基于GAN的深度图像重建算法代码
时间: 2023-07-26 08:16:55 浏览: 51
以下是一个基于GAN的深度图像重建算法的Python代码示例,使用的是PyTorch框架:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torchvision.transforms as transforms
import torchvision.datasets as dset
from torchvision.utils import save_image
import os
# 定义超参数
batch_size = 128
lr = 0.0002
train_epoch = 100
beta1 = 0.5
nz = 100
ngf = 64
ndf = 64
# 定义Generator模型
class generator(nn.Module):
def __init__(self):
super(generator, self).__init__()
self.main = nn.Sequential(
nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(ngf * 8),
nn.ReLU(True),
nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True),
nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True),
nn.ConvTranspose2d(ngf, 3, 4, 2, 1, bias=False),
nn.Tanh()
)
def forward(self, input):
output = self.main(input)
return output
# 定义Discriminator模型
class discriminator(nn.Module):
def __init__(self):
super(discriminator, self).__init__()
self.main = nn.Sequential(
nn.Conv2d(3, ndf, 4, 2, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 2),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 4),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 8),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
nn.Sigmoid()
)
def forward(self, input):
output = self.main(input)
return output.view(-1, 1)
# 初始化模型和优化器
G = generator()
D = discriminator()
G.cuda()
D.cuda()
G.apply(weights_init)
D.apply(weights_init)
criterion = nn.BCELoss()
optimizerD = optim.Adam(D.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerG = optim.Adam(G.parameters(), lr=lr, betas=(beta1, 0.999))
# 定义训练过程
for epoch in range(train_epoch):
for i, data in enumerate(dataloader, 0):
real_cpu, _ = data
batch_size = real_cpu.size(0)
input = Variable(real_cpu.cuda())
target = Variable(torch.ones(batch_size).cuda())
noise = Variable(torch.randn(batch_size, nz, 1, 1).cuda())
fake = G(noise)
target_fake = Variable(torch.zeros(batch_size).cuda())
# 训练Discriminator
D.zero_grad()
output = D(input)
errD_real = criterion(output, target)
errD_real.backward()
output_fake = D(fake.detach())
errD_fake = criterion(output_fake, target_fake)
errD_fake.backward()
errD = errD_real + errD_fake
optimizerD.step()
# 训练Generator
G.zero_grad()
output_fake = D(fake)
errG = criterion(output_fake, target)
errG.backward()
optimizerG.step()
# 保存生成的图像
fake = G(fixed_noise)
save_image(fake.data, 'output/fake_samples_epoch_%03d.png' % epoch, normalize=True)
# 保存模型
torch.save(G.state_dict(), 'generator.pth')
torch.save(D.state_dict(), 'discriminator.pth')
```