用上下文编码加GAN修复128*128带有随机掩膜区域的图像,提供生成器与判别器的代码
时间: 2024-05-14 14:16:40 浏览: 9
好的,我理解您的问题。为了回答您的问题,我需要先解释一下上下文编码和GAN修复的概念。
上下文编码通常是指将原始的输入数据转化为一种更高级的表示形式,这种形式可以帮助模型更好地理解输入数据的结构和语义信息。在图像处理中,上下文编码可以通过卷积神经网络来实现。GAN修复则是一种用于图像修复的技术,它使用生成器和判别器两个网络结构,通过训练生成器来修复图像中的缺陷区域,并通过判别器来判断修复结果的真实性。
下面是一份用上下文编码加GAN修复128*128带有随机掩膜区域的图像的生成器与判别器的代码:
生成器代码:
```python
import torch.nn as nn
import torch.nn.functional as F
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
# Encoder
self.conv1 = nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1)
self.conv2 = nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1)
self.conv3 = nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1)
self.conv4 = nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1)
self.conv5 = nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1)
self.conv6 = nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1)
self.conv7 = nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1)
self.conv8 = nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1)
# Decoder
self.deconv1 = nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2, padding=1)
self.deconv2 = nn.ConvTranspose2d(1024, 512, kernel_size=4, stride=2, padding=1)
self.deconv3 = nn.ConvTranspose2d(1024, 512, kernel_size=4, stride=2, padding=1)
self.deconv4 = nn.ConvTranspose2d(1024, 512, kernel_size=4, stride=2, padding=1)
self.deconv5 = nn.ConvTranspose2d(1024, 256, kernel_size=4, stride=2, padding=1)
self.deconv6 = nn.ConvTranspose2d(512, 128, kernel_size=4, stride=2, padding=1)
self.deconv7 = nn.ConvTranspose2d(256, 64, kernel_size=4, stride=2, padding=1)
self.deconv8 = nn.ConvTranspose2d(128, 3, kernel_size=4, stride=2, padding=1)
def forward(self, x):
# Encoder
e1 = F.leaky_relu(self.conv1(x), negative_slope=0.2)
e2 = F.leaky_relu(self.conv2(e1), negative_slope=0.2)
e3 = F.leaky_relu(self.conv3(e2), negative_slope=0.2)
e4 = F.leaky_relu(self.conv4(e3), negative_slope=0.2)
e5 = F.leaky_relu(self.conv5(e4), negative_slope=0.2)
e6 = F.leaky_relu(self.conv6(e5), negative_slope=0.2)
e7 = F.leaky_relu(self.conv7(e6), negative_slope=0.2)
e8 = F.leaky_relu(self.conv8(e7), negative_slope=0.2)
# Decoder
d1 = F.relu(self.deconv1(e8))
d1 = F.dropout(d1, 0.5, training=True)
d2 = F.relu(self.deconv2(torch.cat([d1, e7], dim=1)))
d2 = F.dropout(d2, 0.5, training=True)
d3 = F.relu(self.deconv3(torch.cat([d2, e6], dim=1)))
d3 = F.dropout(d3, 0.5, training=True)
d4 = F.relu(self.deconv4(torch.cat([d3, e5], dim=1)))
d4 = F.dropout(d4, 0.5, training=True)
d5 = F.relu(self.deconv5(torch.cat([d4, e4], dim=1)))
d6 = F.relu(self.deconv6(torch.cat([d5, e3], dim=1)))
d7 = F.relu(self.deconv7(torch.cat([d6, e2], dim=1)))
d8 = torch.tanh(self.deconv8(torch.cat([d7, e1], dim=1)))
return d8
```
判别器代码:
```python
import torch.nn as nn
import torch.nn.functional as F
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1)
self.conv2 = nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1)
self.conv3 = nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1)
self.conv4 = nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1)
self.conv5 = nn.Conv2d(512, 1, kernel_size=4, stride=1, padding=1)
def forward(self, x):
x = F.leaky_relu(self.conv1(x), negative_slope=0.2)
x = F.leaky_relu(self.conv2(x), negative_slope=0.2)
x = F.leaky_relu(self.conv3(x), negative_slope=0.2)
x = F.leaky_relu(self.conv4(x), negative_slope=0.2)
x = torch.sigmoid(self.conv5(x))
return x
```
注意:这只是一个代码示例,并不是一个完整的可运行代码。您需要使用这个示例代码作为起点,并根据您的数据集和实际需求进行适当的修改。