pytorch使用resnet快速加载官方提供的预训练模型

import torch
import torch.nn as nn
from torchvision import datasets, transforms
from torchvision import models
class base_resnet(nn.Module):
    def __init__(self):
        super(base_resnet, self).__init__()
        self.model = models.resnet50(pretrained=True)
        #self.model.load_state_dict(torch.load('./model/resnet50-19c8e357.pth'))
        self.model.avgpool = nn.AdaptiveAvgPool2d((1, 1))
    def forward(self, x):
        x = self.model.conv1(x)
        x = self.model.bn1(x)
        x = self.model.relu(x)
        x = self.model.maxpool(x)
        x = self.model.layer1(x)
        x = self.model.layer2(x)
        x = self.model.layer3(x)
        x = self.model.layer4(x)
        x = self.model.avgpool(x)
        # x = x.view(x.size(0), x.size(1))
        return x

self.model = models.resnet50(pretrained=True)

 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',

self.model = models.resnet50(pretrained=False)
self.model.load_state_dict(torch.load('./model/resnet50-19c8e357.pth'))

model=resnet()#自己构建的模型，以resnet为例， 需要重新训练的层的名字要和之前的不同。
model_dict = model.state_dict()
pretrained_dict = torch.load('xxx.pkl')
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)

#k是可训练参数的名字，v是包含可训练参数的一个实体
#可以先print（k）,找到自己想进行调整的层，并将该层的名字加入到if语句中：
for k,v in model.named_parameters():
if k!='xxx.weight' and k!='xxx.bias' :
v.requires_grad=False#固定参数

#将要训练的参数放入优化器
optimizer2=torch.optim.Adam(params=[model.xxx.weight，model.xxx.bias],lr=learning_rate,betas=(0.9,0.999),weight_decay=1e-5)

for k,v in model.named_parameters():
if k!='xxx.weight' and k!='xxx.bias' :
print(v.requires_grad)#理想状态下，所有值都是False