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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 | #!/bin/bash
#SBATCH --job-name=ablation_fuse
#SBATCH --partition=gpuA800
#SBATCH --gres=gpu:2
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --mem=64G
#SBATCH --time=4:00:00
#SBATCH --output=${PULSE_ROOT}/results/ablation_fusion_%j.log
# Test confidence-weighted and learned-weight fusion on all multi-modal combos
# Compare against existing mean fusion results
set -e
export PYTHONUNBUFFERED=1
PYTHON=python
BASEDIR=${PULSE_ROOT}
SCRIPT=${BASEDIR}/experiments/train_exp1.py
OUTDIR=${BASEDIR}/results/modality_ablation
COMMON="--model transformer --epochs 100 --batch_size 16 --lr 1e-3 --weight_decay 1e-4 --hidden_dim 128 --downsample 5 --patience 15 --proj_dim 0 --output_dir $OUTDIR"
SEEDS=(42 123 456 789 2024)
PT_IMU=${BASEDIR}/results/exp1_v7/transformer_imu_early/model_best.pt
PT_MOCAP=${BASEDIR}/results/exp1_v8/transformer_mocap_early/model_best.pt
echo "=== Ablation: Confidence & Learned Fusion ==="
# ============================================================
# GPU 0: confidence-weighted fusion
# ============================================================
(
export CUDA_VISIBLE_DEVICES=0
# mocap+imu / confidence / pretrained imu (idx=1)
echo "--- GPU0: mocap+imu / confidence ---"
for seed in "${SEEDS[@]}"; do
echo " mocap+imu confidence seed=$seed"
$PYTHON $SCRIPT --modalities mocap,imu --fusion late --late_agg confidence \
--seed $seed --pretrained_backbone $PT_IMU --freeze_backbone_idx 1 \
--tag ablation_conf_s${seed} $COMMON 2>&1 | tail -3
done
# emg+imu / confidence / pretrained imu (idx=1)
echo "--- GPU0: emg+imu / confidence ---"
for seed in "${SEEDS[@]}"; do
echo " emg+imu confidence seed=$seed"
$PYTHON $SCRIPT --modalities emg,imu --fusion late --late_agg confidence \
--seed $seed --pretrained_backbone $PT_IMU --freeze_backbone_idx 1 \
--tag ablation_conf_s${seed} $COMMON 2>&1 | tail -3
done
# mocap+emg / confidence / pretrained mocap (idx=0)
echo "--- GPU0: mocap+emg / confidence ---"
for seed in "${SEEDS[@]}"; do
echo " mocap+emg confidence seed=$seed"
$PYTHON $SCRIPT --modalities mocap,emg --fusion late --late_agg confidence \
--seed $seed --pretrained_backbone $PT_MOCAP --freeze_backbone_idx 0 \
--tag ablation_conf_s${seed} $COMMON 2>&1 | tail -3
done
# mocap+emg+imu / confidence / pretrained imu (idx=2, modalities=mocap,emg,imu)
echo "--- GPU0: mocap+emg+imu / confidence ---"
for seed in "${SEEDS[@]}"; do
echo " mocap+emg+imu confidence seed=$seed"
$PYTHON $SCRIPT --modalities imu,mocap,emg --fusion late --late_agg confidence \
--seed $seed --pretrained_backbone $PT_IMU --freeze_backbone_idx 0 \
--tag ablation_conf_s${seed} $COMMON 2>&1 | tail -3
done
echo "--- GPU0 Done ---"
) &
PID0=$!
