44 lines
1.3 KiB
Bash
44 lines
1.3 KiB
Bash
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#!/bin/bash
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# 多任务训练最终版本
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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 "BEVFusion 多任务训练"
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echo "任务: 3D检测 + BEV分割（同时训练）"
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echo "=========================================="
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echo ""
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echo "配置信息："
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echo " 配置: multitask.yaml (检测+分割)"
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echo " GPU: 8x Tesla V100S (32GB)"
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echo " Epochs: 20"
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echo " 优化: workers_per_gpu=0"
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echo ""
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echo "预计训练时间: 28-32小时"
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echo "预期性能:"
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echo " - 检测: mAP ~67-69%"
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echo " - 分割: mIoU ~61-62%"
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echo ""
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echo "模型特点:"
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echo " - 一个模型同时输出检测和分割"
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echo " - 共享特征提取（encoder/fuser/decoder）"
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echo " - 两个独立的任务头（object + map）"
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echo " - 两个任务的梯度同时更新模型"
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echo ""
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echo "=========================================="
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echo ""
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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.yaml \
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--model.encoders.camera.backbone.init_cfg.checkpoint pretrained/swint-nuimages-pretrained.pth \
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--load_from pretrained/lidar-only-det.pth \
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--data.workers_per_gpu 0
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echo ""
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echo "=========================================="
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echo "多任务训练完成！"
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echo "=========================================="
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