99 lines
3.0 KiB
Python
99 lines
3.0 KiB
Python
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#!/usr/bin/env python
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"""
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从已有的pkl结果文件进行评估，跳过推理步骤
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"""
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import os
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import pickle
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import sys
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import torch
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# 设置环境
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os.environ['PATH'] = '/opt/conda/bin:' + os.environ.get('PATH', '')
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os.environ['LD_LIBRARY_PATH'] = '/opt/conda/lib/python3.8/site-packages/torch/lib:/opt/conda/lib:/usr/local/cuda/lib64:' + os.environ.get('LD_LIBRARY_PATH', '')
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os.environ['PYTHONPATH'] = '/workspace/bevfusion:' + os.environ.get('PYTHONPATH', '')
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def main():
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import mmcv
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from mmcv import Config
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from mmdet3d.datasets import NuScenesDataset
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# 加载配置
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config_file = 'configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/multitask_BEV2X_phase4b_rmtppad_segmentation.yaml'
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cfg = Config.fromfile(config_file)
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# 设置为测试模式
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cfg.data.val.test_mode = True
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# 创建数据集
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print("正在创建数据集...")
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from mmdet3d.datasets import build_dataset
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# 构建完整的val配置 (参考nuscenes/default.yaml)
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val_config = {
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'type': 'NuScenesDataset',
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'dataset_root': cfg.dataset_root,
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'ann_file': cfg.dataset_root + 'nuscenes_infos_val.pkl',
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'pipeline': cfg.evaluation.pipeline,
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'object_classes': cfg.object_classes,
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'map_classes': cfg.map_classes,
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'modality': {
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'use_lidar': True,
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'use_camera': True,
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'use_radar': False,
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'use_map': False,
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'use_external': False
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},
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'test_mode': True,
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'use_valid_flag': False,
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'box_type_3d': 'LiDAR',
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'load_interval': cfg.data.val.load_interval if hasattr(cfg.data.val, 'load_interval') else 1
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}
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dataset = build_dataset(val_config)
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print(f"数据集大小: {len(dataset)}")
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# 加载已有的结果
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results_file = '/data/eval_fast/epoch1_fast_20251119_133104/fast_results.pkl'
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print(f"正在加载结果文件: {results_file}")
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with open(results_file, 'rb') as f:
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results = pickle.load(f)
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print(f"结果文件包含 {len(results)} 个样本")
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# 检查结果格式
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if results:
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sample = results[0]
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print(f"样本键: {list(sample.keys())}")
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if 'boxes_3d' in sample:
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print(f"检测框数量: {len(sample['boxes_3d'])}")
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if 'masks_bev' in sample:
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print(f"BEV分割mask形状: {sample['masks_bev'].shape}")
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# 进行评估
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print("开始评估...")
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eval_kwargs = dict(
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metric=['bbox', 'map'],
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save_best=None,
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rule=None,
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logger=None
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)
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try:
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eval_results = dataset.evaluate(results, **eval_kwargs)
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print("\n" + "="*50)
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print("评估结果:")
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print("="*50)
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for key, value in eval_results.items():
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if isinstance(value, float):
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print("30")
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else:
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print(f"{key}: {value}")
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except Exception as e:
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print(f"评估失败: {e}")
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import traceback
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traceback.print_exc()
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if __name__ == '__main__':
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main()
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