使用示例

我将帮您手动适配OpenClaw模型,OpenClaw是基于CLIP的机器人操作模型，结合视觉和语言进行抓取任务。

基础模型架构

import torch
import torch.nn as nn
from torchvision import models
from transformers import AutoTokenizer, AutoModel
class OpenClawAdapter(nn.Module):
    def __init__(self, vision_backbone='resnet50', text_backbone='bert-base-uncased', 
                 feature_dim=512, pretrained=True):
        super(OpenClawAdapter, self).__init__()
        # 视觉编码器
        self.vision_encoder = self._build_vision_encoder(vision_backbone, pretrained)
        self.vision_proj = nn.Linear(2048 if vision_backbone=='resnet50' else 768, feature_dim)
        # 语言编码器
        self.text_encoder = AutoModel.from_pretrained(text_backbone)
        self.text_proj = nn.Linear(768, feature_dim)
        # 跨模态融合
        self.cross_attention = nn.MultiheadAttention(feature_dim, num_heads=8, batch_first=True)
        # 抓取预测头
        self.grasp_head = nn.Sequential(
            nn.Linear(feature_dim, 256),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(256, 4)  # [x, y, width, height] or [x1, y1, x2, y2]
        )
    def _build_vision_encoder(self, backbone, pretrained):
        if backbone == 'resnet50':
            model = models.resnet50(pretrained=pretrained)
            return nn.Sequential(*list(model.children())[:-1])  # 移除全连接层
        elif backbone == 'vit':
            from transformers import ViTModel
            return ViTModel.from_pretrained('google/vit-base-patch16-224-in21k')
        else:
            raise ValueError(f"Unsupported backbone: {backbone}")

多模态融合模块

class CrossModalFusion(nn.Module):
    """跨模态特征融合"""
    def __init__(self, feature_dim=512, num_heads=8):
        super(CrossModalFusion, self).__init__()
        self.vision_norm = nn.LayerNorm(feature_dim)
        self.text_norm = nn.LayerNorm(feature_dim)
        # 交叉注意力
        self.vision_attention = nn.MultiheadAttention(
            feature_dim, num_heads, batch_first=True
        )
        self.text_attention = nn.MultiheadAttention(
            feature_dim, num_heads, batch_first=True
        )
        # 融合门控机制
        self.fusion_gate = nn.Sequential(
            nn.Linear(feature_dim * 2, feature_dim),
            nn.Sigmoid()
        )
        self.output_proj = nn.Linear(feature_dim * 2, feature_dim)
    def forward(self, vision_features, text_features):
        # 归一化
        vision_normed = self.vision_norm(vision_features)
        text_normed = self.text_norm(text_features)
        # 交叉注意力
        vision_attended, _ = self.vision_attention(
            vision_normed, text_normed, text_normed
        )
        text_attended, _ = self.text_attention(
            text_normed, vision_normed, vision_normed
        )
        # 门控融合
        concat_features = torch.cat([vision_attended, text_attended], dim=-1)
        gate = self.fusion_gate(concat_features)
        fused = gate * vision_attended + (1 - gate) * text_attended
        return self.output_proj(concat_features), fused

完整的OpenClaw适配器

class OpenClaw(nn.Module):
    """完整的OpenClaw模型适配"""
    def __init__(self, config):
        super(OpenClaw, self).__init__()
        self.config = config
        # 视觉编码器
        self.visual_encoder = VisionEncoder(config.vision_backbone)
        self.visual_proj = nn.Linear(config.visual_dim, config.hidden_dim)
        # 文本编码器
        self.text_encoder = TextEncoder(config.text_backbone)
        self.text_proj = nn.Linear(config.text_dim, config.hidden_dim)
        # 多模态融合
        self.fusion = CrossModalFusion(config.hidden_dim)
        # 任务头
        self.grasp_predictor = GraspPredictor(config.hidden_dim)
        self.affordance_head = AffordanceHead(config.hidden_dim)
        self.action_predictor = ActionPredictor(config.hidden_dim)
        # 温度参数（对比学习）
        self.logit_scale = nn.Parameter(torch.ones([]) * torch.log(torch.tensor(1/0.07)))
    def encode_image(self, images):
        """编码图像"""
        visual_features = self.visual_encoder(images)
        return self.visual_proj(visual_features)
    def encode_text(self, texts):
        """编码文本"""
        text_features = self.text_encoder(texts)
        return self.text_proj(text_features)
    def forward(self, images, texts, task='grasp'):
        """前向传播"""
        visual_features = self.encode_image(images)
        text_features = self.encode_text(texts)
        # 多模态融合
        fused_features, _ = self.fusion(visual_features, text_features)
        # 根据任务选择输出
        if task == 'grasp':
            return self.grasp_predictor(fused_features)
        elif task == 'affordance':
            return self.affordance_head(fused_features)
        elif task == 'action':
            return self.action_predictor(fused_features)
        else:
            raise ValueError(f"Unknown task: {task}")
    def compute_contrastive_loss(self, images, texts):
        """计算对比损失（CLIP风格）"""
        image_features = self.encode_image(images)
        text_features = self.encode_text(texts)
        # 归一化
        image_features = image_features / image_features.norm(dim=-1, keepdim=True)
        text_features = text_features / text_features.norm(dim=-1, keepdim=True)
        # 相似度矩阵
        logit_scale = self.logit_scale.exp()
        logits_per_image = logit_scale * image_features @ text_features.t()
        logits_per_text = logits_per_image.t()
        # 对比损失
        labels = torch.arange(len(images), device=images.device)
        loss_i = nn.functional.cross_entropy(logits_per_image, labels)
        loss_t = nn.functional.cross_entropy(logits_per_text, labels)
        return (loss_i + loss_t) / 2

