"""
PressureMesh (MDP-Net) 架构复现

论文: arXiv 2608.09550
核心: Multi-Device Pressure Network
创新: MoE 融合多压力设备数据 → 3D 人体网格

关键模块:
1. 单设备特征提取器（Temporal CNN）
2. MoE 跨设备融合模块
3. 网格回归头 + 关节回归头
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Dict, Tuple

class SingleDeviceEncoder(nn.Module):
    """
    单设备压力数据编码器
    
    输入: (B, T, H, W) 单个压力垫的时序压力数据
    输出: (B, D) 设备特征向量
    
    结构: 2D CNN 提取空间特征 → 1D Conv 提取时序特征
    """
    
    def __init__(self, input_channels: int = 1, feat_dim: int = 256):
        super().__init__()
        
        # 空间特征提取（逐帧）
        self.spatial_encoder = nn.Sequential(
            nn.Conv2d(input_channels, 32, kernel_size=3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(2),  # H/2, W/2
            
            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(2),  # H/4, W/4
            
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.AdaptiveAvgPool2d((4, 4)),  # 固定输出尺寸
        )
        
        # 时序特征提取
        self.temporal_encoder = nn.Sequential(
            nn.Conv1d(128 * 16, 256, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv1d(256, feat_dim, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.AdaptiveAvgPool1d(1),
        )
        
    def forward(self, pressure_seq: torch.Tensor) -> torch.Tensor:
        """
        Args:
            pressure_seq: (B, T, H, W) 时序压力数据
        Returns:
            feat: (B, feat_dim) 设备特征
        """
        B, T, H, W = pressure_seq.shape
        
        # 逐帧空间编码
        frames = pressure_seq.unsqueeze(2)  # (B, T, 1, H, W)
        frames = frames.view(B * T, 1, H, W)
        spatial_feat = self.spatial_encoder(frames)  # (B*T, 128, 4, 4)
        spatial_feat = spatial_feat.view(B, T, -1)  # (B, T, 128*16)
        
        # 时序编码
        temporal_feat = self.temporal_encoder(spatial_feat.transpose(1, 2))  # (B, feat_dim, 1)
        temporal_feat = temporal_feat.squeeze(-1)  # (B, feat_dim)
        
        return temporal_feat


class MoEFusionModule(nn.Module):
    """
    Mixture of Experts 融合模块
    
    论文描述：受 MoE 启发的多模态融合机制，
    实现跨设备压力信息的有效互补和增强。
    
    核心思想：
    - 每个设备是一个 "Expert"
    - 门控网络根据输入动态分配权重
    - 不同坐姿/姿态下不同设备贡献度不同
    """
    
    def __init__(self, num_devices: int, feat_dim: int = 256):
        super().__init__()
        self.num_devices = num_devices
        self.feat_dim = feat_dim
        
        # 门控网络：根据所有设备特征分配权重
        self.gate = nn.Sequential(
            nn.Linear(feat_dim * num_devices, 512),
            nn.ReLU(),
            nn.Linear(512, num_devices),
            nn.Softmax(dim=1)
        )
        
        # 融合层
        self.fusion = nn.Sequential(
            nn.Linear(feat_dim, 512),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(512, feat_dim),
            nn.ReLU()
        )
        
    def forward(self, device_features: List[torch.Tensor]) -> torch.Tensor:
        """
        Args:
            device_features: 每个设备的特征 (B, feat_dim)
        Returns:
            fused: 融合后的特征 (B, feat_dim)
        """
        B = device_features[0].shape[0]
        
        # 拼接所有设备特征
        concat_feat = torch.cat(device_features, dim=1)  # (B, feat_dim * num_devices)
        
        # 门控权重
        gate_weights = self.gate(concat_feat)  # (B, num_devices)
        
        # 加权求和
        stacked = torch.stack(device_features, dim=1)  # (B, num_devices, feat_dim)
        weighted = stacked * gate_weights.unsqueeze(-1)  # (B, num_devices, feat_dim)
        aggregated = weighted.sum(dim=1)  # (B, feat_dim)
        
        # 融合
        fused = self.fusion(aggregated)
        return fused


class MeshRegressionHead(nn.Module):
    """
    3D 网格回归头
    
    从融合特征回归:
    - 3D 人体网格顶点 (n_verts, 3)
    - 3D 关节位置 (n_joints, 3)
    - 2D 关节投影 (n_joints, 2)
    """
    
    def __init__(self, feat_dim: int = 256, 
                 n_verts: int = 6890,  # SMPL 标准顶点数
                 n_joints: int = 24):  # SMPL 关节数
        super().__init__()
        
        # 网格回归
        self.mesh_head = nn.Sequential(
            nn.Linear(feat_dim, 1024),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(1024, 2048),
            nn.ReLU(),
            nn.Linear(2048, n_verts * 3)  # 每个顶点 xyz
        )
        
