使用反卷积层的VGG网络模型实例
时间: 2023-08-14 18:06:24 浏览: 50
下面是一个使用反卷积层的VGG网络模型实例,该模型用于图像分割任务:
```python
import torch
import torch.nn as nn
class VGG16(nn.Module):
def __init__(self, num_classes):
super(VGG16, self).__init__()
self.num_classes = num_classes
# VGG16的卷积部分
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(128, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(256, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
# VGG16的全连接部分
self.classifier = nn.Sequential(
nn.Linear(512 * 7 * 7, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, self.num_classes),
)
# 反卷积层
self.deconv = nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2, padding=1, output_padding=1)
def forward(self, x):
x = self.features(x)
x = self.deconv(x)
x = self.classifier(x.view(-1, 512 * 7 * 7))
return x
```
在这个模型中,我们将VGG16的卷积层和全连接层都保留,并在其中间添加了一个反卷积层。反卷积层的作用是将特征图的尺寸变大,从而可以更好地进行图像分割任务。在这个模型中,我们使用了一个大小为3x3的反卷积核,步长为2,填充为1,输出填充为1,以保持特征图的大小不变。反卷积层的输入是VGG16的最后一层卷积层的输出,即大小为7x7x512的特征图。输出是大小为14x14x512的特征图,这个特征图将被用于进行图像分割。
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