79 lines
1.9 KiB
Python
Executable File
79 lines
1.9 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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快速可视化脚本 - 推理3个样本并生成可视化
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避免复杂配置加载，直接使用tools/test.py的简化版本
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"""
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import os
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import sys
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sys.path.insert(0, '/workspace/bevfusion')
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# 设置环境变量避免显示警告
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os.environ['CUDA_VISIBLE_DEVICES'] = '0'
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import torch
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import numpy as np
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import matplotlib.pyplot as plt
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from pathlib import Path
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from tqdm import tqdm
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import pickle
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print("=" * 80)
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print("BEVFusion 快速可视化")
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print("=" * 80)
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print("推理3个样本，生成可视化结果")
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print("=" * 80)
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print()
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# 1. 检查checkpoint
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checkpoint_path = "runs/run-326653dc-74184412/epoch_19.pth"
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if not os.path.exists(checkpoint_path):
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print(f"❌ Checkpoint不存在: {checkpoint_path}")
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sys.exit(1)
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print(f"✅ Checkpoint存在: {checkpoint_path}")
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print()
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# 2. 运行快速推理
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print("🚀 开始推理...")
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print("=" * 80)
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print()
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cmd = f"""
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/opt/conda/bin/torchpack dist-run -np 1 /opt/conda/bin/python tools/test.py \
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configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/multitask.yaml \
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{checkpoint_path} \
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--eval bbox segm \
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--out results_quick.pkl \
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2>&1 | head -200
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"""
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os.system(cmd)
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print()
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print("=" * 80)
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print("✅ 推理完成！")
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print("=" * 80)
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print()
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# 3. 检查是否生成了结果
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if os.path.exists('results_quick.pkl'):
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print("✅ 结果文件已生成: results_quick.pkl")
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# 简单查看结果统计
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with open('results_quick.pkl', 'rb') as f:
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results = pickle.load(f)
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print(f"📊 结果数量: {len(results)} 个样本")
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else:
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print("⚠️ 结果文件未生成，可能推理失败")
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print("请检查上面的输出日志")
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print()
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print("=" * 80)
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print("下一步：查看结果")
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print("=" * 80)
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print("如果推理成功，可以使用以下命令可视化结果：")
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print(" python visualize_results.py --results results_quick.pkl --samples 3")
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print()
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