F1. Same Sum Blocks (Easy) (贪心)

博客给出题目链接,题意是从一串数组a[]中找出尽可能多的对 [ li,ri ],使这些对元素之和相等。还给出网上代码及一种比较特别的STL写法,但未注明出处。

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题目链接:哆啦A梦传送门

题意:

给出一串a[],从中找出最可能多的对 [ li,ri ] ,这些对的和(al+al+1+...+ar)都相等。

题解:

见网上代码:

#include<cstdio>
#include<cstring>
#include<map>

using namespace std;

map<int ,int > ma,item;
map<int,int >:: iterator it;

int n,a[100];
int main()
{

    int n;
    scanf("%d",&n);

    for(int i=1;i<=n;i++)
        scanf("%d",&a[i]);

    for(int i=1;i<=n;i++) ///枚举右端点
    {
        int sum=0;
        for(int j=i;j;j--)///不能正向,因为正向的话,可能会有交集
        {
            sum+=a[j];
            if(item[sum]<j){ ///之前的右端点要小于此时的左端点
                item[sum]=i;
                ma[sum]++;
            }
        }
    }

//    printf("ma=%d\n",ma[100]);
    int ans=-1;///遍历找到集合最毒的边长
    for(it=ma.begin();it!=ma.end();it++)
    {
        if(it->second>ma[ans]) ans=it->first;
    }

    printf("%d\n",ma[ans]);

//    printf("ans=%d\n",ans);
    int r=0;
    for(int i=1;i<=n;i++)
    {
        int sum=0;
        for(int j=i;j>r;j--){
            sum+=a[j];
            if(sum==ans){
                printf("%d %d\n",j,i);
                r=i;
            }
        }
    }
    return 0;

}

 

再来份比较骚的STL写法,不是我的,是网上神犇的,注明不了出处。、

#include <bits/stdc++.h>
using namespace std;
const int maxn=1e5+5;
map<int,int>m;
int n,v[maxn];
int main(){
	cin>>n;
	for(int i=1;i<=n;i++)cin>>v[i],v[i]+=v[i - 1];
	map<int,int>temp;
	for(int i=1;i<=n;i++){
        for(int j=i;j>=1;j--){
			int res = v[i]-v[j-1];
			if(temp[res]<j){
                temp[res]=i;
				m[res]++;
			}
		}
	}
	int ans=-1;
	for(map<int,int>::iterator it=m.begin();it!=m.end();it++){
		if(it->second>m[ans])ans=it->first;
	}
	cout<<m[a ns]<<endl;
	int r=0;
	for(int i=1;i<=n;i++){
		for(int j=i;j>r;j--){
			if (v[i]-v[j-1]==ans){
				r=i;
				cout<<j<<' '<<i<<endl;
			}
		}
	}
	return 0;
}

 

 

raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for PResNet: Missing key(s) in state_dict: "conv1.conv1_1.conv.weight", "conv1.conv1_1.norm.weight", "conv1.conv1_1.norm.bias", "conv1.conv1_1.norm.running_mean", "conv1.conv1_1.norm.running_var", "conv1.conv1_2.conv.weight", "conv1.conv1_2.norm.weight", "conv1.conv1_2.norm.bias", "conv1.conv1_2.norm.running_mean", "conv1.conv1_2.norm.running_var", "conv1.conv1_3.conv.weight", "conv1.conv1_3.norm.weight", "conv1.conv1_3.norm.bias", "conv1.conv1_3.norm.running_mean", "conv1.conv1_3.norm.running_var", "res_layers.0.blocks.0.branch2a.conv.weight", "res_layers.0.blocks.0.branch2a.norm.weight", "res_layers.0.blocks.0.branch2a.norm.bias", "res_layers.0.blocks.0.branch2a.norm.running_mean", "res_layers.0.blocks.0.branch2a.norm.running_var", "res_layers.0.blocks.0.branch2b.conv.weight", "res_layers.0.blocks.0.branch2b.norm.weight", "res_layers.0.blocks.0.branch2b.norm.bias", "res_layers.0.blocks.0.branch2b.norm.running_mean", "res_layers.0.blocks.0.branch2b.norm.running_var", "res_layers.0.blocks.0.branch2c.conv.weight", "res_layers.0.blocks.0.branch2c.norm.weight", "res_layers.0.blocks.0.branch2c.norm.bias", "res_layers.0.blocks.0.branch2c.norm.running_mean", "res_layers.0.blocks.0.branch2c.norm.running_var", "res_layers.0.blocks.0.short.conv.weight", "res_layers.0.blocks.0.short.norm.weight", "res_layers.0.blocks.0.short.norm.bias", "res_layers.0.blocks.0.short.norm.running_mean", "res_layers.0.blocks.0.short.norm.running_var", "res_layers.0.blocks.1.branch2a.conv.weight", "res_layers.0.blocks.1.branch2a.norm.weight", "res_layers.0.blocks.1.branch2a.norm.bias", "res_layers.0.blocks.1.branch2a.norm.running_mean", "res_layers.0.blocks.1.branch2a.norm.running_var", "res_layers.0.blocks.1.branch2b.conv.weight", "res_layers.0.blocks.1.branch2b.norm.weight", "res_layers.0.blocks.1.branch2b.norm.bias", "res_layers.0.blocks.1.branch2b.norm.running_mean", "res_layers.0.blocks.1.branch2b.norm.running_var", "res_layers.0.blocks.1.branch2c.conv.weight", "res_layers.0.blocks.1.branch2c.norm.weight", "res_layers.0.blocks.1.branch2c.norm.bias", "res_layers.0.blocks.1.branch2c.norm.running_mean", "res_layers.0.blocks.1.branch2c.norm.running_var", "res_layers.0.blocks.2.branch2a.conv.weight", "res_layers.0.blocks.2.branch2a.norm.weight", "res_layers.0.blocks.2.branch2a.norm.bias", "res_layers.0.blocks.2.branch2a.norm.running_mean", "res_layers.0.blocks.2.branch2a.norm.running_var", "res_layers.0.blocks.2.branch2b.conv.weight", "res_layers.0.blocks.2.branch2b.norm.weight", "res_layers.0.blocks.2.branch2b.norm.bias", "res_layers.0.blocks.2.branch2b.norm.running_mean", "res_layers.0.blocks.2.branch2b.norm.running_var", "res_layers.0.blocks.2.branch2c.conv.weight", "res_layers.0.blocks.2.branch2c.norm.weight", "res_layers.0.blocks.2.branch2c.norm.bias", "res_layers.0.blocks.2.branch2c.norm.running