47 lines
1.7 KiB
Bash
47 lines
1.7 KiB
Bash
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#!/bin/bash
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# 简化版评估脚本 - 使用单GPU快速评估
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# 使用GPU 7，最小资源占用
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set -e
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export PATH=/opt/conda/bin:$PATH
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export LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/torch/lib:/opt/conda/lib:/usr/local/cuda/lib64:$LD_LIBRARY_PATH
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export PYTHONPATH=/workspace/bevfusion:$PYTHONPATH
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cd /workspace/bevfusion
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echo "========================================================================"
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echo "Phase 3 Epoch 23 快速评估 (单GPU)"
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echo "========================================================================"
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echo "Checkpoint: epoch_23.pth"
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echo "使用GPU: 7 (不影响训练)"
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echo "========================================================================"
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echo ""
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# 创建评估输出目录
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EVAL_DIR="eval_results/phase3_epoch23_quick_$(date +%Y%m%d_%H%M%S)"
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mkdir -p "$EVAL_DIR"
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CONFIG="configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/multitask_enhanced_phase1_HIGHRES.yaml"
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CHECKPOINT="runs/enhanced_from_epoch19/epoch_23.pth"
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echo "开始评估..."
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echo ""
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# 使用GPU 4-7进行分布式评估（4卡，避免与训练GPU 0-3冲突）
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CUDA_VISIBLE_DEVICES=4,5,6,7 \
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LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/torch/lib:/opt/conda/lib:/usr/local/cuda/lib64:$LD_LIBRARY_PATH \
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PATH=/opt/conda/bin:$PATH \
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/opt/conda/bin/torchpack dist-run -np 4 /opt/conda/bin/python tools/test.py \
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"$CONFIG" \
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"$CHECKPOINT" \
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--eval bbox \
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--out "$EVAL_DIR/results.pkl" \
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2>&1 | tee "$EVAL_DIR/eval.log"
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echo ""
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echo "========================================================================"
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echo "评估完成！结果保存在: $EVAL_DIR"
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echo "========================================================================"
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