SCENE_DIR="data/360_v2"
RESULT_DIR="results/benchmark_4gpus"
SCENE_LIST="garden bicycle stump bonsai counter kitchen room" # treehill flowers

for SCENE in $SCENE_LIST;
do
    if [ "$SCENE" = "bonsai" ] || [ "$SCENE" = "counter" ] || [ "$SCENE" = "kitchen" ] || [ "$SCENE" = "room" ]; then
        DATA_FACTOR=2
    else
        DATA_FACTOR=4
    fi

    echo "Running $SCENE"

    # train and eval at the last step (30000)
    # 4 GPUs is effectively 4x batch size so we scale down the steps by 4x as well.
    # "--packed" reduces the data transfer between GPUs, which leads to faster training. 
    CUDA_VISIBLE_DEVICES=0,1,2,3 python simple_trainer.py default --eval_steps 30000 --disable_viewer --data_factor $DATA_FACTOR \
        --steps_scaler 0.25 --packed \
        --data_dir data/360_v2/$SCENE/ \
        --result_dir $RESULT_DIR/$SCENE/

done


for SCENE in $SCENE_LIST;
do
    echo "=== Eval Stats ==="

    for STATS in $RESULT_DIR/$SCENE/stats/val_step7499.json;
    do  
        echo $STATS
        cat $STATS; 
        echo
    done

    echo "=== Train Stats ==="

    for STATS in $RESULT_DIR/$SCENE/stats/train_step7499_rank0.json;
    do  
        echo $STATS
        cat $STATS; 
        echo
    done
done