eval_scripts/eval_all_slurm.sh

#!/bin/bash
#SBATCH --job-name=eval-all
#SBATCH --output=/home/ywan0794/MoGe/eval_all_%j.log
#SBATCH --error=/home/ywan0794/MoGe/eval_all_%j.log
#SBATCH --open-mode=append
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=4
#SBATCH --gres=gpu:H100:1
#SBATCH --time=0-12:00:00
#SBATCH --mem=80G
#SBATCH --nodelist=erinyes
# Single sbatch — production run for 7 models on all 10 MoGe benchmarks, serial,
# one H100 held the whole time. Failures don't abort; we log & continue.
# Model order: cheap → expensive (FE2E last so it doesn't block others if it crashes).

export PYTHONUNBUFFERED=1
cd /home/ywan0794/MoGe

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

TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
CONFIG=/home/ywan0794/MoGe/configs/eval/all_benchmarks.json
CONFIG_FE2E=/home/ywan0794/MoGe/configs/eval/fe2e_all_benchmarks.json
OUT_DIR=eval_output
mkdir -p $OUT_DIR

SUMMARY=$OUT_DIR/_eval_all_${TIMESTAMP}.summary.txt
: > $SUMMARY

echo "============================================"
echo "eval-all started at $(date)"
echo "Config (main): $CONFIG"
echo "Config (fe2e): $CONFIG_FE2E"
echo "TIMESTAMP: $TIMESTAMP"
echo "Summary file: $SUMMARY"
echo "============================================"
nvidia-smi

run_model() {
  # Usage: run_model <label> <env> <config> <python invocation ...>
  local label=$1 env=$2 cfg=$3
  shift 3
  echo
  echo "============================================"
  echo "[$label] starting at $(date)  (conda env: $env)"
  echo "============================================"
  conda deactivate 2>/dev/null || true
  conda activate $env
  echo "Active env: $CONDA_DEFAULT_ENV"
  export PYTHONPATH=$PYTHONPATH:$(pwd)
  python -c "import torch; print('CUDA:', torch.cuda.is_available(), torch.cuda.get_device_name(0) if torch.cuda.is_available() else '')"

  local OUTFILE=$OUT_DIR/${label}_${TIMESTAMP}.json

  if "$@" \
        --baseline baselines/${label}.py \
        --config $cfg \
        --output $OUTFILE; then
    if [ -f $OUTFILE ]; then
      local SIZE=$(stat -c%s $OUTFILE 2>/dev/null)
      echo "[OK] $label -> $OUTFILE (${SIZE} bytes) at $(date)" | tee -a $SUMMARY
    else
      echo "[NO-OUTPUT] $label (exited 0 but no JSON) at $(date)" | tee -a $SUMMARY
    fi
  else
    rc=$?
    echo "[FAIL rc=$rc] $label at $(date)" | tee -a $SUMMARY
  fi
}

# ============================================
# 1) DA3-Mono — SKIPPED, already done in eval_output/da3_mono_20260514_010406.json
# ============================================
# REPO=/home/ywan0794/EvalMDE/Depth-Anything-3
# HF_ID=depth-anything/DA3MONO-LARGE
# run_model da3_mono da3 $CONFIG \
#   python moge/scripts/eval_baseline.py \
#         --repo $REPO --hf_id $HF_ID

# ============================================
# 2) Depth Pro — SKIPPED, already done in eval_output/depth_pro_20260514_010406.json
# ============================================
# REPO=/home/ywan0794/EvalMDE/ml-depth-pro
# CKPT=$REPO/checkpoints/depth_pro.pt
# run_model depth_pro depth-pro $CONFIG \
#   python moge/scripts/eval_baseline.py \
#         --repo $REPO --checkpoint $CKPT --precision fp32

# ============================================
# 3) Marigold v1.1 (env: marigold) — paper-canonical via
#    `script/depth/eval/11_infer_nyu.sh`: v1-1 + denoise=1 + ensemble=10 + seed=1234.
#    v1-1 retrained to match v1-0's denoise=50 quality at denoise=1.
# ============================================
REPO=/home/ywan0794/EvalMDE/Marigold
CHECKPOINT=prs-eth/marigold-depth-v1-1
run_model marigold marigold $CONFIG \
  python moge/scripts/eval_baseline.py \
        --repo $REPO --checkpoint $CHECKPOINT \
        --denoise_steps 4 --ensemble_size 1

# ============================================
# 4) Lotus (env: lotus) — paper-canonical eval.sh:
#    generative v2-1-disparity + half_precision + seed=42.
# ============================================
REPO=/home/ywan0794/EvalMDE/Lotus
PRETRAINED=jingheya/lotus-depth-g-v2-1-disparity
run_model lotus lotus $CONFIG \
  python moge/scripts/eval_baseline.py \
        --repo $REPO --pretrained $PRETRAINED --mode generation \
        --task_name depth --disparity --timestep 999 --fp16 --seed 42

# ============================================
# 5) DepthMaster (env: depthmaster)
# ============================================
REPO=/home/ywan0794/EvalMDE/DepthMaster
CKPT=$REPO/ckpt/eval
run_model depthmaster depthmaster $CONFIG \
  python moge/scripts/eval_baseline.py \
        --repo $REPO --checkpoint $CKPT --processing_res 768

# ============================================
# 6) PPD (env: ppd) — needs DA2 vitl semantics
# ============================================
REPO=/home/ywan0794/EvalMDE/Pixel-Perfect-Depth
# Paper-canonical eval.yaml: semantics=MoGe2, ppd_moge.pth, sampling_steps=4
run_model ppd ppd $CONFIG \
  python moge/scripts/eval_baseline.py \
        --repo $REPO --semantics_model MoGe2 \
        --semantics_pth checkpoints/moge2.pt \
        --model_pth checkpoints/ppd_moge.pth --sampling_steps 4

# ============================================
# 7) FE2E (env: fe2e) — slowest, last
# ============================================
REPO=/home/ywan0794/EvalMDE/FE2E
MODEL_PATH=$REPO/pretrain
LORA_PATH=$REPO/lora/LDRN.safetensors
run_model fe2e fe2e $CONFIG_FE2E \
  python moge/scripts/eval_baseline.py \
        --repo $REPO --model_path $MODEL_PATH --lora_path $LORA_PATH \
        --prompt_type empty --single_denoise --cfg_guidance 6.0 --size_level 768

# ============================================
echo
echo "============================================"
echo "eval-all finished at $(date)"
echo "============================================"
echo "=== Summary ==="
cat $SUMMARY