Model

package com.bwei.j.myapplication.cart.model;

/**
 * author:Created by WangZhiQiang on 17.12.19.
 */

    public interface Model_Cart {
        void getFinsh();
    }
-------------------------------------------------
package com.bwei.j.myapplication.cart.model;

import com.bwei.j.myapplication.cart.bean.MyCartBean;
import com.bwei.j.myapplication.tools.MyApi;
import com.bwei.j.myapplication.utils.GsonObjectCallback;
import com.bwei.j.myapplication.utils.OkHttp3Utils;

import java.io.IOException;

import okhttp3.Call;

/**
 * author:Created by WangZhiQiang on 17.12.19.
 */

public class MyMolde_Cart implements Model_Cart {

    OnFinsh onFinsh;

    public interface OnFinsh{
        void Finsh1(MyCartBean bean);
    }

    public void setOnFinsh( OnFinsh onFinsh){
        this.onFinsh = onFinsh;
    }

    @Override
    public void getFinsh() {
        OkHttp3Utils.doGet(MyApi.SELECT_CAR, new GsonObjectCallback<MyCartBean>() {

            @Override
            public void onUi(MyCartBean myCartBean) {
                onFinsh.Finsh1(myCartBean);
            }

            @Override
            public void onFailed(Call call, IOException e) {

            }
        });

