148 lines
5.1 KiB
Bash
Executable File
148 lines
5.1 KiB
Bash
Executable File
#!/bin/bash
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# 🚀🚀🚀 BEVFusion Phase 4B: RMT-PPAD Segmentation Training 🚀🚀🚀
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# 增强数值稳定性版本 - 包含NaN/inf检测和自动恢复机制
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echo "🔧🔧🔧 数值稳定性增强训练启动脚本 🔧🔧🔧"
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echo "Phase 4B: RMT-PPAD Transformer分割集成"
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echo "时间: $(date)"
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echo "==========================================="
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# 配置参数
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CONFIG_FILE="configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/multitask_BEV2X_phase4b_rmtppad_segmentation.yaml"
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WORK_DIR="/data/runs/phase4b_rmtppad_segmentation"
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GPUS=8
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PORT=29500
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# 🔄🔄🔄 恢复训练配置 🔄🔄🔄
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RESUME_FROM="/workspace/bevfusion/runs/run-326653dc-41917c58/latest.pth"
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AUTO_RESUME=false # 是否自动从最新checkpoint恢复
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echo "📋 训练配置:"
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echo " - 配置文件: $CONFIG_FILE"
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echo " - 工作目录: $WORK_DIR"
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echo " - GPU数量: $GPUS"
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echo " - 主端口: $PORT"
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if [ -f "$RESUME_FROM" ]; then
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echo " - 恢复模式: ✅ 从checkpoint恢复"
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echo " - Checkpoint: $RESUME_FROM"
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else
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echo " - 恢复模式: ❌ 从头开始训练"
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fi
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echo ""
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# 创建工作目录
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mkdir -p $WORK_DIR
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# 🔧🔧🔧 数值稳定性监控函数 🔧🔧🔧
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monitor_numerical_stability() {
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echo "📊 启动数值稳定性监控..."
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while true; do
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# 检查是否有新的数值问题
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if grep -q "Warning: Invalid values detected in cost matrix" $WORK_DIR/train.log 2>/dev/null; then
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echo "⚠️ 检测到数值问题，记录已保存到日志"
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fi
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# 检查训练是否仍在运行
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if ! pgrep -f "python.*train.py" > /dev/null; then
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echo "ℹ️ 训练进程已结束"
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break
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fi
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sleep 60 # 每分钟检查一次
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done
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}
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# 启动监控（后台运行）
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monitor_numerical_stability &
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# 🚀 启动分布式训练
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echo "🚀 启动8卡分布式训练..."
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# 检查是否从checkpoint恢复
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RESUME_ARGS=""
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if [ -f "$RESUME_FROM" ]; then
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echo "✅ 找到checkpoint文件: $RESUME_FROM"
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RESUME_ARGS="--resume-from $RESUME_FROM"
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echo "🔄 将从checkpoint恢复训练"
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elif [ "$AUTO_RESUME" = true ]; then
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echo "🔍 启用自动恢复模式，查找最新checkpoint..."
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LATEST_CHECKPOINT=$(find /workspace/bevfusion/runs/ -name "latest.pth" -type l -exec ls -t {} + 2>/dev/null | head -1)
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if [ -n "$LATEST_CHECKPOINT" ]; then
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RESUME_ARGS="--resume-from $LATEST_CHECKPOINT"
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echo "🔄 自动恢复从: $LATEST_CHECKPOINT"
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fi
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fi
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# ✅ 设置环境变量 (参考成功脚本)
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export PATH=/opt/conda/bin:$PATH
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export LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/torch/lib:/opt/conda/lib:/usr/local/cuda/lib64:$LD_LIBRARY_PATH
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export PYTHONPATH=/workspace/bevfusion:$PYTHONPATH
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# 设置GPU
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export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
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# 验证环境
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echo ""
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echo "=== 环境验证 ==="
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/opt/conda/bin/python -c "import torch; print('✅ PyTorch:', torch.__version__)" || {
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echo "❌ PyTorch导入失败"
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exit 1
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}
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/opt/conda/bin/python -c "import mmcv; print('✅ mmcv:', mmcv.__version__)" || {
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echo "❌ mmcv导入失败"
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exit 1
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}
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which torchpack || {
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echo "❌ torchpack未找到"
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exit 1
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}
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echo "✅ torchpack: $(which torchpack)"
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echo "命令: torchpack dist-run -np $GPUS /opt/conda/bin/python tools/train.py $CONFIG_FILE --load_from $RESUME_FROM --data.samples_per_gpu 1 --data.workers_per_gpu 0"
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torchpack dist-run \
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-np $GPUS \
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/opt/conda/bin/python tools/train.py \
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$CONFIG_FILE \
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--load_from "$RESUME_FROM" \
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--data.samples_per_gpu 1 \
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--data.workers_per_gpu 0 \
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> "$WORK_DIR/train.log" 2>&1 &
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# 获取进程ID
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TRAIN_PID=$!
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echo ""
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echo "✅ Phase 4B training started!"
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echo "📊 Check results in: $WORK_DIR"
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echo "📈 Monitor progress: tail -f $WORK_DIR/train.log"
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echo ""
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echo "══════════════════════════════════════════════════════════"
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echo "✅ 训练已在后台启动！"
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echo "══════════════════════════════════════════════════════════"
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echo ""
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echo "进程ID: $TRAIN_PID"
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echo "日志文件: $WORK_DIR/train.log"
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echo ""
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echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
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echo "监控命令:"
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echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
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echo ""
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echo "1. 查看实时日志:"
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echo " tail -f $WORK_DIR/train.log"
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echo ""
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echo "2. 查看关键指标:"
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echo " tail -f $WORK_DIR/train.log | grep -E 'Epoch|loss/map/divider|loss/object'"
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echo ""
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echo "3. 检查进程状态:"
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echo " ps aux | grep $TRAIN_PID"
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echo ""
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echo "4. GPU监控:"
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echo " nvidia-smi -l 5"
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echo ""
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echo "5. 停止训练:"
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echo " kill $TRAIN_PID"
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echo ""
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echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
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