"""
SICGAN: Style-Identified Cycle Consistent GAN
用于座舱合成数据→真实风格迁移

参考: arXiv 2601.16677 (2026年1月)

核心方法：
1. 风格识别器：将图像分类为合成域/真实域
2. CycleGAN翻译：合成→真实风格（保留内容）
3. 混合域训练：原始真实+风格迁移合成

座舱应用：
- 合成DMS图像（Isaac Sim渲染）→ 真实风格DMS图像
- 扩大训练集 10-100x
- 不需要配对数据
"""

import torch
import torch.nn as nn
import numpy as np
from typing import Tuple

class StyleIdentifier(nn.Module):
    """
    风格识别器：判断图像来自合成域还是真实域
    
    论文创新点：
    - 不是二分类，而是风格特征向量提取
    - 风格特征用于指导CycleGAN翻译
    """
    
    def __init__(self, input_channels=3, feature_dim=256):
        super().__init__()
        
        self.encoder = nn.Sequential(
            nn.Conv2d(input_channels, 32, 4, 2, 1),
            nn.LeakyReLU(0.2),
            nn.Conv2d(32, 64, 4, 2, 1),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(0.2),
            nn.Conv2d(64, 128, 4, 2, 1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2),
            nn.Conv2d(128, 256, 4, 2, 1),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(0.2),
            nn.AdaptiveAvgPool2d(1),
        )
        
        self.style_head = nn.Linear(256, feature_dim)
        self.domain_head = nn.Linear(256, 2)  # 合成/真实
    
    def forward(self, x):
        features = self.encoder(x).view(x.size(0), -1)
        style = self.style_head(features)
        domain = self.domain_head(features)
        return style, domain


class CabinCycleGAN:
    """
    座舱数据 Sim-to-Real CycleGAN
    
    组成：
    1. G_S2R: 合成→真实生成器
    2. G_R2S: 真实→合成生成器
    3. D_R: 真实域判别器
    4. D_S: 合成域判别器
    5. Style_ID: 风格识别器（本文创新）
    
    训练损失：
    - 对抗损失：L_GAN
    - 循环一致损失：L_cyc
    - 风格一致损失：L_style（本文新增）
    - 身份损失：L_identity
    """
    
    def __init__(self, image_channels=3):
        # 生成器（U-Net架构）
        self.G_S2R = self._build_generator(image_channels)
        self.G_R2S = self._build_generator(image_channels)
        
        # 判别器（PatchGAN）
        self.D_R = self._build_discriminator(image_channels)
        self.D_S = self._build_discriminator(image_channels)
        
        # 风格识别器
        self.style_id = StyleIdentifier(image_channels)
        
        # 损失权重
        self.lambda_cyc = 10.0
        self.lambda_style = 5.0
        self.lambda_identity = 5.0
    
    def _build_generator(self, channels):
        """U-Net 生成器"""
        return nn.Sequential(
            nn.Conv2d(channels, 64, 4, 2, 1),
            nn.LeakyReLU(0.2),
            nn.Conv2d(64, 128, 4, 2, 1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2),
            # ...更多层...
            nn.ConvTranspose2d(128, 64, 4, 2, 1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.ConvTranspose2d(64, channels, 4, 2, 1),
            nn.Tanh(),
        )
    
    def _build_discriminator(self, channels):
        """PatchGAN 判别器"""
        return nn.Sequential(
            nn.Conv2d(channels, 64, 4, 2, 1),
            nn.LeakyReLU(0.2),
            nn.Conv2d(64, 128, 4, 2, 1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2),
            nn.Conv2d(128, 1, 4, 1, 1),
        )
    
    def translate_sim_to_real(self, synthetic_image):
        """
        将合成座舱图像翻译为真实风格
        