    }
}

raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for YOLO: Missing key(s) in state_dict: "model.model.0.conv.weight", "model.model.0.bn.weight", "model.model.0.bn.bias", "model.model.0.bn.running_mean", "model.model.0.bn.running_var", "model.model.1.conv.weight", "model.model.1.bn.weight", "model.model.1.bn.bias", "model.model.1.bn.running_mean", "model.model.1.bn.running_var", "model.model.2.cv1.conv.weight", "model.model.2.cv1.bn.weight", "model.model.2.cv1.bn.bias", "model.model.2.cv1.bn.running_mean", "model.model.2.cv1.bn.running_var", "model.model.2.cv2.conv.weight", "model.model.2.cv2.bn.weight", "model.model.2.cv2.bn.bias", "model.model.2.cv2.bn.running_mean", "model.model.2.cv2.bn.running_var", "model.model.2.m.0.cv1.conv.weight", "model.model.2.m.0.cv1.bn.weight", "model.model.2.m.0.cv1.bn.bias", "model.model.2.m.0.cv1.bn.running_mean", "model.model.2.m.0.cv1.bn.running_var", "model.model.2.m.0.cv2.conv.weight", "model.model.2.m.0.cv2.bn.weight", "model.model.2.m.0.cv2.bn.bias", "model.model.2.m.0.cv2.bn.running_mean", "model.model.2.m.0.cv2.bn.running_var", "model.model.3.conv.weight", "model.model.3.bn.weight", "model.model.3.bn.bias", "model.model.3.bn.running_mean", "model.model.3.bn.running_var", "model.model.4.cv1.conv.weight", "model.model.4.cv1.bn.weight", "model.model.4.cv1.bn.bias", "model.model.4.cv1.bn.running_mean", "model.model.4.cv1.bn.running_var", "model.model.4.cv2.conv.weight", "model.model.4.cv2.bn.weight", "model.model.4.cv2.bn.bias", "model.model.4.cv2.bn.running_mean", "model.model.4.cv2.bn.running_var", "model.model.4.m.0.cv1.conv.weight", "model.model.4.m.0.cv1.bn.weight", "model.model.4.m.0.cv1.bn.bias", "model.model.4.m.0.cv1.bn.running_mean", "model.model.4.m.0.cv1.bn.running_var", "model.model.4.m.0.cv2.conv.weight", "model.model.4.m.0.cv2.bn.weight", "model.model.4.m.0.cv2.bn.bias", "model.model.4.m.0.cv2.bn.running_mean", "model.model.4.m.0.cv2.bn.running_var", "model.model.5.conv.weight", "model.model.5.bn.weight", "model.model.5.bn.bias", "model.model.5.bn.running_mean", "model.model.5.bn.running_var", "model.model.6.cv1.conv.weight", "model.model.6.cv1.bn.weight", "model.model.6.cv1.bn.bias", "model.model.6.cv1.bn.running_mean", "model.model.6.cv1.bn.running_var", "model.model.6.cv2.conv.weight", "model.model.6.cv2.bn.weight", "model.model.6.cv2.bn.bias", "model.model.6.cv2.bn.running_mean", "model.model.6.cv2.bn.running_var", "model.model.6.m.0.cv1.conv.weight", "model.model.6.m.0.cv1.bn.weight", "model.model.6.m.0.cv1.bn.bias", "model.model.6.m.0.cv1.bn.running_mean", "model.model.6.m.0.cv1.bn.running_var", "model.model.6.m.0.cv2.conv.weight", "model.model.6.m.0.cv2.bn.weight", "model.model.6.m.0.cv2.bn.bias", "model.model.6.m.0.cv2.bn.running_mean", "model.model.6.m.0.cv2.bn.running_var", "model.model.6.m.0.cv3.conv.weight", "model.model.6.m.0.cv3.bn.weight", "model.model.6.m.0.cv3.bn.bias", "model.model.6.m.0.cv3.bn.running_mean", "model.model.6.m.0.cv3.bn.running_var", "model.model.6.m.0.m.0.cv1.conv.weight", "model.model.6.m.0.m.0.cv1.bn.weight", "model.model.6.m.