def visualize_attention_map(model, test_loader, device, class_names, num_samples=3):
    """可视化模型的注意力热力图，展示模型关注的图像区域
    
    参数:
        model: 训练好的PyTorch模型
        test_loader: 测试数据加载器
        device: 计算设备 (cuda/cpu)
        class_names: 类别名称列表
        num_samples: 要可视化的样本数量
    """
    # 反标准化参数 (CIFAR10)
    mean = torch.tensor([0.4914, 0.4822, 0.4465]).view(1, 3, 1, 1)
    std = torch.tensor([0.2023, 0.1994, 0.2010]).view(1, 3, 1, 1)
    
    model.eval()  # 设置为评估模式
    fig, axes = plt.subplots(num_samples, 4, figsize=(16, 4*num_samples))
    
    if num_samples == 1:
        axes = axes.reshape(1, -1)  # 确保单样本时保持2D结构
    
    with torch.no_grad():
        for sample_idx, (images, labels) in enumerate(test_loader):
            if sample_idx >= num_samples:
                break
                
            images, labels = images.to(device), labels.to(device)
            
            # 创建钩子捕获特征图
            activation = []
            hook = model.conv3.register_forward_hook(
                lambda module, input, output: activation.append(output.detach())
            )
            
            # 前向传播
            outputs = model(images)
            hook.remove()  # 立即移除钩子
            
            # 获取预测结果
            _, preds = torch.max(outputs, 1)
            
            # 反标准化图像
            denorm_img = images.cpu() * std + mean
            img_np = denorm_img[0].permute(1, 2, 0).numpy()
            img_np = np.clip(img_np, 0, 1)
            
            # 获取特征图
            feat_map = activation[0][0].cpu()  # 第一个样本
            
            # 计算通道重要性 (全局平均池化)
            channel_weights = torch.mean(feat_map, dim=(1, 2))
            topk_indices = torch.topk(channel_weights, k=3).indices
            
            # 显示原始图像
            ax = axes[sample_idx, 0]
            ax.imshow(img_np)
            ax.set_title(f"Original\nTrue: {class_names[labels[0]]}\nPred: {class_names[preds[0]]}")
            ax.axis('off')
            
            # 显示前3个重要通道的热力图
            for idx, channel_idx in enumerate(topk_indices, start=1):
                # 处理单通道特征图
                channel_heatmap = feat_map[channel_idx].numpy()
                
                # 归一化并缩放到原图尺寸
                channel_heatmap = (channel_heatmap - channel_heatmap.min()) / (channel_heatmap.max() - channel_heatmap.min() + 1e-8)
                resized_heatmap = cv2.resize(channel_heatmap, img_np.shape[:2][::-1])
                
                # 创建叠加可视化
                ax = axes[sample_idx, idx]
                ax.imshow(img_np)
                ax.imshow(resized_heatmap, alpha=0.5, cmap='jet')
                ax.set_title(f"Channel {channel_idx.item()} Attention")
                ax.axis('off')
    
    plt.tight_layout()
    plt.show()

# 调用示例
visualize_attention_map(model, test_loader, device, class_names, num_samples=3)

 @浙大疏锦行