用pytorch写一个FCN网络
时间: 2024-05-12 22:21:13 浏览: 8
以下是一个简单的FCN网络的PyTorch实现:
```python
import torch
import torch.nn as nn
class FCN(nn.Module):
def __init__(self, num_classes):
super().__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(128)
self.relu2 = nn.ReLU(inplace=True)
self.conv3 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1)
self.bn3 = nn.BatchNorm2d(256)
self.relu3 = nn.ReLU(inplace=True)
self.conv4 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1)
self.bn4 = nn.BatchNorm2d(512)
self.relu4 = nn.ReLU(inplace=True)
self.conv5 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1)
self.bn5 = nn.BatchNorm2d(512)
self.relu5 = nn.ReLU(inplace=True)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc6 = nn.Conv2d(in_channels=512, out_channels=4096, kernel_size=7)
self.drop6 = nn.Dropout2d()
self.fc7 = nn.Conv2d(in_channels=4096, out_channels=4096, kernel_size=1)
self.drop7 = nn.Dropout2d()
self.score_fr = nn.Conv2d(in_channels=4096, out_channels=num_classes, kernel_size=1)
self.upscore = nn.ConvTranspose2d(in_channels=num_classes, out_channels=num_classes, kernel_size=64, stride=32, bias=False)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu2(x)
x = self.conv3(x)
x = self.bn3(x)
x = self.relu3(x)
x = self.conv4(x)
x = self.bn4(x)
x = self.relu4(x)
x = self.conv5(x)
x = self.bn5(x)
x = self.relu5(x)
x = self.pool(x)
x = self.fc6(x)
x = self.drop6(x)
x = self.fc7(x)
x = self.drop7(x)
x = self.score_fr(x)
x = self.upscore(x)
return x
```
该网络由一些卷积层、池化层和全连接层组成,其输出通过反卷积层进行上采样,最终生成与输入图像大小相同的语义分割图像。