0.m.0.cv1.bn.bias", "model.model.6.m.0.m.0.cv1.bn.running_mean", "model.model.6.m.0.m.0.cv1.bn.running_var", "model.model.6.m.0.m.0.cv2.conv.weight", "model.model.6.m.0.m.0.cv2.bn.weight", "model.model.6.m.0.m.0.cv2.bn.bias", "model.model.6.m.0.m.0.cv2.bn.running_mean", "model.model.6.m.0.m.0.cv2.bn.running_var", "model.model.6.m.0.m.1.cv1.conv.weight", "model.model.6.m.0.m.1.cv1.bn.weight", "model.model.6.m.0.m.1.cv1.bn.bias", "model.model.6.m.0.m.1.cv1.bn.running_mean", "model.model.6.m.0.m.1.cv1.bn.running_var", "model.model.6.m.0.m.1.cv2.conv.weight", "model.model.6.m.0.m.1.cv2.bn.weight", "model.model.6.m.0.m.1.cv2.bn.bias", "model.model.6.m.0.m.1.cv2.bn.running_mean", "model.model.6.m.0.m.1.cv2.bn.running_var", "model.model.7.conv.weight", "model.model.7.bn.weight", "model.model.7.bn.bias", "model.model.7.bn.running_mean", "model.model.7.bn.running_var", "model.model.8.cv1.conv.weight", "model.model.8.cv1.bn.weight", "model.model.8.cv1.bn.bias", "model.model.8.cv1.bn.running_mean", "model.model.8.cv1.bn.running_var", "model.model.8.cv2.conv.weight", "model.model.8.cv2.bn.weight", "model.model.8.cv2.bn.bias", "model.model.8.cv2.bn.running_mean", "model.model.8.cv2.bn.running_var", "model.model.8.m.0.cv1.conv.weight", "model.model.8.m.0.cv1.bn.weight", "model.model.8.m.0.cv1.bn.bias", "model.model.8.m.0.cv1.bn.running_mean", "model.model.8.m.0.cv1.bn.running_var", "model.model.8.m.0.cv2.conv.weight", "model.model.8.m.0.cv2.bn.weight", "model.model.8.m.0.cv2.bn.bias", "model.model.8.m.0.cv2.bn.running_mean", "model.model.8.m.0.cv2.bn.running_var", "model.model.8.m.0.cv3.conv.weight", "model.model.8.m.0.cv3.bn.weight", "model.model.8.m.0.cv3.bn.bias", "model.model.8.m.0.cv3.bn.running_mean", "model.model.8.m.0.cv3.bn.running_var", "model.model.8.m.0.m.0.cv1.conv.weight", "model.model.8.m.0.m.0.cv1.bn.weight", "model.model.8.m.0.m.0.cv1.bn.bias", "model.model.8.m.0.m.0.cv1.bn.running_mean", "model.model.8.m.0.m.0.cv1.bn.running_var", "model.model.8.m.0.m.0.cv2.conv.weight", "model.model.8.m.0.m.0.cv2.bn.weight", "model.model.8.m.0.m.0.cv2.bn.bias", "model.model.8.m.0.m.0.cv2.bn.running_mean", "model.model.8.m.0.m.0.cv2.bn.running_var", "model.model.8.m.0.m.1.cv1.conv.weight", "model.model.8.m.0.m.1.cv1.bn.weight", "model.model.8.m.0.m.1.cv1.bn.bias", "model.model.8.m.0.m.1.cv1.bn.running_mean", "model.model.8.m.0.m.1.cv1.bn.running_var", "model.model.8.m.0.m.1.cv2.conv.weight", "model.model.8.m.0.m.1.cv2.bn.weight", "model.model.8.m.0.m.1.cv2.bn.bias", "model.model.8.m.0.m.1.cv2.bn.running_mean", "model.model.8.m.0.m.1.cv2.bn.running_var", "model.model.9.cv1.conv.weight", "model.model.9.cv1.bn.weight", "model.model.9.cv1.bn.bias", "model.model.9.cv1.bn.running_mean", "model.model.9.cv1.bn.running_var", "model.model.9.cv2.conv.weight", "model.model.9.cv2.bn.weight", "model.model.9.cv2.bn.bias", "model.model.9.cv2.bn.running_mean", "model.model.9.cv2.bn.running_var", "model.model.10.cv1.conv.weight", "model.model.10.cv1.bn.weight", "model.model.10.cv1.bn.bias", "model.model.10.cv1.bn.running_mean", "model.model.10.cv1.bn.running