#!/bin/bash
# BEVFusion 训练启动脚本
# 使用前请选择要训练的任务（取消注释对应的命令）

set -e

# 设置conda环境
export PATH=/opt/conda/bin:$PATH

# 切换到项目目录
cd /workspace/bevfusion

echo "=========================================="
echo "BEVFusion 训练启动"
echo "=========================================="
echo ""
echo "硬件配置："
echo "  GPU: 8x Tesla V100S (32GB)"
echo "  总显存: 256GB"
echo ""
echo "软件环境："
echo "  Python: $(python --version 2>&1)"
echo "  PyTorch: $(python -c 'import torch; print(torch.__version__)')"
echo "  CUDA: $(python -c 'import torch; print(torch.cuda.is_available())')"
echo ""
echo "数据集："
echo "  位置: /data/nuscenes (已软链接到 ./data)"
echo ""
echo "预训练模型："
echo "  Camera Backbone: pretrained/swint-nuimages-pretrained.pth"
echo "  LiDAR Model: pretrained/lidar-only-det.pth"
echo ""
echo "=========================================="
echo ""

# ============================================
# 选择要训练的任务（取消注释对应的命令）
# ============================================

# 任务1: 3D目标检测训练（推荐）
# 预计时间：20-24小时，预期性能：mAP ~68-70%, NDS ~71-72%
echo "开始训练: 3D目标检测 (Camera + LiDAR)"
echo "配置: 8 GPU, TransFusion, SwinTransformer"
echo ""

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

# ============================================
# 其他训练选项（需要时取消注释）
# ============================================

# 任务2: BEV地图分割训练
# 预计时间：12-15小时，预期性能：mIoU ~62-63%
# echo "开始训练: BEV地图分割 (Camera + LiDAR)"
# torchpack dist-run -np 8 python tools/train.py \
#   configs/nuscenes/seg/fusion-bev256d2-lss.yaml \
#   --model.encoders.camera.backbone.init_cfg.checkpoint pretrained/swint-nuimages-pretrained.pth

# 任务3: 多任务训练（检测 + 分割）
# 预计时间：28-32小时
# echo "开始训练: 多任务 (检测 + 分割)"
# torchpack dist-run -np 8 python tools/train.py \
#   configs/nuscenes/multitask/fusion-det-seg-swint.yaml \
#   --model.encoders.camera.backbone.init_cfg.checkpoint pretrained/swint-nuimages-pretrained.pth \
#   --load_from pretrained/lidar-only-det.pth

echo ""
echo "=========================================="
echo "训练完成！"
echo "=========================================="