145 lines
4.8 KiB
Python
145 lines
4.8 KiB
Python
#!/usr/bin/env python3
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"""
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🔧🔧🔧 数值稳定性测试脚本 🔧🔧🔧
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测试BEVFusion Phase 4B中的数值稳定性修复效果
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"""
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import torch
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import numpy as np
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from mmdet3d.core.bbox.assigners.hungarian_assigner import HungarianAssigner3D
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from mmdet.core.bbox.match_costs import ClassificationCost, BBoxBEVL1Cost, IoU3DCost
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from mmdet.core.bbox.iou_calculators import BboxOverlaps3D
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import time
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def test_numerical_stability():
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"""测试数值稳定性修复"""
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print("🔧 测试数值稳定性修复...")
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# 创建分配器
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assigner = HungarianAssigner3D(
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cls_cost=dict(type='ClassificationCost', weight=1.0),
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reg_cost=dict(type='BBoxBEVL1Cost', weight=1.0),
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iou_cost=dict(type='IoU3DCost', weight=1.0),
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iou_calculator=dict(type='BboxOverlaps3D')
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)
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# 测试场景1: 正常数据
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print("\n📊 测试场景1: 正常数据")
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num_bboxes = 100
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num_gts = 20
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# 生成测试数据
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bboxes = torch.randn(num_bboxes, 9) # [x, y, z, w, l, h, rot, vx, vy]
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gt_bboxes = torch.randn(num_gts, 9)
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gt_labels = torch.randint(0, 10, (num_gts,))
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cls_pred = [torch.randn(10, num_bboxes)] # 10个类别
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train_cfg = {'point_cloud_range': [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]}
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try:
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start_time = time.time()
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result = assigner.assign(bboxes, gt_bboxes, gt_labels, cls_pred, train_cfg)
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elapsed = time.time() - start_time
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print(f"✅ 场景1成功，耗时: {elapsed:.4f}秒")
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except Exception as e:
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print(f"❌ 场景1失败: {e}")
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# 测试场景2: 包含NaN的数据
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print("\n📊 测试场景2: 包含NaN的数据")
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cls_pred_nan = [torch.randn(10, num_bboxes)]
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cls_pred_nan[0][5, 10] = float('nan') # 插入NaN
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try:
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start_time = time.time()
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result = assigner.assign(bboxes, gt_bboxes, gt_labels, cls_pred_nan, train_cfg)
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elapsed = time.time() - start_time
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print(f"✅ 场景2成功，耗时: {elapsed:.4f}秒")
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print("✅ NaN数据处理成功")
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except Exception as e:
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print(f"❌ 场景2失败: {e}")
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# 测试场景3: 包含Inf的数据
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print("\n📊 测试场景3: 包含Inf的数据")
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cls_pred_inf = [torch.randn(10, num_bboxes)]
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cls_pred_inf[0][3, 8] = float('inf') # 插入Inf
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try:
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start_time = time.time()
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result = assigner.assign(bboxes, gt_bboxes, gt_labels, cls_pred_inf, train_cfg)
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elapsed = time.time() - start_time
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print(f"✅ 场景3成功，耗时: {elapsed:.4f}秒")
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print("✅ Inf数据处理成功")
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except Exception as e:
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print(f"❌ 场景3失败: {e}")
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# 测试场景4: 极端值数据
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print("\n📊 测试场景4: 极端值数据")
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cls_pred_extreme = [torch.randn(10, num_bboxes) * 10000] # 大幅放大
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try:
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start_time = time.time()
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result = assigner.assign(bboxes, gt_bboxes, gt_labels, cls_pred_extreme, train_cfg)
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elapsed = time.time() - start_time
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print(f"✅ 场景4成功，耗时: {elapsed:.4f}秒")
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print("✅ 极端值数据处理成功")
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except Exception as e:
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print(f"❌ 场景4失败: {e}")
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print("\n🎯 数值稳定性测试完成！")
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def test_gradient_stability():
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"""测试梯度稳定性"""
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print("\n🔧 测试梯度稳定性...")
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# 创建简单的模型
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model = torch.nn.Linear(10, 1)
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optimizer = torch.optim.AdamW(model.parameters(), lr=5e-7, weight_decay=0.01)
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# 测试梯度裁切
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max_norm = 5.0
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for i in range(10):
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# 前向传播
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x = torch.randn(32, 10)
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y = model(x)
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loss = y.mean()
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# 反向传播
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optimizer.zero_grad()
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loss.backward()
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# 应用梯度裁切
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total_norm = torch.norm(torch.stack([
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torch.norm(p.grad.detach()) for p in model.parameters() if p.grad is not None
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]))
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clip_coef = max_norm / (total_norm + 1e-6)
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clip_coef_clamped = torch.clamp(clip_coef, max=1.0)
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for p in model.parameters():
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if p.grad is not None:
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p.grad.detach().mul_(clip_coef_clamped)
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# 检查梯度范数
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grad_norm = total_norm.item()
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print(f" 梯度范数: {grad_norm:.4f}")
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# 优化器步骤
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optimizer.step()
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if grad_norm > max_norm:
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print(f" ⚠️ 梯度被裁切: {grad_norm:.4f} -> {grad_norm * clip_coef_clamped:.4f}")
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else:
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print(f" ✅ 梯度正常: {grad_norm:.4f}")
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print("✅ 梯度稳定性测试完成！")
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if __name__ == "__main__":
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print("🚀🚀🚀 BEVFusion Phase 4B 数值稳定性测试 🚀🚀🚀")
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print("=" * 60)
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test_numerical_stability()
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test_gradient_stability()
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print("\n" + "=" * 60)
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print("🎯 所有数值稳定性测试完成！")
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print("💡 如果测试通过，可以安全启动训练")
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