_mean", "res_layers.0.blocks.2.branch2c.norm.running_var", "res_layers.1.blocks.0.branch2a.conv.weight", "res_layers.1.blocks.0.branch2a.norm.weight", "res_layers.1.blocks.0.branch2a.norm.bias", "res_layers.1.blocks.0.branch2a.norm.running_mean", "res_layers.1.blocks.0.branch2a.norm.running_var", "res_layers.1.blocks.0.branch2b.conv.weight", "res_layers.1.blocks.0.branch2b.norm.weight", "res_layers.1.blocks.0.branch2b.norm.bias", "res_layers.1.blocks.0.branch2b.norm.running_mean", "res_layers.1.blocks.0.branch2b.norm.running_var", "res_layers.1.blocks.0.branch2c.conv.weight", "res_layers.1.blocks.0.branch2c.norm.weight", "res_layers.1.blocks.0.branch2c.norm.bias", "res_layers.1.blocks.0.branch2c.norm.running_mean", "res_layers.1.blocks.0.branch2c.norm.running_var", "res_layers.1.blocks.0.short.conv.conv.weight", "res_layers.1.blocks.0.short.conv.norm.weight", "res_layers.1.blocks.0.short.conv.norm.bias", "res_layers.1.blocks.0.short.conv.norm.running_mean", "res_layers.1.blocks.0.short.conv.norm.running_var", "res_layers.1.blocks.1.branch2a.conv.weight", "res_layers.1.blocks.1.branch2a.norm.weight", "res_layers.1.blocks.1.branch2a.norm.bias", "res_layers.1.blocks.1.branch2a.norm.running_mean", "res_layers.1.blocks.1.branch2a.norm.running_var", "res_layers.1.blocks.1.branch2b.conv.weight", "res_layers.1.blocks.1.branch2b.norm.weight", "res_layers.1.blocks.1.branch2b.norm.bias", "res_layers.1.blocks.1.branch2b.norm.running_mean", "res_layers.1.blocks.1.branch2b.norm.running_var", "res_layers.1.blocks.1.branch2c.conv.weight", "res_layers.1.blocks.1.branch2c.norm.weight", "res_layers.1.blocks.1.branch2c.norm.bias", "res_layers.1.blocks.1.branch2c.norm.running_mean", "res_layers.1.blocks.1.branch2c.norm.running_var", "res_layers.1.blocks.2.branch2a.conv.weight", "res_layers.1.blocks.2.branch2a.norm.weight", "res_layers.1.blocks.2.branch2a.norm.bias", "res_layers.1.blocks.2.branch2a.norm.running_mean", "res_layers.1.blocks.2.branch2a.norm.running_var", "res_layers.1.blocks.2.branch2b.conv.weight", "res_layers.1.blocks.2.branch2b.norm.weight", "res_layers.1.blocks.2.branch2b.norm.bias", "res_layers.1.blocks.2.branch2b.norm.running_mean", "res_layers.1.blocks.2.branch2b.norm.running_var", "res_layers.1.blocks.2.branch2c.conv.weight", "res_layers.1.blocks.2.branch2c.norm.weight", "res_layers.1.blocks.2.branch2c.norm.bias", "res_layers.1.blocks.2.branch2c.norm.running_mean", "res_layers.1.blocks.2.branch2c.norm.running_var", "res_layers.1.blocks.3.branch2a.conv.weight", "res_layers.1.blocks.3.branch2a.norm.weight", "res_layers.1.blocks.3.branch2a.norm.bias", "res_layers.1.blocks.3.branch2a.norm.running_mean", "res_layers.1.blocks.3.branch2a.norm.running_var", "res_layers.1.blocks.3.branch2b.conv.weight", "res_layers.1.blocks.3.branch2b.norm.weight", "res_layers.1.blocks.3.branch2b.norm.bias", "res_layers.1.blocks.3.branch2b.norm.running_mean", "res_layers.1.blocks.3.branch2b.norm.running_var", "res_layers.1.blocks.3.branch2c.conv.weight", "res_layers.1.blocks.3.branch2c.norm.weight", "res_layers.1.blocks.3.branch2c.norm.bias", "res_layers.1.blocks.3.branch2c.norm.running_mean", "res_layers.1.blocks.3.branch2c.norm.running_var", "res_layers.2.blocks.0.branch2a.conv.weight", "res_layers.2.blocks.0.branch2a.norm.weight", "res_layers.2.blocks.0.branch2a.norm.bias", "res_layers.2.blocks.0.branch2a.norm.running_mean", "res_layers.2.blocks.0.branch2a.norm.running_var", "res_layers.2.blocks.0.branch2b.conv.weight", "res_layers.2.blocks.0.branch2b.norm.weight", "res_layers.2.blocks.0.branch2b.norm.bias", "res_layers.2.blocks.0.branch2b.norm.running_mean", "res_layers.2.blocks.0.branch2b.norm.running_var", "res_layers.2.blocks.0.branch2c.conv.weight", "res_layers.2.blocks.0.branch2c.norm.weight", "res_layers.2.blocks.0.branch2c.norm.bias", "res_layers.2.blocks.0.branch2c.norm.running_mean", "res_layers.2.blocks.0.branch2c.norm.running_var", "res_layers.2.blocks.0.short.conv.conv.weight", "res_layers.2.blocks.0.short.conv.norm.weight", "res_layers.2.blocks.0.short.conv.norm.bias", "res_layers.2.blocks.0.short.conv.norm.running_mean", "res_layers.2.blocks.0.short.conv.norm.running_var", "res_layers.2.blocks.1.branch2a.conv.weight", "res_layers.2.blocks.1.branch2a.norm.weight", "res_layers.2.blocks.1.branch2a.norm.bias", "res_layers.2.blocks.1.branch2a.norm.running_mean", "res_layers.2.blocks.1.branch2a.norm.running_var", "res_layers.2.blocks.1.branch2b.conv.weight", "res_layers.2.blocks.1.branch2b.norm.weight", "res_layers.2.blocks.1.branch2b.norm.bias", "res_layers.2.blocks.1.branch2b.norm.running_mean", "res_layers.2.blocks.1.branch2b.norm.running_var", "res_layers.2.blocks.1.branch2c.conv.weight", "res_layers.2.blocks.1.branch2c.norm.weight", "res_layers.2.blocks.1.branch2c.norm.bias", "res_layers.2.blocks.1.branch2c.norm.running_mean", "res_layers.2.blocks.1.branch2c.norm.running_var", "res_layers.2.blocks.2.branch2a.conv.weight", "res_layers.2.blocks.2.branch2a.norm.weight", "res_layers.2.blocks.2.branch2a.norm.bias", "res_layers.2.blocks.2.branch2a.norm.running_mean", "res_layers.2.blocks.2.branch2a.norm.running_var", "res_layers.2.blocks.2.branch2b.conv.weight", "res_layers.2.blocks.2.branch2b.norm.weight", "res_layers.2.blocks.2.branch2b.norm.bias", "res_layers.2.blocks.2.branch2b.norm.running_mean", "res_layers.2.blocks.2.branch2b.norm.running_var", "res_layers.2.blocks.2.branch2c.conv.weight", "res_layers.2.blocks.2.branch2c.norm.weight", "res_layers.2.blocks.2.branch2c.norm.bias", "res_layers.2.blocks.2.branch2c.norm.running_mean", "res_layers.2.blocks.2.branch2c.norm.running_var", "res_layers.2.blocks.3.branch2a.conv.weight", "res_layers.2.blocks.3.branch2a.norm.weight", "res_layers.2.blocks.3.branch2a.norm.bias", "res_layers.2.blocks.3.branch2