77 lines
1.8 KiB
Bash
Executable File
77 lines
1.8 KiB
Bash
Executable File
#!/bin/bash
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# 启动Phase 4: 完整Enhanced版本
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# 前提：Phase 3 (epoch_29.pth) 已完成
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set -e
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export PATH=/opt/conda/bin:$PATH
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cd /workspace/bevfusion
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echo "=========================================="
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echo "Phase 4: 完整Enhanced版本"
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echo "=========================================="
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echo ""
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echo "增强内容："
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echo " ✅ ASPP (Phase 1)"
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echo " ✅ Dice Loss (Phase 2)"
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echo " ✅ Deep Supervision (Phase 3)"
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echo " ✅ 提高分割权重 (map loss×3.0)"
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echo ""
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echo "训练设置："
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echo " 基础模型: epoch_29.pth"
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echo " 目标: epoch_34.pth"
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echo " 学习率: 8e-5 (稍微提高)"
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echo " 预期提升: mIoU +12-18% (累计)"
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echo " 最终目标: mIoU 50-55%"
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echo ""
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echo "=========================================="
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echo ""
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EPOCH_29_PATH=$(find runs/ -name "epoch_29.pth" -type f | sort -r | head -1)
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if [ -z "$EPOCH_29_PATH" ]; then
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echo "错误: 找不到epoch_29.pth"
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echo "请先完成Phase 3训练"
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exit 1
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fi
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echo "找到Phase 3模型: $EPOCH_29_PATH"
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echo ""
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echo "开始Phase 4训练（最终版本）..."
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echo ""
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LOG_FILE="training_phase4_final_$(date +%Y%m%d_%H%M%S).log"
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torchpack dist-run -np 8 python tools/train.py \
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configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/multitask_enhanced_phase4.yaml \
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--model.encoders.camera.backbone.init_cfg.checkpoint pretrained/swint-nuimages-pretrained.pth \
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--load_from "$EPOCH_29_PATH" \
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--data.workers_per_gpu 0 \
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2>&1 | tee "$LOG_FILE"
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echo ""
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echo "=========================================="
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echo "🎉 Phase 4训练完成！"
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echo "=========================================="
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echo ""
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echo "✅ 所有增强阶段已完成！"
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echo ""
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echo "日志: $LOG_FILE"
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echo ""
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echo "下一步："
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echo " 1. 评估最终性能"
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echo " 2. 对比各Phase提升"
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echo " 3. 准备部署"
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echo ""
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echo "=========================================="
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