108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
#!/usr/bin/env python
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"""
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单GPU测试脚本，用于验证修复后的配置
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"""
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import os
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import sys
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import torch
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import mmcv
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from mmcv import Config
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from mmdet3d.apis import single_gpu_test
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from mmdet3d.datasets import build_dataloader, build_dataset
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from mmdet3d.models import build_model
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from mmcv.runner import load_checkpoint
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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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# 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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# 设置测试模式
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cfg.data.val.test_mode = True
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# 构建数据集
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print("构建数据集...")
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from mmdet3d.datasets import NuScenesDataset
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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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# 构建数据加载器
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data_loader = build_dataloader(
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dataset,
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samples_per_gpu=1,
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workers_per_gpu=0,
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dist=False,
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shuffle=False,
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)
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# 构建模型
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print("构建模型...")
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cfg.model.train_cfg = None
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model = build_model(cfg.model, test_cfg=cfg.get("test_cfg"))
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print(f"模型类型: {type(model)}")
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print(f"模型heads: {list(model.heads.keys())}")
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# 加载checkpoint
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checkpoint_path = 'runs/run-4c8ec7e5-fabdc997/epoch_1.pth'
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print(f"加载checkpoint: {checkpoint_path}")
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checkpoint = load_checkpoint(model, checkpoint_path, map_location="cpu")
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if "CLASSES" in checkpoint.get("meta", {}):
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model.CLASSES = checkpoint["meta"]["CLASSES"]
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else:
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model.CLASSES = dataset.CLASSES
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# 单GPU测试
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print("开始单GPU测试...")
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model = model.cuda()
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outputs = single_gpu_test(model, data_loader)
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print(f"测试完成，结果数量: {len(outputs)}")
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# 检查结果
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if outputs:
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sample = outputs[0]
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print("第一个样本的结果结构:")
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for key, value in sample.items():
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if hasattr(value, 'shape'):
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print(f" {key}: {type(value)}, shape: {value.shape}")
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elif hasattr(value, '__len__') and not isinstance(value, str):
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print(f" {key}: {type(value)}, length: {len(value)}")
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else:
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print(f" {key}: {type(value)}, value: {value}")
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# 检查检测结果
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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 'scores_3d' in sample:
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print(f"分数形状: {sample['scores_3d'].shape}")
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if __name__ == '__main__':
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main()
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