259 lines
8.3 KiB
Python
Executable File
259 lines
8.3 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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验证增强版配置文件是否正确
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检查：
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1. 配置文件能否正确加载
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2. EnhancedBEVSegmentationHead能否正确实例化
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3. 前向传播是否正常
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4. 损失计算是否正常
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"""
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import sys
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import torch
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from mmcv import Config
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def test_enhanced_head():
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"""测试EnhancedBEVSegmentationHead"""
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print("=" * 60)
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print("测试 EnhancedBEVSegmentationHead")
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print("=" * 60)
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try:
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from mmdet3d.models.heads.segm import EnhancedBEVSegmentationHead
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print("✅ 成功导入 EnhancedBEVSegmentationHead")
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except ImportError as e:
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print(f"❌ 导入失败: {e}")
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return False
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# 创建测试实例
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print("\n创建测试实例...")
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try:
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head = EnhancedBEVSegmentationHead(
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in_channels=512,
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grid_transform={
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'input_scope': [[-54.0, 54.0, 0.75], [-54.0, 54.0, 0.75]],
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'output_scope': [[-50, 50, 0.5], [-50, 50, 0.5]],
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},
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classes=['drivable_area', 'ped_crossing', 'walkway',
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'stop_line', 'carpark_area', 'divider'],
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loss='focal',
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focal_alpha=0.25,
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focal_gamma=2.0,
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use_dice_loss=True,
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dice_weight=0.5,
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deep_supervision=True,
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)
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print("✅ 成功创建 EnhancedBEVSegmentationHead")
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# 统计参数量
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num_params = sum(p.numel() for p in head.parameters())
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print(f" 参数量: {num_params:,} ({num_params/1e6:.2f}M)")
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except Exception as e:
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print(f"❌ 创建失败: {e}")
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import traceback
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traceback.print_exc()
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return False
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# 测试前向传播
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print("\n测试前向传播...")
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try:
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batch_size = 2
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x = torch.randn(batch_size, 512, 144, 144) # BEV features
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# 测试推理模式
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head.eval()
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with torch.no_grad():
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output = head(x)
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print(f"✅ 推理输出形状: {output.shape}")
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assert output.shape == (batch_size, 6, 200, 200), f"Expected (2, 6, 200, 200), got {output.shape}"
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# 测试训练模式
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head.train()
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target = torch.randint(0, 2, (batch_size, 6, 200, 200)).float()
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losses = head(x, target)
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print("✅ 训练模式损失:")
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for name, loss in losses.items():
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print(f" {name}: {loss.item():.4f}")
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# 验证损失项
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expected_loss_keys = set()
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for cls in ['drivable_area', 'ped_crossing', 'walkway',
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'stop_line', 'carpark_area', 'divider']:
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expected_loss_keys.add(f"{cls}/focal")
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expected_loss_keys.add(f"{cls}/dice")
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expected_loss_keys.add(f"{cls}/aux_focal")
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actual_loss_keys = set(losses.keys())
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if actual_loss_keys == expected_loss_keys:
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print("✅ 所有预期的损失项都存在")
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else:
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missing = expected_loss_keys - actual_loss_keys
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extra = actual_loss_keys - expected_loss_keys
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if missing:
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print(f"⚠️ 缺少损失项: {missing}")
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if extra:
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print(f"⚠️ 多余损失项: {extra}")
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except Exception as e:
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print(f"❌ 前向传播失败: {e}")
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import traceback
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traceback.print_exc()
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return False
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print("\n" + "=" * 60)
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print("✅ EnhancedBEVSegmentationHead 测试通过！")
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print("=" * 60)
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return True
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def test_config():
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"""测试配置文件"""
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print("\n" + "=" * 60)
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print("测试配置文件")
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print("=" * 60)
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config_file = "configs/nuscenes/multitask/fusion-det-seg-swint-enhanced.yaml"
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try:
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cfg = Config.fromfile(config_file)
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print(f"✅ 成功加载配置文件: {config_file}")
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except Exception as e:
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print(f"❌ 配置文件加载失败: {e}")
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import traceback
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traceback.print_exc()
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return False
