File size: 2,611 Bytes
45b0ed8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | #!/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 "============================================"
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