利用cyclegan实现人脸老化代码

以下是使用CycleGAN实现人脸老化的Python代码，需要使用PyTorch和OpenCV库：

首先，需要安装相关的库：

pip install torch torchvision opencv-python

然后，我们可以定义一些参数，如图像大小、批量大小、学习率、迭代次数等。

import argparse

parser = argparse.ArgumentParser()
parser.add_argument('--dataroot', type=str, default='./data', help='path to dataset')
parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
parser.add_argument('--image_size', type=int, default=256, help='scale images to this size')
parser.add_argument('--epoch', type=int, default=200, help='number of epochs to train for')
parser.add_argument('--lr', type=float, default=0.0002, help='learning rate')
parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam optimizer')
parser.add_argument('--ngpu', type=int, default=1, help='number of GPUs to use')
opt = parser.parse_args()
print(opt)

接下来，我们可以定义模型。在这里，我们使用两个生成器（G_AB和G_BA）和两个判别器（D_A和D_B）来实现CycleGAN。

import torch.nn as nn
import torch.nn.functional as F

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()

        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, 4, stride=2, padding=1),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(64, 128, 4, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(128, 256, 4, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(256, 512, 4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(512, 512, 4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(512, 512, 4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(512, 512, 4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(512, 512, 4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True)
        )

        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(512, 512, 4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),

            nn.ConvTranspose2d(512, 512, 4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),

            nn.ConvTranspose2d(512, 512, 4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),

            nn.ConvTranspose2d(512, 512, 4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),

            nn.ConvTranspose2d(512, 256, 4, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),

            nn.ConvTranspose2d(256, 128, 4, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),

            nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),

            nn.ConvTranspose2d(64, 3, 4, stride=2, padding=1),
            nn.Tanh()
        )

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()

        self.conv = nn.Sequential(
            nn.Conv2d(3, 64, 4, stride=2, padding=1),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(64, 128, 4, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(128, 256, 4, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(256, 512, 4, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(512, 1, 4, stride=1, padding=1),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = self.conv(x)
        return x

接下来，我们可以定义损失函数和优化器。

import torch.optim as optim

criterion_GAN = nn.MSELoss()
criterion_cycle = nn.L1Loss()
criterion_identity = nn.L1Loss()

G_AB = Generator()
G_BA = Generator()
D_A = Discriminator()
D_B = Discriminator()

if opt.ngpu > 0:
    G_AB.cuda()
    G_BA.cuda()
    D_A.cuda()
    D_B.cuda()
    criterion_GAN.cuda()
    criterion_cycle.cuda()
    criterion_identity.cuda()

optimizer_G = optim.Adam(
    itertools.chain(G_AB.parameters(), G_BA.parameters()),
    lr=opt.lr,
    betas=(opt.beta1, 0.999)
)
optimizer_D_A = optim.Adam(D_A.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
optimizer_D_B = optim.Adam(D_B.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))

现在，我们可以加载数据集并进行训练。

import itertools
import os.path
import random
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import transforms
from PIL import Image

class ImageDataset(torch.utils.data.Dataset):
    def __init__(self, root, transform=None, mode='train'):
        self.transform = transform
        self.files_A = sorted(glob.glob(os.path.join(root, '%sA' % mode) + '/*.*'))
        self.files_B = sorted(glob.glob(os.path.join(root, '%sB' % mode) + '/*.*'))

    def __getitem__(self, index):
        item_A = self.transform(Image.open(self.files_A[index % len(self.files_A)]))
        item_B = self.transform(Image.open(self.files_B[random.randint(0, len(self.files_B) - 1)]))
        return {'A': item_A, 'B': item_B}

    def __len__(self):
        return max(len(self.files_A), len(self.files_B))

# Configure data loader
transforms_ = [
    transforms.Resize(int(opt.image_size * 1.12), Image.BICUBIC),
    transforms.RandomCrop(opt.image_size),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))
]
dataloader = DataLoader(
    ImageDataset(opt.dataroot, transforms_=transforms_),
    batch_size=opt.batch_size,
    shuffle=True,
    num_workers=opt.num_workers
)

for epoch in range(opt.epoch):
    for i, batch in enumerate(dataloader):
        real_A = batch['A'].cuda()
        real_B = batch['B'].cuda()

        # 训练生成器 G
        optimizer_G.zero_grad()

        # Identity loss
        same_B = G_AB(real_B)
        loss_identity_B = criterion_identity(same_B, real_B) * 5.0
        same_A = G_BA(real_A)
        loss_identity_A = criterion_identity(same_A, real_A) * 5.0

        # GAN loss
        fake_B = G_AB(real_A)
        pred_fake = D_B(fake_B)
        loss_GAN_AB = criterion_GAN(pred_fake, torch.ones_like(pred_fake))

        fake_A = G_BA(real_B)
        pred_fake = D_A(fake_A)
        loss_GAN_BA = criterion_GAN(pred_fake, torch.ones_like(pred_fake))

        # Cycle loss
        recovered_A = G_BA(fake_B)
        loss_cycle_ABA = criterion_cycle(recovered_A, real_A) * 10.0

        recovered_B = G_AB(fake_A)
        loss_cycle_BAB = criterion_cycle(recovered_B, real_B) * 10.0

        # Total loss
        loss_G = loss_identity_A + loss_identity_B + loss_GAN_AB + loss_GAN_BA + loss_cycle_ABA + loss_cycle_BAB
        loss_G.backward()
        optimizer_G.step()

        # 训练判别器 D_A
        optimizer_D_A.zero_grad()

        pred_real = D_A(real_A)
        loss_D_real = criterion_GAN(pred_real, torch.ones_like(pred_real))

        pred_fake = D_A(fake_A.detach())
        loss_D_fake = criterion_GAN(pred_fake, torch.zeros_like(pred_fake))

        loss_D_A = (loss_D_real + loss_D_fake) * 0.5
        loss_D_A.backward()
        optimizer_D_A.step()

        # 训练判别器 D_B
        optimizer_D_B.zero_grad()

        pred_real = D_B(real_B)
        loss_D_real = criterion_GAN(pred_real, torch.ones_like(pred_real))

        pred_fake = D_B(fake_B.detach())
        loss_D_fake = criterion_GAN(pred_fake, torch.zeros_like(pred_fake))

        loss_D_B = (loss_D_real + loss_D_fake) * 0.5
        loss_D_B.backward()
        optimizer_D_B.step()

        if i % 100 == 0:
            print('[%d/%d][%d/%d] Loss_G: %.4f Loss_D_A: %.4f Loss_D_B: %.4f' % (epoch, opt.epoch, i, len(dataloader), loss_G.item(), loss_D_A.item(), loss_D_B.item()))

训练完成后，我们可以使用以下代码将图片传递给生成器：

with torch.no_grad():
    fake_B = G_AB(real_A)

然后，将生成的图片保存到文件中：

save_image(fake_B, 'output.png')