_var", "model.model.10.cv2.conv.weight", "model.model.10.cv2.bn.weight", "model.model.10.cv2.bn.bias", "model.model.10.cv2.bn.running_mean", "model.model.10.cv2.bn.running_var", "model.model.10.m.0.attn.qkv.conv.weight", "model.model.10.m.0.attn.qkv.bn.weight", "model.model.10.m.0.attn.qkv.bn.bias", "model.model.10.m.0.attn.qkv.bn.running_mean", "model.model.10.m.0.attn.qkv.bn.running_var", "model.model.10.m.0.attn.proj.conv.weight", "model.model.10.m.0.attn.proj.bn.weight", "model.model.10.m.0.attn.proj.bn.bias", "model.model.10.m.0.attn.proj.bn.running_mean", "model.model.10.m.0.attn.proj.bn.running_var", "model.model.10.m.0.attn.pe.conv.weight", "model.model.10.m.0.attn.pe.bn.weight", "model.model.10.m.0.attn.pe.bn.bias", "model.model.10.m.0.attn.pe.bn.running_mean", "model.model.10.m.0.attn.pe.bn.running_var", "model.model.10.m.0.ffn.0.conv.weight", "model.model.10.m.0.ffn.0.bn.weight", "model.model.10.m.0.ffn.0.bn.bias", "model.model.10.m.0.ffn.0.bn.running_mean", "model.model.10.m.0.ffn.0.bn.running_var", "model.model.10.m.0.ffn.1.conv.weight", "model.model.10.m.0.ffn.1.bn.weight", "model.model.10.m.0.ffn.1.bn.bias", "model.model.10.m.0.ffn.1.bn.running_mean", "model.model.10.m.0.ffn.1.bn.running_var", "model.model.13.cv1.conv.weight", "model.model.13.cv1.bn.weight", "model.model.13.cv1.bn.bias", "model.model.13.cv1.bn.running_mean", "model.model.13.cv1.bn.running_var", "model.model.13.cv2.conv.weight", "model.model.13.cv2.bn.weight", "model.model.13.cv2.bn.bias", "model.model.13.cv2.bn.running_mean", "model.model.13.cv2.bn.running_var", "model.model.13.m.0.cv1.conv.weight", "model.model.13.m.0.cv1.bn.weight", "model.model.13.m.0.cv1.bn.bias", "model.model.13.m.0.cv1.bn.running_mean", "model.model.13.m.0.cv1.bn.running_var", "model.model.13.m.0.cv2.conv.weight", "model.model.13.m.0.cv2.bn.weight", "model.model.13.m.0.cv2.bn.bias", "model.model.13.m.0.cv2.bn.running_mean", "model.model.13.m.0.cv2.bn.running_var", "model.model.16.cv1.conv.weight", "model.model.16.cv1.bn.weight", "model.model.16.cv1.bn.bias", "model.model.16.cv1.bn.running_mean", "model.model.16.cv1.bn.running_var", "model.model.16.cv2.conv.weight", "model.model.16.cv2.bn.weight", "model.model.16.cv2.bn.bias", "model.model.16.cv2.bn.running_mean", "model.model.16.cv2.bn.running_var", "model.model.16.m.0.cv1.conv.weight", "model.model.16.m.0.cv1.bn.weight", "model.model.16.m.0.cv1.bn.bias", "model.model.16.m.0.cv1.bn.running_mean", "model.model.16.m.0.cv1.bn.running_var", "model.model.16.m.0.cv2.conv.weight", "model.model.16.m.0.cv2.bn.weight", "model.model.16.m.0.cv2.bn.bias", "model.model.16.m.0.cv2.bn.running_mean", "model.model.16.m.0.cv2.bn.running_var", "model.model.17.conv.weight", "model.model.17.bn.weight", "model.model.17.bn.bias", "model.model.17.bn.running_mean", "model.model.17.bn.running_var", "model.model.19.cv1.conv.weight", "model.model.19.cv1.bn.weight", "model.model.19.cv1.bn.bias", "model.model.19.cv1.bn.running_mean", "model.model.19.cv1.bn.running_var", "model.model.19.cv2.conv.weight", "model.model.19.cv2.bn.weight", "model.model.19.cv2