Errors while Evaluating the Pretrained Model

Thanks for your wonderful work.

I got errors while evaluating using ‘torchpack dist-run -np 1 python evaluate.py configs/semantic_kitti/default.yaml --name SemanticKITTI_val_SPVNAS@65GMACs’,

/bin/sh: -c: line 0: syntax error near unexpected token (' /bin/sh: -c: line 0: mpirun --allow-run-as-root -np 1 -H localhost:1 -bind-to none -map-by slot -x BASH_FUNC_module() -x BLOCKSIZE -x CC -x CLICOLOR -x CONDA_DEFAULT_ENV -x CONDA_EXE -x CONDA_PREFIX -x CONDA_PROMPT_MODIFIER -x CONDA_PYTHON_EXE -x CONDA_SHLVL -x CPATH -x CPLUS_INCLUDE_PATH -x CPPFLAGS -x CUDA_HOME -x CUDA_VISIBLE_DEVICES -x CXX -x EDITOR -x GPU_DEVICE_ORDINAL -x HISTCONTROL -x HISTSIZE -x HOME -x HOSTNAME -x KDEDIRS -x LANG -x LDFLAGS -x LD_LIBRARY_PATH -x LD_RUN_PATH -x LESSOPEN -x LIBRARY_PATH -x LOADEDMODULES -x LOGNAME -x LSCOLORS -x LS_COLORS -x MAIL -x MANPATH -x MASTER_HOST -x MODULEPATH -x MODULESHOME -x OLDPWD -x PATH -x PKG_CONFIG_PATH -x PPL_PATH -x PROJECT_HOME -x PS1 -x PWD -x PYTHONPATH -x QTDIR -x QTINC -x QTLIB -x QT_GRAPHICSSYSTEM_CHECKED -x QT_PLUGIN_PATH -x SELINUX_LEVEL_REQUESTED -x SELINUX_ROLE_REQUESTED -x SELINUX_USE_CURRENT_RANGE -x SHELL -x SHLVL -x SLURMD_NODENAME -x SLURM_CLUSTER_NAME -x SLURM_CPUS_ON_NODE -x SLURM_DISTRIBUTION -x SLURM_GTIDS -x SLURM_JOBID -x SLURM_JOB_ACCOUNT -x SLURM_JOB_CPUS_PER_NODE -x SLURM_JOB_GID -x SLURM_JOB_ID -x SLURM_JOB_NAME -x SLURM_JOB_NODELIST -x SLURM_JOB_NUM_NODES -x SLURM_JOB_PARTITION -x SLURM_JOB_QOS -x SLURM_JOB_UID -x SLURM_JOB_USER -x SLURM_LAUNCH_NODE_IPADDR -x SLURM_LOCALID -x SLURM_NNODES -x SLURM_NODEID -x SLURM_NODELIST -x SLURM_NPROCS -x SLURM_NTASKS -x SLURM_PRIO_PROCESS -x SLURM_PROCID -x SLURM_PTY_PORT -x SLURM_PTY_WIN_COL -x SLURM_PTY_WIN_ROW -x SLURM_SRUN_COMM_HOST -x SLURM_SRUN_COMM_PORT -x SLURM_STEPID -x SLURM_STEP_GPUS -x SLURM_STEP_ID -x SLURM_STEP_LAUNCHER_PORT -x SLURM_STEP_NODELIST -x SLURM_STEP_NUM_NODES -x SLURM_STEP_NUM_TASKS -x SLURM_STEP_TASKS_PER_NODE -x SLURM_SUBMIT_DIR -x SLURM_SUBMIT_HOST -x SLURM_TASKS_PER_NODE -x SLURM_TASK_PID -x SLURM_TOPOLOGY_ADDR -x SLURM_TOPOLOGY_ADDR_PATTERN -x SLURM_UMASK -x SLURM_WORKING_CLUSTER -x SRUN_DEBUG -x SSH_ASKPASS -x SSH_CLIENT -x SSH_CONNECTION -x SSH_TTY -x TERM -x TMPDIR -x TMUX -x TMUX_PANE -x USER -x WORKON_HOME -x XDG_DATA_DIRS -x XDG_RUNTIME_DIR -x XDG_SESSION_ID -x _ -x _CE_CONDA -x _CE_M -x LMFILES -mca pml ob1 -mca btl ^openib -mca btl_tcp_if_exclude docker0,lo python -m torchpack.launch.assets.silentrun python evaluate.py configs/semantic_kitti/default.yaml --name SemanticKITTI_val_SPVNAS@65GMACs’

I checked I got the torchpack and torchsparse installed successfully. Any idea how to solve the issue? Thank you in advance.

[kentang-mit]

Hi,

Thanks for your interest in our work!

I didn’t experience with the same problem when evaluating the pretrained model on my machine so I can only try to find the problem together with you through discussions. Specifically, have you also installed the OpenMPI library successfully on your machine? Also, please try to run

torchpack dist-run -np 1 -v python evaluate.py configs/semantic_kitti/default.yaml --name SemanticKITTI_val_SPVNAS@65GMACs

to see what command is printed, thanks!

Best, Haotian

[HenryJunW]

Cool. I got one more question, how long does it take for you to train the model SemanticKITTI_val_SPVCNN@119GMACs? Can you specify the GPUs # and the training time it takes? As for the evaluation, a single 1080Ti is mentioned in the paper, though. Also, is that possible for you to release the training configuration files for the SPVNAS model? I am wondering whether directly use the file net.config provided in https://hanlab.mit.edu/files/SPVNAS/spvnas_specialized/SemanticKITTI_val_SPVNAS@65GMACs/ as the yaml file. Thanks.