151 lines
4.3 KiB
Bash
151 lines
4.3 KiB
Bash
#!/bin/bash
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# 一键执行剪枝+微调流程
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set -e
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cd /workspace/bevfusion
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echo "========================================================================"
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echo "BEVFusion 模型剪枝+微调 完整流程"
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echo "========================================================================"
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echo ""
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echo "Baseline: Epoch 23"
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echo " - 参数量: 45.72M"
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echo " - NDS: 0.6941, mAP: 0.6446, mIoU: 0.4130"
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echo ""
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echo "目标: 剪枝30% → 32M参数"
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echo "预期: 精度损失<2%"
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echo ""
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# 配置
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CHECKPOINT="runs/enhanced_from_epoch19/epoch_23.pth"
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PRUNED_OUTPUT="pruning_results/bevfusion_pruned_32M.pth"
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TARGET_RATIO=0.70 # 保留70%参数（剪枝30%）
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TIMESTAMP=$(date +%Y%m%d_%H%M%S)
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FINETUNE_DIR="runs/pruned_finetune_${TIMESTAMP}"
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# 检查checkpoint
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if [ ! -f "$CHECKPOINT" ]; then
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echo "错误: Checkpoint不存在: $CHECKPOINT"
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exit 1
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fi
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# 创建输出目录
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mkdir -p pruning_results
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mkdir -p "$FINETUNE_DIR"
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echo "========================================================================"
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echo "阶段1: 模型剪枝（预计15分钟）"
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echo "========================================================================"
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echo ""
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# 执行剪枝
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/opt/conda/bin/python tools/pruning/prune_bevfusion_builtin.py \
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--checkpoint "$CHECKPOINT" \
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--output "$PRUNED_OUTPUT" \
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--target-ratio $TARGET_RATIO \
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2>&1 | tee "pruning_results/pruning_log_${TIMESTAMP}.txt"
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if [ $? -ne 0 ]; then
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echo "错误: 剪枝失败"
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exit 1
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fi
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echo ""
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echo "✅ 剪枝完成！"
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echo " 输出: $PRUNED_OUTPUT"
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echo ""
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# 询问是否继续微调
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echo "========================================================================"
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echo "阶段2: 微调训练（预计12-15小时）"
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echo "========================================================================"
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echo ""
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echo "是否立即开始微调训练？"
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echo ""
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echo "微调配置:"
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echo " - Epochs: 3"
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echo " - 学习率: 5e-6 (很小)"
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echo " - GPU: 8张"
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echo " - 预计时间: 12-15小时"
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echo ""
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echo "选项:"
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echo " [1] 立即开始微调（后台运行）"
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echo " [2] 稍后手动启动"
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echo " [3] 查看剪枝结果后再决定"
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echo ""
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read -p "请选择 [1/2/3]: " choice
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case $choice in
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1)
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echo ""
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echo "启动微调训练..."
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CONFIG="configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/multitask_enhanced_phase1_HIGHRES.yaml"
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# 后台启动微调
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nohup /opt/conda/bin/torchpack dist-run -np 8 /opt/conda/bin/python tools/train.py \
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"$CONFIG" \
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--load_from "$PRUNED_OUTPUT" \
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--run-dir "$FINETUNE_DIR" \
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--cfg-options \
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max_epochs=3 \
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optimizer.lr=5.0e-6 \
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data.samples_per_gpu=2 \
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data.workers_per_gpu=0 \
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2>&1 | tee "${FINETUNE_DIR}/finetune.log" &
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FINETUNE_PID=$!
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echo $FINETUNE_PID > pruning_results/finetune.pid
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echo ""
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echo "✅ 微调训练已启动（后台运行）"
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echo " PID: $FINETUNE_PID"
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echo " 日志: ${FINETUNE_DIR}/finetune.log"
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echo ""
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echo "监控命令:"
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echo " tail -f ${FINETUNE_DIR}/finetune.log | grep 'Epoch'"
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echo ""
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;;
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2)
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echo ""
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echo "稍后手动启动微调。"
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echo ""
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echo "启动命令:"
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echo " torchpack dist-run -np 8 python tools/train.py \\"
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echo " configs/.../multitask_enhanced_phase1_HIGHRES.yaml \\"
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echo " --load_from $PRUNED_OUTPUT \\"
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echo " --cfg-options max_epochs=3 optimizer.lr=5.0e-6"
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echo ""
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;;
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3)
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echo ""
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echo "查看剪枝结果:"
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echo " python tools/analysis/analyze_checkpoint.py $PRUNED_OUTPUT"
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echo ""
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echo "如果满意，再启动微调。"
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echo ""
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;;
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*)
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echo "无效选择，退出。"
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exit 1
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;;
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esac
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echo "========================================================================"
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echo "剪枝流程完成"
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echo "========================================================================"
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echo ""
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echo "已生成文件:"
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echo " - $PRUNED_OUTPUT"
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echo " - pruning_results/pruning_log_${TIMESTAMP}.txt"
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echo ""
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echo "下一步:"
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echo " 1. 等待微调完成（如已启动）"
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echo " 2. 评估剪枝+微调后的模型"
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echo " 3. 进行INT8量化"
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echo ""
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