我是说vgg = models.vgg16(pretrained=True).features怎么修改可以使用别的路径的vgg16.pth
时间: 2024-03-06 15:49:34 浏览: 179
vgg16-397923af.pth
如果你想使用别的路径的`vgg16.pth`,可以先加载预训练模型的参数,然后将参数保存到指定的路径。代码如下:
```
import torch
import torch.nn as nn
import torchvision.models as models
# 加载预训练模型的参数
model = models.vgg16(pretrained=True)
# 保存参数到指定的路径
torch.save(model.state_dict(), 'path/to/your/vgg16.pth')
```
之后,你可以使用下面的代码加载指定路径下的`vgg16.pth`,并提取模型的特征:
```
import torch
import torch.nn as nn
# 加载自定义路径下的vgg16.pth
model = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
model.load_state_dict(torch.load('path/to/your/vgg16.pth'))
# 提取模型的特征
features = model.features
```
这里使用了一个与`vgg16`相同的模型结构,并将预训练模型的参数加载到这个模型中,然后只提取了这个模型的前面一部分卷积层,即与`vgg16`的`features`相同的部分。如果你有其他的模型结构,可以根据需要进行修改。
阅读全文