bev-project/simple_demo.py

#!/usr/bin/env python3
"""
简化的BEVFusion推理脚本
直接加载epoch_19.pth并在少量样本上推理
避免复杂的配置文件系统
"""

import torch
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
import sys
import os
import pickle
from tqdm import tqdm

# 添加路径
sys.path.insert(0, '/workspace/bevfusion')
os.chdir('/workspace/bevfusion')

print("="*80)
print("BEVFusion 简化推理 Demo")
print("="*80)
print("Checkpoint: epoch_19.pth")
print("推理样本: 5个")
print("="*80)
print()

# 1. 加载checkpoint
print("1. 加载checkpoint...")
checkpoint_path = 'runs/run-326653dc-74184412/epoch_19.pth'
checkpoint = torch.load(checkpoint_path, map_location='cpu')

print(f"   ✅ Checkpoint加载成功")
print(f"   - Epoch: {checkpoint.get('meta', {}).get('epoch', 'N/A')}")
print(f"   - Keys: {len(checkpoint['state_dict'])}")
print()

# 2. 使用torchpack简单运行
print("2. 运行推理（使用tools/test.py）...")
print("   配置: configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/multitask.yaml")
print()

# 创建一个简单的运行脚本
run_cmd = """
cd /workspace/bevfusion

# 直接运行test.py，使用单GPU，评估前10个样本
CUDA_VISIBLE_DEVICES=0 /opt/conda/bin/python tools/test.py \
    configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/multitask.yaml \
    runs/run-326653dc-74184412/epoch_19.pth \
    --launcher none \
    --eval bbox segm \
    --out results_epoch19_demo.pkl \
    2>&1 | tee inference_demo.log
"""

# 保存为脚本
with open('run_simple_inference.sh', 'w') as f:
    f.write(run_cmd)

os.chmod('run_simple_inference.sh', 0o755)

print("✅ 推理脚本已创建: run_simple_inference.sh")
print()
print("="*80)
print("执行推理:")
print("="*80)
print("运行命令: bash run_simple_inference.sh")
print()
print("预计时间: 约30-60分钟（完整验证集6019个样本）")
print("="*80)