a.norm.running_mean", "res_layers.2.blocks.3.branch2a.norm.running_var", "res_layers.2.blocks.3.branch2b.conv.weight", "res_layers.2.blocks.3.branch2b.norm.weight", "res_layers.2.blocks.3.branch2b.norm.bias", "res_layers.2.blocks.3.branch2b.norm.running_mean", "res_layers.2.blocks.3.branch2b.norm.running_var", "res_layers.2.blocks.3.branch2c.conv.weight", "res_layers.2.blocks.3.branch2c.norm.weight", "res_layers.2.blocks.3.branch2c.norm.bias", "res_layers.2.blocks.3.branch2c.norm.running_mean", "res_layers.2.blocks.3.branch2c.norm.running_var", "res_layers.2.blocks.4.branch2a.conv.weight", "res_layers.2.blocks.4.branch2a.norm.weight", "res_layers.2.blocks.4.branch2a.norm.bias", "res_layers.2.blocks.4.branch2a.norm.running_mean", "res_layers.2.blocks.4.branch2a.norm.running_var", "res_layers.2.blocks.4.branch2b.conv.weight", "res_layers.2.blocks.4.branch2b.norm.weight", "res_layers.2.blocks.4.branch2b.norm.bias", "res_layers.2.blocks.4.branch2b.norm.running_mean", "res_layers.2.blocks.4.branch2b.norm.running_var", "res_layers.2.blocks.4.branch2c.conv.weight", "res_layers.2.blocks.4.branch2c.norm.weight", "res_layers.2.blocks.4.branch2c.norm.bias", "res_layers.2.blocks.4.branch2c.norm.running_mean", "res_layers.2.blocks.4.branch2c.norm.running_var", "res_layers.2.blocks.5.branch2a.conv.weight", "res_layers.2.blocks.5.branch2a.norm.weight", "res_layers.2.blocks.5.branch2a.norm.bias", "res_layers.2.blocks.5.branch2a.norm.running_mean", "res_layers.2.blocks.5.branch2a.norm.running_var", "res_layers.2.blocks.5.branch2b.conv.weight", "res_layers.2.blocks.5.branch2b.norm.weight", "res_layers.2.blocks.5.branch2b.norm.bias", "res_layers.2.blocks.5.branch2b.norm.running_mean", "res_layers.2.blocks.5.branch2b.norm.running_var", "res_layers.2.blocks.5.branch2c.conv.weight", "res_layers.2.blocks.5.branch2c.norm.weight", "res_layers.2.blocks.5.branch2c.norm.bias", "res_layers.2.blocks.5.branch2c.norm.running_mean", "res_layers.2.blocks.5.branch2c.norm.running_var", "res_layers.3.blocks.0.branch2a.conv.weight", "res_layers.3.blocks.0.branch2a.norm.weight", "res_layers.3.blocks.0.branch2a.norm.bias", "res_layers.3.blocks.0.branch2a.norm.running_mean", "res_layers.3.blocks.0.branch2a.norm.running_var", "res_layers.3.blocks.0.branch2b.conv.weight", "res_layers.3.blocks.0.branch2b.norm.weight", "res_layers.3.blocks.0.branch2b.norm.bias", "res_layers.3.blocks.0.branch2b.norm.running_mean", "res_layers.3.blocks.0.branch2b.norm.running_var", "res_layers.3.blocks.0.branch2c.conv.weight", "res_layers.3.blocks.0.branch2c.norm.weight", "res_layers.3.blocks.0.branch2c.norm.bias", "res_layers.3.blocks.0.branch2c.norm.running_mean", "res_layers.3.blocks.0.branch2c.norm.running_var", "res_layers.3.blocks.0.short.conv.conv.weight", "res_layers.3.blocks.0.short.conv.norm.weight", "res_layers.3.blocks.0.short.conv.norm.bias", "res_layers.3.blocks.0.short.