152 lines
5.2 KiB
Python
152 lines
5.2 KiB
Python
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#!/usr/bin/env python
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"""
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分析TransFusion检测头模型结构
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"""
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import torch
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import torch.nn as nn
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import numpy as np
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def analyze_transfusion_head():
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"""分析TransFusion检测头的结构"""
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print("="*80)
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print("🎯 TransFusion 3D检测头架构分析")
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print("="*80)
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# 1. 整体架构
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print("\n📋 1. 整体架构")
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print("-" * 40)
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print("TransFusionHead 主要组件:")
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print("├── 🔍 Heatmap Head (热力图预测)")
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print("├── 🔄 Shared Conv (共享卷积)")
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print("├── 🎯 Proposal Selection (候选框选择)")
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print("├── 🔧 Category Encoding (类别编码)")
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print("├── 🤖 Transformer Decoder (解码器)")
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print("├── 📊 Prediction Heads (预测头)")
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print("└── 🎛️ NMS & Post-processing (后处理)")
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# 2. 输入输出
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print("\n📥 2. 输入输出规格")
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print("-" * 40)
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print("输入:")
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print(f"├── 特征图: [B, 512, H, W] (典型: [2, 512, 128, 128])")
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print("├── BEV范围: [-54m, 54m] x [-54m, 54m]")
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print("├── 体素大小: [0.075m, 0.075m, 0.2m]")
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print("\n输出:")
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print("├── boxes_3d: [N, 9] (x,y,z,w,l,h,rot_x,rot_y,rot_z)")
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print("├── scores_3d: [N] (检测置信度)")
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print("├── labels_3d: [N] (类别标签)")
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# 3. 核心参数
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print("\n⚙️ 3. 核心参数 (当前配置)")
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print("-" * 40)
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params = {
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"num_proposals": 200,
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"num_classes": 10,
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"num_decoder_layers": 1,
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"num_heads": 8,
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"hidden_channel": 128,
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"ffn_channel": 256
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}
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for key, value in params.items():
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print(f"├── {key}: {value}")
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# 4. 处理流程
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print("\n🔄 4. 处理流程")
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print("-" * 40)
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print("步骤1: 特征提取")
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print("├── 输入BEV特征 → Shared Conv → 特征增强")
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print("└── 输出: [B, hidden_channel, H, W]")
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print("\n步骤2: 热力图预测")
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print("├── 特征图 → Heatmap Head → 类别热力图")
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print("├── 输出: [B, num_classes, H, W]")
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print("└── 作用: 预测每个位置各类别的存在概率")
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print("\n步骤3: 候选框选择")
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print("├── 从热力图中选择Top-K候选位置")
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print("├── K = num_proposals = 200")
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print("├── 为每个候选框提取特征和位置编码")
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print("└── 添加类别嵌入")
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print("\n步骤4: Transformer解码")
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print("├── Query: 候选框特征 [B, hidden_channel, 200]")
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print("├── Key/Value: BEV特征 [B, hidden_channel, H*W]")
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print("├── 多头注意力机制聚合全局上下文")
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print("└── 迭代Refinement (当前配置1层)")
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print("\n步骤5: 预测头")
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print("├── 回归分支: 预测框参数 (中心、尺寸、旋转)")
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print("├── 分类分支: 精炼类别概率")
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print("└── IoU分支: 预测框的质量分数")
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# 5. 类别信息
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print("\n🏷️ 5. 类别信息 (nuScenes)")
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print("-" * 40)
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classes = [
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"car", "truck", "construction_vehicle", "bus", "trailer",
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"barrier", "motorcycle", "bicycle", "pedestrian", "traffic_cone"
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]
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for i, cls in enumerate(classes):
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print("2d")
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# 6. NMS配置
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print("\n🎯 6. NMS配置")
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print("-" * 40)
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print("类型: 圆形NMS (circle)")
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print("├── 大型车辆 (car, truck, bus, etc.): radius=-1 (无NMS)")
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print("├── 行人 (pedestrian): radius=0.175m")
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print("└── 交通锥 (traffic_cone): radius=0.175m")
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# 7. 训练目标
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print("\n🎓 7. 训练目标")
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print("-" * 40)
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losses = {
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"loss_heatmap": "高斯焦点损失 - 热力图预测",
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"loss_cls": "焦点损失 - 类别分类",
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"loss_bbox": "L1损失 - 框回归",
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"loss_iou": "变分焦点损失 - IoU预测"
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}
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for loss_name, description in losses.items():
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print("20s")
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# 8. 当前状态分析
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print("\n📊 8. 当前状态分析")
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print("-" * 40)
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print("✅ BEV分割: 工作正常 (多尺度融合成功)")
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print("❌ 3D检测: 输出为空 (0个检测框)")
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print("\n可能原因:")
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print("├── 模型刚训练1个epoch，权重未充分收敛")
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print("├── Heatmap预测置信度普遍过低")
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print("├── score_threshold=0.0但实际预测分数很低")
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print("└── Transformer解码器可能需要更多层或调整")
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# 9. 优化建议
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print("\n💡 9. 优化建议")
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print("-" * 40)
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print("短期:")
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print("├── 继续训练更多epoch (建议5-10个)")
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print("├── 降低score_threshold到0.05-0.1")
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print("└── 检查heatmap预测质量")
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print("\n中期:")
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print("├── 增加num_decoder_layers到2-3")
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print("├── 调整学习率和warmup策略")
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print("└── 增强数据增强")
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print("\n长期:")
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print("├── 调整模型容量 (hidden_channel, ffn_channel)")
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print("├── 尝试不同的损失函数权重")
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print("└── 使用更大的batch_size")
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print("\n" + "="*80)
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print("🏁 分析完成 - TransFusion检测头架构清晰！")
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print("="*80)
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if __name__ == '__main__':
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analyze_transfusion_head()
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