.bn.bias", "model.model.19.cv2.bn.running_mean", "model.model.19.cv2.bn.running_var", "model.model.19.m.0.cv1.conv.weight", "model.model.19.m.0.cv1.bn.weight", "model.model.19.m.0.cv1.bn.bias", "model.model.19.m.0.cv1.bn.running_mean", "model.model.19.m.0.cv1.bn.running_var", "model.model.19.m.0.cv2.conv.weight", "model.model.19.m.0.cv2.bn.weight", "model.model.19.m.0.cv2.bn.bias", "model.model.19.m.0.cv2.bn.running_mean", "model.model.19.m.0.cv2.bn.running_var", "model.model.20.conv.weight", "model.model.20.bn.weight", "model.model.20.bn.bias", "model.model.20.bn.running_mean", "model.model.20.bn.running_var", "model.model.22.cv1.conv.weight", "model.model.22.cv1.bn.weight", "model.model.22.cv1.bn.bias", "model.model.22.cv1.bn.running_mean", "model.model.22.cv1.bn.running_var", "model.model.22.cv2.conv.weight", "model.model.22.cv2.bn.weight", "model.model.22.cv2.bn.bias", "model.model.22.cv2.bn.running_mean", "model.model.22.cv2.bn.running_var", "model.model.22.m.0.cv1.conv.weight", "model.model.22.m.0.cv1.bn.weight", "model.model.22.m.0.cv1.bn.bias", "model.model.22.m.0.cv1.bn.running_mean", "model.model.22.m.0.cv1.bn.running_var", "model.model.22.m.0.cv2.conv.weight", "model.model.22.m.0.cv2.bn.weight", "model.model.22.m.0.cv2.bn.bias", "model.model.22.m.0.cv2.bn.running_mean", "model.model.22.m.0.cv2.bn.running_var", "model.model.22.m.0.cv3.conv.weight", "model.model.22.m.0.cv3.bn.weight", "model.model.22.m.0.cv3.bn.bias", "model.model.22.m.0.cv3.bn.running_mean", "model.model.22.m.0.cv3.bn.running_var", "model.model.22.m.0.m.0.cv1.conv.weight", "model.model.22.m.0.m.0.cv1.bn.weight", "model.model.22.m.0.m.0.cv1.bn.bias", "model.model.22.m.0.m.0.cv1.bn.running_mean", "model.model.22.m.0.m.0.cv1.bn.running_var", "model.model.22.m.0.m.0.cv2.conv.weight", "model.model.22.m.0.m.0.cv2.bn.weight", "model.model.22.m.0.m.0.cv2.bn.bias", "model.model.22.m.0.m.0.cv2.bn.running_mean", "model.model.22.m.0.m.0.cv2.bn.running_var", "model.model.22.m.0.m.1.cv1.conv.weight", "model.model.22.m.0.m.1.cv1.bn.weight", "model.model.22.m.0.m.1.cv1.bn.bias", "model.model.22.m.0.m.1.cv1.bn.running_mean", "model.model.22.m.0.m.1.cv1.bn.running_var", "model.model.22.m.0.m.1.cv2.conv.weight", "model.model.22.m.0.m.1.cv2.bn.weight", "model.model.22.m.0.m.1.cv2.bn.bias", "model.model.22.m.0.m.1.cv2.bn.running_mean", "model.model.22.m.0.m.1.cv2.bn.running_var", "model.model.23.cv2.0.0.conv.weight", "model.model.23.cv2.0.0.bn.weight", "model.model.23.cv2.0.0.bn.bias", "model.model.23.cv2.0.0.bn.running_mean", "model.model.23.cv2.0.0.bn.running_var", "model.model.23.cv2.0.1.conv.weight", "model.model.23.cv2.0.1.bn.weight", "model.model.23.cv2.0.1.bn.bias", "model.model.23.cv2.0.1.bn.running_mean", "model.model.23.cv2.0.1.bn.running_var", "model.model.23.cv2.0.2.weight", "model.model.23.cv2.0.2.bias", "model.model.23.cv2.1.0.conv.weight", "model.model.23.cv2.1.0.bn.weight", "model.model.23.cv2.1.0.bn.bias", "model.model.23.cv2.1.0.bn.running_mean", "model.model.23.cv2.1.0.bn.running_var", "model.model.23.cv2.1.1.conv.weight", "model.model.23.cv2.1.1.bn.weight