124 lines
4.0 KiB
Bash
Executable File
124 lines
4.0 KiB
Bash
Executable File
#!/bin/bash
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# Epoch 23完整评估 - 检测+分割
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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:$PYTHONPATH
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cd /workspace/bevfusion
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echo "========================================================================"
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echo "Epoch 23 完整评估 (GPU 4-7, 不影响训练)"
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echo "========================================================================"
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echo ""
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TIMESTAMP=$(date +%Y%m%d_%H%M%S)
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EVAL_DIR="eval_results/epoch23_complete_${TIMESTAMP}"
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mkdir -p "$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 "配置文件: $CONFIG"
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echo "Checkpoint: $CHECKPOINT (516MB)"
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echo "输出目录: $EVAL_DIR"
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echo "使用GPU: 4-7 (避开训练GPU 0-3)"
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echo ""
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# 阶段1: 3D检测评估
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echo "========== 阶段1: 3D目标检测评估 =========="
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CUDA_VISIBLE_DEVICES=4,5,6,7 \
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LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/torch/lib:/opt/conda/lib:/usr/local/cuda/lib64:$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/detection_results.pkl" \
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--cfg-options data.workers_per_gpu=0 \
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2>&1 | tee "$EVAL_DIR/detection_eval.log"
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echo ""
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echo "========== 阶段2: BEV分割评估 =========="
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CUDA_VISIBLE_DEVICES=4,5,6,7 \
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LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/torch/lib:/opt/conda/lib:/usr/local/cuda/lib64:$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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"$CHECKPOINT" \
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--eval map \
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--out "$EVAL_DIR/segmentation_results.pkl" \
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--cfg-options data.workers_per_gpu=0 \
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2>&1 | tee "$EVAL_DIR/segmentation_eval.log"
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echo ""
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echo "========== 阶段3: 综合评估 =========="
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CUDA_VISIBLE_DEVICES=4,5,6,7 \
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LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/torch/lib:/opt/conda/lib:/usr/local/cuda/lib64:$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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"$CHECKPOINT" \
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--eval bbox map \
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--out "$EVAL_DIR/complete_results.pkl" \
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--cfg-options data.workers_per_gpu=0 \
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2>&1 | tee "$EVAL_DIR/complete_eval.log"
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echo ""
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echo "========================================================================"
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echo "评估完成！生成报告..."
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echo "========================================================================"
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# 提取关键指标
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python3 << 'PYTHON_SCRIPT'
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import re
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import os
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eval_dir = os.environ.get('EVAL_DIR', 'eval_results/epoch23_complete_*')
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log_files = ['detection_eval.log', 'segmentation_eval.log', 'complete_eval.log']
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report = []
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report.append("=" * 80)
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report.append("Epoch 23 评估报告摘要")
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report.append("=" * 80)
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report.append("")
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for log_file in log_files:
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log_path = f"{eval_dir}/{log_file}"
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if os.path.exists(log_path):
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with open(log_path, 'r') as f:
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content = f.read()
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# 提取NDS和mAP
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nds_match = re.search(r'NDS:\s+([\d\.]+)', content)
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map_match = re.search(r'mAP:\s+([\d\.]+)', content)
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miou_match = re.search(r'mIoU.*?:\s+([\d\.]+)', content)
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report.append(f"--- {log_file} ---")
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if nds_match:
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report.append(f" NDS: {nds_match.group(1)}")
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if map_match:
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report.append(f" mAP: {map_match.group(1)}")
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if miou_match:
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report.append(f" mIoU: {miou_match.group(1)}")
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report.append("")
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report.append("=" * 80)
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report.append(f"完整日志: {eval_dir}/")
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report.append("=" * 80)
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print('\n'.join(report))
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# 保存报告
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with open(f"{eval_dir}/SUMMARY.txt", 'w') as f:
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f.write('\n'.join(report))
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PYTHON_SCRIPT
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echo ""
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echo "评估报告: $EVAL_DIR/SUMMARY.txt"
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echo "完整日志: $EVAL_DIR/"
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echo ""
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