代码下载地址下载地址https://www.lanzouw.com/ipl8Yo37qxi
Anime数据请在Anime Face Dataset | Kaggle下载,其他数据都是pytorch自带,在线下载即可
下面的代码时是用DCGAN生成#选择cifar10, cifar100, mnist, fashion_mnist,STL10,Anime图片
目录情况:
DCGAN3的目录情况
generated_fake目录:
有的模型已经训练,有的没有,如果提示模型文件不存在,请将resume=False
import torch,torchvision
import torch.nn as nn
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
#rusume是否使用预训练模型继续训练,问号处输入模型的编号
resume = True #是继续训练,否重新训练
datasets = 'Anime' #选择cifar10, cifar100, mnist, fashion_mnist,STL10,Anime
if datasets == 'cifar10' or datasets=='cifar100' or datasets=='STL10'or datasets=='Anime':
nc = 3 #图片的通道数
elif datasets == 'mnist' or datasets== 'fashion_mnist':
nc = 1
else:
print('数据集选择错误')
batch_size = 128
nz = 100 #噪声向量的维度
ndf = 64
ngf = 64
real_label = 1
fake_label = 0
start_epoch = 0
#定义模型
#生成器 #(N,nz, 1,1)
netG = nn.Sequential(nn.ConvTranspose2d(nz, ngf*8,4, 1,0, bias=False), nn.BatchNorm2d(ngf*8), nn.LeakyReLU(0.2,inplace=True),
nn.ConvTranspose2d(ngf*8,ngf*4,4,2,1, bias=False), nn.BatchNorm2d(ngf*4), nn.LeakyReLU(0.2,inplace=True),
nn.ConvTranspose2d(ngf*4, ngf*4,4,2, 1,bias=False), nn.BatchNorm2d(ngf*4), nn.LeakyReLU(0.2,inplace=True),
nn.ConvTranspose2d(ngf*4, ngf*2,4,2, 1,bias=False), nn.BatchNorm2d(ngf*2), nn.LeakyReLU(0.2,inplace=True),
nn.ConvTranspose2d(ngf*2, ngf*2,4,2, 1,bias=False), nn.BatchNorm2d(ngf*2), nn.LeakyReLU(0.2,inplace=True),
nn.ConvTranspose2d(ngf*2, nc,4,2,1, bias=False),
nn.Tanh() #(N,nz, 128,128)
)
#判别器 #(N,nc, 128,128)
netD = nn.Sequential(nn.Conv2d(nc, ndf*2, 4,2,1, bias=False), nn.BatchNorm2d(ndf*2),nn.LeakyReLU(0.2,inplace=True),
nn.Conv2d(ndf*2,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*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), #(N,1,1,1)
nn.Flatten(), #(N,1)
nn.Sigmoid()
)
# custom weights initialization called on netG and netD
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
torch.nn.init.normal_(m.weight, 0.0, 0.02)
elif classname.find('BatchNorm') != -1:
torch.nn.init.normal_(m.weight, 1.0, 0.02)
torch.nn.init.zeros_(m.bias)
netD.apply(weights_init)
netG.apply(weights_init)
#加载数据集
apply_transform1 = transforms.Compose([
transforms.Resize(128),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
apply_transform2 = transforms.Compose([
transforms.Resize(128),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,)),
])
if datasets == 'cifar100':
train_dataset = torchvision.datasets.CIFAR100(root='../data/cifar100', train=False, download=True,transform=apply_transform1)
elif datasets == 'cifar10':
train_dataset = torchvision.datasets.CIFAR10(root='../data/cifar10', train=False, download=True,transform=apply_transform1)
elif datasets == 'STL10':
train_dataset = torchvision.datasets.STL10(root='../data/STL10', split='train', download=True,transform=apply_transform1)
elif datasets == 'mnist':
train_dataset = torchvision.datasets.MNIST(root='../data/mnist', train=False, download=True,transform=apply_transform2)
elif datasets == 'fashion_mnist':
train_dataset = torchvision.datasets.FashionMNIST(root='../data/fashion_mnist', train=False, download=True,transform=apply_transform2)