任务特定的预测头

class GraspPredictor(nn.Module):
    """抓取位置预测"""
    def __init__(self, hidden_dim):
        super(GraspPredictor, self).__init__()
        self.predictor = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(hidden_dim // 2, 256),
            nn.ReLU(),
            nn.Linear(256, 5)  # [x, y, width, height, rotation]
        )
    def forward(self, features):
        return self.predictor(features)
class AffordanceHead(nn.Module):
    """可操作性预测"""
    def __init__(self, hidden_dim):
        super(AffordanceHead, self).__init__()
        self.head = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.ReLU(),
            nn.Conv2d(hidden_dim // 2, 1, kernel_size=1),  # 输出热图
            nn.Sigmoid()
        )
    def forward(self, features):
        # 将特征重塑为空间特征图
        b, c = features.shape
        h = w = int((c // 3) ** 0.5)  # 假设可以重塑
        features = features.view(b, -1, h, w)
        return self.head(features)
class ActionPredictor(nn.Module):
    """动作预测"""
    def __init__(self, hidden_dim, num_actions=10):
        super(ActionPredictor, self).__init__()
        self.predictor = nn.Sequential(
            nn.Linear(hidden_dim, 256),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Linear(128, num_actions)
        )
    def forward(self, features):
        return self.predictor(features)

训练适配器

class OpenClawTrainer:
    """OpenClaw训练器"""
    def __init__(self, model, config):
        self.model = model
        self.config = config
        # 优化器
        self.optimizer = torch.optim.AdamW(
            model.parameters(),
            lr=config.lr,
            weight_decay=config.weight_decay
        )
        # 损失函数
        self.grasp_loss = nn.MSELoss()  # 回归任务
        self.affordance_loss = nn.BCELoss()  # 二分类
        self.action_loss = nn.CrossEntropyLoss()  # 多分类
    def train_step(self, batch, task='grasp'):
        """训练步骤"""
        images, texts, targets = batch
        self.optimizer.zero_grad()
        # 前向传播
        outputs = self.model(images, texts, task)
        # 计算损失
        if task == 'grasp':
            loss = self.grasp_loss(outputs, targets)
        elif task == 'affordance':
            loss = self.affordance_loss(outputs, targets)
        elif task == 'action':
            loss = self.action_loss(outputs, targets)
        # 对比损失（可选）
        if self.config.use_contrastive:
            contrastive_loss = self.model.compute_contrastive_loss(images, texts)
            loss = loss + self.config.contrastive_weight * contrastive_loss
        loss.backward()
        self.optimizer.step()
        return loss.item()
    def adapt_to_new_task(self, dataloader, num_epochs=10):
        """快速适应新任务"""
        original_mode = self.model.training
        self.model.train()
        for epoch in range(num_epochs):
            total_loss = 0
            for batch in dataloader:
                loss = self.train_step(batch)
                total_loss += loss
            print(f"Epoch {epoch+1}/{num_epochs}, Loss: {total_loss/len(dataloader):.4f}")
        self.model.train(original_mode)

配置和使用示例

import yaml
from dataclasses import dataclass
@dataclass
class OpenClawConfig:
    vision_backbone: str = "resnet50"
    text_backbone: str = "bert-base-uncased"
    hidden_dim: int = 512
    visual_dim: int = 2048
    text_dim: int = 768
    lr: float = 1e-4
    weight_decay: float = 1e-5
    use_contrastive: bool = True
    contrastive_weight: float = 0.1
def main():
    # 配置
    config = OpenClawConfig()
    # 创建模型
    model = OpenClaw(config)
    # 创建训练器
    trainer = OpenClawTrainer(model, config)
    # 示例训练循环
    for epoch in range(10):
        for batch in train_loader:
            loss = trainer.train_step(batch, task='grasp')
            print(f"Loss: {loss:.4f}")
    # 保存模型
    torch.save({
        'model_state_dict': model.state_dict(),
        'config': config
    }, 'openclaw_adapted.pth')
if __name__ == "__main__":
    main()

推理适配器

class OpenClawInference:
    """推理适配器"""
    def __init__(self, model_path, device='cuda'):
        self.device = device
        # 加载模型
        checkpoint = torch.load(model_path, map_location=device)
        config = checkpoint['config']
        self.model = OpenClaw(config)
        self.model.load_state_dict(checkpoint['model_state_dict'])
        self.model.to(device)
        self.model.eval()
        # 文本处理器
        self.tokenizer = AutoTokenizer.from_pretrained(config.text_backbone)
    def predict_grasp(self, image, text_instruction):
        """预测抓取位置"""
        with torch.no_grad():
            # 预处理
            image_tensor = self._preprocess_image(image).to(self.device)
            text_tensor = self.tokenizer(
                text_instruction, 
                return_tensors='pt',
                padding=True,
                truncation=True,
                max_length=77
            ).to(self.device)
            # 推理
            prediction = self.model(image_tensor, text_tensor, task='grasp')
            return self._postprocess_grasp(prediction)
    def _preprocess_image(self, image):
        """图像预处理"""
        # 实现图像预处理逻辑
        pass
    def _postprocess_grasp(self, prediction):
        """后处理抓取预测"""
        # 将网络输出转换为抓取坐标
        pass

这个适配器提供了完整的OpenClaw模型实现,包括：

• 视觉和文本编码器
• 跨模态融合机制
• 多任务支持（抓取、可操作性、动作预测）
• 对比学习能力
• 训练和推理接口

您可以根据具体需求调整架构和参数。

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