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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 | #!/bin/bash
#SBATCH --job-name=mod_ablation
#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=${PULSE_ROOT}/results/modality_ablation_%j.log
# Modality Ablation Matrix for Scene Recognition (Exp1)
# 7 configs: 3 single + 3 two-modal + 1 three-modal (already done)
# All use Transformer backbone, hidden_dim=128, 5 seeds
# Single modality: early fusion
# Multi modality: late fusion + pretrained strongest branch
set -e
export PYTHONUNBUFFERED=1
PYTHON=python
BASEDIR=${PULSE_ROOT}
SCRIPT=${BASEDIR}/experiments/train_exp1.py
OUTDIR=${BASEDIR}/results/modality_ablation
mkdir -p $OUTDIR
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 $OUTDIR"
SEEDS=(42 123 456 789 2024)
# Pretrained single-modality backbones (seed=42, from v7/v8)
PT_IMU=${BASEDIR}/results/exp1_v7/transformer_imu_early/model_best.pt
PT_MOCAP=${BASEDIR}/results/exp1_v8/transformer_mocap_early/model_best.pt
PT_EMG=${BASEDIR}/results/exp1_v7/transformer_emg_early/model_best.pt
echo "=== Modality Ablation Matrix ==="
echo "Output: $OUTDIR"
# ============================================================
# GPU 0: Single modality (mocap, emg) + two-modal (mocap+emg)
# ============================================================
(
export CUDA_VISIBLE_DEVICES=0
# --- Phase 0: Single modality × 5 seeds ---
echo "--- GPU0: Single modality mocap ---"
for seed in "${SEEDS[@]}"; do
echo " mocap seed=$seed"
$PYTHON $SCRIPT --modalities mocap --fusion early --seed $seed \
--tag ablation_s${seed} $COMMON 2>&1 | tail -5
done
echo "--- GPU0: Single modality emg ---"
for seed in "${SEEDS[@]}"; do
echo " emg seed=$seed"
$PYTHON $SCRIPT --modalities emg --fusion early --seed $seed \
--tag ablation_s${seed} $COMMON 2>&1 | tail -5
done
# --- Phase 1: Two-modal mocap+emg / late+pretrained(emg) ---
# modalities=mocap,emg → idx0=mocap, idx1=emg → pretrain emg (idx=1)
echo "--- GPU0: mocap+emg late+pretrained ---"
for seed in "${SEEDS[@]}"; do
echo " mocap+emg seed=$seed"
$PYTHON $SCRIPT --modalities mocap,emg --fusion late --seed $seed \
--pretrained_backbone $PT_EMG --freeze_backbone_idx 1 \
--tag ablation_pt_s${seed} $COMMON 2>&1 | tail -5
done
echo "--- GPU0 Done ---"
) &
PID0=$!
# ============================================================
# GPU 1: Two-modal (mocap+imu, emg+imu)
# ============================================================
(
export CUDA_VISIBLE_DEVICES=1
# --- mocap+imu / late+pretrained(imu) ---
# modalities=mocap,imu → idx0=mocap, idx1=imu → pretrain imu (idx=1)
echo "--- GPU1: mocap+imu late+pretrained ---"
for seed in "${SEEDS[@]}"; do
echo " mocap+imu seed=$seed"
$PYTHON $SCRIPT --modalities mocap,imu --fusion late --seed $seed \
--pretrained_backbone $PT_IMU --freeze_backbone_idx 1 \
--tag ablation_pt_s${seed} $COMMON 2>&1 | tail -5
done
# --- emg+imu / late+pretrained(imu) ---
# modalities=emg,imu → idx0=emg, idx1=imu → pretrain imu (idx=1)
echo "--- GPU1: emg+imu late+pretrained ---"
for seed in "${SEEDS[@]}"; do
echo " emg+imu seed=$seed"
$PYTHON $SCRIPT --modalities emg,imu --fusion late --seed $seed \
--pretrained_backbone $PT_IMU --freeze_backbone_idx 1 \
--tag ablation_pt_s${seed} $COMMON 2>&1 | tail -5
done
echo "--- GPU1 Done ---"
) &
PID1=$!
wait $PID0 $PID1
# ============================================================
# Collect results
# ============================================================
echo ""
echo "=== Results Summary ==="
$PYTHON -c "
import json, os, numpy as np
base = '$OUTDIR'
configs = [
('mocap / early', 'transformer_mocap_early_ablation_s{}'),
('emg / early', 'transformer_emg_early_ablation_s{}'),
('imu / early', None), # from v8_multiseed
('mocap+emg / late+pt', 'transformer_mocap-emg_late_ablation_pt_s{}'),
('mocap+imu / late+pt', 'transformer_mocap-imu_late_ablation_pt_s{}'),
('emg+imu / late+pt', 'transformer_emg-imu_late_ablation_pt_s{}'),
('mocap+emg+imu / late+pt', None), # from v9
]
seeds = [42, 123, 456, 789, 2024]
v8_base = '${BASEDIR}/results/exp1_v8_multiseed'
v9_base = '${BASEDIR}/results/exp1_v9'
print(f'{\"Config\":<30} {\"F1 (mean±std)\":<20} {\"Acc (mean±std)\":<20} N')
print('-' * 75)
for label, pattern in configs:
f1s, accs = [], []
for s in seeds:
if label == 'imu / early':
path = os.path.join(v8_base, f'transformer_imu_early_s{s}', 'results.json')
elif label == 'mocap+emg+imu / late+pt':
path = os.path.join(v9_base, f'transformer_imu-mocap-emg_late_pt_s{s}', 'results.json')
else:
path = os.path.join(base, pattern.format(s), 'results.json')
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 ==="
|