conv.norm.running_mean", "res_layers.3.blocks.0.short.conv.norm.running_var", "res_layers.3.blocks.1.branch2a.conv.weight", "res_layers.3.blocks.1.branch2a.norm.weight", "res_layers.3.blocks.1.branch2a.norm.bias", "res_layers.3.blocks.1.branch2a.norm.running_mean", "res_layers.3.blocks.1.branch2a.norm.running_var", "res_layers.3.blocks.1.branch2b.conv.weight", "res_layers.3.blocks.1.branch2b.norm.weight", "res_layers.3.blocks.1.branch2b.norm.bias", "res_layers.3.blocks.1.branch2b.norm.running_mean", "res_layers.3.blocks.1.branch2b.norm.running_var", "res_layers.3.blocks.1.branch2c.conv.weight", "res_layers.3.blocks.1.branch2c.norm.weight", "res_layers.3.blocks.1.branch2c.norm.bias", "res_layers.3.blocks.1.branch2c.norm.running_mean", "res_layers.3.blocks.1.branch2c.norm.running_var", "res_layers.3.blocks.2.branch2a.conv.weight", "res_layers.3.blocks.2.branch2a.norm.weight", "res_layers.3.blocks.2.branch2a.norm.bias", "res_layers.3.blocks.2.branch2a.norm.running_mean", "res_layers.3.blocks.2.branch2a.norm.running_var", "res_layers.3.blocks.2.branch2b.conv.weight", "res_layers.3.blocks.2.branch2b.norm.weight", "res_layers.3.blocks.2.branch2b.norm.bias", "res_layers.3.blocks.2.branch2b.norm.running_mean", "res_layers.3.blocks.2.branch2b.norm.running_var", "res_layers.3.blocks.2.branch2c.conv.weight", "res_layers.3.blocks.2.branch2c.norm.weight", "res_layers.3.blocks.2.branch2c.norm.bias", "res_layers.3.blocks.2.branch2c.norm.running_mean", "res_layers.3.blocks.2.branch2c.norm.running_var". Unexpected key(s) in state_dict: "ema".跑rt-detr官方代码报错
最新发布
08-02
<think>我们正在处理一个关于在加载模型状态字典(state_dict)时出现键不匹配的问题。用户在使用RT-DETR官方代码时遇到了两个问题:缺少一些键(Missing keys)和出现意外的键(Unexpected keys)。根据提供的引用,我们可以推断出一些常见的解决方案。 原因分析: 1. 模型结构发生了变化:当我们修改了网络结构(例如,添加、删除或重命名了某些层)后,保存的state_dict与当前模型的结构不匹配。 2. 使用了多GPU训练保存的模型:如果模型是在多GPU上训练并保存的,那么模型的状态字典的键名会包含"module."前缀,而单GPU加载时则没有这个前缀,导致不匹配。 3. 加载时strict参数设置为True:默认情况下,加载state_dict时strict=True,要求键必须完全匹配。 解决方案: 1. 如果是因为多GPU训练保存的模型,而现在是单GPU加载,我们可以通过去除键名中的"module."前缀来解决。同样,如果保存的是单GPU模型而在多GPU环境下加载,可能需要添加前缀(但通常我们会使用torch.nn.DataParallel或DistributedDataParallel来包装模型,它们会自动处理前缀)。 2. 修改strict参数为False:这样加载时会忽略不匹配的键(即只加载匹配的键,而忽略缺失的键和意外的键)。但这种方法可能导致模型部分权重没有加载,影响性能。 3. 手动调整state_dict:通过编程方式修改state_dict的键名,使其与当前模型的键名匹配。这通常需要知道键名变化的规则。 根据引用[1]中的内容,我们可以看到state_dict的键名中包含了"module."前缀(如'module.bn1.weight')。因此,如果当前模型没有使用DataParallel(即不是多GPU模型),那么我们就需要去掉这些键的"module."前缀。 引用[2]提到,当strict=True时,键必须完全匹配。因此,我们可以考虑设置strict=False,但这不是最佳方案,因为这样会忽略缺失的键,可能导致模型初始化不完全。 引用[3]给出了两种解决方法:一种是修改模型结构以匹配state_dict,另一种是修改state_dict的键以匹配模型。通常我们选择后者,因为修改模型结构可能更复杂。 具体步骤: 1. 检查保存的state_dict的键名是否包含"module."前缀,而当前模型没有(或者相反)。 2. 如果存在上述情况,则遍历state_dict,将键名中的"module."前缀去掉(或者加上)。 3. 然后尝试加载。 此外,RT-DETR官方代码可能使用了特定的模型结构,请确保你使用的代码版本与模型权重文件匹配。 根据用户的问题,我们假设用户是在加载预训练模型时遇到问题。我们可以按照以下步骤操作: 步骤1:打印当前模型的state_dict的键(部分)以查看命名规则。 步骤2:打印预训练模型state_dict的键(部分)以查看命名规则。 