elif datasets == 'Anime':
train_dataset = torchvision.datasets.ImageFolder(root='../data/Anime',transform=apply_transform1)
else:
print('数据集不存在')
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True,num_workers=4)
#定义损失函数
criterion = torch.nn.BCELoss()
device = torch.device('cuda'if torch.cuda.is_available() else 'cpu')
# setup optimizer
optimizerD = torch.optim.Adam(netD.parameters(), lr=0.0002,betas=(0.5, 0.999))
optimizerG = torch.optim.Adam(netG.parameters(), lr=0.0002,betas=(0.5,0.999))
#显示16张图片
if datasets=='Anime':
image,label = next(iter(train_loader))
image = (image*0.5+0.5)[:16]
elif datasets=='mnist' or datasets=='fashion_mnist':
image = next(iter(train_loader))[0]
image = image[:16]*0.5+0.5
elif datasets=='STL10' :
image = torch.Tensor(train_dataset.data[:16]/255)
else:
image = torch.Tensor(train_dataset.data[:16]/255).permute(0,3,1,2)
plt.imshow(torchvision.utils.make_grid(image,nrow=4).permute(1,2,0))
#训练和保存模型
#如果继续训练,就加载预训练模型
if resume:
print('==> Resuming from checkpoint..')
checkpoint = torch.load('./checkpoint/GAN_%s_best.pth'%datasets)
netG.load_state_dict(checkpoint['net_G'])
netD.load_state_dict(checkpoint['net_D'])
start_epoch = checkpoint['start_epoch']
print('netG:','\n',netG)
print('netD:','\n',netD)
print('training on: ',device, ' start_epoch',start_epoch)
netD, netG = netD.to(device), netG.to(device)
#固定生成器,训练判别器
for epoch in range(start_epoch,300):
for batch, (data, target) in enumerate(train_loader):
batch_size = data.size(0)
label = torch.full((batch_size,1), real_label).to(device)
#(1)训练判别器
#training real data
netD.zero_grad()
data = data.to(device)
output = netD(data)
loss_D1 = criterion(output, label)
loss_D1.backward()
#training fake data
noise_z = torch.randn(batch_size, nz, 1, 1, device=device)
fake_data = netG(noise_z)
label = torch.full((batch_size,1), fake_label).to(device)
output = netD(fake_data.detach())
loss_D2 = criterion(output, label)
loss_D2.backward()
#更新判别器
optimizerD.step()
#(2)训练生成器
netG.zero_grad()
label = torch.full((batch_size,1), real_label).to(device)
output = netD(fake_data)
lossG = criterion(output, label)
lossG.backward()
#更新生成器
optimizerG.step()
if batch %100==0:
print('epoch: %4d, batch: %4d, discriminator loss: %.4f, generator loss: %.4f'
%(epoch, batch, loss_D1.item()+loss_D2.item(), lossG.item()))
#每2个epoch保存图片
if epoch%2==0:
#如果是单通道图片,那么就转成三通道进行保存
if nc ==1:
fake_data=torch.cat((fake_data,fake_data,fake_data),dim=1) #fake_data(N,1,H,W)->(N,3,H,W)
#保存图片
data = fake_data.detach().cpu().permute(0,2,3,1)
data = np.array(data)
#保存单张图片,将图片归一化到(0,1)
data = (data*0.5+0.5)
plt.imsave('./generated_fake/%s/epoch_%d.png'%(datasets,epoch), data[0])
torchvision.utils.save_image(fake_data[:16], filename='./generated_fake/%s/epoch%d_grid.png'%(datasets,epoch),nrow=4,normalize=True)
#保存模型
state = {
'net_G': netG.state_dict(),
'net_D': netD.state_dict(),
'start_epoch':epoch+1
}
torch.save(state, './checkpoint/GAN_%s_best.pth'%(datasets))
torch.save(state, './checkpoint/GAN_%s_best_copy.pth'%(datasets))
实验结果: