eval_scripts/smoke_evalmde_slurm.sh

#!/bin/bash
#SBATCH --job-name=evalmde-smoke
#SBATCH --output=/home/ywan0794/EvalMDE/smoke_evalmde_%j.log
#SBATCH --error=/home/ywan0794/EvalMDE/smoke_evalmde_%j.log
#SBATCH --open-mode=append
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=4
#SBATCH --gres=gpu:H100:1
#SBATCH --time=0-00:30:00
#SBATCH --mem=40G
#SBATCH --nodelist=erinyes
# Smoke test: Depth Pro inference on EvalMDE sample_data + sample_data_2,
# then compute_metrics in evalmde env. End-to-end pipeline verification.

set -u
export PYTHONUNBUFFERED=1
cd /home/ywan0794/EvalMDE
source /home/ywan0794/miniconda3/etc/profile.d/conda.sh

DATA=/home/ywan0794/EvalMDE/data/smoke
OUT=/home/ywan0794/EvalMDE/output/smoke
mkdir -p $OUT

echo "=== Smoke step 1: depth_pro inference ==="
conda activate depth-pro
export PYTHONPATH=/home/ywan0794/EvalMDE:/home/ywan0794/MoGe:${PYTHONPATH:-}
python scripts/run_inference.py \
    --baseline baselines/depth_pro.py \
    --data-root $DATA \
    --output-root $OUT \
    --model-name depth_pro \
    --repo /home/ywan0794/EvalMDE/ml-depth-pro \
    --checkpoint /home/ywan0794/EvalMDE/ml-depth-pro/checkpoints/depth_pro.pt \
    --precision fp32

echo "=== Smoke step 2: compute_metrics in evalmde env ==="
conda deactivate; conda activate evalmde
python scripts/compute_metrics.py \
    --gt-root   $DATA \
    --pred-root $OUT \
    --model-name depth_pro \
    --output    $OUT/depth_pro_metrics.json

echo "=== Smoke summary ==="
cat $OUT/depth_pro_metrics.json