# ============================================================
# GPU 1: learned-weight fusion
# ============================================================
(
export CUDA_VISIBLE_DEVICES=1
# mocap+imu / learned / pretrained imu (idx=1)
echo "--- GPU1: mocap+imu / learned ---"
for seed in "${SEEDS[@]}"; do
echo " mocap+imu learned seed=$seed"
$PYTHON $SCRIPT --modalities mocap,imu --fusion late --late_agg learned \
--seed $seed --pretrained_backbone $PT_IMU --freeze_backbone_idx 1 \
--tag ablation_lrn_s${seed} $COMMON 2>&1 | tail -3
done
# emg+imu / learned / pretrained imu (idx=1)
echo "--- GPU1: emg+imu / learned ---"
for seed in "${SEEDS[@]}"; do
echo " emg+imu learned seed=$seed"
$PYTHON $SCRIPT --modalities emg,imu --fusion late --late_agg learned \
--seed $seed --pretrained_backbone $PT_IMU --freeze_backbone_idx 1 \
--tag ablation_lrn_s${seed} $COMMON 2>&1 | tail -3
done
# mocap+emg / learned / pretrained mocap (idx=0)
echo "--- GPU1: mocap+emg / learned ---"
for seed in "${SEEDS[@]}"; do
echo " mocap+emg learned seed=$seed"
$PYTHON $SCRIPT --modalities mocap,emg --fusion late --late_agg learned \
--seed $seed --pretrained_backbone $PT_MOCAP --freeze_backbone_idx 0 \
--tag ablation_lrn_s${seed} $COMMON 2>&1 | tail -3
done
# mocap+emg+imu / learned / pretrained imu (idx=0, modalities=imu,mocap,emg)
echo "--- GPU1: mocap+emg+imu / learned ---"
for seed in "${SEEDS[@]}"; do
echo " mocap+emg+imu learned seed=$seed"
$PYTHON $SCRIPT --modalities imu,mocap,emg --fusion late --late_agg learned \
--seed $seed --pretrained_backbone $PT_IMU --freeze_backbone_idx 0 \
--tag ablation_lrn_s${seed} $COMMON 2>&1 | tail -3
done
echo "--- GPU1 Done ---"
) &
PID1=$!
wait $PID0 $PID1
# ============================================================
# Collect results
# ============================================================
echo ""
echo "=== Fusion Comparison ==="
$PYTHON -c "
import json, os, numpy as np
base = '$OUTDIR'
v8_base = '${BASEDIR}/results/exp1_v8_multiseed'
v9_base = '${BASEDIR}/results/exp1_v9'
seeds = [42, 123, 456, 789, 2024]
configs = [
# (label, pattern_template)
# mean (from previous ablation run)
('mocap+imu / mean', base + '/transformer_mocap-imu_late_ablation_pt_s{}/results.json'),
('mocap+imu / confidence', base + '/transformer_mocap-imu_late_ablation_conf_s{}/results.json'),
('mocap+imu / learned', base + '/transformer_mocap-imu_late_ablation_lrn_s{}/results.json'),
('emg+imu / mean', base + '/transformer_emg-imu_late_ablation_pt_s{}/results.json'),
('emg+imu / confidence', base + '/transformer_emg-imu_late_ablation_conf_s{}/results.json'),
('emg+imu / learned', base + '/transformer_emg-imu_late_ablation_lrn_s{}/results.json'),
('mocap+emg / mean', base + '/transformer_mocap-emg_late_ablation_pt_s{}/results.json'),
('mocap+emg / confidence', base + '/transformer_mocap-emg_late_ablation_conf_s{}/results.json'),
('mocap+emg / learned', base + '/transformer_mocap-emg_late_ablation_lrn_s{}/results.json'),
('3mod / mean', v9_base + '/transformer_imu-mocap-emg_late_pt_s{}/results.json'),
('3mod / confidence', base + '/transformer_imu-mocap-emg_late_ablation_conf_s{}/results.json'),
('3mod / learned', base + '/transformer_imu-mocap-emg_late_ablation_lrn_s{}/results.json'),
]
print(f'{\"Config\":<30} {\"F1 (mean±std)\":<20} {\"Acc (mean±std)\":<20} N')
print('-' * 75)
for label, pat in configs:
f1s, accs = [], []
for s in seeds:
path = pat.format(s)
if os.path.exists(path):
with open(path) as f:
d = json.load(f)
f1s.append(d['test_macro_f1'])
accs.append(d['test_accuracy'])
if f1s:
f1 = np.array(f1s)
acc = np.array(accs)
print(f'{label:<30} {f1.mean():.3f}±{f1.std():.3f} {acc.mean():.3f}±{acc.std():.3f} {len(f1s)}')
else:
print(f'{label:<30} (no results)')
"
echo ""
echo "=== All done ==="
|