        # 3D 关节回归
        self.joint_3d_head = nn.Sequential(
            nn.Linear(feat_dim, 512),
            nn.ReLU(),
            nn.Linear(512, n_joints * 3)
        )
        
        # 2D 关节投影
        self.joint_2d_head = nn.Sequential(
            nn.Linear(feat_dim, 256),
            nn.ReLU(),
            nn.Linear(256, n_joints * 2)
        )
        
    def forward(self, feat: torch.Tensor) -> Dict[str, torch.Tensor]:
        return {
            'mesh': self.mesh_head(feat).view(-1, 6890, 3),
            'joints_3d': self.joint_3d_head(feat).view(-1, 24, 3),
            'joints_2d': self.joint_2d_head(feat).view(-1, 24, 2),
        }


class MDPNet(nn.Module):
    """
    完整的 MDP-Net (Multi-Device Pressure Network)
    
    输入: 多个压力设备的时序压力数据
    输出: 3D 人体网格 + 关节位置
    
    性能: 关节位置误差 12.6cm (MDP 数据集)
    
    应用场景:
    - 车辆座椅 OOP 检测
    - 康复评估
    - 人机交互
    """
    
    def __init__(self, num_devices: int = 3, pressure_h: int = 32, pressure_w: int = 32):
        super().__init__()
        self.num_devices = num_devices
        
        # 每个设备一个编码器（共享参数可选）
        self.encoders = nn.ModuleList([
            SingleDeviceEncoder() for _ in range(num_devices)
        ])
        
        # MoE 融合
        self.fusion = MoEFusionModule(num_devices=num_devices)
        
        # 回归头
        self.regression = MeshRegressionHead()
        
    def forward(self, pressure_inputs: List[torch.Tensor]) -> Dict[str, torch.Tensor]:
        """
        Args:
            pressure_inputs: 每个设备的 (B, T, H, W) 压力序列
        Returns:
            Dict with 'mesh', 'joints_3d', 'joints_2d'
        """
        # 1. 单设备编码
        device_feats = [enc(inp) for enc, inp in zip(self.encoders, pressure_inputs)]
        
        # 2. MoE 融合
        fused_feat = self.fusion(device_feats)
        
        # 3. 回归
        outputs = self.regression(fused_feat)
        
        return outputs


# ==================== 测试代码 ====================
if __name__ == "__main__":
    import numpy as np
    import time
    
    # 配置
    num_devices = 3  # 3个压力垫（座垫 + 靠背 + 头枕）
    batch_size = 1
    time_steps = 30  # 1秒@30fps
    pressure_h, pressure_w = 32, 32  # 压力传感器阵列 32x32
    
    # 初始化模型
    model = MDPNet(num_devices=num_devices)
    model.eval()
    
    # 模拟输入
    inputs = [torch.randn(batch_size, time_steps, pressure_h, pressure_w) 
              for _ in range(num_devices)]
    
    # 推理
    start = time.time()
    with torch.no_grad():
        outputs = model(inputs)
    infer_time = (time.time() - start) * 1000
    
    # 结果
    print("=" * 60)
    print("MDP-Net (PressureMesh) 测试结果")
    print("=" * 60)
    print(f"输入设备数: {num_devices}")
    print(f"每设备输入: ({batch_size}, {time_steps}, {pressure_h}, {pressure_w})")
    print(f"输出网格顶点数: {outputs['mesh'].shape[1]}")
    print(f"输出 3D 关节数: {outputs['joints_3d'].shape[1]}")
    print(f"输出 2D 关节数: {outputs['joints_2d'].shape[1]}")
    print(f"\n推理时间: {infer_time:.1f} ms")
    
    # 参数统计
    total_params = sum(p.numel() for p in model.parameters())
    print(f"模型参数量: {total_params:,}")
    
    # 模拟 OOP 检测
    print("\n" + "=" * 60)
    print("OOP 检测模拟")
    print("=" * 60)
    
    # 正常坐姿：关节 y 坐标较高（坐直）
    normal_joints = outputs['joints_3d'][0]
    
    # 检测异常：躯干前倾（OOP）
    hip_y = normal_joints[1, 1].item()  # 髋关节 y
    shoulder_y = normal_joints[15, 1].item()  # 肩关节 y
    torso_angle = np.degrees(np.arctan2(
        normal_joints[15, 0].item() - normal_joints[1, 0].item(),
        abs(shoulder_y - hip_y)
    ))
    
    print(f"躯干前倾角度: {torso_angle:.1f}°")
    if torso_angle > 25:
        print("⚠️ OOP 警告：驾驶员前倾异常")
    else:
        print("✅ 正常坐姿")