使用pytorch进行迁移学习的时候，我们需要下载预训练的模型，但是这个模型通常很大，如果在代码中在线下载的话，很可能会中断，并且一中断之前也就白下载了，这篇文章里我介绍一种离线使用预训练模型的方法。

所谓离线使用预训练模型的方法，实际上就是使用浏览器将模型下载下来（通常浏览器下载会比较稳定，并且如果下载中断还能恢复），下面给出各种模型的下载地址，只需要将对应的url键入到浏览器中就可以建立下载

1. Resnet:
  model_urls = {
      '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',
  }

2. inception:
 model_urls = {
      Inception v3 ported from TensorFlow
     'inception_v3_google': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth',
 }

3. Densenet: 
 model_urls = {
     'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth',
     'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth',
     'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth',
     'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth',
 }

4. Alexnet:
 model_urls = {
     'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
}

5. vggnet:
 model_urls = {
     'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
     'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
     'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
     'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
     'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',
     'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',
     'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',
     'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
 }

下载完后我们就得到了我们想要的pth文件，使用方法如下，假设我们想要加载一个VGG16网络

model_ft = models.vgg16(pretrained=False)
pre = torch.load("vgg16-397923af.pth")
model_ft.load_state_dict(pre)

model.vgg16()里面的参数pretrained要写成False，否则还是会在线下载模型，按照网上的说法，模型是加载在C:\Users\lvnianzu/.cache\torch\checkpoints 这样子的目录里面的，但是我的电脑上没有找到这个目录。

torch.load()里面的参数就是你要加载的模型的地址

下面用一个例子来演示加载和使用VGG16模型的整个过程

import torch
import torchvision.models as models


# 加载模型
model_ft = models.vgg16(pretrained=False)
pre = torch.load("vgg16-397923af.pth")
model_ft.load_state_dict(pre)


from PIL import Image
im = Image.open("dog.jpg")
im = im.resize((224, 224))

from torch.autograd import Variable

import torchvision.transforms as tfs
im = tfs.ToTensor()(im)
# 将图片变成网络输出的维度
tensor = Variable(torch.unsqueeze(im, dim=0).float(), requires_grad=False)
print(tensor.shape)
output = model_ft(tensor)
print(output.shape)
_,pred = torch.max(output, 1)  # dim为1表示索引每行最大值
print(pred)

最后得到的结果如下