怎么把resnet101的特征提取层转化为和vgg16的特征提取层相同的输入输出格式,请给出pytorch代码
时间: 2024-05-09 07:15:08 浏览: 16
可以使用PyTorch中的`nn.Sequential`和`nn.ModuleList`来构建一个新的特征提取网络,使其与VGG16的特征提取层具有相同的输入输出格式。
具体代码如下:
```python
import torchvision.models as models
import torch.nn as nn
# 加载预训练的ResNet101模型
resnet101 = models.resnet101(pretrained=True)
# 获取ResNet101的特征提取层
resnet_features = list(resnet101.children())[:-1]
# 构造一个新的特征提取网络
vgg_features = 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),
)
# 将ResNet101的特征提取层添加到新的特征提取网络中
vgg_features.add_module('resnet_features', nn.ModuleList(resnet_features))
# 打印新的特征提取网络
print(vgg_features)
```
这个新的特征提取网络包含了VGG16的特征提取层和ResNet101的特征提取层,其输入输出格式与VGG16相同。你可以根据需要修改这个新的特征提取网络。
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