slurm_pseudolabel.sh

#!/bin/bash -l
 
#SBATCH --nodes=1 # Allocate *at least* 5 nodes to this job.
#SBATCH --ntasks=1 # Allocate *at most* 5 tasks for job steps in the job
#SBATCH --cpus-per-task=4 # Each task needs only one CPU
#SBATCH --mem=32G # This particular job won't need much memory
#SBATCH --time=3-00:00:00  # 3 days
#SBATCH --mail-user=csimo005@ucr.edu
#SBATCH --mail-type=ALL #SBATCH --job-name="xMUDA Train"
#SBATCH -p vcggpu # You could pick other partitions for other jobs
#SBATCH --gres=gpu:1
#SBATCH --wait-all-nodes=1  # Run once all resources are available
#SBATCH --output=slurm_output/output_%j-%N.txt # logging per job and per host in the current directory. Both stdout and stderr are logged.

# Place any commands you want to run below
# Activate conda Env
conda activate xmuda2

# Calculate NuScenes Pseudo Labels
#python3 xmuda/test.py --cfg=configs/nuscenes/usa_singapore/xmuda_pl_SF.yaml --pselab output/83513/nuscenes/usa_singapore/xmuda_pl_SF/model_2d_100000.pth output/83513/nuscenes/usa_singapore/xmuda_pl_SF/model_3d_100000.pth DATASET_TARGET.TEST "('train_singapore',)" OUTPUT_DIR "/data/AmitRoyChowdhury/xmuda_AF/pselab/@"
#python3 xmuda/test.py --cfg=configs/nuscenes/day_night/xmuda_pl_SF.yaml --pselab output/83535/nuscenes/day_night/xmuda_pl_SF/model_2d_100000.pth output/83535/nuscenes/day_night/xmuda_pl_SF/model_3d_100000.pth DATASET_TARGET.TEST "('train_night',)" OUTPUT_DIR "/data/AmitRoyChowdhury/xmuda_AF/pselab/@"


# Calculate SemanticKITTI Pseudo Labels
python3 xmuda/test.py --cfg=configs/a2d2_semantic_kitti/xmuda_pl_SF.yaml --pselab output/81601/a2d2_semantic_kitti/xmuda_pl_SF/model_2d_100000.pth output/81601/a2d2_semantic_kitti/xmuda_pl_SF/model_3d_100000.pth  DATASET_TARGET.TEST "('train',)" OUTPUT_DIR "/data/AmitRoyChowdhury/xmuda_AF/pselab/@"