68 lines
1.6 KiB
Bash
Executable File
68 lines
1.6 KiB
Bash
Executable File
#!/bin/bash
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# 启动Phase 3: Deep Supervision
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# 前提：Phase 2 (epoch_26.pth) 已完成
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set -e
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export PATH=/opt/conda/bin:$PATH
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cd /workspace/bevfusion
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echo "=========================================="
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echo "Phase 3: 添加Deep Supervision"
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echo "=========================================="
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echo ""
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echo "增强内容："
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echo " ✅ ASPP (Phase 1)"
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echo " ✅ Dice Loss (Phase 2)"
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echo " ✅ Deep Supervision - 深度监督"
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echo " ✅ 更深的Decoder (4层)"
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echo " ❌ 高权重 (Phase 4)"
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echo ""
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echo "训练设置："
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echo " 基础模型: epoch_26.pth"
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echo " 目标: epoch_29.pth"
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echo " 学习率: 5e-5"
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echo " 预期提升: mIoU +5-8% (累计)"
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echo ""
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echo "=========================================="
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echo ""
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EPOCH_26_PATH=$(find runs/ -name "epoch_26.pth" -type f | sort -r | head -1)
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if [ -z "$EPOCH_26_PATH" ]; then
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echo "错误: 找不到epoch_26.pth"
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echo "请先完成Phase 2训练"
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exit 1
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fi
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echo "找到Phase 2模型: $EPOCH_26_PATH"
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echo ""
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echo "开始Phase 3训练..."
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echo ""
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LOG_FILE="training_phase3_$(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_phase3.yaml \
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--model.encoders.camera.backbone.init_cfg.checkpoint pretrained/swint-nuimages-pretrained.pth \
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--load_from "$EPOCH_26_PATH" \
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--data.workers_per_gpu 0 \
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2>&1 | tee "$LOG_FILE"
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echo ""
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echo "=========================================="
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echo "Phase 3训练完成！"
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echo "日志: $LOG_FILE"
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echo "下一步: 运行 bash scripts/start_phase4.sh"
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echo "=========================================="
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