给出edgeconnect的生成器原版代码
时间: 2023-06-15 17:08:36 浏览: 50
以下是 EdgeConnect 生成器的原版代码,使用 PyTorch 框架实现:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Generator(nn.Module):
def __init__(self, opt):
super(Generator, self).__init__()
# Encoder
self.enc_conv0 = nn.Conv2d(opt.input_nc, opt.ngf, 3, stride=2, padding=1)
self.enc_bn0 = nn.BatchNorm2d(opt.ngf)
self.enc_conv1 = nn.Conv2d(opt.ngf, opt.ngf*2, 3, stride=2, padding=1)
self.enc_bn1 = nn.BatchNorm2d(opt.ngf*2)
self.enc_conv2 = nn.Conv2d(opt.ngf*2, opt.ngf*4, 3, stride=2, padding=1)
self.enc_bn2 = nn.BatchNorm2d(opt.ngf*4)
self.enc_conv3 = nn.Conv2d(opt.ngf*4, opt.ngf*8, 3, stride=2, padding=1)
self.enc_bn3 = nn.BatchNorm2d(opt.ngf*8)
self.enc_conv4 = nn.Conv2d(opt.ngf*8, opt.ngf*8, 3, stride=2, padding=1)
self.enc_bn4 = nn.BatchNorm2d(opt.ngf*8)
self.enc_conv5 = nn.Conv2d(opt.ngf*8, opt.ngf*8, 3, stride=2, padding=1)
self.enc_bn5 = nn.BatchNorm2d(opt.ngf*8)
self.enc_conv6 = nn.Conv2d(opt.ngf*8, opt.ngf*8, 3, stride=2, padding=1)
self.enc_bn6 = nn.BatchNorm2d(opt.ngf*8)
self.enc_conv7 = nn.Conv2d(opt.ngf*8, opt.ngf*8, 3, stride=2, padding=1)
self.enc_bn7 = nn.BatchNorm2d(opt.ngf*8)
# Decoder
self.dec_conv0 = nn.ConvTranspose2d(opt.ngf*8, opt.ngf*8, 4, stride=2, padding=1)
self.dec_bn0 = nn.BatchNorm2d(opt.ngf*8)
self.dec_conv1 = nn.ConvTranspose2d(opt.ngf*8*2 + opt.mask_nc, opt.ngf*8, 4, stride=2, padding=1)
self.dec_bn1 = nn.BatchNorm2d(opt.ngf*8)
self.dec_conv2 = nn.ConvTranspose2d(opt.ngf*8*2 + opt.mask_nc, opt.ngf*8, 4, stride=2, padding=1)
self.dec_bn2 = nn.BatchNorm2d(opt.ngf*8)
self.dec_conv3 = nn.ConvTranspose2d(opt.ngf*8*2 + opt.mask_nc, opt.ngf*8, 4, stride=2, padding=1)
self.dec_bn3 = nn.BatchNorm2d(opt.ngf*8)
self.dec_conv4 = nn.ConvTranspose2d(opt.ngf*8*2 + opt.mask_nc, opt.ngf*4, 4, stride=2, padding=1)
self.dec_bn4 = nn.BatchNorm2d(opt.ngf*4)
self.dec_conv5 = nn.ConvTranspose2d(opt.ngf*4*2 + opt.mask_nc, opt.ngf*2, 4, stride=2, padding=1)
self.dec_bn5 = nn.BatchNorm2d(opt.ngf*2)
self.dec_conv6 = nn.ConvTranspose2d(opt.ngf*2*2 + opt.mask_nc, opt.ngf, 4, stride=2, padding=1)
self.dec_bn6 = nn.BatchNorm2d(opt.ngf)
self.dec_conv7 = nn.ConvTranspose2d(opt.ngf*2 + opt.mask_nc, opt.output_nc, 4, stride=2, padding=1)
self.apply(weights_init(opt.init_type, opt.init_gain))
def forward(self, x, m):
# Encoder
enc0 = self.enc_bn0(self.enc_conv0(torch.cat([x, m], dim=1))) # out: batch*ngf*1/2*1/2
enc1 = self.enc_bn1(self.enc_conv1(F.leaky_relu(enc0, 0.2))) # out: batch*(ngf*2)*1/4*1/4
enc2 = self.enc_bn2(self.enc_conv2(F.leaky_relu(enc1, 0.2))) # out: batch*(ngf*4)*1/8*1/8
enc3 = self.enc_bn3(self.enc_conv3(F.leaky_relu(enc2, 0.2))) # out: batch*(ngf*8)*1/16*1/16
enc4 = self.enc_bn4(self.enc_conv4(F.leaky_relu(enc3, 0.2))) # out: batch*(ngf*8)*1/32*1/32
enc5 = self.enc_bn5(self.enc_conv5(F.leaky_relu(enc4, 0.2))) # out: batch*(ngf*8)*1/64*1/64
enc6 = self.enc_bn6(self.enc_conv6(F.leaky_relu(enc5, 0.2))) # out: batch*(ngf*8)*1/128*1/128
enc7 = self.enc_bn7(self.enc_conv7(F.leaky_relu(enc6, 0.2))) # out: batch*(ngf*8)*1/256*1/256
# Decoder with skip-connections
dec0 = F.dropout(F.relu(self.dec_bn0(self.dec_conv0(enc7))), p=0.5, training=True) # out: batch*(ngf*8)*1/128*1/128
dec1 = F.dropout(F.relu(self.dec_bn1(self.dec_conv1(torch.cat([dec0, enc6, m], dim=1)))), p=0.5, training=True) # out: batch*(ngf*8)*1/64*1/64
dec2 = F.dropout(F.relu(self.dec_bn2(self.dec_conv2(torch.cat([dec1, enc5, m], dim=1)))), p=0.5, training=True) # out: batch*(ngf*8)*1/32*1/32
dec3 = F.dropout(F.relu(self.dec_bn3(self.dec_conv3(torch.cat([dec2, enc4, m], dim=1)))), p=0.5, training=True) # out: batch*(ngf*8)*1/16*1/16
dec4 = F.relu(self.dec_bn4(self.dec_conv4(torch.cat([dec3, enc3, m], dim=1)))) # out: batch*(ngf*4)*1/8*1/8
dec5 = F.relu(self.dec_bn5(self.dec_conv5(torch.cat([dec4, enc2, m], dim=1)))) # out: batch*(ngf*2)*1/4*1/4
dec6 = F.relu(self.dec_bn6(self.dec_conv6(torch.cat([dec5, enc1, m], dim=1)))) # out: batch*(ngf*1)*1/2*1/2
dec7 = torch.tanh(self.dec_conv7(torch.cat([dec6, enc0, m], dim=1))) # out: batch*(ngf*1)*1*1
return dec7
```
以上代码实现了 EdgeConnect 生成器的前向传播过程,包括编码器和解码器的操作。其中,编码器使用了 8 个卷积层,解码器使用了 8 个反卷积层,并且在解码器中使用了 skip-connections,以帮助保留原始图像的细节信息。同时,代码中还包括了对权重的初始化方法。
相关推荐
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![exe](https://img-home.csdnimg.cn/images/20210720083343.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)