SignX/eval/simple_benchmark.sh

#!/bin/bash
# 简单的效率基准测试 - 测量真实的推理时间和功耗
set -e

GREEN='\033[0;32m'
BLUE='\033[0;34m'
YELLOW='\033[1;33m'
NC='\033[0m'

SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
PROJECT_ROOT="$(dirname "$SCRIPT_DIR")"
OUTPUT_DIR="${PROJECT_ROOT}/benchmark_results"
mkdir -p "$OUTPUT_DIR"

echo ""
echo "======================================================================"
echo "  SignX Efficiency Benchmark (Simple Version)"
echo "======================================================================"
echo ""

# 激活conda
CONDA_BASE=$(conda info --base 2>/dev/null || echo "")
source "${CONDA_BASE}/etc/profile.d/conda.sh"

# ============================================================
# 1. Latent-only: 只测量 SLTUNET 推理时间
# ============================================================
echo -e "${BLUE}[1/2] Benchmarking Latent-only (SLTUNET only)${NC}"
echo ""

conda activate slt_tf1
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python

# 创建配置文件（使用benchmark专用配置，禁用pose assistance和详细输出）
cat > /tmp/latent_only_config.py <<'EOF'
{
    'sign_cfg': 'smkd/asllrp_baseline_benchmark.yaml',
    'gloss_path': 'smkd/asllrp/gloss_dict.npy',
    'smkd_model_path': 'smkd/work_dir第一次训练的基线/asllrp_smkd/best_model.pt',
    'img_test_file': 'smkd/work_dir第一次训练的基线/asllrp_smkd/test.h5',
    'src_test_file': 'preprocessed-asllrp/test.bpe.gloss',
    'tgt_test_file': 'preprocessed-asllrp/test.bpe.gloss',
    'src_vocab_file': 'preprocessed-asllrp/vocab.asllrp',
    'tgt_vocab_file': 'preprocessed-asllrp/vocab.asllrp',
    'src_codes': 'preprocessed-asllrp/asllrp.bpe',
    'tgt_codes': 'preprocessed-asllrp/asllrp.bpe',
    'output_dir': 'checkpoints_asllrp第一次训练的基线',
    'test_output': '/tmp/latent_only_output.txt',
    'eval_batch_size': 10,
    'gpus': [0],
    'remove_bpe': True,
    'collect_attention_weights': False,  # 禁用attention收集以加速基准测试
}
EOF

echo "Running latent-only inference..."

# 记录GPU功耗（后台进程）
nvidia-smi --query-gpu=power.draw --format=csv,noheader,nounits -l 1 > /tmp/power_latent.log &
POWER_PID=$!

# 测量推理时间
START=$(date +%s.%N)
cd "$PROJECT_ROOT"
python run.py --mode test --config /tmp/latent_only_config.py 2>&1 | grep -E "(BLEU|Evaluating)" || true
END=$(date +%s.%N)

# 停止功耗监控
kill $POWER_PID 2>/dev/null || true

# 计算结果
LATENT_TIME=$(echo "$END - $START" | bc)
LATENT_POWER=$(awk '{ sum += $1; n++ } END { if (n > 0) print sum / n }' /tmp/power_latent.log)

# 计算FPS（使用test集的样本数）
NUM_SAMPLES=$(wc -l < "$PROJECT_ROOT/preprocessed-asllrp/test.bpe.gloss")
LATENT_FPS=$(echo "scale=2; $NUM_SAMPLES / $LATENT_TIME" | bc)

echo -e "${GREEN}✓ Latent-only完成${NC}"
echo "  推理时间: ${LATENT_TIME}s"
echo "  平均功耗: ${LATENT_POWER}W"
echo "  FPS: $LATENT_FPS"
echo ""

# ============================================================
# 2. SMKD Feature Extraction: 测量视频特征提取时间
# ============================================================
echo -e "${BLUE}[2/3] Benchmarking SMKD Feature Extraction${NC}"
echo ""

# 运行 SMKD 基准测试脚本
if [ -f "$SCRIPT_DIR/benchmark_smkd.sh" ]; then
    bash "$SCRIPT_DIR/benchmark_smkd.sh" 2>&1 | grep -E "(FPS|Power|Time)" | tail -3 > /tmp/smkd_results.txt

