75 lines
2.6 KiB
Bash
75 lines
2.6 KiB
Bash
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#!/bin/bash
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# BEVFusion 训练启动脚本
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# 使用前请选择要训练的任务（取消注释对应的命令）
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set -e
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# 设置conda环境
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export PATH=/opt/conda/bin:$PATH
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# 切换到项目目录
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cd /workspace/bevfusion
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echo "=========================================="
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echo "BEVFusion 训练启动"
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echo "=========================================="
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echo ""
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echo "硬件配置："
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echo " GPU: 8x Tesla V100S (32GB)"
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echo " 总显存: 256GB"
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echo ""
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echo "软件环境："
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echo " Python: $(python --version 2>&1)"
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echo " PyTorch: $(python -c 'import torch; print(torch.__version__)')"
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echo " CUDA: $(python -c 'import torch; print(torch.cuda.is_available())')"
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echo ""
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echo "数据集："
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echo " 位置: /data/nuscenes (已软链接到 ./data)"
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echo ""
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echo "预训练模型："
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echo " Camera Backbone: pretrained/swint-nuimages-pretrained.pth"
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echo " LiDAR Model: pretrained/lidar-only-det.pth"
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echo ""
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echo "=========================================="
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echo ""
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# ============================================
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# 选择要训练的任务（取消注释对应的命令）
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# ============================================
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# 任务1: 3D目标检测训练（推荐）
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# 预计时间：20-24小时，预期性能：mAP ~68-70%, NDS ~71-72%
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echo "开始训练: 3D目标检测 (Camera + LiDAR)"
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echo "配置: 8 GPU, TransFusion, SwinTransformer"
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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/convfuser.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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# ============================================
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# 其他训练选项（需要时取消注释）
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# ============================================
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# 任务2: BEV地图分割训练
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# 预计时间：12-15小时，预期性能：mIoU ~62-63%
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# echo "开始训练: BEV地图分割 (Camera + LiDAR)"
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# torchpack dist-run -np 8 python tools/train.py \
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# configs/nuscenes/seg/fusion-bev256d2-lss.yaml \
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# --model.encoders.camera.backbone.init_cfg.checkpoint pretrained/swint-nuimages-pretrained.pth
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# 任务3: 多任务训练（检测 + 分割）
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# 预计时间：28-32小时
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# echo "开始训练: 多任务 (检测 + 分割)"
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# torchpack dist-run -np 8 python tools/train.py \
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# configs/nuscenes/multitask/fusion-det-seg-swint.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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echo ""
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echo "=========================================="
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echo "训练完成！"
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echo "=========================================="
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