#!/bin/bash
# 多任务训练最终版本

set -e

export PATH=/opt/conda/bin:$PATH
cd /workspace/bevfusion

echo "=========================================="
echo "BEVFusion 多任务训练"
echo "任务: 3D检测 + BEV分割（同时训练）"
echo "=========================================="
echo ""
echo "配置信息："
echo "  配置: multitask.yaml (检测+分割)"
echo "  GPU: 8x Tesla V100S (32GB)"
echo "  Epochs: 20"
echo "  优化: workers_per_gpu=0"
echo ""
echo "预计训练时间: 28-32小时"
echo "预期性能:"
echo "  - 检测: mAP ~67-69%"
echo "  - 分割: mIoU ~61-62%"
echo ""
echo "模型特点:"
echo "  - 一个模型同时输出检测和分割"
echo "  - 共享特征提取（encoder/fuser/decoder）"
echo "  - 两个独立的任务头（object + map）"
echo "  - 两个任务的梯度同时更新模型"
echo ""
echo "=========================================="
echo ""

torchpack dist-run -np 8 python tools/train.py \
  configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/multitask.yaml \
  --model.encoders.camera.backbone.init_cfg.checkpoint pretrained/swint-nuimages-pretrained.pth \
  --load_from pretrained/lidar-only-det.pth \
  --data.workers_per_gpu 0

echo ""
echo "=========================================="
echo "多任务训练完成！"
echo "=========================================="