eval_scripts/eval_lotus_v1_slurm.sh

#!/bin/bash
#SBATCH --job-name=moge-lotus-v1
#SBATCH --output=/home/ywan0794/MoGe/moge_lotus_v1_%j.log
#SBATCH --error=/home/ywan0794/MoGe/moge_lotus_v1_%j.log
#SBATCH --open-mode=append
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=4
#SBATCH --gres=gpu:H100:1
#SBATCH --time=0-04:00:00
#SBATCH --mem=40G
#SBATCH --nodelist=erinyes
# MoGe protocol full eval on 10 benchmarks with Lotus depth ckpt v1-0 (depth output).
# Chosen over v2-1-disparity for uniform `depth_affine_invariant` output across 7 models.
# v1-0 is the original Lotus depth ckpt (Lotus paper, 2024-09); v2-1-disparity (2024-11)
# achieves better numbers per README, but emits disparity_affine_invariant — not directly
# comparable in depth space to the other 6 models.

export PYTHONUNBUFFERED=1
cd /home/ywan0794/MoGe

source /home/ywan0794/miniconda3/etc/profile.d/conda.sh
conda activate lotus

export CUDA_HOME=$CONDA_PREFIX
export PATH=$CUDA_HOME/bin:$PATH
export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/python3.10/site-packages/torch/lib:$LD_LIBRARY_PATH
export PYTHONPATH=${PYTHONPATH:-}:$(pwd)

echo "============================================"
echo "Activated conda environment: $CONDA_DEFAULT_ENV"
echo "Ckpt: jingheya/lotus-depth-g-v1-0 (depth output, generation mode)"
echo "============================================"

nvidia-smi
python -c "import torch; print('CUDA:', torch.cuda.is_available(), torch.cuda.get_device_name(0) if torch.cuda.is_available() else '')"

TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
REPO=/home/ywan0794/EvalMDE/Lotus
PRETRAINED=jingheya/lotus-depth-g-v1-0
CONFIG=/home/ywan0794/MoGe/configs/eval/all_benchmarks.json
OUT_DIR=eval_output
mkdir -p $OUT_DIR

echo "============================================"
echo "Starting MoGe Eval for Lotus v1-0 (depth ckpt) at $(date)"
echo "Repo: $REPO"
echo "Checkpoint: $PRETRAINED"
echo "Config: $CONFIG"
echo "============================================"

# Paper-canonical from Lotus eval.sh: generation mode, fp16, seed=42, timestep=999.
# NO --disparity flag (v1-0 outputs depth, not disparity).
# Wrapper auto-emits `depth_affine_invariant` when --disparity is absent.
python moge/scripts/eval_baseline.py \
    --baseline baselines/lotus.py \
    --config $CONFIG \
    --output ${OUT_DIR}/lotus_v1_${TIMESTAMP}.json \
    --repo $REPO \
    --pretrained $PRETRAINED \
    --mode generation \
    --task_name depth \
    --timestep 999 \
    --fp16 \
    --seed 42

echo "============================================"
echo "Evaluation completed at $(date)"
echo "============================================"