本文参考官方博客以及李宏毅老师讲解。
另参考/sunqiande88/article/details/80219842
关于其中转置卷积和卷积的问题，会另外开一篇。
理论和GAN理论类似，只是在两个模型上做了改进，
论文中详细说明了改进方法。如下：
1.将pooling层替换成带strides的卷积层。
2.消除顶层卷积特征中的全连接层，为了实现更深的网络。顶层特征指的是生成器的输入，以及判别器的输出。
3.使用BatchNorm。直接对所有层使用batchnorm会导致震荡和不稳定。所以在生成器的输出层和辨别其的输入层不用。
4.在生成器中使用ReLU激活，除了输出层，用的是tanh激活。辨别器使用的是leakyReLU激活，尤其对于高分辨率建模。
/yfy1127yfy/article/details/88822125
导入包：
import argparseimport torchimport torchvisionimport torchvision.utils as vutilsimport torch.nn as nnfrom random import randint
参数：
parser = argparse.ArgumentParser()parser.add_argument('--batchSize', type=int, default=64)parser.add_argument('--imageSize', type=int, default=96)parser.add_argument('--nz', type=int, default=100, help='size of the latent z vector')parser.add_argument('--ngf', type=int, default=64)parser.add_argument('--ndf', type=int, default=64)parser.add_argument('--epoch', type=int, default=25, help='number of epochs to train for')parser.add_argument('--lr', type=float, default=0.0002, help='learning rate, default=0.0002')parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')parser.add_argument('--data_path', default='./face_data/', help='folder to train data')parser.add_argument('--outf', default='./imgs/', help='folder to output images and model checkpoints')opt = parser.parse_args()
数据处理：
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")print(torch.cuda.is_available())#图像读入与预处理transforms = pose([torchvision.transforms.Resize([96, 96]),torchvision.transforms.ToTensor(),torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])dataset = torchvision.datasets.ImageFolder(opt.data_path, transform=transforms)dataloader = torch.utils.data.DataLoader(dataset=dataset,batch_size=opt.batchSize,shuffle=True,drop_last=True,)
torch.utils.data.DataLoader（）中drop_last参数。
如果数据集大小不能被批处理大小整除，则将其设置为True以删除最后一个不完整的批处理。如果为False且数据集的大小不能被批处理大小整除，则最后一个批处理将更小。(默认值:False)。建议True。
模型：
class NetG(nn.Module):def __init__(self, ngf, nz):super(NetG, self).__init__()# layer1输入的是一个100x1x1的随机噪声, 输出尺寸(ngf*8)x4x4self.layer1 = nn.Sequential(nn.ConvTranspose2d(nz, ngf * 8, kernel_size=4, stride=1, padding=0, bias=False),nn.BatchNorm2d(ngf * 8),nn.ReLU(inplace=True))# layer2输出尺寸(ngf*4)x8x8self.layer2 = nn.Sequential(nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),nn.BatchNorm2d(ngf * 4),nn.ReLU(inplace=True))# layer3输出尺寸(ngf*2)x16x16self.layer3 = nn.Sequential(nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),nn.BatchNorm2d(ngf * 2),nn.ReLU(inplace=True))# layer4输出尺寸(ngf)x32x32self.layer4 = nn.Sequential(nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),nn.BatchNorm2d(ngf),nn.ReLU(inplace=True))# layer5输出尺寸 3x96x96self.layer5 = nn.Sequential(nn.ConvTranspose2d(ngf, 3, 5, 3, 1, bias=False),nn.Tanh())# 定义NetG的前向传播def forward(self, x):out = self.layer1(x)out = self.layer2(out)out = self.layer3(out)out = self.layer4(out)out = self.layer5(out)return out# 定义鉴别器网络Dclass NetD(nn.Module):def __init__(self, ndf):super(NetD, self).__init__()# layer1 输入 3 x 96 x 96, 输出 (ndf) x 32 x 32#64self.layer1 = nn.Sequential(nn.Conv2d(3, ndf, kernel_size=5, stride=3, padding=1, bias=False),nn.BatchNorm2d(ndf),nn.LeakyReLU(0.2, inplace=True))# layer2 输出 (ndf*2) x 16 x 16self.layer2 = nn.Sequential(nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),nn.BatchNorm2d(ndf * 2),nn.LeakyReLU(0.2, inplace=True))# layer3 输出 (ndf*4) x 8 x 8self.layer3 = nn.Sequential(nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),nn.BatchNorm2d(ndf * 4),nn.LeakyReLU(0.2, inplace=True))# layer4 输出 (ndf*8) x 4 x 4self.layer4 = nn.Sequential(nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),nn.BatchNorm2d(ndf * 8),nn.LeakyReLU(0.2, inplace=True))# layer5 输出一个数(概率)self.layer5 = nn.Sequential(nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),nn.Sigmoid())# 定义NetD的前向传播def forward(self,x):out = self.layer1(x)out = self.layer2(out)out = self.layer3(out)out = self.layer4(out)out = self.layer5(out)return out
训练前的设置：
netG = NetG(opt.ngf, opt.nz).to(device)netD = NetD(opt.ndf).to(device)criterion = nn.BCELoss()optimizerG = torch.optim.Adam(netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))optimizerD = torch.optim.Adam(netD.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))label = torch.FloatTensor(opt.batchSize)real_label = 1fake_label = 0
训练：
for epoch in range(1, opt.epoch + 1):for i, (imgs,_) in enumerate(dataloader):# 固定生成器G，训练鉴别器DoptimizerD.zero_grad()## 让D尽可能的把真图片判别为1imgs=imgs.to(device)output = netD(imgs)label.data.fill_(real_label)label=label.to(device)errD_real = criterion(output, label)errD_real.backward()## 让D尽可能把假图片判别为0label.data.fill_(fake_label)noise = torch.randn(opt.batchSize, opt.nz, 1, 1)noise=noise.to(device)fake = netG(noise) # 生成假图output = netD(fake.detach()) #避免梯度传到G，因为G不用更新errD_fake = criterion(output, label)errD_fake.backward()errD = errD_fake + errD_realoptimizerD.step()# 固定鉴别器D，训练生成器GoptimizerG.zero_grad()# 让D尽可能把G生成的假图判别为1label.data.fill_(real_label)label = label.to(device)output = netD(fake)errG = criterion(output, label)errG.backward()optimizerG.step()print('[%d/%d][%d/%d] Loss_D: %.3f Loss_G %.3f'% (epoch, opt.epoch, i, len(dataloader), errD.item(), errG.item()))vutils.save_image(fake.data,'%s/fake_samples_epoch_%03d.png' % (opt.outf, epoch),normalize=True)torch.save(netG.state_dict(), '%s/netG_%03d.pth' % (opt.outf, epoch))torch.save(netD.state_dict(), '%s/netD_%03d.pth' % (opt.outf, epoch))