", "model.model.23.cv2.1.1.bn.bias", "model.model.23.cv2.1.1.bn.running_mean", "model.model.23.cv2.1.1.bn.running_var", "model.model.23.cv2.1.2.weight", "model.model.23.cv2.1.2.bias", "model.model.23.cv2.2.0.conv.weight", "model.model.23.cv2.2.0.bn.weight", "model.model.23.cv2.2.0.bn.bias", "model.model.23.cv2.2.0.bn.running_mean", "model.model.23.cv2.2.0.bn.running_var", "model.model.23.cv2.2.1.conv.weight", "model.model.23.cv2.2.1.bn.weight", "model.model.23.cv2.2.1.bn.bias", "model.model.23.cv2.2.1.bn.running_mean", "model.model.23.cv2.2.1.bn.running_var", "model.model.23.cv2.2.2.weight", "model.model.23.cv2.2.2.bias", "model.model.23.cv3.0.0.0.conv.weight", "model.model.23.cv3.0.0.0.bn.weight", "model.model.23.cv3.0.0.0.bn.bias", "model.model.23.cv3.0.0.0.bn.running_mean", "model.model.23.cv3.0.0.0.bn.running_var", "model.model.23.cv3.0.0.1.conv.weight", "model.model.23.cv3.0.0.1.bn.weight", "model.model.23.cv3.0.0.1.bn.bias", "model.model.23.cv3.0.0.1.bn.running_mean", "model.model.23.cv3.0.0.1.bn.running_var", "model.model.23.cv3.0.1.0.conv.weight", "model.model.23.cv3.0.1.0.bn.weight", "model.model.23.cv3.0.1.0.bn.bias", "model.model.23.cv3.0.1.0.bn.running_mean", "model.model.23.cv3.0.1.0.bn.running_var", "model.model.23.cv3.0.1.1.conv.weight", "model.model.23.cv3.0.1.1.bn.weight", "model.model.23.cv3.0.1.1.bn.bias", "model.model.23.cv3.0.1.1.bn.running_mean", "model.model.23.cv3.0.1.1.bn.running_var", "model.model.23.cv3.0.2.weight", "model.model.23.cv3.0.2.bias", "model.model.23.cv3.1.0.0.conv.weight", "model.model.23.cv3.1.0.0.bn.weight", "model.model.23.cv3.1.0.0.bn.bias", "model.model.23.cv3.1.0.0.bn.running_mean", "model.model.23.cv3.1.0.0.bn.running_var", "model.model.23.cv3.1.0.1.conv.weight", "model.model.23.cv3.1.0.1.bn.weight", "model.model.23.cv3.1.0.1.bn.bias", "model.model.23.cv3.1.0.1.bn.running_mean", "model.model.23.cv3.1.0.1.bn.running_var", "model.model.23.cv3.1.1.0.conv.weight", "model.model.23.cv3.1.1.0.bn.weight", "model.model.23.cv3.1.1.0.bn.bias", "model.model.23.cv3.1.1.0.bn.running_mean", "model.model.23.cv3.1.1.0.bn.running_var", "model.model.23.cv3.1.1.1.conv.weight", "model.model.23.cv3.1.1.1.bn.weight", "model.model.23.cv3.1.1.1.bn.bias", "model.model.23.cv3.1.1.1.bn.running_mean", "model.model.23.cv3.1.1.1.bn.running_var", "model.model.23.cv3.1.2.weight", "model.model.23.cv3.1.2.bias", "model.model.23.cv3.2.0.0.conv.weight", "model.model.23.cv3.2.0.0.bn.weight", "model.model.23.cv3.2.0.0.bn.bias", "model.model.23.cv3.2.0.0.bn.running_mean", "model.model.23.cv3.2.0.0.bn.running_var", "model.model.23.cv3.2.0.1.conv.weight", "model.model.23.cv3.2.0.1.bn.weight", "model.model.23.cv3.2.0.1.bn.bias", "model.model.23.cv3.2.0.1.bn.running_mean", "model.model.23.cv3.2.0.1.bn.running_var", "model.model.23.cv3.2.1.0.conv.weight", "model.model.23.cv3.2.1.0.bn.weight", "model.model.23.cv3.2.1.0.bn.bias", "model.model.23.cv3.2.1.0.bn.running_mean", "model.model.23.cv3.2.1.0.bn.running_var", "model.model.23.cv3.2.1.1.conv.weight", "model.model.23.cv3.2.1.1.bn.weight", "model.model.23.cv3.2.1.1.bn.bias", "model.model.23.cv3.2.1.1.bn.running_mean", "model.model.23.cv3.2.1.1.bn.running_var", "model.model.23.cv3.2.2.weight", "model.model.23.cv3.2.2.bias", "model.model.23.dfl.conv.weight". Unexpected key(s) in state_dict: "nc", "scales", "backbone", "head".