        Args:
            synthetic_image: Isaac Sim 渲染的合成DMS图像
        
        Returns:
            real_style_image: 真实风格DMS图像
        """
        with torch.no_grad():
            real_style = self.G_S2R(synthetic_image)
        return real_style
    
    def compute_losses(self, real_batch, synth_batch):
        """
        计算训练损失
        
        关键创新：风格一致损失
        翻译后的图像应保留原始内容，只改变风格
        """
        # 合成→真实→合成（循环）
        fake_real = self.G_S2R(synth_batch)
        recon_synth = self.G_R2S(fake_real)
        cyc_loss = torch.mean((recon_synth - synth_batch)**2)
        
        # 风格一致
        style_synth, _ = self.style_id(synth_batch)
        style_fake_real, _ = self.style_id(fake_real)
        style_loss = torch.mean((style_synth - style_fake_real)**2)
        
        # 对抗损失
        disc_real = self.D_R(fake_real)
        adv_loss = -torch.mean(disc_real)  # 生成器希望判别器输出高
        
        # 身份损失（真实图像应保持真实）
        identity_real = self.G_S2R(real_batch)
        identity_loss = torch.mean((identity_real - real_batch)**2)
        
        total_loss = (
            adv_loss +
            self.lambda_cyc * cyc_loss +
            self.lambda_style * style_loss +
            self.lambda_identity * identity_loss
        )
        
        return {
            'total': total_loss,
            'adversarial': adv_loss.item(),
            'cycle': cyc_loss.item(),
            'style': style_loss.item(),
            'identity': identity_loss.item(),
        }


class SimToRealTransferLearning:
    """
    迁移学习方案（路径3）
    
    参考: MDPI Mathematics 13(23):3884 (2025)
    "Simulation-Driven Deep Transfer Learning Framework"
    
    方法：
    1. 在大规模合成数据上预训练
    2. 冻结底层特征提取器
    3. 用少量真实数据微调顶层
    
    论文关键发现：
    - 数据增强 > 不增强
    - 冻结底层+微调顶层 > 全量微调
    - ResNet backbone > VGG backbone
    """
    
    def __init__(self, model: nn.Module):
        self.model = model
    
    def pretrain_on_synthetic(self, synth_loader, epochs=50):
        """在合成数据上预训练"""
        print(f"Phase 1: 合成数据预训练 ({epochs} epochs)")
        # 标准训练流程
        # ...
    
    def finetune_on_real(self, real_loader, 
                         freeze_backbone: bool = True,
                         epochs: int = 10):
        """
        在真实数据上微调
        
        Args:
            freeze_backbone: 是否冻结底层
            epochs: 微调轮数（通常5-20）
        """
        if freeze_backbone:
            # 冻结底层参数
            for param in self.model.backbone.parameters():
                param.requires_grad = False
            print(f"Phase 2: 微调顶层（冻结backbone, {epochs} epochs）")
        else:
            print(f"Phase 2: 全量微调 ({epochs} epochs)")
        
        # 微调训练流程
        # ...
    
    def evaluate_transfer(self, test_loader):
        """评估迁移效果"""
        return {
            'synthetic_only_acc': 0.75,  # 示例
            'finetuned_acc': 0.94,
            'improvement': 0.19,
            'real_data_used': '500 images',
        }


class DomainRandomization:
    """
    域随机化方案（路径1）
    
    参考: ResearchGate (2025) "Sim-to-Real Domain Adaptation 
    with Randomized Data for Autonomous Driving"
    
    方法：
    在Isaac Sim中随机化：
    1. 光照条件（角度/颜色/强度）
    2. 内饰颜色/材质
    3. 乘员外观（性别/年龄/肤色/发型/眼镜）
    4. 摄像头参数（焦距/角度/噪声）
    5. 背景/窗外场景
    
    理论：如果模型在足够多变的虚拟环境中泛化，
    真实世界只是"另一个域"
    """
    
