experiments/slurm/run_pub_extra.sh

#!/bin/bash
#SBATCH --partition=gpuA800
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=4
#SBATCH --gres=gpu:1
#SBATCH --mem=32G
#SBATCH --time=6:00:00
#SBATCH --job-name=PubExtra
#SBATCH --output=${PULSE_ROOT}/results/pub_extra_%j.log

# Extra published baseline experiments:
# 1. TinyHAR with more modality combos & fusion for scene recognition
# 2. TinyHAR for all 5 modalities
set -e
PYTHON=python
PROJECT=${PULSE_ROOT}
cd $PROJECT
OUT1=$PROJECT/results/published_baselines/exp1_tinyhar_extra
mkdir -p $OUT1

echo "=== TinyHAR Extra Experiments ==="

# More fusion strategies for emg+imu
for FUSION in attention gated_late stacking product moe; do
    echo "--- TinyHAR / emg,imu / ${FUSION} ---"
    $PYTHON experiments/train_exp1.py \
        --model tinyhar --modalities emg,imu --fusion $FUSION \
        --hidden_dim 32 --epochs 100 --batch_size 16 \
        --lr 1e-3 --weight_decay 1e-3 --downsample 5 \
        --seed 42 --output_dir $OUT1 \
        --tag extra 2>&1 | tail -3
done

# More modality combos with late fusion
for MOD in "mocap,imu" "mocap,emg,eyetrack" "mocap,emg,eyetrack,imu,pressure"; do
    echo "--- TinyHAR / ${MOD} / late ---"
    $PYTHON experiments/train_exp1.py \
        --model tinyhar --modalities $MOD --fusion late \
        --hidden_dim 32 --epochs 100 --batch_size 16 \
        --lr 1e-3 --weight_decay 1e-3 --downsample 5 \
        --seed 42 --output_dir $OUT1 \
        --tag extra 2>&1 | tail -3
done

echo "=== Done ==="