuda 0, 序列 02]
CUDA_VISIBLE_DEVICES=0 python merge_events.py
# 生成事件[cuda 1, 序列 00]
CUDA_VISIBLE_DEVICES=1 python ./esim_torch/scripts/generate_events.py \
--input_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/imageFiles_Upsample_cuda1 \
--output_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/events
# 生成时间戳[序列 00]
python generate_timestamp.py
# 合并事件[tmux1, cuda 1, 序列 00]
CUDA_VISIBLE_DEVICES=1 python merge_events.py
# 此前生成事件时裁剪了图像边界 重新生成，不裁剪而是resize; 同时改进vid2e，从静态时间戳改为动态时间戳
上采样[cuda 0, 序列 00]
CUDA_VISIBLE_DEVICES=1 python upsample.py \
--input_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/images_reorganize_cuda1 \
--output_dir=/workspace/mnt/storage/shihao/EventSSC/SemanticKITTI/kitti/dataset/imageFiles_Upsample_cuda1