150 lines
4.7 KiB
Bash
Executable File
150 lines
4.7 KiB
Bash
Executable File
#!/bin/bash
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# BEVFusion Docker环境检查
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export PATH=/opt/conda/bin:$PATH
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echo "=========================================="
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echo "BEVFusion Docker环境检查"
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echo "=========================================="
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echo ""
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PASS=0
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FAIL=0
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check_pass() {
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echo " ✓ $1"
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((PASS++))
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}
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check_fail() {
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echo " ✗ $1"
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((FAIL++))
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}
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# 1. Python和PyTorch
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echo "[1/10] Python和PyTorch"
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PYTHON_VERSION=$(python --version 2>&1)
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check_pass "$PYTHON_VERSION"
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PYTORCH_INFO=$(python -c "import torch; print(f'PyTorch {torch.__version__}, CUDA: {torch.cuda.is_available()}, GPUs: {torch.cuda.device_count()}')" 2>&1)
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check_pass "$PYTORCH_INFO"
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# 2. torchpack
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echo ""
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echo "[2/10] torchpack"
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python -c "import torchpack; print('torchpack installed')" 2>/dev/null
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if [ $? -eq 0 ]; then
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check_pass "torchpack 已安装"
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else
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check_fail "torchpack 未安装"
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fi
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# 3. mmcv
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echo ""
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echo "[3/10] mmcv"
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MMCV_VERSION=$(python -c "import mmcv; print(mmcv.__version__)" 2>/dev/null)
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if [ $? -eq 0 ]; then
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check_pass "mmcv $MMCV_VERSION"
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else
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check_fail "mmcv 未安装"
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fi
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# 4. mmdet
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echo ""
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echo "[4/10] mmdetection"
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MMDET_VERSION=$(python -c "import mmdet; print(mmdet.__version__)" 2>/dev/null)
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if [ $? -eq 0 ]; then
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check_pass "mmdetection $MMDET_VERSION"
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else
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check_fail "mmdetection 未安装"
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fi
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# 5. nuscenes-devkit
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echo ""
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echo "[5/10] nuscenes-devkit"
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python -c "from nuscenes import NuScenes" 2>/dev/null
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if [ $? -eq 0 ]; then
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check_pass "nuscenes-devkit 已安装"
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else
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check_fail "nuscenes-devkit 未安装"
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fi
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# 6. 其他依赖
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echo ""
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echo "[6/10] 其他Python依赖"
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python -c "import tqdm" 2>/dev/null && check_pass "tqdm" || check_fail "tqdm"
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python -c "from PIL import Image" 2>/dev/null && check_pass "Pillow" || check_fail "Pillow"
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python -c "import numpy" 2>/dev/null && check_pass "numpy" || check_fail "numpy"
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# 7. 自定义算子
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echo ""
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echo "[7/10] 自定义CUDA算子"
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python -c "from mmdet3d.ops import bev_pool_v2" 2>/dev/null && check_pass "BEV Pool" || check_fail "BEV Pool (需要: python setup.py develop)"
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python -c "from mmdet3d.ops import Voxelization" 2>/dev/null && check_pass "Voxelization" || check_fail "Voxelization"
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python -c "from mmdet3d.ops.spconv import SparseConv3d" 2>/dev/null && check_pass "Sparse Conv" || check_fail "Sparse Conv"
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# 8. GPU信息
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echo ""
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echo "[8/10] GPU信息"
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nvidia-smi --query-gpu=index,name,memory.total,memory.free --format=csv,noheader | while read line; do
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check_pass "GPU $line"
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done
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# 9. 数据集
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echo ""
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echo "[9/10] nuScenes数据集"
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if [ -d "data/nuscenes" ]; then
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check_pass "数据集目录存在"
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[ -f "data/nuscenes/nuscenes_infos_train.pkl" ] && check_pass "训练info文件" || check_fail "训练info文件缺失"
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[ -f "data/nuscenes/nuscenes_infos_val.pkl" ] && check_pass "验证info文件" || check_fail "验证info文件缺失"
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else
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check_fail "数据集目录不存在"
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fi
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# 10. 预训练模型
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echo ""
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echo "[10/10] 预训练模型"
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if [ -d "pretrained" ]; then
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check_pass "预训练目录存在"
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[ -f "pretrained/swint-nuimages-pretrained.pth" ] && check_pass "SwinT预训练模型" || check_fail "SwinT预训练模型缺失"
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[ -f "pretrained/lidar-only-det.pth" ] && check_pass "LiDAR检测模型" || check_fail "LiDAR检测模型缺失"
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else
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check_fail "预训练目录不存在"
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fi
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# 总结
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echo ""
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echo "=========================================="
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echo "检查完成: 通过 $PASS 项, 失败 $FAIL 项"
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echo "=========================================="
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if [ $FAIL -eq 0 ]; then
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echo ""
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echo "✅ 环境完整，可以开始训练！"
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echo ""
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echo "推荐训练命令："
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echo ""
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echo "1. 3D检测训练（8 GPU）："
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echo " torchpack dist-run -np 8 python tools/train.py \\"
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echo " configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/convfuser.yaml \\"
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echo " --model.encoders.camera.backbone.init_cfg.checkpoint pretrained/swint-nuimages-pretrained.pth \\"
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echo " --load_from pretrained/lidar-only-det.pth"
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echo ""
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echo "2. BEV分割训练（8 GPU）："
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echo " torchpack dist-run -np 8 python tools/train.py \\"
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echo " configs/nuscenes/seg/fusion-bev256d2-lss.yaml \\"
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echo " --model.encoders.camera.backbone.init_cfg.checkpoint pretrained/swint-nuimages-pretrained.pth"
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echo ""
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echo "3. 多任务训练（检测+分割，8 GPU）："
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echo " torchpack dist-run -np 8 python tools/train.py \\"
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echo " configs/nuscenes/multitask/fusion-det-seg-swint.yaml \\"
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echo " --model.encoders.camera.backbone.init_cfg.checkpoint pretrained/swint-nuimages-pretrained.pth \\"
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echo " --load_from pretrained/lidar-only-det.pth"
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exit 0
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else
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echo ""
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echo "⚠️ 发现 $FAIL 个问题需要解决"
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exit 1
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fi
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