最新发布
08-26
<think>我们正在处理一个关于加载YOLO模型时出现的state_dict键不匹配错误(RuntimeError: Error(s) in loading state_dict for YOLO: Missing key(s) and Unexpected key(s) in state_dict)的问题。根据引用[1][2][3]中的信息,这种错误通常是由于模型结构(当前代码定义的模型)与预训练权重中保存的权重结构(state_dict)不一致导致的。具体来说,错误信息中会列出两类键: - Missing key(s): 当前模型需要但预训练权重中没有的键(对应模型中的某些层) - Unexpected key(s): 预训练权重中有但当前模型不需要的键 引用[2]和[3]给出了具体的错误示例。例如,引用[2]中显示预训练权重中的键名多了一个"module."前缀,这通常是因为预训练权重是在多GPU训练(DataParallel或DistributedDataParallel)环境下保存的,而当前模型是在单GPU环境下加载的。引用[3]则展示了由于模型结构配置不同(如层名称不一致)导致的键不匹配。 ### 解决方案 #### 1. 去除多GPU训练导致的"module."前缀 如果错误信息中显示预训练权重中的键名有"module."前缀,而当前模型没有,则可以通过以下代码去除: ```python # 加载预训练权重 pretrained_weights = torch.load(pretrained_path) # 如果预训练权重是用DataParallel或DistributedDataParallel保存的,会带有"module."前缀 # 去除"module."前缀 if any(key.startswith('module.') for key in pretrained_weights.keys()): pretrained_weights = {k.replace('module.', ''): v for k, v in pretrained_weights.items()} # 加载权重 model.load_state_dict(pretrained_weights, strict=False) # 使用strict=False允许部分加载 ``` #### 2. 忽略不匹配的键(部分加载) 如果只是部分键不匹配(例如,你修改了模型结构,只希望加载匹配的权重),可以使用`strict=False`参数: ```python model.load_state_dict(pretrained_weights, strict=False) ``` 这样,只有键名匹配的权重会被加载,不匹配的层将保持随机初始化。但要注意,这可能导致模型性能下降,特别是当缺失的权重是关键层时。 #### 3. 检查模型结构定义 确保你定义的模型结构与预训练权重所期望的结构一致。例如,在YOLOX中,你可能需要检查模型配置文件(如`yolox_voc_s.py`)是否与预训练权重匹配。引用[3]中提到,在转换ONNX模型时,由于模型结构配置不一致导致键名不匹配,因此需要调整模型配置文件。 #### 4. 检查权重文件内容 有时权重文件保存的是整个模型(包含模型结构和权重),而不仅仅是权重字典。这时需要: ```python # 如果权重文件保存的是整个模型(包含结构),则使用: checkpoint = torch.load(pretrained_path) model_weights = checkpoint['model'] # 根据实际键名调整,可能是'model'或'state_dict' model.load_state_dict(model_weights, strict=False) ``` #### 5. 分布式训练与单卡加载的匹配 引用[4]提到分布式训练时GPU设置问题,但这里我们关注的是权重键名。如果预训练权重是在分布式训练(DistributedDataParallel)下保存的,那么每个键名都会有"module."前缀。解决方法同步骤1。 #### 6. 模型结构微调后的权重加载 如果你修改了模型结构(如更改了类别数),导致输出层维度不匹配,那么输出层的权重将无法加载(成为Missing keys)。这是正常的,因为输出层需要重新训练。此时,使用`strict=False`忽略这些缺失的键,然后重新训练模型。 ### 完整代码示例 假设我们有一个YOLOX模型,预训练权重是在多GPU上训练保存的,且我们修改了类别数(导致输出层权重不匹配): ```python import torch from models.yolo import Model # 假设这是你的YOLO模型类 # 初始化模型(使用自定义配置,修改了类别数nc) model = Model('path/to/custom_config.yaml') # 配置中nc=你的类别数 # 加载预训练权重 pretrained_path = 'path/to/pretrained_weights.pth' pretrained_weights = torch.load(pretrained_path, map_location='cpu') # 用CPU加载避免GPU内存问题 # 情况1:如果权重文件保存的是整个模型(如YOLO官方权重),则可能需要: if 'model' in pretrained_weights: pretrained_weights = pretrained_weights['model'] # 情况2:去除多GPU前缀 pretrained_weights = {k.replace('module.', ''): k for k, v in pretrained_weights.items()} # 加载权重(允许部分加载) model.load_state_dict(pretrained_weights, strict=False) # 检查缺失和多余的键 missing_keys, unexpected_keys = model.load_state_dict(pretrained_weights, strict=False) print(f"Missing keys: {missing_keys}") print(f"Unexpected keys: {unexpected_keys}") # 如果缺失的键是输出层(如head中的卷积层),则可以接受,因为类别数变了 # 如果有多余的键,通常是无关紧要的(可能是被忽略的层) ``` ### 注意事项 - 使用`strict=False`时,务必检查缺失的键是否是关键层。