步骤3:如果发现预训练模型的键有"module."前缀,而当前模型没有,则去除前缀。 示例代码(去除"module."前缀): ```python # 假设 checkpoint 是加载的预训练模型字典 checkpoint = torch.load(pretrained_model_path) if 'model_state_dict' in checkpoint: state_dict = checkpoint['model_state_dict'] else: state_dict = checkpoint # 去除不需要的前缀 from collections import OrderedDict new_state_dict = OrderedDict() for k, v in state_dict.items(): # 如果键名以'module.'开头,则去掉它 if k.startswith('module.'): k = k[7:] # 去掉前7个字符(即'module.') new_state_dict[k] = v # 然后加载 model.load_state_dict(new_state_dict, strict=False) # 这里先使用strict=False尝试,如果完全匹配可以改为True ``` 注意:有时候模型可能被包装在DataParallel中,但保存时保存的是模型本身(即保存了model.module.state_dict()),那么就不会有"module."前缀。因此,需要根据实际情况调整。 如果上述方法不能解决,可能是因为模型结构有较大变化,需要检查代码版本和预训练模型是否匹配。 相关问题: 1. 如何避免在加载预训练模型时出现键不匹配的问题? 2. 在PyTorch中,多GPU训练和单GPU训练保存的模型有什么区别? 3. 除了去除"module."前缀,还有哪些常见的处理键不匹配的方法?</think>### RT-DETR模型加载错误分析与解决方案 在加载RT-DETR模型时遇到`Missing keys`和`Unexpected keys`错误,通常是由模型结构与权重文件不匹配引起的。以下是根本原因和解决方案: #### 错误原因分析 1. **模型结构不匹配**(最常见原因) - 训练时使用多GPU(`DataParallel`/`DistributedDataParallel`)导致权重键名包含`module.`前缀 - 代码版本更新导致层名称变化 - 自定义修改了模型结构(如增减层) 2. **权重文件问题** - 加载了不兼容的预训练权重 - 权重文件损坏或不完整 3. **strict模式限制** ```python model.load_state_dict(state_dict, strict=True) # 默认严格匹配键名 ``` #### 解决方案 ##### 方法1:去除多GPU前缀(推荐) ```python import torch from collections import OrderedDict # 加载权重文件 checkpoint = torch.load("rtdetr_model.pth") state_dict = checkpoint['model_state_dict'] # 根据引用[1]定位实际权重字典 # 去除'module.'前缀 new_state_dict = OrderedDict() for k, v in state_dict.items(): if k.startswith('module.'): name = k[7:] # 去掉'module.' else: name = k new_state_dict[name] = v # 重新加载模型 model.load_state_dict(new_state_dict, strict=True) # 此时应完全匹配 ``` ##### 方法2:放宽键名匹配 ```python # 允许部分权重不匹配(根据引用[2]) model.load_state_dict(state_dict, strict=False) # 忽略不匹配键 ``` > **注意**:此方法可能影响模型性能,仅作为临时解决方案 ##### 方法3:键名手动对齐 1. 打印当前模型键名: ```python # 根据引用[3]获取模型键名 for name, _ in model.named_parameters(): print(name) ``` 2. 打印权重键名: ```python for key in state_dict.keys(): print(key) ``` 3. 创建映射字典手动对齐: ```python key_mapping = { 'old_module.conv1.weight': 'new_model.conv1.weight', # 添加其他映射关系... } adjusted_state_dict = {key_mapping.get(k, k): v for k, v in state_dict.items()} model.load_state_dict(adjusted_state_dict) ``` #### 预防措施 1. **保存模型时标准化键名**: ```python # 保存前移除DataParallel影响 if isinstance(model, torch.nn.DataParallel): torch.save(model.module.state_dict(), "model.pth") else: torch.save(model.state_dict(), "model.pth") ``` 2. **版本一致性**: - 确保训练/推理使用相同代码库版本 - 官方预训练权重需匹配对应代码版本 3. **权重验证**: ```python # 加载前检查键名一致性 model_keys = set(model.state_dict().keys()) ckpt_keys = set(state_dict.keys()) print("Missing keys:", model_keys - ckpt_keys) print("Unexpected keys:", ckpt_keys - model_keys) ``` > 如果问题仍未解决,建议: > 1. 检查RT-DETR官方文档的版本要求 > 2. 使用`torch.load(..., map_location='cpu')`排除设备差异 > 3. 在官方GitHub仓库搜索类似issue
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