import torch
import torch.nn as nn

class InfantPoseDistillation(nn.Module):
    """
    婴儿3D姿态跨模型蒸馏
    
    论文: ECCV 2026 Workshop MoCha
    教师: Sapiens 2 (冻结, 2D姿态)
    学生: SAM 3D Body (可训练, 3D mesh)
    
    输入: 无标注婴儿视频
    输出: 婴儿3D姿态
    
    关键创新: 可微分渲染对齐2D伪标签和3D mesh
    """
    
    def __init__(self, num_keypoints=17):
        super().__init__()
        
        # 教师: 冻结的Sapiens 2
        self.teacher = self._load_sapiens2()
        for p in self.teacher.parameters():
            p.requires_grad = False
        
        # 学生: 可训练的SAM 3D Body
        self.student = self._load_sam3d()
        
        # 可微分渲染器
        self.renderer = DifferentiableRenderer(
            image_size=224,
            mesh_type='smplx'
        )
        
        # 损失函数
        self.kp_2d_loss = nn.MSELoss()  # 2D关键点对齐
        self.mesh_loss = nn.L1Loss()    # mesh正则化
        
    def _load_sapiens2(self):
        """加载Sapiens 2 2D姿态模型"""
        # Meta Sapiens 2: 2D人体关键点
        model = torch.hub.load('facebookresearch/sapiens', 'sapiens_2_2d')
        return model
    
    def _load_sam3d(self):
        """加载SAM 3D Body模型"""
        # SAM 3D Body: 3D人体mesh
        model = torch.hub.load('facebookresearch/sam3d', 'body')
        return model
    
    def forward(self, image: torch.Tensor) -> dict:
        """
        蒸馏前向传播
        
        Args:
            image: (B, 3, H, W) 无标注婴儿图像
            
        Returns:
            dict: {
                'student_3d': (B, 17, 3) 学生3D关节,
                'teacher_2d': (B, 17, 2) 教师2D关键点,
                'rendered_2d': (B, 17, 2) 渲染的2D投影,
                'loss': 蒸馏损失
            }
        """
        B = image.shape[0]
        
        # 1. 教师生成伪标签（不计算梯度）
        with torch.no_grad():
            teacher_kp_2d = self.teacher(image)  # (B, 17, 2)
            teacher_heatmap = self.teacher.get_heatmap(image)  # (B, 17, H, W)
        
        # 2. 学生预测3D mesh
        student_mesh = self.student(image)  # (B, V, 3) 顶点
        student_3d_joints = self.mesh_to_joints(student_mesh)  # (B, 17, 3)
        
        # 3. 可微分渲染到2D
        rendered_2d = self.renderer.render_keypoints(
            student_3d_joints, 
            camera_params=self.estimate_camera(image)
        )  # (B, 17, 2)
        
        # 4. 蒸馏损失: 渲染的2D vs 教师伪标签
        loss_2d = self.kp_2d_loss(rendered_2d, teacher_kp_2d)
        
        # mesh正则化（防止不合理的3D形状）
        loss_mesh = self.mesh_loss(student_mesh, self.student.template_mesh)
        
        total_loss = loss_2d + 0.01 * loss_mesh
        
        return {
            'student_3d': student_3d_joints,
            'teacher_2d': teacher_kp_2d,
            'rendered_2d': rendered_2d,
            'loss': total_loss,
            'loss_2d': loss_2d.item(),
            'loss_mesh': loss_mesh.item()
        }
    
    def mesh_to_joints(self, mesh: torch.Tensor) -> torch.Tensor:
        """从mesh顶点提取关节位置"""
        # SMPL-X模型有预定义的关节回归矩阵
        J_regressor = self.student.J_regressor  # (17, V)
        joints = torch.einsum('jv,bvc->bjc', J_regressor, mesh)
        return joints  # (B, 17, 3)
    
    def estimate_camera(self, image):
        """估计相机参数（弱透视）"""
        B, C, H, W = image.shape
        # 简化: 使用固定相机参数
        return {
            'focal': torch.tensor([W, W]).expand(B, 2),
            'center': torch.tensor([W/2, H/2]).expand(B, 2)
        }


class DifferentiableRenderer(nn.Module):
    """可微分3D→2D渲染器"""
    
    def __init__(self, image_size=224, mesh_type='smplx'):
        super().__init__()
        self.image_size = image_size
        self.mesh_type = mesh_type
        
    def render_keypoints(self, joints_3d, camera_params):
        """
        将3D关节投影到2D
        
        Args:
            joints_3d: (B, 17, 3) 3D关节坐标
            camera_params: dict with focal, center
            
        Returns:
            joints_2d: (B, 17, 2) 2D投影坐标
        """
        focal = camera_params['focal']  # (B, 2)
        center = camera_params['center']  # (B, 2)
        
        B, J, _ = joints_3d.shape
        
        # 弱透视投影
        z = joints_3d[:, :, 2:3]  # (B, 17, 1)
        z = torch.clamp(z, min=0.1)  # 避免除零
        
        x_2d = joints_3d[:, :, 0:1] * focal.unsqueeze(1) / z + center.unsqueeze(1)
        y_2d = joints_3d[:, :, 1:2] * focal.unsqueeze(1) / z + center.unsqueeze(1)
        
        joints_2d = torch.cat([x_2d, y_2d], dim=-1)  # (B, 17, 2)
        
        return joints_2d


# 测试
if __name__ == "__main__":
    model = InfantPoseDistillation()
    
    # 模拟婴儿图像
    image = torch.randn(4, 3, 224, 224)
    
    output = model(image)
    
    print("=== 婴儿姿态蒸馏测试 ===")
    print(f"输入: {image.shape} (无标注婴儿图像)")
    print(f"教师2D标签: {output['teacher_2d'].shape}")
    print(f"学生3D姿态: {output['student_3d'].shape}")
    print(f"渲染2D: {output['rendered_2d'].shape}")
    print(f"蒸馏损失: {output['loss'].item():.4f}")
    print()
    print("论文结果:")
    print(f"  PCK@10px: 0.22 → 0.42 (+91%)")
    print(f"  MPJPE: 25.5 → 22.2 mm (-13%)")
    print(f"  评估: 11名婴儿, 18 sessions, 173段录像")
    print()
    print("核心价值: 无需标注数据，用无标注视频蒸馏")

class IMSDistillationStrategy:
    """
    IMS跨模型蒸馏策略
    
    应用论文方法到IMS场景:
    1. 成人DMS模型→婴儿/儿童模型蒸馏
    2. 通用姿态→座舱特定姿态蒸馏
    3. 大模型→边缘小模型蒸馏
    """
    
    STRATEGIES = {
        # 策略1: 成人→儿童姿态蒸馏
        'adult_to_child': {
            'teacher': 'Sapiens 2 (adult-trained)',
            'student': 'SAM 3D Body',
            'data': '无标注儿童座舱视频',
            'target': 'CPD儿童姿态检测'
        },
        # 策略2: 通用→座舱场景蒸馏
        'general_to_cabin': {
            'teacher': '通用姿态模型',
            'student': '座舱专用模型',
            'data': '无标注座舱视频',
            'target': 'OOP异常姿态'
        },
        # 策略3: 大→小模型蒸馏
        'large_to_edge': {
            'teacher': 'DINOv3-Large',
            'student': 'DINOv3-Small',
            'data': '标注驾驶视频',
            'target': '边缘部署'
        }
    }