61 lines
1.7 KiB
Bash
Executable File
61 lines
1.7 KiB
Bash
Executable File
#!/bin/bash
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# 启动Phase 1: ASPP增强训练
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# 前提：Baseline (epoch_20.pth) 已完成
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set -e
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export PATH=/opt/conda/bin:$PATH
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cd /workspace/bevfusion
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echo "=========================================="
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echo "Phase 1: ASPP多尺度特征增强"
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echo "=========================================="
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echo ""
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echo "增强内容："
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echo " ✅ ASPP - 多尺度特征提取"
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echo " ❌ Dice Loss (Phase 2)"
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echo " ❌ Deep Supervision (Phase 3)"
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echo " ❌ 高权重 (Phase 4)"
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echo ""
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echo "训练设置："
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echo " 基础模型: epoch_20.pth"
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echo " 目标: epoch_23.pth"
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echo " 学习率: 5e-5"
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echo " 预期提升: mIoU +2-3%"
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echo ""
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echo "=========================================="
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echo ""
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# 查找epoch_19.pth或epoch_20.pth
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EPOCH_BASE_PATH=$(find runs/ -name "epoch_19.pth" -type f | sort -r | head -1)
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if [ -z "$EPOCH_BASE_PATH" ]; then
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echo "错误: 找不到epoch_19.pth或epoch_20.pth"
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echo "请先完成Baseline训练"
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exit 1
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fi
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echo "找到Baseline模型: $EPOCH_BASE_PATH"
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echo ""
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echo "开始Phase 1训练..."
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echo ""
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# 生成日志文件名
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LOG_FILE="training_phase1_$(date +%Y%m%d_%H%M%S).log"
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# 启动训练
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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_phase1.yaml \
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--model.encoders.camera.backbone.init_cfg.checkpoint pretrained/swint-nuimages-pretrained.pth \
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--load_from "$EPOCH_BASE_PATH" \
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--data.workers_per_gpu 0 \
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2>&1 | tee "$LOG_FILE"
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echo ""
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echo "=========================================="
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echo "Phase 1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_phase2.sh"
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echo "=========================================="
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