conda create -p [YourEnv] python=3.9
conda activate [YourEnv]
conda install -c anaconda libstdcxx-ng
conda install -c menpo opencv 
conda install -c conda-forge plyfile==0.8.1
pip install tqdm imageio
pip install torch==2xx # find the torch version that works for your cuda device
pip install torchmetrics
pip install requests 
pip install tensorboard 
pip install scipy
pip install kornia
pip install lightning=2.2.1 # recommend to use this version for stability!
pip install "jsonargparse[signatures]"
pip install wandb
pip install lpips
pip install pytorch-msssim
pip install ninja
pip install timm==0.4.5
# install from local folders 
pip install submodules/diff-gaussian-rasterization
pip install submodules/depth-diff-gaussian-rasterization
pip install submodules/gaussian-rasterization_ch3
pip install submodules/gaussian-rasterization_ch9
pip install submodules/simple-knn

exp_group_name="vanilla"
exp_name="${scene}_${method}"
python runner.py \
    --config_file configs/custom/${method}/vanilla1.yaml \
    --group ${exp_group_name}_${scene} \
    --name ${exp_name} \
    --dataset data/custom/${scene} \
    --slurm_script slurms/custom.sh \
    --output_dir output/custom/${exp_group_name}/${scene}/${method}

base="${dataset}/${scene}/${method}"
name="vanilla1"
variant="${base}/${name%?}1"
output_path="./output/${base}"
python main.py fit \
    --config configs/${variant}.yaml \
    --output ${output_path} \
    --name "${base##*/}_$name" 
python main.py test \
    --config configs/${variant}.yaml \
    --ckpt_path  last \
    --output ${output_path} \
    --name "${base##*/}_$name"