基于cross-attention算法关联文本和图像、图像和动作

问题：

基于cross-attention算法关联文本和图像，可以举一个可以运行的例子吗？

基本实现框架：

import torch
import torch.nn as nn
import matplotlib.pyplot as plt

class TextImageCrossAttention(nn.Module):
    def __init__(self, text_dim=512, image_dim=768, att_dim=64):
        super().__init__()
        self.Wq = nn.Linear(text_dim, att_dim)  # 文本作为Query
        self.Wk = nn.Linear(image_dim, att_dim) # 图像作为Key
        self.Wv = nn.Linear(image_dim, att_dim) # 图像作为Value

    def forward(self, text_feats, image_feats):
        # text_feats: [batch, seq_len, text_dim]
        # image_feats: [batch, h*w, image_dim]
        
        Q = self.Wq(text_feats)  # [batch, seq, att_dim]
        K = self.Wk(image_feats) # [batch, h*w, att_dim]
        V = self.Wv(image_feats)
        
        attn_scores = torch.bmm(Q, K.transpose(1,2)) / (att_dim**0.5)
        attn_weights = torch.softmax(attn_scores, dim=-1)
        
        context = torch.bmm(attn_weights, V)  # [batch, seq, att_dim]
        return context, attn_weights

图像描述生成的例子（特征对齐、上下文建模、多模态融合）：

准备输入数据：

# 模拟CLIP特征提取结果
text_feats = torch.randn(1, 5, 512)  # 文本序列："A cat sits on grass"
image_feats = torch.randn(1, 196, 768) # 14x14图像特征图

# 实例化注意力模块
cross_attn = TextImageCrossAttention()

# 计算交叉注意力
context, attn_weights = cross_attn(text_feats, image_feats)

注意力可视化：

def visualize_attention(attn, text_tokens, img_size=14):
    # 取第一个样本的注意力权重
    attn_map = attn[0].mean(dim=0)  # [seq_len, h*w]
    
    # 创建可视化画布
    fig, axes = plt.subplots(1, len(text_tokens), figsize=(15,3))
    
    for i, token in enumerate(text_tokens):
        # 将注意力权重转换为特征图尺寸
        heatmap = attn_map[i].view(img_size, img_size).detach().numpy()
        
        axes[i].imshow(heatmap, cmap='viridis')
        axes[i].set_title(f'"{token}"')
        axes[i].axis('off')
    
    plt.colorbar(axes[-1].images[0], ax=axes, shrink=0.5)
    plt.show()

# 示例文本标记
text_tokens = ["[CLS]", "A", "cat", "sits", "grass"]

# 执行可视化
visualize_attention(attn_weights, text_tokens)

个性化图像生成场景：

# 示例代码片段（基于网页2思想）
def nested_attention(orig_attn, theme_attn):
    # 融合原始注意力与主题注意力
    return alpha*orig_attn + (1-alpha)*theme_attn

实时图像编辑场景：

# 示例编辑操作：增强"grass"的注意力权重
attn_weights[:,3] *= 1.5  # 对应"grass"的位置索引
attn_weights = torch.softmax(attn_weights, dim=-1)

问题：

基于cross-attention算法关联动作策略和图像，可以举一个可以运行的例子吗？

模拟机器人视觉导航场景的核心代码实现：

import torch
import torch.nn as nn
import torchvision.models as models
import matplotlib.pyplot as plt

class ActionPolicyCrossAttention(nn.Module):
    def __init__(self, policy_dim=256, image_dim=512, att_dim=64):
        super().__init__()
        # 图像特征提取（预训练ResNet18）
        self.cnn = models.resnet18(pretrained=True)
        self.cnn = nn.Sequential(*list(self.cnn.children())[:-2]) # 输出14x14特征图
        
        # Cross-Attention模块
        self.Wq = nn.Linear(policy_dim, att_dim)  # 策略作为Query
        self.Wk = nn.Linear(image_dim, att_dim)    # 图像作为Key
        self.Wv = nn.Linear(image_dim, att_dim)    # 图像作为Value
        
        # 策略生成层
        self.policy_head = nn.Linear(att_dim, 4)  # 输出动作：前/后/左/右

    def forward(self, image, policy_state):
        """
        image: [batch, 3, 224, 224] 
        policy_state: [batch, policy_dim] (当前策略状态)
        """
        # 提取图像特征
        img_feats = self.cnn(image)  # [batch, 512, 14, 14]
        img_feats = img_feats.view(img_feats.size(0), img_feats.size(1), -1).permute(0,2,1) # [batch, 196, 512]
        
        # 计算交叉注意力
        Q = self.Wq(policy_state.unsqueeze(1))  # [batch, 1, att_dim]
        K = self.Wk(img_feats)                  # [batch, 196, att_dim]
        V = self.Wv(img_feats)
        
        attn_scores = torch.bmm(Q, K.transpose(1,2)) / (att_dim**0.5)
        attn_weights = torch.softmax(attn_scores, dim=-1)
        
        context = torch.bmm(attn_weights, V)  # [batch, 1, att_dim]
        
        # 生成动作策略
        action_logits = self.policy_head(context.squeeze(1))
        return action_logits, attn_weights

模拟避障场景（特征交互机制、动态权重分配、多模态融合架构）：

初始化与模型输入：

# 模拟输入
image_input = torch.randn(1, 3, 224, 224)  # 摄像头图像
policy_state = torch.randn(1, 256)        # 当前策略状态向量

# 初始化模型
model = ActionPolicyCrossAttention()

# 前向计算
action_probs, attn = model(image_input, policy_state)
print(f"动作概率分布: {torch.softmax(action_probs, dim=-1).detach().numpy()}")

注意力可视化：

def visualize_action_attention(attn_weights, img_size=14):
    heatmap = attn_weights[0].view(img_size, img_size).detach().numpy()
    plt.imshow(heatmap, cmap='viridis')
    plt.title("策略关注的关键图像区域")
    plt.colorbar()
    plt.show()

visualize_action_attention(attn)

游戏AI控制：

# 实现动作策略与游戏画面的关联
class GameAgent(nn.Module):
    def __init__(self):
        super().__init__()
        self.visual_encoder = models.vgg16(pretrained=True).features
        self.policy_net = nn.LSTM(input_size=128, hidden_size=256)
        self.cross_attn = CrossAttention(policy_dim=256, image_dim=512)

机器人实时决策（通过时间序列扩展实现连续决策）：

class RecurrentCrossAttn(nn.Module):
    def __init__(self):
        self.lstm = nn.LSTMCell(input_size=64, hidden_size=256)
        self.cross_attn = CrossAttention(policy_dim=256, image_dim=512)