    # 提取结果
    SMKD_FPS=$(grep "FPS:" /tmp/smkd_results.txt | awk '{print $2}')
    SMKD_POWER=$(grep "Power:" /tmp/smkd_results.txt | awk '{print $2}' | sed 's/W//')

    echo -e "${GREEN}✓ SMKD Feature Extraction完成${NC}"
    echo "  FPS: $SMKD_FPS"
    echo "  功耗: ${SMKD_POWER}W"
    echo ""
else
    echo "Warning: benchmark_smkd.sh not found, skipping SMKD test"
    SMKD_FPS="N/A"
    SMKD_POWER="N/A"
fi

# ============================================================
# 3. Full Pipeline: 测量 inference.sh 的总时间
# ============================================================
echo -e "${BLUE}[3/3] Benchmarking Full Pipeline (SMKD + SLTUNET)${NC}"
echo ""

TEST_VIDEO="${PROJECT_ROOT}/eval/tiny_test_data/videos/666.mp4"

if [ ! -f "$TEST_VIDEO" ]; then
    echo "Warning: Test video not found, skipping full pipeline test"
else
    echo "Running full pipeline inference..."

    # 记录GPU功耗
    nvidia-smi --query-gpu=power.draw --format=csv,noheader,nounits -l 1 > /tmp/power_full.log &
    POWER_PID=$!

    # 测量推理时间
    START=$(date +%s.%N)
    cd "$PROJECT_ROOT"
    bash inference.sh "$TEST_VIDEO" /tmp/full_pipeline_output.txt 2>&1 | grep -E "(完成|BLEU)" || true
    END=$(date +%s.%N)

    # 停止功耗监控
    kill $POWER_PID 2>/dev/null || true

    # 计算结果
    FULL_TIME=$(echo "$END - $START" | bc)
    FULL_POWER=$(awk '{ sum += $1; n++ } END { if (n > 0) print sum / n }' /tmp/power_full.log)
    FULL_FPS=$(echo "scale=2; 1 / $FULL_TIME" | bc)  # 单个视频

    echo -e "${GREEN}✓ Full Pipeline完成${NC}"
    echo "  推理时间: ${FULL_TIME}s"
    echo "  平均功耗: ${FULL_POWER}W"
    echo "  FPS: $FULL_FPS"
    echo ""
fi

# ============================================================
# 4. 生成LaTeX表格
# ============================================================
echo -e "${BLUE}[4/4] Generating LaTeX Table${NC}"
echo ""

cat > "${OUTPUT_DIR}/efficiency_comparison_table.tex" <<EOF
\begin{table}[t]
\centering
\caption{\textbf{Inference Efficiency on ASLLRP:} SignX achieves real-time performance by operating in latent space.}
\label{tab:efficiency}
\begin{tabular}{lcc}
\toprule
Method & FPS \$\\uparrow\$ & Power (W) \$\\downarrow\$ \\\\
\midrule
SignX (Full Pipeline) & ${FULL_FPS:-N/A} & ${FULL_POWER:-N/A} \\\\
SignX (SMKD Feature Extraction) & ${SMKD_FPS:-N/A} & ${SMKD_POWER:-N/A} \\\\
SignX (Latent-only) & $LATENT_FPS & $LATENT_POWER \\\\
\bottomrule
\end{tabular}
\end{table}
EOF

echo "======================================================================"
echo "  Benchmark Results"
echo "======================================================================"
echo ""
echo "Configuration                      | FPS      | Power (W)"
echo "-----------------------------------|----------|----------"
echo "Full Pipeline                      | ${FULL_FPS:-N/A}      | ${FULL_POWER:-N/A}"
echo "SMKD Feature Extraction (视频→特征) | ${SMKD_FPS:-N/A}      | ${SMKD_POWER:-N/A}"
echo "Latent-only (特征→gloss)           | $LATENT_FPS      | $LATENT_POWER"
echo ""
echo -e "${GREEN}✓ LaTeX table saved to: ${OUTPUT_DIR}/efficiency_comparison_table.tex${NC}"
echo ""

# 清理
rm -f /tmp/latent_only_config.py /tmp/power_*.log /tmp/latent_only_output.txt /tmp/full_pipeline_output.txt /tmp/smkd_results.txt
rm -rf /tmp/detailed_*  # 删除任何详细输出目录

echo -e "${GREEN}✓ Benchmark complete!${NC}"
echo ""