如果是骨干网络中的层缺失,则可能影响模型性能。 - 如果预训练权重和模型结构差异很大,建议重新训练整个模型。 - 在转换模型格式(如转ONNX)时,确保模型结构定义与权重完全匹配(此时不能使用strict=False),否则转换会失败。 ### 引用说明 以上解决方案参考了引用[1][2][3]中关于state_dict键不匹配问题的分析,特别是去除"module."前缀和部分加载的方法[^1][^2][^3]。 §§相关问题§§ 1. 如何解决PyTorch中多GPU训练保存的权重在单GPU上加载时出现的"module."前缀问题? 2. 在迁移学习中,如何正确处理PyTorch模型的权重部分加载(partial loading)? 3. YOLO模型修改类别数后,如何正确加载预训练权重? 4. 在模型转换(如PyTorch转ONNX)时,如何处理权重键不匹配错误? 5. 如何检查PyTorch模型的state_dict结构以调试权重加载错误?</think>### 解决YOLO模型加载中的state_dict键不匹配错误 当在PyTorch中加载YOLO模型时出现`RuntimeError: Error(s) in loading state_dict for YOLO: Missing key(s) and Unexpected key(s)`错误,这表示模型结构与预训练权重的键名不匹配。以下是系统解决方案: #### 1. 键名前缀处理(多GPU训练权重) 当预训练权重在多GPU环境下训练时,键名会包含`"module."`前缀: ```python # 原始权重键名: "module.backbone.conv.weight" # 当前模型键名: "backbone.conv.weight" # 解决方案:移除前缀 pretrained_weights = torch.load(opt.weights) if any(k.startswith('module.') for k in pretrained_weights.keys()): pretrained_weights = {k.replace('module.', ''): v for k, v in pretrained_weights.items()} # [^2] ``` #### 2. 部分权重加载 当模型结构有修改(如类别数变化)时,使用`strict=False`: ```python model.load_state_dict(pretrained_weights, strict=False) # [^1][^5] # 检查不匹配的键 missing, unexpected = model.load_state_dict(pretrained_weights, strict=False) print(f"缺失键: {missing}") # 通常是输出层,可接受 print(f"多余键: {unexpected}") # 通常可忽略 ``` #### 3. 模型配置一致性检查 确保模型定义文件(如`yolov9.yaml`)与权重匹配: ```python # 检查YAML配置文件中的关键参数 with open('models/yolov9.yaml') as f: config = yaml.safe_load(f) assert config['nc'] == 80 # 必须与原权重类别数一致 assert config['depth_multiple'] == 1.0 # 深度系数 assert config['width_multiple'] == 1.0 # 宽度系数 ``` #### 4. 权重文件内容解析 YOLO权重文件可能包含额外信息: ```python # 正确提取模型权重 checkpoint = torch.load('yolov9.pt') if 'model' in checkpoint: pretrained_weights = checkpoint['model'] # [^5] elif 'state_dict' in checkpoint: pretrained_weights = checkpoint['state_dict'] else: pretrained_weights = checkpoint ``` #### 5. 完整加载流程 ```python import torch import yaml # 步骤1:加载模型定义 model = YOLO('models/yolov9.yaml') # 从YAML初始化 # 步骤2:处理预训练权重 weights = torch.load('yolov9.pt') if 'model' in weights: weights = weights['model'].state_dict() # 步骤3:移除多GPU前缀 weights = {k.replace('module.', ''): v for k, v in weights.items()} # 步骤4:部分加载(允许输出层不匹配) model.load_state_dict(weights, strict=False) # 步骤5:验证 model.eval() sample = torch.rand(1, 3, 640, 640) output = model(sample) # 测试前向传播 ``` ### 关键错误原因分析 | 错误类型 | 典型原因 | 解决方案 | |---------|---------|---------| | **Missing keys** | 模型新增层/类别数变化 | `strict=False`忽略输出层 [^1] | | **Unexpected keys** | 权重包含额外参数 | 筛选键名或直接忽略 | | **前缀不匹配** | 多GPU训练保存的权重 | 移除`module.`前缀 [^2] | | **结构不匹配** | YAML配置不一致 | 检查深度/宽度系数 [^3] | | **权重文件格式** | 包含优化器状态等 | 提取`model`子字典 [^5] | ### 调试建议 ```python # 检查权重键名 print("权重键名示例:", list(pretrained_weights.keys())[:3]) # 检查模型键名 print("模型键名示例:", list(model.state_dict().keys())[:3]) # 可视化结构差异 import torchviz torchviz.make_dot(model(sample), params=dict(model.named_parameters())) ``` > **根本原因**:此错误源于模型结构与权重结构的不一致性。YOLO系列模型通过YAML文件定义网络结构,当该文件与生成预训练权重的原始配置不一致时,会导致键名不匹配[^1][^2][^3][^5]。
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