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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 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 | #!/bin/bash
#SBATCH --partition=gpuA800
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --gres=gpu:2
#SBATCH --mem=64G
#SBATCH --time=12:00:00
#SBATCH --job-name=PubBaselines
#SBATCH --output=${PULSE_ROOT}/results/published_baselines_%j.log
# Published Baselines for DailyAct-5M
# ASFormer (Yi et al., BMVC 2021) - Temporal Segmentation & Contact Detection
# TinyHAR (Zhou et al., ISWC 2022 Best Paper) - Scene Recognition
set -e
PYTHON=python
PROJECT=${PULSE_ROOT}
cd $PROJECT
EXP1_OUT=$PROJECT/results/published_baselines/exp1_tinyhar
EXP2_OUT=$PROJECT/results/published_baselines/exp2_asformer
EXP3_OUT=$PROJECT/results/published_baselines/exp3_asformer
mkdir -p $EXP1_OUT $EXP2_OUT $EXP3_OUT
echo "=========================================="
echo "Published Baselines - $(date)"
echo "=========================================="
# ============================================================
# Group 1: TinyHAR for Scene Recognition (Exp 1)
# Run on GPU 0
# ============================================================
(
export CUDA_VISIBLE_DEVICES=0
echo ""
echo "=== [GPU0] Exp1: TinyHAR Scene Recognition ==="
# Single modalities
for MOD in imu mocap emg eyetrack pressure; do
echo "--- TinyHAR / ${MOD} / early ---"
$PYTHON experiments/train_exp1.py \
--model tinyhar --modalities $MOD --fusion early \
--hidden_dim 32 --epochs 100 --batch_size 16 \
--lr 1e-3 --weight_decay 1e-3 --downsample 5 \
--seed 42 --output_dir $EXP1_OUT \
--tag published 2>&1 | tail -5
done
# Best multi-modal combos
for MOD in "emg,imu" "mocap,emg,imu" "mocap,emg,eyetrack,imu"; do
echo "--- TinyHAR / ${MOD} / early ---"
$PYTHON experiments/train_exp1.py \
--model tinyhar --modalities $MOD --fusion early \
--hidden_dim 32 --epochs 100 --batch_size 16 \
--lr 1e-3 --weight_decay 1e-3 --downsample 5 \
--seed 42 --output_dir $EXP1_OUT \
--tag published 2>&1 | tail -5
done
# TinyHAR with late fusion (emg + imu)
for FUSION in late weighted_late feat_concat; 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 $EXP1_OUT \
--tag published 2>&1 | tail -5
done
echo "[GPU0] TinyHAR experiments complete."
) &
PID_GPU0=$!
# ============================================================
# Group 2: ASFormer for Segmentation (Exp 2) + Contact (Exp 3)
# Run on GPU 1
# ============================================================
(
export CUDA_VISIBLE_DEVICES=1
echo ""
echo "=== [GPU1] Exp2: ASFormer Temporal Segmentation ==="
# Key modality combinations
for MOD in mocap emg "mocap,emg,eyetrack" "mocap,emg,eyetrack,imu" "mocap,emg,eyetrack,imu,pressure"; do
echo "--- ASFormer / ${MOD} ---"
$PYTHON experiments/train_exp2.py \
--model asformer --modalities $MOD \
--hidden_dim 64 --epochs 80 --batch_size 16 \
--lr 5e-4 --weight_decay 1e-4 --downsample 2 \
--seed 42 --output_dir $EXP2_OUT 2>&1 | tail -5
done
echo ""
echo "=== [GPU1] Exp3: ASFormer Contact Detection ==="
# Key modality combinations
for MOD in mocap emg imu "mocap,emg" "mocap,emg,eyetrack" "mocap,emg,eyetrack,imu"; do
echo "--- ASFormer / ${MOD} ---"
$PYTHON experiments/train_exp3.py \
--model asformer --modalities $MOD \
--hidden_dim 64 --epochs 50 --batch_size 32 \
--lr 1e-3 --weight_decay 1e-4 --downsample 2 \
--seed 42 --output_dir $EXP3_OUT 2>&1 | tail -5
done
echo "[GPU1] ASFormer experiments complete."
) &
PID_GPU1=$!
# Wait for both GPU groups
wait $PID_GPU0
wait $PID_GPU1
echo ""
echo "=========================================="
echo "All published baseline experiments complete - $(date)"
echo "=========================================="
# ============================================================
# Collect results summary
# ============================================================
echo ""
echo "=== Results Summary ==="
echo ""
echo "--- Exp1: TinyHAR Scene Recognition ---"
for f in $EXP1_OUT/*/results.json; do
if [ -f "$f" ]; then
$PYTHON -c "
import json
with open('$f') as fp:
r = json.load(fp)
mods = ','.join(r.get('modalities', []))
fus = r.get('fusion', 'early')
f1 = r.get('test_macro_f1', 0)
acc = r.get('test_accuracy', 0)
print(f' TinyHAR | {mods:<30} | {fus:<12} | F1={f1:.4f} Acc={acc:.4f}')
"
fi
done
echo ""
echo "--- Exp2: ASFormer Temporal Segmentation ---"
for f in $EXP2_OUT/*/results.json; do
if [ -f "$f" ]; then
$PYTHON -c "
import json
with open('$f') as fp:
r = json.load(fp)
mods = ','.join(r.get('modalities', []))
m = r.get('test_metrics', {})
print(f' ASFormer | {mods:<35} | Acc={m.get(\"frame_acc\",0):.4f} F1={m.get(\"frame_f1\",0):.4f} Seg@50={m.get(\"seg_f1@50\",0):.4f}')
"
fi
done
echo ""
echo "--- Exp3: ASFormer Contact Detection ---"
for f in $EXP3_OUT/*/results.json; do
if [ -f "$f" ]; then
$PYTHON -c "
import json
with open('$f') as fp:
r = json.load(fp)
mods = ','.join(r.get('input_modalities', []))
m = r.get('test_metrics', {})
print(f' ASFormer | {mods:<30} | R_F1={m.get(\"right_f1\",0):.4f} L_F1={m.get(\"left_f1\",0):.4f} Avg_F1={m.get(\"avg_f1\",0):.4f}')
"
fi
done
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