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# 验证关键配置
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print("\n验证关键配置项:")
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# 1. 检查分割头类型
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if cfg.model.heads.get('map', {}).get('type') == 'EnhancedBEVSegmentationHead':
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print("✅ 分割头类型: EnhancedBEVSegmentationHead")
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else:
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print(f"⚠️ 分割头类型: {cfg.model.heads.get('map', {}).get('type')}")
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# 2. 检查损失权重
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map_scale = cfg.model.get('loss_scale', {}).get('map', 1.0)
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print(f"✅ 分割损失权重: {map_scale}x")
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if map_scale < 2.0:
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print(" ⚠️ 建议设置为 3.0 以平衡多任务学习")
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# 3. 检查focal loss参数
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map_cfg = cfg.model.heads.get('map', {})
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focal_alpha = map_cfg.get('focal_alpha', -1)
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focal_gamma = map_cfg.get('focal_gamma', 2.0)
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print(f"✅ Focal Loss参数: alpha={focal_alpha}, gamma={focal_gamma}")
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if focal_alpha <= 0:
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print(" ⚠️ alpha应设置为0.25以启用类别平衡")
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# 4. 检查dice loss
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use_dice = map_cfg.get('use_dice_loss', False)
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dice_weight = map_cfg.get('dice_weight', 0.0)
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print(f"✅ Dice Loss: {'启用' if use_dice else '禁用'} (权重={dice_weight})")
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# 5. 检查deep supervision
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deep_sup = map_cfg.get('deep_supervision', False)
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print(f"✅ Deep Supervision: {'启用' if deep_sup else '禁用'}")
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# 6. 检查类别权重
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loss_weight = map_cfg.get('loss_weight', {})
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print(f"✅ 类别权重配置:")
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for cls in ['drivable_area', 'ped_crossing', 'walkway',
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'stop_line', 'carpark_area', 'divider']:
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weight = loss_weight.get(cls, 1.0)
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print(f" {cls:15s}: {weight}x")
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print("\n" + "=" * 60)
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print("✅ 配置文件验证通过！")
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print("=" * 60)
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return True
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def test_focal_loss_fix():
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"""测试focal loss修复"""
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print("\n" + "=" * 60)
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print("测试 Focal Loss 修复")
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print("=" * 60)
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try:
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from mmdet3d.models.heads.segm.vanilla import sigmoid_focal_loss
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# 创建测试数据
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pred = torch.randn(2, 100, 100)
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target = torch.randint(0, 2, (2, 100, 100)).float()
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# 测试默认参数（应该alpha=0.25）
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loss_default = sigmoid_focal_loss(pred, target)
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print(f"✅ Focal Loss (默认参数): {loss_default.item():.4f}")
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# 测试显式alpha
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loss_alpha = sigmoid_focal_loss(pred, target, alpha=0.25, gamma=2.0)
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print(f"✅ Focal Loss (alpha=0.25): {loss_alpha.item():.4f}")
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# 测试老版本（alpha=-1应该不再有效）
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loss_old = sigmoid_focal_loss(pred, target, alpha=-1)
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print(f"✅ Focal Loss (alpha=-1): {loss_old.item():.4f}")
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# 验证修复：alpha=-1时应该仍然应用默认的alpha
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# 因为我们移除了 if alpha >= 0 的条件
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print("\n验证修复:")
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print(f" loss_default ≈ loss_alpha: {abs(loss_default - loss_alpha) < 1e-5}")
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print("\n" + "=" * 60)
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print("✅ Focal Loss 修复验证通过！")
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print("=" * 60)
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return True
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except Exception as e:
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print(f"❌ Focal Loss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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def main():
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"""主测试函数"""
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print("\n")
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print("╔" + "=" * 58 + "╗")
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print("║" + " " * 10 + "BEVFusion 增强版配置验证" + " " * 20 + "║")
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print("╚" + "=" * 58 + "╝")
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print()
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results = []
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# 1. 测试focal loss修复
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results.append(("Focal Loss修复", test_focal_loss_fix()))
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# 2. 测试EnhancedBEVSegmentationHead
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results.append(("EnhancedBEVSegmentationHead", test_enhanced_head()))
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# 3. 测试配置文件
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results.append(("配置文件", test_config()))
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# 总结
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print("\n" + "=" * 60)
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print("测试总结")
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print("=" * 60)
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for name, passed in results:
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status = "✅ 通过" if passed else "❌ 失败"
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print(f"{name:30s}: {status}")
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all_passed = all(passed for _, passed in results)
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print("=" * 60)
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if all_passed:
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print("🎉 所有测试通过！可以开始训练。")
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print("\n训练命令:")
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print("bash scripts/train_enhanced_multitask.sh")
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return 0
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else:
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print("❌ 部分测试失败，请检查配置。")
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return 1
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if __name__ == "__main__":
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sys.exit(main())
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