86 lines
2.5 KiB
Python
86 lines
2.5 KiB
Python
#!/usr/bin/env python3
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"""
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测试Phase 4B完整配置是否能正确构建模型
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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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def test_phase4b_config():
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"""测试Phase 4B配置"""
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try:
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print("🔧 测试Phase 4B完整配置...")
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# 加载配置
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from mmcv import Config
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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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print("✅ 配置文件加载成功")
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# 简化配置用于测试
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cfg.data.samples_per_gpu = 1
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cfg.data.workers_per_gpu = 0
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# 设置训练模式
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cfg.model.train_cfg = {
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'object': {
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'grid_size': [1440, 1440, 41]
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},
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'map': {}
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}
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print("🔧 检查模型组件...")
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# 检查所有必要的组件
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model_config = cfg.model
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required_components = [
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'encoders.camera.backbone',
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'encoders.camera.neck',
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'encoders.camera.vtransform',
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'encoders.lidar.backbone',
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'fuser',
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'decoder.backbone',
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'decoder.neck',
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'heads.object',
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'heads.map'
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]
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for component in required_components:
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keys = component.split('.')
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value = model_config
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try:
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for key in keys:
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value = value[key]
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print(f"✅ {component}: {value.get('type', 'configured')}")
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except (KeyError, TypeError):
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print(f"❌ 缺少组件: {component}")
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return False
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print("🎉 Phase 4B配置验证通过！")
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print("\n配置摘要:")
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print(f"- Camera: SwinTransformer + GeneralizedLSSFPN + DepthLSSTransform")
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print(f"- LiDAR: SparseEncoder")
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print(f"- Fusion: ConvFuser")
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print(f"- Decoder: SECOND + SECONDFPN")
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print(f"- Heads: TransFusionHead + EnhancedTransformerSegmentationHead")
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print(f"- 分割类别数: {len(cfg.map_classes)}")
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print(f"- 检测类别数: {len(cfg.object_classes)}")
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return True
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except Exception as e:
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print(f"❌ 配置测试失败: {e}")
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import traceback
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traceback.print_exc()
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return False
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if __name__ == "__main__":
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success = test_phase4b_config()
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if success:
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print("\n🚀 Phase 4B配置准备就绪，可以开始训练！")
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else:
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print("\n❌ 配置存在问题，需要修复")
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