    RANDOMIZATION_PARAMS = {
        'lighting': {
            'angle': (0, 360),  # 度
            'intensity': (100, 1000),  # lux
            'color_temp': (2700, 6500),  # K
        },
        'interior': {
            'colors': ['black', 'beige', 'gray', 'brown', 'red'],
            'materials': ['leather', 'fabric', 'plastic', 'wood'],
        },
        'person': {
            'gender': ['male', 'female'],
            'age': (18, 75),
            'skin_tone': (0.2, 0.9),
            'hair': ['short', 'long', 'bald', 'covered'],
            'accessories': ['none', 'glasses', 'sunglasses', 'mask', 'hat'],
        },
        'camera': {
            'fov': (50, 90),
            'position': (±0.05, ±0.05, ±0.05),  # m
            'noise_std': (0, 0.02),
            'exposure': (0.5, 2.0),
        },
    }
    
    def generate_random_scene(self):
        """生成随机化场景配置"""
        config = {}
        for category, params in self.RANDOMIZATION_PARAMS.items():
            config[category] = {}
            for param, values in params.items():
                if isinstance(values, tuple):
                    config[category][param] = np.random.uniform(*values)
                elif isinstance(values, list):
                    config[category][param] = np.random.choice(values)
        return config


# ==================== 测试 ====================
if __name__ == "__main__":
    np.random.seed(42)
    
    print("=== Sim-to-Real 域适配三路径 ===")
    
    # 路径1: 域随机化
    dr = DomainRandomization()
    scene = dr.generate_random_scene()
    print(f"\n1. 域随机化场景:")
    for cat, params in scene.items():
        print(f"  {cat}: {params}")
    
    # 路径2: CycleGAN
    print(f"\n2. SICGAN 风格迁移:")
    cyclegan = CabinCycleGAN()
    # 模拟合成图像 (1, 3, 128, 128)
    synth_img = torch.randn(1, 3, 128, 128)
    real_style = cyclegan.translate_sim_to_real(synth_img)
    print(f"  输入: {synth_img.shape}")
    print(f"  输出: {real_style.shape}")
    print(f"  损失权重: cyc={cyclegan.lambda_cyc}, "
          f"style={cyclegan.lambda_style}, id={cyclegan.lambda_identity}")
    
    # 路径3: 迁移学习
    print(f"\n3. 迁移学习:")
    # 创建简化模型
    model = nn.Sequential(
        nn.Conv2d(3, 32, 3, padding=1),
        nn.AdaptiveAvgPool2d(1),
        nn.Flatten(),
        nn.Linear(32, 3)
    )
    model.backbone = model[0]  # 标记backbone
    tl = SimToRealTransferLearning(model)
    result = tl.evaluate_transfer(None)
    print(f"  纯合成准确率: {result['synthetic_only_acc']:.1%}")
    print(f"  微调后准确率: {result['finetuned_acc']:.1%}")
    print(f"  提升: +{result['improvement']:.1%}")
    
    # 路径对比
    print(f"\n=== 三路径对比 ===")
    print(f"{'路径':<20} {'准确率':<10} {'真实数据需求':<15} {'计算量':<10}")
    print(f"{'域随机化':<20} {'85-90%':<10} {'0':<15} {'高(仿真)':<10}")
    print(f"{'CycleGAN':<20} {'90-95%':<10} {'100-500张':<15} {'高(GAN)':<10}")
    print(f"{'迁移学习':<20} {'88-93%':<10} {'500-5000张':<15} {'低(微调)':<10}")
    print(f"{'组合方案':<20} {'92-97%':<10} {'500-1000张':<15} {'中':<10}")
    
    print(f"\n=== 座舱 Sim-to-Real 最佳实践 ===")
    print(f"  1. 先域随机化生成大量合成数据（100K+）")
    print(f"  2. CycleGAN翻译为真实风格（减少外观差距）")
    print(f"  3. 迁移学习微调（少量真实数据适配）")
    print(f"  4. 持续收集真实数据迭代优化")
    print(f"  5. 数据增强（翻转/裁剪/颜色抖动）")