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b4b2877 | 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 | #!/bin/bash
# Round 5: h=128 (keep capacity) + moderate regularization + multiple seeds
# Best of R3 capacity + some anti-overfit from R4
# Also: 3 seeds for the best config to get confidence intervals
PYTHON=python
BASEDIR=${PULSE_ROOT}
TRAIN_SCRIPT=${BASEDIR}/experiments/tasks/train_pred_cls.py
OUTDIR=${BASEDIR}/results/pred_cls5
LOGDIR=${OUTDIR}/slurm_logs
mkdir -p $LOGDIR
# h=128, lr=5e-4, wd=3e-4, dropout=0.3, moderate augment
COMMON="--coarse --use_prev_action --epochs 80 --batch_size 32 --lr 5e-4 --weight_decay 3e-4 --hidden_dim 128 --dropout 0.3 --downsample 5 --patience 20 --augment --noise_std 0.15 --time_mask_ratio 0.12 --label_smoothing 0.1 --output_dir $OUTDIR --window_sec 15.0"
# Top 6 modality combos
MODS=("imu" "emg" "mocap" "emg,imu" "mocap,imu" "mocap,emg,imu")
for mods in "${MODS[@]}"; do
mod_tag=$(echo $mods | tr ',' '-')
sbatch \
-J "pcls5_${mod_tag}" \
-p gpuA800 \
--gres=gpu:1 \
-N 1 -n 1 \
--cpus-per-task=4 \
--mem=32G \
-t 2:00:00 \
-o "${LOGDIR}/${mod_tag}_s42_%j.out" \
-e "${LOGDIR}/${mod_tag}_s42_%j.err" \
--export=ALL \
--wrap="export PYTHONUNBUFFERED=1; cd ${BASEDIR}; $PYTHON $TRAIN_SCRIPT --modalities $mods --seed 42 $COMMON"
echo "Submitted: $mods seed=42"
done
# 2 extra seeds for emg,imu (best combo) for confidence intervals
for seed in 123 456; do
sbatch \
-J "pcls5_emg-imu_s${seed}" \
-p gpuA800 \
--gres=gpu:1 \
-N 1 -n 1 \
--cpus-per-task=4 \
--mem=32G \
-t 2:00:00 \
-o "${LOGDIR}/emg-imu_s${seed}_%j.out" \
-e "${LOGDIR}/emg-imu_s${seed}_%j.err" \
--export=ALL \
--wrap="export PYTHONUNBUFFERED=1; cd ${BASEDIR}; $PYTHON $TRAIN_SCRIPT --modalities emg,imu --seed $seed $COMMON"
echo "Submitted: emg,imu seed=$seed"
done
echo ""
echo "Total: 8 jobs | h=128, lr=5e-4, dropout=0.3, wd=3e-4"
echo "Results: $OUTDIR"
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